From 31408a88f19776dc02ddeb98de2b53093e6c4ee8 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Mon, 29 Jul 2024 16:35:37 +0200 Subject: [PATCH 01/35] corrected broken links and broken references --- README.md | 14 +++++++------- conda/amadesuGPT.yml | 2 -- conda/install_cpu.sh | 6 +++--- conda/install_gpu.sh | 6 +++--- conda/install_minimal.sh | 6 +++--- 5 files changed, 16 insertions(+), 18 deletions(-) diff --git a/README.md b/README.md index 9c4af75..317d61e 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ In our original work (NeurIPS 2023) we used GPT3.5 and GPT4 as part of our agent Conda is an easy-to-use Python interface that supports launching [Jupyter Notebooks](https://jupyter.org/). If you are completely new to this, we recommend checking out the [docs here for getting conda installed](https://deeplabcut.github.io/DeepLabCut/docs/beginner-guides/beginners-guide.html#beginner-user-guide). Otherwise, proceed to use one of [our supplied conda files](https://github.com/AdaptiveMotorControlLab/AmadeusGPT/tree/main/conda). As you will see we have minimal dependencies to get started, and [here is a simple step-by-step guide](https://deeplabcut.github.io/DeepLabCut/docs/installation.html#step-2-build-an-env-using-our-conda-file) you can reference for setting it up (or see [BONUS](README.md#bonus---customized-your-conda-env) below). Here is the quick start command: ```bash -conda env create -f amadeusGPT.yml +conda env create -f conda/amadeusGPT.yml ``` To note, some modules AmadeusGPT can use benefit from GPU support, therefore we recommend also having an NVIDIA GPU available and installing CUDA. @@ -72,12 +72,12 @@ You can git clone (or download) this repo to grab a copy and go. We provide exam ### Here are a few demos that could fuel your own work, so please check them out! -1) [Draw a region of interest (ROI) and ask, "when is the animal in the ROI?"](notebook/EPM_demo.ipynb) -2) [Use a DeepLabCut SuperAnimal pose model to do video inference](notebook/custom_mouse_demo.ipynb) - (make sure you use a GPU if you don't have corresponding DeepLabCut keypoint files already! -3) [Write you own integration modules and use them](notebook/Horse_demo.ipynb). Bonus: [source code](amadeusgpt/integration_modules). Make sure you delete the cached modules_embedding.pickle if you add new modules! -4) [Multi-Animal social interactions](notebook/MABe_demo.ipynb) -5) [Reuse the task program generated by LLM and run it on different videos](notebook/MABe_demo.ipynb) -7) You can ask one query across multiple videos. Put your keypoint files and video files (pairs) in the same folder and specify the `data_folder` as shown in this [Demo](notebook/custom_mouse_video.ipynb). Make sure your video file and keypoint file follows the normal DeepLabCut convention, i.e., `prefix.mp4` `prefix*.h5`. +1) [Draw a region of interest (ROI) and ask, "when is the animal in the ROI?"](notebooks/EPM_demo.ipynb) +2) [Use a DeepLabCut SuperAnimal pose model to do video inference](notebooks/custom_mouse_demo.ipynb) - (make sure you use a GPU if you don't have corresponding DeepLabCut keypoint files already! +3) [Write you own integration modules and use them](notebooks/Horse_demo.ipynb). Bonus: [source code](amadeusgpt/integration_modules). Make sure you delete the cached modules_embedding.pickle if you add new modules! +4) [Multi-Animal social interactions](notebooks/MABe_demo.ipynb) +5) [Reuse the task program generated by LLM and run it on different videos](notebooks/MABe_demo.ipynb) +7) You can ask one query across multiple videos. Put your keypoint files and video files (pairs) in the same folder and specify the `data_folder` as shown in this [Demo](notebooks/custom_mouse_video.ipynb). Make sure your video file and keypoint file follows the normal DeepLabCut convention, i.e., `prefix.mp4` `prefix*.h5`. ### Minimal example diff --git a/conda/amadesuGPT.yml b/conda/amadesuGPT.yml index d82a235..128c79d 100644 --- a/conda/amadesuGPT.yml +++ b/conda/amadesuGPT.yml @@ -6,6 +6,4 @@ dependencies: - python==3.10 - pytables==3.8.0 - hdf5 - - pip - jupyter - - amadeusGPT diff --git a/conda/install_cpu.sh b/conda/install_cpu.sh index 9188ed8..3dc3584 100644 --- a/conda/install_cpu.sh +++ b/conda/install_cpu.sh @@ -1,10 +1,10 @@ #!/bin/bash source /Users/shaokaiye/miniforge3/bin/activate -conda env create -f conda/amadesuGPT-cpu.yml -conda activate amadeusgpt-cpu +conda env create -f conda/amadesuGPT.yml +conda activate amadeusgpt conda install pytorch torchvision cpuonly -c pytorch pip install "git+https://github.com/DeepLabCut/DeepLabCut.git@pytorch_dlc#egg=deeplabcut" pip install pycocotools pip install -e .[streamlit] # install the python kernel -python -m ipykernel install --user --name amadeusgpt-cpu --display-name "amadeusgpt-cpu" +python -m ipykernel install --user --name amadeusgpt --display-name "amadeusgpt" diff --git a/conda/install_gpu.sh b/conda/install_gpu.sh index 8057933..b779c5c 100644 --- a/conda/install_gpu.sh +++ b/conda/install_gpu.sh @@ -1,11 +1,11 @@ #!/bin/bash source /mnt/md0/shaokai/miniconda3/bin/activate -conda env create -f conda/amadesuGPT-gpu.yml -conda activate amadeusgpt-gpu +conda env create -f conda/amadesuGPT.yml +conda activate amadeusgpt # adjust this line according to your cuda version conda install pytorch torchvision torchaudio pytorch-cuda=12.1 -c pytorch -c nvidia pip install "git+https://github.com/DeepLabCut/DeepLabCut.git@pytorch_dlc#egg=deeplabcut" pip install pycocotools pip install -e .[streamlit] # install the python kernel -python -m ipykernel install --user --name amadeusgpt-gpu --display-name "amadeusgpt-gpu" +python -m ipykernel install --user --name amadeusgpt --display-name "amadeusgpt" diff --git a/conda/install_minimal.sh b/conda/install_minimal.sh index 4941bc5..097d7f7 100644 --- a/conda/install_minimal.sh +++ b/conda/install_minimal.sh @@ -1,8 +1,8 @@ #!/bin/bash # change this to your own miniconda / miniforge path source /Users/shaokaiye/miniforge3/bin/activate -conda env create -f conda/amadesuGPT-minimal.yml -conda activate amadeusgpt-minimal +conda env create -f conda/amadesuGPT.yml +conda activate amadeusgpt pip install pycocotools pip install -e .[streamlit] -python -m ipykernel install --user --name amadeusgpt-minimal --display-name "amadeusgpt-minimal" \ No newline at end of file +python -m ipykernel install --user --name amadeusgpt --display-name "amadeusgpt" \ No newline at end of file From 38e7ce822233f139cd0b02cf8eb17c609cfecbaf Mon Sep 17 00:00:00 2001 From: Mackenzie Mathis Date: Tue, 30 Jul 2024 10:42:06 +0200 Subject: [PATCH 02/35] Update setup.cfg - bump to stable v1 and v0.1.1 --- setup.cfg | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/setup.cfg b/setup.cfg index 05c4c7f..c6fdf8f 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,6 +1,6 @@ [metadata] name = amadeusgpt -version = 0.1.1rc2 +version = 0.1.1 author = Shaokai Ye, Jessy Lauer, Mu Zhou, Alexander Mathis, Mackenzie Mathis author_email = mackenzie@post.harvard.edu description = AmadeusGPT🎻: We turn natural language descriptions of behaviors into machine-executable code @@ -12,7 +12,7 @@ url = https://github.com/AdaptiveMotorControlLab/AmadeusGPT project_urls = Bug Tracker = https://github.com/AdaptiveMotorControlLab/AmadeusGPT/issues classifiers = - Development Status :: 3 - Alpha + Development Status :: 5 - Production/Stable Environment :: GPU :: NVIDIA CUDA Intended Audience :: Science/Research Operating System :: OS Independent From 591d056d18949fb8377c172b73c581fccfc1e5e3 Mon Sep 17 00:00:00 2001 From: Mackenzie Mathis Date: Tue, 30 Jul 2024 10:42:19 +0200 Subject: [PATCH 03/35] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 4f4cd90..72af39e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,7 +7,7 @@ name = "AmadeusGPT" readme = "README.md" requires-python = ">=3.10" dynamic = ["version"] -version = "0.1.1rc2" +version = "0.1.1" [tool.setuptools] packages = ["amadeusgpt"] From 84e01eefa293d6f401ecb797023760b4efb41171 Mon Sep 17 00:00:00 2001 From: Mackenzie Mathis Date: Tue, 30 Jul 2024 10:42:32 +0200 Subject: [PATCH 04/35] Update version.py --- amadeusgpt/version.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/amadeusgpt/version.py b/amadeusgpt/version.py index 1be166e..09e1a53 100644 --- a/amadeusgpt/version.py +++ b/amadeusgpt/version.py @@ -6,5 +6,5 @@ # # Licensed under Apache 2.0 -__version__ = "0.1.1rc2" +__version__ = "0.1.1" VERSION = __version__ From 5aea5a63b621f4d5724dc123c5b3a0a60ff87ab4 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 30 Jul 2024 11:42:41 +0200 Subject: [PATCH 05/35] Corrected typo. Added config yamls in setup --- conda/{amadesuGPT.yml => amadeusGPT.yml} | 0 setup.cfg | 4 ++++ 2 files changed, 4 insertions(+) rename conda/{amadesuGPT.yml => amadeusGPT.yml} (100%) diff --git a/conda/amadesuGPT.yml b/conda/amadeusGPT.yml similarity index 100% rename from conda/amadesuGPT.yml rename to conda/amadeusGPT.yml diff --git a/setup.cfg b/setup.cfg index c6fdf8f..97189c3 100644 --- a/setup.cfg +++ b/setup.cfg @@ -52,6 +52,10 @@ dev = static/*.* static/images/*.* static/styles/*.* + configs/Horse_template.yaml + configs/EPM_template.yaml + configs/MausHaus_template.yaml + configs/MABe_template.yaml [options.entry_points] console_scripts = From 4c4e3e75d31a352b439c8185a7f721dbb4a57133 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 30 Jul 2024 11:43:20 +0200 Subject: [PATCH 06/35] Removed config files that are no longer needed --- amadeusgpt/configs/Custom_template.yaml | 12 ------------ amadeusgpt/configs/template.yaml | 11 ----------- 2 files changed, 23 deletions(-) delete mode 100644 amadeusgpt/configs/Custom_template.yaml delete mode 100644 amadeusgpt/configs/template.yaml diff --git a/amadeusgpt/configs/Custom_template.yaml b/amadeusgpt/configs/Custom_template.yaml deleted file mode 100644 index a505854..0000000 --- a/amadeusgpt/configs/Custom_template.yaml +++ /dev/null @@ -1,12 +0,0 @@ -keypoint_info: -llm_info: - keep_last_n_messages: 2 -object_info: - load_objects_from_disk: false -video_info: - scene_frame_number: 100 - video_file_path: -data_info: - data_folder: 'examples/Custom' - video_suffix: '.mp4' - result_folder: 'results' \ No newline at end of file diff --git a/amadeusgpt/configs/template.yaml b/amadeusgpt/configs/template.yaml deleted file mode 100644 index 5422097..0000000 --- a/amadeusgpt/configs/template.yaml +++ /dev/null @@ -1,11 +0,0 @@ -keypoint_info: -object_info: - load_objects_from_disk: false -llm_info: - keep_last_n_messages: 2 -video_info: - scene_frame_number: 100 -data_info: - data_folder: '/Users/shaokaiye/maushaus_workspace' - video_suffix: '.mp4' - result_folder: 'results' \ No newline at end of file From 0077578249b822df49d5558a1831ccb8ff99c3f6 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 30 Jul 2024 15:03:22 +0200 Subject: [PATCH 07/35] changed work from to pull the repo from git --- .github/workflows/pytest.yml | 2 +- notebooks/custom_mouse_video.ipynb | 35 ++++-------------------------- 2 files changed, 5 insertions(+), 32 deletions(-) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index bf58f0f..afd37bc 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -43,5 +43,5 @@ jobs: env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} run: | - pip install '.[streamlit]' + pip install git+https://github.com/${{ github.repository }}.git@${{ github.sha }} pytest tests --timeout=400 diff --git a/notebooks/custom_mouse_video.ipynb b/notebooks/custom_mouse_video.ipynb index e326a9f..486b1e3 100644 --- a/notebooks/custom_mouse_video.ipynb +++ b/notebooks/custom_mouse_video.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "f01f49c5", "metadata": {}, "outputs": [], @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "bceb3204-2a87-4671-8135-2533a7a51771", "metadata": {}, "outputs": [], @@ -41,37 +41,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "be76dc87-fbe8-452f-b85c-2af3e95a03bf", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Project created at temp_result_folder. Results will be saved to temp_result_folder\n", - "The project will load video files (*..mp4) and optionally keypoint files from temp_data_folder\n", - "A copy of the project config file is saved at temp_result_folder/config.yaml\n", - "{'data_info': {'data_folder': 'temp_data_folder',\n", - " 'result_folder': 'temp_result_folder',\n", - " 'video_suffix': '.mp4'},\n", - " 'llm_info': {'max_tokens': 4096, 'temperature': 1.0}}\n", - "No video files found in the data folder temp_data_folder. Please check the data folder and the video suffix\n" - ] - }, - { - "ename": "AttributeError", - "evalue": "'AMADEUS' object has no attribute 'sandbox'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[3], line 13\u001b[0m\n\u001b[1;32m 10\u001b[0m config[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscene_frame_number\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m scene_frame_number\n\u001b[1;32m 12\u001b[0m amadeus \u001b[38;5;241m=\u001b[39m AMADEUS(config)\n\u001b[0;32m---> 13\u001b[0m video_file_paths \u001b[38;5;241m=\u001b[39m \u001b[43mamadeus\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_video_file_paths\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[38;5;28mprint\u001b[39m (video_file_paths)\n", - "File \u001b[0;32m~/AmadeusGPT-dev/amadeusgpt/main.py:121\u001b[0m, in \u001b[0;36mAMADEUS.get_video_file_paths\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 120\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mget_video_file_paths\u001b[39m(\u001b[38;5;28mself\u001b[39m) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[\u001b[38;5;28mstr\u001b[39m]:\n\u001b[0;32m--> 121\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msandbox\u001b[49m\u001b[38;5;241m.\u001b[39mvideo_file_paths\n", - "\u001b[0;31mAttributeError\u001b[0m: 'AMADEUS' object has no attribute 'sandbox'" - ] - } - ], + "outputs": [], "source": [ "scene_frame_number = 400\n", "\n", From 8bd38845d9f192f2027d0559763c2d7b4b81dbe8 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 30 Jul 2024 15:05:32 +0200 Subject: [PATCH 08/35] Added comments to remind people to pay attentino to data folder in the demo notebooks --- notebooks/EPM_demo.ipynb | 2 ++ notebooks/Horse_demo.ipynb | 2 ++ notebooks/MABe_demo.ipynb | 2 ++ notebooks/MausHaus_demo.ipynb | 3 ++- 4 files changed, 8 insertions(+), 1 deletion(-) diff --git a/notebooks/EPM_demo.ipynb b/notebooks/EPM_demo.ipynb index b4b1b77..60a7b50 100644 --- a/notebooks/EPM_demo.ipynb +++ b/notebooks/EPM_demo.ipynb @@ -51,6 +51,8 @@ "amadeus_root = Path(amadeusgpt.__file__).parent.parent\n", "config = Config(amadeus_root / \"amadeusgpt/configs/EPM_template.yaml\")\n", "config['video_info']['scene_frame_number'] = scene_frame_number\n", + "# if you install from pypl or you used the conda file, you have to adjust the path of data folder to\n", + "# repo_root/examples/EPM\n", "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder']\n", "\n", "amadeus = AMADEUS(config)\n", diff --git a/notebooks/Horse_demo.ipynb b/notebooks/Horse_demo.ipynb index a3aacda..b2d9425 100644 --- a/notebooks/Horse_demo.ipynb +++ b/notebooks/Horse_demo.ipynb @@ -41,6 +41,8 @@ "config = Config(amadeus_root / \"amadeusgpt/configs/Horse_template.yaml\")\n", "\n", "config['video_info']['scene_frame_number'] = scene_frame_number\n", + "# if you install from pypl or you used the conda file, you have to adjust the path of data folder to\n", + "# repo_root/examples/Horse\n", "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder'] \n", "\n", "amadeus = AMADEUS(config)\n", diff --git a/notebooks/MABe_demo.ipynb b/notebooks/MABe_demo.ipynb index 93431e6..53f69bc 100644 --- a/notebooks/MABe_demo.ipynb +++ b/notebooks/MABe_demo.ipynb @@ -38,6 +38,8 @@ "source": [ "amadeus_root = Path(amadeusgpt.__file__).parent.parent\n", "config = Config(amadeus_root / \"amadeusgpt/configs/MABe_template.yaml\")\n", + "# if you install from pypl or you used the conda file, you have to adjust the path of data folder to\n", + "# repo_root/examples/MABe\n", "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder']\n", "\n", "amadeus = AMADEUS(config)\n", diff --git a/notebooks/MausHaus_demo.ipynb b/notebooks/MausHaus_demo.ipynb index 99bb727..1b1b905 100644 --- a/notebooks/MausHaus_demo.ipynb +++ b/notebooks/MausHaus_demo.ipynb @@ -43,7 +43,8 @@ "config = Config(amadeus_root / \"amadeusgpt/configs/MausHaus_template.yaml\")\n", "\n", "config['video_info']['scene_frame_number'] = scene_frame_number\n", - "\n", + "# if you install from pypl or you used the conda file, you have to adjust the path of data folder to\n", + "# repo_root/examples/MausHaus\n", "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder']\n", "\n", "amadeus = AMADEUS(config)\n", From b5f76291c98c5553755381daf8b0427c6d423022 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 30 Jul 2024 16:09:10 +0200 Subject: [PATCH 09/35] fixed pypi typo --- notebooks/EPM_demo.ipynb | 2 +- notebooks/Horse_demo.ipynb | 2 +- notebooks/MABe_demo.ipynb | 2 +- notebooks/MausHaus_demo.ipynb | 2 +- 4 files changed, 4 insertions(+), 4 deletions(-) diff --git a/notebooks/EPM_demo.ipynb b/notebooks/EPM_demo.ipynb index 60a7b50..d9e9d65 100644 --- a/notebooks/EPM_demo.ipynb +++ b/notebooks/EPM_demo.ipynb @@ -51,7 +51,7 @@ "amadeus_root = Path(amadeusgpt.__file__).parent.parent\n", "config = Config(amadeus_root / \"amadeusgpt/configs/EPM_template.yaml\")\n", "config['video_info']['scene_frame_number'] = scene_frame_number\n", - "# if you install from pypl or you used the conda file, you have to adjust the path of data folder to\n", + "# if you install from pypi or you used the conda file, you have to adjust the path of data folder to\n", "# repo_root/examples/EPM\n", "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder']\n", "\n", diff --git a/notebooks/Horse_demo.ipynb b/notebooks/Horse_demo.ipynb index b2d9425..164cbdd 100644 --- a/notebooks/Horse_demo.ipynb +++ b/notebooks/Horse_demo.ipynb @@ -41,7 +41,7 @@ "config = Config(amadeus_root / \"amadeusgpt/configs/Horse_template.yaml\")\n", "\n", "config['video_info']['scene_frame_number'] = scene_frame_number\n", - "# if you install from pypl or you used the conda file, you have to adjust the path of data folder to\n", + "# if you install from pypi or you used the conda file, you have to adjust the path of data folder to\n", "# repo_root/examples/Horse\n", "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder'] \n", "\n", diff --git a/notebooks/MABe_demo.ipynb b/notebooks/MABe_demo.ipynb index 53f69bc..4e80b16 100644 --- a/notebooks/MABe_demo.ipynb +++ b/notebooks/MABe_demo.ipynb @@ -38,7 +38,7 @@ "source": [ "amadeus_root = Path(amadeusgpt.__file__).parent.parent\n", "config = Config(amadeus_root / \"amadeusgpt/configs/MABe_template.yaml\")\n", - "# if you install from pypl or you used the conda file, you have to adjust the path of data folder to\n", + "# if you install from pypi or you used the conda file, you have to adjust the path of data folder to\n", "# repo_root/examples/MABe\n", "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder']\n", "\n", diff --git a/notebooks/MausHaus_demo.ipynb b/notebooks/MausHaus_demo.ipynb index 1b1b905..c717557 100644 --- a/notebooks/MausHaus_demo.ipynb +++ b/notebooks/MausHaus_demo.ipynb @@ -43,7 +43,7 @@ "config = Config(amadeus_root / \"amadeusgpt/configs/MausHaus_template.yaml\")\n", "\n", "config['video_info']['scene_frame_number'] = scene_frame_number\n", - "# if you install from pypl or you used the conda file, you have to adjust the path of data folder to\n", + "# if you install from pypi or you used the conda file, you have to adjust the path of data folder to\n", "# repo_root/examples/MausHaus\n", "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder']\n", "\n", From e14c42f02e6ace3078ff1465f104f7a565b85187 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Wed, 31 Jul 2024 15:10:07 +0200 Subject: [PATCH 10/35] Fixed a bug in create_project. Changed default use_vlm to False. Updated demo notebooks --- amadeusgpt/analysis_objects/animal.py | 4 ++-- amadeusgpt/analysis_objects/relationship.py | 6 ++++-- amadeusgpt/main.py | 3 +-- amadeusgpt/managers/animal_manager.py | 13 ++++++++++++- amadeusgpt/managers/object_manager.py | 4 +++- amadeusgpt/managers/visual_manager.py | 2 +- amadeusgpt/project.py | 8 +++++++- notebooks/EPM_demo.ipynb | 2 +- notebooks/Horse_demo.ipynb | 2 +- notebooks/MABe_demo.ipynb | 2 +- notebooks/MausHaus_demo.ipynb | 2 +- notebooks/custom_mouse_video.ipynb | 2 +- tests/test_project_creation.py | 4 ++-- 13 files changed, 37 insertions(+), 17 deletions(-) diff --git a/amadeusgpt/analysis_objects/animal.py b/amadeusgpt/analysis_objects/animal.py index aa7ffca..529b143 100644 --- a/amadeusgpt/analysis_objects/animal.py +++ b/amadeusgpt/analysis_objects/animal.py @@ -27,8 +27,8 @@ class AnimalSeq(Animal): body center, left, right, above, top are relative to the subset of keypoints. Attributes ---------- - self._coords: arr potentially subset of keypoints - self.wholebody: full set of keypoints. This is important for overlap relationship + self.wholebody: np.ndarray of keypoints of all bodyparts + self.keypoint """ def __init__(self, animal_name: str, keypoints: ndarray, keypoint_names: List[str]): diff --git a/amadeusgpt/analysis_objects/relationship.py b/amadeusgpt/analysis_objects/relationship.py index c098f0b..0c58de1 100644 --- a/amadeusgpt/analysis_objects/relationship.py +++ b/amadeusgpt/analysis_objects/relationship.py @@ -48,13 +48,15 @@ def calc_angle_between_2d_coordinate_systems(cs1, cs2): return np.rad2deg(np.arccos(dot)) -def get_pairwise_distance(arr1, arr2): +def get_pairwise_distance(arr1:np.ndarray, + arr2:np.ndarray): # we want to make sure this uses a fast implementation - # (n_frame, n_kpts, 2) + # arr: (n_frame, n_kpts, 2) assert len(arr1.shape) == 3 and len(arr2.shape) == 3 # pariwise distance (n_frames, n_kpts, n_kpts) pairwise_distances = np.ones((arr1.shape[0], arr1.shape[1], arr2.shape[1])) * 100000 for frame_id in range(arr1.shape[0]): + # should we use the mean of all keypooints for the distance? pairwise_distances[frame_id] = cdist(arr1[frame_id], arr2[frame_id]) return pairwise_distances diff --git a/amadeusgpt/main.py b/amadeusgpt/main.py index 6fcaf5b..55ebddf 100644 --- a/amadeusgpt/main.py +++ b/amadeusgpt/main.py @@ -2,7 +2,6 @@ from amadeusgpt.config import Config from amadeusgpt.programs.sandbox import Sandbox -import yaml ########## # all these are providing the customized classes for the code execution ########## @@ -21,7 +20,7 @@ class AMADEUS: - def __init__(self, config: Config | dict, use_vlm=True): + def __init__(self, config: Config | dict, use_vlm=False): self.config = config ### fields that decide the behavior of the application self.use_self_debug = True diff --git a/amadeusgpt/managers/animal_manager.py b/amadeusgpt/managers/animal_manager.py index 96a8406..8a01904 100644 --- a/amadeusgpt/managers/animal_manager.py +++ b/amadeusgpt/managers/animal_manager.py @@ -101,7 +101,6 @@ def configure_animal_from_meta(self, meta_info): self.superanimal_name = None def init_pose(self): - if not os.path.exists(self.keypoint_file_path): # no need to initialize here return @@ -111,6 +110,9 @@ def init_pose(self): elif self.keypoint_file_path.endswith(".json"): # could be coco format all_keypoints = self._process_keypoint_file_from_json() + elif self.keypoint_file_path.endswith(".npy"): + # assuming it's for 3D keypoints + all_keypoints = self._process_keypoint_file_from_npy() for individual_id in range(self.n_individuals): animal_name = f"animal_{individual_id}" # by default, we initialize all animals with the same keypoints and all the keypoint names @@ -130,6 +132,15 @@ def init_pose(self): self.animals.append(animalseq) + def _process_keypoint_file_from_npy(self) -> ndarray: + + all_keypoints = np.load(self.keypoint_file_path) + # (n_frames, n_keypoints, 4 -> (x,y,z, confidence)) + all_keypoints = all_keypoints[..., :3] + + + return all_keypoints + def _process_keypoint_file_from_h5(self) -> ndarray: df = pd.read_hdf(self.keypoint_file_path) self.full_keypoint_names = list( diff --git a/amadeusgpt/managers/object_manager.py b/amadeusgpt/managers/object_manager.py index 12b90f7..cbd8edc 100644 --- a/amadeusgpt/managers/object_manager.py +++ b/amadeusgpt/managers/object_manager.py @@ -28,7 +28,9 @@ def __init__( self.animal_manager = animal_manager self.roi_objects = [] self.seg_objects = [] - self.load_from_disk = self.config["object_info"]["load_objects_from_disk"] + + self.load_from_disk = self.config["object_info"].get("load_objects_from_disk", False) + if self.load_from_disk: self.load_objects_from_disk() else: diff --git a/amadeusgpt/managers/visual_manager.py b/amadeusgpt/managers/visual_manager.py index 9724d7c..437cc7b 100644 --- a/amadeusgpt/managers/visual_manager.py +++ b/amadeusgpt/managers/visual_manager.py @@ -51,7 +51,7 @@ def __init__( self.object_manager = object_manager def get_scene_image(self): - scene_frame_index = self.config["video_info"]["scene_frame_number"] + scene_frame_index = self.config["video_info"].get("scene_frame_number", 1) cap = cv2.VideoCapture(self.video_file_path) cap.set(cv2.CAP_PROP_POS_FRAMES, scene_frame_index) ret, frame = cap.read() diff --git a/amadeusgpt/project.py b/amadeusgpt/project.py index 1eddd0d..9e20e21 100644 --- a/amadeusgpt/project.py +++ b/amadeusgpt/project.py @@ -20,6 +20,12 @@ def create_project(data_folder, "temperature": 0.0, "keep_last_n_messages": 2 }, + "object_info": { + "load_objects_from_disk": False, + "use_grid_objects": False + }, + "result_info" :{}, + "video_info": {} } # save the dictionary config to yaml @@ -31,7 +37,7 @@ def create_project(data_folder, yaml.dump(config, f) print (f"Project created at {result_folder}. Results will be saved to {result_folder}") - print (f"The project will load video files (*.{video_suffix}) and optionally keypoint files from {data_folder}") + print (f"The project will load video files (*{video_suffix}) and optionally keypoint files from {data_folder}") print (f"A copy of the project config file is saved at {file_path}") pprint.pprint(config) diff --git a/notebooks/EPM_demo.ipynb b/notebooks/EPM_demo.ipynb index d9e9d65..6680c20 100644 --- a/notebooks/EPM_demo.ipynb +++ b/notebooks/EPM_demo.ipynb @@ -55,7 +55,7 @@ "# repo_root/examples/EPM\n", "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder']\n", "\n", - "amadeus = AMADEUS(config)\n", + "amadeus = AMADEUS(config, use_vlm=True)\n", "video_file_paths = amadeus.get_video_file_paths()\n", "print (video_file_paths) " ] diff --git a/notebooks/Horse_demo.ipynb b/notebooks/Horse_demo.ipynb index 164cbdd..c98f6c7 100644 --- a/notebooks/Horse_demo.ipynb +++ b/notebooks/Horse_demo.ipynb @@ -45,7 +45,7 @@ "# repo_root/examples/Horse\n", "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder'] \n", "\n", - "amadeus = AMADEUS(config)\n", + "amadeus = AMADEUS(config, use_vlm = True)\n", "video_file_paths = amadeus.get_video_file_paths()\n", "print (video_file_paths) " ] diff --git a/notebooks/MABe_demo.ipynb b/notebooks/MABe_demo.ipynb index 4e80b16..51fd2b9 100644 --- a/notebooks/MABe_demo.ipynb +++ b/notebooks/MABe_demo.ipynb @@ -42,7 +42,7 @@ "# repo_root/examples/MABe\n", "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder']\n", "\n", - "amadeus = AMADEUS(config)\n", + "amadeus = AMADEUS(config, use_vlm=True)\n", "video_file_paths = amadeus.get_video_file_paths()\n", "print (video_file_paths)" ] diff --git a/notebooks/MausHaus_demo.ipynb b/notebooks/MausHaus_demo.ipynb index c717557..9ebfe04 100644 --- a/notebooks/MausHaus_demo.ipynb +++ b/notebooks/MausHaus_demo.ipynb @@ -47,7 +47,7 @@ "# repo_root/examples/MausHaus\n", "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder']\n", "\n", - "amadeus = AMADEUS(config)\n", + "amadeus = AMADEUS(config, use_vlm = True)\n", "video_file_paths = amadeus.get_video_file_paths()\n", "print (video_file_paths) " ] diff --git a/notebooks/custom_mouse_video.ipynb b/notebooks/custom_mouse_video.ipynb index 486b1e3..bcbe720 100644 --- a/notebooks/custom_mouse_video.ipynb +++ b/notebooks/custom_mouse_video.ipynb @@ -57,7 +57,7 @@ "\n", "config[\"scene_frame_number\"] = scene_frame_number\n", "\n", - "amadeus = AMADEUS(config)\n", + "amadeus = AMADEUS(config, use_vlm = True)\n", "video_file_paths = amadeus.get_video_file_paths()\n", "print (video_file_paths)" ] diff --git a/tests/test_project_creation.py b/tests/test_project_creation.py index f3bf076..f42b98d 100644 --- a/tests/test_project_creation.py +++ b/tests/test_project_creation.py @@ -8,13 +8,13 @@ # Create a project -data_folder = "temp_data_folder" +data_folder = "examples/EPM/" result_folder = "temp_result_folder" config = create_project(data_folder, result_folder) # Create an AMADEUS instance -amadeus = AMADEUS(config) +amadeus = AMADEUS(config, use_vlm=True) # query = "Plot the trajectory of the animal using the animal center and color it by time" # qa_message = amadeus.step(query) From 9d81d5484fa6a714690f04796e2879eed15cffa1 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Wed, 31 Jul 2024 15:22:53 +0200 Subject: [PATCH 11/35] removed WIP 3d keypoints --- amadeusgpt/managers/animal_manager.py | 15 ++------------- 1 file changed, 2 insertions(+), 13 deletions(-) diff --git a/amadeusgpt/managers/animal_manager.py b/amadeusgpt/managers/animal_manager.py index 8a01904..bda7e25 100644 --- a/amadeusgpt/managers/animal_manager.py +++ b/amadeusgpt/managers/animal_manager.py @@ -110,9 +110,7 @@ def init_pose(self): elif self.keypoint_file_path.endswith(".json"): # could be coco format all_keypoints = self._process_keypoint_file_from_json() - elif self.keypoint_file_path.endswith(".npy"): - # assuming it's for 3D keypoints - all_keypoints = self._process_keypoint_file_from_npy() + for individual_id in range(self.n_individuals): animal_name = f"animal_{individual_id}" # by default, we initialize all animals with the same keypoints and all the keypoint names @@ -130,16 +128,7 @@ def init_pose(self): self.config["keypoint_info"]["head_orientation_keypoints"] ) - self.animals.append(animalseq) - - def _process_keypoint_file_from_npy(self) -> ndarray: - - all_keypoints = np.load(self.keypoint_file_path) - # (n_frames, n_keypoints, 4 -> (x,y,z, confidence)) - all_keypoints = all_keypoints[..., :3] - - - return all_keypoints + self.animals.append(animalseq) def _process_keypoint_file_from_h5(self) -> ndarray: df = pd.read_hdf(self.keypoint_file_path) From dbcb6c57b2c4da50ca04cfdf4151fc4f13d742b3 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Wed, 31 Jul 2024 15:29:43 +0200 Subject: [PATCH 12/35] Fixed one more --- amadeusgpt/project.py | 1 + 1 file changed, 1 insertion(+) diff --git a/amadeusgpt/project.py b/amadeusgpt/project.py index 9e20e21..a63cdd1 100644 --- a/amadeusgpt/project.py +++ b/amadeusgpt/project.py @@ -24,6 +24,7 @@ def create_project(data_folder, "load_objects_from_disk": False, "use_grid_objects": False }, + "keypoint_info": {}, "result_info" :{}, "video_info": {} } From c60a506c0830d28e57656e6fd3f9ce711f45dda4 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Fri, 2 Aug 2024 11:38:33 +0200 Subject: [PATCH 13/35] WIP --- amadeusgpt/managers/animal_manager.py | 17 ++++------------- amadeusgpt/programs/sandbox.py | 2 +- 2 files changed, 5 insertions(+), 14 deletions(-) diff --git a/amadeusgpt/managers/animal_manager.py b/amadeusgpt/managers/animal_manager.py index 8a01904..6367dc3 100644 --- a/amadeusgpt/managers/animal_manager.py +++ b/amadeusgpt/managers/animal_manager.py @@ -84,6 +84,7 @@ def __init__(self, identifier: Identifier): self.animals: List[AnimalSeq] = [] self.full_keypoint_names = [] self.superanimal_predicted_video = None + self.superanimal_name = None self.init_pose() def configure_animal_from_meta(self, meta_info): @@ -101,6 +102,8 @@ def configure_animal_from_meta(self, meta_info): self.superanimal_name = None def init_pose(self): + + print ('self.keypoint_file_path', self.keypoint_file_path) if not os.path.exists(self.keypoint_file_path): # no need to initialize here return @@ -109,10 +112,7 @@ def init_pose(self): all_keypoints = self._process_keypoint_file_from_h5() elif self.keypoint_file_path.endswith(".json"): # could be coco format - all_keypoints = self._process_keypoint_file_from_json() - elif self.keypoint_file_path.endswith(".npy"): - # assuming it's for 3D keypoints - all_keypoints = self._process_keypoint_file_from_npy() + all_keypoints = self._process_keypoint_file_from_json() for individual_id in range(self.n_individuals): animal_name = f"animal_{individual_id}" # by default, we initialize all animals with the same keypoints and all the keypoint names @@ -132,15 +132,6 @@ def init_pose(self): self.animals.append(animalseq) - def _process_keypoint_file_from_npy(self) -> ndarray: - - all_keypoints = np.load(self.keypoint_file_path) - # (n_frames, n_keypoints, 4 -> (x,y,z, confidence)) - all_keypoints = all_keypoints[..., :3] - - - return all_keypoints - def _process_keypoint_file_from_h5(self) -> ndarray: df = pd.read_hdf(self.keypoint_file_path) self.full_keypoint_names = list( diff --git a/amadeusgpt/programs/sandbox.py b/amadeusgpt/programs/sandbox.py index 626078f..01dfec3 100644 --- a/amadeusgpt/programs/sandbox.py +++ b/amadeusgpt/programs/sandbox.py @@ -178,7 +178,7 @@ def __init__( for video_file_path, keypoint_file_path in zip( self.video_file_paths, self.keypoint_file_paths - ): + ): self.analysis_dict[video_file_path] = create_analysis( Identifier(self.config, video_file_path, keypoint_file_path) ) From 53f2f259408f069d723a0b13c62be11b5bdc580e Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Fri, 2 Aug 2024 13:46:40 +0200 Subject: [PATCH 14/35] enforcing the use of create_project in demo notebooks and modified the test --- amadeusgpt/project.py | 20 +++++++++++++++----- notebooks/EPM_demo.ipynb | 30 +++++++++++++++++++----------- notebooks/Horse_demo.ipynb | 14 +++++++++----- notebooks/MABe_demo.ipynb | 20 +++++++++++++++----- notebooks/MausHaus_demo.ipynb | 24 +++++++++++++++--------- tests/test_demo_data.py | 3 ++- tests/test_project_creation.py | 22 +++++++++++++++++----- 7 files changed, 92 insertions(+), 41 deletions(-) diff --git a/amadeusgpt/project.py b/amadeusgpt/project.py index a63cdd1..7e4a5db 100644 --- a/amadeusgpt/project.py +++ b/amadeusgpt/project.py @@ -1,11 +1,10 @@ -from amadeusgpt.config import Config import os import pprint import yaml def create_project(data_folder, result_folder, - video_suffix=".mp4"): + **kwargs): """ Create a project config file. Save the config file to the result folder """ @@ -13,7 +12,7 @@ def create_project(data_folder, "data_info": { "data_folder": data_folder, "result_folder": result_folder, - "video_suffix": video_suffix, + "video_suffix": ".mp4", }, "llm_info": { "max_tokens": 4096, @@ -24,12 +23,23 @@ def create_project(data_folder, "load_objects_from_disk": False, "use_grid_objects": False }, - "keypoint_info": {}, + "keypoint_info": {"use_3d": False, + "include_confidence": False, + }, "result_info" :{}, "video_info": {} } # save the dictionary config to yaml + def set_nested_value(d, keys, value): + for key in keys[:-1]: + d = d.setdefault(key, {}) + d[keys[-1]] = value + + for key, value in kwargs.items(): + keys = key.split('.') + set_nested_value(config, keys, value) + os.makedirs(result_folder, exist_ok=True) file_path = os.path.join(result_folder, "config.yaml") @@ -38,7 +48,7 @@ def create_project(data_folder, yaml.dump(config, f) print (f"Project created at {result_folder}. Results will be saved to {result_folder}") - print (f"The project will load video files (*{video_suffix}) and optionally keypoint files from {data_folder}") + print (f"The project will load video files (*{config['data_info']['video_suffix']}) and optionally keypoint files from {data_folder}") print (f"A copy of the project config file is saved at {file_path}") pprint.pprint(config) diff --git a/notebooks/EPM_demo.ipynb b/notebooks/EPM_demo.ipynb index 6680c20..8fecf7b 100644 --- a/notebooks/EPM_demo.ipynb +++ b/notebooks/EPM_demo.ipynb @@ -10,7 +10,9 @@ "#If th openai api key is not set already, please set it here.\n", "import os\n", "if 'OPENAI_API_KEY' not in os.environ: \n", - " os.environ['OPENAI_API_KEY'] = 'your key'\n" + " os.environ['OPENAI_API_KEY'] = 'your key'\n", + "import amadeusgpt\n", + "amadeusgpt.__file__" ] }, { @@ -20,15 +22,13 @@ "metadata": {}, "outputs": [], "source": [ - "import matplotlib\n", "%matplotlib widget\n", "from amadeusgpt import AMADEUS\n", - "from amadeusgpt.config import Config\n", "import amadeusgpt\n", "from pathlib import Path\n", - "import matplotlib.pyplot as plt\n", - "import cv2\n", - "from amadeusgpt.utils import parse_result" + "import amadeusgpt\n", + "from amadeusgpt.utils import parse_result\n", + "from amadeusgpt import create_project" ] }, { @@ -49,12 +49,20 @@ "source": [ "scene_frame_number = 400\n", "amadeus_root = Path(amadeusgpt.__file__).parent.parent\n", - "config = Config(amadeus_root / \"amadeusgpt/configs/EPM_template.yaml\")\n", - "config['video_info']['scene_frame_number'] = scene_frame_number\n", - "# if you install from pypi or you used the conda file, you have to adjust the path of data folder to\n", - "# repo_root/examples/EPM\n", - "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder']\n", "\n", + "kwargs = {\n", + " \"keypoint_info.body_orientation_keypoints.neck\" : \"nose\",\n", + " \"keypoint_info.body_orientation_keypoints.tail_base\" : \"tail_base\",\n", + " \"keypoint_info.body_orientation_keypoints.animal_center\" : \"neck\",\n", + " \"keypoint_info.head_orientation_keypoints.nose\" : \"nose\",\n", + " \"keypoint_info.head_orientation_keypoints.neck\" : \"neck\",\n", + " \"video_info.scene_frame_number\" : scene_frame_number,\n", + "}\n", + "\n", + "config = create_project(data_folder = \"../examples/EPM\",\n", + " result_folder = \"results\",\n", + " **kwargs\n", + " )\n", "amadeus = AMADEUS(config, use_vlm=True)\n", "video_file_paths = amadeus.get_video_file_paths()\n", "print (video_file_paths) " diff --git a/notebooks/Horse_demo.ipynb b/notebooks/Horse_demo.ipynb index c98f6c7..93c3b40 100644 --- a/notebooks/Horse_demo.ipynb +++ b/notebooks/Horse_demo.ipynb @@ -26,7 +26,7 @@ "from pathlib import Path\n", "import matplotlib.pyplot as plt\n", "from amadeusgpt.utils import parse_result\n", - "import cv2" + "from amadeusgpt import create_project" ] }, { @@ -40,10 +40,14 @@ "amadeus_root = Path(amadeusgpt.__file__).parent.parent\n", "config = Config(amadeus_root / \"amadeusgpt/configs/Horse_template.yaml\")\n", "\n", - "config['video_info']['scene_frame_number'] = scene_frame_number\n", - "# if you install from pypi or you used the conda file, you have to adjust the path of data folder to\n", - "# repo_root/examples/Horse\n", - "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder'] \n", + "kwargs = { \n", + " \"video_info.scene_frame_number\" : scene_frame_number,\n", + "}\n", + "\n", + "config = create_project(data_folder = \"../examples/Horse\",\n", + " result_folder = \"results\",\n", + " **kwargs\n", + " )\n", "\n", "amadeus = AMADEUS(config, use_vlm = True)\n", "video_file_paths = amadeus.get_video_file_paths()\n", diff --git a/notebooks/MABe_demo.ipynb b/notebooks/MABe_demo.ipynb index 51fd2b9..336c12e 100644 --- a/notebooks/MABe_demo.ipynb +++ b/notebooks/MABe_demo.ipynb @@ -26,7 +26,7 @@ "from amadeusgpt.utils import parse_result\n", "from pathlib import Path\n", "import matplotlib.pyplot as plt\n", - "import cv2" + "from amadeusgpt import create_project" ] }, { @@ -37,10 +37,20 @@ "outputs": [], "source": [ "amadeus_root = Path(amadeusgpt.__file__).parent.parent\n", - "config = Config(amadeus_root / \"amadeusgpt/configs/MABe_template.yaml\")\n", - "# if you install from pypi or you used the conda file, you have to adjust the path of data folder to\n", - "# repo_root/examples/MABe\n", - "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder']\n", + "\n", + "kwargs = {\n", + " \"keypoint_info.body_orientation_keypoints.neck\" : \"nose\",\n", + " \"keypoint_info.body_orientation_keypoints.tail_base\" : \"tail base\",\n", + " \"keypoint_info.body_orientation_keypoints.animal_center\" : \"neck\",\n", + " \"keypoint_info.head_orientation_keypoints.nose\" : \"nose\",\n", + " \"keypoint_info.head_orientation_keypoints.neck\" : \"neck\",\n", + " \"video_info.scene_frame_number\" : 1400,\n", + "}\n", + "\n", + "config = create_project(data_folder = \"../examples/MABe\",\n", + " result_folder = \"results\",\n", + " **kwargs\n", + " )\n", "\n", "amadeus = AMADEUS(config, use_vlm=True)\n", "video_file_paths = amadeus.get_video_file_paths()\n", diff --git a/notebooks/MausHaus_demo.ipynb b/notebooks/MausHaus_demo.ipynb index 9ebfe04..3d2c106 100644 --- a/notebooks/MausHaus_demo.ipynb +++ b/notebooks/MausHaus_demo.ipynb @@ -23,12 +23,10 @@ "import matplotlib\n", "%matplotlib widget\n", "from amadeusgpt import AMADEUS\n", - "from amadeusgpt.config import Config\n", "import amadeusgpt\n", "from pathlib import Path\n", - "import matplotlib.pyplot as plt\n", - "import cv2\n", - "from amadeusgpt.utils import parse_result" + "from amadeusgpt.utils import parse_result\n", + "from amadeusgpt import create_project" ] }, { @@ -40,12 +38,20 @@ "source": [ "scene_frame_number = 400\n", "amadeus_root = Path(amadeusgpt.__file__).parent.parent\n", - "config = Config(amadeus_root / \"amadeusgpt/configs/MausHaus_template.yaml\")\n", "\n", - "config['video_info']['scene_frame_number'] = scene_frame_number\n", - "# if you install from pypi or you used the conda file, you have to adjust the path of data folder to\n", - "# repo_root/examples/MausHaus\n", - "config['data_info']['data_folder'] = amadeus_root / config['data_info']['data_folder']\n", + "kwargs = {\n", + " \"keypoint_info.body_orientation_keypoints.neck\" : \"nose\",\n", + " \"keypoint_info.body_orientation_keypoints.tail_base\" : \"tail_base\",\n", + " \"keypoint_info.body_orientation_keypoints.animal_center\" : \"neck\",\n", + " \"keypoint_info.head_orientation_keypoints.nose\" : \"nose\",\n", + " \"keypoint_info.head_orientation_keypoints.neck\" : \"neck\",\n", + " \"video_info.scene_frame_number\" : 100,\n", + "}\n", + "\n", + "config = create_project(data_folder = \"../examples/MausHaus\",\n", + " result_folder = \"results\",\n", + " **kwargs\n", + " )\n", "\n", "amadeus = AMADEUS(config, use_vlm = True)\n", "video_file_paths = amadeus.get_video_file_paths()\n", diff --git a/tests/test_demo_data.py b/tests/test_demo_data.py index 67ba00b..c336513 100644 --- a/tests/test_demo_data.py +++ b/tests/test_demo_data.py @@ -17,7 +17,8 @@ def test_demo_data(template_name): assert len(video_file_paths) == 1 assert len(keypoint_file_paths) == 1 assert os.path.exists(os.path.join(config['data_info']['data_folder'], 'example.json')) - + query = "plot the trajectory of the animal" + qa_message = amadeus.step(query) diff --git a/tests/test_project_creation.py b/tests/test_project_creation.py index f42b98d..aa6c097 100644 --- a/tests/test_project_creation.py +++ b/tests/test_project_creation.py @@ -11,11 +11,23 @@ data_folder = "examples/EPM/" result_folder = "temp_result_folder" -config = create_project(data_folder, result_folder) +kwargs = { + "llm_info.max_tokens": 2000, + "llm_info.temperature": 0.0, + "llm_info.keep_last_n_messages": 2, + "object_info.load_objects_from_disk": False, + "object_info.use_grid_objects": False, + "keypoint_info.use_3d": False, + "keypoint_info.include_confidence": False +} + +config = create_project(data_folder, result_folder, **kwargs) + +print (config) # Create an AMADEUS instance -amadeus = AMADEUS(config, use_vlm=True) +amadeus = AMADEUS(config, use_vlm=False) -# query = "Plot the trajectory of the animal using the animal center and color it by time" -# qa_message = amadeus.step(query) -# parse_result(amadeus, qa_message) \ No newline at end of file +# let's start testing a simple query using openai api +query = "Plot the trajectory of the animal using the animal center and color it by time" +qa_message = amadeus.step(query) From d38c619a74316d1414b77c6471189ae710bb1d57 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Mon, 5 Aug 2024 20:40:03 +0200 Subject: [PATCH 15/35] 3D supported. Better tests. More flexible identifier --- amadeusgpt/analysis_objects/animal.py | 8 +- amadeusgpt/analysis_objects/llm.py | 15 ++- amadeusgpt/analysis_objects/visualization.py | 74 +++++++----- .../behavior_analysis/analysis_factory.py | 6 +- amadeusgpt/behavior_analysis/identifier.py | 20 +++- amadeusgpt/main.py | 111 ++++++++++++------ amadeusgpt/managers/animal_manager.py | 23 ++-- amadeusgpt/managers/base.py | 2 +- amadeusgpt/managers/event_manager.py | 61 +++------- amadeusgpt/managers/visual_manager.py | 41 ++++--- amadeusgpt/programs/sandbox.py | 83 ++++++------- amadeusgpt/project.py | 3 +- amadeusgpt/system_prompts/code_generator.py | 13 +- amadeusgpt/utils.py | 26 ++-- ...shaus_superanimal_topviewmouse_hrnetw32.h5 | Bin 0 -> 87469 bytes ...al_topviewmouse_hrnetw32_before_adapt.json | 1 + examples/MausHaus3D/keypoints_3d_short.h5 | Bin 0 -> 2846528 bytes notebooks/Use_Task_Program.ipynb | 5 +- tests/test_3d.py | 33 ++++++ tests/test_project_creation.py | 39 +++--- tests/test_superanimal.py | 21 ++++ tests/test_task_program.py | 38 ++++++ 22 files changed, 387 insertions(+), 236 deletions(-) create mode 100644 examples/DummyVideo/dummy_maushaus_superanimal_topviewmouse_hrnetw32.h5 create mode 100644 examples/DummyVideo/dummy_maushaus_superanimal_topviewmouse_hrnetw32_before_adapt.json create mode 100644 examples/MausHaus3D/keypoints_3d_short.h5 create mode 100644 tests/test_3d.py create mode 100644 tests/test_superanimal.py create mode 100644 tests/test_task_program.py diff --git a/amadeusgpt/analysis_objects/animal.py b/amadeusgpt/analysis_objects/animal.py index 529b143..8a670a2 100644 --- a/amadeusgpt/analysis_objects/animal.py +++ b/amadeusgpt/analysis_objects/animal.py @@ -95,8 +95,6 @@ def get_path(self, ind): return mpath.Path(verts, codes) def get_keypoints(self, average_keypoints=False) -> ndarray: - # the shape should be (n_frames, n_keypoints, 2) - # extending to 3D? assert ( len(self.keypoints.shape) == 3 ), f"keypoints shape is {self.keypoints.shape}" @@ -122,6 +120,12 @@ def get_ymin(self): def get_ymax(self): return np.nanmax(self.keypoints[..., 1], axis=1) + + def get_zmin(self): + return np.nanmin(self.keypoints[..., 2], axis=1) + + def get_zmax(self): + return np.nanmax(self.keypoints[..., 2], axis=1) def get_keypoint_names(self): return self.keypoint_names diff --git a/amadeusgpt/analysis_objects/llm.py b/amadeusgpt/analysis_objects/llm.py index 979a2b8..b7626a6 100644 --- a/amadeusgpt/analysis_objects/llm.py +++ b/amadeusgpt/analysis_objects/llm.py @@ -27,8 +27,9 @@ class LLM(AnalysisObject): def __init__(self, config): self.config = config - self.max_tokens = config.get("max_tokens", 4096) - self.gpt_model = config.get("gpt_model", "gpt-4o-mini") + + self.max_tokens = config["llm_info"].get("max_tokens", 4096) + self.gpt_model = config["llm_info"].get("gpt_model", "gpt-4o-mini") self.keep_last_n_messages = config.get("keep_last_n_messages", 2) # the list that is actually sent to gpt @@ -293,11 +294,11 @@ def speak( task_program_docs = sandbox.get_task_program_docs() if share_video_file: - video_file_path = sandbox.video_file_paths[0] + identifier = sandbox.identifiers[0] else: raise NotImplementedError("This is not implemented yet") - behavior_analysis = sandbox.analysis_dict[video_file_path] + behavior_analysis = sandbox.analysis_dict[identifier] scene_image = behavior_analysis.visual_manager.get_scene_image() keypoint_names = behavior_analysis.animal_manager.get_keypoint_names() object_names = behavior_analysis.object_manager.get_object_names() @@ -310,7 +311,7 @@ def speak( keypoint_names, object_names, animal_names, - ) + ) self.update_history("system", self.system_prompt) @@ -338,6 +339,10 @@ def speak( with open("temp_answer.json", "w") as f: obj = {} obj["chain_of_thought"] = text + obj['code'] = function_code + obj['video_file_paths'] = sandbox.video_file_paths + obj['keypoint_file_paths'] = sandbox.keypoint_file_paths + obj['config'] = str(sandbox.config) json.dump(obj, f, indent=4) return qa_message diff --git a/amadeusgpt/analysis_objects/visualization.py b/amadeusgpt/analysis_objects/visualization.py index 6922448..2be3111 100644 --- a/amadeusgpt/analysis_objects/visualization.py +++ b/amadeusgpt/analysis_objects/visualization.py @@ -13,6 +13,7 @@ from matplotlib.ticker import FuncFormatter from mpl_toolkits.axes_grid1 import make_axes_locatable from PIL import Image +from mpl_toolkits.mplot3d import Axes3D from scipy.signal import medfilt from amadeusgpt.analysis_objects.event import get_fps, get_video_length @@ -125,7 +126,8 @@ def draw(self, **kwargs) -> None: self._draw_seg_objects() self._draw_roi_objects() - self.axs.imshow(self.scene_frame) + if self.scene_frame is not None: + self.axs.imshow(self.scene_frame) class KeypointVisualization(MatplotlibVisualization): @@ -284,37 +286,47 @@ def _event_plot_trajectory(self, **kwargs): masked_data = medfilt(masked_data, kernel_size=(k, 1)) if masked_data.shape[0] == 0: continue - x, y = masked_data[:, 0], masked_data[:, 1] - x = x[x.nonzero()] - y = y[y.nonzero()] - if len(x) < 1: - continue - scatter = self.axs.plot( - x, - y, - label=f"event{event_id}", - color=line_colors[event_id], - alpha=0.5, - ) - scatter = self.axs.scatter( - x[0], - y[0], - marker="*", - s=100, - color=line_colors[event_id], - alpha=0.5, - **kwargs, - ) - self.axs.scatter( - x[-1], - y[-1], - marker="x", - s=100, - color=line_colors[event_id], - alpha=0.5, - **kwargs, - ) + if not kwargs.get("use_3d", False): + x, y = masked_data[:, 0], masked_data[:, 1] + _mask = (x!=0) & (y!=0) + + x = x[_mask] + y = y[_mask] + if len(x) < 1: + continue + + scatter = self.axs.plot( + x, + y, + label=f"event{event_id}", + color=line_colors[event_id], + alpha=0.5, + ) + scatter = self.axs.scatter( + x[0], + y[0], + marker="*", + s=100, + color=line_colors[event_id], + alpha=0.5, + **kwargs, + ) + self.axs.scatter( + x[-1], + y[-1], + marker="x", + s=100, + color=line_colors[event_id], + alpha=0.5, + **kwargs, + ) + else: + #TODO + # implement 3d event plot + pass + + return self.axs def display(self): diff --git a/amadeusgpt/behavior_analysis/analysis_factory.py b/amadeusgpt/behavior_analysis/analysis_factory.py index d248cbb..9113648 100644 --- a/amadeusgpt/behavior_analysis/analysis_factory.py +++ b/amadeusgpt/behavior_analysis/analysis_factory.py @@ -7,6 +7,6 @@ def create_analysis(identifier: Identifier): - if str(identifier) not in analysis_fac: - analysis_fac[str(identifier)] = AnimalBehaviorAnalysis(identifier) - return analysis_fac[str(identifier)] + if identifier not in analysis_fac: + analysis_fac[identifier] = AnimalBehaviorAnalysis(identifier) + return analysis_fac[identifier] diff --git a/amadeusgpt/behavior_analysis/identifier.py b/amadeusgpt/behavior_analysis/identifier.py index 79d882d..b95fdd5 100644 --- a/amadeusgpt/behavior_analysis/identifier.py +++ b/amadeusgpt/behavior_analysis/identifier.py @@ -11,17 +11,27 @@ class Identifier: Can be more in the future """ - def __init__(self, config: Config, video_file_path: str, keypoint_file_path: str): + def __init__(self, config: Config | dict, video_file_path: str, keypoint_file_path: str): self.config = config self.video_file_path = video_file_path self.keypoint_file_path = keypoint_file_path def __str__(self): - return os.path.abspath(self.video_file_path) - + return f"""------ +video_file_path: {self.video_file_path} +keypoint_file_path: {self.keypoint_file_path} +config: {self.config} +------ +""" def __eq__(self, other): - return self.video_file_path == other.video_file_path + if os.path.exists(self.video_file_path): + return os.path.abspath(self.video_file_path) == os.path.abspath(other.video_file_path) + else: + return os.path.abspath(self.keypoint_file_path) == os.path.abspath(other.keypoint_file_path) def __hash__(self): - return hash(self.video_file_path) + if os.path.exists(self.video_file_path): + return hash(os.path.abspath(self.video_file_path)) + else: + return hash(os.path.abspath(self.keypoint_file_path)) diff --git a/amadeusgpt/main.py b/amadeusgpt/main.py index 55ebddf..0f16ed1 100644 --- a/amadeusgpt/main.py +++ b/amadeusgpt/main.py @@ -17,10 +17,14 @@ VisualLLM) from amadeusgpt.integration_module_hub import IntegrationModuleHub from amadeusgpt.programs.task_program_registry import TaskProgramLibrary +from amadeusgpt.behavior_analysis.identifier import Identifier class AMADEUS: - def __init__(self, config: Config | dict, use_vlm=False): + def __init__(self, + config: Config | dict, + use_vlm=False, + movie2keypoint_func=None): self.config = config ### fields that decide the behavior of the application self.use_self_debug = True @@ -35,28 +39,17 @@ def __init__(self, config: Config | dict, use_vlm=False): ### For the sake of multiple animal, we store multiple sandboxes ### the example {video_file_path : sandbox } - data_info = config["data_info"] - self.result_folder: str = data_info["result_folder"] - - data_folder = Path(data_info["data_folder"]) - video_suffix = data_info["video_suffix"] - video_file_paths = glob.glob(str(data_folder / f"*{video_suffix}")) - - if len(video_file_paths) == 0: - print (f"No video files found in the data folder {data_folder}. Please check the data folder and the video suffix") - return - # optionally get the corresponding keypoint files - keypoint_file_paths = self.get_DLC_keypoint_files(video_file_paths) - - assert len(video_file_paths) == len( - keypoint_file_paths - ), "The number of video files and keypoint files should be the same" - - self.sandbox = Sandbox(config, video_file_paths, keypoint_file_paths) - - self.code_generator_llm = CodeGenerationLLM(config.get("llm_info", {})) - self.self_debug_llm = SelfDebugLLM(config.get("llm_info", {})) - self.visual_llm = VisualLLM(config.get("llm_info", {})) + self.result_folder: str = config["data_info"]["result_folder"] + self.data_folder = config["data_info"]['data_folder'] + self.video_suffix = config["data_info"]["video_suffix"] + + self.video_file_paths, self.keypoint_file_paths = self.fetch_data_from_data_folder(movie2keypoint_func) + + self.sandbox = Sandbox(config, self.video_file_paths, self.keypoint_file_paths) + + self.code_generator_llm = CodeGenerationLLM(config) + self.self_debug_llm = SelfDebugLLM(config) + self.visual_llm = VisualLLM(config) #### @@ -71,6 +64,55 @@ def __init__(self, config: Config | dict, use_vlm=False): if use_vlm: self.sandbox.configure_using_vlm() + def fetch_data_from_data_folder(self, movie2keypoint_func): + """ + 1) video file exists, keypoint file does not exist + 2) video file and keypoint file both exist and one-to-one mapping + 3) many video files and one keypoint file (many-to-one mapping), perhaps for 3D data + 4) none of video file and keypoint file exist + 5) only keypoint file exists + + Returns + ------- + video_file_paths: list[str] + a list of video file paths + keypoint_file_paths: list[str] + a list of keypoint file paths + + """ + video_files = glob.glob(os.path.join(self.data_folder, f"*{self.video_suffix}")) + video_files = [video_file for video_file in video_files if "labeled" not in video_file] + keypoint_files = glob.glob(os.path.join(self.data_folder, "*.h5")) + # case 1 + if movie2keypoint_func is None: + if len(video_files) > 0 and len(keypoint_files) == 0: + return video_files, [""] * len(video_files) + # case 2 + elif len(video_files) > 0 and len(video_files) == len(keypoint_files): + return video_files, self.get_DLC_keypoint_files(video_files) + # case 3 + elif len(video_files) > 1 and len(keypoint_files) == 1: + print ("We assume this is 3D data with multiple video files and one keypoint file") + return video_files, [keypoint_files[0]] * len(video_files) + # case 4 + elif len(video_files) == 0 and len(keypoint_files) == 0: + raise ValueError("No video files and keypoint files found in the data folder") + # case 5 + elif len(video_files) == 0 and len(keypoint_files) > 0: + print ("No video found. We proceed with the keypoint file only") + return [""] * len(keypoint_files), keypoint_files + else: + return video_files, keypoint_files + else: + if len(video_files) > 0: + return video_files, [movie2keypoint_func(video_file) for video_file in video_files] + else: + raise ValueError("No video files found in the data folder") + + + + + def get_DLC_keypoint_files(self, video_file_paths: list[str]): ret = [] # how to get the filename from the path file @@ -107,33 +149,28 @@ def match_integration_module(self, user_query: str) -> list: def step(self, user_query: str) -> QA_Message: integration_module_names = self.match_integration_module(user_query) - - # print ('integration modules?') - # print (integration_module_names) - + self.sandbox.update_matched_integration_modules(integration_module_names) qa_message = self.sandbox.llm_step(user_query) return qa_message def get_video_file_paths(self) -> list[str]: - data_info = self.config["data_info"] - data_folder = data_info['data_folder'] - video_suffix = data_info['video_suffix'] - video_file_paths = glob.glob(os.path.join(data_folder, f"*{video_suffix}")) - - return video_file_paths - + return self.video_file_paths def get_keypoint_file_paths(self) -> list[str]: - return self.sandbox.keypoint_file_paths + return self.keypoint_file_paths + + def get_behavior_analysis(self, + video_file_path: str = "", + keypoint_file_path: str = ""): - def get_behavior_analysis(self, video_file_path: str): """ Every sandbox stores a unique "behavior analysis" instance in its namespace Therefore, get analysis gets the current sandbox's analysis. """ - analysis = self.sandbox.namespace_dict[video_file_path]["behavior_analysis"] + identifier = Identifier(self.config, video_file_path, keypoint_file_path) + analysis = self.sandbox.namespace_dict[identifier]["behavior_analysis"] return analysis diff --git a/amadeusgpt/managers/animal_manager.py b/amadeusgpt/managers/animal_manager.py index 6367dc3..94da999 100644 --- a/amadeusgpt/managers/animal_manager.py +++ b/amadeusgpt/managers/animal_manager.py @@ -102,8 +102,7 @@ def configure_animal_from_meta(self, meta_info): self.superanimal_name = None def init_pose(self): - - print ('self.keypoint_file_path', self.keypoint_file_path) + if not os.path.exists(self.keypoint_file_path): # no need to initialize here return @@ -145,9 +144,15 @@ def _process_keypoint_file_from_h5(self) -> ndarray: self.n_frames = df.shape[0] self.n_kpts = len(self.keypoint_names) - df_array = df.to_numpy().reshape( - (self.n_frames, self.n_individuals, self.n_kpts, -1) - )[..., :2] + # whether to keep the 3rd dimension in the last axis + if self.config['keypoint_info'].get('use_3d', False) == True or self.config['keypoint_info'].get('include_confidence', False) == True: + df_array = df.to_numpy().reshape( + (self.n_frames, self.n_individuals, self.n_kpts, -1) + ) + else: + df_array = df.to_numpy().reshape( + (self.n_frames, self.n_individuals, self.n_kpts, -1) + )[..., :2] df_array = reject_outlier_keypoints(df_array) df_array = ast_fillna_2d(df_array) @@ -257,7 +262,9 @@ def get_animal_by_name(self, name: str) -> AnimalSeq: @register_core_api def get_keypoints(self) -> ndarray: """ - Get the keypoints of animals. The shape is of shape n_frames, n_individuals, n_kpts, n_dims + Get the keypoints of animals. The keypoints are of shape (n_frames, n_individuals, n_kpts, n_dims) + n_dims is 2 (x,y) for 2D keypoints and 3 (x,y,z) for 3D keypoints. + Do not forget the n_individuals dimension. If there is only one animal, the n_individuals dimension is 1. Optionally, you can pass a list of events to filter the keypoints based on the events. """ @@ -312,7 +319,7 @@ def get_speed( @register_core_api def get_velocity(self) -> ndarray: """ - Get the velocity. The shape is (n_frames, n_individuals, n_kpts, 2) # 2 is the x and y components + Get the velocity. The shape is (n_frames, n_individuals, n_kpts, n_dim) n_dim is 2 or 3 The velocity is a vector. """ return np.stack([animal.get_velocity() for animal in self.animals], axis=1) @@ -344,7 +351,7 @@ def get_n_kpts(self) -> int: @register_core_api def get_keypoint_names(self) -> List[str]: """ - Get the names of the bodyparts. + Get the names of the bodyparts. This is used to index the keypoints for a specific bodypart. """ # this is to initialize self.get_keypoints() diff --git a/amadeusgpt/managers/base.py b/amadeusgpt/managers/base.py index 438b6f2..e4f3fcb 100644 --- a/amadeusgpt/managers/base.py +++ b/amadeusgpt/managers/base.py @@ -70,7 +70,7 @@ def __call__(self, *args, **kwargs): class Manager(BaseManager): - def __init__(self, config: Config, use_cache: bool = False): + def __init__(self, config: Config|dict, use_cache: bool = False): self.config = config self.use_cache = use_cache self._cache = LRUCache(maxsize=128) diff --git a/amadeusgpt/managers/event_manager.py b/amadeusgpt/managers/event_manager.py index f23cffd..7702c08 100644 --- a/amadeusgpt/managers/event_manager.py +++ b/amadeusgpt/managers/event_manager.py @@ -153,74 +153,41 @@ def get_animals_object_events( @register_core_api def get_animals_state_events( self, - query: str, - bodypart_names: Optional[List[str]] = None, - min_window: Optional[int] = 0, - max_window: Optional[int] = 1000000, - smooth_window_size: Optional[int] = 3, + mask: np.ndarray, + min_window: int = 10, + max_window: int = 1000000, ) -> List[Event]: """ Parameters - ---------- - query: str - Takes the form of {type_of_query}{comparison operator}{numerical value}. - For example, at 'speed>50', type_of_query is 'speed', comparison operator is '>', and numerical value is 50. - Valid type_of_query ONLY INCLUDE "speed", "acceleration_mag" (magnitude of acceleration), "bodypart_pairwise_distance". - There can only be one compasion operator in the query. + ---------- + mask: np.ndarray, optional. + The mask must be of shape (n_frames, n_individuals). It is a boolean mask that describes the condition for the behavior. + If n_individuals is 1, the shape should be (n_frames, 1) Returns ------- List[Event] -------- - """ - if min_window is None: - min_window = 0 - if max_window is None: - max_window = 1000000 - if smooth_window_size is None: - smooth_window_size = 3 + if len(mask.shape) == 1: + mask = mask.reshape(-1, 1) + - if bodypart_names is not None: - self.animal_manager.update_roi_keypoint_by_names(bodypart_names) ret_events = [] - pattern = r"(==|<=|>=|<|>)" - # note we need to strip off the spaces - comparison_operator = re.findall(pattern, query)[0].strip() - query_name = query.split(comparison_operator)[0].strip() - comparison = comparison_operator + "".join(query.split(comparison_operator)[1:]) - - for sender_animal_name in self.animal_manager.get_animal_names(): - # to construct the events - - state = self.animal_manager.query_animal_states( - sender_animal_name, query_name - ) - - # must be of shape (n_frames, n_kpts, n_dim) - assert ( - len(state.shape) == 3 - ), f"state shape is {state.shape}. It must be of shape (n_frames, n_kpts, n_dim)" - if len(state.shape) == 3: - state = np.nanmedian(state, axis=(1, 2)) - relation_string = "state" + comparison - - mask = eval(relation_string) + for animal_idx, sender_animal_name in enumerate(self.animal_manager.get_animal_names()): + # to construct the events events = Event.mask2events( - mask, + mask[:, animal_idx], self.video_file_path, sender_animal_name, set(), set(), - smooth_window_size=smooth_window_size, ) events = Event.filter_events_by_duration(events, min_window, max_window) ret_events.extend(events) - ret_events = sorted(ret_events, key=lambda x: x.start) - if bodypart_names is not None: - self.animal_manager.restore_roi_keypoint() + ret_events = sorted(ret_events, key=lambda x: x.start) return ret_events diff --git a/amadeusgpt/managers/visual_manager.py b/amadeusgpt/managers/visual_manager.py index 437cc7b..bb0a020 100644 --- a/amadeusgpt/managers/visual_manager.py +++ b/amadeusgpt/managers/visual_manager.py @@ -42,20 +42,20 @@ def __init__( super().__init__(identifier.config) self.config = identifier.config self.video_file_path = identifier.video_file_path - self.keypoint_file_path = identifier.keypoint_file_path - - if not os.path.exists(self.video_file_path): - return + self.keypoint_file_path = identifier.keypoint_file_path self.animal_manager = animal_manager self.object_manager = object_manager def get_scene_image(self): scene_frame_index = self.config["video_info"].get("scene_frame_number", 1) - cap = cv2.VideoCapture(self.video_file_path) - cap.set(cv2.CAP_PROP_POS_FRAMES, scene_frame_index) - ret, frame = cap.read() - return frame + if os.path.exists(self.video_file_path): + cap = cv2.VideoCapture(self.video_file_path) + cap.set(cv2.CAP_PROP_POS_FRAMES, scene_frame_index) + ret, frame = cap.read() + return frame + else: + return None def get_scene_visualization( self, @@ -218,6 +218,7 @@ def get_keypoint_visualization( self.animal_manager.get_n_individuals(), average_keypoints=average_keypoints, events=events, + use_3d = self.config['keypoint_info'].get('use_3d', False) ) scene_vis.draw() keypoint_vis.draw() @@ -230,10 +231,15 @@ def get_keypoint_visualization( fig, axs = plt.subplots(self.animal_manager.get_n_individuals()) axs = np.atleast_1d(axs) + for idx, sender_animal in enumerate(self.animal_manager.get_animals()): - scene_vis = self.get_scene_visualization( - self.config["video_info"]["scene_frame_number"], axs=axs[idx] - ) + if not self.config['keypoint_info'].get('use_3d', False): + + scene_vis = self.get_scene_visualization( + self.config["video_info"]["scene_frame_number"], axs=axs[idx] + ) + scene_vis.draw() + axs[idx].set_ylabel(sender_animal.get_name()) # always feed the full keypoints to visualization full_keypoints = sender_animal.get_keypoints( @@ -252,7 +258,7 @@ def get_keypoint_visualization( average_keypoints=average_keypoints, events=events, ) - scene_vis.draw() + keypoint_vis.draw() if render: @@ -531,11 +537,12 @@ def write_video(self, out_folder, video_file_path, out_name, events): if time_slice[0] <= current_frame < time_slice[1]: # select the keypoint based on the frame number - frame = self.sender_visual_cone_on_frame( - self.animal_manager.get_animal_by_name(sender_animal_name), - frame, - current_frame, - ) + if self.config['keypoint_info'].get('head_orientation_keypoints', False): + frame = self.sender_visual_cone_on_frame( + self.animal_manager.get_animal_by_name(sender_animal_name), + frame, + current_frame, + ) sender_location = sender_keypoints[current_frame] diff --git a/amadeusgpt/programs/sandbox.py b/amadeusgpt/programs/sandbox.py index 01dfec3..c7a764f 100644 --- a/amadeusgpt/programs/sandbox.py +++ b/amadeusgpt/programs/sandbox.py @@ -175,17 +175,15 @@ def __init__( self.keypoint_file_paths = keypoint_file_paths self.namespace_dict = {} self.analysis_dict = {} - + self.identifiers = [] + for video_file_path, keypoint_file_path in zip( self.video_file_paths, self.keypoint_file_paths - ): - self.analysis_dict[video_file_path] = create_analysis( - Identifier(self.config, video_file_path, keypoint_file_path) - ) - - for video_file_path in self.video_file_paths: - self.namespace_dict[video_file_path] = {"__builtins__": __builtins__} - + ): + identifier = Identifier(self.config, video_file_path, keypoint_file_path) + self.identifiers.append(identifier) + self.analysis_dict[identifier] = create_analysis(identifier) + self.namespace_dict[identifier] = {"__builtins__": __builtins__} # update_namespace initializes behavior analysis self.update_namespace() @@ -210,7 +208,7 @@ def __init__( self.message_cache: defaultdict[str, QA_Message] = defaultdict() # configure how to save the results to a result folder self.result_folder = Path( - self.config["result_info"].get("result_folder", "./results") + self.config["data_info"].get("result_folder", "results") ) def configure_using_vlm(self): @@ -223,11 +221,10 @@ def configure_using_vlm(self): "background_objects": ["laboratory equipment", "white surface", "colored dots"] } """ - for video_file_path, analysis in self.analysis_dict.items(): - scene_image = analysis.visual_manager.get_scene_image() + for identifier, analysis in self.analysis_dict.items(): + scene_image = analysis.visual_manager.get_scene_image() json_obj = self.llms["visual_llm"].speak(self, scene_image) - - self.meta_info[video_file_path] = json_obj + self.meta_info[identifier] = json_obj # configure meta info on the analysis managers analysis.animal_manager.configure_animal_from_meta(json_obj) @@ -275,12 +272,12 @@ def get_query_block(self) -> str: ret = f"```query\n {query}\n```" return ret - def get_analysis(self, video_file_path): + def get_analysis(self, identifier): """ Every sandbox stores a unique "behavior analysis" instance in its namespace Therefore, get analysis gets the current sandbox's analysis. """ - analysis = self.analysis_dict[video_file_path] + analysis = self.analysis_dict[identifier] return analysis def update_matched_integration_modules(self, matched_modules): @@ -289,10 +286,10 @@ def update_matched_integration_modules(self, matched_modules): def update_namespace(self): # we need to manage the scope of the session # there are potentially new variables, new task programs, new apis - for video_file_path, analysis in self.analysis_dict.items(): - - namespace = self.namespace_dict[video_file_path] - + for video_file_path, keypoint_file_path in zip(self.video_file_paths, self.keypoint_file_paths): + identifier = Identifier(self.config, video_file_path, keypoint_file_path) + analysis = self.analysis_dict[identifier] + namespace = self.namespace_dict[identifier] for api in self.api_registry.values(): f = wrap_instance_method(analysis, api["name"]) namespace[api["name"]] = f @@ -324,7 +321,7 @@ def update_namespace(self): # to allow the program to access existing task programs namespace["task_programs"] = TaskProgramLibrary.get_task_programs() - def code_execution(self, qa_message: QA_Message) -> QA_Message: + def code_execution(self, qa_message: QA_Message, debug = True) -> QA_Message: # update the namespace in the beginning of code execution makes sure that # if there is a change in the config, we always use the newest config self.update_namespace() @@ -332,29 +329,33 @@ def code_execution(self, qa_message: QA_Message) -> QA_Message: for video_file_path, keypoint_file_path in zip( self.video_file_paths, self.keypoint_file_paths ): - namespace = self.namespace_dict[video_file_path] + identifier = Identifier(self.config, video_file_path, keypoint_file_path) + namespace = self.namespace_dict[identifier] + namespace["identifier"] = identifier + code = qa_message.code # not need to do further if´ there was no code found if code is None: continue exec(code, namespace) - - identifier = Identifier(self.config, video_file_path, keypoint_file_path) - namespace["identifier"] = identifier - + # call the main function function_name = self.get_function_name_from_string(code) call_str = f"{function_name}(identifier)" try: exec(f"result = {call_str}", namespace) - qa_message.error_message[video_file_path] = None + qa_message.error_message[identifier] = None except Exception as e: + + print("error occurs in code execution") # use traceback to get full error full_traceback = traceback.format_exc() print(full_traceback) - qa_message.error_message[video_file_path] = str(full_traceback) + qa_message.error_message[identifier] = str(full_traceback) + if not debug: + return qa_message qa_message = self.llms["self_debug"].speak( qa_message ) @@ -363,7 +364,7 @@ def code_execution(self, qa_message: QA_Message) -> QA_Message: ) return qa_message result = namespace["result"] - qa_message.function_rets[video_file_path] = result + qa_message.function_rets[identifier] = result return qa_message @@ -395,11 +396,10 @@ def register_llm(self, name, llm): self.llms[name] = llm def events_to_videos( - self, video_file_path: str, events: list[Event], function_name: str + self, identifier: Identifier, events: list[Event], function_name: str ): - analysis = self.analysis_dict[video_file_path] - + analysis = self.analysis_dict[identifier] visual_manager = analysis.visual_manager # save video clips to the result folder out_folder = str(self.result_folder) @@ -416,13 +416,14 @@ def render_qa_message(self, qa_message: QA_Message) -> QA_Message: if the function returns is a tuple of axe and figure, we put them into the plots filed """ - for video_file_path in self.video_file_paths: + for video_file_path, keypoint_file_path in zip(self.video_file_paths, self.keypoint_file_paths): + identifier = Identifier(self.config, video_file_path, keypoint_file_path) - namespace = self.namespace_dict[video_file_path] - function_rets = qa_message.function_rets[video_file_path] + namespace = self.namespace_dict[identifier] + function_rets = qa_message.function_rets[identifier] behavior_analysis = namespace["behavior_analysis"] bodypart_names = behavior_analysis.animal_manager.get_keypoint_names() - qa_message.pose_video[video_file_path] = ( + qa_message.pose_video[identifier] = ( behavior_analysis.animal_manager.superanimal_predicted_video ) visual_manager = behavior_analysis.visual_manager @@ -450,9 +451,9 @@ def render_qa_message(self, qa_message: QA_Message) -> QA_Message: bodypart_names=bodypart_names, events=e ) ) - qa_message.out_videos[video_file_path] = ( + qa_message.out_videos[identifier] = ( self.events_to_videos( - video_file_path, + identifier, e, self.get_function_name_from_string(qa_message.code), ) @@ -472,13 +473,13 @@ def render_qa_message(self, qa_message: QA_Message) -> QA_Message: plots.append( visual_manager.get_ethogram_visualization(events=function_rets) ) - qa_message.out_videos[video_file_path] = self.events_to_videos( - video_file_path, + qa_message.out_videos[identifier] = self.events_to_videos( + identifier, function_rets, self.get_function_name_from_string(qa_message.code), ) - qa_message.plots[video_file_path].extend(plots) + qa_message.plots[identifier].extend(plots) return qa_message def llm_step(self, user_query: str): diff --git a/amadeusgpt/project.py b/amadeusgpt/project.py index 7e4a5db..cb488c3 100644 --- a/amadeusgpt/project.py +++ b/amadeusgpt/project.py @@ -26,8 +26,7 @@ def create_project(data_folder, "keypoint_info": {"use_3d": False, "include_confidence": False, }, - "result_info" :{}, - "video_info": {} + "video_info": {"scene_frame_number": 1} } # save the dictionary config to yaml diff --git a/amadeusgpt/system_prompts/code_generator.py b/amadeusgpt/system_prompts/code_generator.py index 410d728..2cd764b 100644 --- a/amadeusgpt/system_prompts/code_generator.py +++ b/amadeusgpt/system_prompts/code_generator.py @@ -6,11 +6,12 @@ def code_related_prompt( object_names, animal_names, ): - - image_h, image_w = scene_image.shape[:2] + if scene_image is not None: + image_h, image_w = scene_image.shape[:2] + else: + image_h, image_w = "not available", "not available" prompt = f""" - We provide you additionl apis and task programs to help you write code. coreapidocs: this block contains information about the core apis for class AnimalBehaviorAnalysis. They do not contain implementation details but you can use them to write code @@ -77,6 +78,8 @@ def get_watching_events(identifier): 7) if your plotting code plots coordinates of keypoints, make sure you invert y axis so that the plot is consistent with the image 8) make sure the xlim and ylim covers the whole image. The image (h,w) is ({image_h},{image_w}) 9) Do not define your own objects (including grid objects). Only use objects that are given to you. +10) You MUST use the index from get_keypoint_names to access the keypoint data of specific keyponit names. Do not assume the order of the bodypart. +11) You MUST call functions in api docs on the analysis object. """ return prompt @@ -92,7 +95,6 @@ def _get_system_prompt( system_prompt = f""" You are helpful AI assistant. Your job is to answer user queries. Importantly, before you write the code, you need to explain whether the question can be answered accurately by code. If not, ask users to give more information. - {code_related_prompt(core_api_docs, task_program_docs, scene_image, @@ -104,6 +106,5 @@ def _get_system_prompt( If the question can be answered by code: - YOU MUST only write one function and no other classes or functions when you write code. -""" - +""" return system_prompt diff --git a/amadeusgpt/utils.py b/amadeusgpt/utils.py index 40cd0b9..0b65e07 100644 --- a/amadeusgpt/utils.py +++ b/amadeusgpt/utils.py @@ -215,8 +215,11 @@ def create_qa_message(query: str, video_file_paths: list[str]) -> QA_Message: from IPython.display import Markdown, Video, display -def parse_result(amadeus, qa_message): - display(Markdown(qa_message.chain_of_thought)) +def parse_result(amadeus, qa_message, use_ipython = True): + if use_ipython: + display(Markdown(qa_message.chain_of_thought)) + else: + print (qa_message.chain_of_thought) sandbox = amadeus.sandbox qa_message = sandbox.code_execution(qa_message) qa_message = sandbox.render_qa_message(qa_message) @@ -225,13 +228,16 @@ def parse_result(amadeus, qa_message): print( "Open it with media player if it does not properly display in the notebook" ) - if len(qa_message.out_videos) > 0: - for video_path, event_videos in qa_message.out_videos.items(): - for event_video in event_videos: - display(Video(event_video, embed=True)) - - if len(qa_message.function_rets) > 0: - for video_file_path in qa_message.function_rets: - display(Markdown(str(qa_message.function_rets[video_file_path]))) + if use_ipython: + if len(qa_message.out_videos) > 0: + for video_path, event_videos in qa_message.out_videos.items(): + for event_video in event_videos: + display(Video(event_video, embed=True)) + + if use_ipython: + if len(qa_message.function_rets) > 0: + for video_file_path in qa_message.function_rets: + display(Markdown(str(qa_message.function_rets[video_file_path]))) + return qa_message diff --git a/examples/DummyVideo/dummy_maushaus_superanimal_topviewmouse_hrnetw32.h5 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-114,7 +115,7 @@ "metadata": {}, "outputs": [], "source": [ - "behavior_analysis = amadeus.get_behavior_analysis('/Users/shaokaiye/AmadeusGPT-dev/examples/MABe/EGS8X2MN4SSUGFWAV976.mp4')\n", + "behavior_analysis = amadeus.get_behavior_analysis(video_file_path='/Users/shaokaiye/AmadeusGPT-dev/examples/MABe/EGS8X2MN4SSUGFWAV976.mp4')\n", "scene_image = behavior_analysis.visual_manager.get_scene_image()\n", "plt.imshow(scene_image)" ] diff --git a/tests/test_3d.py b/tests/test_3d.py new file mode 100644 index 0000000..f78576f --- /dev/null +++ b/tests/test_3d.py @@ -0,0 +1,33 @@ + + +from amadeusgpt import create_project +from amadeusgpt import AMADEUS +from amadeusgpt.utils import parse_result + + +def test_3d_maushaus(): + + kwargs = { + 'keypoint_info.use_3d': True, + 'llm_info.gpt_model': "gpt-4o-mini" + } + + config = create_project(data_folder="examples/MausHaus3D", + result_folder="3d_results", + **kwargs) + + + amadeus = AMADEUS(config, use_vlm=False) + + behavior_analysis = amadeus.get_behavior_analysis(keypoint_file_path=amadeus.get_keypoint_file_paths()[0]) + + assert behavior_analysis.get_keypoints().shape == (1000, 1, 30, 3) + assert behavior_analysis.get_velocity().shape == (1000, 1, 30, 3) + assert behavior_analysis.get_speed().shape == (1000, 1, 30, 1) + assert behavior_analysis.get_acceleration_mag().shape == (1000, 1, 30, 1) + + query = "plot the 3D trajectory of the animal." + + qa_message = amadeus.step(query) + + parse_result(amadeus, qa_message, use_ipython=False) \ No newline at end of file diff --git a/tests/test_project_creation.py b/tests/test_project_creation.py index aa6c097..36e242a 100644 --- a/tests/test_project_creation.py +++ b/tests/test_project_creation.py @@ -1,33 +1,34 @@ import os from amadeusgpt import create_project from amadeusgpt import AMADEUS -from amadeusgpt.utils import parse_result +import pytest if 'OPENAI_API_KEY' not in os.environ: os.environ['OPENAI_API_KEY'] = 'your key' # Create a project -data_folder = "examples/EPM/" -result_folder = "temp_result_folder" +@pytest.mark.parametrize("example_name", ["MausHaus", "MausHaus3D"]) +def test_project_creation(example_name): -kwargs = { - "llm_info.max_tokens": 2000, - "llm_info.temperature": 0.0, - "llm_info.keep_last_n_messages": 2, - "object_info.load_objects_from_disk": False, - "object_info.use_grid_objects": False, - "keypoint_info.use_3d": False, - "keypoint_info.include_confidence": False -} + data_folder = f"examples/{example_name}" + result_folder = "temp_result_folder" -config = create_project(data_folder, result_folder, **kwargs) + kwargs = { + "llm_info.max_tokens": 2000, + "llm_info.temperature": 0.0, + "llm_info.keep_last_n_messages": 2, + "object_info.load_objects_from_disk": False, + "object_info.use_grid_objects": False, + "keypoint_info.use_3d": False, + "keypoint_info.include_confidence": False + } -print (config) + config = create_project(data_folder, result_folder, **kwargs) -# Create an AMADEUS instance -amadeus = AMADEUS(config, use_vlm=False) + # Create an AMADEUS instance + amadeus = AMADEUS(config, use_vlm=False) -# let's start testing a simple query using openai api -query = "Plot the trajectory of the animal using the animal center and color it by time" -qa_message = amadeus.step(query) + # let's start testing a simple query using openai api + query = "Plot the trajectory of the animal using the animal center and color it by time" + qa_message = amadeus.step(query) diff --git a/tests/test_superanimal.py b/tests/test_superanimal.py new file mode 100644 index 0000000..e1c0d7d --- /dev/null +++ b/tests/test_superanimal.py @@ -0,0 +1,21 @@ +from amadeusgpt import AMADEUS +from amadeusgpt import create_project +from amadeusgpt.utils import parse_result + + +def test_superanimal(): + # the dummy video only contains 6 frames. + kwargs = { + 'video_info.scene_frame_number': 1, + 'llm_info.gpt_model': "gpt-4o" + } + data_folder = "examples/DummyVideo" + result_folder = "temp_result_folder" + + config = create_project(data_folder, result_folder, **kwargs) + amadeus = AMADEUS(config, use_vlm=True) + qa_message = amadeus.step("plot the trajectory") + parse_result(amadeus, qa_message, use_ipython=False) + +if __name__ == "__main__": + test_superanimal() diff --git a/tests/test_task_program.py b/tests/test_task_program.py new file mode 100644 index 0000000..1d06654 --- /dev/null +++ b/tests/test_task_program.py @@ -0,0 +1,38 @@ +from amadeusgpt import AMADEUS +from amadeusgpt.behavior_analysis.identifier import Identifier +import pytest +from amadeusgpt import create_project +from amadeusgpt.programs.task_program_registry import TaskProgramLibrary + + +@TaskProgramLibrary.register_task_program(creator="human") +def plot_trajectory(identifier : Identifier): + """ + This task program describes the approach events between any pair of two animals. + """ + # behavior_analysis was defined in the namespace. Just take this as syntax + analysis = create_analysis(identifier) + fig, axs = analysis.visual_manager.get_keypoint_visualization() + + return fig, axs + +@pytest.mark.parametrize("example_name", ["MausHaus", "MausHaus3D"]) +def test_task_program(example_name): + data_folder = f"examples/{example_name}" + result_folder = "temp_result_folder" + + kwargs = { + "llm_info.max_tokens": 2000, + "llm_info.temperature": 0.0, + "llm_info.keep_last_n_messages": 2, + "object_info.load_objects_from_disk": False, + "object_info.use_grid_objects": False, + "keypoint_info.use_3d": False, + "keypoint_info.include_confidence": False + } + + config = create_project(data_folder, result_folder, **kwargs) + + # Create an AMADEUS instance + amadeus = AMADEUS(config, use_vlm=False) + amadeus.run_task_program("plot_trajectory") \ No newline at end of file From 435174db50c8c9e89c8b92fec95f2331fa9ce8a4 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Mon, 5 Aug 2024 20:40:47 +0200 Subject: [PATCH 16/35] black and isort --- amadeusgpt/__init__.py | 3 +- amadeusgpt/analysis_objects/animal.py | 4 +- amadeusgpt/analysis_objects/llm.py | 14 ++--- amadeusgpt/analysis_objects/object.py | 2 + amadeusgpt/analysis_objects/relationship.py | 3 +- amadeusgpt/analysis_objects/visualization.py | 11 ++-- amadeusgpt/behavior_analysis/identifier.py | 15 ++++-- .../integration_modules/embedding/cebra.py | 10 ++-- amadeusgpt/main.py | 54 ++++++++++--------- amadeusgpt/managers/animal_manager.py | 23 +++++--- amadeusgpt/managers/base.py | 2 +- amadeusgpt/managers/event_manager.py | 11 ++-- amadeusgpt/managers/object_manager.py | 4 +- amadeusgpt/managers/visual_manager.py | 18 ++++--- amadeusgpt/programs/sandbox.py | 47 ++++++++-------- amadeusgpt/project.py | 42 +++++++-------- amadeusgpt/system_prompts/code_generator.py | 2 +- amadeusgpt/utils.py | 5 +- 18 files changed, 144 insertions(+), 126 deletions(-) diff --git a/amadeusgpt/__init__.py b/amadeusgpt/__init__.py index 35e9d2c..01c5b6b 100644 --- a/amadeusgpt/__init__.py +++ b/amadeusgpt/__init__.py @@ -9,8 +9,9 @@ from amadeusgpt.integration_modules import * from amadeusgpt.main import AMADEUS -from amadeusgpt.version import VERSION, __version__ from amadeusgpt.project import create_project +from amadeusgpt.version import VERSION, __version__ + params = { "axes.labelsize": 10, "legend.fontsize": 10, diff --git a/amadeusgpt/analysis_objects/animal.py b/amadeusgpt/analysis_objects/animal.py index 8a670a2..61b7cc9 100644 --- a/amadeusgpt/analysis_objects/animal.py +++ b/amadeusgpt/analysis_objects/animal.py @@ -120,10 +120,10 @@ def get_ymin(self): def get_ymax(self): return np.nanmax(self.keypoints[..., 1], axis=1) - + def get_zmin(self): return np.nanmin(self.keypoints[..., 2], axis=1) - + def get_zmax(self): return np.nanmax(self.keypoints[..., 2], axis=1) diff --git a/amadeusgpt/analysis_objects/llm.py b/amadeusgpt/analysis_objects/llm.py index b7626a6..bbc1b14 100644 --- a/amadeusgpt/analysis_objects/llm.py +++ b/amadeusgpt/analysis_objects/llm.py @@ -262,8 +262,8 @@ def speak(self, sandbox: Sandbox, image: np.ndarray): response = self.connect_gpt(self.context_window, max_tokens=2000) text = response.choices[0].message.content.strip() - print ('description of the image frame provided') - print (text) + print("description of the image frame provided") + print(text) pattern = r"```json(.*?)```" if len(re.findall(pattern, text, re.DOTALL)) == 0: @@ -311,7 +311,7 @@ def speak( keypoint_names, object_names, animal_names, - ) + ) self.update_history("system", self.system_prompt) @@ -339,10 +339,10 @@ def speak( with open("temp_answer.json", "w") as f: obj = {} obj["chain_of_thought"] = text - obj['code'] = function_code - obj['video_file_paths'] = sandbox.video_file_paths - obj['keypoint_file_paths'] = sandbox.keypoint_file_paths - obj['config'] = str(sandbox.config) + obj["code"] = function_code + obj["video_file_paths"] = sandbox.video_file_paths + obj["keypoint_file_paths"] = sandbox.keypoint_file_paths + obj["config"] = str(sandbox.config) json.dump(obj, f, indent=4) return qa_message diff --git a/amadeusgpt/analysis_objects/object.py b/amadeusgpt/analysis_objects/object.py index 2b49952..a7a8c3d 100644 --- a/amadeusgpt/analysis_objects/object.py +++ b/amadeusgpt/analysis_objects/object.py @@ -1,5 +1,6 @@ import matplotlib.path as mpath import numpy as np + from .base import AnalysisObject @@ -141,6 +142,7 @@ def __init__(self, name: str, masks: dict): _seg: dict = self.masks.get("segmentation") # this is rle format from pycocotools import mask as mask_decoder + if "counts" in _seg: self.segmentation = mask_decoder.decode(_seg) else: diff --git a/amadeusgpt/analysis_objects/relationship.py b/amadeusgpt/analysis_objects/relationship.py index 0c58de1..3db396e 100644 --- a/amadeusgpt/analysis_objects/relationship.py +++ b/amadeusgpt/analysis_objects/relationship.py @@ -48,8 +48,7 @@ def calc_angle_between_2d_coordinate_systems(cs1, cs2): return np.rad2deg(np.arccos(dot)) -def get_pairwise_distance(arr1:np.ndarray, - arr2:np.ndarray): +def get_pairwise_distance(arr1: np.ndarray, arr2: np.ndarray): # we want to make sure this uses a fast implementation # arr: (n_frame, n_kpts, 2) assert len(arr1.shape) == 3 and len(arr2.shape) == 3 diff --git a/amadeusgpt/analysis_objects/visualization.py b/amadeusgpt/analysis_objects/visualization.py index 2be3111..c1cd784 100644 --- a/amadeusgpt/analysis_objects/visualization.py +++ b/amadeusgpt/analysis_objects/visualization.py @@ -12,8 +12,8 @@ from matplotlib.figure import Figure from matplotlib.ticker import FuncFormatter from mpl_toolkits.axes_grid1 import make_axes_locatable -from PIL import Image from mpl_toolkits.mplot3d import Axes3D +from PIL import Image from scipy.signal import medfilt from amadeusgpt.analysis_objects.event import get_fps, get_video_length @@ -288,9 +288,9 @@ def _event_plot_trajectory(self, **kwargs): continue if not kwargs.get("use_3d", False): - x, y = masked_data[:, 0], masked_data[:, 1] - _mask = (x!=0) & (y!=0) - + x, y = masked_data[:, 0], masked_data[:, 1] + _mask = (x != 0) & (y != 0) + x = x[_mask] y = y[_mask] if len(x) < 1: @@ -322,11 +322,10 @@ def _event_plot_trajectory(self, **kwargs): **kwargs, ) else: - #TODO + # TODO # implement 3d event plot pass - return self.axs def display(self): diff --git a/amadeusgpt/behavior_analysis/identifier.py b/amadeusgpt/behavior_analysis/identifier.py index b95fdd5..f10f337 100644 --- a/amadeusgpt/behavior_analysis/identifier.py +++ b/amadeusgpt/behavior_analysis/identifier.py @@ -11,7 +11,9 @@ class Identifier: Can be more in the future """ - def __init__(self, config: Config | dict, video_file_path: str, keypoint_file_path: str): + def __init__( + self, config: Config | dict, video_file_path: str, keypoint_file_path: str + ): self.config = config self.video_file_path = video_file_path @@ -24,14 +26,19 @@ def __str__(self): config: {self.config} ------ """ + def __eq__(self, other): if os.path.exists(self.video_file_path): - return os.path.abspath(self.video_file_path) == os.path.abspath(other.video_file_path) + return os.path.abspath(self.video_file_path) == os.path.abspath( + other.video_file_path + ) else: - return os.path.abspath(self.keypoint_file_path) == os.path.abspath(other.keypoint_file_path) + return os.path.abspath(self.keypoint_file_path) == os.path.abspath( + other.keypoint_file_path + ) def __hash__(self): if os.path.exists(self.video_file_path): return hash(os.path.abspath(self.video_file_path)) - else: + else: return hash(os.path.abspath(self.keypoint_file_path)) diff --git a/amadeusgpt/integration_modules/embedding/cebra.py b/amadeusgpt/integration_modules/embedding/cebra.py index 03e15ec..49128cb 100644 --- a/amadeusgpt/integration_modules/embedding/cebra.py +++ b/amadeusgpt/integration_modules/embedding/cebra.py @@ -20,7 +20,7 @@ def get_cebra_embedding(self, inputs: np.ndarray, n_dimension=3) -> np.ndarray: features = inputs.reshape(inputs.shape[0], -1) features = np.nan_to_num(features) - print ('features shape', features.shape) + print("features shape", features.shape) cebra_params = dict( model_architecture="offset10-model", batch_size=512, @@ -34,11 +34,11 @@ def get_cebra_embedding(self, inputs: np.ndarray, n_dimension=3) -> np.ndarray: verbose=True, time_offsets=10, ) - print ('got here1') + print("got here1") model = CEBRA(**cebra_params) - print ('got here2') + print("got here2") model.fit(features) - print ('got here3') + print("got here3") embeddings = model.transform(features) - print ('got here4') + print("got here4") return embeddings diff --git a/amadeusgpt/main.py b/amadeusgpt/main.py index 0f16ed1..fe0a8cb 100644 --- a/amadeusgpt/main.py +++ b/amadeusgpt/main.py @@ -15,16 +15,13 @@ from amadeusgpt.analysis_objects.llm import (CodeGenerationLLM, SelfDebugLLM, VisualLLM) +from amadeusgpt.behavior_analysis.identifier import Identifier from amadeusgpt.integration_module_hub import IntegrationModuleHub from amadeusgpt.programs.task_program_registry import TaskProgramLibrary -from amadeusgpt.behavior_analysis.identifier import Identifier class AMADEUS: - def __init__(self, - config: Config | dict, - use_vlm=False, - movie2keypoint_func=None): + def __init__(self, config: Config | dict, use_vlm=False, movie2keypoint_func=None): self.config = config ### fields that decide the behavior of the application self.use_self_debug = True @@ -40,11 +37,13 @@ def __init__(self, ### For the sake of multiple animal, we store multiple sandboxes ### the example {video_file_path : sandbox } self.result_folder: str = config["data_info"]["result_folder"] - self.data_folder = config["data_info"]['data_folder'] + self.data_folder = config["data_info"]["data_folder"] self.video_suffix = config["data_info"]["video_suffix"] - - self.video_file_paths, self.keypoint_file_paths = self.fetch_data_from_data_folder(movie2keypoint_func) - + + self.video_file_paths, self.keypoint_file_paths = ( + self.fetch_data_from_data_folder(movie2keypoint_func) + ) + self.sandbox = Sandbox(config, self.video_file_paths, self.keypoint_file_paths) self.code_generator_llm = CodeGenerationLLM(config) @@ -78,10 +77,12 @@ def fetch_data_from_data_folder(self, movie2keypoint_func): a list of video file paths keypoint_file_paths: list[str] a list of keypoint file paths - + """ video_files = glob.glob(os.path.join(self.data_folder, f"*{self.video_suffix}")) - video_files = [video_file for video_file in video_files if "labeled" not in video_file] + video_files = [ + video_file for video_file in video_files if "labeled" not in video_file + ] keypoint_files = glob.glob(os.path.join(self.data_folder, "*.h5")) # case 1 if movie2keypoint_func is None: @@ -92,27 +93,29 @@ def fetch_data_from_data_folder(self, movie2keypoint_func): return video_files, self.get_DLC_keypoint_files(video_files) # case 3 elif len(video_files) > 1 and len(keypoint_files) == 1: - print ("We assume this is 3D data with multiple video files and one keypoint file") + print( + "We assume this is 3D data with multiple video files and one keypoint file" + ) return video_files, [keypoint_files[0]] * len(video_files) # case 4 elif len(video_files) == 0 and len(keypoint_files) == 0: - raise ValueError("No video files and keypoint files found in the data folder") + raise ValueError( + "No video files and keypoint files found in the data folder" + ) # case 5 elif len(video_files) == 0 and len(keypoint_files) > 0: - print ("No video found. We proceed with the keypoint file only") + print("No video found. We proceed with the keypoint file only") return [""] * len(keypoint_files), keypoint_files else: return video_files, keypoint_files else: if len(video_files) > 0: - return video_files, [movie2keypoint_func(video_file) for video_file in video_files] + return video_files, [ + movie2keypoint_func(video_file) for video_file in video_files + ] else: raise ValueError("No video files found in the data folder") - - - - def get_DLC_keypoint_files(self, video_file_paths: list[str]): ret = [] # how to get the filename from the path file @@ -149,7 +152,7 @@ def match_integration_module(self, user_query: str) -> list: def step(self, user_query: str) -> QA_Message: integration_module_names = self.match_integration_module(user_query) - + self.sandbox.update_matched_integration_modules(integration_module_names) qa_message = self.sandbox.llm_step(user_query) @@ -157,14 +160,13 @@ def step(self, user_query: str) -> QA_Message: def get_video_file_paths(self) -> list[str]: return self.video_file_paths - + def get_keypoint_file_paths(self) -> list[str]: return self.keypoint_file_paths - def get_behavior_analysis(self, - video_file_path: str = "", - keypoint_file_path: str = ""): - + def get_behavior_analysis( + self, video_file_path: str = "", keypoint_file_path: str = "" + ): """ Every sandbox stores a unique "behavior analysis" instance in its namespace Therefore, get analysis gets the current sandbox's analysis. @@ -193,7 +195,7 @@ def get_messages(self): def get_task_programs(self): return TaskProgramLibrary.get_task_programs() - + if __name__ == "__main__": from amadeusgpt.analysis_objects.llm import VisualLLM diff --git a/amadeusgpt/managers/animal_manager.py b/amadeusgpt/managers/animal_manager.py index 94da999..28a0a9b 100644 --- a/amadeusgpt/managers/animal_manager.py +++ b/amadeusgpt/managers/animal_manager.py @@ -102,7 +102,7 @@ def configure_animal_from_meta(self, meta_info): self.superanimal_name = None def init_pose(self): - + if not os.path.exists(self.keypoint_file_path): # no need to initialize here return @@ -111,7 +111,7 @@ def init_pose(self): all_keypoints = self._process_keypoint_file_from_h5() elif self.keypoint_file_path.endswith(".json"): # could be coco format - all_keypoints = self._process_keypoint_file_from_json() + all_keypoints = self._process_keypoint_file_from_json() for individual_id in range(self.n_individuals): animal_name = f"animal_{individual_id}" # by default, we initialize all animals with the same keypoints and all the keypoint names @@ -119,12 +119,18 @@ def init_pose(self): animalseq = AnimalSeq( animal_name, all_keypoints[:, individual_id], self.keypoint_names ) - if self.config["keypoint_info"] and "body_orientation_keypoints" in self.config["keypoint_info"]: + if ( + self.config["keypoint_info"] + and "body_orientation_keypoints" in self.config["keypoint_info"] + ): animalseq.set_body_orientation_keypoints( self.config["keypoint_info"]["body_orientation_keypoints"] ) - if self.config["keypoint_info"] and "head_orientation_keypoints" in self.config["keypoint_info"]: + if ( + self.config["keypoint_info"] + and "head_orientation_keypoints" in self.config["keypoint_info"] + ): animalseq.set_head_orientation_keypoints( self.config["keypoint_info"]["head_orientation_keypoints"] ) @@ -145,10 +151,13 @@ def _process_keypoint_file_from_h5(self) -> ndarray: self.n_kpts = len(self.keypoint_names) # whether to keep the 3rd dimension in the last axis - if self.config['keypoint_info'].get('use_3d', False) == True or self.config['keypoint_info'].get('include_confidence', False) == True: - df_array = df.to_numpy().reshape( + if ( + self.config["keypoint_info"].get("use_3d", False) == True + or self.config["keypoint_info"].get("include_confidence", False) == True + ): + df_array = df.to_numpy().reshape( (self.n_frames, self.n_individuals, self.n_kpts, -1) - ) + ) else: df_array = df.to_numpy().reshape( (self.n_frames, self.n_individuals, self.n_kpts, -1) diff --git a/amadeusgpt/managers/base.py b/amadeusgpt/managers/base.py index e4f3fcb..3ca56d2 100644 --- a/amadeusgpt/managers/base.py +++ b/amadeusgpt/managers/base.py @@ -70,7 +70,7 @@ def __call__(self, *args, **kwargs): class Manager(BaseManager): - def __init__(self, config: Config|dict, use_cache: bool = False): + def __init__(self, config: Config | dict, use_cache: bool = False): self.config = config self.use_cache = use_cache self._cache = LRUCache(maxsize=128) diff --git a/amadeusgpt/managers/event_manager.py b/amadeusgpt/managers/event_manager.py index 7702c08..9fa6a5c 100644 --- a/amadeusgpt/managers/event_manager.py +++ b/amadeusgpt/managers/event_manager.py @@ -159,7 +159,7 @@ def get_animals_state_events( ) -> List[Event]: """ Parameters - ---------- + ---------- mask: np.ndarray, optional. The mask must be of shape (n_frames, n_individuals). It is a boolean mask that describes the condition for the behavior. If n_individuals is 1, the shape should be (n_frames, 1) @@ -170,11 +170,12 @@ def get_animals_state_events( """ if len(mask.shape) == 1: mask = mask.reshape(-1, 1) - ret_events = [] - for animal_idx, sender_animal_name in enumerate(self.animal_manager.get_animal_names()): - # to construct the events + for animal_idx, sender_animal_name in enumerate( + self.animal_manager.get_animal_names() + ): + # to construct the events events = Event.mask2events( mask[:, animal_idx], @@ -187,7 +188,7 @@ def get_animals_state_events( events = Event.filter_events_by_duration(events, min_window, max_window) ret_events.extend(events) - ret_events = sorted(ret_events, key=lambda x: x.start) + ret_events = sorted(ret_events, key=lambda x: x.start) return ret_events diff --git a/amadeusgpt/managers/object_manager.py b/amadeusgpt/managers/object_manager.py index cbd8edc..592b35f 100644 --- a/amadeusgpt/managers/object_manager.py +++ b/amadeusgpt/managers/object_manager.py @@ -29,7 +29,9 @@ def __init__( self.roi_objects = [] self.seg_objects = [] - self.load_from_disk = self.config["object_info"].get("load_objects_from_disk", False) + self.load_from_disk = self.config["object_info"].get( + "load_objects_from_disk", False + ) if self.load_from_disk: self.load_objects_from_disk() diff --git a/amadeusgpt/managers/visual_manager.py b/amadeusgpt/managers/visual_manager.py index bb0a020..d74697e 100644 --- a/amadeusgpt/managers/visual_manager.py +++ b/amadeusgpt/managers/visual_manager.py @@ -42,7 +42,7 @@ def __init__( super().__init__(identifier.config) self.config = identifier.config self.video_file_path = identifier.video_file_path - self.keypoint_file_path = identifier.keypoint_file_path + self.keypoint_file_path = identifier.keypoint_file_path self.animal_manager = animal_manager self.object_manager = object_manager @@ -218,7 +218,7 @@ def get_keypoint_visualization( self.animal_manager.get_n_individuals(), average_keypoints=average_keypoints, events=events, - use_3d = self.config['keypoint_info'].get('use_3d', False) + use_3d=self.config["keypoint_info"].get("use_3d", False), ) scene_vis.draw() keypoint_vis.draw() @@ -231,12 +231,12 @@ def get_keypoint_visualization( fig, axs = plt.subplots(self.animal_manager.get_n_individuals()) axs = np.atleast_1d(axs) - for idx, sender_animal in enumerate(self.animal_manager.get_animals()): - if not self.config['keypoint_info'].get('use_3d', False): - + if not self.config["keypoint_info"].get("use_3d", False): + scene_vis = self.get_scene_visualization( - self.config["video_info"]["scene_frame_number"], axs=axs[idx] + self.config["video_info"]["scene_frame_number"], + axs=axs[idx], ) scene_vis.draw() @@ -258,7 +258,7 @@ def get_keypoint_visualization( average_keypoints=average_keypoints, events=events, ) - + keypoint_vis.draw() if render: @@ -537,7 +537,9 @@ def write_video(self, out_folder, video_file_path, out_name, events): if time_slice[0] <= current_frame < time_slice[1]: # select the keypoint based on the frame number - if self.config['keypoint_info'].get('head_orientation_keypoints', False): + if self.config["keypoint_info"].get( + "head_orientation_keypoints", False + ): frame = self.sender_visual_cone_on_frame( self.animal_manager.get_animal_by_name(sender_animal_name), frame, diff --git a/amadeusgpt/programs/sandbox.py b/amadeusgpt/programs/sandbox.py index c7a764f..4899092 100644 --- a/amadeusgpt/programs/sandbox.py +++ b/amadeusgpt/programs/sandbox.py @@ -176,14 +176,14 @@ def __init__( self.namespace_dict = {} self.analysis_dict = {} self.identifiers = [] - + for video_file_path, keypoint_file_path in zip( self.video_file_paths, self.keypoint_file_paths - ): - identifier = Identifier(self.config, video_file_path, keypoint_file_path) + ): + identifier = Identifier(self.config, video_file_path, keypoint_file_path) self.identifiers.append(identifier) - self.analysis_dict[identifier] = create_analysis(identifier) - self.namespace_dict[identifier] = {"__builtins__": __builtins__} + self.analysis_dict[identifier] = create_analysis(identifier) + self.namespace_dict[identifier] = {"__builtins__": __builtins__} # update_namespace initializes behavior analysis self.update_namespace() @@ -221,8 +221,8 @@ def configure_using_vlm(self): "background_objects": ["laboratory equipment", "white surface", "colored dots"] } """ - for identifier, analysis in self.analysis_dict.items(): - scene_image = analysis.visual_manager.get_scene_image() + for identifier, analysis in self.analysis_dict.items(): + scene_image = analysis.visual_manager.get_scene_image() json_obj = self.llms["visual_llm"].speak(self, scene_image) self.meta_info[identifier] = json_obj # configure meta info on the analysis managers @@ -286,10 +286,12 @@ def update_matched_integration_modules(self, matched_modules): def update_namespace(self): # we need to manage the scope of the session # there are potentially new variables, new task programs, new apis - for video_file_path, keypoint_file_path in zip(self.video_file_paths, self.keypoint_file_paths): + for video_file_path, keypoint_file_path in zip( + self.video_file_paths, self.keypoint_file_paths + ): identifier = Identifier(self.config, video_file_path, keypoint_file_path) analysis = self.analysis_dict[identifier] - namespace = self.namespace_dict[identifier] + namespace = self.namespace_dict[identifier] for api in self.api_registry.values(): f = wrap_instance_method(analysis, api["name"]) namespace[api["name"]] = f @@ -321,7 +323,7 @@ def update_namespace(self): # to allow the program to access existing task programs namespace["task_programs"] = TaskProgramLibrary.get_task_programs() - def code_execution(self, qa_message: QA_Message, debug = True) -> QA_Message: + def code_execution(self, qa_message: QA_Message, debug=True) -> QA_Message: # update the namespace in the beginning of code execution makes sure that # if there is a change in the config, we always use the newest config self.update_namespace() @@ -338,7 +340,7 @@ def code_execution(self, qa_message: QA_Message, debug = True) -> QA_Message: if code is None: continue exec(code, namespace) - + # call the main function function_name = self.get_function_name_from_string(code) call_str = f"{function_name}(identifier)" @@ -347,7 +349,6 @@ def code_execution(self, qa_message: QA_Message, debug = True) -> QA_Message: qa_message.error_message[identifier] = None except Exception as e: - print("error occurs in code execution") # use traceback to get full error full_traceback = traceback.format_exc() @@ -356,12 +357,8 @@ def code_execution(self, qa_message: QA_Message, debug = True) -> QA_Message: if not debug: return qa_message - qa_message = self.llms["self_debug"].speak( - qa_message - ) - qa_message = self.code_execution( - qa_message - ) + qa_message = self.llms["self_debug"].speak(qa_message) + qa_message = self.code_execution(qa_message) return qa_message result = namespace["result"] qa_message.function_rets[identifier] = result @@ -416,7 +413,9 @@ def render_qa_message(self, qa_message: QA_Message) -> QA_Message: if the function returns is a tuple of axe and figure, we put them into the plots filed """ - for video_file_path, keypoint_file_path in zip(self.video_file_paths, self.keypoint_file_paths): + for video_file_path, keypoint_file_path in zip( + self.video_file_paths, self.keypoint_file_paths + ): identifier = Identifier(self.config, video_file_path, keypoint_file_path) namespace = self.namespace_dict[identifier] @@ -451,12 +450,10 @@ def render_qa_message(self, qa_message: QA_Message) -> QA_Message: bodypart_names=bodypart_names, events=e ) ) - qa_message.out_videos[identifier] = ( - self.events_to_videos( - identifier, - e, - self.get_function_name_from_string(qa_message.code), - ) + qa_message.out_videos[identifier] = self.events_to_videos( + identifier, + e, + self.get_function_name_from_string(qa_message.code), ) elif ( diff --git a/amadeusgpt/project.py b/amadeusgpt/project.py index cb488c3..2544a6e 100644 --- a/amadeusgpt/project.py +++ b/amadeusgpt/project.py @@ -1,10 +1,10 @@ import os import pprint + import yaml -def create_project(data_folder, - result_folder, - **kwargs): + +def create_project(data_folder, result_folder, **kwargs): """ Create a project config file. Save the config file to the result folder """ @@ -12,21 +12,15 @@ def create_project(data_folder, "data_info": { "data_folder": data_folder, "result_folder": result_folder, - "video_suffix": ".mp4", + "video_suffix": ".mp4", }, - "llm_info": { - "max_tokens": 4096, - "temperature": 0.0, - "keep_last_n_messages": 2 + "llm_info": {"max_tokens": 4096, "temperature": 0.0, "keep_last_n_messages": 2}, + "object_info": {"load_objects_from_disk": False, "use_grid_objects": False}, + "keypoint_info": { + "use_3d": False, + "include_confidence": False, }, - "object_info": { - "load_objects_from_disk": False, - "use_grid_objects": False - }, - "keypoint_info": {"use_3d": False, - "include_confidence": False, - }, - "video_info": {"scene_frame_number": 1} + "video_info": {"scene_frame_number": 1}, } # save the dictionary config to yaml @@ -36,9 +30,9 @@ def set_nested_value(d, keys, value): d[keys[-1]] = value for key, value in kwargs.items(): - keys = key.split('.') + keys = key.split(".") set_nested_value(config, keys, value) - + os.makedirs(result_folder, exist_ok=True) file_path = os.path.join(result_folder, "config.yaml") @@ -46,9 +40,13 @@ def set_nested_value(d, keys, value): with open(file_path, "w") as f: yaml.dump(config, f) - print (f"Project created at {result_folder}. Results will be saved to {result_folder}") - print (f"The project will load video files (*{config['data_info']['video_suffix']}) and optionally keypoint files from {data_folder}") - print (f"A copy of the project config file is saved at {file_path}") + print( + f"Project created at {result_folder}. Results will be saved to {result_folder}" + ) + print( + f"The project will load video files (*{config['data_info']['video_suffix']}) and optionally keypoint files from {data_folder}" + ) + print(f"A copy of the project config file is saved at {file_path}") pprint.pprint(config) - return config \ No newline at end of file + return config diff --git a/amadeusgpt/system_prompts/code_generator.py b/amadeusgpt/system_prompts/code_generator.py index 2cd764b..3537436 100644 --- a/amadeusgpt/system_prompts/code_generator.py +++ b/amadeusgpt/system_prompts/code_generator.py @@ -106,5 +106,5 @@ def _get_system_prompt( If the question can be answered by code: - YOU MUST only write one function and no other classes or functions when you write code. -""" +""" return system_prompt diff --git a/amadeusgpt/utils.py b/amadeusgpt/utils.py index 0b65e07..d97ffb6 100644 --- a/amadeusgpt/utils.py +++ b/amadeusgpt/utils.py @@ -215,11 +215,11 @@ def create_qa_message(query: str, video_file_paths: list[str]) -> QA_Message: from IPython.display import Markdown, Video, display -def parse_result(amadeus, qa_message, use_ipython = True): +def parse_result(amadeus, qa_message, use_ipython=True): if use_ipython: display(Markdown(qa_message.chain_of_thought)) else: - print (qa_message.chain_of_thought) + print(qa_message.chain_of_thought) sandbox = amadeus.sandbox qa_message = sandbox.code_execution(qa_message) qa_message = sandbox.render_qa_message(qa_message) @@ -238,6 +238,5 @@ def parse_result(amadeus, qa_message, use_ipython = True): if len(qa_message.function_rets) > 0: for video_file_path in qa_message.function_rets: display(Markdown(str(qa_message.function_rets[video_file_path]))) - return qa_message From a827c3b0ffc301e3020c35b87e07d54bf1a20238 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Mon, 5 Aug 2024 20:43:41 +0200 Subject: [PATCH 17/35] added dlc to test requirement --- .github/workflows/pytest.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index afd37bc..64d2ca7 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -36,6 +36,7 @@ jobs: run: | python -m pip install --upgrade pip pip install pytest numpy==1.23.5 tables==3.8.0 + pip install git+https://github.com/DeepLabCut/DeepLabCut.git@pytorch_dlc#egg=deeplabcut[modelzoo] pip install pytest pip install pytest-timeout if [ -f requirements.txt ]; then pip install -r requirements.txt; fi From b96bb5e84f5aa7f0a5152d9f1f42deb1c308ddfe Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Mon, 5 Aug 2024 21:08:38 +0200 Subject: [PATCH 18/35] Made test use stronger gpt. Added dummy video --- amadeusgpt/programs/sandbox.py | 1 + examples/DummyVideo/dummy_maushaus.mp4 | Bin 0 -> 26565 bytes ...aushaus_superanimal_topviewmouse_hrnetw32.h5 | Bin 87469 -> 0 bytes ...imal_topviewmouse_hrnetw32_before_adapt.json | 1 - tests/test_3d.py | 2 +- 5 files changed, 2 insertions(+), 2 deletions(-) create mode 100644 examples/DummyVideo/dummy_maushaus.mp4 delete mode 100644 examples/DummyVideo/dummy_maushaus_superanimal_topviewmouse_hrnetw32.h5 delete mode 100644 examples/DummyVideo/dummy_maushaus_superanimal_topviewmouse_hrnetw32_before_adapt.json diff --git a/amadeusgpt/programs/sandbox.py b/amadeusgpt/programs/sandbox.py index 4899092..0000cc7 100644 --- a/amadeusgpt/programs/sandbox.py +++ b/amadeusgpt/programs/sandbox.py @@ -222,6 +222,7 @@ def configure_using_vlm(self): } """ for identifier, analysis in self.analysis_dict.items(): + scene_image = analysis.visual_manager.get_scene_image() json_obj = self.llms["visual_llm"].speak(self, scene_image) 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129.1796875, 0.8142274022102356], [423.0546875, 129.1796875, 0.713253915309906], [404.5703125, 126.5390625, 0.7891340851783752], [391.3671875, 126.5390625, 0.7491600513458252], [378.1640625, 134.4609375, 0.6272221803665161], [364.9609375, 142.3828125, 0.6443408727645874], [364.9609375, 142.3828125, 0.39580416679382324], [364.9609375, 152.9453125, 0.04275646433234215], [486.4296875, 118.6171875, 0.012246779166162014], [473.2265625, 118.6171875, 0.7474603652954102], [449.4609375, 113.3359375, 0.8204736113548279], [417.7734375, 110.6953125, 0.8033183813095093], [475.8671875, 147.6640625, 0.7803322672843933], [449.4609375, 150.3046875, 0.9713789224624634], [420.4140625, 155.5859375, 0.930040180683136], [404.5703125, 134.4609375, 0.06275403499603271], [502.2734375, 131.8203125, 0.7241994738578796]]], "bboxes": [[361.5312805175781, 89.57644653320312, 168.49923706054688, 82.95301818847656]], "bbox_scores": [0.9997017979621887]}] \ No newline at end of file diff --git a/tests/test_3d.py b/tests/test_3d.py index f78576f..af99a02 100644 --- a/tests/test_3d.py +++ b/tests/test_3d.py @@ -9,7 +9,7 @@ def test_3d_maushaus(): kwargs = { 'keypoint_info.use_3d': True, - 'llm_info.gpt_model': "gpt-4o-mini" + 'llm_info.gpt_model': "gpt-4o" } config = create_project(data_folder="examples/MausHaus3D", From 0107838377c18c2535b12bdbd4aabd23b0fbb29a Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Mon, 5 Aug 2024 21:40:01 +0200 Subject: [PATCH 19/35] easier superanimal test --- tests/test_superanimal.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tests/test_superanimal.py b/tests/test_superanimal.py index e1c0d7d..03a738d 100644 --- a/tests/test_superanimal.py +++ b/tests/test_superanimal.py @@ -14,8 +14,10 @@ def test_superanimal(): config = create_project(data_folder, result_folder, **kwargs) amadeus = AMADEUS(config, use_vlm=True) - qa_message = amadeus.step("plot the trajectory") - parse_result(amadeus, qa_message, use_ipython=False) + behavior_analysis = amadeus.get_behavior_analysis(video_file_path=amadeus.get_video_file_paths()[0]) + keypoints = behavior_analysis.get_keypoints() + assert keypoints.shape == (5, 1, 27, 2) + if __name__ == "__main__": test_superanimal() From 2c0a728d8875f34bc23fa44a88d79159dfe6145d Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 6 Aug 2024 10:58:42 +0200 Subject: [PATCH 20/35] Better 3D prompt and fixed self-debug --- amadeusgpt/analysis_objects/llm.py | 12 +++++++----- amadeusgpt/programs/sandbox.py | 6 ++++-- amadeusgpt/system_prompts/code_generator.py | 10 +++++++++- 3 files changed, 20 insertions(+), 8 deletions(-) diff --git a/amadeusgpt/analysis_objects/llm.py b/amadeusgpt/analysis_objects/llm.py index bbc1b14..a4d72e6 100644 --- a/amadeusgpt/analysis_objects/llm.py +++ b/amadeusgpt/analysis_objects/llm.py @@ -302,8 +302,9 @@ def speak( scene_image = behavior_analysis.visual_manager.get_scene_image() keypoint_names = behavior_analysis.animal_manager.get_keypoint_names() object_names = behavior_analysis.object_manager.get_object_names() - animal_names = behavior_analysis.animal_manager.get_animal_names() - + animal_names = behavior_analysis.animal_manager.get_animal_names() + use_3d = sandbox.config['keypoint_info'].get('use_3d', False) + self.system_prompt = _get_system_prompt( core_api_docs, task_program_docs, @@ -311,7 +312,8 @@ def speak( keypoint_names, object_names, animal_names, - ) + use_3d=use_3d, + ) self.update_history("system", self.system_prompt) @@ -366,12 +368,11 @@ def speak(self, qa_message): query = f""" The code that caused error was {code} And the error message was {error_message}. All the modules were already imported so you don't need to import them again. -Can you correct the code? +Can you correct the code? Make sure you only write one function which is the updated function. """ self.update_history("user", query) response = self.connect_gpt(self.context_window, max_tokens=700) text = response.choices[0].message.content.strip() - print(text) pattern = r"```python(.*?)```" @@ -381,6 +382,7 @@ def speak(self, qa_message): qa_message.chain_of_thought = text + return qa_message if __name__ == "__main__": from amadeusgpt.config import Config diff --git a/amadeusgpt/programs/sandbox.py b/amadeusgpt/programs/sandbox.py index 0000cc7..a003361 100644 --- a/amadeusgpt/programs/sandbox.py +++ b/amadeusgpt/programs/sandbox.py @@ -359,8 +359,10 @@ def code_execution(self, qa_message: QA_Message, debug=True) -> QA_Message: if not debug: return qa_message qa_message = self.llms["self_debug"].speak(qa_message) - qa_message = self.code_execution(qa_message) - return qa_message + print ("after self debug") + print (qa_message.code) + return self.code_execution(qa_message) + result = namespace["result"] qa_message.function_rets[identifier] = result diff --git a/amadeusgpt/system_prompts/code_generator.py b/amadeusgpt/system_prompts/code_generator.py index 3537436..ca23d9a 100644 --- a/amadeusgpt/system_prompts/code_generator.py +++ b/amadeusgpt/system_prompts/code_generator.py @@ -5,12 +5,17 @@ def code_related_prompt( keypoint_names, object_names, animal_names, + use_3d = False, ): if scene_image is not None: image_h, image_w = scene_image.shape[:2] else: image_h, image_w = "not available", "not available" + if use_3d: + keypoint_description = "the last axis of the keypoint data is 3, which means it is 3D keypoint data. They are x,y,z coordinates and y is the height and z is the depth" + else: + keypoint_description = "the last axis of the keypoint data is 2, which means it is 2D keypoint data. They are x,y coordinates. The x axis is the width and y axis is the height" prompt = f""" We provide you additionl apis and task programs to help you write code. @@ -67,6 +72,7 @@ def get_watching_events(identifier): The keypoint names for the animals are: {keypoint_names}. Don't assume there are other keypoints. Available objects are: {object_names}. Don't assume there exist other objects. DO NOT define new objects. Present animals are: {animal_names}. Don't assume there exist other animals. +{keypoint_description} RULES: 1) If you are asked to provide plotting code, make sure you don't call plt.show() but return a tuple figure, axs @@ -91,6 +97,7 @@ def _get_system_prompt( keypoint_names, object_names, animal_names, + use_3d = False, ): system_prompt = f""" You are helpful AI assistant. Your job is to answer user queries. @@ -100,7 +107,8 @@ def _get_system_prompt( scene_image, keypoint_names, object_names, - animal_names + animal_names, + use_3d = use_3d )} If the question can be answered by code: From 18dd33944f9ca4fbf93425f8ec2ba2170fc1b353 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 6 Aug 2024 11:07:02 +0200 Subject: [PATCH 21/35] preventing infinite loop --- amadeusgpt/analysis_objects/llm.py | 3 --- amadeusgpt/programs/sandbox.py | 3 ++- 2 files changed, 2 insertions(+), 4 deletions(-) diff --git a/amadeusgpt/analysis_objects/llm.py b/amadeusgpt/analysis_objects/llm.py index a4d72e6..29d4ab5 100644 --- a/amadeusgpt/analysis_objects/llm.py +++ b/amadeusgpt/analysis_objects/llm.py @@ -374,12 +374,9 @@ def speak(self, qa_message): response = self.connect_gpt(self.context_window, max_tokens=700) text = response.choices[0].message.content.strip() print(text) - pattern = r"```python(.*?)```" function_code = re.findall(pattern, text, re.DOTALL)[0] - qa_message.code = function_code - qa_message.chain_of_thought = text return qa_message diff --git a/amadeusgpt/programs/sandbox.py b/amadeusgpt/programs/sandbox.py index a003361..90244b1 100644 --- a/amadeusgpt/programs/sandbox.py +++ b/amadeusgpt/programs/sandbox.py @@ -361,7 +361,8 @@ def code_execution(self, qa_message: QA_Message, debug=True) -> QA_Message: qa_message = self.llms["self_debug"].speak(qa_message) print ("after self debug") print (qa_message.code) - return self.code_execution(qa_message) + # set debug = False to avoid infinite loop + return self.code_execution(qa_message, debug = False) result = namespace["result"] qa_message.function_rets[identifier] = result From 91e8255bda1976d892018334dae43b34bd6fddc2 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 6 Aug 2024 11:30:08 +0200 Subject: [PATCH 22/35] better prompt for 3D --- amadeusgpt/system_prompts/code_generator.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/amadeusgpt/system_prompts/code_generator.py b/amadeusgpt/system_prompts/code_generator.py index ca23d9a..bab03c5 100644 --- a/amadeusgpt/system_prompts/code_generator.py +++ b/amadeusgpt/system_prompts/code_generator.py @@ -13,9 +13,13 @@ def code_related_prompt( image_h, image_w = "not available", "not available" if use_3d: - keypoint_description = "the last axis of the keypoint data is 3, which means it is 3D keypoint data. They are x,y,z coordinates and y is the height and z is the depth" + keypoint_description = """ +the last axis of the keypoint data is 3, which means it is 3D keypoint data. They are x,y,z coordinates and y is the height and z is the depth. +The higher the z value, the further the object is to the camera. +The higher the y value, the higher the object is in the image. Saying object A is higher than object B means the y value of object A is higher than object B. + """ else: - keypoint_description = "the last axis of the keypoint data is 2, which means it is 2D keypoint data. They are x,y coordinates. The x axis is the width and y axis is the height" + keypoint_description = "the last axis of the keypoint data is 2, which means it is 2D keypoint data. They are x,y coordinates. The x axis is the width and y axis is the height. The higher the y value, the higher the object is in the image. " prompt = f""" We provide you additionl apis and task programs to help you write code. @@ -81,7 +85,7 @@ def get_watching_events(identifier): 4) Make sure you do not import any libraries in your code. All needed libraries are imported already. 5) Make sure you disintuigh positional and keyword arguments when you call functions in api docs 6) If you are writing code that uses matplotlib to plot, make sure you comment shape of the data to be plotted to double-check -7) if your plotting code plots coordinates of keypoints, make sure you invert y axis so that the plot is consistent with the image +7) if your plotting code plots coordinates of keypoints, make sure you invert y axis (only during plotting) so that the plot is consistent with the image 8) make sure the xlim and ylim covers the whole image. The image (h,w) is ({image_h},{image_w}) 9) Do not define your own objects (including grid objects). Only use objects that are given to you. 10) You MUST use the index from get_keypoint_names to access the keypoint data of specific keyponit names. Do not assume the order of the bodypart. From 4c181f4a88694a0abe6e686b12b2a658cc46f5d5 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 6 Aug 2024 11:30:40 +0200 Subject: [PATCH 23/35] better prompt for 3D --- amadeusgpt/managers/event_manager.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/amadeusgpt/managers/event_manager.py b/amadeusgpt/managers/event_manager.py index 9fa6a5c..27570f4 100644 --- a/amadeusgpt/managers/event_manager.py +++ b/amadeusgpt/managers/event_manager.py @@ -168,6 +168,8 @@ def get_animals_state_events( List[Event] -------- """ + + print ('mask shape', mask.shape) if len(mask.shape) == 1: mask = mask.reshape(-1, 1) From d4afe7133d3559282a783a45a416b1e442493235 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 6 Aug 2024 11:39:27 +0200 Subject: [PATCH 24/35] better prompt --- amadeusgpt/managers/event_manager.py | 12 +++++++----- amadeusgpt/system_prompts/code_generator.py | 1 + 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/amadeusgpt/managers/event_manager.py b/amadeusgpt/managers/event_manager.py index 27570f4..bb216c2 100644 --- a/amadeusgpt/managers/event_manager.py +++ b/amadeusgpt/managers/event_manager.py @@ -163,17 +163,20 @@ def get_animals_state_events( mask: np.ndarray, optional. The mask must be of shape (n_frames, n_individuals). It is a boolean mask that describes the condition for the behavior. If n_individuals is 1, the shape should be (n_frames, 1) + min_window : int, optional, default 10 + Only include events that are longer than min_window + max_window : int, optional, default 1000000 + Only include events that are shorter than max_window Returns ------- List[Event] -------- """ - print ('mask shape', mask.shape) if len(mask.shape) == 1: mask = mask.reshape(-1, 1) - ret_events = [] + ret_events = [] for animal_idx, sender_animal_name in enumerate( self.animal_manager.get_animal_names() ): @@ -185,9 +188,8 @@ def get_animals_state_events( sender_animal_name, set(), set(), - ) - - events = Event.filter_events_by_duration(events, min_window, max_window) + ) + events = Event.filter_events_by_duration(events, min_window, max_window) ret_events.extend(events) ret_events = sorted(ret_events, key=lambda x: x.start) diff --git a/amadeusgpt/system_prompts/code_generator.py b/amadeusgpt/system_prompts/code_generator.py index bab03c5..f6f78e7 100644 --- a/amadeusgpt/system_prompts/code_generator.py +++ b/amadeusgpt/system_prompts/code_generator.py @@ -90,6 +90,7 @@ def get_watching_events(identifier): 9) Do not define your own objects (including grid objects). Only use objects that are given to you. 10) You MUST use the index from get_keypoint_names to access the keypoint data of specific keyponit names. Do not assume the order of the bodypart. 11) You MUST call functions in api docs on the analysis object. +12) For api functions that require min_window and max_window, make sure you leave them as default values unless you are asked to change them. """ return prompt From e79391d646d49999346e2cbac8fb329c1d754107 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 6 Aug 2024 12:32:30 +0200 Subject: [PATCH 25/35] updates --- amadeusgpt/analysis_objects/llm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/amadeusgpt/analysis_objects/llm.py b/amadeusgpt/analysis_objects/llm.py index 29d4ab5..ec774de 100644 --- a/amadeusgpt/analysis_objects/llm.py +++ b/amadeusgpt/analysis_objects/llm.py @@ -344,7 +344,7 @@ def speak( obj["code"] = function_code obj["video_file_paths"] = sandbox.video_file_paths obj["keypoint_file_paths"] = sandbox.keypoint_file_paths - obj["config"] = str(sandbox.config) + obj["config"] = str(json.dumps(sandbox.config)) json.dump(obj, f, indent=4) return qa_message From 5f0cafaa4688219ee7439b767890e5abf0f24264 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 6 Aug 2024 14:31:10 +0200 Subject: [PATCH 26/35] fixed serialization --- amadeusgpt/analysis_objects/llm.py | 5 ++++- amadeusgpt/config.py | 3 +++ 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/amadeusgpt/analysis_objects/llm.py b/amadeusgpt/analysis_objects/llm.py index ec774de..d2455f6 100644 --- a/amadeusgpt/analysis_objects/llm.py +++ b/amadeusgpt/analysis_objects/llm.py @@ -344,7 +344,10 @@ def speak( obj["code"] = function_code obj["video_file_paths"] = sandbox.video_file_paths obj["keypoint_file_paths"] = sandbox.keypoint_file_paths - obj["config"] = str(json.dumps(sandbox.config)) + if not isinstance(sandbox.config, dict): + obj["config"] = sandbox.config.to_dict() + else: + obj["config"] = sandbox.config json.dump(obj, f, indent=4) return qa_message diff --git a/amadeusgpt/config.py b/amadeusgpt/config.py index f22b18f..e53ea85 100644 --- a/amadeusgpt/config.py +++ b/amadeusgpt/config.py @@ -22,6 +22,9 @@ def __repr__(self): def __setitem__(self, key, value): self.data[key] = value + def to_dict(self): + return self.data + def load_config(self): # Load the YAML config file if os.path.exists(self.config_file_path): From 4383d46022fb3d2f3d61d59abd430790bb748b21 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 6 Aug 2024 16:29:03 +0200 Subject: [PATCH 27/35] extension to support animation. Made self-debugging work with bigger output. Allowing to skip code execution in parse result --- amadeusgpt/analysis_objects/llm.py | 2 +- amadeusgpt/programs/sandbox.py | 5 +++-- amadeusgpt/system_prompts/code_generator.py | 5 ++++- amadeusgpt/utils.py | 5 +++-- 4 files changed, 11 insertions(+), 6 deletions(-) diff --git a/amadeusgpt/analysis_objects/llm.py b/amadeusgpt/analysis_objects/llm.py index d2455f6..28c7701 100644 --- a/amadeusgpt/analysis_objects/llm.py +++ b/amadeusgpt/analysis_objects/llm.py @@ -374,7 +374,7 @@ def speak(self, qa_message): Can you correct the code? Make sure you only write one function which is the updated function. """ self.update_history("user", query) - response = self.connect_gpt(self.context_window, max_tokens=700) + response = self.connect_gpt(self.context_window, max_tokens=4096) text = response.choices[0].message.content.strip() print(text) pattern = r"```python(.*?)```" diff --git a/amadeusgpt/programs/sandbox.py b/amadeusgpt/programs/sandbox.py index 90244b1..ce4f18c 100644 --- a/amadeusgpt/programs/sandbox.py +++ b/amadeusgpt/programs/sandbox.py @@ -321,14 +321,15 @@ def update_namespace(self): namespace["Event"] = Event # numpy might be needed for raw kinematics namespace["np"] = np + import matplotlib.animation as animation + namespace["animation"] = animation # to allow the program to access existing task programs namespace["task_programs"] = TaskProgramLibrary.get_task_programs() def code_execution(self, qa_message: QA_Message, debug=True) -> QA_Message: # update the namespace in the beginning of code execution makes sure that # if there is a change in the config, we always use the newest config - self.update_namespace() - + self.update_namespace() for video_file_path, keypoint_file_path in zip( self.video_file_paths, self.keypoint_file_paths ): diff --git a/amadeusgpt/system_prompts/code_generator.py b/amadeusgpt/system_prompts/code_generator.py index f6f78e7..62d91e9 100644 --- a/amadeusgpt/system_prompts/code_generator.py +++ b/amadeusgpt/system_prompts/code_generator.py @@ -79,7 +79,7 @@ def get_watching_events(identifier): {keypoint_description} RULES: -1) If you are asked to provide plotting code, make sure you don't call plt.show() but return a tuple figure, axs +1) If you are asked to provide plotting code, make sure you don't call plt.show() but return a tuple (figure, axs) or an instance of animation.FuncAnimation. 2) Make sure you must write a clear docstring for your code. 3) Make sure your function signature looks like func_name(identifier: Identifier) 4) Make sure you do not import any libraries in your code. All needed libraries are imported already. @@ -91,6 +91,9 @@ def get_watching_events(identifier): 10) You MUST use the index from get_keypoint_names to access the keypoint data of specific keyponit names. Do not assume the order of the bodypart. 11) You MUST call functions in api docs on the analysis object. 12) For api functions that require min_window and max_window, make sure you leave them as default values unless you are asked to change them. + +HOW TO AVOID BUGS: +You should always comment the shape of the any numpy array you are working with to avoid bugs. YOU MUST DO IT. """ return prompt diff --git a/amadeusgpt/utils.py b/amadeusgpt/utils.py index d97ffb6..9da4019 100644 --- a/amadeusgpt/utils.py +++ b/amadeusgpt/utils.py @@ -215,13 +215,14 @@ def create_qa_message(query: str, video_file_paths: list[str]) -> QA_Message: from IPython.display import Markdown, Video, display -def parse_result(amadeus, qa_message, use_ipython=True): +def parse_result(amadeus, qa_message, use_ipython=True, skip_code_execution=False): if use_ipython: display(Markdown(qa_message.chain_of_thought)) else: print(qa_message.chain_of_thought) sandbox = amadeus.sandbox - qa_message = sandbox.code_execution(qa_message) + if not skip_code_execution: + qa_message = sandbox.code_execution(qa_message) qa_message = sandbox.render_qa_message(qa_message) if len(qa_message.out_videos) > 0: print(f"videos generated to {qa_message.out_videos}") From 8e9c56181ba9cb784a917c8f90f98ea3f8567611 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Tue, 6 Aug 2024 19:44:00 +0200 Subject: [PATCH 28/35] better interpolation and corrected x,y,z convention --- amadeusgpt/analysis_objects/animal.py | 2 +- amadeusgpt/managers/animal_manager.py | 65 ++++++++++++--------- amadeusgpt/system_prompts/code_generator.py | 6 +- 3 files changed, 43 insertions(+), 30 deletions(-) diff --git a/amadeusgpt/analysis_objects/animal.py b/amadeusgpt/analysis_objects/animal.py index 61b7cc9..bf21b67 100644 --- a/amadeusgpt/analysis_objects/animal.py +++ b/amadeusgpt/analysis_objects/animal.py @@ -4,7 +4,6 @@ import numpy as np from numpy import ndarray from scipy.spatial import ConvexHull - from amadeusgpt.analysis_objects.object import Object @@ -129,6 +128,7 @@ def get_zmax(self): def get_keypoint_names(self): return self.keypoint_names + def query_states(self, query: str) -> ndarray: assert query in [ diff --git a/amadeusgpt/managers/animal_manager.py b/amadeusgpt/managers/animal_manager.py index 28a0a9b..189cd50 100644 --- a/amadeusgpt/managers/animal_manager.py +++ b/amadeusgpt/managers/animal_manager.py @@ -14,7 +14,7 @@ register_core_api) from .base import Manager - +from scipy.interpolate import interp1d def get_orientation_vector(cls, b1_name, b2_name): b1 = cls.get_keypoints()[:, :, cls.get_bodypart_index(b1_name), :] @@ -22,34 +22,45 @@ def get_orientation_vector(cls, b1_name, b2_name): return b1 - b2 -def ast_fillna_2d(arr: ndarray) -> ndarray: +def interpolate_keypoints(keypoints): """ - Fills NaN values in a 4D keypoints array using linear interpolation. - + Interpolate missing (NaN or 0) keypoints in neighboring frames. + Parameters: - arr (np.ndarray): A 4D numpy array of shape (n_frames, n_individuals, n_kpts, n_dims). - + keypoints (numpy array): Array of shape (n_frames, n_individuals, n_keypoints, n_dim). + Returns: - np.ndarray: The 4D array with NaN values filled. + numpy array: Interpolated keypoints array. """ - n_frames, n_individuals, n_kpts, n_dims = arr.shape - arr_reshaped = arr.reshape(n_frames, -1) - x = np.arange(n_frames) - for i in range(arr_reshaped.shape[1]): - valid_mask = ~np.isnan(arr_reshaped[:, i]) - if np.all(valid_mask): - continue - elif np.any(valid_mask): - # Perform interpolation when there are some valid points - arr_reshaped[:, i] = np.interp( - x, x[valid_mask], arr_reshaped[valid_mask, i] - ) - else: - # Handle the case where all values are NaN - # Replace with a default value or another suitable handling - arr_reshaped[:, i].fill(0) # Example: filling with 0 - - return arr_reshaped.reshape(n_frames, n_individuals, n_kpts, n_dims) + n_frames, n_individuals, n_keypoints, n_dim = keypoints.shape + + # Replace zeros with NaNs for interpolation purposes + keypoints[keypoints == 0] = np.nan + + # Function to interpolate along the frames axis + def interpolate_along_frames(data): + for individual in range(n_individuals): + for keypoint in range(n_keypoints): + for dim in range(n_dim): + # Extract the data for the current dimension + values = data[:, individual, keypoint, dim] + valid_mask = ~np.isnan(values) + + if valid_mask.sum() > 1: + # Interpolate only if we have more than one valid value + interp_fn = interp1d(np.flatnonzero(valid_mask), values[valid_mask], bounds_error=False, fill_value="extrapolate") + values[~valid_mask] = interp_fn(np.flatnonzero(~valid_mask)) + data[:, individual, keypoint, dim] = values + + return data + + # Interpolate missing values + keypoints = interpolate_along_frames(keypoints) + + # Replace NaNs back with zeros if needed + keypoints[np.isnan(keypoints)] = 0 + + return keypoints def reject_outlier_keypoints(keypoints: ndarray, threshold_in_stds: int = 2): @@ -164,9 +175,11 @@ def _process_keypoint_file_from_h5(self) -> ndarray: )[..., :2] df_array = reject_outlier_keypoints(df_array) - df_array = ast_fillna_2d(df_array) + df_array = interpolate_keypoints(df_array) return df_array + + def _process_keypoint_file_from_json(self) -> ndarray: # default as the mabe predicted keypoints from mmpose-superanimal-topviewmouse # {'0': ['bbox':[], 'keypoints':[]} diff --git a/amadeusgpt/system_prompts/code_generator.py b/amadeusgpt/system_prompts/code_generator.py index 62d91e9..f00c3ef 100644 --- a/amadeusgpt/system_prompts/code_generator.py +++ b/amadeusgpt/system_prompts/code_generator.py @@ -14,9 +14,9 @@ def code_related_prompt( if use_3d: keypoint_description = """ -the last axis of the keypoint data is 3, which means it is 3D keypoint data. They are x,y,z coordinates and y is the height and z is the depth. -The higher the z value, the further the object is to the camera. -The higher the y value, the higher the object is in the image. Saying object A is higher than object B means the y value of object A is higher than object B. +the last axis of the keypoint data is 3, which means it is 3D keypoint data. They are x,y,z coordinates and y is the depth and z is the height. +The higher the y value, the further the object is to the camera. +The higher the z value, the higher the object is in the image. """ else: keypoint_description = "the last axis of the keypoint data is 2, which means it is 2D keypoint data. They are x,y coordinates. The x axis is the width and y axis is the height. The higher the y value, the higher the object is in the image. " From 0953af5cd77cbc4866bc5fbde816df662e838753 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Wed, 7 Aug 2024 08:36:31 +0200 Subject: [PATCH 29/35] incorporated suggestions --- .github/workflows/pytest.yml | 2 +- README.md | 2 +- amadeusgpt/analysis_objects/relationship.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/pytest.yml b/.github/workflows/pytest.yml index 64d2ca7..86d595e 100644 --- a/.github/workflows/pytest.yml +++ b/.github/workflows/pytest.yml @@ -36,7 +36,7 @@ jobs: run: | python -m pip install --upgrade pip pip install pytest numpy==1.23.5 tables==3.8.0 - pip install git+https://github.com/DeepLabCut/DeepLabCut.git@pytorch_dlc#egg=deeplabcut[modelzoo] + pip install deeplabcut==3.0.0rc4 pip install pytest pip install pytest-timeout if [ -f requirements.txt ]; then pip install -r requirements.txt; fi diff --git a/README.md b/README.md index 5cb03f7..bd3cd30 100644 --- a/README.md +++ b/README.md @@ -36,7 +36,7 @@ In our original work (NeurIPS 2023) we used GPT3.5 and GPT4 as part of our agent Conda is an easy-to-use Python interface that supports launching [Jupyter Notebooks](https://jupyter.org/). If you are completely new to this, we recommend checking out the [docs here for getting conda installed](https://deeplabcut.github.io/DeepLabCut/docs/beginner-guides/beginners-guide.html#beginner-user-guide). Otherwise, proceed to use one of [our supplied conda files](https://github.com/AdaptiveMotorControlLab/AmadeusGPT/tree/main/conda). As you will see we have minimal dependencies to get started, and [here is a simple step-by-step guide](https://deeplabcut.github.io/DeepLabCut/docs/installation.html#step-2-build-an-env-using-our-conda-file) you can reference for setting it up (or see [BONUS](README.md#bonus---customized-your-conda-env) below). Here is the quick start command: ```bash -conda env create -f conda/amadeusGPT.yml +conda env create -f amadeusGPT.yml ``` To note, some modules AmadeusGPT can use benefit from GPU support, therefore we recommend also having an NVIDIA GPU available and installing CUDA. diff --git a/amadeusgpt/analysis_objects/relationship.py b/amadeusgpt/analysis_objects/relationship.py index 3db396e..71e8414 100644 --- a/amadeusgpt/analysis_objects/relationship.py +++ b/amadeusgpt/analysis_objects/relationship.py @@ -55,7 +55,7 @@ def get_pairwise_distance(arr1: np.ndarray, arr2: np.ndarray): # pariwise distance (n_frames, n_kpts, n_kpts) pairwise_distances = np.ones((arr1.shape[0], arr1.shape[1], arr2.shape[1])) * 100000 for frame_id in range(arr1.shape[0]): - # should we use the mean of all keypooints for the distance? + # should we use the mean of all keypoints for the distance? pairwise_distances[frame_id] = cdist(arr1[frame_id], arr2[frame_id]) return pairwise_distances From d22d2d1fd085d9a48a5f4786d01af8ecc40ffb24 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Thu, 8 Aug 2024 10:33:56 +0200 Subject: [PATCH 30/35] add a test plot keypoint label --- amadeusgpt/managers/visual_manager.py | 10 ++++++++-- tests/test_plot_keypoint_labels.py | 28 +++++++++++++++++++++++++++ 2 files changed, 36 insertions(+), 2 deletions(-) create mode 100644 tests/test_plot_keypoint_labels.py diff --git a/amadeusgpt/managers/visual_manager.py b/amadeusgpt/managers/visual_manager.py index d74697e..6ee306b 100644 --- a/amadeusgpt/managers/visual_manager.py +++ b/amadeusgpt/managers/visual_manager.py @@ -47,8 +47,14 @@ def __init__( self.animal_manager = animal_manager self.object_manager = object_manager - def get_scene_image(self): - scene_frame_index = self.config["video_info"].get("scene_frame_number", 1) + @register_core_api + def get_scene_image(self, scene_frame_index: int| None = None): + """ + Returns the frame given the index in the video. + For visualizing keypoints or keypoint labels, it's nice to overlay the keypoints on the scene image. + """ + if scene_frame_index is None: + scene_frame_index = self.config["video_info"].get("scene_frame_number", 1) if os.path.exists(self.video_file_path): cap = cv2.VideoCapture(self.video_file_path) cap.set(cv2.CAP_PROP_POS_FRAMES, scene_frame_index) diff --git a/tests/test_plot_keypoint_labels.py b/tests/test_plot_keypoint_labels.py new file mode 100644 index 0000000..457e840 --- /dev/null +++ b/tests/test_plot_keypoint_labels.py @@ -0,0 +1,28 @@ +from amadeusgpt import AMADEUS +from amadeusgpt import create_project +from amadeusgpt.utils import parse_result + + +def test_name_plotting(): + # the dummy video only contains 6 frames. + kwargs = { + 'video_info.scene_frame_number': 1, + 'llm_info.gpt_model': "gpt-4o" + } + data_folder = "examples/DummyVideo" + result_folder = "temp_result_folder" + + config = create_project(data_folder, result_folder, **kwargs) + amadeus = AMADEUS(config, use_vlm=True) + + query = """ plot the keypoint names next to the keypoints """ + + qa_message = amadeus.step(query) + + parse_result(amadeus, qa_message, use_ipython=False) + + #import matplotlib.pyplot as plt + #plt.show() + +if __name__ == "__main__": + test_name_plotting() From d08492b5bdeef707f137d2f7da47fa63a27eb079 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Thu, 8 Aug 2024 15:11:41 +0200 Subject: [PATCH 31/35] Fixed a bug. Changed hardcoded path to relative path in notebooks --- amadeusgpt/analysis_objects/visualization.py | 1 + notebooks/EPM_demo.ipynb | 6 +- notebooks/Horse_demo.ipynb | 192 ++++++++++++++++++- notebooks/MABe_demo.ipynb | 3 +- notebooks/MausHaus_demo.ipynb | 4 +- 5 files changed, 192 insertions(+), 14 deletions(-) diff --git a/amadeusgpt/analysis_objects/visualization.py b/amadeusgpt/analysis_objects/visualization.py index c1cd784..078be2a 100644 --- a/amadeusgpt/analysis_objects/visualization.py +++ b/amadeusgpt/analysis_objects/visualization.py @@ -143,6 +143,7 @@ def __init__( n_individuals: int, average_keypoints: Optional[bool] = True, events: Optional[List[BaseEvent]] = None, + use_3d: Optional[bool] = False, ): assert len(keypoints.shape) == 3 super().__init__(axs) diff --git a/notebooks/EPM_demo.ipynb b/notebooks/EPM_demo.ipynb index 8fecf7b..d88482e 100644 --- a/notebooks/EPM_demo.ipynb +++ b/notebooks/EPM_demo.ipynb @@ -84,7 +84,7 @@ "metadata": {}, "outputs": [], "source": [ - "behavior_analysis = amadeus.get_behavior_analysis('/Users/shaokaiye/AmadeusGPT-dev/examples/EPM/EPM_11.mp4')\n", + "behavior_analysis = amadeus.get_behavior_analysis('../examples/EPM/EPM_11.mp4')\n", "behavior_analysis.gui_manager.add_roi_from_video_selection()" ] }, @@ -174,9 +174,9 @@ ], "metadata": { "kernelspec": { - "display_name": "amadeusgpt-minimal", + "display_name": "amadeusgpt-cpu", "language": "python", - "name": "python3" + "name": "amadeusgpt-cpu" }, "language_info": { "codemirror_mode": { diff --git a/notebooks/Horse_demo.ipynb b/notebooks/Horse_demo.ipynb index 93c3b40..ae3ec73 100644 --- a/notebooks/Horse_demo.ipynb +++ b/notebooks/Horse_demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "6245b791", "metadata": {}, "outputs": [], @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "bceb3204-2a87-4671-8135-2533a7a51771", "metadata": {}, "outputs": [], @@ -31,10 +31,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "94210808-364c-44a9-a548-b600e75c5c25", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Project created at results. Results will be saved to results\n", + "The project will load video files (*.mp4) and optionally keypoint files from ../examples/Horse\n", + "A copy of the project config file is saved at results/config.yaml\n", + "{'data_info': {'data_folder': '../examples/Horse',\n", + " 'result_folder': 'results',\n", + " 'video_suffix': '.mp4'},\n", + " 'keypoint_info': {'include_confidence': False, 'use_3d': False},\n", + " 'llm_info': {'keep_last_n_messages': 2,\n", + " 'max_tokens': 4096,\n", + " 'temperature': 0.0},\n", + " 'object_info': {'load_objects_from_disk': False, 'use_grid_objects': False},\n", + " 'video_info': {'scene_frame_number': 100}}\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current total cost 0.0014 $\n", + "current input tokens 8666\n", + "current accumulated tokens 8791\n", + "description of the image frame provided\n", + "In the image, there is a person walking alongside a horse in a sunny outdoor setting. The horse is in a side view, and the person appears to be guiding it. The background features trees and structures, suggesting a stable or farm environment.\n", + "\n", + "Now, I will fill in the JSON string based on this description:\n", + "\n", + "```json\n", + "{\n", + " \"description\": \"A person walking alongside a horse in a sunny outdoor setting with trees and structures in the background.\",\n", + " \"individuals\": 1,\n", + " \"species\": \"sideview_quadruped\",\n", + " \"background_objects\": [\"trees\", \"stable structures\"]\n", + "}\n", + "```\n", + "['../examples/Horse/BrownHorseinShadow.mp4']\n" + ] + } + ], "source": [ "scene_frame_number = 100\n", "amadeus_root = Path(amadeusgpt.__file__).parent.parent\n", @@ -56,22 +105,131 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "0b8af8f4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "behavior_analysis = amadeus.get_behavior_analysis('/Users/shaokaiye/AmadeusGPT-dev/examples/Horse/BrownHorseinShadow.mp4')\n", + "behavior_analysis = amadeus.get_behavior_analysis('../examples/Horse/BrownHorseinShadow.mp4')\n", "scene_image = behavior_analysis.visual_manager.get_scene_image()\n", "plt.imshow(scene_image)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "d28b3f10-ecba-4ecf-a283-142d2d43ea8f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "current total cost 0.002 $\n", + "current input tokens 2925\n", + "current accumulated tokens 12076\n" + ] + }, + { + "data": { + "text/markdown": [ + "To plot the gait analysis using the specified keypoints (Offfrontfoot, Offfrontfetlock, Offknee, Elbow, and Shoulder), I will define a function that runs the gait analysis and then plots the results. \n", + "\n", + "The function will use the `run_gait_analysis` method to compute the gait parameters and the `plot_gait_analysis_results` method to visualize the results. \n", + "\n", + "Here’s the code:\n", + "\n", + "```python\n", + "def plot_gait_analysis(identifier):\n", + " '''\n", + " Parameters:\n", + " ----------\n", + " identifier: Identifier. Contains information about the video, keypoint and config.\n", + " \n", + " This function computes and plots the gait analysis results for the specified keypoints.\n", + " The keypoints used for the analysis are Offfrontfoot, Offfrontfetlock, Offknee, Elbow, and Shoulder.\n", + " '''\n", + " # create_analysis returns an instance of AnimalBehaviorAnalysis\n", + " analysis = create_analysis(identifier)\n", + " \n", + " # Define the keypoints for gait analysis\n", + " limb_keypoint_names = ['Offfrontfoot', 'Offfrontfetlock', 'Offknee', 'Elbow', 'Shoulder']\n", + " \n", + " # Run gait analysis\n", + " gait_analysis_results = analysis.run_gait_analysis(limb_keypoint_names)\n", + " \n", + " # Plot the gait analysis results\n", + " figure, axs = analysis.plot_gait_analysis_results(gait_analysis_results, limb_keypoint_names)\n", + " \n", + " return figure, axs\n", + "```\n", + "\n", + "In this code:\n", + "- The `run_gait_analysis` function is called with the specified keypoints.\n", + "- The `plot_gait_analysis_results` function is used to visualize the results.\n", + "- The function returns the figure and axes for further manipulation or display. \n", + "\n", + "Make sure to call this function with the appropriate `identifier` to execute the gait analysis and plotting." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "(
, )" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "query = \"Plot the gait analysis using Offfrontfoot; Offfrontfetlock; Offknee; Elbow and Shoulder.\"\n", "qa_message = amadeus.step(query)\n", @@ -85,6 +243,22 @@ "metadata": {}, "outputs": [], "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f5031b41", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e93c4b3d", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/notebooks/MABe_demo.ipynb b/notebooks/MABe_demo.ipynb index 336c12e..57a526f 100644 --- a/notebooks/MABe_demo.ipynb +++ b/notebooks/MABe_demo.ipynb @@ -64,7 +64,8 @@ "metadata": {}, "outputs": [], "source": [ - "behavior_analysis = amadeus.get_behavior_analysis('/Users/shaokaiye/AmadeusGPT-dev/examples/MABe/EGS8X2MN4SSUGFWAV976.mp4')\n", + "behavior_analysis = amadeus.get_behavior_analysis(video_file_path='../examples/MABe/EGS8X2MN4SSUGFWAV976.mp4',\n", + " keypoint_file_path='../examples/MABe/EGS8X2MN4SSUGFWAV976.h5')\n", "scene_image = behavior_analysis.visual_manager.get_scene_image()\n", "plt.imshow(scene_image)" ] diff --git a/notebooks/MausHaus_demo.ipynb b/notebooks/MausHaus_demo.ipynb index 3d2c106..7a25fca 100644 --- a/notebooks/MausHaus_demo.ipynb +++ b/notebooks/MausHaus_demo.ipynb @@ -65,7 +65,9 @@ "metadata": {}, "outputs": [], "source": [ - "behavior_analysis = amadeus.get_behavior_analysis('/Users/shaokaiye/AmadeusGPT-dev/examples/MausHaus/maushaus_trimmed.mp4')\n", + "behavior_analysis = amadeus.get_behavior_analysis(video_file_path='../examples/MausHaus/maushaus_trimmed.mp4',\n", + " keypoint_file_path='../examples/MausHaus/maushaus_trimmed.h5')\n", + "\n", "behavior_analysis.gui_manager.add_roi_from_video_selection()" ] }, From f9ce60eae0aea6a6d27eb7504743ccd461b36888 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Fri, 9 Aug 2024 10:03:49 +0200 Subject: [PATCH 32/35] updated vlm prompt to be more robust --- amadeusgpt/system_prompts/code_generator.py | 1 + amadeusgpt/system_prompts/visual_llm.py | 2 +- tests/test_3d.py | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) diff --git a/amadeusgpt/system_prompts/code_generator.py b/amadeusgpt/system_prompts/code_generator.py index f00c3ef..8c0be27 100644 --- a/amadeusgpt/system_prompts/code_generator.py +++ b/amadeusgpt/system_prompts/code_generator.py @@ -92,6 +92,7 @@ def get_watching_events(identifier): 11) You MUST call functions in api docs on the analysis object. 12) For api functions that require min_window and max_window, make sure you leave them as default values unless you are asked to change them. + HOW TO AVOID BUGS: You should always comment the shape of the any numpy array you are working with to avoid bugs. YOU MUST DO IT. """ diff --git a/amadeusgpt/system_prompts/visual_llm.py b/amadeusgpt/system_prompts/visual_llm.py index 83e52c0..7637a7c 100644 --- a/amadeusgpt/system_prompts/visual_llm.py +++ b/amadeusgpt/system_prompts/visual_llm.py @@ -11,7 +11,7 @@ def _get_system_prompt(): ``` The "description" has high level description of the image. The "individuals" indicates the number of animals in the image - The "species" indicates the species of the animals in the image. You can only choose from one of "topview_mouse", "sideview_quadruped" or "others". + The "species" indicates the species of the animals in the image. You can only choose from one of "topview_mouse", "sideview_quadruped" or "others". Note all quadruped animals should be considered as sideview_quadruped. The "background_objects" is a list of background objects in the image. Explain your answers before you fill the answers. Make sure you only return one json string. """ diff --git a/tests/test_3d.py b/tests/test_3d.py index af99a02..ee1f0a6 100644 --- a/tests/test_3d.py +++ b/tests/test_3d.py @@ -30,4 +30,4 @@ def test_3d_maushaus(): qa_message = amadeus.step(query) - parse_result(amadeus, qa_message, use_ipython=False) \ No newline at end of file + parse_result(amadeus, qa_message, use_ipython=False) From 07e4ae2e8ca14d718e16e12947757354f345b4e4 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Fri, 9 Aug 2024 13:56:23 +0200 Subject: [PATCH 33/35] deleted y axis inversion prompt --- amadeusgpt/managers/visual_manager.py | 9 ++++++++- amadeusgpt/system_prompts/code_generator.py | 11 +++++------ 2 files changed, 13 insertions(+), 7 deletions(-) diff --git a/amadeusgpt/managers/visual_manager.py b/amadeusgpt/managers/visual_manager.py index 6ee306b..a7a06da 100644 --- a/amadeusgpt/managers/visual_manager.py +++ b/amadeusgpt/managers/visual_manager.py @@ -48,9 +48,16 @@ def __init__( self.object_manager = object_manager @register_core_api - def get_scene_image(self, scene_frame_index: int| None = None): + def get_scene_image(self, scene_frame_index: int |None = None)-> np.ndarray: """ Returns the frame given the index in the video. + Parameter + --------- + scene_frame_index: int (optional) that specifies the index of the video frame. + Returns + ------- + An ndarray image + For visualizing keypoints or keypoint labels, it's nice to overlay the keypoints on the scene image. """ if scene_frame_index is None: diff --git a/amadeusgpt/system_prompts/code_generator.py b/amadeusgpt/system_prompts/code_generator.py index 8c0be27..8f13287 100644 --- a/amadeusgpt/system_prompts/code_generator.py +++ b/amadeusgpt/system_prompts/code_generator.py @@ -85,12 +85,11 @@ def get_watching_events(identifier): 4) Make sure you do not import any libraries in your code. All needed libraries are imported already. 5) Make sure you disintuigh positional and keyword arguments when you call functions in api docs 6) If you are writing code that uses matplotlib to plot, make sure you comment shape of the data to be plotted to double-check -7) if your plotting code plots coordinates of keypoints, make sure you invert y axis (only during plotting) so that the plot is consistent with the image -8) make sure the xlim and ylim covers the whole image. The image (h,w) is ({image_h},{image_w}) -9) Do not define your own objects (including grid objects). Only use objects that are given to you. -10) You MUST use the index from get_keypoint_names to access the keypoint data of specific keyponit names. Do not assume the order of the bodypart. -11) You MUST call functions in api docs on the analysis object. -12) For api functions that require min_window and max_window, make sure you leave them as default values unless you are asked to change them. +7) make sure the xlim and ylim covers the whole image. The image (h,w) is ({image_h},{image_w}) +8) Do not define your own objects (including grid objects). Only use objects that are given to you. +9) You MUST use the index from get_keypoint_names to access the keypoint data of specific keyponit names. Do not assume the order of the bodypart. +10) You MUST call functions in api docs on the analysis object. +11) For api functions that require min_window and max_window, make sure you leave them as default values unless you are asked to change them. HOW TO AVOID BUGS: From 71a8d1be2938c0dc4d70cbcfcd4bd5a422d9bb13 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Fri, 9 Aug 2024 15:02:33 +0200 Subject: [PATCH 34/35] Added animation support and added animation in horse demo --- .../integration_modules/embedding/__init__.py | 7 +- amadeusgpt/project.py | 6 +- amadeusgpt/system_prompts/code_generator.py | 1 + amadeusgpt/utils.py | 15 +- notebooks/Horse_demo.ipynb | 202 ++---------------- ...ustom_mouse_video.ipynb => YourData.ipynb} | 50 +++-- tests/test_plot_keypoint_labels.py | 28 --- 7 files changed, 78 insertions(+), 231 deletions(-) rename notebooks/{custom_mouse_video.ipynb => YourData.ipynb} (58%) delete mode 100644 tests/test_plot_keypoint_labels.py diff --git a/amadeusgpt/integration_modules/embedding/__init__.py b/amadeusgpt/integration_modules/embedding/__init__.py index 3985cfb..1406ccb 100644 --- a/amadeusgpt/integration_modules/embedding/__init__.py +++ b/amadeusgpt/integration_modules/embedding/__init__.py @@ -1,2 +1,7 @@ -from .cebra import * +try: + import cebra + from .cebra import * +except: + print ('not able to import cebra') + from .umap import * diff --git a/amadeusgpt/project.py b/amadeusgpt/project.py index 2544a6e..ccf6697 100644 --- a/amadeusgpt/project.py +++ b/amadeusgpt/project.py @@ -14,7 +14,11 @@ def create_project(data_folder, result_folder, **kwargs): "result_folder": result_folder, "video_suffix": ".mp4", }, - "llm_info": {"max_tokens": 4096, "temperature": 0.0, "keep_last_n_messages": 2}, + "llm_info": {"max_tokens": 4096, + "temperature": 0.0, + # let's use the best model by default + "gpt_model": "gpt-4o", + "keep_last_n_messages": 2}, "object_info": {"load_objects_from_disk": False, "use_grid_objects": False}, "keypoint_info": { "use_3d": False, diff --git a/amadeusgpt/system_prompts/code_generator.py b/amadeusgpt/system_prompts/code_generator.py index 8f13287..70d9488 100644 --- a/amadeusgpt/system_prompts/code_generator.py +++ b/amadeusgpt/system_prompts/code_generator.py @@ -90,6 +90,7 @@ def get_watching_events(identifier): 9) You MUST use the index from get_keypoint_names to access the keypoint data of specific keyponit names. Do not assume the order of the bodypart. 10) You MUST call functions in api docs on the analysis object. 11) For api functions that require min_window and max_window, make sure you leave them as default values unless you are asked to change them. +12) When making plots of keypoints of making animation about keypoints, try to overlap the plots with the scene frame if feasible. HOW TO AVOID BUGS: diff --git a/amadeusgpt/utils.py b/amadeusgpt/utils.py index 9da4019..dd99dd8 100644 --- a/amadeusgpt/utils.py +++ b/amadeusgpt/utils.py @@ -212,7 +212,7 @@ def create_qa_message(query: str, video_file_paths: list[str]) -> QA_Message: return QA_Message(query, video_file_paths) -from IPython.display import Markdown, Video, display +from IPython.display import Markdown, Video, display, HTML def parse_result(amadeus, qa_message, use_ipython=True, skip_code_execution=False): @@ -231,13 +231,20 @@ def parse_result(amadeus, qa_message, use_ipython=True, skip_code_execution=Fals ) if use_ipython: if len(qa_message.out_videos) > 0: - for video_path, event_videos in qa_message.out_videos.items(): + for identifier, event_videos in qa_message.out_videos.items(): for event_video in event_videos: display(Video(event_video, embed=True)) if use_ipython: + from matplotlib.animation import FuncAnimation if len(qa_message.function_rets) > 0: - for video_file_path in qa_message.function_rets: - display(Markdown(str(qa_message.function_rets[video_file_path]))) + for identifier, rets in qa_message.function_rets.items(): + if not isinstance(rets, (tuple, list)): + rets = [rets] + for ret in rets: + if isinstance(ret, FuncAnimation): + display(HTML(ret.to_jshtml())) + else: + display(Markdown(str(qa_message.function_rets[identifier]))) return qa_message diff --git a/notebooks/Horse_demo.ipynb b/notebooks/Horse_demo.ipynb index ae3ec73..69a24a1 100644 --- a/notebooks/Horse_demo.ipynb +++ b/notebooks/Horse_demo.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "6245b791", "metadata": {}, "outputs": [], @@ -15,7 +15,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "bceb3204-2a87-4671-8135-2533a7a51771", "metadata": {}, "outputs": [], @@ -31,59 +31,10 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "94210808-364c-44a9-a548-b600e75c5c25", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Project created at results. Results will be saved to results\n", - "The project will load video files (*.mp4) and optionally keypoint files from ../examples/Horse\n", - "A copy of the project config file is saved at results/config.yaml\n", - "{'data_info': {'data_folder': '../examples/Horse',\n", - " 'result_folder': 'results',\n", - " 'video_suffix': '.mp4'},\n", - " 'keypoint_info': {'include_confidence': False, 'use_3d': False},\n", - " 'llm_info': {'keep_last_n_messages': 2,\n", - " 'max_tokens': 4096,\n", - " 'temperature': 0.0},\n", - " 'object_info': {'load_objects_from_disk': False, 'use_grid_objects': False},\n", - " 'video_info': {'scene_frame_number': 100}}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current total cost 0.0014 $\n", - "current input tokens 8666\n", - "current accumulated tokens 8791\n", - "description of the image frame provided\n", - "In the image, there is a person walking alongside a horse in a sunny outdoor setting. The horse is in a side view, and the person appears to be guiding it. The background features trees and structures, suggesting a stable or farm environment.\n", - "\n", - "Now, I will fill in the JSON string based on this description:\n", - "\n", - "```json\n", - "{\n", - " \"description\": \"A person walking alongside a horse in a sunny outdoor setting with trees and structures in the background.\",\n", - " \"individuals\": 1,\n", - " \"species\": \"sideview_quadruped\",\n", - " \"background_objects\": [\"trees\", \"stable structures\"]\n", - "}\n", - "```\n", - "['../examples/Horse/BrownHorseinShadow.mp4']\n" - ] - } - ], + "outputs": [], "source": [ "scene_frame_number = 100\n", "amadeus_root = Path(amadeusgpt.__file__).parent.parent\n", @@ -91,6 +42,10 @@ "\n", "kwargs = { \n", " \"video_info.scene_frame_number\" : scene_frame_number,\n", + " \"llm_info\": {\n", + " \"gpt_model\": \"gpt-4o\",\n", + " }\n", + "\n", "}\n", "\n", "config = create_project(data_folder = \"../examples/Horse\",\n", @@ -105,131 +60,22 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "0b8af8f4", "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "behavior_analysis = amadeus.get_behavior_analysis('../examples/Horse/BrownHorseinShadow.mp4')\n", + "behavior_analysis = amadeus.get_behavior_analysis(video_file_path = '../examples/Horse/BrownHorseinShadow.mp4')\n", "scene_image = behavior_analysis.visual_manager.get_scene_image()\n", "plt.imshow(scene_image)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "d28b3f10-ecba-4ecf-a283-142d2d43ea8f", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", - "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "current total cost 0.002 $\n", - "current input tokens 2925\n", - "current accumulated tokens 12076\n" - ] - }, - { - "data": { - "text/markdown": [ - "To plot the gait analysis using the specified keypoints (Offfrontfoot, Offfrontfetlock, Offknee, Elbow, and Shoulder), I will define a function that runs the gait analysis and then plots the results. \n", - "\n", - "The function will use the `run_gait_analysis` method to compute the gait parameters and the `plot_gait_analysis_results` method to visualize the results. \n", - "\n", - "Here’s the code:\n", - "\n", - "```python\n", - "def plot_gait_analysis(identifier):\n", - " '''\n", - " Parameters:\n", - " ----------\n", - " identifier: Identifier. Contains information about the video, keypoint and config.\n", - " \n", - " This function computes and plots the gait analysis results for the specified keypoints.\n", - " The keypoints used for the analysis are Offfrontfoot, Offfrontfetlock, Offknee, Elbow, and Shoulder.\n", - " '''\n", - " # create_analysis returns an instance of AnimalBehaviorAnalysis\n", - " analysis = create_analysis(identifier)\n", - " \n", - " # Define the keypoints for gait analysis\n", - " limb_keypoint_names = ['Offfrontfoot', 'Offfrontfetlock', 'Offknee', 'Elbow', 'Shoulder']\n", - " \n", - " # Run gait analysis\n", - " gait_analysis_results = analysis.run_gait_analysis(limb_keypoint_names)\n", - " \n", - " # Plot the gait analysis results\n", - " figure, axs = analysis.plot_gait_analysis_results(gait_analysis_results, limb_keypoint_names)\n", - " \n", - " return figure, axs\n", - "```\n", - "\n", - "In this code:\n", - "- The `run_gait_analysis` function is called with the specified keypoints.\n", - "- The `plot_gait_analysis_results` function is used to visualize the results.\n", - "- The function returns the figure and axes for further manipulation or display. \n", - "\n", - "Make sure to call this function with the appropriate `identifier` to execute the gait analysis and plotting." - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/markdown": [ - "(
, )" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "query = \"Plot the gait analysis using Offfrontfoot; Offfrontfetlock; Offknee; Elbow and Shoulder.\"\n", "qa_message = amadeus.step(query)\n", @@ -242,23 +88,11 @@ "id": "e394c4e0", "metadata": {}, "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5031b41", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e93c4b3d", - "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "query = \"\"\" make an animation of the horse keypoints over time. Overlap the image frame on it. Save the animation on the disk. \"\"\"\n", + "qa_message = amadeus.step(query)\n", + "qa_message = parse_result(amadeus, qa_message)" + ] } ], "metadata": { diff --git a/notebooks/custom_mouse_video.ipynb b/notebooks/YourData.ipynb similarity index 58% rename from notebooks/custom_mouse_video.ipynb rename to notebooks/YourData.ipynb index bcbe720..848575f 100644 --- a/notebooks/custom_mouse_video.ipynb +++ b/notebooks/YourData.ipynb @@ -21,12 +21,8 @@ "outputs": [], "source": [ "from amadeusgpt import AMADEUS\n", - "from amadeusgpt.config import Config\n", "from amadeusgpt.utils import parse_result\n", - "import amadeusgpt\n", - "from amadeusgpt import create_project\n", - "import matplotlib.pyplot as plt\n", - "import cv2" + "from amadeusgpt import create_project" ] }, { @@ -35,7 +31,7 @@ "metadata": {}, "source": [ "### Note that unlike other notebooks, we don't have keypoint_file_path here (as it's not provided)\n", - "### By default, we use gpt-4o to determine which SuperAnimal models to run and it will run SuperAnimal in the first time the keypoints related queries are asked\n", + "### By default, we use gpt-4o to determine which SuperAnimal models to run and it will run SuperAnimal in the first time the keypoints related queries are asked. Note to use superanimal, you will need to install the newest DeepLabCut.\n", "### Make sure you use a short video clips if you are not using GPUs in Linux (Mac silicon support to be added)" ] }, @@ -46,16 +42,44 @@ "metadata": {}, "outputs": [], "source": [ - "scene_frame_number = 400\n", - "\n", "# where you store you video and (optionally) keypoint files\n", - "data_folder = \"temp_data_folder\"\n", + "# If you don't have keypoint files, we would try to run SuperAnimal on your video\n", + "# If you have pair of video and keypoint files, make sure they follow the naming convention as following:\n", + "\n", + "# your_folder\n", + "# - cat.mp4\n", + "# - cat.h5 (DLC output)\n", + "# - dog.mp4\n", + "# - dog.h5 (DLC output)\n", + "\n", + "data_folder = \"../examples/Horse\"\n", "result_folder = \"temp_result_folder\"\n", "video_suffix = \".mp4\"\n", "\n", - "config = create_project(data_folder, result_folder, video_suffix = video_suffix)\n", + "# if you want to overwrite the default config, you can do it here\n", + "kwargs = {\n", + " \"data_info\": {\n", + " \"data_folder\": data_folder,\n", + " \"result_folder\": result_folder,\n", + " # can only locate videos specified in video_suffix\n", + " \"video_suffix\": \".mp4\",\n", + " },\n", + " \n", + " \"llm_info\": {\"max_tokens\": 4096, \n", + " \"temperature\": 0.0, \n", + " # one can swtich this to gpt-4o-mini for cheaper inference with the cost of worse performance.\n", + " \"gpt_model\": \"gpt-4o\",\n", + " # We only keep conversation history of 2. You can make it longer with more cost. We are switching to a different form of long-term memory.\n", + " \"keep_last_n_messages\": 2},\n", + " \"keypoint_info\": {\n", + " # only set True if you work with 3D keypoint \n", + " \"use_3d\": False,\n", + " },\n", + " # this is the frame index for gpt-4o to match the right superanimal model.\n", + " \"video_info\": {\"scene_frame_number\": 1},\n", + " }\n", "\n", - "config[\"scene_frame_number\"] = scene_frame_number\n", + "config = create_project(data_folder, result_folder, video_suffix = video_suffix, **kwargs)\n", "\n", "amadeus = AMADEUS(config, use_vlm = True)\n", "video_file_paths = amadeus.get_video_file_paths()\n", @@ -89,9 +113,9 @@ ], "metadata": { "kernelspec": { - "display_name": "amadeusgpt-cpu", + "display_name": "amadeusgpt-minimal", "language": "python", - "name": "amadeusgpt-cpu" + "name": "python3" }, "language_info": { "codemirror_mode": { diff --git a/tests/test_plot_keypoint_labels.py b/tests/test_plot_keypoint_labels.py deleted file mode 100644 index 457e840..0000000 --- a/tests/test_plot_keypoint_labels.py +++ /dev/null @@ -1,28 +0,0 @@ -from amadeusgpt import AMADEUS -from amadeusgpt import create_project -from amadeusgpt.utils import parse_result - - -def test_name_plotting(): - # the dummy video only contains 6 frames. - kwargs = { - 'video_info.scene_frame_number': 1, - 'llm_info.gpt_model': "gpt-4o" - } - data_folder = "examples/DummyVideo" - result_folder = "temp_result_folder" - - config = create_project(data_folder, result_folder, **kwargs) - amadeus = AMADEUS(config, use_vlm=True) - - query = """ plot the keypoint names next to the keypoints """ - - qa_message = amadeus.step(query) - - parse_result(amadeus, qa_message, use_ipython=False) - - #import matplotlib.pyplot as plt - #plt.show() - -if __name__ == "__main__": - test_name_plotting() From f940c94787f7a1cfb191fb448db8f1b766769e78 Mon Sep 17 00:00:00 2001 From: shaokaiye Date: Fri, 9 Aug 2024 15:06:15 +0200 Subject: [PATCH 35/35] edited readme --- README.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 94aec9b..b4f71ef 100644 --- a/README.md +++ b/README.md @@ -73,7 +73,7 @@ You can git clone (or download) this repo to grab a copy and go. We provide exam ### Here are a few demos that could fuel your own work, so please check them out! 1) [Draw a region of interest (ROI) and ask, "when is the animal in the ROI?"](https://github.com/AdaptiveMotorControlLab/AmadeusGPT/tree/main/notebooks/EPM_demo.ipynb) -2) [Use a DeepLabCut SuperAnimal pose model to do video inference](https://github.com/AdaptiveMotorControlLab/AmadeusGPT/tree/main/notebooks/custom_mouse_demo.ipynb) - (make sure you use a GPU if you don't have corresponding DeepLabCut keypoint files already! +2) [Use your own data](https://github.com/AdaptiveMotorControlLab/AmadeusGPT/tree/main/notebooks/YourData.ipynb) - (make sure you use a GPU to run SuperAnimal if you don't have corresponding DeepLabCut keypoint files already! 3) [Write you own integration modules and use them](https://github.com/AdaptiveMotorControlLab/AmadeusGPT/tree/main/notebooks/Horse_demo.ipynb). Bonus: [source code](amadeusgpt/integration_modules). Make sure you delete the cached modules_embedding.pickle if you add new modules! 4) [Multi-Animal social interactions](https://github.com/AdaptiveMotorControlLab/AmadeusGPT/tree/main/notebooks/MABe_demo.ipynb) 5) [Reuse the task program generated by LLM and run it on different videos](https://github.com/AdaptiveMotorControlLab/AmadeusGPT/tree/main/notebooks/MABe_demo.ipynb) @@ -126,6 +126,8 @@ the key dependencies that need installed are: pip install notebook conda install hdf5 conda install pytables==3.8 +# pip install deeplabcut==3.0.0rc4 if you want to use SuperAnimal on your own videos + pip install amadeusgpt ``` ## Citation