Created camera.py to test detections on IP camera streams.

YOLOv3_TF2/README.md

@@ -60,4 +60,8 @@ to the weights folder.
 
    python video.py
 
+5. Test an rtsp IP camera stream. Update the URL in camera.py to test.
+
+   python camera.py
+
 Good luck

YOLOv3_TF2/camera.py

@@ -0,0 +1,124 @@
+"""
+Amendment:
+Updated video.py to camera.py, this sample is tailored to accept an RTSP camera stream.
+Adjustments were made so that the frame grab only captures the most recent frame for detection processing.
+
+Originally Written by: Rahmad Sadli
+Website : https://machinelearningspace.com
+
+I finally made this program simple and readable
+Hopefully, this program will help some beginners like me to understand better object detection.
+If you want to redistribute it, just keep the author's name.
+
+In oder to execute this program, you need to install TensorFlow 2.0 and opencv 4.x
+
+For more details about how this program works. I explained well about it, just click the link below:
+https://machinelearningspace.com/the-beginners-guide-to-implementing-yolo-v3-in-tensorflow-2-0-part-1/
+
+Credit to:
+Ayoosh Kathuria who shared his great work using pytorch, really appreaciated it.
+https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/
+
+"""
+
+
+import tensorflow as tf
+
+from utils import load_class_names, output_boxes, draw_outputs, resize_image
+import cv2
+import time
+
+from yolov3 import YOLOv3Net
+
+#If you don't have enough GPU hardware device available in your machine, uncomment the following three lines:
+physical_devices = tf.config.experimental.list_physical_devices('GPU')
+assert len(physical_devices) > 0, "Not enough GPU hardware devices available"
+tf.config.experimental.set_memory_growth(physical_devices[0], True)
+
+
+model_size = (416, 416,3)
+num_classes = 80
+class_name = './data/coco.names'
+max_output_size = 100
+max_output_size_per_class= 20
+iou_threshold = 0.5
+confidence_threshold = 0.5
+
+
+cfgfile = 'cfg/yolov3.cfg'
+weightfile = 'weights/yolov3_weights.tf'
+
+def main():
+
+    model = YOLOv3Net(cfgfile,model_size,num_classes)
+
+    model.load_weights(weightfile)
+
+    class_names = load_class_names(class_name)
+
+
+
+    win_name = 'Yolov3 detection'
+    cv2.namedWindow(win_name)
+
+    # Specify the camera url.
+    # For camera, just change the camera URL to match your IP camera RTSP stream or MPEG stream.
+    cap = cv2.VideoCapture("rtsp://user:password@172.168.50.208:554/cam/realmonitor?channel=1&subtype=1")
+    frame_size = (cap.get(cv2.CAP_PROP_FRAME_WIDTH),
+                  cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    try:
+        while True:
+            start = time.time()
+            cap.grab() # Grab the most recent frame from the camera stream
+            ret, frame = cap.read() # Read it into a frame buffer
+            if not ret:
+               break
+
+            resized_frame = tf.expand_dims(frame, 0)
+            resized_frame = resize_image(resized_frame, (model_size[0],model_size[1]))
+
+            pred = model.predict(resized_frame)
+
+            boxes, scores, classes, nums = output_boxes( \
+                pred, model_size,
+                max_output_size=max_output_size,
+                max_output_size_per_class=max_output_size_per_class,
+                iou_threshold=iou_threshold,
+                confidence_threshold=confidence_threshold)
+
+            img = draw_outputs(frame, boxes, scores, classes, nums, class_names)
+            cv2.imshow(win_name, img)
+
+            stop = time.time()
+
+            seconds = stop - start
+            # print("Time taken : {0} seconds".format(seconds))
+
+            # Calculate frames per second
+            fps = 1 / seconds
+            print("Estimated frames per second : {0}".format(fps))
+
+            key = cv2.waitKey(1) & 0xFF
+            if key == ord('q'):
+                break
+            if key == 27:
+                break
+
+            # Adjust frame rate
+            #if fps > 30:
+            #    fps = fps * 0.5
+            #    cap.set(cv2.CAP_PROP_FPS, int(fps))
+            #    print("Changing frame rate to: {0}".format(int(fps)))
+            #else:
+            #    cap.set(cv2.CAP_PROP_FPS, 10)
+            #    print("Changing frame rate to: {0}".format(int(fps)))
+
+    finally:
+        cv2.destroyAllWindows()
+        cap.release()
+        print('Detections have been performed successfully.')
+
+
+if __name__ == '__main__':
+    main()