0.0.6

.gitignore

@@ -0,0 +1,10 @@
+# cache
+.DS_Store
+*/.DS_Store
+.vscode/
+*/__pycache__
+
+# Pytorch
+*.pth
+*.py~
+*.sh~

BaseML/Classification.py

@@ -1,18 +1,26 @@
 import pandas as pd
 import numpy as np
 import os
-from sklearn.metrics import accuracy_score, mean_squared_error
+import matplotlib.pyplot as plt
+import seaborn
+from sklearn.metrics import accuracy_score, mean_squared_error, precision_score, recall_score, confusion_matrix
 from sklearn.naive_bayes import GaussianNB
 from sklearn.ensemble import AdaBoostClassifier
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.neighbors import KNeighborsClassifier
 from sklearn.neural_network import MLPClassifier
+from sklearn.ensemble import RandomForestClassifier
 from sklearn.model_selection import train_test_split
 from sklearn.svm import SVC
 from sklearn.cluster import KMeans
 from sklearn.metrics import silhouette_score, silhouette_samples
 import joblib
+import cv2
 import random
+from skimage.feature import hog
+from scipy.spatial.distance import cdist
+
+from demo.face import extract_hog_features
 
 class cls:
     def __init__(self, algorithm='KNN', n_neighbors=5, n_estimators=100, N_CLUSTERS=5):
@@ -23,6 +31,8 @@ def __init__(self, algorithm='KNN', n_neighbors=5, n_estimators=100, N_CLUSTERS=
         self.test_size = 0.2
         self.test_set = ' '
         self.x_train, self.x_test, self.y_train, self.y_test = 0, 0, 0, 0
+        self.X = 0
+        self.Y = 0
         if self.algorithm == 'KNN':
             self.model = KNeighborsClassifier(n_neighbors=n_neighbors)
         elif self.algorithm == 'SVM':
@@ -38,6 +48,8 @@ def __init__(self, algorithm='KNN', n_neighbors=5, n_estimators=100, N_CLUSTERS=
             self.model = KMeans(self.n)
         elif self.algorithm == 'MLP':
             self.model = MLPClassifier(solver='lbfgs')
+        elif self.algorithm == 'RandomForest':
+            self.model == RandomForestClassifier(n_estimators=n_estimators,random_state=0)
 
     def train(self, seed=0, data_type='csv'):
         if self.algorithm in ['AdaBoost','SVM','NaiveBayes', 'MLP']:
@@ -72,14 +84,19 @@ def train(self, seed=0, data_type='csv'):
 
             self.x_train = np.delete(self.x_train, delete_list, axis=1)
             self.model.fit(self.x_train)
+
+        elif self.algorithm == 'RandomForest':
+            self.x_train, self.x_test, self.y_train, self.y_test = train_test_split(self.X, self.Y, test_size=0.25, random_state=42)
+            self.model.fit(self.x_train, self.y_train)
+
 
     def inference(self, data = np.nan):
         if data is not np.nan: # 对data进行了指定
             self.x_test = data
 
-        if self.algorithm in ['AdaBoost','SVM','NaiveBayes', 'MLP','KNN','CART']:
-            pred = self.model.predict(self.x_test)
-            return pred
+        if self.algorithm in ['AdaBoost','SVM','NaiveBayes', 'MLP','KNN','CART', 'RandomForest']:
+            self.pred = self.model.predict(self.x_test)
+            return self.pred
 
         elif self.algorithm == 'Kmeans':
             labels = self.model.labels_      # 获取聚类标签
@@ -166,3 +183,98 @@ def get_data(self,X,y,x_column,y_column):
             self.y_train = y[:,y_column]
             if self.y_train.shape[0]:
                 self.dataset = np.concatenate((self.x_train,self.y_train),axis=1) # 按列进行拼接
+
+    def extract_hog_features(self, X):
+        image_descriptors = []
+        for i in range(len(X)):                                         # 此处的X为之前训练部分所有图像的矩阵形式拼接而来，
+            # print(i)                                                  # 所以len(X)实为X中矩阵的个数，即训练部分图像的个数
+            fd, _ = hog(X[i], orientations=9, pixels_per_cell=(16, 16), cells_per_block=(16, 16),
+                                block_norm='L2-Hys', visualize=True)    # 此处的参数细节详见其他文章
+            image_descriptors.append(fd)                                # 拼接得到所有图像的hog特征
+        return image_descriptors                                        # 返回的是训练部分所有图像的hog特征
+
+
+    def extract_hog_features_single(self, X):
+        image_descriptors_single = []
+        fd, _ = hog(X, orientations=9, pixels_per_cell=(16, 16), cells_per_block=(16, 16),
+                                block_norm='L2-Hys', visualize=True)
+        image_descriptors_single.append(fd)
+        return image_descriptors_single
+
+
+    def read_data_and_pre(self, label2id, path):
+        X = []
+        Y = []
+        path =r'../dataset/JAFFE/training_set'
+        for label in os.listdir(path):
+            for img_file in os.listdir(os.path.join(path, label)):              # 遍历
+                image = cv2.imread(os.path.join(path, label, img_file))         # 读取图像
+                result = image/255.0                                            # 图像归一化
+                res = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)                   # 转灰度
+                # cv2.waitKey(0)
+                cv2.destroyAllWindows()
+                X.append(res)                                                   # 将读取到的所有图像的矩阵形式拼接在一起
+                Y.append(label2id[label])                                       # 将读取到的所有图像的标签拼接在一起
+        self.X = self.extract_hog_features(X)
+        self.Y = Y
+        return self.extract_hog_features(X), Y                                                             # 返回的X,Y分别是图像的矩阵表达和图像的标签
+
+
+    def plot(self):
+        acc = accuracy_score(self.y_test, self.pred)
+        precision = precision_score(self.y_test, self.pred, average='macro')
+        recall = recall_score(self.y_test, self.pred, average='macro')
+        cm = confusion_matrix(self.y_test, self.pred)
+        print(cm)
+        print('Acc: ', acc)
+        print('Precision: ', precision)
+        print('Recall: ', recall)
+
+        xtick = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+        ytick = xtick
+
+        f, ax = plt.subplots(figsize=(7, 5))
+        ax.tick_params(axis='y', labelsize=15)
+        ax.tick_params(axis='x', labelsize=15)
+
+        seaborn.set(font_scale=1.2)
+        plt.rc('font',family='Times New Roman', size=15)
+
+        seaborn.heatmap(cm,fmt='g', cmap='Blues', annot=True, cbar=True,xticklabels=xtick, yticklabels=ytick, ax=ax)
+
+        plt.title('Confusion Matrix', fontsize='x-large')
+
+        plt.show()
+
+
+    def test(self, path):
+    # 下面为同一文件夹下多张图片的表情识别
+    # labelid2 = {0:'angry',1: 'disgust',2: 'fear',3:'happy',4:'neutral',5:'sad',6:'surprise'}
+        path=path
+        i = 1
+        for dir in os.listdir((path)):
+            temp_path = os.path.join(path, dir)
+            for image_file in os.listdir(temp_path):
+                image = cv2.imread(os.path.join(temp_path,image_file))
+                # result = image/255.0
+                # cv2.waitKey(1000)
+                # cv2.destroyAllWindows()
+                result = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+                X_Single = self.extract_hog_features_single(result)
+                predict = self.model.predict(X_Single)         # 可以在这里选择分类器的类别
+                print(i)
+                i += 1
+                if predict == 0:
+                    print('angry')
+                elif predict == 1:
+                    print('disgust')
+                elif predict == 2:
+                    print('fear')
+                elif predict == 3:
+                    print('happy')
+                elif predict == 4:
+                    print('neutral')
+                elif predict == 5:
+                    print('sad')
+                elif predict == 6:
+                    print('surprise')

BaseNN/__init__.py

@@ -0,0 +1 @@
+from .ops import *