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预测表情（main）.py

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+import numpy as np
+import cv2
+from keras.preprocessing.image import ImageDataGenerator
+from keras.layers import Dense, Dropout, Flatten
+from keras.layers import Conv2D
+from keras.optimizers import Adam
+from keras.layers import MaxPooling2D
+from keras.models import Sequential
+
+# 照片路径
+train_dir = 'D:\\my\\college\\31\\machinelearning\\archive\\train'
+val_dir = 'D:\\my\\college\\31\\machinelearning\\archive\\test'
+
+# 把灰度值从0-255映射到0-1
+train_datagen = ImageDataGenerator(rescale=1./255)
+val_datagen = ImageDataGenerator(rescale=1./255)
+
+# 图像增强
+train_generator = train_datagen.flow_from_directory(
+        train_dir,
+        target_size=(48,48),
+        batch_size=64,
+        color_mode="grayscale",
+        class_mode='categorical')
+
+validation_generator = val_datagen.flow_from_directory(
+        val_dir,
+        target_size=(48,48),
+        batch_size=64,
+        color_mode="grayscale",
+        class_mode='categorical')
+
+emotion_model = Sequential()
+
+emotion_model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))
+emotion_model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
+emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
+emotion_model.add(Dropout(0.25))
+
+emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
+emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
+emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
+emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
+emotion_model.add(Dropout(0.25))
+
+emotion_model.add(Flatten())
+emotion_model.add(Dense(1024, activation='relu'))
+emotion_model.add(Dropout(0.5))
+emotion_model.add(Dense(7, activation='softmax'))
+
+emotion_model.load_weights('emotion_model.h5')
+frame = cv2.imread('angry1.jpg')
+gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+cropped_frame = np.expand_dims(np.expand_dims(cv2.resize(gray_frame, (48, 48)), -1), 0)
+
+emotion_prediction = emotion_model.predict(cropped_frame)
+maxindex = int(np.argmax(emotion_prediction))
+emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
+print(emotion_dict[maxindex])