qyuan.github.io/train.py at main · kcabkooltonod/qyuan.github.io · GitHub

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import numpy as np
import cv2
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D
from keras.optimizers import Adam
from keras.layers import MaxPooling2D
from keras.preprocessing.image import ImageDataGenerator
# 需要调用scipy库才能运行keras神经网络
import scipy
# 确保训练集和测试集的名称对应当前目录下的文件名
train_dir = 'train'
val_dir = 'test'
# 将图片转换成数据
train_datagen = ImageDataGenerator(rescale=1. / 255)
val_datagen = ImageDataGenerator(rescale=1. / 255)
# 生成图片训练集数据
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')
# 构建卷积神经网络
emotion_model = Sequential()
# 卷积，池化，随机丢弃
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')

# 损失函数和adam优化器
emotion_model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001, decay=1e-6), metrics=['accuracy'])
# 开始训练 50轮 批大小64
emotion_model_info = emotion_model.fit_generator(
    train_generator,
    steps_per_epoch=28709 // 64,
    epochs=50,
    validation_data=validation_generator,
    validation_steps=7178 // 64)
emotion_model.save_weights('emotion_model.h5')

cv2.ocl.setUseOpenCL(False)
# 表情标签
emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}

# 获取摄像头
cap = cv2.VideoCapture(0)
while True:
    # Find haar cascade to draw bounding box around face
    ret, frame = cap.read()
    if not ret:
        break
# 调用opencv库中的人脸特征分类器，确保工具的位置对应路径
    bounding_box = cv2.CascadeClassifier(
        'haarcascade_frontalface_default.xml')
# 彩图转为灰图 cv2.COLOR_BGR2gray_frame换成cv2.COLOR_BGR2GRAY
    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# opencv的人脸检测器
    num_faces = bounding_box.detectMultiScale(gray_frame, 1.3, 5)
# 生成表情画框
    for (x, y, w, h) in num_faces:
        cv2.rectangle(frame, (x, y - 50), (x + w, y + h + 10), (255, 0, 0), 2)
        roi_gray_frame = gray_frame[y:y + h, x:x + w]
        cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
        emotion_prediction = emotion_model.predict(cropped_img)
        maxindex = int(np.argmax(emotion_prediction))
        cv2.putText(frame, emotion_dict[maxindex], (x + 20, y - 60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2,
                    cv2.LINE_AA)

    cv2.imshow('Video', cv2.resize(frame, (1200, 860), interpolation=cv2.INTER_CUBIC))
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()