CV：利用cv2+自定義load_detection_model(加載人臉識別xml文件及detectMultiScale函數得到人臉列表)+keras的load_model(加載表情hdf5、性別hdf5)實現標注臉部表情和性別label
CV：利用cv2(加載人臉識別xml文件及detectMultiScale函數得到人臉列表)+keras的load_model(加載表情hdf5、性別hdf5)并標注代碼實現

目錄

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設計思路

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輸出結果

設計思路

核心代碼

gitee鏈接：

https://gitee.com/yunyaniu/Python_GUI/tree/master/SR%20and%20TD%20with%20BCT%20to%20Automatic%20Alarm%20GDCup/CV_face_classification_emotion_genderhttps://gitee.com/yunyaniu/Python_GUI/tree/master/SR%20and%20TD%20with%20BCT%20to%20Automatic%20Alarm%20GDCup/CV_face_classification_emotion_gender

#CV：基于Keras利用cv2+自定義load_detection_model(加載人臉識別xml文件及detectMultiScale函數得到人臉列表)+keras的load_model(加載表情hdf5、性別hdf5)實現標注臉部表情和性別label——Jason Niu import sysimport cv2 from keras.models import load_model import numpy as npimage_path ="F:/File_Python/Resources/hezhao05.jpg" detection_model_path = '../trained_models/detection_models/haarcascade_frontalface_default.xml' emotion_model_path = '../trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5' gender_model_path = '../trained_models/gender_models/simple_CNN.81-0.96.hdf5' emotion_labels = get_labels('fer2013') gender_labels = get_labels('imdb') font = cv2.FONT_HERSHEY_SIMPLEX gender_offsets = (30, 60) gender_offsets = (10, 10) emotion_offsets = (20, 40) emotion_offsets = (0, 0)face_detection = load_detection_model(detection_model_path) emotion_classifier = load_model(emotion_model_path, compile=False) gender_classifier = load_model(gender_model_path, compile=False)emotion_target_size = emotion_classifier.input_shape[1:3] gender_target_size = gender_classifier.input_shape[1:3]rgb_image = load_image(image_path, grayscale=False) gray_image = load_image(image_path, grayscale=True) gray_image = np.squeeze(gray_image) gray_image = gray_image.astype('uint8') faces = detect_faces(face_detection, gray_image)for face_coordinates in faces: x1, x2, y1, y2 = apply_offsets(face_coordinates, gender_offsets)rgb_face = rgb_image[y1:y2, x1:x2] x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)gray_face = gray_image[y1:y2, x1:x2] try:rgb_face = cv2.resize(rgb_face, (gender_target_size))gray_face = cv2.resize(gray_face, (emotion_target_size))except:continuergb_face = preprocess_input(rgb_face, False)rgb_face = np.expand_dims(rgb_face, 0) gender_prediction = gender_classifier.predict(rgb_face) gender_label_arg = np.argmax(gender_prediction)gender_text = gender_labels[gender_label_arg] gray_face = preprocess_input(gray_face, True)gray_face = np.expand_dims(gray_face, 0)gray_face = np.expand_dims(gray_face, -1)emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))emotion_text = emotion_labels[emotion_label_arg]if gender_text == gender_labels[0]: color = (255, 255, 0)else:color = (255, 0, 0)draw_bounding_box(face_coordinates, rgb_image, color) draw_text(face_coordinates, rgb_image, gender_text, color, 0, -20, 1, 2)draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1, 2)bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR) save_img='F:/File_Python/Resources/hezhao041.jpg' cv2.imwrite(save_img, bgr_image)cv2.imshow('Emotion and Gender test', rgb_image) cv2.waitKey(0) cv2.destroyAllWindows()

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CV：利用cv2+自定義load_detection_model(加載人臉識別xml文件及detectMultiScale函數得到人臉列表)+keras的load_model(加載表情hdf5、性別hdf5)實現標注臉部表情和性別label

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