CV:基于keras利用cv2自带两步检测法对《跑男第六季第五期》之如花片段(或调用摄像头)进行实时性别&脸部表情检测
目录
- from statistics import mode
-
- import cv2
- from keras.models import load_model
- import numpy as np
-
-
- 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
-
- frame_window = 10
- gender_offsets = (30, 60)
- emotion_offsets = (20, 40)
-
- 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]
-
- gender_window = []
- emotion_window = []
-
- cv2.namedWindow('window_frame_by_Jason_Niu')
- video_capture = cv2.VideoCapture(0)
- video_capture = cv2.VideoCapture("F:\File_Python\Python_example\YOLOv3_use_TF\RunMan5.mp4")
- while True:
-
- bgr_image = video_capture.read()[1]
- gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY) 分别将读取的图像进行灰化、RGB化处理
- rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
- 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:
- continue
- gray_face = preprocess_input(gray_face, False)
- 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]
- emotion_window.append(emotion_text)
-
- rgb_face = np.expand_dims(rgb_face, 0)
- rgb_face = preprocess_input(rgb_face, False)
- gender_prediction = gender_classifier.predict(rgb_face)
- gender_label_arg = np.argmax(gender_prediction)
- gender_text = gender_labels[gender_label_arg]
- gender_window.append(gender_text)
-
- if len(gender_window) > frame_window:
- emotion_window.pop(0)
- gender_window.pop(0)
- try:
- emotion_mode = mode(emotion_window)
- gender_mode = mode(gender_window)
- except:
- continue
-
- if gender_text == gender_labels[0]:
- color = (0, 0, 255)
- else:
- color = (255, 0, 0)
-
- draw_bounding_box(face_coordinates, rgb_image, color)
- draw_text(face_coordinates, rgb_image, gender_mode,
- color, 0, -20, 1, 4)
- draw_text(face_coordinates, rgb_image, emotion_mode,
- color, 0, -45, 1, 4)
-
-
- bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
- cv2.namedWindow("window_frame_by_Jason_Niu",0);
- cv2.resizeWindow("window_frame_by_Jason_Niu", 640, 380);
- cv2.imshow('window_frame_by_Jason_Niu', bgr_image)
- if cv2.waitKey(1) & 0xFF == ord('q'):
- break
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