75 lines
2.4 KiB
Python
75 lines
2.4 KiB
Python
# 摄像头实时人脸特征描述子计算 / Real-time face descriptor compute
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import dlib # 人脸识别的库 Dlib
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import cv2 # 图像处理的库 OpenCV
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import time
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# 1. Dlib 正向人脸检测器
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detector = dlib.get_frontal_face_detector()
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# 2. Dlib 人脸 landmark 特征点检测器
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predictor = dlib.shape_predictor('data/data_dlib/shape_predictor_68_face_landmarks.dat')
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# 3. Dlib Resnet 人脸识别模型，提取 128D 的特征矢量
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face_reco_model = dlib.face_recognition_model_v1("data/data_dlib/dlib_face_recognition_resnet_model_v1.dat")
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class Face_Descriptor:
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def __init__(self):
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self.frame_time = 0
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self.frame_start_time = 0
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self.fps = 0
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def update_fps(self):
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now = time.time()
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self.frame_time = now - self.frame_start_time
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self.fps = 1.0 / self.frame_time
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self.frame_start_time = now
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def run(self):
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cap = cv2.VideoCapture(0)
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cap.set(3, 480)
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self.process(cap)
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cap.release()
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cv2.destroyAllWindows()
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def process(self, stream):
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while stream.isOpened():
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flag, img_rd = stream.read()
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k = cv2.waitKey(1)
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faces = detector(img_rd, 0)
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font = cv2.FONT_HERSHEY_SIMPLEX
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# 检测到人脸
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if len(faces) != 0:
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for face in faces:
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face_shape = predictor(img_rd, face)
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face_desc = face_reco_model.compute_face_descriptor(img_rd, face_shape)
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# 添加说明
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cv2.putText(img_rd, "Face Descriptor", (20, 40), font, 1, (255, 255, 255), 1, cv2.LINE_AA)
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cv2.putText(img_rd, "FPS: " + str(self.fps.__round__(2)), (20, 100), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
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cv2.putText(img_rd, "Faces: " + str(len(faces)), (20, 140), font, 0.8, (0, 255, 0), 1, cv2.LINE_AA)
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cv2.putText(img_rd, "S: Save current face", (20, 400), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
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cv2.putText(img_rd, "Q: Quit", (20, 450), font, 0.8, (255, 255, 255), 1, cv2.LINE_AA)
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# 按下 'q' 键退出
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if k == ord('q'):
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break
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self.update_fps()
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cv2.namedWindow("camera", 1)
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cv2.imshow("camera", img_rd)
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def main():
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Face_Descriptor_con = Face_Descriptor()
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Face_Descriptor_con.run()
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if __name__ == '__main__':
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main() |