def findFace(self, detect, img):
if detect == 'mtcnn':
return mtcnn_findFace(img)
elif detect == 'cv':
return cv_findFace(img)
elif detect == 'dlib_cnn':
return dlib_findFace(img, mode="cnn", image_size=160)
elif detect == 'dlib':
return dlib_findFace(img, mode="normal", image_size=160)
else:
return [], []
shape = sp(img, convert_to_rect(rect))
# 把特征点画出来，检验程序是否正确
for pt in shape.parts():
pt_pos = (pt.x, pt.y)
cv2.circle(show_img, pt_pos, 2, (0, 255, 0), 1)
# 将原图像和特征点传入人脸识别模型，得到人脸特征
face_descriptor = facerec.compute_face_descriptor(img, shape)
feature.append(face_descriptor)
feature = np.array(feature)
# 结果保存到本地文件
misc.imsave("./dlib_feature_extraction_{}.png".format(mode), show_img)
# 返回人脸特征
return feature
if __name__ == '__main__':
# 先通过MTCNN取得人脸框位置
image_path = "./frame_tmp.jpg"
img = misc.imread(os.path.expanduser(image_path), mode='RGB')
images, rects = mtcnn_findFace(img)
img = misc.imread(os.path.expanduser(image_path), mode='RGB')
feature_extraction(img, rects, "68")
img = misc.imread(os.path.expanduser(image_path), mode='RGB')
feature_extraction(img, rects, "5")