def Feature_extract_batch(fun, files_list, names_list):
asf_embeddings = [] # 特征
asf_label_list = [] # 标签
for img_path, name in zip(files_list, names_list):
print("processing image: {}".format(img_path))
im = face_class.IM()
im.filepath = img_path
im = fun.LoadImg(im) # 加载图片
ret = fun.face_detect(im) # 人脸检测
if ret == -1:
print('人脸检测失败:', ret)
continue
faces = ret
if faces.faceNum != 1:
print("-----image total {} faces, continue...".format(faces.faceNum))
continue
ft = fun.getSingleFace(faces, 0) # 从faces集中，提取第0个人的特征
# print("ft:", ft.faceRect.left1, ft.faceRect.top1, ft.faceRect.right1, ft.faceRect.bottom1, ft.faceOrient)
ret, fea = fun.Feature_extract(im, ft) # 返回tuple，(标识, 特征)
if ret != 0:
print("特征提取失败！")
continue
# asf_embeddings_size.append(fea.featureSize) # 特征的大小，feature.featureSize
asf_embeddings.append(fea) # feature，ASF_FaceFeature类型，包括：feature.featureSize, feature.feature
asf_label_list.append(name) # 标签
return asf_embeddings, asf_label_list
print('初始化失败:', ret)
# 3.加载库中图片特征及标签
asf_dataset_emb, asf_name_list = fun.loadFeatureFromDB()
print("asf_dataset_emb:", asf_dataset_emb)
print("asf_name_list:", asf_name_list)
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read() # frame.shape:(480, 640, 3)，（高，宽，通道）
if frame is None:
break
frame = cv2.resize(frame, (frame.shape[1] // 4 * 4, frame.shape[0] // 4 * 4)) # 宽高要reshape成4的倍数，否则会报错：90127
im = face_class.IM()
im.data = frame
im.width = frame.shape[1]
im.height = frame.shape[0]
ret = fun.face_detect(im) # 人脸检测
if ret == -1:
print('人脸检测失败:', ret)
pass
# 5.显示检测结果，这时face_detect()返回的是faces
faces = ret
for i in range(0, faces.faceNum):
ra = faces.faceRect[i]
ft = fun.getSingleFace(faces, i) # 从faces集中，提取第0个人的特征