def face_recognition_image(model_path, dataset_path, filename, image_path):
# 加载数据库的数据
dataset_emb, names_list = load_dataset(dataset_path, filename)
# 初始化mtcnn人脸检测
face_detect = face_recognition.FaceDetection()
# 初始化facenet
face_net = face_recognition.facenetEmbedding(model_path)
# 读取待检图片
image = image_processing.read_image_gbk(image_path)
print("image_processing.read_image_gbk:", type(image),
image.shape) # <class 'numpy.ndarray'>, (616, 922, 3)，（高，宽，通道）
# 获取 判断标识 bounding_box crop_image
bboxes, landmarks = face_detect.detect_face(image)
bboxes, landmarks = face_detect.get_square_bboxes(
bboxes, landmarks, fixed="height") # 以高为基准，获得等宽的举行
if bboxes == [] or landmarks == []:
print("-----no face")
exit(0)
print("-----image have {} faces".format(len(bboxes)))
face_images = image_processing.get_bboxes_image(
image, bboxes, resize_height, resize_width) # 按照bboxes截取矩形框
face_images = image_processing.get_prewhiten_images(face_images) # 图像归一化
pred_emb = face_net.get_embedding(face_images) # 获取facenet特征
pred_name, pred_score = compare_embadding(pred_emb, dataset_emb,
names_list)
# 在图像上绘制人脸边框和识别的结果
show_info = [n + ':' + str(s)[:5] for n, s in zip(pred_name, pred_score)]
image_processing.show_image_bboxes_text("face_reco", image, bboxes,
show_info)
def get_face_embedding(model_path, files_list, names_list):
'''
获得embedding数据
:param files_list: 图像列表
:param names_list: 与files_list一一的名称列表
:return:
'''
# 转换颜色空间RGB or BGR
colorSpace = "RGB"
# 初始化mtcnn人脸检测
face_detect = face_recognition.FaceDetection()
# 初始化facenet
face_net = face_recognition.facenetEmbedding(model_path)
embeddings = [] # 用于保存人脸特征数据库
label_list = [] # 保存人脸label的名称，与embeddings一一对应
for image_path, name in zip(files_list, names_list):
print("processing image: {}".format(image_path))
image = image_processing.read_image_gbk(image_path,
colorSpace=colorSpace)
# 进行人脸检测，获得bounding_box
bboxes, landmarks = face_detect.detect_face(image)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes,
landmarks,
fixed="height")
# image_processing.show_image_boxes("image",image,bboxes)
if bboxes == [] or landmarks == []:
print("-----no face")
continue
if len(bboxes) >= 2 or len(landmarks) >= 2:
print("-----image total {} faces".format(len(bboxes)))
continue
# 获得人脸区域
face_images = image_processing.get_bboxes_image(
image, bboxes, resize_height, resize_width)
# 人脸预处理，归一化
face_images = image_processing.get_prewhiten_images(face_images,
normalization=True)
# 获得人脸特征
pred_emb = face_net.get_embedding(face_images)
embeddings.append(pred_emb)
# 可以选择保存image_list或者names_list作为人脸的标签
# 测试时建议保存image_list，这样方便知道被检测人脸与哪一张图片相似
# label_list.append(image_path)
label_list.append(name)
return embeddings, label_list
fps_display_interval = 5 # seconds
frame_rate = 0
frame_count = 0
normalization = False # 是否标准化，默认否
input_path = "D:/奇辉电子/120_150_2.mp4"
output_path = "D:/奇辉电子/120_150_2-人脸识别.mp4" # 输出文件，为空则认为不需要输出
if __name__ == '__main__':
model_path = 'models/20180408-102900'
dataset_path = 'dataset/emb/faceEmbedding.npy' # 人脸特征
filename = 'dataset/emb/name.txt' # 人脸列表
dataset_emb, names_list = load_dataset(dataset_path, filename) # 加载数据库的数据
face_detect = face_recognition.FaceDetection() # 初始化mtcnn
face_net = face_recognition.facenetEmbedding(model_path) # 初始化facenet
cap = cv2.VideoCapture(input_path)
start_time = time.time()
video_FourCC = int(cap.get(cv2.CAP_PROP_FOURCC))
video_fps = cap.get(cv2.CAP_PROP_FPS)
video_size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
isOutput = True if output_path != "" else False
if isOutput:
print("!!! TYPE:", type(output_path), type(video_FourCC), type(video_fps), type(video_size))
out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)
total_frame_count = cap.get(7) # 获取总帧数