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)
image = image_processing.read_image_gbk(image_path)
# 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")
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)
face_images = image_processing.get_prewhiten_images(face_images)
pred_emb = face_net.get_embedding(face_images)
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_recognition", image, bboxes,
show_info)
def face_recognition_video(model_path,dataset_path, filename,video_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)
cap = cv2.VideoCapture(video_path) # 获取视频数据
face_cnt = 0 # 控制视频读取时候，每秒截取指定数量图片进行一次识别
while cap.isOpened():
ok, frame = cap.read()
face_cnt += 1
if not ok:
break
if(face_cnt % 5 == 0):
frame = image_processing.read_image1(frame)
bboxes, landmarks = face_detect.detect_face(frame)
bboxes, landmarks = face_detect.get_square_bboxes(bboxes, landmarks, fixed="height")
if bboxes == [] or landmarks == []:
print("-----no face")
continue
print("-----image have {} faces".format(len(bboxes)))
face_images = image_processing.get_bboxes_image(frame, bboxes, resize_height, resize_width)
face_images = image_processing.get_prewhiten_images(face_images)
pred_emb = face_net.get_embedding(face_images)
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)]
print("showinfo:", show_info)
image_processing.show_image_bboxes_text("face_recognition", frame, bboxes, show_info)
cap.release()
cv2.destroyWindow("face_recognition")
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 batch_detect_image(image_list, class_names, prob_threshold, show=False):
predictor = select_net(model_path, net_type, len(class_names))
for image_path in image_list:
orig_image = cv2.imread(image_path)
rgb_image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)
boxes, labels, probs = predict_image(predictor, rgb_image,
prob_threshold)
label_names = file_processing.decode_label(labels,
name_table=class_names)
info = image_processing.combile_label_prob(label_names, probs)
print("predict:{}".format(info))
if show:
image_processing.show_image_bboxes_text("image", rgb_image, boxes,
info)
def linedataset_test(filename, image_dir=None, show=True):
'''
label_data:[image_path,boxes_nums,x1, y1, w, h, label_id,x1, y1, w, h, label_id]
:param filename:
:param image_dir:
:param show:
:return:
'''
with open(filename) as f:
lines = f.readlines()
for line in lines:
image_id, box, label = face_body.read_line_image_label(line)
if show:
if image_dir:
image_path = os.path.join(image_dir, image_id)
else:
image_path = image_id
image = image_processing.read_image(image_path)
image_processing.show_image_bboxes_text(
"image_dict", image, box, label)