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elem.text.strip(): + elem.text = i + "\t" + if not elem.tail or not elem.tail.strip(): + elem.tail = i + for elem in elem: + indent(elem, level+1) + if not elem.tail or not elem.tail.strip(): + elem.tail = i + else: + if level and (not elem.tail or not elem.tail.strip()): + elem.tail = i + + +def add_xml(inforsDict,xmlFilePath): + + result = inforsDict + + for re in result: + # if re['score'] > 0.5: + + # 获得标注信息 + ObjName = list(re.keys())[0] + xmin = int(list(re.values())[0][0]) + ymin = int(list(re.values())[0][1]) + xmax = int(list(re.values())[0][2]) + ymax = int(list(re.values())[0][3]) + # xmax = xmin + r + # ymax = ymin + z + + #if ObjName == 'person': + + tree = ElementTree() + tree.parse(xmlFilePath) + + # 得到根目录 + root = tree.getroot() + + # 创建一级目录 + elementOjb = Element('object') + + elementBox = Element('bndbox') + + # 创建二级目录 + one = Element('name') + one.text = ObjName # 二级目录的值 #结果展示：1 + elementOjb.append(one) # 将二级目录加到一级目录里 + + two = Element('pose') + two.text = 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{label_name_list[int(labels_ids_list[i])]:bbox_list[i]} + result_list.append(result_dict) + + + return result_list \ No newline at end of file diff --git a/analysis_result/same_model_img.py b/analysis_result/same_model_img.py new file mode 100644 index 0000000..4ec2766 --- /dev/null +++ b/analysis_result/same_model_img.py @@ -0,0 +1,48 @@ +from analysis_result.tools_analysis import select_bbox_by_labels, iou_result_two + + +def same_model_img_analysis_labels(example_list, result_dicts_list, relevancy, relevancy_para): + ''' + example_list: 需要关联处理的目标标签集合 + result_dicts_list: 当前图片检测结果，格式为[{labels1:result1},{labels2:result2},{labels2:result2}...] + ''' + + # 获得当前结果的标签集 + result_labels_list = [list(re.keys())[0] for re in result_dicts_list] + # print('result_labels_list:',result_labels_list) + + # 判断是否存在example_list中的所有标签 + continue_para = False + compara_labellist = [ + label for label in example_list if label not in result_labels_list] + + if compara_labellist: + return continue_para + + else: + + result_dicts_list_change = select_bbox_by_labels( + example_list, result_dicts_list) + + if relevancy == 'overlap': + + if len(example_list) == 2: + + overlap_list = iou_result_two( + result_dicts_list_change, relevancy_para) + + return overlap_list + + if relevancy == 'in_bbox': + + pass + + +def model_labels_selet(example_list, result_dict_list): + ''' + 直接从result中获得目标列表 + ''' + compara_labellist = [result_dict for result_dict in result_dict_list if list( + result_dict.keys())[0] in example_list] + + return compara_labellist diff --git a/analysis_result/tools_analysis.py b/analysis_result/tools_analysis.py new file mode 100644 index 0000000..7cdcbd1 --- /dev/null +++ b/analysis_result/tools_analysis.py @@ -0,0 +1,74 @@ +def iou_result_two(result_dict_list,iou_para): + + result_0 = list(result_dict_list[0].values())[0] + result_1 = list(result_dict_list[1].values())[0] + label_0 = list(result_dict_list[0].keys())[0] + label_1 = list(result_dict_list[1].keys())[0] + + return_lists = [] + + for re_0 in result_0: + + for re_1 in result_1: + + iou = calculate_iou(re_0, re_1) + + if iou > iou_para: + + label_dict_0 = {label_0: re_0} + label_dict_1 = {label_1: re_1} + + return_lists.append(label_dict_0) + return_lists.append(label_dict_1) + + return return_lists + + +def calculate_iou(box1, box2): + """ + 计算两个边界框之间的IoU值 + + 参数: + box1: 边界框1的坐标（x1, y1, x2, y2） + box2: 边界框2的坐标（x1, y1, x2, y2） + + 返回值: + iou: 两个边界框之间的IoU值 + """ + x1 = max(box1[0], box2[0]) + y1 = max(box1[1], box2[1]) + x2 = min(box1[2], box2[2]) + y2 = min(box1[3], box2[3]) + + # 计算交集区域面积 + intersection_area = max(0, x2 - x1 + 1) * max(0, y2 - y1 + 1) + + # 计算边界框1和边界框2的面积 + box1_area = (box1[2] - box1[0] + 1) * (box1[3] - box1[1] + 1) + box2_area = (box2[2] - box2[0] + 1) * (box2[3] - box2[1] + 1) + + # 计算并集区域面积 + union_area = box1_area + box2_area - intersection_area + + # 计算IoU值 + iou = intersection_area / union_area + + return iou + + +def select_bbox_by_labels(example_list, result_dicts_list): + ''' + 将result列表中的example列表内的bbox挑选出来,放到同一字典下 + ''' + + result_dict_list_change = [] + for label in example_list: + bbox_list = [] + for result in result_dicts_list: + if list(result.keys())[0] == label: + bbox_list.append(list(result.values())[0]) + + result_dict_change = {label: bbox_list} + result_dict_list_change.append(result_dict_change) + + return result_dict_list_change diff --git a/config_det.yaml b/config_det.yaml new file mode 100644 index 0000000..59fd899 --- /dev/null +++ b/config_det.yaml @@ -0,0 +1,32 @@ +# detect_0: +# source : rtsp://admin:@192.168.10.203 +# model: ../config_phone.yaml + +detect_1: + source : rtsp://admin:@192.168.10.203 + model: ../config_phone.yaml + +# detect_2: +# source : E:/Bank_files/Bank_03/dataset/dataset_now/phone/4/images +# model: ../config_sleep.yaml + +# detect_3: +# source : rtsp://admin:@192.168.10.11 +# model: /home/yaxin/xbank/xbank_poc_test/config_sleep.yaml + +# detect_4: +# source : rtsp://admin:@192.168.10.11 +# model: /home/yaxin/xbank/xbank_poc_test/config_phone.yaml + +# detect_5: +# source : rtsp://admin:@192.168.10.18 +# model: /home/yaxin/xbank/xbank_poc_test/config_phone.yaml + +# detect_6: +# source : /home/yaxin/xbank/xbank_poc_test/images_test/images_del +# model: /home/yaxin/xbank/xbank_poc_test/config_phone.yaml + +# detect_7: +# source : /home/xbank/xbank_poc_test_use/images_test/images_del + + diff --git a/config_person.yaml b/config_person.yaml new file mode 100644 index 0000000..e1a91e5 --- /dev/null +++ b/config_person.yaml @@ -0,0 +1,30 @@ +# load model file +model: ./model_file/yolov8_person.onnx +model_cache: ./tensort_cache/yolov8_person.trt + +# label and bbox message set +model_parameter: + device : cpu + label_names: ["person","bicycle","car",motorcycle,airplane,bus,train,truck,boat,traffic light,fire hydrant,stop sign,parking meter,bench,bird,cat, dog,horse,sheep, cow,elephant, bear,zebra,giraffe, backpack,umbrella,handbag,tie,suitcase,frisbee,skis,snowboard,sports ball,kite,baseball bat,baseball glove,skateboard,surfboard,tennis racket, bottle, wine glass,cup,fork,knife,spoon, bowl,banana,apple,sandwich,orange,broccoli,carrot,hot dog, pizza,donut,cake,chair,couch,potted plant,bed,dining table,toilet,tv,laptop,mouse,remote,keyboard,cell phone,microwave, oven,toaster,sink,refrigerator,book,clock,vase,scissors,teddy bear,hair drier,toothbrush] # model labels + # label_names: ["person"] + compara_label_names: ["person"] # + compara_relevancy: False # 'object_num' + relevancy_para : False + object_num_min : False # 视频中最低人数 + confidence : 0.2 + + +save_path_original : False +test_path : False +save_annotations : False +save_path : False + +# save videos +save_videos : False + +# detect time set +detect_time : 30 +detect_time_small : 10 +detect_ratio : 0.5 + + diff --git a/config_phone.yaml b/config_phone.yaml new file mode 100644 index 0000000..006ee04 --- /dev/null +++ b/config_phone.yaml @@ -0,0 +1,30 @@ +# load model file +model: E:/Bank_files/Bank_03/xbank_poc_test_use/model_file/yolov5_4best.onnx +model_cache: E:/Bank_files/Bank_03/xbank_poc_test_use/tensort_cache/yolov5_4best.trt + +# label and bbox message set +model_parameter: + device : cpu + label_names: ["Keypad","hands","keyboard", "mouse","phone"] # model labels + compara_label_names: ["hands","phone"] # + compara_relevancy: overlap # 'in_bbox','overlap' + relevancy_para : 0.01 + object_num_min : False + confidence : 0.2 + +save_path : False +test_path : False +save_path_original : False +save_annotations : False + + +# save videos +save_videos : E:/Bank_files/Bank_03/xbank_poc_test_use/video_save_path/sleep + +# detect time set +detect_time : 30 +detect_time_small : 10 +detect_ratio : 0.5 + + + diff --git a/config_sleep.yaml b/config_sleep.yaml new file mode 100644 index 0000000..e490cc3 --- /dev/null +++ b/config_sleep.yaml @@ -0,0 +1,29 @@ +# load model file +model: ./model_file/yolov5_640_sleep.onnx +model_cache: ./tensort_cache/yolov5_640_sleep.trt + +# label and bbox message set +model_parameter: + device : cpu + label_names: ["person","sleep"] # model labels + compara_label_names: ["sleep","person"] # + compara_relevancy: False # 'in_bbox' + relevancy_para : False + object_num_min : False + confidence : 0.2 + + +save_path_original : False +test_path : False +save_annotations : False +save_path : False + +# save videos +save_videos : ./video_save_path/sleep + +# detect time set +detect_time : 30 +detect_time_small : 10 +detect_ratio : 0.5 + + diff --git a/create_xml.py b/create_xml.py new file mode 100644 index 0000000..873b424 --- /dev/null +++ b/create_xml.py @@ -0,0 +1,53 @@ + +from lxml.etree import Element, SubElement, tostring + + +def create_xml(boxs, img_shape, xml_path): + """ + 创建xml文件，依次写入xml文件必备关键字 + :param boxs: txt文件中的box + :param img_shape: 图片信息，xml中需要写入WHC + :return: + """ + node_root = Element('annotation') + node_folder = SubElement(node_root, 'folder') + node_folder.text = 'Images' + node_filename = SubElement(node_root, 'filename') + node_filename.text = str(img_shape[3]) + node_size = SubElement(node_root, 'size') + node_width = SubElement(node_size, 'width') + node_width.text = str(img_shape[1]) + node_height = SubElement(node_size, 'height') + node_height.text = str(img_shape[0]) + node_depth = SubElement(node_size, 'depth') + node_depth.text = str(img_shape[2]) + + if len(boxs) >= 1: # 循环写入box + for box in boxs: + node_object = SubElement(node_root, 'object') + node_name = SubElement(node_object, 'name') + # if str(list_[4]) == "person": # 根据条件筛选需要标注的标签,例如这里只标记person这类，不符合则直接跳过 + # node_name.text = str(list_[4]) + # else: + # continue + node_name.text = str(list(box.keys())[0]) + node_difficult = SubElement(node_object, 'difficult') + node_difficult.text = '0' + node_bndbox = SubElement(node_object, 'bndbox') + node_xmin = SubElement(node_bndbox, 'xmin') + node_xmin.text = str(int(list(box.values())[0][0])) + node_ymin = SubElement(node_bndbox, 'ymin') + node_ymin.text = str(int(list(box.values())[0][1])) + node_xmax = SubElement(node_bndbox, 'xmax') + node_xmax.text = str(int(list(box.values())[0][2])) + node_ymax = SubElement(node_bndbox, 'ymax') + node_ymax.text = str(int(list(box.values())[0][3])) + + xml = tostring(node_root, pretty_print=True) # 格式化显示，该换行的换行 + + # file_name = img_shape[3].split(".")[0] + # filename = xml_path+"/{}.xml".format(file_name) + + f = open(xml_path, "wb") + f.write(xml) + f.close() diff --git a/drawing_img/__pycache__/drawing_img.cpython-310.pyc b/drawing_img/__pycache__/drawing_img.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c47114b341b2febf883fbe63b7b36de1f2d2b22f GIT binary patch literal 979 zcmZuw&2AGh5VpPlo3<(vqAf_M;uZ--G~6o0Uujh&D2Vm|q1Cdy;Z-7vrfd}xF-#h{cDU7{o$pNqY`J2x(fZioR+ET$I&!_kD`XjQ`x zm@t0cS!?(OH8=&R-)#jZ{|q4U1GH1`V*%_4AEN=BxPbb;vB|j|2ulV7jFI$MS>l9% zPbh5ZgTFP0fQ17*hx(=5F4TTWI7hBT!jpl{IKYP4n(Y$9opaSKRB20c>|g|uzC^P0 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literal 0 HcmV?d00001 diff --git a/drawing_img/drawing_img.py b/drawing_img/drawing_img.py new file mode 100644 index 0000000..c0f3040 --- /dev/null +++ b/drawing_img/drawing_img.py @@ -0,0 +1,36 @@ +import cv2 + + +def drawing_frame(images_frame, + result_list, + line_1=3, + line_2=1, + color_1=(0, 0, 255), + color_2=(0, 255, 255), + txt_num=0.75, + scores_list_id=False): + ''' + result_dicts_list: 格式为[{labels1:result1},{labels2:result2},{labels2:result2}...] + ''' + + # img = images_frame.copy() + + for result_list in result_list: + + label_name = list(result_list.keys())[0] + bbox_list = list(result_list.values())[0] + + print(bbox_list) + + if scores_list_id: + scores_list = result_list['scores_list'] + lables = str(label_name) + "__" + str(scores_list) + else: + lables = str(label_name) + + cv2.rectangle(images_frame, (int(bbox_list[0]), int(bbox_list[1])), (int( + bbox_list[2]), int(bbox_list[3])), color_1, line_1) + cv2.putText(images_frame, lables, (int(bbox_list[0]) - 10, int( + bbox_list[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, txt_num, color_2, line_2) + + return images_frame diff --git a/main.py b/main.py new file mode 100644 index 0000000..dfb7739 --- /dev/null +++ b/main.py @@ -0,0 +1,61 @@ +#coding: utf-8 +from main_process import data_load +from analysis_data.config_load import get_configs +from multiprocessing import Pool +from loguru import logger +import os + +# 日志配置 +log_path = os.path.join(__file__, "../logs/xbank.log") +logger.add( + log_path, rotation="60 MB", enqueue=True, backtrace=True, diagnose=True, retention=30 +) +logger.info("*************************** xbank start ***************************") + + +def get_args_list(args_data): + + args_list = [] + + for args in args_data: + + args_det = args_data[args] + det_config = args_det['model'] + det_source = args_det['source'] + det_list = [det_source, det_config] + args_list.append(det_list) + + return args_list + +def start_worker(args_list_n): + while True: + try: + logger.info(f" process {args_list_n} crashed. Starting...") + + data_load(args_list_n) + + except Exception as e: + + logger.info(f" process {args_list_n} crashed. Restarting...") + logger.debug(e) + + +def main(): + + # 加载配置文件 + args = '../config_det.yaml' + args_data = get_configs(ymal_files=args) + args_list = get_args_list(args_data) + + process_num = len(args_list) + + with Pool(process_num) as pool: + pool.map(start_worker, args_list) + + +if __name__ == "__main__": + + main() + + + diff --git a/main_process.py b/main_process.py new file mode 100644 index 0000000..6ef1673 --- /dev/null +++ b/main_process.py @@ -0,0 +1,536 @@ +from analysis_result.get_model_result import det_img +from analysis_result.same_model_img import same_model_img_analysis_labels, model_labels_selet +from model_load.model_load import Load_model +from drawing_img.drawing_img import drawing_frame +from analysis_data.data_rtsp import rtsp_para +from analysis_data.data_dir_file import get_dir_file +from analysis_data.config_load import get_configs +from add_xml import add_xml +from create_xml import create_xml +from analysis_data.change_video import mp4_to_H264 + +import cv2 +import os +import time +from datetime import datetime +import json +from loguru import logger + +import logging +import logstash + +host = '192.168.10.96' + +xbank_logger = logging.getLogger('python-logstash-logger') +xbank_logger.setLevel(logging.INFO) +xbank_logger.addHandler(logstash.LogstashHandler(host, 5959, version=1)) + + +def data_load(args): + + source = args[0] + model_ymal = args[1] + + # 数据加载 + rtsp_source = rtsp_para(source) + dir_source = os.path.isdir(source) + file_source = os.path.isfile(source) + + # # 模型加载 + model_data = get_configs(model_ymal) + model_inference = Load_model(model_file=model_data["model"], + device=model_data["model_parameter"]['device'], + cache_file=model_data["model_cache"]) + + if rtsp_source: + + rtsp_detect_process(source=source, model_data=model_data, + model_inference=model_inference) + + if dir_source: + dir_source_process(source, model_inference, model_data) + + if file_source: + + file_source_process(source, model_inference, model_data) + + +def rtsp_detect_process(source, model_data, model_inference): + + cap = cv2.VideoCapture(source) + logger.info(f"视频流{source}读取中...") + + # 视频流信息 + fps = int(cap.get(cv2.CAP_PROP_FPS)) + fps_num = fps*model_data['detect_time'] + fps_num_small = fps*model_data['detect_time_small'] + size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)), + int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))) + + i = 0 + j = 0 + n = 0 + + det_t_num = 0 + nodet_t_num = 0 + + det_img = [] + + video_name_time = 0 + det_fps_time = [] + + while True: + + try: + ret, frame = cap.read() + + i += 1 + j += 1 + + # 读取到当前视频帧时间 + data_now = datetime.now() + get_time = str(data_now.strftime("%H")) + \ + str(data_now.strftime("%M")) + str(data_now.strftime("%S")) + \ + str(data_now.strftime("%f")) + + imgframe_dict = {"path": source, 'frame': frame, + 'get_fps': j, 'get_time': get_time} + + # 视频暂时保存路径 + if video_name_time == 0: + + video_name_time = get_time + video_path = video_name( + video_name_base=video_name_time, save_path=model_data['save_videos'], save_file='temp') + + out_video = cv2.VideoWriter( + video_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, size) + + logger.info(f"视频{video_path}已经暂时保存...") + + # 模型推理 + images_det_result = img_process( + imgframe_dict, model_inference, model_data) + + images_update = save_process( + imgframe_dict, images_det_result, model_data) + + # 结果判断，t + if images_det_result: + + det_t_num += 1 + + if len(det_img) == 0: + img_dict = images_update.copy() + det_img.append(img_dict) + + if not images_det_result and len(det_img) > 0: + + nodet_t_num += 1 + + if (det_t_num + nodet_t_num) >= fps_num_small: + + para = determine_time( + det_num=det_t_num, nodet_num=nodet_t_num, ratio_set=model_data['detect_ratio']) + + if para: + + first_fps_time = det_img[0] + first_fps_time.update( + {"dert_fps": (j-int(first_fps_time['get_fps'])+1)}) + + det_fps_time.append(first_fps_time) + + det_img.clear() + det_t_num = 0 + nodet_t_num = 0 + + # 视频保存 + out_video.write(images_update['frame']) + + # 结果判断 ，T + if j >= fps_num: + + try: + out_video.release() + + except Exception: + logger.exception(f"视频release失败") + else: + logger.info("视频release成功") + + # T时间截至，判断t时间结果。 + if det_img: + + para = determine_time( + det_num=det_t_num, nodet_num=nodet_t_num, ratio_set=model_data['detect_ratio']) + + first_fps_time = det_img[0] + time_1 = (j-int(first_fps_time['get_fps'])+1) + + if para and time_1 >= (fps_num_small/2): + + first_fps_time.update( + {"dert_fps": (j-int(first_fps_time['get_fps'])+1)}) + det_fps_time.append(first_fps_time) + + if det_fps_time: + + det_fps_time = determine_duration(result_list=det_fps_time) + + # 转换后视频保存路径 + save_video_name = os.path.basename(video_path) + only_video_name = save_video_name.split('.')[0] + save_video_path = os.path.join(model_data['save_videos'], (str(data_now.strftime( + "%Y")) + str(data_now.strftime("%m")) + str(data_now.strftime("%d")))) + save_video_path = os.path.join( + save_video_path, only_video_name) + + # 路径 + save_video = os.path.join(save_video_path, save_video_name) + json_path = os.path.join( + save_video_path, only_video_name + '.json') + images_path = os.path.join(save_video_path, 'images') + + # 转换视频、保存视频 + change_video = mp4_to_H264() + change_video.convert_byfile(video_path, save_video) + + # 保存图片 + update_det_fps = video_cut_images_save( + det_list=det_fps_time, images_path=images_path) + + # print(update_det_fps) + + # 保存json文件 + re_list, result_lables = json_get( + time_list=update_det_fps, video_path=save_video, fps=fps) + result_path = json_save(re_list, json_path) + + # send_message(update_det_fps=update_det_fps, result_path=result_path, + # source=source, result_lables=result_lables) + + else: + # print(video_path) + os.remove(video_path) + logger.info(f"未检测到目标信息的视频{video_path}删除成功") + + logger.info('开始信息重置') + det_img.clear() + det_fps_time.clear() + det_t_num = 0 + nodet_t_num = 0 + video_name_time = 0 + j = 0 + + # print('det_fps_time:', det_fps_time,'det_img:',det_img) + + # t2 = time.time() + # tx = t2 - t1 + # logger.info(f'检测一张图片的时间为：{tx}.') + except Exception as e: + + logger.debug(f"读帧率失败{source}未读到...") + logger.debug(e) + cap.release() + cap = cv2.VideoCapture(source) + logger.info(f"摄像头{source}重新读取") + + # break + + +def video_name(video_name_base, save_path, save_file): + + video_name_base = video_name_base + + savePath = os.path.join(save_path, save_file) + + if not os.path.exists(savePath): + os.makedirs(savePath) + + video_path = os.path.join( + savePath, video_name_base + '.mp4') + + return video_path + + +def dir_source_process(source, model_inference, model_data): + + img_ext = [".jpg", ".JPG", ".bmp"] + video_ext = [".mp4", ".avi", ".MP4"] + + img_list = get_dir_file(source, img_ext) + video_list = get_dir_file(source, video_ext) + + if img_list: + + for img in img_list: + + t1 = time.time() + images = cv2.imread(img) + + imgframe_dict = {"path": img, 'frame': images} + + images_update = img_process( + imgframe_dict, model_inference, model_data) + + t2 = time.time() + tx = t2 - t1 + print('检测一张图片的时间为：', tx) + + if video_list: + + pass + + +def file_source_process(source, model_inference, model_data): + + img_para = True + + if img_para: + images = cv2.imread(source) + imgframe_dict = {"path": source, 'frame': images} + + images_update = img_process( + imgframe_dict, model_inference, model_data) + + +def img_process(images, model_inference, model_data): + + # t1 = time.time() + # 检测每帧图片，返回推理结果 + results = det_img(model_inference=model_inference, + images_frame=images['frame'], + confidence=model_data["model_parameter"]['confidence'], + label_name_list=model_data["model_parameter"]['label_names']) + + # print(results) + + # print(images['path']) + + # 根据需要挑选标注框信息 + select_labels_list = model_labels_selet(example_list=model_data["model_parameter"]['compara_label_names'], + result_dict_list=results) + + if model_data["model_parameter"]['compara_relevancy']: + + # 需要根据的逻辑判断标注框信息 + determine_bbox = same_model_img_analysis_labels(example_list=model_data["model_parameter"]['compara_label_names'], + result_dicts_list=select_labels_list, + relevancy=model_data["model_parameter"]['compara_relevancy'], + relevancy_para=model_data["model_parameter"]['relevancy_para']) + else: + + determine_bbox = select_labels_list + + # print(determine_bbox) + + if model_data['model_parameter']['object_num_min']: + if len(determine_bbox) >= model_data["model_parameter"]['object_num_min']: + + determine_bbox.clear() + + # logger.debug(f"正确获得检测后的信息{determine_bbox}...") + + # 返回检测后结果 + return determine_bbox + + +def save_process(images, determine_bbox, model_data): + + if determine_bbox: + + images.update({"results": determine_bbox}) + + if model_data['save_path_original']: + imgname_original = images_save(images=images, + save_path=model_data["save_path_original"]) + + img_save = drawing_frame( + images_frame=images['frame'], result_list=determine_bbox) + + images.update({"img_save": img_save}) + + if model_data["save_path"]: + + imgname = images_save( + images=images, save_path=model_data["save_path"]) + + if model_data["save_annotations"]: + + if not os.path.exists(model_data["save_annotations"]): + + os.makedirs(model_data["save_annotations"]) + save_annotations_xml( + xml_save_file=model_data["save_annotations"], save_infors=determine_bbox, images=images['path']) + + else: + pass + + else: + # 没检测出来的图片是否保存 + if model_data["test_path"]: + imgname = images_save( + images=images, save_path=model_data["test_path"]) + # print('no:',images['path'],imgname) + + # 展示显示 + # if images['path'] == 'rtsp://admin:@192.168.10.11': + # cv2.namedWindow('11', cv2.WINDOW_NORMAL) + # cv2.imshow('11',images['frame']) + # cv2.waitKey(1) + # cv2.destroyAllWindows() + + # t2 = time.time() + + return images + + +def images_save(images, save_path): + + # 保存时候时间为图片名 + images_name = images['get_time'] + '.jpg' + + if not os.path.exists(save_path): + os.makedirs(save_path) + + full_name = os.path.join(save_path, images_name) + + cv2.imwrite(full_name, images['img_save']) + + return full_name + + +def save_annotations_xml(xml_save_file, save_infors, images): + + results = save_infors + img = os.path.basename(images) + img_frame = cv2.imread(images) + xml_save_path = os.path.join(xml_save_file, img.split('.')[0] + '.xml') + w, h, d = img_frame.shape + img_shape = (w, h, d, img) + + if os.path.isfile(xml_save_path): + + add_xml(inforsDict=results, + xmlFilePath=xml_save_path) + else: + create_xml(boxs=results, + img_shape=img_shape, + xml_path=xml_save_path) + + +def determine_time(det_num, nodet_num, ratio_set): + + ratio = det_num / (det_num + nodet_num) + + if ratio >= ratio_set: + return True + else: + return False + + +def determine_duration(result_list): + i = 0 + + while i < len(result_list) - 1: + dict_i = result_list[i] + dict_j = result_list[i + 1] + + if 'get_fps' in dict_i and 'dert_fps' in dict_i and 'get_fps' in dict_j: + num_i = int(dict_i['get_fps']) + dura_i = int(dict_i['dert_fps']) + num_j = int(dict_j['get_fps']) + + if num_i + dura_i == num_j: + dura_j = int(dict_j['dert_fps']) + dura_update = dura_i + dura_j + + dict_i['dert_fps'] = dura_update + result_list.pop(i + 1) + else: + i += 1 + else: + i += 1 + + return result_list + + # print('2:', result_list) + + +def json_get(time_list, video_path, fps): + + result_dict = {'info': {'video_path': video_path, 'fps': fps}} + re_dict = {} + for i, det_dict in enumerate(time_list): + + list_hands = ["Keypad", "hands", "keyboard", "mouse", "phone"] + list_sleep = ["sleep"] + list_person = ["person"] + + if list(det_dict['results'][0].keys())[0] in list_hands: + + result_lables = 'playing_phone' + + if list(det_dict['results'][0].keys())[0] in list_sleep: + + result_lables = "sleep" + + if list(det_dict['results'][0].keys())[0] in list_person: + + result_lables = "person" + + fps_dict = {'time': det_dict['get_fps'], + 'duration': det_dict['dert_fps'], + 'images_path': det_dict['images_path']} + + re_dict.update({('id_' + str(i)): fps_dict}) + + result_dict.update({'result': re_dict}) + + return result_dict, result_lables + + +def json_save(result_dict, json_path): + + result = json.dumps(result_dict) + + f = open(json_path, 'w') + f.write(result + '\n') + f.close + + return json_path + + +def video_cut_images_save(det_list, images_path): + + for det_dict in det_list: + + images_path_full = images_save(images=det_dict, save_path=images_path) + + del det_dict['frame'] + del det_dict['get_time'] + det_dict.update({'images_path': images_path_full}) + + return det_list + + +def send_message(update_det_fps, result_path, source, result_lables): + + for det_dict in update_det_fps: + + extra = { + 'worker': 'xbank', + 'time': det_dict['get_fps'], + 'config_file': result_path, + 'source': source, + 'type': result_lables + } + + xbank_logger.info('xBank_infer', extra=extra) + logger.info(f'发送信息{extra}') + +# if __name__ == '__main__': + +# 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build_option( + device=device, backend="trt", cache_file=cache_file) + model_inference = [] + + if model_name == "yolov5": + model = fd.vision.detection.YOLOv5( + model_file, runtime_option=runtime_option) + model_inference = model + + elif model_name == "yolov8": + model = fd.vision.detection.YOLOv8( + model_file, runtime_option=runtime_option) + model_inference = model + + + # print(model_inference) + return model_inference