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from analysis_result.get_model_result import det_img
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from analysis_result.same_model_img import same_model_img_analysis_labels, model_labels_selet
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from model_load.model_load import Load_model
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from drawing_img.drawing_img import drawing_frame
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from analysis_data.data_rtsp import rtsp_para
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from analysis_data.data_dir_file import get_dir_file, get_imgframe
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from analysis_data.config_load import get_configs
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from add_xml import add_xml
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from create_xml import create_xml
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import yaml
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import cv2
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import os
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from pathlib import Path
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import time
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from datetime import datetime
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import glob
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import json
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def data_load(args):
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source = args[0]
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model_ymal = args[1]
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# 数据加载
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rtsp_source = rtsp_para(source)
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dir_source = os.path.isdir(source)
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file_source = os.path.isfile(source)
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# # 模型加载
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model_data = get_configs(model_ymal)
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model_inference = Load_model(model_file=model_data["model"],
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device=model_data["model_parameter"]['device'],
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cache_file=model_data["model_cache"])
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if rtsp_source:
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rtsp_detect_process(source=source, model_data=model_data,
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model_inference=model_inference)
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if dir_source:
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dir_source_process(source, model_inference, model_data)
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if file_source:
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file_source_process(source, model_inference, model_data)
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def rtsp_detect_process(source, model_data, model_inference):
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cap = cv2.VideoCapture(source)
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# 视频流信息
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fps = int(cap.get(cv2.CAP_PROP_FPS))
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fps_num = fps*model_data['detect_time']
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fps_num_small = fps*model_data['detect_time_small']
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size = (int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)),
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int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)))
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try:
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i = 0
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j = 0
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det_t_num = 0
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nodet_t_num = 0
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det_img = []
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video_name_time = 0
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det_fps_time = []
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while True:
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ret, frame = cap.read()
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t1 = time.time()
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if not ret:
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continue # 如果未成功读取到视频帧，则继续读取下一帧
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i = i + 1
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j = j + 1
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# 读取到当前视频帧时间
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data_now = datetime.now()
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get_time = str(data_now.strftime("%H")) + \
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str(data_now.strftime("%M")) + str(data_now.strftime("%S")) + \
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str(data_now.strftime("%f"))
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# 视频保存
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if video_name_time == 0:
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video_name_time = get_time
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savePath = os.path.join(model_data['save_videos'], (str(data_now.strftime(
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"%Y")) + str(data_now.strftime("%m")) + str(data_now.strftime("%d"))))
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if not os.path.exists(savePath):
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os.makedirs(savePath)
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video_path = os.path.join(
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savePath, video_name_time + '.avi')
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print('video_path:', video_path)
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out_video = cv2.VideoWriter(
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video_path, cv2.VideoWriter_fourcc(*'DIVX'), fps, size)
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print(source, data_now, i, j,video_path)
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# 推理部分
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imgframe_dict = {"path": source, 'frame': frame, 'get_fps': j}
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images_det_result = img_process(
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imgframe_dict, model_inference, model_data)
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images_update = save_process(
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imgframe_dict, images_det_result, model_data)
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# print('images_det_result:', len(images_det_result))
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# 结果判断，t
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if images_det_result:
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det_t_num = det_t_num + 1
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if len(det_img) == 0:
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img_dict = images_update.copy()
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del img_dict['frame']
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det_img.append(img_dict)
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if not images_det_result and len(det_img) > 0:
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nodet_t_num = nodet_t_num + 1
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if (det_t_num + nodet_t_num) >= fps_num_small:
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para = determine_time(
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det_num=det_t_num, nodet_num=nodet_t_num, ratio_set=model_data['detect_ratio'])
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if para:
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first_fps_time = det_img[0]
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first_fps_time.update(
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{"dert_fps": (j-int(first_fps_time['get_fps'])+1)})
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det_fps_time.append(first_fps_time)
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det_img.clear()
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det_t_num = 0
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nodet_t_num = 0
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# 视频保存
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out_video.write(images_update['frame'])
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# 结果判断 ，T
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if j >= fps_num:
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out_video.release()
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# T时间截至，判断t时间结果。
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if det_img:
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para = determine_time(
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det_num=det_t_num, nodet_num=nodet_t_num, ratio_set=model_data['detect_ratio'])
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first_fps_time = det_img[0]
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# print(j-int(first_fps_time['get_fps'])+1)
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# print(fps_num_small/2)
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if (j-int(first_fps_time['get_fps'])+1) >= (fps_num_small/2):
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first_fps_time.update(
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{"dert_fps": (j-int(first_fps_time['get_fps'])+1)})
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det_fps_time.append(first_fps_time)
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# print('det_fps_time:', det_fps_time)
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if det_fps_time:
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re_list = json_get(
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time_list=det_fps_time, video_path=video_path,fps=fps)
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json_save(re_list)
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else:
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print(video_path)
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os.remove(video_path)
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print('----------------------------------------------clear videos-----------------------------------------------')
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# 重置
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print('----------------------------------------------next-----------------------------------------------')
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det_img.clear()
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det_fps_time.clear()
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det_t_num = 0
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nodet_t_num = 0
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video_name_time = 0
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j = 0
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# print('det_fps_time:', det_fps_time,'det_img:',det_img)
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t2 = time.time()
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tx = t2 - t1
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print('检测一张图片的时间为：', tx)
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except Exception as e:
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# 处理异常或错误
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print(str(e))
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cap.release()
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def dir_source_process(source, model_inference, model_data):
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img_ext = [".jpg", ".JPG", ".bmp"]
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video_ext = [".mp4", ".avi", ".MP4"]
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img_list = get_dir_file(source, img_ext)
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video_list = get_dir_file(source, video_ext)
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if img_list:
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for img in img_list:
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t1 = time.time()
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images = cv2.imread(img)
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imgframe_dict = {"path": img, 'frame': images}
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images_update = img_process(
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imgframe_dict, model_inference, model_data)
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t2 = time.time()
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tx = t2 - t1
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print('检测一张图片的时间为：', tx)
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if video_list:
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pass
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def file_source_process(source, model_inference, model_data):
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img_para = True
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if img_para:
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images = cv2.imread(source)
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imgframe_dict = {"path": source, 'frame': images}
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images_update = img_process(
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imgframe_dict, model_inference, model_data)
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def img_process(images, model_inference, model_data):
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# t1 = time.time()
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# 检测每帧图片，返回推理结果
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results = det_img(model_inference=model_inference,
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images_frame=images['frame'],
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confidence=model_data["model_parameter"]['confidence'],
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label_name_list=model_data["model_parameter"]['label_names'])
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# print(images['path'])
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# 根据需要挑选标注框信息
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select_labels_list = model_labels_selet(example_list=model_data["model_parameter"]['compara_label_names'],
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result_dict_list=results)
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if model_data["model_parameter"]['compara_relevancy']:
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# 需要根据的逻辑判断标注框信息
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determine_bbox = same_model_img_analysis_labels(example_list=model_data["model_parameter"]['compara_label_names'],
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result_dicts_list=select_labels_list,
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relevancy=model_data["model_parameter"]['compara_relevancy'],
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relevancy_para=model_data["model_parameter"]['relevancy_para'])
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else:
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determine_bbox = select_labels_list
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# print(determine_bbox)
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if model_data['model_parameter']['object_num_min']:
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if len(determine_bbox) >= model_data["model_parameter"]['object_num_min']:
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# print(len(determine_bbox))
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determine_bbox.clear()
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# 返回检测后结果
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return determine_bbox
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def save_process(images, determine_bbox, model_data):
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if determine_bbox:
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images.update({"results": determine_bbox})
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img_save = drawing_frame(
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images_frame=images['frame'], result_list=determine_bbox)
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images.update({"frame": img_save})
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if model_data["save_path"]:
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imgname = images_save(
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images=images, save_path=model_data["save_path"])
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if model_data['save_path_original']:
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imgname_original = images_save(images=images,
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save_path=model_data["save_path_original"])
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if model_data["save_annotations"]:
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if not os.path.exists(model_data["save_annotations"]):
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os.makedirs(model_data["save_annotations"])
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save_annotations_xml(
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xml_save_file=model_data["save_annotations"], save_infors=determine_bbox, images=images['path'])
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else:
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pass
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else:
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# 没检测出来的图片是否保存
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if model_data["test_path"]:
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imgname = images_save(
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images=images, save_path=model_data["test_path"])
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# print('no:',images['path'],imgname)
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# 展示显示
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# if images['path'] == 'rtsp://admin:@192.168.10.11':
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# cv2.namedWindow('11', cv2.WINDOW_NORMAL)
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# cv2.imshow('11',images['frame'])
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# cv2.waitKey(1)
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# cv2.destroyAllWindows()
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# t2 = time.time()
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return images
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def images_save(images, save_path):
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# 保存时候时间为图片名
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# data_now = datetime.now()
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# images_name = str(data_now.strftime("%Y")) + str(data_now.strftime("%m")) + str(data_now.strftime("%d")) + str(data_now.strftime("%H")) + \
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# str(data_now.strftime("%M")) + str(data_now.strftime("%S")) + \
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# str(data_now.strftime("%f")) + '.jpg'
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# img_save_path = save_path + '/' + str(
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# data_now.year) + '/' + str(data_now.month) + '_' + str(data_now.day) + '/'
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img_save_path = os.path.join(save_path, str(images['path'].split('.')[-1]))
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images_name = images['get_time'] + '.jpg'
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if not os.path.exists(img_save_path):
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os.makedirs(img_save_path)
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full_name = os.path.join(img_save_path, images_name)
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cv2.imwrite(full_name, images['frame'])
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return full_name
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def save_annotations_xml(xml_save_file, save_infors, images):
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results = save_infors
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img = os.path.basename(images)
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img_frame = cv2.imread(images)
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xml_save_path = os.path.join(xml_save_file, img.split('.')[0] + '.xml')
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w, h, d = img_frame.shape
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img_shape = (w, h, d, img)
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if os.path.isfile(xml_save_path):
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add_xml(inforsDict=results,
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xmlFilePath=xml_save_path)
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else:
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create_xml(boxs=results,
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img_shape=img_shape,
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xml_path=xml_save_path)
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def determine_time(det_num, nodet_num, ratio_set):
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ratio = det_num / (det_num + nodet_num)
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# print(det_num, nodet_num, ratio)
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if ratio >= ratio_set:
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return True
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else:
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return False
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def video_synthesis(imglist, savePath, size, fps, videoname):
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if not os.path.exists(savePath):
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os.makedirs(savePath)
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print(videoname)
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video_path = os.path.join(savePath, videoname + '.avi')
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out = cv2.VideoWriter(
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video_path, cv2.VideoWriter_fourcc(*'DIVX'), fps, size)
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sorted_list = sorted(imglist, key=lambda x: x['get_time'])
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for filename in sorted_list:
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out.write(filename['frame'])
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out.release()
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def json_get(time_list, video_path,fps):
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result_dict ={'info': {'video_path': video_path,'fps':fps}}
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re_dict = {}
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for i, det_dict in enumerate(time_list):
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list_hands = ["Keypad", "hands", "keyboard", "mouse", "phone"]
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list_sleep = ["person", "sleep"]
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if list(det_dict['results'][0].keys())[0] in list_hands:
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result_lables = 'playing_phone'
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if list(det_dict['results'][0].keys())[0] in list_sleep:
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result_lables = "sleep"
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fps_dict = {'time': det_dict['get_fps'],
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'duration': det_dict['dert_fps'], 'result': result_lables}
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re_dict.update({('id_' + str(i)): fps_dict})
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result_dict.update({'result':re_dict})
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return result_dict
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def json_save(result_dict):
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json_path = result_dict['info`']['video_path'].split('.')[0] + '.json'
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result = json.dumps(result_dict)
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f = open(json_path, 'w')
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f.write(result + '\n')
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f.close
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# if __name__ == '__main__':
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# data_load(['rtsp://admin:@192.168.10.203',
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# 'E:/Bank_files/Bank_03/xbank_poc_test_use/config_phone.yaml'])
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