1102-wangying-update

add_xml.py

@@ -0,0 +1,95 @@
+
+from xml.etree.ElementTree import ElementTree, Element
+
+
+# xml换行
+
+
+def indent(elem, level=0):
+    i = "\n" + level*"\t"
+    if len(elem):
+        if not elem.text or not 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：<id>1</id>
+            elementOjb.append(one)  # 将二级目录加到一级目录里
+
+            two = Element('pose')
+            two.text = "Unspecified"
+            elementOjb.append(two)
+
+            three = Element('truncated')
+            three.text = "0"
+            elementOjb.append(three)
+
+            four = Element('difficult')
+            four.text = "0"
+            elementOjb.append(four)
+
+            five = Element('xmin')
+            five.text = str(xmin)
+            elementBox.append(five)
+
+            six = Element('xmax')
+            six.text = str(xmax)
+            elementBox.append(six)
+
+            seven = Element('ymin')
+            seven.text = str(ymin)
+            elementBox.append(seven)
+
+            eight = Element('ymax')
+            eight.text = str(ymax)
+            elementBox.append(eight)
+
+            # 将一级目录加到根目录里
+            elementOjb.append(elementBox)
+            root.append(elementOjb)
+            # 换行缩进
+            indent(elementOjb)
+            indent(elementBox)
+            # 让结果保存进文件就可以了
+            tree.write(xmlFilePath, encoding='utf-8', xml_declaration=True)
+
+

analysis_data/change_video.py

@@ -0,0 +1,43 @@
+import os
+import threading
+
+class mp4_to_H264():
+    def __init__(self):
+        pass
+    
+    def convert_avi(self, input_file, output_file, ffmpeg_exec="ffmpeg"):
+        ffmpeg = '{ffmpeg} -y -i "{infile}" -c:v libx264 -strict -2 "{outfile}"'.format(ffmpeg=ffmpeg_exec,
+                                                                                        infile=input_file,
+                                                                                        outfile=output_file)
+        f = os.popen(ffmpeg)
+        ffmpegresult = f.readline()
+ 
+        # s = os.stat(output_file)
+        # fsize = s.st_size
+ 
+        return ffmpegresult
+ 
+    def convert_avi_to_webm(self, input_file, output_file, ffmpeg_exec="ffmpeg"):
+        return self.convert_avi(input_file, output_file, ffmpeg_exec="ffmpeg")
+ 
+    def convert_avi_to_mp4(self, input_file, output_file, ffmpeg_exec="ffmpeg"):
+        return self.convert_avi(input_file, output_file, ffmpeg_exec="ffmpeg")
+ 
+    def convert_to_avcmp4(self, input_file, output_file, ffmpeg_exec="ffmpeg"):
+        email = threading.Thread(target=self.convert_avi, args=(input_file, output_file, ffmpeg_exec,))
+        email.start()
+ 
+    def convert_byfile(self, from_path, to_path):
+        if not os.path.exists(from_path):
+            print("Sorry, you must create the directory for the output files first")
+        if not os.path.exists(os.path.dirname(to_path)):
+            os.makedirs(os.path.dirname(to_path), exist_ok=True)
+        directory, file_name = os.path.split(from_path)
+        raw_name, extension = os.path.splitext(file_name)
+        print("Converting ", from_path)
+        self.convert_avi_to_mp4(from_path, to_path)
+
+# a = mp4_to_H264()
+# from_path = '/home/xbank/xbank_poc_test_use/video_save_path/hands/20231012/100932541744.mp4'
+# to_path = '/home/xbank/xbank_poc_test_use/video_save_path/hands/20231012/100932541744.mp4'
+# a.convert_byfile(from_path, to_path)

analysis_data/config_load.py

@@ -0,0 +1,7 @@
+import yaml
+from pathlib import Path
+
+def get_configs(ymal_files):
+    yaml_path = Path(__file__).parent / ymal_files
+    with yaml_path.open("r", encoding="utf-8") as f:
+        return yaml.load(f, Loader=yaml.FullLoader)

analysis_data/data_dir_file.py

@@ -0,0 +1,25 @@
+import cv2
+import os
+
+def get_dir_file(path,ext_list):
+    # video_ext = [".mp4", ".avi", ".MP4"]
+    file_names = []
+    for maindir, subdir, file_name_list in os.walk(path):
+        for filename in file_name_list:
+            apath = os.path.join(maindir, filename)
+            ext = os.path.splitext(apath)[1]
+            if ext in ext_list:
+                file_names.append(apath)
+    return file_names
+
+def get_imgframe(img_path_list):
+
+    imgframe_list = []
+    for img in img_path_list:
+
+        images = cv2.imread(img)
+        imgframe_dict = {"path":img,'frame':images}
+        imgframe_list.append(imgframe_dict)
+    
+    return imgframe_list
+

analysis_data/data_rtsp.py

@@ -0,0 +1,9 @@
+
+
+def rtsp_para(scource):
+
+    if scource.split('://')[0] == 'rtsp':
+        return True
+    else:
+        return False
+

analysis_result/get_model_result.py

@@ -0,0 +1,23 @@
+
+
+def det_img(model_inference,images_frame,confidence,label_name_list):
+
+    result = model_inference.predict(images_frame)
+
+    bbox_list = result.boxes
+    labels_ids_list = result.label_ids
+    scores_list = result.scores
+
+    result_list = []
+
+    for i in range(len(labels_ids_list)):
+
+        if scores_list[i] > confidence:
+
+            if int(labels_ids_list[i]) in range(len(label_name_list)):
+                
+                result_dict = {label_name_list[int(labels_ids_list[i])]:bbox_list[i]}
+                result_list.append(result_dict)
+    
+        
+    return result_list

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

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

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
+
+

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
+
+

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
+
+
+

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
+
+

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()

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

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()
+
+    
+    

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__':
+
+#     data_load(['rtsp://admin:@192.168.10.203',
+#               'E:/Bank_files/Bank_03/xbank_poc_test_use/config_phone.yaml'])

model_load/model_load.py

@@ -0,0 +1,50 @@
+import fastdeploy as fd
+import os.path as op
+
+
+def build_option(device, backend, cache_file):
+    """
+       创建Runtime运行选项
+       device：设备CPU or GPU
+       backend:TensorRT引擎
+    """
+    option = fd.RuntimeOption()
+    option.use_cpu()
+    option.trt_option.serialize_file = cache_file
+    if device.lower() == "gpu":
+        option.use_gpu(0)
+    if backend.lower() == "trt":
+        assert device.lower(
+        ) == "gpu", "TensorRT backend require inference on device GPU."
+        option.use_trt_backend()
+    return option
+
+
+def Load_model(model_file, device, cache_file):
+    """
+    加载模型的tensorRT引擎
+    model_file：模型权重   格式：".onnx"
+    device：设备选择
+    """
+
+    model_name = op.basename(model_file).split('_')[0]
+
+    # print(model_file)
+
+    runtime_option = 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