0921更新代码

Bank_second_part/xbank_detect_process/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)

Bank_second_part/xbank_detect_process/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
+

Bank_second_part/xbank_detect_process/analysis_data/data_rtsp.py

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

Bank_second_part/xbank_detect_process/analysis_result/get_model_result.py

@@ -0,0 +1,22 @@
+
+
+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:
+
+            result_dict = {label_name_list[int(labels_ids_list[i])]:bbox_list[i]}
+
+            result_list.append(result_dict)
+    
+        
+    return result_list

Bank_second_part/xbank_detect_process/analysis_result/same_model_img.py

@@ -1,4 +1,4 @@
-from tools_analysis import select_bbox_by_labels, iou_result_two
+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):
@@ -9,6 +9,7 @@ def same_model_img_analysis_labels(example_list, result_dicts_list, relevancy, r
 
     # 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
@@ -42,6 +43,6 @@ 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()) in example_list]
+        result_dict.keys())[0] in example_list]
 
     return compara_labellist

Bank_second_part/xbank_detect_process/analysis_result/tools_analysis.py

@@ -65,8 +65,8 @@ def select_bbox_by_labels(example_list, result_dicts_list):
     for label in example_list:
         bbox_list = []
         for result in result_dicts_list:
-            if list(result.keys()) == label:
-                bbox_list.append(list(result.values()))
+            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)

Bank_second_part/xbank_detect_process/config_det.yaml

@@ -0,0 +1,32 @@
+detect_0: 
+  source : rtsp://admin:@192.168.10.18
+  model: /home/yaxin/xbank/xbank_poc_test/config_phone.yaml
+
+detect_1: 
+  source : rtsp://admin:@192.168.10.12
+  model: /home/yaxin/xbank/xbank_poc_test/config_sleep.yaml
+
+detect_2: 
+  source : rtsp://admin:@192.168.10.18
+  model: /home/yaxin/xbank/xbank_poc_test/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/yaxin/xbank/xbank_poc_test/images_test/images_del
+#   model: /home/yaxin/xbank/xbank_poc_test/config_sleep.yaml
+

Bank_second_part/xbank_detect_process/config_phone.yaml

@@ -0,0 +1,21 @@
+# load model file
+model: /home/yaxin/xbank/xbank_poc_test/model_file/yolov5.onnx
+model_cache: /home/yaxin/xbank/xbank_poc_test/tensort_cache/yolov5.trt
+
+# label and bbox message set 
+model_parameter:
+  device : gpu 
+  label_names:  ["Keypad","hands","keyboard", "mouse","phone"] # model labels
+  compara_label_names:  ["hands","phone"] # 
+  compara_relevancy: 'overlap' # 'in_bbox'
+  relevancy_para : 0
+  confidence : 0.2
+
+save_path : /home/yaxin/xbank/xbank_poc_test/save_path/hands
+# save_path_original : /home/yaxin/xbank/xbank_poc_test/save_path_original/hands
+# test_path : /home/yaxin/xbank/xbank_poc_test/test_save_path/hands
+save_path_original : False
+test_path : False
+
+
+

Bank_second_part/xbank_detect_process/config_sleep.yaml

@@ -0,0 +1,20 @@
+# load model file
+model: /home/yaxin/xbank/xbank_poc_test/model_file/yolov8.onnx
+model_cache: /home/yaxin/xbank/xbank_poc_test/tensort_cache/yolov8.trt
+
+# label and bbox message set 
+model_parameter:
+  device : gpu 
+  label_names:  ["person","sleep"] # model labels
+  compara_label_names:  ["person","sleep"] # 
+  compara_relevancy: False # 'in_bbox'
+  relevancy_para : False
+  confidence : 0.2
+
+save_path : /home/yaxin/xbank/xbank_poc_test/save_path/sleep
+# save_path_original : /home/yaxin/xbank/xbank_poc_test/save_path_original/sleep
+# test_path : /home/yaxin/xbank/xbank_poc_test/test_save_path/sleep
+save_path_original : False
+test_path : False
+
+

Bank_second_part/xbank_detect_process/drawing_img/drawing_img.py

@@ -0,0 +1,34 @@
+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]
+
+        if scores_list_id:
+            scores_list = result_list['scores_list']
+            lables = str(label_name) + "__" + str(scores_list)
+        else:
+            lables = str(label_name)
+
+        cv2.rectangle(img, (int(bbox_list[0]), int(bbox_list[1])), (int(
+            bbox_list[2]), int(bbox_list[3])), color_1, line_1)
+        cv2.putText(img, lables, (int(bbox_list[0]) - 10, int(
+            bbox_list[1]) - 10), cv2.FONT_HERSHEY_SIMPLEX, txt_num, color_2, line_2)
+
+    return img

Bank_second_part/xbank_detect_process/main.py

@@ -0,0 +1,37 @@
+#coding: utf-8
+from main_process import data_load
+import multiprocessing
+from analysis_data.config_load import get_configs
+from model_load.model_load import Load_model
+import time
+from multiprocessing import Pool
+
+
+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
+
+
+if __name__ == "__main__":
+
+    # 加载配置文件
+    args = '/home/yaxin/xbank/xbank_poc_test/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(data_load, args_list)
+    
+    

Bank_second_part/xbank_detect_process/main_process.py

@@ -0,0 +1,201 @@
+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, get_imgframe
+from analysis_data.data_load import data_load
+from analysis_data.config_load import get_configs
+
+import yaml
+import cv2
+import os
+from pathlib import Path
+import time
+from datetime import datetime
+
+
+def data_load(args):
+
+    # print('正在运行的进程',msg)
+    # print(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:
+
+        cap = cv2.VideoCapture(source)
+
+        try:
+            i = 0
+            while True:
+                ret, frame = cap.read()
+
+                if not ret:
+                    continue  # 如果未成功读取到视频帧，则继续读取下一帧
+                print(source,datetime.today(), i)
+                # if source == 'rtsp://admin:@192.168.10.18':
+
+                #     cv2.imshow('18',frame)
+
+                t1 = time.time()
+                imgframe_dict = {"path": source, 'frame': frame}
+                images_update = img_process(
+                    imgframe_dict, model_inference, model_data)
+                
+                # print(type(images_update['frame']))
+                t2 = time.time()
+                tx = t2 - t1
+                print('检测一张图片的时间为：',tx)
+
+                # if source == 'rtsp://admin:@192.168.10.18':
+                #     cv2.namedWindow('18',0)
+                #     cv2.imshow('18',images_update['frame'])
+
+                i = i+1
+                
+
+        except Exception as e:
+            # 处理异常或错误
+            print(str(e))
+
+        cap.release()
+
+    if dir_source:
+
+        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
+
+    if file_source:
+
+        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(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
+
+    # 判断后的信息返回结果，这里是画图，可返回结果
+    if determine_bbox:
+
+        images.update({"results": determine_bbox})
+
+        img_save = drawing_frame(
+            images_frame=images['frame'], result_list=determine_bbox)
+        
+        images.update({"frame": img_save})
+
+        imgname = images_save(
+            images=images['frame'], save_path=model_data["save_path"])
+
+        print('sleep:', images['path'], imgname)
+
+        if model_data['save_path_original']:
+            images_save(images=images['frame'],
+                        save_path=model_data["save_path_original"])
+        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['frame'], save_path=model_data["test_path"])
+            # print('no:',images['path'],imgname)
+
+        else:
+            pass
+    
+    # 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
+    data_now = datetime.today()
+    images_name = str(data_now.year) + str(data_now.month) + str(data_now.day) + str(data_now.hour) + \
+        str(data_now.minute) + str(data_now.second) + \
+        str(data_now.microsecond) + '.jpg'
+    img_save_path = save_path + '/' + str(
+        data_now.year) + '/' + str(data_now.month) + '_' + str(data_now.day) + '/'
+
+    if not os.path.exists(img_save_path):
+        os.makedirs(img_save_path)
+
+    full_name = img_save_path + images_name
+
+    cv2.imwrite(full_name, images)
+
+    return full_name
+

Bank_second_part/xbank_detect_process/model_load/model_load.py

@@ -0,0 +1,44 @@
+import fastdeploy as fd
+
+
+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 = model_file.split("/")[-1].split(".")[0]
+
+    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

Bank_second_part/xbank_detect_process/readme.md

@@ -0,0 +1,29 @@
+1、主程序运行main.py文件
+2、main.py文件中args加载检测配置文件config_det.yaml，为了保证路径没问题，请设置绝对路径
+3、config_det.yaml中配置
+    detect_0:                                                       # 名称随意，不可重复
+        source : rtsp://admin:@192.168.10.18                        # scource   设置rtsp流或者图片文件夹
+        model: /home/yaxin/xbank/xbank_poc_test/config_phone.yaml   # 配置检测的模型配置文件
+
+4、模型配置文件，config_检测名称.yaml
+    # load model file
+    model: /home/yaxin/xbank/xbank_poc_test/model_file/yolov5.onnx                          # 模型的绝对路径
+    model_cache: /home/yaxin/xbank/xbank_poc_test/tensort_cache/yolov5.trt                  # 设置cache，不用更改
+
+    # label and bbox message set 
+    model_parameter:                                                                        # 模型检测中需要的参数
+    device : gpu                                                                            # 使用gpu检测
+    label_names:  ["Keypad","hands","keyboard", "mouse","phone"] # model labels             # 检测模型训练时的标签名列表，标签名顺序需要跟训练时候分配数据集时候的顺序一致，不用更改
+    compara_label_names:  ["hands","phone"] #                                               # 检测到的类别中需要挑选出来的目标
+    compara_relevancy: 'overlap' # 'in_bbox'                                                # 是否对挑选出来进行推理的目标进行其标签框之间关系进行推理，如果不需要就设置成FALSE，默认不用更改
+    relevancy_para : 0                                                                      # 需要的参数
+    confidence : 0.2                                                                        # 设置检测出来的目标过滤的置信度
+
+    save_path : /home/yaxin/xbank/xbank_poc_test/save_path/hands                            # 保存检测后的图片的路径，默认不需要更改
+    # save_path_original : /home/yaxin/xbank/xbank_poc_test/save_path_original/hands        # 检测到的图片的原始图片的路径，不保存使用False
+    # test_path : /home/yaxin/xbank/xbank_poc_test/test_save_path/hands                     # 未检测到目标的图片，不保存设置为False，默认保存
+
+5、使用本地环境
+    conda activate fastdeploy
+    修改过配置文件后，运行Python main.py
+