1

AI_example.py

 import time
 
 import cv2
+import numpy as np
 import torch
 
 from DMPRUtils.DMPR_process import DMPR_process, plot_points
     device_ = '0'  ##选定模型，可选 cpu,'0','1'
 
     ##以下参数目前不可改
-    Detweights = 'weights/urbanManagement/yolo/best1023.pt'
+    Detweights = 'weights/urbanManagement/yolo/best1201.pt'
     seg_nclass = 2
-    DMPRweights = "weights/urbanManagement/DMPR/dp_detector_299_1023.pth"
+    DMPRweights = "weights/urbanManagement/DMPR/dp_detector_372_1204.pth"
     conf_thres, iou_thres, classes = 0.25, 0.45, 3
     labelnames = "weights/yolov5/class5/labelnames.json"
     rainbows = [[0, 0, 255], [0, 255, 0], [255, 0, 0], [255, 0, 255], [255, 255, 0], [255, 129, 0], [255, 0, 127],
 
     # 图像测试
     # impth = 'images/input'
-    impth = 'images/debug'
+    # impth = 'images/debug'
+    # impth = '/home/thsw/ssd/zjc/cityManagement_test'
+    impth = '/home/thsw/WJ/zjc/AI/images/pic2'
     # impth = '/home/thsw/WJ/zjc/AI_old/images/input_0'
     # outpth = 'images/output'
     outpth = 'images/debug_out'
         t_stdc = time.time()
         # STDC process
         det2 = STDC_process(img0, STDC_model, device, args.stdc_new_hw)
-        # det2[det2 == 1] = 255
+        det2[det2 == 1] = 255
         t_stdc_inf = time.time()
         print(f't_stdc_inf. ({t_stdc_inf - t_stdc:.3f}s)')
         # STDC joint yolo
         print(f't_dmpr. ({t_dmpr - t_yolo:.3f}s)')
 
         # 绘制角点
-        # plot_points(img0, det1)
+        plot_points(img0, det1)
 
         # yolo joint DMPR
         cls = 0     #需要过滤的box类别
         t_end = time.time()
         print(f'Done. ({t_end - t_start:.3f}s)')
         # 绘制膨胀box
-        # for *xyxy, flag in dilate_box:
-        #     plot_one_box(xyxy, img0, color=rainbows[int(cls)], line_thickness=2)
+        for *xyxy, flag in dilate_box:
+            plot_one_box(xyxy, img0, color=rainbows[int(cls)], line_thickness=2)
         # #
         # # 绘制删除满足 在膨胀框内 && 角度差小于90度 的box
-        # for *xyxy, conf, cls, flag in reversed(joint_det):
-        #     if flag == 0:
-        #         # label = f'{int(cls)} {conf:.2f}'
-        #         label = None
-        #         plot_one_box(xyxy, img0, label=label, color=rainbows[int(cls)], line_thickness=2)
+        for *xyxy, conf, cls, flag in reversed(joint_det):
+            if flag == 0:
+                # label = f'{int(cls)} {conf:.2f}'
+                label = None
+                plot_one_box(xyxy, img0, label=label, color=rainbows[int(cls)], line_thickness=2)
 
         # save
-        # save_path = os.path.join(outpth, file)
-        # cv2.imwrite(save_path, file)
+        mask = det2[..., np.newaxis].repeat(3, 2)
+        img_seg = 0.3*mask + img0
+        save_path = os.path.join(outpth, file)
+        cv2.imwrite(save_path, img_seg)
 
 
 

DMPRUtils/yolo_net.py

         #         y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
         #         z.append(y.view(bs, -1, self.no))
 
-        prediction = self.m[1](x[1])
+        prediction = self.m[0](x[0])
         point_pred, angle_pred = torch.split(prediction, 4, dim=1)
         point_pred = torch.sigmoid(point_pred)
         angle_pred = torch.tanh(angle_pred)

conf/config.py

 
 def add_common_arguments(parser):
     """Add common arguments for training and inference."""
-    parser.add_argument('--detector_weights', default=r'E:\pycharmProject\DMPR-PS\weights\dp_detector_299.pth',
+    parser.add_argument('--detector_weights', default=r'E:\pycharmProject\DMPR-PS\weights\dp_detector_372_1204.pth',
                         help="The weights of pretrained detector.")
     parser.add_argument('--cfg', type=str, default='models/yolov5s.yaml',
                         help='model.yaml path')
                         help='class-agnostic NMS')
     parser.add_argument('--classes', nargs='+', type=int,
                         help='filter by class: --class 0, or --class 0 2 3')
-    parser.add_argument('--dmpr-thresh', type=float, default=0.3,
+    parser.add_argument('--dmpr-thresh', type=float, default=0.1,
                         help="Detection threshold.")
     # STDC
     parser.add_argument('--n-classes', type=int, default=2, help='number of classes for segment')
     parser.add_argument('--backbone', type=str, default='STDCNet813', help='STDC backbone')
-    parser.add_argument('--respth', type=str, default='weights/urbanManagement/STDC/model_final_1023.pth',
+    parser.add_argument('--respth', type=str, default='weights/urbanManagement/STDC/model_final_1123.pth',
                         help='The weights of STDC')
     parser.add_argument('--stdc-new-hw', nargs='+', type=int, default=[360, 640], help='The new hw of STDC')
     parser.add_argument('--use-boundary-2', type=bool, default=False, help='')