fix bug

README.md

@@ -19,7 +19,7 @@ python detect_plate.py --detect_model weights/plate_detect.pt  --rec_model weigh
 #### 视频测试demo  [2.MP4](https://pan.baidu.com/s/1O1sT8hCEwJZmVScDwBHgOg)  提取码：41aq
 
 ```
-python detect_plate.py --detect_model weights/plate_detect.pt  --rec_model weights/plate_rec.pth --video 2.mp4
+python detect_plate.py --detect_model weights/plate_detect.pt  --rec_model weights/plate_rec_color.pth --video 2.mp4
 ```
 
 视频文件为2.mp4  保存为result.mp4
@@ -83,10 +83,10 @@ python detect_plate.py --detect_model weights/plate_detect.pt  --rec_model weigh
 - [X] 5.教练车牌
 - [X] 6.武警车牌
 - [X] 7.双层黄牌
-- [X] 8.双层武警
+- [X] 8.双层白牌
 - [X] 9.使馆车牌
 - [X] 10.港澳粤Z牌
-- [X] 11.双层农用车牌
+- [X] 11.双层绿牌
 - [X] 12.民航车牌
 
 

detect_plate.py

@@ -19,7 +19,6 @@
 from plate_recognition.plate_rec import get_plate_result,allFilePath,init_model,cv_imread
 # from plate_recognition.plate_cls import cv_imread
 from plate_recognition.double_plate_split_merge import get_split_merge
-from plate_recognition.color_rec import plate_color_rec,init_color_model
 
 clors = [(255,0,0),(0,255,0),(0,0,255),(255,255,0),(0,255,255)]
 danger=['危','险']
@@ -35,7 +34,8 @@ def order_points(pts):                   #四个点按照左上 右上 右下 
 
 
 def four_point_transform(image, pts):                       #透视变换得到车牌小图
-    rect = order_points(pts)
+    # rect = order_points(pts)
+    rect = pts.astype('float32')
     (tl, tr, br, bl) = rect
     widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
     widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
@@ -107,9 +107,6 @@ def get_plate_rec_landmark(img, xyxy, conf, landmarks, class_num,device,plate_re
         plate_number,rec_prob = get_plate_result(roi_img,device,plate_rec_model,is_color=is_color)                 #对车牌小图进行识别
     else:
         plate_number,rec_prob,plate_color,color_conf=get_plate_result(roi_img,device,plate_rec_model,is_color=is_color) 
-    for dan in danger:                                                           #只要出现‘危’或者‘险’就是危险品车牌
-        if dan in plate_number:
-            plate_number='危险品'
     # cv2.imwrite("roi.jpg",roi_img)
     result_dict['rect']=rect                      #车牌roi区域
     result_dict['detect_conf']=conf              #检测区域得分
@@ -217,10 +214,7 @@ def draw_result(orgimg,dict_list,is_color=False):   # 车牌结果画出来
             cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1)
         cv2.rectangle(orgimg,(rect_area[0],rect_area[1]),(rect_area[2],rect_area[3]),(0,0,255),2) #画框
         if len(result)>=1:
-            if "危险品" in result_p: #如果是危险品车牌，文字就画在下面
-                orgimg=cv2ImgAddText(orgimg,result_p,rect_area[0],rect_area[3],(0,255,0),height_area)
-            else:
-                orgimg=cv2ImgAddText(orgimg,result_p,rect_area[0]-height_area,rect_area[1]-height_area-10,(0,255,0),height_area)
+            orgimg=cv2ImgAddText(orgimg,result_p,rect_area[0]-height_area,rect_area[1]-height_area-10,(0,255,0),height_area)
 
     print(result_str)
     return orgimg
@@ -237,10 +231,10 @@ def get_second(capture):
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('--detect_model', nargs='+', type=str, default='weights/plate_detect.pt', help='model.pt path(s)')  #检测模型
+    parser.add_argument('--detect_model', nargs='+', type=str, default='runs/train/exp40/weights/best.pt', help='model.pt path(s)')  #检测模型
     parser.add_argument('--rec_model', type=str, default='weights/plate_rec_color.pth', help='model.pt path(s)')#车牌识别+颜色识别模型
     parser.add_argument('--is_color',type=bool,default=True,help='plate color')     #是否识别颜色
-    parser.add_argument('--image_path', type=str, default='imgs', help='source') 
+    parser.add_argument('--image_path', type=str, default='gangao/Test', help='source') 
     parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)')
     parser.add_argument('--output', type=str, default='result1', help='source') 
     parser.add_argument('--video', type=str, default='', help='source') 

processing_json.py

@@ -70,9 +70,9 @@ def four_point_transform(image, pts):
 
 if __name__ == "__main__":
     pic_file_list = []
-    pic_file = r"test"
+    pic_file = r"/mnt/Gpan/Mydata/pytorchPorject/datasets/ccpd/train_bisai/train_bisai"
     save_small_path = "small"
-    label_file = ['single','double']
+    label_file = ['0','1']
     allFilePath(pic_file,pic_file_list)
     count=0
     index = 0
@@ -96,7 +96,7 @@ def four_point_transform(image, pts):
                     label=data_message['label']
                     points = data_message['points']
                     pts = np.array(points)
-                    pts=order_points(pts)
+                    # pts=order_points(pts)
                     new_img = four_point_transform(img,pts)
                     roi_img_name = label+"_"+str(index)+".jpg"
                     save_path=os.path.join(save_small_path,roi_img_name)

train.py

@@ -473,8 +473,8 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
     opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1
     set_logging(opt.global_rank)
-    if opt.global_rank in [-1, 0]:
-        check_git_status()
+    # if opt.global_rank in [-1, 0]:
+    #     check_git_status()
 
     # Resume
     if opt.resume:  # resume an interrupted run

utils/face_datasets.py

@@ -41,6 +41,8 @@ def img2label_paths(img_paths):
     sa, sb = os.sep + 'images' + os.sep, os.sep + 'labels' + os.sep  # /images/, /labels/ substrings
     return [x.replace(sa, sb, 1).replace('.' + x.split('.')[-1], '.txt') for x in img_paths]
 
+    # return [x.replace(".jpg",".txt") for x in img_paths]
+
 def exif_size(img):
     # Returns exif-corrected PIL size
     s = img.size  # (width, height)