modify

.gitignore

@@ -18,6 +18,8 @@ pretrained_model/
 cfg1/
 test_imgs/
 weixian/
+moto/
+pic/
 # 不忽略下面指定的文件类型
 !*.cpp
 !*.h

cfg/yolov7-lite-s-detect.yaml

@@ -0,0 +1,47 @@
+# parameters
+nc: 6  # number of classes
+nkpt: 0 # number of keypoints
+depth_multiple: 1.0  # model depth multiple
+width_multiple: 1.0  # layer channel multiple
+
+anchors:
+  - [ 19,27,  44,40,  38,94 ]  # P3/8
+  - [ 96,68,  86,152,  180,137 ]  # P4/16
+  - [ 140,301,  303,264,  238,542 ]  # P5/32
+  - [ 436,615,  739,380,  925,792 ]  # P6/64
+# custom backbone
+
+backbone:
+  # [from, number, module, args]
+  [ [ -1, 1, StemBlock, [32, 3, 2] ],    # 0-P2/4
+    [ -1, 1, Shuffle_Block, [96, 2]], # 1-P3/8
+    [ -1, 3, Shuffle_Block, [96, 1]], # 2
+    [ -1, 1, Shuffle_Block, [192, 2]], # 3-P4/16
+    [ -1, 7, Shuffle_Block, [192, 1]], # 4
+    [ -1, 1, Shuffle_Block, [384, 2]], # 5-P5/32
+    [ -1, 3, Shuffle_Block, [384, 1]], # 6
+    [ -1, 1, SPPF, [384, 5]],
+  ]
+
+# v5lite-e head
+head:
+  [ [ -1, 1, Conv, [96, 1, 1]],
+    [ -1, 1, nn.Upsample, [ None, 2, 'nearest']],
+    [[ -1, 4], 1, Concat, [1]],  # cat backbone P4
+    [ -1, 1, DWConvblock, [96, 3, 1]],  # 11
+
+    [ -1, 1, Conv, [96, 1, 1]],
+    [ -1, 1, nn.Upsample, [ None, 2, 'nearest']],
+    [[ -1, 2], 1, Concat, [1]],  # cat backbone P3
+    [ -1, 1, DWConvblock, [96, 3, 1] ],  # 15 (P3/8-small)
+
+    [-1, 1, DWConvblock, [96, 3, 2]],
+    [[ -1, 12], 1, ADD, [1]],  # cat head P4
+    [ -1, 1, DWConvblock, [96, 3, 1]],  # 18 (P4/16-medium)
+
+    [ -1, 1, DWConvblock, [96, 3, 2]],
+    [[ -1, 8], 1, ADD, [1]],  # cat head P5
+    [ -1, 1, DWConvblock, [96, 3, 1]],  # 21 (P5/32-large)
+
+    [[ 15, 18, 21], 1, IKeypoint, [nc, anchors, nkpt]],  # Detect(P3, P4, P5)
+  ]

data/plate.yaml

@@ -8,8 +8,8 @@
 
 
 # train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/]
-train: /mnt/Gpan/Mydata/pytorchPorject/datasets/ccpd/train_detect
-val: /mnt/Gpan/Mydata/pytorchPorject/datasets/ccpd/val_detect/
+train: /mnt/Gu/trainData/Detect/plate_detect/train_detect 
+val: /mnt/Gu/trainData/Detect/plate_detect/val_detect
 #val: /ssd_1t/derron/yolov5-face/data/widerface/train/  # 4952 images
 
 # number of classes

detect_rec_plate.py

@@ -30,7 +30,9 @@ def order_points(pts):  #关键点按照（左上，右上，右下，左下）
 
 
 def four_point_transform(image, pts):  #透视变换
-    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))
@@ -145,14 +147,14 @@ def draw_result(orgimg,dict_list):
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--detect_model', nargs='+', type=str, default='weights/yolov7-lite-s.pt', help='model.pt path(s)')
-    parser.add_argument('--rec_model', type=str, default='weights/plate_rec.pth', help='model.pt path(s)')
+    parser.add_argument('--rec_model', type=str, default='weights/plate_rec_small.pth', help='model.pt path(s)')
     parser.add_argument('--source', type=str, default='imgs', help='source')  # file/folder, 0 for webcam
     # parser.add_argument('--img-size', nargs= '+', type=int, default=640, help='inference size (pixels)')
     parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)')
     parser.add_argument('--output', type=str, default='result', help='source') 
     parser.add_argument('--kpt-label', type=int, default=4, help='number of keypoints')
-    device  =torch.device("cuda" if torch.cuda.is_available() else "cpu")
-    # device = torch.device("cpu")
+    # device  =torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cpu")
     opt = parser.parse_args()
     print(opt)
     model = attempt_load(opt.detect_model, map_location=device)

models/export.py

@@ -26,7 +26,7 @@ from utils.torch_utils import select_device
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--weights', type=str, default='./yolov5s.pt', help='weights path')
-    parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='image size')  # height, width
+    parser.add_argument('--img-size', nargs='+', type=int, default=[320, 320], help='image size')  # height, width
     parser.add_argument('--batch-size', type=int, default=1, help='batch size')
     parser.add_argument('--grid', action='store_true', help='export Detect() layer grid')
     parser.add_argument('--device', default='cpu', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')

onnx/yolov7_plate_onnx_infer.py

@@ -232,10 +232,10 @@ def draw_result(orgimg,dict_list):
 if __name__ == "__main__":
    
     parser = argparse.ArgumentParser()
-    parser.add_argument('--detect_model',type=str, default=r'weights/plate_detect.onnx', help='model.pt path(s)')  #检测模型
+    parser.add_argument('--detect_model',type=str, default=r'runs/train/yolov714/weights/best.onnx', help='model.pt path(s)')  #检测模型
     parser.add_argument('--rec_model', type=str, default='weights/plate_rec.onnx', help='model.pt path(s)')#识别模型
-    parser.add_argument('--image_path', type=str, default=r'imgs', help='source') 
-    parser.add_argument('--img_size', type=int, default=640, help='inference size (pixels)')
+    parser.add_argument('--image_path', type=str, default=r'pic', help='source') 
+    parser.add_argument('--img_size', type=int, default=320, help='inference size (pixels)')
     parser.add_argument('--output', type=str, default='result', help='source') 
     opt = parser.parse_args()
     file_list = []