zishuo/yolov7_plate
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we0091234
2023-02-17 15:05:02 +08:00
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commit 623fb642c8
33 changed files with 1110 additions and 191 deletions
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2
.gitignore vendored
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@@ -14,6 +14,8 @@ result/
plate/
wan/
label_imgs/
pretrained_model/
cfg1/
# 不忽略下面指定的文件类型
!*.cpp
!*.h

10
README.md
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@@ -14,9 +14,9 @@
**12 民航车牌**
**测试demo:**
以yolov7-lite-s 为例:
```
python detect_rec_plate.py --detect_model weights/plate_detect.pt --rec_model weights/plate_rec.pth --source imgs --output result
python detect_rec_plate.py --detect_model weights/yolov7-lite-s.pt --rec_model weights/plate_rec.pth --source imgs --output result
```
测试文件夹imgs，结果保存再 result 文件夹中
@@ -45,9 +45,9 @@ python detect_rec_plate.py --detect_model weights/plate_detect.pt --rec_model w
```
3. 训练
以yolov7-lite-s 为例:
```
python train.py --batch-size 32 --data data/plate.yaml --img 640 640 --cfg cfg/yolov7-lite-e-plate.yaml --weights weights/yolov7-lite-e.pt --name yolov7 --hyp data/hyp.face.yaml
python train.py --batch-size 32 --data data/plate.yaml --img 640 640 --cfg cfg/yolov7-lite-s.yaml --weights weights/yolov7-lite-s.pt --name yolov7 --hyp data/hyp.face.yaml
```
结果存在run文件夹中
@@ -72,7 +72,7 @@ python detect_rec_plate.py --detect_model weights/plate_detect.pt --rec_model w
## References
* [https://github.com/derronqi/yolov7-face](https://github.com/derronqi/yolov7-face)
* [https://github.com/qinggangwu/yolov7-pose_Npoint_Ncla](https://github.com/qinggangwu/yolov7-pose_Npoint_Ncla)
* [https://github.com/WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7)
* [https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose](https://github.com/TexasInstruments/edgeai-yolov5/tree/yolo-pose)
* [https://github.com/qinggangwu/yolov7-pose_Npoint_Ncla](https://github.com/qinggangwu/yolov7-pose_Npoint_Ncla)
* [https://github.com/WongKinYiu/yolov7/tree/pose](https://github.com/WongKinYiu/yolov7/tree/pose)

49
cfg/yolov5s-face.yaml
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@@ -1,49 +0,0 @@
# parameters
nc: 1 # number of classes
nkpt: 5 # number of keypoints
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple
dw_conv_kpt: False
anchors:
- [4,5, 6,8, 10,12] # P3/8
- [15,19, 23,30, 39,52] # P4/16
- [72,97, 123,164, 209,297] # P5/32
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 3, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 3]], # 9
]
# YOLOv5 v6.0 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 13
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 14], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 10], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [1024, False]], # 23 (P5/32-large)
[[17, 20, 23], 1, IKeypoint, [nc, anchors, nkpt]], # Detect(P3, P4, P5)
]

8
cfg/yolov7-face.yaml
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@@ -1,9 +1,9 @@
# parameters
nc: 1 # number of classes
nkpt: 5 # number of keypoints
nc: 2 # number of classes
nkpt: 4 # number of keypoints
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
dw_conv_kpt: True
dw_conv_kpt: False
anchors:
- [4,5, 6,8, 10,12] # P3/8
@@ -73,7 +73,7 @@ backbone:
# yolov7 head
head:
[[-1, 1, SPPCSPC, [512]], # 51
[[-1, 1, SPPFCSPC, [512]], # 51
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],

46
cfg/yolov7-lite-e-plate.yaml
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@@ -1,46 +0,0 @@
# parameters
nc: 2 # number of classes
nkpt: 4 # number of keypoints
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
dw_conv_kpt: True
anchors:
- [4,5, 6,8, 10,12] # P3/8
- [15,19, 23,30, 39,52] # P4/16
- [72,97, 123,164, 209,297] # P5/32
# custom backbone
backbone:
# [from, number, module, args]
[ [ -1, 1, conv_bn_relu_maxpool, [ 32 ] ], # 0-P2/4
[ -1, 1, Shuffle_Block, [ 116, 2 ] ], # 1-P3/8
[ -1, 3, Shuffle_Block, [ 116, 1 ] ], # 2
[ -1, 1, Shuffle_Block, [ 232, 2 ] ], # 3-P4/16
[ -1, 7, Shuffle_Block, [ 232, 1 ] ], # 4
[ -1, 1, Shuffle_Block, [ 464, 2 ] ], # 5-P5/32
[ -1, 1, Shuffle_Block, [ 464, 1 ] ], # 6
]
# 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] ], # 10
[ -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] ], # 14 (P3/8-small)
[-1, 1, DWConvblock, [96, 3, 2]],
[ [ -1, 11 ], 1, ADD, [ 1 ] ], # cat head P4
[ -1, 1, DWConvblock, [96, 3, 1] ], # 17 (P4/16-medium)
[ -1, 1, DWConvblock, [ 96, 3, 2 ] ],
[ [ -1, 7 ], 1, ADD, [ 1 ] ], # cat head P5
[ -1, 1, DWConvblock, [96, 3, 1] ], # 20 (P5/32-large)
[ [ 14, 17, 20 ], 1, IKeypoint, [nc, anchors, nkpt]], # Detect(P3, P4, P5)
]

46
cfg/yolov7-lite-e.yaml
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@@ -1,46 +0,0 @@
# parameters
nc: 2 # number of classes
nkpt: 4 # number of keypoints
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
dw_conv_kpt: True
anchors:
- [4,5, 6,8, 10,12] # P3/8
- [15,19, 23,30, 39,52] # P4/16
- [72,97, 123,164, 209,297] # P5/32
# custom backbone
backbone:
# [from, number, module, args]
[ [ -1, 1, conv_bn_relu_maxpool, [ 32 ] ], # 0-P2/4
[ -1, 1, Shuffle_Block, [ 116, 2 ] ], # 1-P3/8
[ -1, 3, Shuffle_Block, [ 116, 1 ] ], # 2
[ -1, 1, Shuffle_Block, [ 232, 2 ] ], # 3-P4/16
[ -1, 7, Shuffle_Block, [ 232, 1 ] ], # 4
[ -1, 1, Shuffle_Block, [ 464, 2 ] ], # 5-P5/32
[ -1, 1, Shuffle_Block, [ 464, 1 ] ], # 6
]
# 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] ], # 10
[ -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] ], # 14 (P3/8-small)
[-1, 1, DWConvblock, [96, 3, 2]],
[ [ -1, 11 ], 1, ADD, [ 1 ] ], # cat head P4
[ -1, 1, DWConvblock, [96, 3, 1] ], # 17 (P4/16-medium)
[ -1, 1, DWConvblock, [ 96, 3, 2 ] ],
[ [ -1, 7 ], 1, ADD, [ 1 ] ], # cat head P5
[ -1, 1, DWConvblock, [96, 3, 1] ], # 20 (P5/32-large)
[ [ 14, 17, 20 ], 1, IKeypoint, [nc, anchors, nkpt]], # Detect(P3, P4, P5)
]

47
cfg/yolov7-lite-s.yaml Normal file
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@@ -0,0 +1,47 @@
# parameters
nc: 2 # number of classes
nkpt: 4 # number of keypoints
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
dw_conv_kpt: False
anchors:
- [4,5, 6,8, 10,12] # P3/8
- [15,19, 23,30, 39,52] # P4/16
- [72,97, 123,164, 209,297] # P5/32
# 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)
]

47
cfg/yolov7-lite-t.yaml Normal file
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@@ -0,0 +1,47 @@
# parameters
nc: 2 # number of classes
nkpt: 4 # number of keypoints
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
dw_conv_kpt: False
anchors:
- [4,5, 6,8, 10,12] # P3/8
- [15,19, 23,30, 39,52] # P4/16
- [72,97, 123,164, 209,297] # P5/32
# custom backbone
backbone:
# [from, number, module, args]
[ [ -1, 1, StemBlock, [16, 3, 2] ], # 0-P2/4
[ -1, 1, Shuffle_Block, [48, 2]], # 1-P3/8
[ -1, 2, Shuffle_Block, [48, 1]], # 2
[ -1, 1, Shuffle_Block, [96, 2]], # 3-P4/16
[ -1, 5, Shuffle_Block, [96, 1]], # 4
[ -1, 1, Shuffle_Block, [192, 2]], # 5-P5/32
[ -1, 2, Shuffle_Block, [192, 1]], # 6
[ -1, 1, SPPF, [192, 5]],
]
# v5lite-e head
head:
[ [ -1, 1, Conv, [48, 1, 1]],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest']],
[[ -1, 4], 1, Concat, [1]], # cat backbone P4
[ -1, 1, DWConvblock, [48, 3, 1]], # 11
[ -1, 1, Conv, [48, 1, 1]],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest']],
[[ -1, 2], 1, Concat, [1]], # cat backbone P3
[ -1, 1, DWConvblock, [48, 3, 1] ], # 15 (P3/8-small)
[-1, 1, DWConvblock, [48, 3, 2]],
[[ -1, 12], 1, ADD, [1]], # cat head P4
[ -1, 1, DWConvblock, [48, 3, 1]], # 18 (P4/16-medium)
[ -1, 1, DWConvblock, [48, 3, 2]],
[[ -1, 8], 1, ADD, [1]], # cat head P5
[ -1, 1, DWConvblock, [48, 3, 1]], # 21 (P5/32-large)
[[ 15, 18, 21], 1, IKeypoint, [nc, anchors, nkpt]], # Detect(P3, P4, P5)
]

10
cfg/yolov7-tiny-face.yaml
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@@ -1,6 +1,6 @@
# parameters
nc: 1 # number of classes
nkpt: 5 # number of keypoints
nc: 2 # number of classes
nkpt: 4 # number of keypoints
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
dw_conv_kpt: True
@@ -53,9 +53,9 @@ backbone:
head:
[[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-2, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[-1, 1, SP, [5]],
[-2, 1, SP, [9]],
[-3, 1, SP, [13]],
[-1, 1, SPF, [5]],
[-2, 1, SPF, [9]],
[-3, 1, SPF, [13]],
[[-1, -2, -3, -4], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1, None, 1, nn.LeakyReLU(0.1)]],
[[-1, -7], 1, Concat, [1]],

139
cfg/yolov7s-face.yaml Normal file
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@@ -0,0 +1,139 @@
# parameters
nc: 2 # number of classes
nkpt: 4 # number of keypoints
depth_multiple: 1.0 # model depth multiple
width_multiple: 0.4 # layer channel multiple
dw_conv_kpt: False
anchors:
- [4,5, 6,8, 10,12] # P3/8
- [15,19, 23,30, 39,52] # P4/16
- [72,97, 123,164, 209,297] # P5/32
# yolov7 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 3, 2]], # 0-P1/2
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [128, 3, 2]], # 2-P2/4
[-1, 1, Conv, [64, 1, 1]],
[-2, 1, Conv, [64, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 10
[-1, 1, MP, []],
[-1, 1, Conv, [128, 1, 1]],
[-3, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 15-P3/8
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]], # 23
[-1, 1, MP, []],
[-1, 1, Conv, [256, 1, 1]],
[-3, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 28-P4/16
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [1024, 1, 1]], # 36
[-1, 1, MP, []],
[-1, 1, Conv, [512, 1, 1]],
[-3, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [512, 3, 2]],
[[-1, -3], 1, Concat, [1]], # 41-P5/32
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -3, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [1024, 1, 1]], # 49
]
# yolov7 head
head:
[[-1, 1, SPPF, [512, 3]], # 50
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[36, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 62
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[23, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]],
[-2, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Conv, [64, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [128, 1, 1]], # 74
[-1, 1, MP, []],
[-1, 1, Conv, [128, 1, 1]],
[-3, 1, Conv, [128, 1, 1]],
[-1, 1, Conv, [128, 3, 2]],
[[-1, -3, 62], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]],
[-2, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [256, 1, 1]], # 87
[-1, 1, MP, []],
[-1, 1, Conv, [256, 1, 1]],
[-3, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [256, 3, 2]],
[[-1, -3, 50], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]],
[-2, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[[-1, -2, -3, -4, -5, -6], 1, Concat, [1]],
[-1, 1, Conv, [512, 1, 1]], # 100
[74, 1, Conv, [256, 1, 1]],
[87, 1, Conv, [512, 1, 1]],
[100, 1, Conv, [1024,1, 1]],
[[101, 102, 103], 1, IKeypoint, [nc, anchors, nkpt]], # Detect(P3, P4, P5)
]

2
data/plate.yaml
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@@ -9,7 +9,7 @@
# 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
val: /mnt/Gpan/Mydata/pytorchPorject/datasets/ccpd/val_detect/
#val: /ssd_1t/derron/yolov5-face/data/widerface/train/ # 4952 images
# number of classes

4
detect.py
View File

@@ -166,7 +166,7 @@ def detect(opt):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--weights', nargs='+', type=str, default='yolov7-lite-s.pt', help='model.pt path(s)')
parser.add_argument('--source', type=str, default='data/images', 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)')
@@ -190,7 +190,7 @@ if __name__ == '__main__':
parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
parser.add_argument('--kpt-label', type=int, default=4, help='number of keypoints')
parser.add_argument('--kpt-label', type=int, default=5, help='number of keypoints')
opt = parser.parse_args()
print(opt)
check_requirements(exclude=('tensorboard', 'pycocotools', 'thop'))

18
detect_rec_plate.py
View File

@@ -66,10 +66,16 @@ def get_plate_rec_landmark(img, xyxy, conf, landmarks, class_num,device,plate_re
class_label= int(class_num) #车牌的的类型0代表单牌，1代表双层车牌
roi_img = four_point_transform(img,landmarks_np) #透视变换得到车牌小图
if class_label: #判断是否是双层车牌，是双牌的话进行分割后然后拼接
# cv2.imwrite("roi.jpg",roi_img)
# roi_img_h = roi_img.shape[0]
# roi_img_w = roi_img.shape[1]
# if roi_img_w/roi_img_h<3:
# class_label=
# h_w_r = roi_img_w/roi_img_h
if class_label : #判断是否是双层车牌，是双牌的话进行分割后然后拼接
roi_img=get_split_merge(roi_img)
plate_number = get_plate_result(roi_img,device,plate_rec_model) #对车牌小图进行识别
# cv2.imwrite("roi.jpg",roi_img)
result_dict['rect']=rect
result_dict['landmarks']=landmarks_np.tolist()
result_dict['plate_no']=plate_number
@@ -92,7 +98,7 @@ def detect_Recognition_plate(model, orgimg, device,plate_rec_model,img_size):
if img.ndimension() == 3:
img = img.unsqueeze(0)
pred = model(img)[0]
pred = non_max_suppression(pred, conf_thres=conf_thres, iou_thres=iou_thres, kpt_label=4)
pred = non_max_suppression(pred, conf_thres=conf_thres, iou_thres=iou_thres, kpt_label=4,agnostic=True)
for i, det in enumerate(pred):
if len(det):
# Rescale boxes from img_size to im0 size
@@ -124,10 +130,10 @@ def draw_result(orgimg,dict_list):
height_area = result['roi_height']
landmarks=result['landmarks']
result = result['plate_no']+" "+"{:.2f}".format(result['score'])
result = result['plate_no']
result_str+=result+" "
cv2.rectangle(orgimg,(rect_area[0],rect_area[1]),(rect_area[2],rect_area[3]),(0,0,255),2) #画框
if len(result)>=7:
if len(result)>6:
for i in range(4): #关键点
cv2.circle(orgimg, (int(landmarks[i][0]), int(landmarks[i][1])), 5, clors[i], -1)
@@ -138,7 +144,7 @@ def draw_result(orgimg,dict_list):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--detect_model', nargs='+', type=str, default='runs/train/yolov75/weights/best.pt', help='model.pt path(s)')
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('--source', type=str, default='wan', help='source') # file/folder, 0 for webcam
# parser.add_argument('--img-size', nargs= '+', type=int, default=640, help='inference size (pixels)')

11
get_small_pic.py
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@@ -1,4 +1,4 @@
from detect_rec_plate import detect_Recognition_plate,attempt_load,init_model,allFilePath,cv_imread,draw_result,four_point_transform,order_points
from detect_rec_plate_macao import detect_Recognition_plate,attempt_load,init_model,allFilePath,cv_imread,draw_result,four_point_transform,order_points
import os
import argparse
import torch
@@ -23,13 +23,13 @@ def is_car_number(pattern, string):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--detect_model', nargs='+', type=str, default=r'runs/train/yolov75/weights/best.pt', help='model.pt path(s)')
parser.add_argument('--detect_model', nargs='+', type=str, default=r'runs/train/yolov711/weights/best.pt', help='model.pt path(s)')
parser.add_argument('--rec_model', type=str, default=r'weights/plate_rec.pth', help='model.pt path(s)')
parser.add_argument('--source', type=str, default=r'/mnt/Gpan/BaiduNetdiskDownload/VehicleColour/VehicleColour/', help='source') # file/folder, 0 for webcam
parser.add_argument('--source', type=str, default=r'/mnt/Gpan/Mydata/pytorchPorject/datasets/macao_plate/download/', help='source') # file/folder, 0 for webcam
# parser.add_argument('--source', type=str, default=r'test2', help='source')
# 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=r'/mnt/Gpan/BaiduNetdiskDownload/VehicleColour/result1', help='source')
parser.add_argument('--output', type=str, default=r'/mnt/Gpan/Mydata/pytorchPorject/datasets/macao_plate/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")
@@ -74,7 +74,8 @@ if __name__ == '__main__':
landmarks_np = np.array(landmarks).reshape(-1,2)
img_roi = four_point_transform(img,landmarks_np)
plate_no= result_['plate_no']
if len(plate_no)<6 or not is_car_number(pattern_str,plate_no):
# if len(plate_no)<6 or not is_car_number(pattern_str,plate_no):
if len(plate_no)<6:
continue
height = result_['roi_height']
# if height<48:

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71
models/common.py
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@@ -42,6 +42,15 @@ class SP(nn.Module):
def forward(self, x):
return self.m(x)
class SPF(nn.Module):
def __init__(self, k=3, s=1):
super(SPF, self).__init__()
self.n = (k - 1) // 2
self.m = nn.Sequential(*[nn.MaxPool2d(kernel_size=3, stride=s, padding=1) for _ in range(self.n)])
def forward(self, x):
return self.m(x)
class ImplicitA(nn.Module):
def __init__(self, channel):
@@ -302,6 +311,27 @@ class SPPCSPC(nn.Module):
y2 = self.cv2(x)
return self.cv7(torch.cat((y1, y2), dim=1))
class SPPFCSPC(nn.Module):
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=5):
super(SPPFCSPC, self).__init__()
c_ = int(2 * c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c1, c_, 1, 1)
self.cv3 = Conv(c_, c_, 3, 1)
self.cv4 = Conv(c_, c_, 1, 1)
self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
self.cv5 = Conv(4 * c_, c_, 1, 1)
self.cv6 = Conv(c_, c_, 3, 1)
self.cv7 = Conv(2 * c_, c2, 1, 1)
def forward(self, x):
x1 = self.cv4(self.cv3(self.cv1(x)))
x2 = self.m(x1)
x3 = self.m(x2)
y1 = self.cv6(self.cv5(torch.cat((x1,x2,x3, self.m(x3)),1)))
y2 = self.cv2(x)
return self.cv7(torch.cat((y1, y2), dim=1))
class SPPF(nn.Module):
# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
@@ -389,13 +419,30 @@ class Concat(nn.Module):
return torch.cat(x, self.d)
# yolov7-lite
class StemBlock(nn.Module):
def __init__(self, c1, c2, k=3, s=2, p=None, g=1, act=True):
super(StemBlock, self).__init__()
self.stem_1 = Conv(c1, c2, k, s, p, g, act)
self.stem_2a = Conv(c2, c2 // 2, 1, 1, 0)
self.stem_2b = Conv(c2 // 2, c2, 3, 2, 1)
self.stem_2p = nn.MaxPool2d(kernel_size=2,stride=2,ceil_mode=True)
self.stem_3 = Conv(c2 * 2, c2, 1, 1, 0)
def forward(self, x):
stem_1_out = self.stem_1(x)
stem_2a_out = self.stem_2a(stem_1_out)
stem_2b_out = self.stem_2b(stem_2a_out)
stem_2p_out = self.stem_2p(stem_1_out)
out = self.stem_3(torch.cat((stem_2b_out,stem_2p_out),1))
return out
class conv_bn_relu_maxpool(nn.Module):
def __init__(self, c1, c2): # ch_in, ch_out
super(conv_bn_relu_maxpool, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(c1, c2, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(c2),
nn.ReLU(inplace=True),
nn.SiLU(inplace=True),
)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
@@ -404,23 +451,23 @@ class conv_bn_relu_maxpool(nn.Module):
class DWConvblock(nn.Module):
"Depthwise conv + Pointwise conv"
def __init__(self, in_channels, out_channels, k, s):
super(DWConvblock, self).__init__()
self.p = k // 2
self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=k, stride=s, padding=self.p, groups=in_channels,
bias=False)
self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=k, stride=s, padding=self.p, groups=in_channels, bias=False)
self.bn1 = nn.BatchNorm2d(in_channels)
self.act1 = nn.SiLU(inplace=True)
self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
self.act2 = nn.SiLU(inplace=True)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = F.relu(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
x = F.relu(x)
x = self.act2(x)
return x
class ADD(nn.Module):
@@ -438,14 +485,10 @@ def channel_shuffle(x, groups):
channels_per_group = num_channels // groups
# reshape
x = x.view(batchsize, groups,
channels_per_group, height, width)
x = x.view(batchsize, groups, channels_per_group, height, width)
x = torch.transpose(x, 1, 2).contiguous()
# flatten
x = x.view(batchsize, -1, height, width)
return x
class Shuffle_Block(nn.Module):
@@ -465,19 +508,19 @@ class Shuffle_Block(nn.Module):
nn.BatchNorm2d(inp),
nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
nn.SiLU(inplace=True),
)
self.branch2 = nn.Sequential(
nn.Conv2d(inp if (self.stride > 1) else branch_features,
branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
nn.SiLU(inplace=True),
self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
nn.BatchNorm2d(branch_features),
nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
nn.SiLU(inplace=True),
)
@staticmethod

696
models/common_ori.py Normal file
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@@ -0,0 +1,696 @@
# This file contains modules common to various models
import math
from copy import copy
from pathlib import Path
import numpy as np
import pandas as pd
import requests
import torch
import torch.nn as nn
from PIL import Image
from torch.cuda import amp
import torch.nn.functional as F
from utils.datasets import letterbox
from utils.general import non_max_suppression, non_max_suppression_export, make_divisible, scale_coords, increment_path, xyxy2xywh, save_one_box
from utils.plots import colors, plot_one_box
from utils.torch_utils import time_synchronized
def autopad(k, p=None): # kernel, padding
# Pad to 'same'
if p is None:
p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad
return p
class MP(nn.Module):
def __init__(self, k=2):
super(MP, self).__init__()
self.m = nn.MaxPool2d(kernel_size=k, stride=k)
def forward(self, x):
return self.m(x)
class SP(nn.Module):
def __init__(self, k=3, s=1):
super(SP, self).__init__()
self.m = nn.MaxPool2d(kernel_size=k, stride=s, padding=k // 2)
def forward(self, x):
return self.m(x)
class ImplicitA(nn.Module):
def __init__(self, channel):
super(ImplicitA, self).__init__()
self.channel = channel
self.implicit = nn.Parameter(torch.zeros(1, channel, 1, 1))
nn.init.normal_(self.implicit, std=.02)
def forward(self, x):
return self.implicit.expand_as(x) + x
class ImplicitM(nn.Module):
def __init__(self, channel):
super(ImplicitM, self).__init__()
self.channel = channel
self.implicit = nn.Parameter(torch.ones(1, channel, 1, 1))
nn.init.normal_(self.implicit, mean=1., std=.02)
def forward(self, x):
return self.implicit.expand_as(x) * x
class ReOrg(nn.Module):
def __init__(self):
super(ReOrg, self).__init__()
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
return torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
def DWConv(c1, c2, k=1, s=1, act=True):
# Depthwise convolution
return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act)
class Conv(nn.Module):
# Standard convolution
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Conv, self).__init__()
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
self.bn = nn.BatchNorm2d(c2)
if act != "ReLU":
self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())
else:
self.act = nn.ReLU(inplace=True)
def forward(self, x):
return self.act(self.bn(self.conv(x)))
def fuseforward(self, x):
return self.act(self.conv(x))
class TransformerLayer(nn.Module):
# Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
def __init__(self, c, num_heads):
super().__init__()
self.q = nn.Linear(c, c, bias=False)
self.k = nn.Linear(c, c, bias=False)
self.v = nn.Linear(c, c, bias=False)
self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads)
self.fc1 = nn.Linear(c, c, bias=False)
self.fc2 = nn.Linear(c, c, bias=False)
def forward(self, x):
x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x
x = self.fc2(self.fc1(x)) + x
return x
class TransformerBlock(nn.Module):
# Vision Transformer https://arxiv.org/abs/2010.11929
def __init__(self, c1, c2, num_heads, num_layers):
super().__init__()
self.conv = None
if c1 != c2:
self.conv = Conv(c1, c2)
self.linear = nn.Linear(c2, c2) # learnable position embedding
self.tr = nn.Sequential(*[TransformerLayer(c2, num_heads) for _ in range(num_layers)])
self.c2 = c2
def forward(self, x):
if self.conv is not None:
x = self.conv(x)
b, _, w, h = x.shape
p = x.flatten(2)
p = p.unsqueeze(0)
p = p.transpose(0, 3)
p = p.squeeze(3)
e = self.linear(p)
x = p + e
x = self.tr(x)
x = x.unsqueeze(3)
x = x.transpose(0, 3)
x = x.reshape(b, self.c2, w, h)
return x
class Bottleneck(nn.Module):
# Standard bottleneck
def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, act=True): # ch_in, ch_out, shortcut, groups, expansion
super(Bottleneck, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1, act=act)
self.cv2 = Conv(c_, c2, 3, 1, g=g, act=act)
self.add = shortcut and c1 == c2
def forward(self, x):
return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x))
class BottleneckCSP(nn.Module):
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super(BottleneckCSP, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
self.cv4 = Conv(2 * c_, c2, 1, 1)
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
self.act = nn.SiLU()
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
def forward(self, x):
y1 = self.cv3(self.m(self.cv1(x)))
y2 = self.cv2(x)
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
class BottleneckCSPF(nn.Module):
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super(BottleneckCSPF, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
#self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False)
self.cv4 = Conv(2 * c_, c2, 1, 1)
self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3)
self.act = nn.SiLU()
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
def forward(self, x):
y1 = self.m(self.cv1(x))
y2 = self.cv2(x)
return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
class BottleneckCSP2(nn.Module):
# CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion
super(BottleneckCSP2, self).__init__()
c_ = int(c2) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = nn.Conv2d(c_, c_, 1, 1, bias=False)
self.cv3 = Conv(2 * c_, c2, 1, 1)
self.bn = nn.BatchNorm2d(2 * c_)
self.act = nn.SiLU()
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)])
def forward(self, x):
x1 = self.cv1(x)
y1 = self.m(x1)
y2 = self.cv2(x1)
return self.cv3(self.act(self.bn(torch.cat((y1, y2), dim=1))))
class C3(nn.Module):
# CSP Bottleneck with 3 convolutions
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, act=True): # ch_in, ch_out, number, shortcut, groups, expansion
super(C3, self).__init__()
c_ = int(c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1, act=act)
self.cv2 = Conv(c1, c_, 1, 1, act=act)
self.cv3 = Conv(2 * c_, c2, 1, act=act) # act=FReLU(c2)
self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0, act=act) for _ in range(n)])
# self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)])
def forward(self, x):
return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))
class C3TR(C3):
# C3 module with TransformerBlock()
def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
super().__init__(c1, c2, n, shortcut, g, e)
c_ = int(c2 * e)
self.m = TransformerBlock(c_, c_, 4, n)
class SPP(nn.Module):
# Spatial pyramid pooling layer used in YOLOv3-SPP
def __init__(self, c1, c2, k=(3, 3, 3)):
print(k)
super(SPP, self).__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1)
num_3x3_maxpool = []
max_pool_module_list = []
for pool_kernel in k:
assert (pool_kernel-3)%2==0; "Required Kernel size cannot be implemented with kernel_size of 3"
num_3x3_maxpool = 1 + (pool_kernel-3)//2
max_pool_module_list.append(nn.Sequential(*num_3x3_maxpool*[nn.MaxPool2d(kernel_size=3, stride=1, padding=1)]))
#max_pool_module_list[-1] = nn.ModuleList(max_pool_module_list[-1])
self.m = nn.ModuleList(max_pool_module_list)
#self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
def forward(self, x):
x = self.cv1(x)
return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1))
class SPPCSP(nn.Module):
# CSP SPP https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
super(SPPCSP, self).__init__()
c_ = int(2 * c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False)
self.cv3 = Conv(c_, c_, 3, 1)
self.cv4 = Conv(c_, c_, 1, 1)
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
self.cv5 = Conv(4 * c_, c_, 1, 1)
self.cv6 = Conv(c_, c_, 3, 1)
self.bn = nn.BatchNorm2d(2 * c_)
self.act = nn.SiLU()
self.cv7 = Conv(2 * c_, c2, 1, 1)
def forward(self, x):
x1 = self.cv4(self.cv3(self.cv1(x)))
y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
y2 = self.cv2(x)
return self.cv7(self.act(self.bn(torch.cat((y1, y2), dim=1))))
class SPPCSPC(nn.Module):
# CSP SPP https://github.com/WongKinYiu/CrossStagePartialNetworks
def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
super(SPPCSPC, self).__init__()
c_ = int(2 * c2 * e) # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c1, c_, 1, 1)
self.cv3 = Conv(c_, c_, 3, 1)
self.cv4 = Conv(c_, c_, 1, 1)
self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
self.cv5 = Conv(4 * c_, c_, 1, 1)
self.cv6 = Conv(c_, c_, 3, 1)
self.cv7 = Conv(2 * c_, c2, 1, 1)
def forward(self, x):
x1 = self.cv4(self.cv3(self.cv1(x)))
y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
y2 = self.cv2(x)
return self.cv7(torch.cat((y1, y2), dim=1))
class SPPF(nn.Module):
# Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
def __init__(self, c1, c2, k=5): # equivalent to SPP(k=(5, 9, 13))
super().__init__()
c_ = c1 // 2 # hidden channels
self.cv1 = Conv(c1, c_, 1, 1)
self.cv2 = Conv(c_ * 4, c2, 1, 1)
self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
def forward(self, x):
x = self.cv1(x)
y1 = self.m(x)
y2 = self.m(y1)
return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1))
class Focus(nn.Module):
# Focus wh information into c-space
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(Focus, self).__init__()
self.contract = Contract(gain=2)
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
if hasattr(self, "contract"):
x = self.contract(x)
elif hasattr(self, "conv_slice"):
x = self.conv_slice(x)
else:
x = torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
return self.conv(x)
class ConvFocus(nn.Module):
# Focus wh information into c-space
def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups
super(ConvFocus, self).__init__()
slice_kernel = 3
slice_stride = 2
self.conv_slice = Conv(c1, c1*4, slice_kernel, slice_stride, p, g, act)
self.conv = Conv(c1 * 4, c2, k, s, p, g, act)
def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2)
if hasattr(self, "conv_slice"):
x = self.conv_slice(x)
else:
x = torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)
x = self.conv(x)
return x
class Contract(nn.Module):
# Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
N, C, H, W = x.size() # assert (H / s == 0) and (W / s == 0), 'Indivisible gain'
s = self.gain
x = x.view(N, C, H // s, s, W // s, s) # x(1,64,40,2,40,2)
x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40)
return x.view(N, C * s * s, H // s, W // s) # x(1,256,40,40)
class Expand(nn.Module):
# Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
def __init__(self, gain=2):
super().__init__()
self.gain = gain
def forward(self, x):
N, C, H, W = x.size() # assert C / s ** 2 == 0, 'Indivisible gain'
s = self.gain
x = x.view(N, s, s, C // s ** 2, H, W) # x(1,2,2,16,80,80)
x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2)
return x.view(N, C // s ** 2, H * s, W * s) # x(1,16,160,160)
class Concat(nn.Module):
# Concatenate a list of tensors along dimension
def __init__(self, dimension=1):
super(Concat, self).__init__()
self.d = dimension
def forward(self, x):
return torch.cat(x, self.d)
# yolov7-lite
class conv_bn_relu_maxpool(nn.Module):
def __init__(self, c1, c2): # ch_in, ch_out
super(conv_bn_relu_maxpool, self).__init__()
self.conv = nn.Sequential(
nn.Conv2d(c1, c2, kernel_size=3, stride=2, padding=1, bias=False),
nn.BatchNorm2d(c2),
nn.ReLU(inplace=True),
)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)
def forward(self, x):
return self.maxpool(self.conv(x))
class DWConvblock(nn.Module):
"Depthwise conv + Pointwise conv"
def __init__(self, in_channels, out_channels, k, s):
super(DWConvblock, self).__init__()
self.p = k // 2
self.conv1 = nn.Conv2d(in_channels, in_channels, kernel_size=k, stride=s, padding=self.p, groups=in_channels,
bias=False)
self.bn1 = nn.BatchNorm2d(in_channels)
self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
self.bn2 = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = F.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = F.relu(x)
return x
class ADD(nn.Module):
# Stortcut a list of tensors along dimension
def __init__(self, alpha=0.5):
super(ADD, self).__init__()
self.a = alpha
def forward(self, x):
x1, x2 = x[0], x[1]
return torch.add(x1, x2, alpha=self.a)
def channel_shuffle(x, groups):
batchsize, num_channels, height, width = x.data.size()
channels_per_group = num_channels // groups
# reshape
x = x.view(batchsize, groups,
channels_per_group, height, width)
x = torch.transpose(x, 1, 2).contiguous()
# flatten
x = x.view(batchsize, -1, height, width)
return x
class Shuffle_Block(nn.Module):
def __init__(self, inp, oup, stride):
super(Shuffle_Block, self).__init__()
if not (1 <= stride <= 3):
raise ValueError('illegal stride value')
self.stride = stride
branch_features = oup // 2
assert (self.stride != 1) or (inp == branch_features << 1)
if self.stride > 1:
self.branch1 = nn.Sequential(
self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
nn.BatchNorm2d(inp),
nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
)
self.branch2 = nn.Sequential(
nn.Conv2d(inp if (self.stride > 1) else branch_features,
branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
nn.BatchNorm2d(branch_features),
nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
)
@staticmethod
def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
def forward(self, x):
if self.stride == 1:
x1, x2 = x.chunk(2, dim=1)
out = torch.cat((x1, self.branch2(x2)), dim=1)
else:
out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
out = channel_shuffle(out, 2)
return out
# end of yolov7-lite
class NMS(nn.Module):
# Non-Maximum Suppression (NMS) module
iou = 0.45 # IoU threshold
classes = None # (optional list) filter by class
def __init__(self, conf=0.25, kpt_label=False):
super(NMS, self).__init__()
self.conf=conf
self.kpt_label = kpt_label
def forward(self, x):
return non_max_suppression(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes, kpt_label=self.kpt_label)
class NMS_Export(nn.Module):
# Non-Maximum Suppression (NMS) module used while exporting ONNX model
iou = 0.45 # IoU threshold
classes = None # (optional list) filter by class
def __init__(self, conf=0.001, kpt_label=False):
super(NMS_Export, self).__init__()
self.conf = conf
self.kpt_label = kpt_label
def forward(self, x):
return non_max_suppression_export(x[0], conf_thres=self.conf, iou_thres=self.iou, classes=self.classes, kpt_label=self.kpt_label)
class autoShape(nn.Module):
# input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
conf = 0.25 # NMS confidence threshold
iou = 0.45 # NMS IoU threshold
classes = None # (optional list) filter by class
def __init__(self, model):
super(autoShape, self).__init__()
self.model = model.eval()
def autoshape(self):
print('autoShape already enabled, skipping... ') # model already converted to model.autoshape()
return self
@torch.no_grad()
def forward(self, imgs, size=640, augment=False, profile=False):
# Inference from various sources. For height=640, width=1280, RGB images example inputs are:
# filename: imgs = 'data/images/zidane.jpg'
# URI: = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/zidane.jpg'
# OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3)
# PIL: = Image.open('image.jpg') # HWC x(640,1280,3)
# numpy: = np.zeros((640,1280,3)) # HWC
# torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values)
# multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images
t = [time_synchronized()]
p = next(self.model.parameters()) # for device and type
if isinstance(imgs, torch.Tensor): # torch
with amp.autocast(enabled=p.device.type != 'cpu'):
return self.model(imgs.to(p.device).type_as(p), augment, profile) # inference
# Pre-process
n, imgs = (len(imgs), imgs) if isinstance(imgs, list) else (1, [imgs]) # number of images, list of images
shape0, shape1, files = [], [], [] # image and inference shapes, filenames
for i, im in enumerate(imgs):
f = f'image{i}' # filename
if isinstance(im, str): # filename or uri
im, f = np.asarray(Image.open(requests.get(im, stream=True).raw if im.startswith('http') else im)), im
elif isinstance(im, Image.Image): # PIL Image
im, f = np.asarray(im), getattr(im, 'filename', f) or f
files.append(Path(f).with_suffix('.jpg').name)
if im.shape[0] < 5: # image in CHW
im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1)
im = im[:, :, :3] if im.ndim == 3 else np.tile(im[:, :, None], 3) # enforce 3ch input
s = im.shape[:2] # HWC
shape0.append(s) # image shape
g = (size / max(s)) # gain
shape1.append([y * g for y in s])
imgs[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update
shape1 = [make_divisible(x, int(self.stride.max())) for x in np.stack(shape1, 0).max(0)] # inference shape
x = [letterbox(im, new_shape=shape1, auto=False)[0] for im in imgs] # pad
x = np.stack(x, 0) if n > 1 else x[0][None] # stack
x = np.ascontiguousarray(x.transpose((0, 3, 1, 2))) # BHWC to BCHW
x = torch.from_numpy(x).to(p.device).type_as(p) / 255. # uint8 to fp16/32
t.append(time_synchronized())
with amp.autocast(enabled=p.device.type != 'cpu'):
# Inference
y = self.model(x, augment, profile)[0] # forward
t.append(time_synchronized())
# Post-process
y = non_max_suppression(y, conf_thres=self.conf, iou_thres=self.iou, classes=self.classes) # NMS
for i in range(n):
scale_coords(shape1, y[i][:, :4], shape0[i])
t.append(time_synchronized())
return Detections(imgs, y, files, t, self.names, x.shape)
class Detections:
# detections class for YOLOv5 inference results
def __init__(self, imgs, pred, files, times=None, names=None, shape=None):
super(Detections, self).__init__()
d = pred[0].device # device
gn = [torch.tensor([*[im.shape[i] for i in [1, 0, 1, 0]], 1., 1.], device=d) for im in imgs] # normalizations
self.imgs = imgs # list of images as numpy arrays
self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls)
self.names = names # class names
self.files = files # image filenames
self.xyxy = pred # xyxy pixels
self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels
self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized
self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized
self.n = len(self.pred) # number of images (batch size)
self.t = tuple((times[i + 1] - times[i]) * 1000 / self.n for i in range(3)) # timestamps (ms)
self.s = shape # inference BCHW shape
def display(self, pprint=False, show=False, save=False, crop=False, render=False, save_dir=Path('')):
for i, (im, pred) in enumerate(zip(self.imgs, self.pred)):
str = f'image {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} '
if pred is not None:
for c in pred[:, -1].unique():
n = (pred[:, -1] == c).sum() # detections per class
str += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string
if show or save or render or crop:
for *box, conf, cls in pred: # xyxy, confidence, class
label = f'{self.names[int(cls)]} {conf:.2f}'
if crop:
save_one_box(box, im, file=save_dir / 'crops' / self.names[int(cls)] / self.files[i])
else: # all others
plot_one_box(box, im, label=label, color=colors(cls))
im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np
if pprint:
print(str.rstrip(', '))
if show:
im.show(self.files[i]) # show
if save:
f = self.files[i]
im.save(save_dir / f) # save
print(f"{'Saved' * (i == 0)} {f}", end=',' if i < self.n - 1 else f' to {save_dir}\n')
if render:
self.imgs[i] = np.asarray(im)
def print(self):
self.display(pprint=True) # print results
print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {tuple(self.s)}' % self.t)
def show(self):
self.display(show=True) # show results
def save(self, save_dir='runs/hub/exp'):
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True) # increment save_dir
self.display(save=True, save_dir=save_dir) # save results
def crop(self, save_dir='runs/hub/exp'):
save_dir = increment_path(save_dir, exist_ok=save_dir != 'runs/hub/exp', mkdir=True) # increment save_dir
self.display(crop=True, save_dir=save_dir) # crop results
print(f'Saved results to {save_dir}\n')
def render(self):
self.display(render=True) # render results
return self.imgs
def pandas(self):
# return detections as pandas DataFrames, i.e. print(results.pandas().xyxy[0])
new = copy(self) # return copy
ca = 'xmin', 'ymin', 'xmax', 'ymax', 'confidence', 'class', 'name' # xyxy columns
cb = 'xcenter', 'ycenter', 'width', 'height', 'confidence', 'class', 'name' # xywh columns
for k, c in zip(['xyxy', 'xyxyn', 'xywh', 'xywhn'], [ca, ca, cb, cb]):
a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update
setattr(new, k, [pd.DataFrame(x, columns=c) for x in a])
return new
def tolist(self):
# return a list of Detections objects, i.e. 'for result in results.tolist():'
x = [Detections([self.imgs[i]], [self.pred[i]], self.names, self.s) for i in range(self.n)]
for d in x:
for k in ['imgs', 'pred', 'xyxy', 'xyxyn', 'xywh', 'xywhn']:
setattr(d, k, getattr(d, k)[0]) # pop out of list
return x
def __len__(self):
return self.n
class Classify(nn.Module):
# Classification head, i.e. x(b,c1,20,20) to x(b,c2)
def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups
super(Classify, self).__init__()
self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1)
self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g) # to x(b,c2,1,1)
self.flat = nn.Flatten()
def forward(self, x):
z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list
return self.flat(self.conv(z)) # flatten to x(b,c2)

2
models/yolo.py
View File

@@ -531,7 +531,7 @@ def parse_model(d, ch): # model_dict, input_channels(3)
n = max(round(n * gd), 1) if n > 1 else n # depth gain
if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, ConvFocus, CrossConv, BottleneckCSP,
C3, C3TR, BottleneckCSPF, BottleneckCSP2, SPPCSP, SPPCSPC, SPPF, conv_bn_relu_maxpool, Shuffle_Block, DWConvblock]:
C3, C3TR, BottleneckCSPF, BottleneckCSP2, SPPCSP, SPPCSPC, SPPF, conv_bn_relu_maxpool, Shuffle_Block, DWConvblock,StemBlock]:
c1, c2 = ch[f], args[0]
if c2 != no: # if not output
c2 = make_divisible(c2 * gw, 8)

79
ncnn/ncnn_export.py Normal file
View File

@@ -0,0 +1,79 @@
"""Exports a YOLOv5 *.pt model to ONNX and TorchScript formats
Usage:
$ export PYTHONPATH="$PWD" && python models/export.py --weights yolov5s.pt --img 640 --batch 1
"""
import argparse
import sys
import time
from pathlib import Path
sys.path.append(Path(__file__).parent.parent.absolute().__str__()) # to run '$ python *.py' files in subdirectories
import onnx
import torch
import torch.nn as nn
from torch.utils.mobile_optimizer import optimize_for_mobile
import cv2
import numpy as np
import models
from models.experimental import attempt_load
from utils.activations import Hardswish, SiLU
from utils.general import colorstr, check_img_size, check_requirements, file_size, set_logging
from utils.torch_utils import select_device
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='runs/train/yolov714/weights/best.pt', help='weights path')
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')
opt = parser.parse_args()
opt.img_size *= 2 if len(opt.img_size) == 1 else 1 # expand
print(opt)
set_logging()
t = time.time()
# Load PyTorch model
device = select_device(opt.device)
model = attempt_load(opt.weights, map_location=device) # load FP32 model
labels = model.names
# Checks
gs = int(max(model.stride)) # grid size (max stride)
opt.img_size = [check_img_size(x, gs) for x in opt.img_size] # verify img_size are gs-multiples
# Input
img = torch.zeros(opt.batch_size, 3, *opt.img_size).to(device) # image size(1,3,320,192) iDetection
# Update model
for k, m in model.named_modules():
m._non_persistent_buffers_set = set() # pytorch 1.6.0 compatibility
if isinstance(m, models.common.Conv): # assign export-friendly activations
if isinstance(m.act, nn.Hardswish):
m.act = Hardswish()
elif isinstance(m.act, nn.SiLU):
m.act = SiLU()
# elif isinstance(m, models.yolo.Detect):
# m.forward = m.forward_export # assign forward (optional)
model.model[-1].export = True # set Detect() layer grid export
for _ in range(2):
y = model(img) # dry runs
output_names = None
print(f'starting export with onnx {onnx.__version__}...')
f = opt.weights.replace('.pt', '.onnx') # filename
torch.onnx.export(model, img, f, verbose=False, opset_version=11, input_names=['data'],
output_names=['stride_' + str(int(x)) for x in model.stride])
# Checks
model_onnx = onnx.load(f) # load onnx model
onnx.checker.check_model(model_onnx) # check onnx model
# print(onnx.helper.printable_graph(model_onnx.graph)) # print
# Finish
print(f'\nExport complete ({time.time() - t:.2f}s). Visualize with https://github.com/lutzroeder/netron.')

2
onnx/yolov7_plate_onnx_infer.py
View File

@@ -214,7 +214,7 @@ def draw_result(orgimg,dict_list):
for i in range(4): #关键点
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 len(result)<0:
orgimg=cv2ImgAddText(orgimg,result,rect_area[0]-height_area,rect_area[1]-height_area-10,(255,0,0),height_area)
print(result_str)
return orgimg

2
plate_recognition/plateNet.py
View File

@@ -14,7 +14,7 @@ class myNet_ocr(nn.Module):
# self.loc = nn.MaxPool2d((2, 2), (5, 1), (0, 1),ceil_mode=True)
# self.loc = nn.AvgPool2d((2, 2), (5, 2), (0, 1),ceil_mode=False)
self.loc = nn.MaxPool2d((5, 2), (1, 1),(0,1),ceil_mode=False)
self.newCnn=nn.Conv2d(256,num_classes,1,1)
self.newCnn=nn.Conv2d(cfg[-1],num_classes,1,1)
# self.newBn=nn.BatchNorm2d(num_classes)
def make_layers(self, cfg, batch_norm=False):
layers = []

4
plate_recognition/plate_rec.py
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@@ -20,7 +20,7 @@ def allFilePath(rootPath,allFIleList):
else:
allFilePath(os.path.join(rootPath,temp),allFIleList)
device = torch.device('cuda') if torch.cuda.is_available() else torch.device("cpu")
plateName=r"#京沪津渝冀晋蒙辽吉黑苏浙皖闽赣鲁豫鄂湘粤桂琼川贵云藏陕甘青宁新学警港澳挂使领民航深0123456789ABCDEFGHJKLMNPQRSTUVWXYZ"
plateName=r"#京沪津渝冀晋蒙辽吉黑苏浙皖闽赣鲁豫鄂湘粤桂琼川贵云藏陕甘青宁新学警港澳挂使领民航危0123456789ABCDEFGHJKLMNPQRSTUVWXYZ险品"
mean_value,std_value=(0.588,0.193)
def decodePlate(preds):
pre=0
@@ -64,7 +64,7 @@ def init_model(device,model_path):
check_point = torch.load(model_path,map_location=device)
model_state=check_point['state_dict']
cfg=check_point['cfg']
model_path = os.sep.join([sys.path[0],model_path])
# model_path = os.sep.join([sys.path[0],model_path])
model = myNet_ocr(num_classes=78,export=True,cfg=cfg)
model.load_state_dict(model_state)

6
train.py
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@@ -480,11 +480,11 @@ def train(hyp, opt, device, tb_writer=None):
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--weights', type=str, default='weights/yolov7-lite-e.pt', help='initial weights path')
parser.add_argument('--cfg', type=str, default='cfg/yolov7-lite-e-plate.yaml', help='model.yaml path')
parser.add_argument('--weights', type=str, default='yolov7-tiny.pt', help='initial weights path')
parser.add_argument('--cfg', type=str, default='cfg/yolov7-tiny-face.yaml', help='model.yaml path')
parser.add_argument('--data', type=str, default='data/plate.yaml', help='data.yaml path')
parser.add_argument('--hyp', type=str, default='data/hyp.face.yaml', help='hyperparameters path')
parser.add_argument('--epochs', type=int, default=120)
parser.add_argument('--epochs', type=int, default=200)
parser.add_argument('--batch-size', type=int, default=32, help='total batch size for all GPUs')
parser.add_argument('--img-size', nargs='+', type=int, default=[640, 640], help='[train, test] image sizes')
parser.add_argument('--rect', action='store_true', help='rectangular training')

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