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我们开源数据集啦，感谢所有参与标注的同学
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‎C++_inference_openvino_kpt/yolov7_kpt.h‎

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@@ -25,21 +25,21 @@ using namespace InferenceEngine;
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#define IMG_SIZE 416 //推理图像大小，如果不是640 和 416 需要自己在下面添加anchor
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#define ANCHOR 3 //anchor 数量
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#define DETECT_MODE 1 //ARMOR 0 WIN 1 BOARD 2
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#define DEVICE "CPU" // 设备选择
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#define DEVICE "GPU" // 设备选择
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#define VIDEO //是否展示推理视频
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#if DETECT_MODE == 0 // 装甲板四点模型
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#define KPT_NUM 4
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#define CLS_NUM 14
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#define MODEL_PATH "/home/knight/C++_inference_openvino_old/demo_weight/yolov8tiny.onnx"
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#define MODEL_PATH ""
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#elif DETECT_MODE == 1 // 能量机关五点模型
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#define KPT_NUM 5
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#define CLS_NUM 4
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#define MODEL_PATH "/home/knight/C++_inference_openvino_old/demo_weight/yolov8tiny-win.onnx"
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#define MODEL_PATH ""
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#elif DETECT_MODE == 2 // 视觉识别版检测模型
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#define KPT_NUM 0
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#define CLS_NUM 4
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#define MODEL_PATH "/home/knight/Sharefolder_Knight/best_board_416.onnx"
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#define MODEL_PATH ""
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#endif
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class yolo_kpt {
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public:

‎README.md‎

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##### 装甲板四点模型
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+ 西交利物浦大学RM2023赛季场地数据集数据集录制 2500张。
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+ RM视觉开源站数据集 3000张。
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+ 共计 5500张。
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+ 西交利物浦大学RM2023赛季场地数据集数据集录制约 2500张。
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+ RM视觉开源站数据集约 3000张。
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+ 视频内录约 15000张
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+ 共计约 20500张。
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__数据集开源地址: __
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+ **```注意```**
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1.```YOLO格式数据集```中有部分数据集使用的也是四点格式，不影响使用。(YOLO格式按照识别版模型识别就行,只关注label，xywh)<br>
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2.```四点格式数据集```中均为四点格式，数量较少。（人手真的不够，标不了）
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##### 能量机关五点模型
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+ 西交利物浦大学RM2023赛季场地数据集数据集录制 1000张。
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+ 共计 1000张。
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+ 西交利物浦大学RM2023赛季场地数据集数据集录制约 1000张。
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+ 东北大学能量机关西交利物浦步兵相机内录数据集约 1100张。
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+ 手机模拟西交利物浦大学步兵相机内录场地风车数据集约 100张。
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+ 其他学校提供的内录视频并按照西交利物浦标注格式标注的数据集约 200张。
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+ 官方样例能量数据集约 500张。
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+ 共计约 3000张。
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<br><br>
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__数据集开源地址: 链接: https://pan.baidu.com/s/1ayRI1MMw40ae4kuFZCXK_Q?pwd=XPGM 提取码: XPGM__
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##### 视觉识别板检测模型
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+ 西交利物浦大学RM2023赛季场地数据集数据集录制 2000张。
@@ -203,6 +213,7 @@
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+ 该项目针对RMUC2023赛季，如果你有好的建议，欢迎给我留言哦。
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+ 如果你觉得项目对你有帮助，请给个star吧~
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+ 如果你有更好的建议，欢迎提出PR，或者直接联系我哦，大家一起学习！
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+ __感谢西浦GMaster战队在比赛期间标注数据的嵌入式组和机械组的战友，你们都为视觉组做出了一份重要的贡献！__
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## 联系方式
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‎cfg/armor/shufflenet-0.5-armor.yaml‎

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@@ -1,9 +1,7 @@
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# XJTLU GMaster zR
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nc: 14 # number of classes
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nkpt: 4 # number of keypoints
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depth_multiple: 0.33 # model depth multiple
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width_multiple: 0.5 # layer channel multiple
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dw_conv_kpt: True
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# anchors
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anchors:
@@ -40,5 +38,5 @@ head:
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 7], 1, Concat, [1]], # cat head P5
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[-1, 3, C2f, [1024]], # 27 (P5/32-large)
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[[13, 16, 19], 1, IKeypoint, [nc, anchors, nkpt]], # Detect(P3, P4, P5)
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[[13, 16, 19], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
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]

‎data/armor/armor_detect.yaml‎

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train: /media/zr/Data/RoboMaster_data/Smart_Car_1/images/train/
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val: /media/zr/Data/RoboMaster_data/Smart_Car_1/images/val/
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test: /media/zr/Data/RoboMaster_data/Smart_Car_1/images/test/
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nc: 28
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names: ['Plant_Corn_1', 'Plant_Corn_2', 'Plant_Corn_3', 'Plant_Corn_4',
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'Plant_Cucumber_1', 'Plant_Cucumber_2', 'Plant_Cucumber_3', 'Plant_Cucumber_4',
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'Plant_Rice_1', 'Plant_Rice_2', 'Plant_Rice_3', 'Plant_Rice_4',
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'Plant_Wheat_1', 'Plant_Wheat_2', 'Plant_Wheat_3', 'Plant_Wheat_4',
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'Fruit_Corn_1', 'Fruit_Corn_2', 'Fruit_Corn_3', 'Fruit_Corn_4',
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'Fruit_Cucumber_1', 'Fruit_Cucumber_2', 'Fruit_Cucumber_3', 'Fruit_Cucumber_4',
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'Fruit_Watermelon_1', 'Fruit_Watermelon_2', 'Fruit_Watermelon_3', 'Fruit_Watermelon_4']
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train: /media/zr/Data/RoboMaster_data/dataset/XJTLU_detect/images/train/
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val: /media/zr/Data/RoboMaster_data/dataset/XJTLU_detect/images/val/
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test:
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nc: 14
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names: ['B1','B2','B3','B4','B5','BO','BS','R1','R2','R3','R4','R5','RO','RS']

‎data/armor/armor_kpt.yaml‎

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train: /media/zr/Data/RoboMaster_data/dataset/XJTLU_2022_keypoints/images/train/
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val: /media/zr/Data/RoboMaster_data/dataset/XJTLU_2022_keypoints/images/val/
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test: /media/zr/Data/RoboMaster_data/dataset/XJTLU_2022_keypoints/images/test/
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train: /media/zr/Data/RoboMaster_data/dataset/XJTLU_detect/images/train/images/train/
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val: /media/zr/Data/RoboMaster_data/dataset/XJTLU_detect/images/val/images/train/
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test:
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nc: 14
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names: ['B1','B2','B3','B4','B5','BO','BS','R1','R2','R3','R4','R5','RO','RS']

‎data/armor/hyp.armor.yaml‎

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@@ -15,10 +15,10 @@ iou_t: 0.20 # IoU training threshold
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anchor_t: 4.0 # anchor-multiple threshold
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# anchors: 3 # anchors per output layer (0 to ignore)
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fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
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hsv_h: 0.7 # image HSV-Hue augmentation (fraction)
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hsv_s: 0.5 # image HSV-Saturation augmentation (fraction)
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hsv_v: 0.3 # image HSV-Value augmentation (fraction)
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degrees: 0.0 # image rotation (+/- deg)
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hsv_h: 0.05 # image HSV-Hue augmentation (fraction)
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hsv_s: 0.3 # image HSV-Saturation augmentation (fraction)
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hsv_v: 0.5 # image HSV-Value augmentation (fraction)
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degrees: 0.3 # image rotation (+/- deg)
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translate: 0.1 # image translation (+/- fraction)
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scale: 0.1 # image scale (+/- gain)
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shear: 0.0 # image shear (+/- deg)

‎data/winmill/win_kpt.yaml‎

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train: /media/zr/Data/RoboMaster_data/dataset/XJTLU_WIN
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val: /media/zr/Data/RoboMaster_data/dataset/XJTLU_WIN
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test: /media/zr/Data/RoboMaster_data/dataset/XJTLU_WIN
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train: /media/zr/Data/RoboMaster_data/dataset/XJTLU_WIN/images/train/
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val: /media/zr/Data/RoboMaster_data/dataset/XJTLU_WIN/images/train/
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nc: 4
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names: ['RR','RW','BR','BW']
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# 红Right(正确打击) 红 Wrong(已经打击) 蓝 Right(正确打击) 蓝 Wrong(已经打击)

‎detect.py‎

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@@ -172,7 +172,7 @@ def detect(opt):
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', nargs='+', type=str,
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default='../win_kpt/runs/train/exp/weights/best.pt',help='model.pt path(s)')
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parser.add_argument('--source', type=str, default='/media/zr/Data/RoboMaster_data/record/red-win.MP4', help='source')
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parser.add_argument('--source', type=str, default='/media/zr/Data/Sharefolder_zR_PC/output.mp4', help='source')
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parser.add_argument('--img-size', nargs='+', type=int, default=416, help='inference size (pixels)')
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parser.add_argument('--conf-thres', type=float, default=0.5, help='object confidence threshold')
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parser.add_argument('--iou-thres', type=float, default=0.4, help='IOU threshold for NMS')

‎export.py‎

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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--weights', type=str, default='../win_kpt/runs/train/exp2/weights/best.pt', help='weights path')
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parser.add_argument('--weights', type=str, default='../armor_detct_new/runs/train/exp/weights/best.pt', help='weights path')
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# parser.add_argument('--weights', type=str, default='../win_kpt/runs/train/exp/weights/best.pt', help='weights path')
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parser.add_argument('--img-size', nargs='+', type=int, default=[416, 416], help='image size') # height, width
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parser.add_argument('--batch-size', type=int, default=1, help='batch size')
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parser.add_argument('--grid', action='store_true', help='export Detect() layer grid')
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parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--device', default='1', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
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parser.add_argument('--dynamic', action='store_true', help='dynamic ONNX axes') # ONNX-only
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parser.add_argument('--simplify', type=bool, default=True, help='simplify ONNX model') # ONNX-only
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parser.add_argument('--export-nms', action='store_true',

‎pre-processing_script/change_anchor.py‎

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sys.path.append('./')
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import utils.autoanchor as autoAC
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new_anchors = autoAC.kmean_anchors('data/winmill/win_kpt.yaml', 9, 416, 5.0, 1000, True)
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new_anchors = autoAC.kmean_anchors('../data/winmill/win_kpt.yaml', 9, 416, 5.0, 1000, True)
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print("生成的anchor如下:")
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print(new_anchors)

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