├── LICENSE ├── README.md ├── fumx.pth ├── imgs ├── label │ ├── 100_pseudo.png │ ├── 101_pseudo.png │ ├── 102_pseudo.png │ ├── 103_pseudo.png │ ├── 104_pseudo.png │ ├── 105_pseudo.png │ ├── 106_pseudo.png │ ├── 107_pseudo.png │ ├── 108_pseudo.png │ ├── 109_pseudo.png │ ├── 10_pseudo.png │ ├── 110_pseudo.png │ ├── 111_pseudo.png │ ├── 112_pseudo.png │ ├── 113_pseudo.png │ ├── 114_pseudo.png │ ├── 115_pseudo.png │ ├── 116_pseudo.png │ ├── 117_pseudo.png │ ├── 118_pseudo.png │ ├── 119_pseudo.png │ ├── 11_pseudo.png │ ├── 120_pseudo.png │ ├── 121_pseudo.png │ ├── 122_pseudo.png │ ├── 123_pseudo.png │ ├── 124_pseudo.png │ ├── 125_pseudo.png │ ├── 126_pseudo.png │ ├── 127_pseudo.png │ ├── 128_pseudo.png │ ├── 129_pseudo.png │ ├── 12_pseudo.png │ ├── 130_pseudo.png │ ├── 131_pseudo.png │ ├── 132_pseudo.png │ ├── 133_pseudo.png │ ├── 134_pseudo.png │ ├── 135_pseudo.png │ ├── 136_pseudo.png │ ├── 137_pseudo.png │ ├── 138_pseudo.png │ ├── 139_pseudo.png │ ├── 13_pseudo.png │ ├── 140_pseudo.png │ ├── 141_pseudo.png │ ├── 142_pseudo.png │ ├── 143_pseudo.png │ ├── 144_pseudo.png │ ├── 145_pseudo.png │ ├── 146_pseudo.png │ ├── 147_pseudo.png │ ├── 148_pseudo.png │ ├── 149_pseudo.png │ ├── 14_pseudo.png │ ├── 15_pseudo.png │ ├── 16_pseudo.png │ ├── 17_pseudo.png │ ├── 18_pseudo.png │ ├── 19_pseudo.png │ ├── 1_pseudo.png │ ├── 20_pseudo.png │ ├── 21_pseudo.png │ ├── 22_pseudo.png │ ├── 23_pseudo.png │ ├── 24_pseudo.png │ ├── 25_pseudo.png │ ├── 26_pseudo.png │ ├── 27_pseudo.png │ ├── 28_pseudo.png │ ├── 29_pseudo.png │ ├── 2_pseudo.png │ ├── 30_pseudo.png │ ├── 31_pseudo.png │ ├── 32_pseudo.png │ ├── 33_pseudo.png │ ├── 34_pseudo.png │ ├── 35_pseudo.png │ ├── 36_pseudo.png │ ├── 37_pseudo.png │ ├── 38_pseudo.png │ ├── 39_pseudo.png │ ├── 3_pseudo.png │ ├── 40_pseudo.png │ ├── 41_pseudo.png │ ├── 42_pseudo.png │ ├── 43_pseudo.png │ ├── 44_pseudo.png │ ├── 45_pseudo.png │ ├── 46_pseudo.png │ ├── 47_pseudo.png │ ├── 48_pseudo.png │ ├── 49_pseudo.png │ ├── 4_pseudo.png │ ├── 50_pseudo.png │ ├── 51_pseudo.png │ ├── 52_pseudo.png │ ├── 53_pseudo.png │ ├── 54_pseudo.png │ ├── 55_pseudo.png │ ├── 56_pseudo.png │ ├── 57_pseudo.png │ ├── 58_pseudo.png │ ├── 59_pseudo.png │ ├── 5_pseudo.png │ ├── 60_pseudo.png │ ├── 61_pseudo.png │ ├── 62_pseudo.png │ ├── 63_pseudo.png │ ├── 64_pseudo.png │ ├── 65_pseudo.png │ ├── 66_pseudo.png │ ├── 67_pseudo.png │ ├── 68_pseudo.png │ ├── 69_pseudo.png │ ├── 6_pseudo.png │ ├── 70_pseudo.png │ ├── 71_pseudo.png │ ├── 72_pseudo.png │ ├── 73_pseudo.png │ ├── 74_pseudo.png │ ├── 75_pseudo.png │ ├── 76_pseudo.png │ ├── 77_pseudo.png │ ├── 78_pseudo.png │ ├── 79_pseudo.png │ ├── 7_pseudo.png │ ├── 80_pseudo.png │ ├── 81_pseudo.png │ ├── 82_pseudo.png │ ├── 83_pseudo.png │ ├── 84_pseudo.png │ ├── 85_pseudo.png │ ├── 86_pseudo.png │ ├── 87_pseudo.png │ ├── 88_pseudo.png │ ├── 89_pseudo.png │ ├── 8_pseudo.png │ ├── 90_pseudo.png │ ├── 91_pseudo.png │ ├── 92_pseudo.png │ ├── 93_pseudo.png │ ├── 94_pseudo.png │ ├── 95_pseudo.png │ ├── 96_pseudo.png │ ├── 97_pseudo.png │ ├── 98_pseudo.png │ ├── 99_pseudo.png │ └── 9_pseudo.png └── target │ ├── 1.jpg │ ├── 10.jpg │ ├── 100.jpg │ ├── 101.jpg │ ├── 102.jpg │ ├── 103.jpg │ ├── 104.jpg │ ├── 105.jpg │ ├── 106.jpg │ ├── 107.jpg │ ├── 108.jpg │ ├── 109.jpg │ ├── 11.jpg │ ├── 110.jpg │ ├── 111.jpg │ ├── 112.jpg │ ├── 113.jpg │ ├── 114.jpg │ ├── 115.jpg │ ├── 116.jpg │ ├── 117.jpg │ ├── 118.jpg │ ├── 119.jpg │ ├── 12.jpg │ ├── 120.jpg │ ├── 121.jpg │ ├── 122.jpg │ ├── 123.jpg │ ├── 124.jpg │ ├── 125.jpg │ ├── 126.jpg │ ├── 127.jpg │ ├── 128.jpg │ ├── 129.jpg │ ├── 13.jpg │ ├── 130.jpg │ ├── 131.jpg │ ├── 132.jpg │ ├── 133.jpg │ ├── 134.jpg │ ├── 135.jpg │ ├── 136.jpg │ ├── 137.jpg │ ├── 138.jpg │ ├── 139.jpg │ ├── 14.jpg │ ├── 140.jpg │ ├── 141.jpg │ ├── 142.jpg │ ├── 143.jpg │ ├── 144.jpg │ ├── 145.jpg │ ├── 146.jpg │ ├── 147.jpg │ ├── 148.jpg │ ├── 149.jpg │ ├── 15.jpg │ ├── 16.jpg │ ├── 17.jpg │ ├── 18.jpg │ ├── 19.jpg │ ├── 2.jpg │ ├── 20.jpg │ ├── 21.jpg │ ├── 22.jpg │ ├── 23.jpg │ ├── 24.jpg │ ├── 25.jpg │ ├── 26.jpg │ ├── 27.jpg │ ├── 28.jpg │ ├── 29.jpg │ ├── 3.jpg │ ├── 30.jpg │ ├── 31.jpg │ ├── 32.jpg │ ├── 33.jpg │ ├── 34.jpg │ ├── 35.jpg │ ├── 36.jpg │ ├── 37.jpg │ ├── 38.jpg │ ├── 39.jpg │ ├── 4.jpg │ ├── 40.jpg │ ├── 41.jpg │ ├── 42.jpg │ ├── 43.jpg │ ├── 44.jpg │ ├── 45.jpg │ ├── 46.jpg │ ├── 47.jpg │ ├── 48.jpg │ ├── 49.jpg │ ├── 5.jpg │ ├── 50.jpg │ ├── 51.jpg │ ├── 52.jpg │ ├── 53.jpg │ ├── 54.jpg │ ├── 55.jpg │ ├── 56.jpg │ ├── 57.jpg │ ├── 58.jpg │ ├── 59.jpg │ ├── 6.jpg │ ├── 60.jpg │ ├── 61.jpg │ ├── 62.jpg │ ├── 63.jpg │ ├── 64.jpg │ ├── 65.jpg │ ├── 66.jpg │ ├── 67.jpg │ ├── 68.jpg │ ├── 69.jpg │ ├── 7.jpg │ ├── 70.jpg │ ├── 71.jpg │ ├── 72.jpg │ ├── 73.jpg │ ├── 74.jpg │ ├── 75.jpg │ ├── 76.jpg │ ├── 77.jpg │ ├── 78.jpg │ ├── 79.jpg │ ├── 8.jpg │ ├── 80.jpg │ ├── 81.jpg │ ├── 82.jpg │ ├── 83.jpg │ ├── 84.jpg │ ├── 85.jpg │ ├── 86.jpg │ ├── 87.jpg │ ├── 88.jpg │ ├── 89.jpg │ ├── 9.jpg │ ├── 90.jpg │ ├── 91.jpg │ ├── 92.jpg │ ├── 93.jpg │ ├── 94.jpg │ ├── 95.jpg │ ├── 96.jpg │ ├── 97.jpg │ ├── 98.jpg │ └── 99.jpg ├── main.py ├── requirements.txt ├── test.jpg └── test.py /LICENSE: -------------------------------------------------------------------------------- 1 | Apache License 2 | Version 2.0, January 2004 3 | http://www.apache.org/licenses/ 4 | 5 | TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 6 | 7 | 1. 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-------------------------------------------------------------------------------- 1 | ## 手势识别 2 | 3 | 例子是x东的 4 | 5 | 原图片 和 标注图片 分别保存在imgs/target和imgs/label 6 | 7 | 启动main.py 运行脚本训练 运行失败 可以把 torch.permute(image, (2, 0, 1)) 替换为torch.permute(image, (0, 1,2)) 8 | 9 | test.py 查看结果 需要注意opencv版本 10 | 运行失败可以 11 | torch.permute(transform, (2, 0, 1)) 12 | 这里交换维度 改为 13 | torch.permute(transform, (0, 1, 2)) 14 | 15 | 16 | 使用EISeg工具去标注 17 | 18 | 19 | https://blog.csdn.net/qq_37541097/article/details/120154543 20 | 21 | ![image](test.jpg) 22 | 23 | 欢迎加入星球哦 24 | 星球链接https://t.zsxq.com/06bIUvBEM 25 | -------------------------------------------------------------------------------- /fumx.pth: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/2833844911/Gesture_recognition_captcha/425add428949dc28faf4c5fb3992153a139be772/fumx.pth -------------------------------------------------------------------------------- /imgs/label/100_pseudo.png: 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/2833844911/Gesture_recognition_captcha/425add428949dc28faf4c5fb3992153a139be772/imgs/target/99.jpg -------------------------------------------------------------------------------- /main.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | import torch.optim as optim 4 | from torch.utils.data import Dataset, DataLoader 5 | import cv2 6 | import os 7 | import torchvision.transforms as transforms 8 | import numpy as np 9 | from tqdm import tqdm 10 | import torch.nn.functional as F 11 | os.environ['CUDA_LAUNCH_BLOCKING'] = '1' 12 | 13 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 14 | 15 | 16 | class DiceLoss(nn.Module): 17 | def __init__(self, smooth=1): 18 | super(DiceLoss, self).__init__() 19 | self.smooth = smooth 20 | 21 | def forward(self, logits, targets): 22 | probs = F.softmax(logits, dim=1)[:, 1, :, :] 23 | targets = (targets == 1).float() 24 | intersection = torch.sum(probs * targets) 25 | union = torch.sum(probs) + torch.sum(targets) 26 | dice = (2. * intersection + self.smooth) / (union + self.smooth) 27 | loss = 1 - dice 28 | return loss 29 | 30 | class CombinedLoss(nn.Module): 31 | def __init__(self, alpha=0.95, smooth=1): 32 | super(CombinedLoss, self).__init__() 33 | self.alpha = alpha 34 | self.dice_loss = DiceLoss(smooth) 35 | self.ce_loss = nn.CrossEntropyLoss() 36 | 37 | def forward(self, logits, targets): 38 | dice_loss = self.dice_loss(logits, targets) 39 | ce_loss = self.ce_loss(logits, targets) 40 | combined_loss = self.alpha * dice_loss + (1 - self.alpha) * ce_loss 41 | return combined_loss, ce_loss 42 | 43 | # 定义U-Net模型 44 | class UNet(nn.Module): 45 | def __init__(self, in_channels, out_channels): 46 | super(UNet, self).__init__() 47 | 48 | # Encoder 49 | self.enc1 = self.conv_block(in_channels, 64) 50 | self.enc2 = self.conv_block(64, 128) 51 | self.enc3 = self.conv_block(128, 256) 52 | self.enc4 = self.conv_block(256, 512) 53 | 54 | # Decoder 55 | self.dec1 = self.conv_block(512 + 256, 256) 56 | self.dec2 = self.conv_block(256 + 128, 128) 57 | self.dec3 = self.conv_block(128 + 64, 64) 58 | self.final_conv = nn.Conv2d(64, out_channels, kernel_size=1) 59 | 60 | self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) 61 | self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) 62 | 63 | def conv_block(self, in_channels, out_channels): 64 | return nn.Sequential( 65 | nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), 66 | nn.ReLU(inplace=True), 67 | nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), 68 | nn.ReLU(inplace=True) 69 | ) 70 | 71 | def forward(self, x): 72 | # Encoder 73 | x1 = self.enc1(x) 74 | x2 = self.enc2(self.maxpool(x1)) 75 | x3 = self.enc3(self.maxpool(x2)) 76 | x4 = self.enc4(self.maxpool(x3)) 77 | 78 | # Decoder with skip connections 79 | x = self.upsample(x4) 80 | x = torch.cat([x, x3], dim=1) 81 | x = self.dec1(x) 82 | 83 | x = self.upsample(x) 84 | x = torch.cat([x, x2], dim=1) 85 | x = self.dec2(x) 86 | 87 | x = self.upsample(x) 88 | x = torch.cat([x, x1], dim=1) 89 | x = self.dec3(x) 90 | 91 | x = self.final_conv(x) 92 | return x 93 | 94 | 95 | class MyLoss(nn.Module): 96 | def __init__(self): 97 | super().__init__() 98 | self.crossEn = nn.CrossEntropyLoss() 99 | 100 | def forward(self,da,ta): 101 | # loss1 = self.crossEn(da,ta) 102 | dn, ind = torch.max(da, dim=1) 103 | b = torch.sum(dn[(ind == 1) & (ta == 1)]) 104 | bm = torch.sum( dn[(ind == 1) | (ta == 1)]) 105 | diss = 1 - (b)/bm 106 | 107 | b = torch.sum(dn[(ind == 0) & (ta == 0)]) 108 | bm = torch.sum(dn[(ind == 0) | (ta == 0)]) 109 | diss2 = 1 - (b) / bm 110 | 111 | # loss1 +=( diss + diss2) 112 | loss1 =( diss + diss2) 113 | 114 | return loss1, diss+diss2 115 | 116 | 117 | # 生成数据 118 | class CustomDataset(Dataset): 119 | def __init__(self): 120 | self.dataFiles = os.listdir('./imgs/target') 121 | self.length = len(self.dataFiles) 122 | 123 | def __getitem__(self, index): 124 | fname = self.dataFiles[index] 125 | image = cv2.imread('./imgs/target/'+fname) 126 | image = cv2.resize(image, (280, 176)) 127 | mask = cv2.imread('./imgs/label/'+fname.replace('.jpg', '_pseudo.png'), 0) 128 | mask = cv2.resize(mask, (280, 176)) 129 | mask[mask>0] = 1 130 | transform = transforms.Compose([ 131 | transforms.ToTensor() 132 | ]) 133 | image = transform(image) 134 | image = torch.permute(image, (2, 0, 1)) 135 | return image, torch.tensor(mask, dtype=torch.long) 136 | def __len__(self): 137 | return self.length 138 | 139 | # 训练函数 140 | def train(model, device, train_loader, optimizer, criterion, epoch): 141 | model.train() 142 | train_loader = tqdm(train_loader) 143 | allloss = 0 144 | mogg = 0 145 | for batch_idx, (data, target) in enumerate(train_loader): 146 | data, target = data.to(device), target.to(device) 147 | optimizer.zero_grad() 148 | output = model(data) 149 | fm = target 150 | output = torch.softmax(output, dim=1) 151 | loss, mmloss = criterion(output, fm) 152 | loss.backward() 153 | optimizer.step() 154 | allloss += loss.item() 155 | mogg += mmloss.item() 156 | 157 | train_loader.set_description(desc='Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f} mfloss: {:.6f}'.format( 158 | epoch, batch_idx * len(data), len(train_loader), 159 | 100. * batch_idx / len(train_loader), allloss/(batch_idx+1), mogg/(batch_idx+1) )) 160 | 161 | def main(): 162 | 163 | model = UNet(3, 2).to(device) 164 | optimizer = optim.Adam(model.parameters(), lr=0.0001) 165 | # criterion = MyLoss() 166 | # criterion = DiceLoss() 167 | criterion = CombinedLoss() 168 | 169 | # 数据集和数据加载器 170 | train_dataset = CustomDataset() 171 | train_loader = DataLoader(train_dataset, batch_size=10, shuffle=True) 172 | # 训练过程 173 | num_epochs = 200 174 | for epoch in range(1, num_epochs + 1): 175 | train(model, device, train_loader, optimizer, criterion, epoch) 176 | torch.save(model,"./fumx.pth") 177 | 178 | 179 | if __name__ == '__main__': 180 | main() 181 | 182 | -------------------------------------------------------------------------------- /requirements.txt: -------------------------------------------------------------------------------- 1 | numpy==1.23.5 2 | opencv_python_headless==4.7.0.72 3 | torch==1.13.1+cu117 4 | torchvision==0.1.6 5 | tqdm==4.64.1 6 | -------------------------------------------------------------------------------- /test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/2833844911/Gesture_recognition_captcha/425add428949dc28faf4c5fb3992153a139be772/test.jpg -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn as nn 3 | from skimage import morphology 4 | import cv2 5 | import os 6 | import numpy as np 7 | import torchvision.transforms as transforms 8 | 9 | os.environ['CUDA_LAUNCH_BLOCKING'] = '1' 10 | device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 11 | 12 | 13 | # 定义U-Net模型 14 | class UNet(nn.Module): 15 | def __init__(self, in_channels, out_channels): 16 | super(UNet, self).__init__() 17 | 18 | # Encoder 19 | self.enc1 = self.conv_block(in_channels, 64) 20 | self.enc2 = self.conv_block(64, 128) 21 | self.enc3 = self.conv_block(128, 256) 22 | self.enc4 = self.conv_block(256, 512) 23 | 24 | # Decoder 25 | self.dec1 = self.conv_block(512 + 256, 256) 26 | self.dec2 = self.conv_block(256 + 128, 128) 27 | self.dec3 = self.conv_block(128 + 64, 64) 28 | self.final_conv = nn.Conv2d(64, out_channels, kernel_size=1) 29 | 30 | self.maxpool = nn.MaxPool2d(kernel_size=2, stride=2) 31 | self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True) 32 | 33 | def conv_block(self, in_channels, out_channels): 34 | return nn.Sequential( 35 | nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1), 36 | nn.ReLU(inplace=True), 37 | nn.Conv2d(out_channels, out_channels, kernel_size=3, padding=1), 38 | nn.ReLU(inplace=True) 39 | ) 40 | 41 | def forward(self, x): 42 | # Encoder 43 | x1 = self.enc1(x) 44 | x2 = self.enc2(self.maxpool(x1)) 45 | x3 = self.enc3(self.maxpool(x2)) 46 | x4 = self.enc4(self.maxpool(x3)) 47 | 48 | # Decoder with skip connections 49 | x = self.upsample(x4) 50 | x = torch.cat([x, x3], dim=1) 51 | x = self.dec1(x) 52 | 53 | x = self.upsample(x) 54 | x = torch.cat([x, x2], dim=1) 55 | x = self.dec2(x) 56 | 57 | x = self.upsample(x) 58 | x = torch.cat([x, x1], dim=1) 59 | x = self.dec3(x) 60 | 61 | x = self.final_conv(x) 62 | return x 63 | 64 | model = torch.load("./fumx.pth") 65 | 66 | 67 | def getInfo(path): 68 | df2 = cv2.imread(path) 69 | image = cv2.resize(df2, (280, 176)) 70 | transform = transforms.Compose([ 71 | transforms.ToTensor() 72 | ])(image).to(device) 73 | image = torch.permute(transform, (2, 0, 1)).unsqueeze(dim=0) 74 | out = model(image) 75 | out = torch.argmax(out, dim=1) 76 | j = torch.zeros((1, 176, 280)) 77 | j[out == 1] = 255 78 | 79 | kp = j.repeat(3, 1, 1) 80 | kp = torch.permute(kp, (1, 2, 0)) 81 | 82 | gj = morphology.skeletonize(np.array( kp.tolist(), dtype=np.uint8)) 83 | 84 | # 得到轨迹点 85 | hn = np.transpose(np.nonzero(gj)) 86 | print(hn) 87 | 88 | # 画图 89 | es = np.array( kp.tolist(), dtype=np.uint8) 90 | for i in hn: 91 | es[i[0],i[1],:] = 100 92 | cv2.imshow("target", es) 93 | cv2.imshow("src", df2) 94 | cv2.waitKey(0) # 等待用户按下任意键 95 | cv2.destroyAllWindows() 96 | 97 | 98 | 99 | if __name__ == '__main__': 100 | getInfo('./imgs/target/37.jpg') 101 | 102 | 103 | --------------------------------------------------------------------------------