├── test.jpg ├── test ├── 0_327.jpg ├── 0_57.jpg ├── 11_54.jpg ├── 12_66.jpg ├── 4_216.jpg ├── 5_311.jpg ├── 5_350.jpg ├── 8_221.jpg └── 9_299.jpg ├── imgs ├── img_0806.jpg ├── img_0807.jpg ├── img_0808.jpg ├── img_0809.jpg ├── img_0810.jpg ├── img_0811.jpg ├── img_0812.jpg ├── img_0813.jpg ├── img_0814.jpg ├── img_0815.jpg ├── img_0816.jpg ├── img_0817.jpg ├── img_0818.jpg ├── img_0819.jpg ├── img_0820.jpg ├── img_0821.jpg ├── img_0822.jpg ├── img_0823.jpg ├── img_0824.jpg ├── img_0825.jpg ├── img_0826.jpg ├── img_0827.jpg ├── img_0828.jpg ├── img_0829.jpg ├── img_0830.jpg ├── img_0831.jpg ├── img_0832.jpg ├── img_0833.jpg ├── img_0834.jpg ├── img_0835.jpg ├── img_0836.jpg ├── img_0837.jpg ├── img_0838.jpg ├── img_0839.jpg ├── img_0840.jpg ├── img_0841.jpg ├── img_0842.jpg ├── img_0843.jpg ├── img_0844.jpg ├── img_0845.jpg ├── img_0846.jpg ├── img_0847.jpg ├── img_0848.jpg ├── img_0849.jpg ├── img_0850.jpg ├── img_0851.jpg ├── img_0852.jpg ├── img_0853.jpg ├── img_0855.jpg ├── img_0856.jpg ├── img_0857.jpg ├── img_0858.jpg ├── img_0859.jpg ├── img_0860.jpg ├── img_0861.jpg ├── img_0862.jpg ├── img_0863.jpg ├── img_0864.jpg ├── img_0865.jpg ├── img_0866.jpg ├── img_0867.jpg ├── img_0868.jpg ├── img_0869.jpg ├── img_0870.jpg ├── img_0871.jpg ├── img_0872.jpg ├── img_0873.jpg ├── img_0874.jpg ├── img_0875.jpg ├── img_0876.jpg ├── img_0877.jpg ├── img_0878.jpg ├── img_0879.jpg ├── img_0880.jpg ├── img_0881.jpg ├── img_0882.jpg ├── img_0883.jpg ├── img_0884.jpg ├── img_0885.jpg ├── img_0886.jpg ├── img_0887.jpg ├── img_0889.jpg ├── img_0923.jpg ├── img_0924.jpg ├── img_0925.jpg ├── img_0926.jpg ├── img_0927.jpg ├── img_0928.jpg ├── img_0929.jpg ├── img_0930.jpg ├── img_0931.jpg ├── img_0932.jpg ├── img_0933.jpg ├── img_0934.jpg ├── img_0935.jpg ├── img_0936.jpg ├── img_0937.jpg ├── img_0938.jpg ├── img_0939.jpg ├── img_0941.jpg ├── img_0942.jpg ├── img_0943.jpg ├── img_0944.jpg ├── img_0945.jpg ├── img_0946.jpg ├── img_0947.jpg ├── img_0948.jpg ├── img_0949.jpg ├── img_0950.jpg ├── img_0952.jpg ├── img_0953.jpg ├── img_0954.jpg ├── img_0955.jpg ├── img_0956.jpg ├── img_0957.jpg ├── img_0958.jpg ├── img_0959.jpg ├── img_0960.jpg ├── img_0961.jpg ├── img_0962.jpg ├── img_0963.jpg ├── img_0964.jpg ├── img_0965.jpg ├── img_0967.jpg ├── img_0968.jpg ├── img_0969.jpg ├── img_0970.jpg ├── img_0971.jpg ├── img_0972.jpg ├── img_0973.jpg ├── img_0974.jpg ├── p1010678.jpg ├── p1010680.jpg ├── p1010681.jpg ├── p1010682.jpg ├── p1010683.jpg ├── p1010684.jpg ├── p1010685.jpg ├── p1010686.jpg ├── p1010687.jpg ├── p1010688.jpg ├── p1010689.jpg ├── p1010690.jpg ├── p1010691.jpg ├── p1010692.jpg ├── p1010693.jpg ├── p1010694.jpg ├── p1010695.jpg ├── p1010696.jpg ├── p1010697.jpg ├── p1010698.jpg ├── p1010699.jpg ├── p1010700.jpg ├── p1010701.jpg ├── p1010702.jpg ├── p1010703.jpg ├── p1010704.jpg ├── p1010705.jpg ├── p1010706.jpg ├── p1010707.jpg ├── p1010708.jpg ├── p1010709.jpg ├── p1010710.jpg ├── p1010712.jpg ├── p1010713.jpg ├── p1010714.jpg ├── p1010715.jpg ├── p1010716.jpg ├── p1010717.jpg ├── p1010718.jpg ├── p1010719.jpg ├── p1010720.jpg ├── p1010721.jpg ├── p1010722.jpg ├── p1010723.jpg ├── p1010724.jpg ├── p1010725.jpg ├── p1010726.jpg ├── p1010727.jpg ├── p1010728.jpg ├── p1010729.jpg ├── p1010730.jpg ├── p1010731.jpg ├── p1010732.jpg ├── p1010733.jpg ├── p1010734.jpg ├── p1010735.jpg ├── p1010736.jpg ├── p1010737.jpg ├── p1010738.jpg ├── p1010739.jpg ├── p1010740.jpg ├── p1010741.jpg ├── p1010742.jpg ├── p1010743.jpg ├── p1010744.jpg ├── p1010745.jpg ├── p1010746.jpg ├── p1010747.jpg ├── p1010748.jpg ├── p1010749.jpg ├── p1010750.jpg ├── p1010751.jpg ├── p1010752.jpg ├── p1010753.jpg ├── p1010754.jpg ├── p1010755.jpg ├── p1010756.jpg ├── p1010757.jpg ├── p1010758.jpg ├── p1010759.jpg ├── p1010760.jpg ├── p1010761.jpg ├── p1010762.jpg ├── p1010763.jpg ├── p1010764.jpg ├── p1010765.jpg ├── p1010766.jpg ├── p1010767.jpg ├── p1010768.jpg ├── p1010769.jpg ├── p1010770.jpg ├── p1010771.jpg ├── p1010772.jpg ├── p1010773.jpg ├── p1010774.jpg ├── p1010775.jpg ├── p1010776.jpg ├── p1010777.jpg ├── p1010778.jpg ├── p1010779.jpg ├── p1010780.jpg ├── p1010781.jpg ├── p1010782.jpg ├── p1010783.jpg ├── p1010784.jpg ├── p1010785.jpg ├── p1010786.jpg ├── p1010787.jpg ├── p1010788.jpg ├── p1010789.jpg ├── p1010790.jpg ├── p1010791.jpg ├── p1010792.jpg ├── p1010793.jpg ├── p1010795.jpg ├── p1010796.jpg ├── p1010797.jpg ├── p1010798.jpg ├── p1010799.jpg ├── p1010800.jpg ├── p1010801.jpg ├── p1010802.jpg ├── p1010804.jpg ├── p1010805.jpg ├── p1010806.jpg ├── p1010808.jpg ├── p1010810.jpg ├── p1010811.jpg ├── p1010812.jpg ├── p1010813.jpg ├── p1010814.jpg ├── p1010815.jpg ├── p1010816.jpg ├── p1010817.jpg ├── p1010818.jpg ├── p1010819.jpg ├── p1010820.jpg ├── p1010821.jpg ├── p1010822.jpg ├── p1010823.jpg ├── p1010824.jpg ├── p1010825.jpg ├── p1010826.jpg ├── p1010827.jpg ├── p1010828.jpg ├── p1010829.jpg ├── p1010831.jpg ├── p1010832.jpg ├── p1010834.jpg ├── p1010835.jpg ├── p1010836.jpg ├── p1010837.jpg ├── p1010838.jpg ├── p1010839.jpg ├── p1010840.jpg ├── p1010841.jpg ├── p1010842.jpg ├── p1010843.jpg ├── p1010844.jpg ├── p1010845.jpg ├── p1010846.jpg ├── p1010847.jpg ├── p1010848.jpg ├── p1010849.jpg ├── p1010850.jpg ├── p1010851.jpg ├── p1010852.jpg ├── p1010853.jpg ├── p1010854.jpg ├── p1010855.jpg ├── p1010856.jpg ├── p1010857.jpg ├── p1010858.jpg ├── p1010859.jpg ├── p1010860.jpg ├── p1010861.jpg ├── p1010862.jpg ├── p1010863.jpg ├── p1010864.jpg ├── p1010866.jpg ├── p1010867.jpg ├── p1010868.jpg ├── p1010869.jpg ├── p1010870.jpg ├── p1010871.jpg ├── p1010872.jpg ├── p1010873.jpg ├── p1010874.jpg ├── p1010875.jpg ├── p1010876.jpg ├── p1010877.jpg ├── p1010878.jpg ├── p1010879.jpg ├── p1010880.jpg ├── p1010881.jpg ├── p1010882.jpg ├── p1010883.jpg ├── p1010884.jpg ├── p1010885.jpg ├── p1010886.jpg ├── p1010887.jpg ├── p1010890.jpg ├── p1010891.jpg ├── p1010892.jpg ├── p1010893.jpg ├── p1010894.jpg ├── p1010895.jpg ├── p1010896.jpg ├── p1010897.jpg ├── p1010898.jpg ├── p1010899.jpg ├── p1010900.jpg ├── p1010901.jpg ├── p1010902.jpg ├── p1010903.jpg ├── p1010904.jpg ├── p1010905.jpg ├── p1010906.jpg ├── p1010907.jpg ├── p1010908.jpg ├── p1010909.jpg ├── p1010910.jpg ├── p1010911.jpg ├── p1010912.jpg ├── p1010913.jpg ├── p1010914.jpg ├── p1010915.jpg ├── p1010916.jpg ├── p1010917.jpg ├── p1010918.jpg ├── p1010919.jpg ├── p1010920.jpg ├── p1010921.jpg ├── p1010922.jpg ├── p1010923.jpg ├── p1010924.jpg ├── p1010925.jpg ├── p1010926.jpg ├── p1010927.jpg ├── p1010931.jpg ├── p1010932.jpg ├── p1010933.jpg ├── p1010934.jpg ├── p1010935.jpg ├── p1010936.jpg ├── p1010937.jpg ├── p1010938.jpg ├── p1010939.jpg ├── p1010940.jpg ├── p1010941.jpg ├── p1010942.jpg ├── p1010943.jpg ├── p1010944.jpg ├── p1010945.jpg ├── p1010946.jpg ├── p1010947.jpg ├── p1010948.jpg ├── p1010949.jpg ├── p1010950.jpg ├── p1010951.jpg ├── p1010952.jpg ├── p1010953.jpg ├── p1010954.jpg ├── p1010955.jpg ├── p1010956.jpg ├── p1010957.jpg ├── p1010958.jpg ├── p1010959.jpg ├── p1010960.jpg ├── p1010961.jpg ├── p1010962.jpg ├── p1010963.jpg ├── p1010964.jpg ├── p1010966.jpg ├── p1010967.jpg ├── p1010968.jpg ├── p1010969.jpg ├── p1010970.jpg ├── p1010971.jpg ├── p1010972.jpg ├── p1010973.jpg ├── p1010974.jpg ├── p1010975.jpg ├── p1010976.jpg ├── p1010977.jpg └── p1010978.jpg ├── requirements.txt ├── README.md ├── test.py ├── train.py └── LICENSE /test.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/2833844911/Rotate-the-verification-code-pytorch/HEAD/test.jpg -------------------------------------------------------------------------------- /test/0_327.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/2833844911/Rotate-the-verification-code-pytorch/HEAD/test/0_327.jpg -------------------------------------------------------------------------------- /test/0_57.jpg: 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训练好的模型 [下面这个训练好的模型只能识别百度旋转的图片，不要用其他图片测试，直接用百度旋转的图片测试] 6 | 链接：https://pan.baidu.com/s/1mcz58nquhL0DA8tCX5f_ww 7 | 提取码：39ev 8 | 9 | 10 | ### 注意: 需要设置训练轮数 epochs 11 | ### 导入图片大小最好要为正方形，不为正方形也可以训练 12 | ![image](test.jpg) 13 | -------------------------------------------------------------------------------- /test.py: -------------------------------------------------------------------------------- 1 | import torchvision.transforms as transforms 2 | import torch 3 | import cv2 4 | import os 5 | import torch.nn as nn 6 | import numpy as np 7 | import torchvision.models as models 8 | os.environ['CUDA_LAUNCH_BLOCKING'] = '1' 9 | # 将模型移动到GPU上（如果有可用的话） 10 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 11 | 12 | num_classes = 360 13 | 14 | class CustomResNet50(nn.Module): 15 | def __init__(self, num_classes=num_classes): 16 | super(CustomResNet50, self).__init__() 17 | resnet50 = models.resnet50(weights=models.ResNet50_Weights.DEFAULT) 18 | self.resnet_layers = nn.Sequential(*list(resnet50.children())[:-1]) 19 | self.flatten = nn.Flatten() 20 | # self.fc = nn.Linear(2048, num_classes) 21 | self.fc = nn.Linear(8192, num_classes) 22 | 23 | 24 | def forward(self, x): 25 | x = self.resnet_layers(x) 26 | x = self.flatten(x) 27 | x = self.fc(x) 28 | return x 29 | 30 | def crop_max_square(img): 31 | size = min(img.shape[:2]) 32 | x = (img.shape[1] - size) // 2 33 | y = (img.shape[0] - size) // 2 34 | return img[y:y+size, x:x+size] 35 | def getImgae(path): 36 | print(path) 37 | img = cv2.imread(path) 38 | 39 | rotated =cv2.resize(img, (480, 480), interpolation=cv2.INTER_AREA) 40 | 41 | img = rotated 42 | 43 | transform = transforms.Compose([ 44 | transforms.ToTensor() 45 | ]) 46 | img_tensor = transform(img) 47 | kk = img_tensor.shape[0] 48 | if kk != 3: 49 | img_tensor = torch.permute(img_tensor, (2, 0, 1)) 50 | img_tensor = img_tensor.unsqueeze(0) 51 | img_tensor = img_tensor.to(device) 52 | return img_tensor 53 | 54 | def getDu(data): 55 | k = data.tolist()[0] 56 | for i in range(len(k)): 57 | k[i] = [k[i], i] 58 | k.sort() 59 | k = k[::-1] 60 | 61 | print() 62 | return k[0][1] 63 | 64 | 65 | resnet = torch.load('./modelyzm.pth',map_location=device) 66 | resnet.to(device) 67 | 68 | # 需要识别的图片 69 | pathfile = './test/5_110.jpg' 70 | # pathfile = './test/104_257.jpg' 71 | # pathfile = './test/105_101.jpg' 72 | # pathfile = './test/115_101.jpg' 73 | # pathfile = './test/294_181.jpg' 74 | # pathfile = './test/321_219.jpg' 75 | imgTensor = getImgae(pathfile) 76 | # print(imgTensor.shape) 77 | outInfo = resnet(imgTensor) 78 | o = getDu(outInfo) 79 | 80 | 81 | # 展示图片 82 | angle = o 83 | print("旋转角度",angle) 84 | # 计算旋转矩阵 85 | data = cv2.imread(pathfile) 86 | center = (data.shape[1] // 2, data.shape[0] // 2) 87 | scale = 1 88 | M = cv2.getRotationMatrix2D(center, 360 - angle, scale) 89 | 90 | # 应用旋转矩阵 91 | rotated = cv2.warpAffine(data, M, (data.shape[1], data.shape[0])) 92 | cv2.imshow('original', data) 93 | 94 | cv2.imshow('Rotated Image', rotated) 95 | cv2.waitKey(0) 96 | cv2.destroyAllWindows() 97 | -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | ''' 2 | author: cbb 3 | email: 2833844911@qq.com 4 | date: 2023-04-18 5 | ''' 6 | 7 | import torchvision.transforms as transforms 8 | from torch.utils.data import DataLoader, Dataset 9 | 10 | import torch 11 | import cv2 12 | import random 13 | import os 14 | import torch.optim as optim 15 | import torch.nn as nn 16 | 17 | import torchvision.models as models 18 | from tqdm import tqdm 19 | os.environ['CUDA_LAUNCH_BLOCKING'] = '1' 20 | # 将模型移动到GPU上（如果有可用的话） 21 | device = torch.device("cuda" if torch.cuda.is_available() else "cpu") 22 | 23 | 24 | num_classes = 360 25 | 26 | 27 | # 导入图片大小 28 | daorimg = (480, 480) 29 | 30 | class CustomResNet50(nn.Module): 31 | def __init__(self, num_classes=num_classes): 32 | super(CustomResNet50, self).__init__() 33 | # 加载ResNet50模型 34 | resnet50 = models.resnet50(pretrained=True) 35 | self.resnet_layers = nn.Sequential(*list(resnet50.children())[:-1]) 36 | self.flatten = nn.Flatten() 37 | self.fc = nn.Linear(2048, num_classes) 38 | # self.fc = nn.Linear(8192, num_classes) 39 | self.sfm = nn.Sigmoid() 40 | 41 | def forward(self, x): 42 | # print(x.shape) 43 | x = self.resnet_layers(x) 44 | x = self.flatten(x) 45 | x = self.fc(x) 46 | x = self.sfm(x) 47 | return x 48 | 49 | resnet = CustomResNet50(num_classes=num_classes) 50 | # resnet = torch.load('./modelyzm.pth') 51 | resnet.to(device) 52 | 53 | def get_jpg_paths(folder_path): 54 | jpg_paths = [] 55 | for file in os.listdir(folder_path): 56 | jpg_paths.append(folder_path+'/'+file) 57 | 58 | return jpg_paths 59 | def crop_max_square(img): 60 | size = min(img.shape[:2]) 61 | x = (img.shape[1] - size) // 2 62 | y = (img.shape[0] - size) // 2 63 | return img[y:y+size, x:x+size] 64 | folder_path = './imgs' 65 | allfile = get_jpg_paths(folder_path) 66 | random.shuffle(allfile) 67 | huaf = int(len(allfile)*0.9) 68 | tranfiles = allfile[:huaf] 69 | textfiles = allfile[huaf:] 70 | def add_gaussian_noise(image, mean=0, sigma=25): 71 | """ 72 | 添加高斯噪声到图像 73 | :param image: 输入图像 74 | :param mean: 噪声均值 75 | :param sigma: 噪声方差 76 | :return: 带有高斯噪声的图像 77 | """ 78 | # 将图像转换为浮点数，以防止整数溢出 79 | # noisy_image = np.array(image, dtype=np.float64) 80 | 81 | # 生成与图像尺寸相同的高斯噪声 82 | # noise = np.random.normal(mean, sigma, image.shape) 83 | # 84 | # # 将噪声添加到图像 85 | # noisy_image = image + noise 86 | 87 | # 确保结果在0到255范围内 88 | # noisy_image = np.clip(noisy_image, 0, 255) 89 | 90 | # 将图像类型转换回原始类型 91 | # noisy_image = noisy_image.astype(np.uint8) 92 | 93 | return image 94 | class data_Dataset(Dataset): 95 | def __init__(self, data) -> None: 96 | super().__init__() 97 | self.data = data 98 | 99 | def __getitem__(self, index): 100 | tmfile = self.data[index] 101 | data = cv2.imread(tmfile) 102 | 103 | 104 | 105 | data =cv2.resize(data, daorimg, interpolation=cv2.INTER_AREA) 106 | 107 | # 计算旋转矩阵 108 | center = (data.shape[1] // 2, data.shape[0] // 2) 109 | angle = random.randint(0, num_classes-1) 110 | scale = 1 111 | M = cv2.getRotationMatrix2D(center, angle, scale) 112 | 113 | # 应用旋转矩阵 114 | croppedcj = cv2.warpAffine(data, M, (data.shape[1], data.shape[0]),borderValue=(255,255,255)) 115 | 116 | 117 | 118 | transform = transforms.Compose([ 119 | transforms.ToTensor() 120 | ]) 121 | 122 | img_tensor = transform(croppedcj) 123 | kk = img_tensor.shape[0] 124 | if kk != 3: 125 | 126 | img_tensor = torch.permute(img_tensor, (2, 0, 1)) 127 | # print(img_tensor.shape) 128 | return img_tensor.to(device), torch.tensor( angle).to(device) 129 | 130 | def __len__(self): 131 | return len(self.data) -1 132 | 133 | 134 | train_dataset = data_Dataset(tranfiles) 135 | 136 | test_dataset = data_Dataset(textfiles) 137 | batch_size = 4 138 | train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True) 139 | test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False) 140 | 141 | 142 | class CustomClassificationLoss(nn.Module): 143 | def __init__(self, num_classes, alpha=18): 144 | super(CustomClassificationLoss, self).__init__() 145 | self.num_classes = num_classes 146 | self.alpha = alpha 147 | 148 | 149 | def forward(self, logits, targets): 150 | 151 | ce_loss = 0 152 | for i in range(logits.shape[0]): 153 | wz = targets[i].item() 154 | kj = wz 155 | lossOne = (1-logits[i][wz]) 156 | for r in range(1, 180): 157 | wz -= 1 158 | if wz <0: 159 | wz = 359 160 | lossOne += torch.abs((0.98 ** r) - logits[i][wz]) 161 | 162 | for r in range(1, 180): 163 | kj += 1 164 | if kj >359: 165 | kj = 0 166 | lossOne += torch.abs((0.98 ** r) - logits[i][kj]) 167 | ce_loss += lossOne 168 | 169 | 170 | # 组合损失 171 | loss = ce_loss/targets.shape[0] 172 | return loss, 0, loss.item() 173 | 174 | # model = LeNet5() 175 | criterion = CustomClassificationLoss(360) 176 | optimizer = optim.Adam(resnet.parameters(), lr=0.0001) 177 | 178 | max_xl = 1000 179 | 180 | # 训练轮数 181 | epochs = 100 182 | for epoch in range(epochs): 183 | resnet.train() 184 | running_loss = 0 185 | xiao_loss = 0 186 | mmmm_loss = 0 187 | max_xlk = 0 188 | train_loader = tqdm(train_loader) 189 | for i, (inputs, labels) in enumerate(train_loader, 0): 190 | bashz = inputs.shape[0] 191 | if bashz != batch_size: 192 | continue 193 | optimizer.zero_grad() 194 | outputs = resnet(inputs) 195 | loss, xiao, mmmm = criterion(outputs, labels) 196 | loss.backward() 197 | optimizer.step() 198 | running_loss += loss.item() 199 | xiao_loss += xiao 200 | mmmm_loss += mmmm 201 | max_xlk = running_loss/((i+1)) 202 | train_loader.set_description(desc='Epoch %d loss: %.3f xiao_loss %.3f mmmm_loss: %.3f' % (epoch+1, running_loss/((i+1)), xiao_loss/((i+1)) , mmmm_loss/((i+1)))) 203 | if max_xl > max_xlk: 204 | torch.save(resnet, './modelyzm.pth') 205 | max_xl = max_xlk 206 | 207 | 208 | 209 | lossall = 0 210 | alllen = 0 211 | 212 | with torch.no_grad(): 213 | test_loader = tqdm(test_loader) 214 | resnet.eval() 215 | for data in test_loader: 216 | images, labels = data 217 | bashz = images.shape[0] 218 | if bashz != batch_size: 219 | continue 220 | alllen+=1 221 | outputs = resnet(images) 222 | loss, xiao, mmmm = criterion(outputs, labels) 223 | lossall += loss.item() 224 | 225 | test_loader.set_description(desc='test loss: {}'.format(lossall / alllen)) 226 | 227 | -------------------------------------------------------------------------------- /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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