基于python和cv2、pytorch实现的车牌定位、字符分割、字符识别项目.zip

## 问题描述 车牌的检测和识别的应用非常广泛，比如交通违章车牌追踪，小区或地下车库门禁。在对车牌识别和检测的过程中，因为车牌往往是规整的矩形，长宽比相对固定，色调纹理相对固定，常用的方法有：基于形状、基于色调、基于纹理、基于文字特征等方法，近年来随着深度学习的发展也会使用目标检测的一些深度学习方法。 ## 实现思路 ### 车牌定位  ### 车牌分割  ### 文字识别  ## 工程和算法 ### 目录结构 源码和数据位于src文件夹中，src文件夹中各个子文件功能如下： * characterData：内含有所有简体中文车牌包含的字符图片数据集（包括省份汉字、字母、数字） * singledigit：(请自己创建该空文件夹），用于存储提取出的单个字符图片 * Car.jpg：示例图片，可以用来测试模型识别效果 * getplate.py：车牌定位、字符分割源码 * main.py：模型训练，字符识别源码 ### <span id="run">运行</span> * clone到本地 * 在src文件夹下添加空文件夹，名为 singledigit * 先运行getplate.py * 再运行main.py，控制台输出结果 ### 车牌定位 ```python import matplotlib.pyplot as plt import numpy as np import torch import cv2 import os def find_card(I): # 识别出车牌区域并返回该区域的图像 [y, x, z] = I.shape # y取值范围分析 Blue_y = np.zeros((y, 1)) for i in range(y): for j in range(x): # 蓝色rgb范围 temp = I[i, j, :] if (I[i, j, 2] <= 30) and (I[i, j, 0] >= 119): Blue_y[i][0] += 1 MaxY = np.argmax(Blue_y) PY1 = MaxY while (Blue_y[PY1, 0] >= 5) and (PY1 > 0): PY1 -= 1 PY2 = MaxY while (Blue_y[PY2, 0] >= 5) and (PY2 < y - 1): PY2 += 1 # x取值 Blue_x = np.zeros((1, x)) for i in range(x): for j in range(PY1, PY2): if (I[j, i, 2] <= 30) and (I[j, i, 0] >= 119): Blue_x[0][i] += 1 PX1 = 0 while (Blue_x[0, PX1] < 3) and (PX1 < x - 1): PX1 += 1 PX2 = x - 1 while (Blue_x[0, PX2] < 3) and (PX2 > PX1): PX2 -= 1 # 对车牌区域的修正 PX1 -= 2 PX2 += 2 return I[PY1:PY2, PX1 - 2: PX2, :] def divide(I): [y, x, z] = I.shape White_x = np.zeros((x, 1)) for i in range(x): for j in range(y): if I[j, i, 1] > 176: White_x[i][0] += 1 return White_x ``` ### 车牌分割 ```pytho def divide_each_character(I): [y, x, z] = I.shape White_x = np.zeros((x, 1)) for i in range(x): for j in range(y): if I[j, i, 1] > 176: White_x[i][0] += 1 res = [] length = 0 for i in range(White_x.shape[0]): # 使用超参数经验分割 t = I.shape[1] / 297 num = White_x[i] if num > 8: length += 1 elif length > 20 * t: res.append([i - length - 2, i + 2]) length = 0 else: length = 0 return res ``` 定位和分割主函数 ```py if __name__ == '__main__': I = cv2.imread('Car.jpg') Plate = find_card(I) # White_x = divide(Plate) plt.imshow(Plate) plt.show() # plt.plot(np.arange(Plate.shape[1]), White_x) res = divide_each_character(Plate) plate_save_path = './singledigit/' for t in range(len(res)): plt.subplot(1, 7, t + 1) temp = res[t] save_img = cv2.cvtColor(Plate[:, temp[0]:temp[1], :],cv2.COLOR_BGR2GRAY) ma = max(save_img.shape[0], save_img.shape[1]) mi = min(save_img.shape[0], save_img.shape[1]) ans = np.zeros(shape=(ma, ma, 3),dtype=np.uint8) start =int(ma/2-mi/2) for i in range(mi): for j in range(ma): if save_img[j,i] > 125: for k in range(3): ans[j,start+i,k]=255 ans=cv2.merge([ans[:,:,0],ans[:,:,1],ans[:,:,2]]) ans=cv2.resize(ans,(25,25)) dir_name=plate_save_path+str(t) os.mkdir(dir_name) cv2.imwrite(dir_name+'/'+str(t)+'.jpg',ans) plt.imshow(ans) plt.show() ``` ### 文字识别 ```py from torchvision.datasets import ImageFolder from torchvision import transforms from torch.utils.data import DataLoader,SubsetRandomSampler from torch import nn, optim import numpy as np import torch import time import sys import cv2 import os #车牌字符数组 match = {0: '0', 1: '1', 2: '2', 3: '3', 4: '4', 5: '5', 6: '6', 7: '7', 8: '8', 9: '9', 10: 'A', 11: 'B', 12: 'C',13: '川', 14: 'D', 15: 'E', 16: '鄂',17: 'F', 18: 'G', 19: '赣', 20: '甘', 21: '贵', 22: '桂', 23: 'H', 24: '黑', 25: '沪', 26: 'J', 27: '冀', 28: '津',29: '京', 30: '吉', 31: 'K', 32: 'L', 33: '辽',34: '鲁', 35: 'M', 36: '蒙', 37: '闽', 38: 'N', 39: '宁', 40: 'P', 41: 'Q', 42: '青', 43: '琼', 44: 'R', 45: 'S',46: '陕', 47: '苏', 48: '晋', 49: 'T', 50: 'U',51: 'V', 52: 'W ', 53: '皖', 54: 'X', 55: '湘', 56: '新', 57: 'Y', 58: '豫', 59: '渝', 60: '粤', 61: '云', 62: 'Z',63: '藏', 64: '浙'} data_path = './characterData/data' data_transform=transforms.Compose([ transforms.Grayscale(), transforms.ToTensor() ]) dataset=ImageFolder(data_path,transform=data_transform) validation_split=.1 shuffle_dataset = True random_seed= 42 batch_size=100 dataset_size = len(dataset) indices = list(range(dataset_size)) split = int(np.floor(validation_split * dataset_size)) if shuffle_dataset : np.random.seed(random_seed) np.random.shuffle(indices) train_indices, val_indices = indices[split:], indices[:split] train_sampler = SubsetRandomSampler(train_indices) test_sampler = SubsetRandomSampler(val_indices) train_iter = DataLoader(dataset, batch_size=batch_size, sampler=train_sampler) test_iter = DataLoader(dataset, batch_size=batch_size, sampler=test_sampler) #lenet训练 sys.path.append("..") device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') class LeNet(nn.Module): def __init__(self): super(LeNet, self).__init__() self.conv = nn.Sequential( nn.Conv2d(1, 6, 5), # in_channels, out_channels, kernel_size nn.Sigmoid(), nn.MaxPool2d(2, 2), # kernel_size, stride nn.Conv2d(6, 16, 5), nn.Sigmoid(), nn.MaxPool2d(2, 2) ) self.fc = nn.Sequential( nn.Linear(16*4, 120), nn.Sigmoid(), nn.Linear(120, 84), nn.Sigmoid(), nn.Linear(84, 65) ) def forward(self, img): feature = self.conv(img) output = self.fc(feature.view(img.shape[0], -1)) return output def evaluate_accuracy(data_iter, net, device=None): if device is None and isinstance(net, torch.nn.Module): # 如果没指定device就使用net的device device = list(net.parameters())[0].device acc_sum, n = 0.0, 0 with torch.no_grad(): for X, y in data_iter: if isinstance(net, torch.nn.Module): net.eval() # 评估模式, 这会关闭dropout acc_sum += (net(X.to(device)).argmax(dim=1) == y.to(device)).float().sum().cpu().item() net.train() # 改回训练模式 else: # 自定义的模型, 3.13节之后不会用到, 不考虑GPU if('is_training' in net.__code__.co_varnames): # 如果有is_training这个参数 # 将is_training设置成False acc_sum += (net(X, is_training=False).argmax(dim=1) == y).float().sum().item() else: acc_sum += (net(X).argmax(dim=1) == y).float().sum().item() n += y