Repository: duanshengliu/End-to-end-for-chinese-plate-recognition Branch: master Commit: e47edd4aaf1f Files: 12 Total size: 19.5 MB Directory structure: gitextract__ge8bvgl/ ├── .gitignore ├── LICENSE ├── License-plate-recognition/ │ ├── CNN.py │ ├── UI.py │ ├── Unet.py │ ├── __init__.py │ ├── cnn.h5 │ ├── core.py │ ├── train.py │ └── unet.h5 ├── README.md └── requirements.txt ================================================ FILE CONTENTS ================================================ ================================================ FILE: .gitignore ================================================ __pycache__/ ================================================ FILE: LICENSE ================================================ Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. 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See the License for the specific language governing permissions and limitations under the License. ================================================ FILE: License-plate-recognition/CNN.py ================================================ # -*- coding:utf-8 -*- # author: DuanshengLiu from tensorflow.keras import layers, losses, models import numpy as np import cv2 import os def cnn_train(): char_dict = {"京": 0, "沪": 1, "津": 2, "渝": 3, "冀": 4, "晋": 5, "蒙": 6, "辽": 7, "吉": 8, "黑": 9, "苏": 10, "浙": 11, "皖": 12, "闽": 13, "赣": 14, "鲁": 15, "豫": 16, "鄂": 17, "湘": 18, "粤": 19, "桂": 20, "琼": 21, "川": 22, "贵": 23, "云": 24, "藏": 25, "陕": 26, "甘": 27, "青": 28, "宁": 29, "新": 30, "0": 31, "1": 32, "2": 33, "3": 34, "4": 35, "5": 36, "6": 37, "7": 38, "8": 39, "9": 40, "A": 41, "B": 42, "C": 43, "D": 44, "E": 45, "F": 46, "G": 47, "H": 48, "J": 49, "K": 50, "L": 51, "M": 52, "N": 53, "P": 54, "Q": 55, "R": 56, "S": 57, "T": 58, "U": 59, "V": 60, "W": 61, "X": 62, "Y": 63, "Z": 64} # 读取数据集 path = 'home/cnn_datasets/' # 车牌号数据集路径(车牌图片宽240，高80) pic_name = sorted(os.listdir(path)) n = len(pic_name) X_train, y_train = [], [] for i in range(n): print("正在读取第%d张图片" % i) img = cv2.imdecode(np.fromfile(path + pic_name[i], dtype=np.uint8), -1) # cv2.imshow无法读取中文路径图片，改用此方式 label = [char_dict[name] for name in pic_name[i][0:7]] # 图片名前7位为车牌标签 X_train.append(img) y_train.append(label) X_train = np.array(X_train) y_train = [np.array(y_train)[:, i] for i in range(7)] # y_train是长度为7的列表，其中每个都是shape为(n,)的ndarray，分别对应n张图片的第一个字符，第二个字符....第七个字符 # cnn模型 Input = layers.Input((80, 240, 3)) # 车牌图片shape(80,240,3) x = Input x = layers.Conv2D(filters=16, kernel_size=(3, 3), strides=1, padding='same', activation='relu')(x) x = layers.MaxPool2D(pool_size=(2, 2), padding='same', strides=2)(x) for i in range(3): x = layers.Conv2D(filters=32 * 2 ** i, kernel_size=(3, 3), padding='valid', activation='relu')(x) x = layers.Conv2D(filters=32 * 2 ** i, kernel_size=(3, 3), padding='valid', activation='relu')(x) x = layers.MaxPool2D(pool_size=(2, 2), padding='same', strides=2)(x) x = layers.Dropout(0.5)(x) x = layers.Flatten()(x) x = layers.Dropout(0.3)(x) Output = [layers.Dense(65, activation='softmax', name='c%d' % (i + 1))(x) for i in range(7)] # 7个输出分别对应车牌7个字符，每个输出都为65个类别类概率 model = models.Model(inputs=Input, outputs=Output) model.summary() model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', # y_train未进行one-hot编码，所以loss选择sparse_categorical_crossentropy metrics=['accuracy']) # 模型训练 print("开始训练cnn") model.fit(X_train, y_train, epochs=35) # 总loss为7个loss的和 model.save('cnn.h5') print('cnn.h5保存成功!!!') def cnn_predict(cnn, Lic_img): characters = ["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", "0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "J", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"] Lic_pred = [] for lic in Lic_img: lic_pred = cnn.predict(lic.reshape(1, 80, 240, 3)) # 预测形状应为(1,80,240,3) lic_pred = np.array(lic_pred).reshape(7, 65) # 列表转为ndarray，形状为(7,65) if len(lic_pred[lic_pred >= 0.8]) >= 4: # 统计其中预测概率值大于80%以上的个数，大于等于4个以上认为识别率高，识别成功 chars = '' for arg in np.argmax(lic_pred, axis=1): # 取每行中概率值最大的arg,将其转为字符 chars += characters[arg] chars = chars[0:2] + '·' + chars[2:] Lic_pred.append((lic, chars)) # 将车牌和识别结果一并存入Lic_pred return Lic_pred ================================================ FILE: License-plate-recognition/UI.py ================================================ # -*- coding:utf-8 -*- # author: DuanshengLiu import cv2 import sys import numpy as np from tkinter import * from tkinter.filedialog import askopenfilename from PIL import Image, ImageTk from tensorflow import keras from core import locate_and_correct from Unet import unet_predict from CNN import cnn_predict class Window: def __init__(self, win, ww, wh): self.win = win self.ww = ww self.wh = wh self.win.geometry("%dx%d+%d+%d" % (ww, wh, 200, 50)) # 界面启动时的初始位置 self.win.title("车牌定位，矫正和识别软件---by DuanshengLiu") self.img_src_path = None self.label_src = Label(self.win, text='原图:', font=('微软雅黑', 13)).place(x=0, y=0) self.label_lic1 = Label(self.win, text='车牌区域1:', font=('微软雅黑', 13)).place(x=615, y=0) self.label_pred1 = Label(self.win, text='识别结果1:', font=('微软雅黑', 13)).place(x=615, y=85) self.label_lic2 = Label(self.win, text='车牌区域2:', font=('微软雅黑', 13)).place(x=615, y=180) self.label_pred2 = Label(self.win, text='识别结果2:', font=('微软雅黑', 13)).place(x=615, y=265) self.label_lic3 = Label(self.win, text='车牌区域3:', font=('微软雅黑', 13)).place(x=615, y=360) self.label_pred3 = Label(self.win, text='识别结果3:', font=('微软雅黑', 13)).place(x=615, y=445) self.can_src = Canvas(self.win, width=512, height=512, bg='white', relief='solid', borderwidth=1) # 原图画布 self.can_src.place(x=50, y=0) self.can_lic1 = Canvas(self.win, width=245, height=85, bg='white', relief='solid', borderwidth=1) # 车牌区域1画布 self.can_lic1.place(x=710, y=0) self.can_pred1 = Canvas(self.win, width=245, height=65, bg='white', relief='solid', borderwidth=1) # 车牌识别1画布 self.can_pred1.place(x=710, y=90) self.can_lic2 = Canvas(self.win, width=245, height=85, bg='white', relief='solid', borderwidth=1) # 车牌区域2画布 self.can_lic2.place(x=710, y=175) self.can_pred2 = Canvas(self.win, width=245, height=65, bg='white', relief='solid', borderwidth=1) # 车牌识别2画布 self.can_pred2.place(x=710, y=265) self.can_lic3 = Canvas(self.win, width=245, height=85, bg='white', relief='solid', borderwidth=1) # 车牌区域3画布 self.can_lic3.place(x=710, y=350) self.can_pred3 = Canvas(self.win, width=245, height=65, bg='white', relief='solid', borderwidth=1) # 车牌识别3画布 self.can_pred3.place(x=710, y=440) self.button1 = Button(self.win, text='选择文件', width=10, height=1, command=self.load_show_img) # 选择文件按钮 self.button1.place(x=680, y=wh - 30) self.button2 = Button(self.win, text='识别车牌', width=10, height=1, command=self.display) # 识别车牌按钮 self.button2.place(x=780, y=wh - 30) self.button3 = Button(self.win, text='清空所有', width=10, height=1, command=self.clear) # 清空所有按钮 self.button3.place(x=880, y=wh - 30) self.unet = keras.models.load_model('unet.h5') self.cnn = keras.models.load_model('cnn.h5') print('正在启动中,请稍等...') cnn_predict(self.cnn, [np.zeros((80, 240, 3))]) print("已启动,开始识别吧！") def load_show_img(self): self.clear() sv = StringVar() sv.set(askopenfilename()) self.img_src_path = Entry(self.win, state='readonly', text=sv).get() # 获取到所打开的图片 img_open = Image.open(self.img_src_path) if img_open.size[0] * img_open.size[1] > 240 * 80: img_open = img_open.resize((512, 512), Image.ANTIALIAS) self.img_Tk = ImageTk.PhotoImage(img_open) self.can_src.create_image(258, 258, image=self.img_Tk, anchor='center') def display(self): if self.img_src_path == None: # 还没选择图片就进行预测 self.can_pred1.create_text(32, 15, text='请选择图片', anchor='nw', font=('黑体', 28)) else: img_src = cv2.imdecode(np.fromfile(self.img_src_path, dtype=np.uint8), -1) # 从中文路径读取时用 h, w = img_src.shape[0], img_src.shape[1] if h * w <= 240 * 80 and 2 <= w / h <= 5: # 满足该条件说明可能整个图片就是一张车牌,无需定位,直接识别即可 lic = cv2.resize(img_src, dsize=(240, 80), interpolation=cv2.INTER_AREA)[:, :, :3] # 直接resize为(240,80) img_src_copy, Lic_img = img_src, [lic] else: # 否则就需通过unet对img_src原图预测,得到img_mask,实现车牌定位,然后进行识别 img_src, img_mask = unet_predict(self.unet, self.img_src_path) img_src_copy, Lic_img = locate_and_correct(img_src, img_mask) # 利用core.py中的locate_and_correct函数进行车牌定位和矫正 Lic_pred = cnn_predict(self.cnn, Lic_img) # 利用cnn进行车牌的识别预测,Lic_pred中存的是元祖(车牌图片,识别结果) if Lic_pred: img = Image.fromarray(img_src_copy[:, :, ::-1]) # img_src_copy[:, :, ::-1]将BGR转为RGB self.img_Tk = ImageTk.PhotoImage(img) self.can_src.delete('all') # 显示前,先清空画板 self.can_src.create_image(258, 258, image=self.img_Tk, anchor='center') # img_src_copy上绘制出了定位的车牌轮廓,将其显示在画板上 for i, lic_pred in enumerate(Lic_pred): if i == 0: self.lic_Tk1 = ImageTk.PhotoImage(Image.fromarray(lic_pred[0][:, :, ::-1])) self.can_lic1.create_image(5, 5, image=self.lic_Tk1, anchor='nw') self.can_pred1.create_text(35, 15, text=lic_pred[1], anchor='nw', font=('黑体', 28)) elif i == 1: self.lic_Tk2 = ImageTk.PhotoImage(Image.fromarray(lic_pred[0][:, :, ::-1])) self.can_lic2.create_image(5, 5, image=self.lic_Tk2, anchor='nw') self.can_pred2.create_text(40, 15, text=lic_pred[1], anchor='nw', font=('黑体', 28)) elif i == 2: self.lic_Tk3 = ImageTk.PhotoImage(Image.fromarray(lic_pred[0][:, :, ::-1])) self.can_lic3.create_image(5, 5, image=self.lic_Tk3, anchor='nw') self.can_pred3.create_text(40, 15, text=lic_pred[1], anchor='nw', font=('黑体', 28)) else: # Lic_pred为空说明未能识别 self.can_pred1.create_text(47, 15, text='未能识别', anchor='nw', font=('黑体', 27)) def clear(self): self.can_src.delete('all') self.can_lic1.delete('all') self.can_lic2.delete('all') self.can_lic3.delete('all') self.can_pred1.delete('all') self.can_pred2.delete('all') self.can_pred3.delete('all') self.img_src_path = None def closeEvent(): # 关闭前清除session(),防止'NoneType' object is not callable keras.backend.clear_session() sys.exit() if __name__ == '__main__': win = Tk() ww = 1000 # 窗口宽设定1000 wh = 600 # 窗口高设定600 Window(win, ww, wh) win.protocol("WM_DELETE_WINDOW", Window.closeEvent) win.mainloop() ================================================ FILE: License-plate-recognition/Unet.py ================================================ # -*- coding:utf-8 -*- # author: DuanshengLiu import numpy as np import os import cv2 from tensorflow.keras import layers, losses, models def unet_train(): height = 512 width = 512 path = 'D:/desktop/unet_datasets/' input_name = os.listdir(path + 'train_image') n = len(input_name) print(n) X_train, y_train = [], [] for i in range(n): print("正在读取第%d张图片" % i) img = cv2.imread(path + 'train_image/%d.png' % i) label = cv2.imread(path + 'train_label/%d.png' % i) X_train.append(img) y_train.append(label) X_train = np.array(X_train) y_train = np.array(y_train) def Conv2d_BN(x, nb_filter, kernel_size, strides=(1, 1), padding='same'): x = layers.Conv2D(nb_filter, kernel_size, strides=strides, padding=padding)(x) x = layers.BatchNormalization(axis=3)(x) x = layers.LeakyReLU(alpha=0.1)(x) return x def Conv2dT_BN(x, filters, kernel_size, strides=(2, 2), padding='same'): x = layers.Conv2DTranspose(filters, kernel_size, strides=strides, padding=padding)(x) x = layers.BatchNormalization(axis=3)(x) x = layers.LeakyReLU(alpha=0.1)(x) return x inpt = layers.Input(shape=(height, width, 3)) conv1 = Conv2d_BN(inpt, 8, (3, 3)) conv1 = Conv2d_BN(conv1, 8, (3, 3)) pool1 = layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same')(conv1) conv2 = Conv2d_BN(pool1, 16, (3, 3)) conv2 = Conv2d_BN(conv2, 16, (3, 3)) pool2 = layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same')(conv2) conv3 = Conv2d_BN(pool2, 32, (3, 3)) conv3 = Conv2d_BN(conv3, 32, (3, 3)) pool3 = layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same')(conv3) conv4 = Conv2d_BN(pool3, 64, (3, 3)) conv4 = Conv2d_BN(conv4, 64, (3, 3)) pool4 = layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2), padding='same')(conv4) conv5 = Conv2d_BN(pool4, 128, (3, 3)) conv5 = layers.Dropout(0.5)(conv5) conv5 = Conv2d_BN(conv5, 128, (3, 3)) conv5 = layers.Dropout(0.5)(conv5) convt1 = Conv2dT_BN(conv5, 64, (3, 3)) concat1 = layers.concatenate([conv4, convt1], axis=3) concat1 = layers.Dropout(0.5)(concat1) conv6 = Conv2d_BN(concat1, 64, (3, 3)) conv6 = Conv2d_BN(conv6, 64, (3, 3)) convt2 = Conv2dT_BN(conv6, 32, (3, 3)) concat2 = layers.concatenate([conv3, convt2], axis=3) concat2 = layers.Dropout(0.5)(concat2) conv7 = Conv2d_BN(concat2, 32, (3, 3)) conv7 = Conv2d_BN(conv7, 32, (3, 3)) convt3 = Conv2dT_BN(conv7, 16, (3, 3)) concat3 = layers.concatenate([conv2, convt3], axis=3) concat3 = layers.Dropout(0.5)(concat3) conv8 = Conv2d_BN(concat3, 16, (3, 3)) conv8 = Conv2d_BN(conv8, 16, (3, 3)) convt4 = Conv2dT_BN(conv8, 8, (3, 3)) concat4 = layers.concatenate([conv1, convt4], axis=3) concat4 = layers.Dropout(0.5)(concat4) conv9 = Conv2d_BN(concat4, 8, (3, 3)) conv9 = Conv2d_BN(conv9, 8, (3, 3)) conv9 = layers.Dropout(0.5)(conv9) outpt = layers.Conv2D(filters=3, kernel_size=(1, 1), strides=(1, 1), padding='same', activation='relu')(conv9) # TODO:activation='softmax' model = models.Model(inpt, outpt) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.summary() print("开始训练u-net") model.fit(X_train, y_train, epochs=100, batch_size=15)#epochs和batch_size看个人情况调整，batch_size不要过大，否则内存容易溢出 #我11G显存也只能设置15-20左右，我训练最终loss降低至250左右，acc约95%左右 model.save('unet.h5') print('unet.h5保存成功!!!') def unet_predict(unet, img_src_path): img_src = cv2.imdecode(np.fromfile(img_src_path, dtype=np.uint8), -1) # 从中文路径读取时用 # img_src=cv2.imread(img_src_path) if img_src.shape != (512, 512, 3): img_src = cv2.resize(img_src, dsize=(512, 512), interpolation=cv2.INTER_AREA)[:, :, :3] # dsize=(宽度,高度),[:,:,:3]是防止图片为4通道图片，后续无法reshape img_src = img_src.reshape(1, 512, 512, 3) # 预测图片shape为(1,512,512,3) img_mask = unet.predict(img_src) # 归一化除以255后进行预测 img_src = img_src.reshape(512, 512, 3) # 将原图reshape为3维 img_mask = img_mask.reshape(512, 512, 3) # 将预测后图片reshape为3维 img_mask = img_mask / np.max(img_mask) * 255 # 归一化后乘以255 img_mask[:, :, 2] = img_mask[:, :, 1] = img_mask[:, :, 0] # 三个通道保持相同 img_mask = img_mask.astype(np.uint8) # 将img_mask类型转为int型 return img_src, img_mask ================================================ FILE: License-plate-recognition/__init__.py ================================================ ================================================ FILE: License-plate-recognition/cnn.h5 ================================================ [File too large to display: 19.5 MB] ================================================ FILE: License-plate-recognition/core.py ================================================ # -*- coding:utf-8 -*- # author: DuanshengLiu import cv2 import numpy as np def locate_and_correct(img_src, img_mask): """ 该函数通过cv2对img_mask进行边缘检测，获取车牌区域的边缘坐标(存储在contours中)和最小外接矩形4个端点坐标, 再从车牌的边缘坐标中计算出和最小外接矩形4个端点最近的点即为平行四边形车牌的四个端点,从而实现车牌的定位和矫正 :param img_src: 原始图片 :param img_mask: 通过u_net进行图像分隔得到的二值化图片，车牌区域呈现白色，背景区域为黑色 :return: 定位且矫正后的车牌 """ # cv2.imshow('img_mask',img_mask) # cv2.waitKey(0) # ret,thresh = cv2.threshold(img_mask[:,:,0],0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU) #二值化 # cv2.imshow('thresh',thresh) # cv2.waitKey(0) try: contours, hierarchy = cv2.findContours(img_mask[:, :, 0], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) except: # 防止opencv版本不一致报错 ret, contours, hierarchy = cv2.findContours(img_mask[:, :, 0], cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) if not len(contours): # contours1长度为0说明未检测到车牌 # print("未检测到车牌") return [], [] else: Lic_img = [] img_src_copy = img_src.copy() # img_src_copy用于绘制出定位的车牌轮廓 for ii, cont in enumerate(contours): x, y, w, h = cv2.boundingRect(cont) # 获取最小外接矩形 img_cut_mask = img_mask[y:y + h, x:x + w] # 将标签车牌区域截取出来 # cv2.imshow('img_cut_mask',img_cut_mask) # cv2.waitKey(0) # print('w,h,均值,宽高比',w,h,np.mean(img_cut_mask),w/h) # contours中除了车牌区域可能会有宽或高都是1或者2这样的小噪点， # 而待选车牌区域的均值应较高，且宽和高不会非常小，因此通过以下条件进行筛选 if np.mean(img_cut_mask) >= 75 and w > 15 and h > 15: rect = cv2.minAreaRect(cont) # 针对坐标点获取带方向角的最小外接矩形，中心点坐标，宽高，旋转角度 box = cv2.boxPoints(rect).astype(np.int32) # 获取最小外接矩形四个顶点坐标 # cv2.drawContours(img_mask, contours, -1, (0, 0, 255), 2) # cv2.drawContours(img_mask, [box], 0, (0, 255, 0), 2) # cv2.imshow('img_mask',img_mask) # cv2.waitKey(0) cont = cont.reshape(-1, 2).tolist() # 由于转换矩阵的两组坐标位置需要一一对应，因此需要将最小外接矩形的坐标进行排序，最终排序为[左上，左下，右上，右下] box = sorted(box, key=lambda xy: xy[0]) # 先按照左右进行排序，分为左侧的坐标和右侧的坐标 box_left, box_right = box[:2], box[2:] # 此时box的前2个是左侧的坐标，后2个是右侧的坐标 box_left = sorted(box_left, key=lambda x: x[1]) # 再按照上下即y进行排序，此时box_left中为左上和左下两个端点坐标 box_right = sorted(box_right, key=lambda x: x[1]) # 此时box_right中为右上和右下两个端点坐标 box = np.array(box_left + box_right) # [左上，左下，右上，右下] # print(box) x0, y0 = box[0][0], box[0][1] # 这里的4个坐标即为最小外接矩形的四个坐标，接下来需获取平行(或不规则)四边形的坐标 x1, y1 = box[1][0], box[1][1] x2, y2 = box[2][0], box[2][1] x3, y3 = box[3][0], box[3][1] def point_to_line_distance(X, Y): if x2 - x0: k_up = (y2 - y0) / (x2 - x0) # 斜率不为无穷大 d_up = abs(k_up * X - Y + y2 - k_up * x2) / (k_up ** 2 + 1) ** 0.5 else: # 斜率无穷大 d_up = abs(X - x2) if x1 - x3: k_down = (y1 - y3) / (x1 - x3) # 斜率不为无穷大 d_down = abs(k_down * X - Y + y1 - k_down * x1) / (k_down ** 2 + 1) ** 0.5 else: # 斜率无穷大 d_down = abs(X - x1) return d_up, d_down d0, d1, d2, d3 = np.inf, np.inf, np.inf, np.inf l0, l1, l2, l3 = (x0, y0), (x1, y1), (x2, y2), (x3, y3) for each in cont: # 计算cont中的坐标与矩形四个坐标的距离以及到上下两条直线的距离，对距离和进行权重的添加，成功计算选出四边形的4个顶点坐标 x, y = each[0], each[1] dis0 = (x - x0) ** 2 + (y - y0) ** 2 dis1 = (x - x1) ** 2 + (y - y1) ** 2 dis2 = (x - x2) ** 2 + (y - y2) ** 2 dis3 = (x - x3) ** 2 + (y - y3) ** 2 d_up, d_down = point_to_line_distance(x, y) weight = 0.975 if weight * d_up + (1 - weight) * dis0 < d0: # 小于则更新 d0 = weight * d_up + (1 - weight) * dis0 l0 = (x, y) if weight * d_down + (1 - weight) * dis1 < d1: d1 = weight * d_down + (1 - weight) * dis1 l1 = (x, y) if weight * d_up + (1 - weight) * dis2 < d2: d2 = weight * d_up + (1 - weight) * dis2 l2 = (x, y) if weight * d_down + (1 - weight) * dis3 < d3: d3 = weight * d_down + (1 - weight) * dis3 l3 = (x, y) # print([l0,l1,l2,l3]) # for l in [l0, l1, l2, l3]: # cv2.circle(img=img_mask, color=(0, 255, 255), center=tuple(l), thickness=2, radius=2) # cv2.imshow('img_mask',img_mask) # cv2.waitKey(0) p0 = np.float32([l0, l1, l2, l3]) # 左上角，左下角，右上角，右下角，p0和p1中的坐标顺序对应，以进行转换矩阵的形成 p1 = np.float32([(0, 0), (0, 80), (240, 0), (240, 80)]) # 我们所需的长方形 transform_mat = cv2.getPerspectiveTransform(p0, p1) # 构成转换矩阵 lic = cv2.warpPerspective(img_src, transform_mat, (240, 80)) # 进行车牌矫正 # cv2.imshow('lic',lic) # cv2.waitKey(0) Lic_img.append(lic) cv2.drawContours(img_src_copy, [np.array([l0, l1, l3, l2])], -1, (0, 255, 0), 2) # 在img_src_copy上绘制出定位的车牌轮廓，(0, 255, 0)表示绘制线条为绿色 return img_src_copy, Lic_img ================================================ FILE: License-plate-recognition/train.py ================================================ # -*- coding:utf-8 -*- # author: DuanshengLiu from Unet import unet_train from CNN import cnn_train unet_train()#训练后得到unet.h5,用于车牌定位 cnn_train()#训练后得到cnn.h5，用于车牌识别 ================================================ FILE: README.md ================================================ # End-to-end-for-chinese-plate-recognition ## 基于u-net，cv2以及cnn的中文车牌定位，矫正和端到端识别软件，其中unet和cv2用于车牌定位和矫正，cnn进行车牌识别，unet和cnn都是基于tensorflow的keras实现 ## 环境：python:3.6, tensorflow:1.15.2, opencv: 4.1.0.25, keras: 2.3.1 ### 整体思路：1. 利用u-net图像分割得到二值化图像，2. 再使用cv2进行边缘检测获得车牌区域坐标，并将车牌图形矫正，3. 利用卷积神经网络cnn进行车牌多标签端到端识别，具体描述可见CSDN博客：https://blog.csdn.net/qq_32194791/article/details/106748685 ### 实现效果：拍摄角度倾斜、强曝光或昏暗环境等都能较好地识别，甚至有些百度AI车牌识别未能识别的图片也能识别 ### 注意：若是直接识别类似下图的无需定位的完整车牌，那么请确保图片尺寸小于等于240 * 80，否则会被认为图片中含其余区域而进行定位，反而识别效果不佳 ![](https://github.com/duanshengliu/End-to-end-for-chinese-plate-recognition/blob/master/test_pic/lic.png) ### 其余的没什么问题，正常识别都可以 ### 部分效果图： ![](https://github.com/duanshengliu/End-to-end-for-chinese-plate-recognition/blob/master/test_pic/0.png) ![](https://github.com/duanshengliu/End-to-end-for-chinese-plate-recognition/blob/master/test_pic/1.png) ![](https://github.com/duanshengliu/End-to-end-for-chinese-plate-recognition/blob/master/test_pic/2.png) ![](https://github.com/duanshengliu/End-to-end-for-chinese-plate-recognition/blob/master/test_pic/3.png) ![](https://github.com/duanshengliu/End-to-end-for-chinese-plate-recognition/blob/master/test_pic/4.png) ![](https://github.com/duanshengliu/End-to-end-for-chinese-plate-recognition/blob/master/test_pic/5.png) ![](https://github.com/duanshengliu/End-to-end-for-chinese-plate-recognition/blob/master/test_pic/6.png) ![](https://github.com/duanshengliu/End-to-end-for-chinese-plate-recognition/blob/master/test_pic/7.png) ================================================ FILE: requirements.txt ================================================ tensorflow==1.15.2 opencv-python==4.1.0.25 keras==2.3.1 pillow==9.0.0