毕业设计-车牌识别程序

#coding=utf-8 import cv2 import numpy as np from keras import backend as K from keras.models import * from keras.layers import * from PIL import Image chars = [ u"京", u"沪", u"津", u"渝", u"冀", u"晋", u"蒙", u"辽", u"吉", u"黑", u"苏", u"浙", u"皖", u"闽", u"赣", u"鲁", u"豫", u"鄂", u"湘", u"粤", u"桂",u"琼", u"川", u"贵", u"云", u"藏", u"陕", u"甘", u"青", u"宁", u"新", u"0", u"1", u"2", u"3", u"4", u"5", u"6", u"7", u"8", u"9", u"A",u"B", u"C", u"D", u"E", u"F", u"G", u"H", u"J", u"K", u"L",u"M", u"N", u"P", u"Q", u"R", u"S", u"T", u"U", u"V", u"W", u"X",u"Y", u"Z",u"港",u"学",u"使",u"警",u"澳",u"挂",u"军",u"北",u"南", u"广",u"沈",u"兰",u"成",u"济",u"海",u"民",u"航",u"空"] class LPR(): #导入模型: def __init__(self,model_detection,model_finemapping,model_seq_rec): self.watch_cascade = cv2.CascadeClassifier(model_detection) #导入cascade.xml 检测模型 self.modelFineMapping = self.model_finemapping() self.modelFineMapping.load_weights(model_finemapping) #导入边界回归模型 self.modelSeqRec = self.model_seq_rec(model_seq_rec) #导入神经网络模型 # 剪切图片: def computeSafeRegion(self,shape,bounding_rect): top = bounding_rect[1] # y bottom = bounding_rect[1] + bounding_rect[3] # y + h left = bounding_rect[0] # x right = bounding_rect[0] + bounding_rect[2] # x + w min_top = 0 max_bottom = shape[0] min_left = 0 max_right = shape[1] if top < min_top: top = min_top if left < min_left: left = min_left if bottom > max_bottom: bottom = max_bottom if right > max_right: right = max_right return [left,top,right-left,bottom-top] # 剪切图片: def cropImage(self,image,rect): x, y, w, h = self.computeSafeRegion(image.shape,rect) return image[y:y+h,x:x+w] # 车牌粗定位: def detectPlateRough(self,image_gray,resize_h = 720,en_scale =1.08 ,top_bottom_padding_rate = 0.05): #if top_bottom_padding_rate>0.2: # print("error:top_bottom_padding_rate > 0.2:",top_bottom_padding_rate) # exit(1) height = image_gray.shape[0] # 原图的高 padding = int(height*top_bottom_padding_rate) # 需要剪掉的尺寸 scale = image_gray.shape[1]/float(image_gray.shape[0]) # 原图的长宽比 image = cv2.resize(image_gray, (int(scale*resize_h), resize_h)) # 将图片按照原图比例变化尺寸 image_color_cropped = image[padding:resize_h-padding,0:image_gray.shape[1]] # 剪切图片 image_gray = cv2.cvtColor(image_color_cropped,cv2.COLOR_RGB2GRAY) # 将图片转换为灰度图 watches = self.watch_cascade.detectMultiScale(image_gray, en_scale, 2, minSize=(36, 9),maxSize=(36*40, 9*40)) cropped_images = [] for (x, y, w, h) in watches: # 减小x和y,放大w和h x -= w * 0.14 w += w * 0.28 y -= h * 0.15 h += h * 0.3 cropped = self.cropImage(image_color_cropped, (int(x), int(y), int(w), int(h))) # 剪切图片 cropped_images.append([cropped,[x, y+padding, w, h]]) return cropped_images # 计算置信度: def fastdecode(self,y_pred): results = "" confidence = 0.0 table_pred = y_pred.reshape(-1, len(chars)+1) res = table_pred.argmax(axis=1) for i,one in enumerate(res): if one<len(chars) and (i==0 or (one!=res[i-1])): results+= chars[one] confidence+=table_pred[i][one] confidence/= len(results) return results,confidence # 搭建ocr的网络模型: def model_seq_rec(self,model_path): width, height, n_len, n_class = 164, 48, 7, len(chars)+ 1 rnn_size = 256 input_tensor = Input((164, 48, 3)) x = input_tensor base_conv = 32 for i in range(3): x = Conv2D(base_conv * (2 ** (i)), (3, 3))(x) x = BatchNormalization()(x) x = Activation('relu')(x) x = MaxPooling2D(pool_size=(2, 2))(x) conv_shape = x.get_shape() x = Reshape(target_shape=(int(conv_shape[1]), int(conv_shape[2] * conv_shape[3])))(x) x = Dense(32)(x) x = BatchNormalization()(x) x = Activation('relu')(x) gru_1 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru1')(x) gru_1b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru1_b')(x) gru1_merged = add([gru_1, gru_1b]) gru_2 = GRU(rnn_size, return_sequences=True, kernel_initializer='he_normal', name='gru2')(gru1_merged) gru_2b = GRU(rnn_size, return_sequences=True, go_backwards=True, kernel_initializer='he_normal', name='gru2_b')(gru1_merged) x = concatenate([gru_2, gru_2b]) x = Dropout(0.25)(x) x = Dense(n_class, kernel_initializer='he_normal', activation='softmax')(x) base_model = Model(inputs=input_tensor, outputs=x) base_model.load_weights(model_path) return base_model # 边界回归模型: def model_finemapping(self): input = Input(shape=[16, 66, 3]) # x = Conv2D(10, (3, 3), strides=1, padding='valid', name='conv1')(input) x = Activation("relu", name='relu1')(x) x = MaxPool2D(pool_size=2)(x) x = Conv2D(16, (3, 3), strides=1, padding='valid', name='conv2')(x) x = Activation("relu", name='relu2')(x) x = Conv2D(32, (3, 3), strides=1, padding='valid', name='conv3')(x) x = Activation("relu", name='relu3')(x) x = Flatten()(x) output = Dense(2,name = "dense")(x) output = Activation("relu", name='relu4')(output) model = Model([input], [output]) return model # 车牌精定位: def finemappingVertical(self,image,rect): resized = cv2.resize(image,(66,16)) # 将图像尺寸转换为66*16*3 resized = resized.astype(np.float)/255 # 将原来灰度图颜色通道[0, 255]转化为float类型[0,1] res_raw= self.modelFineMapping.predict(np.array([resized]))[0] # 将图像输入边界回归模型 res =res_raw*image.shape[1] res = res.astype(np.int) H,T = res H-=3 if H<0: H=0 T+=2; if T>= image.shape[1]-1: T= image.shape[1]-1 rect[2] -= rect[2]*(1-res_raw[1] + res_raw[0]) rect[0]+=res[0] image = image[:,H:T+2] image = cv2.resize(image, (int(136), int(36))) return image,rect # OCR识别: def recognizeOne(self,src): x_tempx = src x_temp = cv2.resize(x_tempx,( 164,48)) x_temp = x_temp.transpose(1, 0, 2) # 转置图像 y_pred = self.modelSeqRec.predict(np.array([x_temp])) # 输入神经网络模型 y_pred = y_pred[:,2:,:] return self.fastdecode(y_pred) # 输入为一个Mat类型的图片,输出为识别的车牌字符串，以及confidence可信度: def SimpleRecognizePlateByE2E(self,image): images = self.detectPlateRough(image,image.shape[0],top_bottom_padding_rate=0.1) res_set = [] for j,plate in enumerate(images): plate,rect = plate image_rgb,rect_refine = self.finemappingVertical(plate,rect) cv2.imshow("image_rgb",image_rgb) cv2.waitKey(0) res,confidence = self.recognizeOne(image_rgb) res_set.append([res,confidence,rect_refine]) # 填充数组 return res_set