python — cnn+opencv 识别车牌

过几天再补细节（）

import cv2
import os
import sys
import numpy as np
import tensorflow as tfchar_table = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K','L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z', '川', '鄂', '赣', '甘', '贵','桂', '黑', '沪', '冀', '津', '京', '吉', '辽', '鲁', '蒙', '闽', '宁', '青', '琼', '陕', '苏', '晋','皖', '湘', '新', '豫', '渝', '粤', '云', '藏', '浙']def hist_image(img):assert img.ndim==2hist = [0 for i in range(256)]img_h,img_w = img.shape[0],img.shape[1]for row in range(img_h):for col in range(img_w):hist[img[row,col]] += 1p = [hist[n]/(img_w*img_h) for n in range(256)]p1 = np.cumsum(p)for row in range(img_h):for col in range(img_w):v = img[row,col]img[row,col] = p1[v]*255return imgdef find_board_area(img):assert img.ndim==2img_h,img_w = img.shape[0],img.shape[1]top,bottom,left,right = 0,img_h,0,img_wflag = Falseh_proj = [0 for i in range(img_h)]v_proj = [0 for i in range(img_w)]for row in range(round(img_h*0.5),round(img_h*0.8),3):for col in range(img_w):if img[row,col]==255:h_proj[row] += 1if flag==False and h_proj[row]>12:flag = Truetop = rowif flag==True and row>top+8 and h_proj[row]<12:bottom = rowflag = Falsefor col in range(round(img_w*0.3),img_w,1):for row in range(top,bottom,1):if img[row,col]==255:v_proj[col] += 1if flag==False and (v_proj[col]>10 or v_proj[col]-v_proj[col-1]>5):left = colbreakreturn left,top,120,bottom-top-10def verify_scale(rotate_rect):error = 0.4aspect = 4#4.7272min_area = 10*(10*aspect)max_area = 150*(150*aspect)min_aspect = aspect*(1-error)max_aspect = aspect*(1+error)theta = 30# 宽或高为0，不满足矩形直接返回Falseif rotate_rect[1][0]==0 or rotate_rect[1][1]==0:return Falser = rotate_rect[1][0]/rotate_rect[1][1]r = max(r,1/r)area = rotate_rect[1][0]*rotate_rect[1][1]if area>min_area and area<max_area and r>min_aspect and r<max_aspect:# 矩形的倾斜角度在不超过thetaif ((rotate_rect[1][0] < rotate_rect[1][1] and rotate_rect[2] >= -90 and rotate_rect[2] < -(90 - theta)) or(rotate_rect[1][1] < rotate_rect[1][0] and rotate_rect[2] > -theta and rotate_rect[2] <= 0)):return Truereturn Falsedef img_Transform(car_rect,image):img_h,img_w = image.shape[:2]rect_w,rect_h = car_rect[1][0],car_rect[1][1]angle = car_rect[2]return_flag = Falseif car_rect[2]==0:return_flag = Trueif car_rect[2]==-90 and rect_w<rect_h:rect_w, rect_h = rect_h, rect_wreturn_flag = Trueif return_flag:car_img = image[int(car_rect[0][1]-rect_h/2):int(car_rect[0][1]+rect_h/2),int(car_rect[0][0]-rect_w/2):int(car_rect[0][0]+rect_w/2)]return car_imgcar_rect = (car_rect[0],(rect_w,rect_h),angle)box = cv2.boxPoints(car_rect)heigth_point = right_point = [0,0]left_point = low_point = [car_rect[0][0], car_rect[0][1]]for point in box:if left_point[0] > point[0]:left_point = pointif low_point[1] > point[1]:low_point = pointif heigth_point[1] < point[1]:heigth_point = pointif right_point[0] < point[0]:right_point = pointif left_point[1] <= right_point[1]:  # 正角度new_right_point = [right_point[0], heigth_point[1]]pts1 = np.float32([left_point, heigth_point, right_point])pts2 = np.float32([left_point, heigth_point, new_right_point])  # 字符只是高度需要改变M = cv2.getAffineTransform(pts1, pts2)dst = cv2.warpAffine(image, M, (round(img_w*2), round(img_h*2)))car_img = dst[int(left_point[1]):int(heigth_point[1]), int(left_point[0]):int(new_right_point[0])]elif left_point[1] > right_point[1]:  # 负角度new_left_point = [left_point[0], heigth_point[1]]pts1 = np.float32([left_point, heigth_point, right_point])pts2 = np.float32([new_left_point, heigth_point, right_point])  # 字符只是高度需要改变M = cv2.getAffineTransform(pts1, pts2)dst = cv2.warpAffine(image, M, (round(img_w*2), round(img_h*2)))car_img = dst[int(right_point[1]):int(heigth_point[1]), int(new_left_point[0]):int(right_point[0])]return car_imgdef pre_process(orig_img):gray_img = cv2.cvtColor(orig_img, cv2.COLOR_BGR2GRAY)cv2.imshow('gray_img', gray_img)blur_img = cv2.blur(gray_img, (3, 3))cv2.imshow('blur', blur_img)sobel_img = cv2.Sobel(blur_img, cv2.CV_16S, 1, 0, ksize=3)sobel_img = cv2.convertScaleAbs(sobel_img)cv2.imshow('sobel', sobel_img)hsv_img = cv2.cvtColor(orig_img, cv2.COLOR_BGR2HSV)h, s, v = hsv_img[:, :, 0], hsv_img[:, :, 1], hsv_img[:, :, 2]# 黄色色调区间[26，34],蓝色色调区间:[100,124]blue_img = (((h > 26) & (h < 34)) | ((h > 100) & (h < 124))) & (s > 70) & (v > 70)blue_img = blue_img.astype('float32')mix_img = np.multiply(sobel_img, blue_img)cv2.imshow('mix', mix_img)mix_img = mix_img.astype(np.uint8)ret, binary_img = cv2.threshold(mix_img, 0, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)cv2.imshow('binary',binary_img)kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(21,5))close_img = cv2.morphologyEx(binary_img, cv2.MORPH_CLOSE, kernel)cv2.imshow('close', close_img)return close_img# 给候选车牌区域做漫水填充算法，一方面补全上一步求轮廓可能存在轮廓歪曲的问题，
# 另一方面也可以将非车牌区排除掉
def verify_color(rotate_rect,src_image):img_h,img_w = src_image.shape[:2]mask = np.zeros(shape=[img_h+2,img_w+2],dtype=np.uint8)connectivity = 4 #种子点上下左右4邻域与种子颜色值在[loDiff,upDiff]的被涂成new_value，也可设置8邻域loDiff,upDiff = 30,30new_value = 255flags = connectivityflags |= cv2.FLOODFILL_FIXED_RANGE  #考虑当前像素与种子象素之间的差，不设置的话则和邻域像素比较flags |= new_value << 8flags |= cv2.FLOODFILL_MASK_ONLY #设置这个标识符则不会去填充改变原始图像，而是去填充掩模图像（mask）rand_seed_num = 5000 #生成多个随机种子valid_seed_num = 200 #从rand_seed_num中随机挑选valid_seed_num个有效种子adjust_param = 0.1box_points = cv2.boxPoints(rotate_rect)box_points_x = [n[0] for n in box_points]box_points_x.sort(reverse=False)adjust_x = int((box_points_x[2]-box_points_x[1])*adjust_param)col_range = [box_points_x[1]+adjust_x,box_points_x[2]-adjust_x]box_points_y = [n[1] for n in box_points]box_points_y.sort(reverse=False)adjust_y = int((box_points_y[2]-box_points_y[1])*adjust_param)row_range = [box_points_y[1]+adjust_y, box_points_y[2]-adjust_y]# 如果以上方法种子点在水平或垂直方向可移动的范围很小，则采用旋转矩阵对角线来设置随机种子点if (col_range[1]-col_range[0])/(box_points_x[3]-box_points_x[0])<0.4\or (row_range[1]-row_range[0])/(box_points_y[3]-box_points_y[0])<0.4:points_row = []points_col = []for i in range(2):pt1,pt2 = box_points[i],box_points[i+2]x_adjust,y_adjust = int(adjust_param*(abs(pt1[0]-pt2[0]))),int(adjust_param*(abs(pt1[1]-pt2[1])))if (pt1[0] <= pt2[0]):pt1[0], pt2[0] = pt1[0] + x_adjust, pt2[0] - x_adjustelse:pt1[0], pt2[0] = pt1[0] - x_adjust, pt2[0] + x_adjustif (pt1[1] <= pt2[1]):pt1[1], pt2[1] = pt1[1] + adjust_y, pt2[1] - adjust_yelse:pt1[1], pt2[1] = pt1[1] - y_adjust, pt2[1] + y_adjusttemp_list_x = [int(x) for x in np.linspace(pt1[0],pt2[0],int(rand_seed_num /2))]temp_list_y = [int(y) for y in np.linspace(pt1[1],pt2[1],int(rand_seed_num /2))]points_col.extend(temp_list_x)points_row.extend(temp_list_y)else:points_row = np.random.randint(row_range[0],row_range[1],size=rand_seed_num)points_col = np.linspace(col_range[0],col_range[1],num=rand_seed_num).astype(np.uint16)points_row = np.array(points_row)points_col = np.array(points_col)hsv_img = cv2.cvtColor(src_image, cv2.COLOR_BGR2HSV)h,s,v = hsv_img[:,:,0],hsv_img[:,:,1],hsv_img[:,:,2]# 将随机生成的多个种子依次做漫水填充,理想情况是整个车牌被填充flood_img = src_image.copy()seed_cnt = 0for i in range(rand_seed_num):rand_index = np.random.choice(rand_seed_num,1,replace=False)row,col = points_row[rand_index],points_col[rand_index]# 限制随机种子必须是车牌背景色if (((h[row,col]>26)&(h[row,col]<34))|((h[row,col]>100)&(h[row,col]<124)))&(s[row,col]>70)&(v[row,col]>70):cv2.floodFill(src_image, mask, (col,row), (255, 255, 255), (loDiff,) * 3, (upDiff,) * 3, flags)cv2.circle(flood_img,center=(col,row),radius=2,color=(0,0,255),thickness=2)seed_cnt += 1if seed_cnt >= valid_seed_num:break#======================调试用======================#show_seed = np.random.uniform(1,100,1).astype(np.uint16)cv2.imshow('floodfill'+str(show_seed),flood_img)cv2.imshow('flood_mask'+str(show_seed),mask)#======================调试用======================## 获取掩模上被填充点的像素点，并求点集的最小外接矩形mask_points = []for row in range(1,img_h+1):for col in range(1,img_w+1):if mask[row,col] != 0:mask_points.append((col-1,row-1))mask_rotateRect = cv2.minAreaRect(np.array(mask_points))if verify_scale(mask_rotateRect):return True,mask_rotateRectelse:return False,mask_rotateRect# 车牌定位
def locate_carPlate(orig_img,pred_image):carPlate_list = []temp1_orig_img = orig_img.copy() #调试用temp2_orig_img = orig_img.copy() #调试用cloneImg,contours,heriachy = cv2.findContours(pred_image,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)for i,contour in enumerate(contours):cv2.drawContours(temp1_orig_img, contours, i, (0, 255, 255), 2)# 获取轮廓最小外接矩形，返回值rotate_rectrotate_rect = cv2.minAreaRect(contour)# 根据矩形面积大小和长宽比判断是否是车牌if verify_scale(rotate_rect):ret,rotate_rect2 = verify_color(rotate_rect,temp2_orig_img)if ret == False:continue# 车牌位置矫正car_plate = img_Transform(rotate_rect2, temp2_orig_img)car_plate = cv2.resize(car_plate,(car_plate_w,car_plate_h)) #调整尺寸为后面CNN车牌识别做准备#========================调试看效果========================#box = cv2.boxPoints(rotate_rect2)for k in range(4):n1,n2 = k%4,(k+1)%4cv2.line(temp1_orig_img,(box[n1][0],box[n1][1]),(box[n2][0],box[n2][1]),(255,0,0),2)cv2.imshow('opencv_' + str(i), car_plate)#========================调试看效果========================#carPlate_list.append(car_plate)cv2.imshow('contour', temp1_orig_img)return carPlate_list# 左右切割
def horizontal_cut_chars(plate):char_addr_list = []area_left,area_right,char_left,char_right= 0,0,0,0img_w = plate.shape[1]# 获取车牌每列边缘像素点个数def getColSum(img,col):sum = 0for i in range(img.shape[0]):sum += round(img[i,col]/255)return sum;sum = 0for col in range(img_w):sum += getColSum(plate,col)# 每列边缘像素点必须超过均值的60%才能判断属于字符区域col_limit = 0#round(0.5*sum/img_w)# 每个字符宽度也进行限制charWid_limit = [round(img_w/12),round(img_w/5)]is_char_flag = Falsefor i in range(img_w):colValue = getColSum(plate,i)if colValue > col_limit:if is_char_flag == False:area_right = round((i+char_right)/2)area_width = area_right-area_leftchar_width = char_right-char_leftif (area_width>charWid_limit[0]) and (area_width<charWid_limit[1]):char_addr_list.append((area_left,area_right,char_width))char_left = iarea_left = round((char_left+char_right) / 2)is_char_flag = Trueelse:if is_char_flag == True:char_right = i-1is_char_flag = False# 手动结束最后未完成的字符分割if area_right < char_left:area_right,char_right = img_w,img_warea_width = area_right - area_leftchar_width = char_right - char_leftif (area_width > charWid_limit[0]) and (area_width < charWid_limit[1]):char_addr_list.append((area_left, area_right, char_width))return char_addr_listdef get_chars(car_plate):img_h,img_w = car_plate.shape[:2]h_proj_list = [] # 水平投影长度列表h_temp_len,v_temp_len = 0,0h_startIndex,h_end_index = 0,0 # 水平投影记索引h_proj_limit = [0.2,0.8] # 车牌在水平方向得轮廓长度少于20%或多余80%过滤掉char_imgs = []# 将二值化的车牌水平投影到Y轴，计算投影后的连续长度，连续投影长度可能不止一段h_count = [0 for i in range(img_h)]for row in range(img_h):temp_cnt = 0for col in range(img_w):if car_plate[row,col] == 255:temp_cnt += 1h_count[row] = temp_cntif temp_cnt/img_w<h_proj_limit[0] or temp_cnt/img_w>h_proj_limit[1]:if h_temp_len != 0:h_end_index = row-1h_proj_list.append((h_startIndex,h_end_index))h_temp_len = 0continueif temp_cnt > 0:if h_temp_len == 0:h_startIndex = rowh_temp_len = 1else:h_temp_len += 1else:if h_temp_len > 0:h_end_index = row-1h_proj_list.append((h_startIndex,h_end_index))h_temp_len = 0# 手动结束最后得水平投影长度累加if h_temp_len != 0:h_end_index = img_h-1h_proj_list.append((h_startIndex, h_end_index))# 选出最长的投影，该投影长度占整个截取车牌高度的比值必须大于0.5h_maxIndex,h_maxHeight = 0,0for i,(start,end) in enumerate(h_proj_list):if h_maxHeight < (end-start):h_maxHeight = (end-start)h_maxIndex = iif h_maxHeight/img_h < 0.5:return char_imgschars_top,chars_bottom = h_proj_list[h_maxIndex][0],h_proj_list[h_maxIndex][1]plates = car_plate[chars_top:chars_bottom+1,:]cv2.imwrite('./carIdentityData/opencv_output/car.jpg',car_plate)cv2.imwrite('./carIdentityData/opencv_output/plate.jpg', plates)char_addr_list = horizontal_cut_chars(plates)for i,addr in enumerate(char_addr_list):char_img = car_plate[chars_top:chars_bottom+1,addr[0]:addr[1]]char_img = cv2.resize(char_img,(char_w,char_h))char_imgs.append(char_img)return char_imgsdef extract_char(car_plate):gray_plate = cv2.cvtColor(car_plate,cv2.COLOR_BGR2GRAY)ret,binary_plate = cv2.threshold(gray_plate,0,255,cv2.THRESH_BINARY|cv2.THRESH_OTSU)char_img_list = get_chars(binary_plate)return char_img_listdef cnn_select_carPlate(plate_list,model_path):if len(plate_list) == 0:return False,plate_listg1 = tf.Graph()sess1 = tf.Session(graph=g1)with sess1.as_default():with sess1.graph.as_default():model_dir = os.path.dirname(model_path)saver = tf.train.import_meta_graph(model_path)saver.restore(sess1, tf.train.latest_checkpoint(model_dir))graph = tf.get_default_graph()net1_x_place = graph.get_tensor_by_name('x_place:0')net1_keep_place = graph.get_tensor_by_name('keep_place:0')net1_out = graph.get_tensor_by_name('out_put:0')input_x = np.array(plate_list)net_outs = tf.nn.softmax(net1_out)preds = tf.argmax(net_outs,1) #预测结果probs = tf.reduce_max(net_outs,reduction_indices=[1]) #结果概率值pred_list,prob_list = sess1.run([preds,probs],feed_dict={net1_x_place:input_x,net1_keep_place:1.0})# 选出概率最大的车牌result_index,result_prob = -1,0.for i,pred in enumerate(pred_list):if pred==1 and prob_list[i]>result_prob:result_index,result_prob = i,prob_list[i]if result_index == -1:return False,plate_list[0]else:return True,plate_list[result_index]def cnn_recongnize_char(img_list,model_path):g2 = tf.Graph()sess2 = tf.Session(graph=g2)text_list = []if len(img_list) == 0:return text_listwith sess2.as_default():with sess2.graph.as_default():model_dir = os.path.dirname(model_path)saver = tf.train.import_meta_graph(model_path)saver.restore(sess2, tf.train.latest_checkpoint(model_dir))graph = tf.get_default_graph()net2_x_place = graph.get_tensor_by_name('x_place:0')net2_keep_place = graph.get_tensor_by_name('keep_place:0')net2_out = graph.get_tensor_by_name('out_put:0')data = np.array(img_list)# 数字、字母、汉字，从67维向量找到概率最大的作为预测结果net_out = tf.nn.softmax(net2_out)preds = tf.argmax(net_out,1)my_preds= sess2.run(preds, feed_dict={net2_x_place: data, net2_keep_place: 1.0})for i in my_preds:text_list.append(char_table[i])return text_listif __name__ == '__main__':cur_dir = sys.path[0]car_plate_w,car_plate_h = 136,36char_w,char_h = 20,20plate_model_path = os.path.join(cur_dir, './carIdentityData/model/plate_recongnize/model.ckpt-540.meta')char_model_path = os.path.join(cur_dir,'./carIdentityData/model/char_recongnize/model.ckpt-510.meta')img = cv2.imread('./carIdentityData/test/4.jpg')# 预处理pred_img = pre_process(img)# 车牌定位car_plate_list = locate_carPlate(img,pred_img)# CNN车牌过滤ret,car_plate = cnn_select_carPlate(car_plate_list,plate_model_path)if ret == False:print("未检测到车牌")sys.exit(-1)cv2.imshow('cnn_plate',car_plate)# 字符提取char_img_list = extract_char(car_plate)num = 0for plate_chars in char_img_list:cv2.imwrite('./carIdentityData/opencv_output/char' + str(num) + '.jpg', plate_chars)num = num + 1# CNN字符识别text = cnn_recongnize_char(char_img_list,char_model_path)print(text)cv2.waitKey(0)

import sys
import os
import numpy as np
import cv2
import tensorflow as tf
from sklearn.model_selection import train_test_splitnumbers = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9']
alphbets = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T','U', 'V', 'W', 'X', 'Y', 'Z']
chinese = ['zh_cuan', 'zh_e', 'zh_gan', 'zh_gan1', 'zh_gui', 'zh_gui1', 'zh_hei', 'zh_hu', 'zh_ji', 'zh_jin','zh_jing', 'zh_jl', 'zh_liao', 'zh_lu', 'zh_meng', 'zh_min', 'zh_ning', 'zh_qing', 'zh_qiong','zh_shan', 'zh_su', 'zh_sx', 'zh_wan', 'zh_xiang', 'zh_xin', 'zh_yu', 'zh_yu1', 'zh_yue', 'zh_yun','zh_zang', 'zh_zhe']class char_cnn_net:def __init__(self):self.dataset = numbers + alphbets + chineseself.dataset_len = len(self.dataset)self.img_size = 20self.y_size = len(self.dataset)self.batch_size = 100self.x_place = tf.placeholder(dtype=tf.float32, shape=[None, self.img_size, self.img_size], name='x_place')self.y_place = tf.placeholder(dtype=tf.float32, shape=[None, self.y_size], name='y_place')self.keep_place = tf.placeholder(dtype=tf.float32, name='keep_place')def cnn_construct(self):x_input = tf.reshape(self.x_place, shape=[-1, 20, 20, 1])cw1 = tf.Variable(tf.random_normal(shape=[3, 3, 1, 32], stddev=0.01), dtype=tf.float32)cb1 = tf.Variable(tf.random_normal(shape=[32]), dtype=tf.float32)conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x_input,filter=cw1,strides=[1,1,1,1],padding='SAME'),cb1))conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')conv1 = tf.nn.dropout(conv1, self.keep_place)cw2 = tf.Variable(tf.random_normal(shape=[3, 3, 32, 64], stddev=0.01), dtype=tf.float32)cb2 = tf.Variable(tf.random_normal(shape=[64]), dtype=tf.float32)conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1,filter=cw2,strides=[1,1,1,1],padding='SAME'),cb2))conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')conv2 = tf.nn.dropout(conv2, self.keep_place)cw3 = tf.Variable(tf.random_normal(shape=[3, 3, 64, 128], stddev=0.01), dtype=tf.float32)cb3 = tf.Variable(tf.random_normal(shape=[128]), dtype=tf.float32)conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2,filter=cw3,strides=[1,1,1,1],padding='SAME'),cb3))conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')conv3 = tf.nn.dropout(conv3, self.keep_place)conv_out = tf.reshape(conv3, shape=[-1, 3 * 3 * 128])fw1 = tf.Variable(tf.random_normal(shape=[3 * 3 * 128, 1024], stddev=0.01), dtype=tf.float32)fb1 = tf.Variable(tf.random_normal(shape=[1024]), dtype=tf.float32)fully1 = tf.nn.relu(tf.add(tf.matmul(conv_out, fw1), fb1))fully1 = tf.nn.dropout(fully1, self.keep_place)fw2 = tf.Variable(tf.random_normal(shape=[1024, 1024], stddev=0.01), dtype=tf.float32)fb2 = tf.Variable(tf.random_normal(shape=[1024]), dtype=tf.float32)fully2 = tf.nn.relu(tf.add(tf.matmul(fully1, fw2), fb2))fully2 = tf.nn.dropout(fully2, self.keep_place)fw3 = tf.Variable(tf.random_normal(shape=[1024, self.dataset_len], stddev=0.01), dtype=tf.float32)fb3 = tf.Variable(tf.random_normal(shape=[self.dataset_len]), dtype=tf.float32)fully3 = tf.add(tf.matmul(fully2, fw3), fb3, name='out_put')return fully3def train(self,data_dir,save_model_path):print('ready load train dataset')X, y = self.init_data(data_dir)print('success load' + str(len(y)) + 'datas')train_x, test_x, train_y, test_y = train_test_split(X, y, test_size=0.2, random_state=0)out_put = self.cnn_construct()predicts = tf.nn.softmax(out_put)predicts = tf.argmax(predicts, axis=1)actual_y = tf.argmax(self.y_place, axis=1)accuracy = tf.reduce_mean(tf.cast(tf.equal(predicts, actual_y), dtype=tf.float32))cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out_put, labels=self.y_place))opt = tf.train.AdamOptimizer(learning_rate=0.001)train_step = opt.minimize(cost)with tf.Session() as sess:init = tf.global_variables_initializer()sess.run(init)step = 0saver = tf.train.Saver()while True:train_index = np.random.choice(len(train_x), self.batch_size, replace=False)train_randx = train_x[train_index]train_randy = train_y[train_index]_, loss = sess.run([train_step, cost],feed_dict={self.x_place:train_randx,self.y_place:train_randy,self.keep_place:0.75})step += 1if step % 10 == 0:test_index = np.random.choice(len(test_x), self.batch_size, replace=False)test_randx = test_x[test_index]test_randy = test_y[test_index]acc = sess.run(accuracy,feed_dict={self.x_place : test_randx, self.y_place : test_randy,self.keep_place : 1.0})print(step, loss)if step % 50 == 0:print('accuracy:' + str(acc))if step % 500 == 0:saver.save(sess, save_model_path, global_step=step)if acc > 0.99 and step > 500:saver.save(sess, save_model_path, global_step=step)breakdef test(self,x_images,model_path):text_list = []out_put = self.cnn_construct()predicts = tf.nn.softmax(out_put)predicts = tf.argmax(predicts, axis=1)saver = tf.train.Saver()with tf.Session() as sess:sess.run(tf.global_variables_initializer())saver.restore(sess, model_path)preds = sess.run(predicts, feed_dict={self.x_place: x_images, self.keep_place: 1.0})for i in range(len(preds)):pred = preds[i].astype(int)text_list.append(self.dataset[pred])return text_listdef list_all_files(self,root):files = []list = os.listdir(root)for i in range(len(list)):element = os.path.join(root, list[i])if os.path.isdir(element):temp_dir = os.path.split(element)[-1]if temp_dir in self.dataset:files.extend(self.list_all_files(element))elif os.path.isfile(element):files.append(element)return filesdef init_data(self,dir):X = []y = []if not os.path.exists(data_dir):raise ValueError('没有找到文件夹')files = self.list_all_files(dir)for file in files:src_img = cv2.imread(file, cv2.COLOR_BGR2GRAY)if src_img.ndim == 3:continueresize_img = cv2.resize(src_img, (20, 20))X.append(resize_img)# 获取图片文件全目录dir = os.path.dirname(file)# 获取图片文件上一级目录名dir_name = os.path.split(dir)[-1]vector_y = [0 for i in range(len(self.dataset))]index_y = self.dataset.index(dir_name)vector_y[index_y] = 1y.append(vector_y)X = np.array(X)y = np.array(y).reshape(-1, self.dataset_len)return X, ydef init_testData(self,dir):test_X = []if not os.path.exists(test_dir):raise ValueError('没有找到文件夹')files = self.list_all_files(test_dir)for file in files:src_img = cv2.imread(file, cv2.COLOR_BGR2GRAY)if src_img.ndim == 3:continueresize_img = cv2.resize(src_img, (20, 20))test_X.append(resize_img)test_X = np.array(test_X)return test_Xif __name__ == '__main__':cur_dir = sys.path[0]data_dir = os.path.join(cur_dir, 'carIdentityData/cnn_char_train')test_dir = os.path.join(cur_dir, 'carIdentityData/cnn_char_test')train_model_path = os.path.join(cur_dir, './carIdentityData/model/char_recongnize/model.ckpt')model_path = os.path.join(cur_dir,'./carIdentityData/model/char_recongnize/model.ckpt-580')train_flag = 1net = char_cnn_net()if train_flag == 1:# 训练模型net.train(data_dir,train_model_path)else:# 测试部分test_X = net.init_testData(test_dir)text = net.test(test_X,model_path)print(text)

import sys
import os
import numpy as np
import cv2
import tensorflow as tf
from sklearn.model_selection import train_test_splitclass plate_cnn_net:def __init__(self):self.img_w,self.img_h = 136,36self.y_size = 2self.batch_size = 100self.learn_rate = 0.001self.x_place = tf.placeholder(dtype=tf.float32, shape=[None, self.img_h, self.img_w, 3], name='x_place')self.y_place = tf.placeholder(dtype=tf.float32, shape=[None, self.y_size], name='y_place')self.keep_place = tf.placeholder(dtype=tf.float32, name='keep_place')def cnn_construct(self):x_input = tf.reshape(self.x_place, shape=[-1, self.img_h, self.img_w, 3])cw1 = tf.Variable(tf.random_normal(shape=[3, 3, 3, 32], stddev=0.01), dtype=tf.float32)cb1 = tf.Variable(tf.random_normal(shape=[32]), dtype=tf.float32)conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x_input, filter=cw1, strides=[1, 1, 1, 1], padding='SAME'), cb1))conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')conv1 = tf.nn.dropout(conv1, self.keep_place)cw2 = tf.Variable(tf.random_normal(shape=[3, 3, 32, 64], stddev=0.01), dtype=tf.float32)cb2 = tf.Variable(tf.random_normal(shape=[64]), dtype=tf.float32)conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, filter=cw2, strides=[1, 1, 1, 1], padding='SAME'), cb2))conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')conv2 = tf.nn.dropout(conv2, self.keep_place)cw3 = tf.Variable(tf.random_normal(shape=[3, 3, 64, 128], stddev=0.01), dtype=tf.float32)cb3 = tf.Variable(tf.random_normal(shape=[128]), dtype=tf.float32)conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, filter=cw3, strides=[1, 1, 1, 1], padding='SAME'), cb3))conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')conv3 = tf.nn.dropout(conv3, self.keep_place)conv_out = tf.reshape(conv3, shape=[-1, 17 * 5 * 128])fw1 = tf.Variable(tf.random_normal(shape=[17 * 5 * 128, 1024], stddev=0.01), dtype=tf.float32)fb1 = tf.Variable(tf.random_normal(shape=[1024]), dtype=tf.float32)fully1 = tf.nn.relu(tf.add(tf.matmul(conv_out, fw1), fb1))fully1 = tf.nn.dropout(fully1, self.keep_place)fw2 = tf.Variable(tf.random_normal(shape=[1024, 1024], stddev=0.01), dtype=tf.float32)fb2 = tf.Variable(tf.random_normal(shape=[1024]), dtype=tf.float32)fully2 = tf.nn.relu(tf.add(tf.matmul(fully1, fw2), fb2))fully2 = tf.nn.dropout(fully2, self.keep_place)fw3 = tf.Variable(tf.random_normal(shape=[1024, self.y_size], stddev=0.01), dtype=tf.float32)fb3 = tf.Variable(tf.random_normal(shape=[self.y_size]), dtype=tf.float32)fully3 = tf.add(tf.matmul(fully2, fw3), fb3, name='out_put')return fully3def train(self,data_dir,model_save_path):print('ready load train dataset')X, y = self.init_data(data_dir)print('success load ' + str(len(y)) + ' datas')train_x, test_x, train_y, test_y = train_test_split(X, y, test_size=0.2, random_state=0)out_put = self.cnn_construct()predicts = tf.nn.softmax(out_put)predicts = tf.argmax(predicts, axis=1)actual_y = tf.argmax(self.y_place, axis=1)accuracy = tf.reduce_mean(tf.cast(tf.equal(predicts, actual_y), dtype=tf.float32))cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=out_put, labels=self.y_place))opt = tf.train.AdamOptimizer(self.learn_rate)train_step = opt.minimize(cost)with tf.Session() as sess:init = tf.global_variables_initializer()sess.run(init)step = 0saver = tf.train.Saver()while True:train_index = np.random.choice(len(train_x), self.batch_size, replace=False)train_randx = train_x[train_index]train_randy = train_y[train_index]_, loss = sess.run([train_step, cost], feed_dict={self.x_place: train_randx,self.y_place: train_randy, self.keep_place: 0.75})step += 1print(step, loss)if step % 10 == 0:test_index = np.random.choice(len(test_x), self.batch_size, replace=False)test_randx = test_x[test_index]test_randy = test_y[test_index]acc = sess.run(accuracy, feed_dict={self.x_place: test_randx,self.y_place: test_randy, self.keep_place: 1.0})print('accuracy:' + str(acc))if acc > 0.99 and step > 500:saver.save(sess, model_save_path, global_step=step)breakdef test(self,x_images,model_path):out_put = self.cnn_construct()predicts = tf.nn.softmax(out_put)probabilitys = tf.reduce_max(predicts, reduction_indices=[1])predicts = tf.argmax(predicts, axis=1)saver = tf.train.Saver()with tf.Session() as sess:sess.run(tf.global_variables_initializer())saver.restore(sess, model_path)preds, probs = sess.run([predicts, probabilitys], feed_dict={self.x_place: x_images, self.keep_place: 1.0})return preds,probsdef list_all_files(self,root):files = []list = os.listdir(root)for i in range(len(list)):element = os.path.join(root, list[i])if os.path.isdir(element):files.extend(self.list_all_files(element))elif os.path.isfile(element):files.append(element)return filesdef init_data(self,dir):X = []y = []if not os.path.exists(dir):raise ValueError('没有找到文件夹')files = self.list_all_files(dir)labels = [os.path.split(os.path.dirname(file))[-1] for file in files]for i, file in enumerate(files):src_img = cv2.imread(file)if src_img.ndim != 3:continueresize_img = cv2.resize(src_img, (136, 36))X.append(resize_img)y.append([[0, 1] if labels[i] == 'has' else [1, 0]])X = np.array(X)y = np.array(y).reshape(-1, 2)return X, ydef init_testData(self,dir):test_X = []if not os.path.exists(dir):raise ValueError('没有找到文件夹')files = self.list_all_files(dir)for file in files:src_img = cv2.imread(file, cv2.COLOR_BGR2GRAY)if src_img.ndim != 3:continueresize_img = cv2.resize(src_img, (136, 36))test_X.append(resize_img)test_X = np.array(test_X)return test_Xif __name__ == '__main__':cur_dir = sys.path[0]data_dir = os.path.join(cur_dir, './carIdentityData/cnn_plate_train')test_dir = os.path.join(cur_dir, './carIdentityData/cnn_plate_test')train_model_path = os.path.join(cur_dir, './carIdentityData/model/plate_recongnize/model.ckpt')model_path = os.path.join(cur_dir,'./carIdentityData/model/plate_recongnize/model.ckpt-520')train_flag = 1net = plate_cnn_net()if train_flag == 1:# 训练模型net.train(data_dir,train_model_path)else:# 测试部分test_X = net.init_testData(test_dir)preds,probs = net.test(test_X,model_path)for i in range(len(preds)):pred = preds[i].astype(int)prob = probs[i]if pred == 1:print('plate',prob)else:print('no',prob)

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