关于rpc的问题~
xmlrpc.client.Fault: <Fault 1: "<class 'xml.parsers.expat.ExpatError'>:not well-formed (invalid token): line 6, column 89">有人遇到这种问题嘛~
我把详细代码附在评论区 服务端：import cv2
import os
import sys
import numpy as np
import tensorflow as tf
car_plate_w,car_plate_h = 136,36
char_w,char_h = 20,20
char_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==2
hist =
img_h,img_w = img.shape,img.shape
for row in range(img_h):
for col in range(img_w):
hist] += 1
p = /(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
img = p1*255
return img
def find_board_area(img):
assert img.ndim==2
img_h,img_w = img.shape,img.shape
top,bottom,left,right = 0,img_h,0,img_w
flag = False
h_proj =
v_proj =
for row in range(round(img_h*0.5),round(img_h*0.8),3):
for col in range(img_w):
if img==255:
h_proj += 1
if flag==False and h_proj>12:
flag = True
top = row
if flag==True and row>top+8 and h_proj<12:
bottom = row
flag = False
for col in range(round(img_w*0.3),img_w,1):
for row in range(top,bottom,1):
if img==255:
v_proj += 1
if flag==False and (v_proj>10 or v_proj-v_proj>5):
left = col
break
return left,top,120,bottom-top-10
def verify_scale(rotate_rect):
error = 0.4
aspect = 4#4.7272
min_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False
if rotate_rect==0 or rotate_rect==0:
return False
r = rotate_rect/rotate_rect
r = max(r,1/r)
area = rotate_rect*rotate_rect
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theta
if ((rotate_rect < rotate_rect and rotate_rect >= -90 and rotate_rect < -(90 - theta)) or
(rotate_rect < rotate_rect and rotate_rect > -theta and rotate_rect <= 0)):
return True
return False
def img_Transform(car_rect,image):
img_h,img_w = image.shape[:2]
rect_w,rect_h = car_rect,car_rect
angle = car_rect
return_flag = False
if car_rect==0:
return_flag = True
if car_rect==-90 and rect_w<rect_h:
rect_w, rect_h = rect_h, rect_w
return_flag = True
if return_flag:
car_img = image-rect_h/2):int(car_rect+rect_h/2),
int(car_rect-rect_w/2):int(car_rect+rect_w/2)]
return car_img
car_rect = (car_rect,(rect_w,rect_h),angle)
box = cv2.boxPoints(car_rect)
heigth_point = right_point =
left_point = low_point = , car_rect]
for point in box:
if left_point > point:
left_point = point
if low_point > point:
low_point = point
if heigth_point < point:
heigth_point = point
if right_point < point:
right_point = point
if left_point <= right_point:# 正角度
new_right_point = , heigth_point]
pts1 = np.float32()
pts2 = np.float32()# 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(heigth_point), int(left_point):int(new_right_point)]
elif left_point > right_point:# 负角度
new_left_point = , heigth_point]
pts1 = np.float32()
pts2 = np.float32()# 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(heigth_point), int(new_left_point):int(right_point)]
return car_img
def pre_process(orig_img):
gray_img = cv2.cvtColor(orig_img, cv2.COLOR_BGR2GRAY)
# cv2.imshow('gray_img', gray_img)
# cv2.waitKey(0)
blur_img = cv2.blur(gray_img, (3, 3))
# cv2.imshow('blur', blur_img)
# cv2.waitKey(0)
sobel_img = cv2.Sobel(blur_img, cv2.CV_16S, 1, 0, ksize=3)
sobel_img = cv2.convertScaleAbs(sobel_img)
# cv2.imshow('sobel', sobel_img)
# cv2.waitKey(0)
hsv_img = cv2.cvtColor(orig_img, cv2.COLOR_BGR2HSV)
# cv2.imshow('hsv', hsv_img)
# cv2.waitKey(0)
h, s, v = hsv_img[:, :, 0], hsv_img[:, :, 1], hsv_img[:, :, 2]
# 黄色色调区间,蓝色色调区间:
blue_img = (((h > 26) & (h < 34)) | ((h > 100) & (h < 124))) & (s > 70) & (v > 70)
blue_img = blue_img.astype('float32')
# cv2.imshow('blue', blue_img)
# cv2.waitKey(0)
mix_img = np.multiply(sobel_img, blue_img)
# cv2.imshow('mix', mix_img)
# cv2.waitKey(0)
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)
# cv2.waitKey(0)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(21,5))
close_img = cv2.morphologyEx(binary_img, cv2.MORPH_CLOSE, kernel)
# cv2.imshow('close', close_img)
# cv2.waitKey(0)
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=,dtype=np.uint8)
connectivity = 4 #种子点上下左右4邻域与种子颜色值在的被涂成new_value，也可设置8邻域
loDiff,upDiff = 30,30
new_value = 255
flags = connectivity
flags |= 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 << 8
flags |= 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 = 0.1
box_points = cv2.boxPoints(rotate_rect)
box_points_x = for n in box_points]
box_points_x.sort(reverse=False)
adjust_x = int((box_points_x-box_points_x)*adjust_param)
col_range = +adjust_x,box_points_x-adjust_x]
box_points_y = for n in box_points]
box_points_y.sort(reverse=False)
adjust_y = int((box_points_y-box_points_y)*adjust_param)
row_range = +adjust_y, box_points_y-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-col_range)/(box_points_x-box_points_x)<0.4\
or (row_range-row_range)/(box_points_y-box_points_y)<0.4:
points_row = []
points_col = []
for i in range(2):
pt1,pt2 = box_points,box_points
x_adjust,y_adjust = int(adjust_param*(abs(pt1-pt2))),int(adjust_param*(abs(pt1-pt2)))
if (pt1 <= pt2):
pt1, pt2 = pt1 + x_adjust, pt2 - x_adjust
else:
pt1, pt2 = pt1 - x_adjust, pt2 + x_adjust
if (pt1 <= pt2):
pt1, pt2 = pt1 + adjust_y, pt2 - adjust_y
else:
pt1, pt2 = pt1 - y_adjust, pt2 + y_adjust
temp_list_x = ,pt2,int(rand_seed_num /2))]
temp_list_y = ,pt2,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,row_range,size=rand_seed_num)
points_col = np.linspace(col_range,col_range,num=rand_seed_num).astype(np.int)
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 = 0
for i in range(rand_seed_num):
rand_index = np.random.choice(rand_seed_num,1,replace=False)
row,col = points_row,points_col
# 限制随机种子必须是车牌背景色
if (((h>26)&(h<34))|((h>100)&(h<124)))&(s>70)&(v>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 += 1
if 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 != 0:
mask_points.append((col-1,row-1))
mask_rotateRect = cv2.minAreaRect(np.array(mask_points))
if verify_scale(mask_rotateRect):
return True,mask_rotateRect
else:
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_rect
rotate_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)%4
cv2.line(temp1_orig_img,(box,box),(box,box),(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= 0,0,0,0
img_w = plate.shape
# 获取车牌每列边缘像素点个数
def getColSum(img,col):
sum = 0
for i in range(img.shape):
sum += round(img/255)
return sum;
sum = 0
for 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 =
is_char_flag = False
for 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_left
char_width = char_right-char_left
if (area_width>charWid_limit) and (area_width<charWid_limit):
char_addr_list.append((area_left,area_right,char_width))
char_left = i
area_left = round((char_left+char_right) / 2)
is_char_flag = True
else:
if is_char_flag == True:
char_right = i-1
is_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_w
area_width = area_right - area_left
char_width = char_right - char_left
if (area_width > charWid_limit) and (area_width < charWid_limit):
char_addr_list.append((area_left, area_right, char_width))
return char_addr_list
def 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 = 0,0
h_startIndex,h_end_index = 0,0 # 水平投影记索引
h_proj_limit = # 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 =
for row in range(img_h):
temp_cnt = 0
for col in range(img_w):
if car_plate == 255:
temp_cnt += 1
h_count = temp_cnt
if temp_cnt/img_w<h_proj_limit or temp_cnt/img_w>h_proj_limit:
if h_temp_len != 0:
h_end_index = row-1
h_proj_list.append((h_startIndex,h_end_index))
h_temp_len = 0
continue
if temp_cnt > 0:
if h_temp_len == 0:
h_startIndex = row
h_temp_len = 1
else:
h_temp_len += 1
else:
if h_temp_len > 0:
h_end_index = row-1
h_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-1
h_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0.5
h_maxIndex,h_maxHeight = 0,0
for i,(start,end) in enumerate(h_proj_list):
if h_maxHeight < (end-start):
h_maxHeight = (end-start)
h_maxIndex = i
if h_maxHeight/img_h < 0.5:
return char_imgs
chars_top,chars_bottom = h_proj_list,h_proj_list
plates = car_plate
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:addr]
char_img = cv2.resize(char_img,(char_w,char_h))
char_imgs.append(char_img)
return char_imgs
def 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_list
def cnn_select_carPlate(plate_list,model_path):
if len(plate_list) == 0:
return False,plate_list
g1 = 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=) #结果概率值
pred_list,prob_list = sess1.run(,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>result_prob:
result_index,result_prob = i,prob_list
if result_index == -1:
return False,plate_list
else:
return True,plate_list
def cnn_recongnize_char(img_list,model_path):
g2 = tf.Graph()
sess2 = tf.Session(graph=g2)
text_list = []
pro_list = []
if len(img_list) == 0:
return text_list
with 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)
probs = tf.reduce_max(net_out, reduction_indices=)# 结果概率值
my_preds,my_probs= sess2.run(, feed_dict={net2_x_place: data, net2_keep_place: 1.0})
# print(my_preds)
print(my_probs)
for i in my_preds:
text_list.append(char_table)
prob = 0
for i in my_probs:
prob = prob + i
prob=prob/len(my_probs)
return text_list,prob
# if __name__ == '__main__':
# cur_dir = sys.path
# car_plate_w,car_plate_h = 136,36
# char_w,char_h = 20,20
# plate_model_path = os.path.join(cur_dir, './carIdentityData/model/plate_recongnize/model.ckpt-510.meta')
# char_model_path = os.path.join(cur_dir,'./carIdentityData/model/char_recongnize/model.ckpt-520.meta')
# img = cv2.imread('carIdentityData/images/43.jpg')
#
# # 预处理
# pred_img = pre_process(img)
# # cv2.imshow('pred_img', pred_img)
# # cv2.waitKey(0)
#
# # 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)
# # cv2.waitKey(0)
#
# # 字符提取
# char_img_list = extract_char(car_plate)
# print(len(char_img_list))
# # CNN字符识别
# text,pro = cnn_recongnize_char(char_img_list,char_model_path)
# print(text)
# print(pro)
def recognizePlatestr(src):
cur_dir = sys.path
car_plate_w,car_plate_h = 136,36
char_w,char_h = 20,20
plate_model_path = os.path.join(cur_dir, './carIdentityData/model/plate_recongnize/model.ckpt-510.meta')
char_model_path = os.path.join(cur_dir,'./carIdentityData/model/char_recongnize/model.ckpt-520.meta')
# img = cv2.imread('./carIdentityData/images/32.jpg')
img = cv2.imread(src)
# 预处理
pred_img = pre_process(img)
# cv2.imshow('pred_img', pred_img)
# cv2.waitKey(0)
# 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)
# 字符提取
char_img_list = extract_char(car_plate)
print(len(char_img_list))
# CNN字符识别
text,confidence = cnn_recongnize_char(char_img_list,char_model_path)
confidence = str(round(confidence, 3))
str1 = ''.join(text)
laststr = str1 + "#" + confidence
return laststr
from xmlrpc.server import SimpleXMLRPCServer
server = SimpleXMLRPCServer(('localhost', 8888)) # 初始化
server.register_function(recognizePlatestr, "get_platestr") # 注册函数
print ("Listening for Client")
server.serve_forever() # 保持等待调用状态
客户端:
from xmlrpc.client import ServerProxy
server = ServerProxy("http://localhost:8888") # 初始化服务器
path = 'D:\pycharm\djangocode\project6\myApp\plateRecognize\carIdentityData\images\23.jpg'
print(server.get_platestr(path))
platestr=server.get_platestr(path)
print("222") 愿你 发表于 2020-4-1 10:16
服务端：
客户端:
好长…… _2_ 发表于 2020-4-1 10:20
好长……
服务端只要看最后那几行就好了~
我单在文件里独运行那个recognizePlatestr，是能够有结果的。
如下：
str = recognizePlatestr('./carIdentityData/images/23.jpg')
print(str)
但是我远程调用就不行了 不知道为什么
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