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 = [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]] += 1
p = [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] * 255
return img
def find_board_area(img):
assert img.ndim == 2
img_h, img_w = img.shape[0], img.shape[1]
top, bottom, left, right = 0, img_h, 0, img_w
flag = False
h_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] += 1
if flag == False and h_proj[row] > 12:
flag = True
top = row
if flag == True and row > top + 8 and h_proj[row] < 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[row, col] == 255:
v_proj[col] += 1
if flag == False and (v_proj[col] > 10 or v_proj[col] - v_proj[col - 1] > 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[1][0] == 0 or rotate_rect[1][1] == 0:
return False
r = 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theta
if ((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 True
return False
def 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 = False
if car_rect[2] == 0:
return_flag = True
if car_rect[2] == -90 and rect_w < rect_h:
rect_w, rect_h = rect_h, rect_w
return_flag = True
if 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_img
car_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 = point
if low_point[1] > point[1]:
low_point = point
if heigth_point[1] < point[1]:
heigth_point = point
if right_point[0] < point[0]:
right_point = point
if 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_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[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')
# 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=[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, 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 = [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_adjust
else:
pt1[0], pt2[0] = pt1[0] - x_adjust, pt2[0] + x_adjust
if (pt1[1] <= pt2[1]):
pt1[1], pt2[1] = pt1[1] + adjust_y, pt2[1] - adjust_y
else:
pt1[1], pt2[1] = pt1[1] - y_adjust, pt2[1] + y_adjust
temp_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.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[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 += 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[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_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
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[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, 0
img_w = plate.shape[1]
# 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[0]):
sum += round(img[i, col] / 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 = [round(img_w / 12), round(img_w / 5)]
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[0]) and (area_width < charWid_limit[1]):
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[0]) and (area_width < charWid_limit[1]):
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 = [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 = 0
for col in range(img_w):
if car_plate[row, col] == 255:
temp_cnt += 1
h_count[row] = temp_cnt
if 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 - 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_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_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=[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 = []
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=[1]) # 结果概率值
my_preds, my_probs = sess2.run([preds, probs], 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[i])
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[0]
# 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[0]
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
# str = recognizePlatestr('./carIdentityData/images/23.jpg')
# print(str)
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狗仔卡
发表于 2020-3-20 11:39:20
置顶卡
千斤顶
显身卡
楼主
发表于 2020-3-20 13:12:22
