#!/usr/bin/env python
# -*- coding: utf-8 -*-
# author:ros
import config
import time
# 修改全局变量
a = 0
while a<10:
time.sleep(1)
a=a+1
config.set_name('new_name')
print (config.get_name())
def img_color_contours(self, img_contours, oldimg):
"""
:param img_contours: 预处理好的图像
:param oldimg: 原图像
:return: 已经定位好的车牌
"""
if img_contours.any():
config.set_name(img_contours)
pic_hight, pic_width = img_contours.shape[:2]
card_contours = img_math.img_findContours(img_contours)
card_imgs = img_math.img_Transform(card_contours, oldimg, pic_width,
pic_hight)
colors, car_imgs = img_math.img_color(card_imgs)
predict_result = []
roi = None
card_color = None
for i, color in enumerate(colors):
if color in ("blue", "yello", "green"):
card_img = card_imgs[i]
gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)
# 黄、绿车牌字符比背景暗、与蓝车牌刚好相反，所以黄、绿车牌需要反向
if color == "green" or color == "yello":
gray_img = cv2.bitwise_not(gray_img)
ret, gray_img = cv2.threshold(
gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
x_histogram = np.sum(gray_img, axis=1)
x_min = np.min(x_histogram)
x_average = np.sum(x_histogram) / x_histogram.shape[0]
x_threshold = (x_min + x_average) / 2
wave_peaks = img_math.find_waves(x_threshold, x_histogram)
if len(wave_peaks) == 0:
print("peak less 0:")
continue
# 认为水平方向，最大的波峰为车牌区域
wave = max(wave_peaks, key=lambda x: x[1] - x[0])
gray_img = gray_img[wave[0]:wave[1]]
# 查找垂直直方图波峰
row_num, col_num = gray_img.shape[:2]
# 去掉车牌上下边缘1个像素，避免白边影响阈值判断
gray_img = gray_img[1:row_num - 1]
y_histogram = np.sum(gray_img, axis=0)
y_min = np.min(y_histogram)
y_average = np.sum(y_histogram) / y_histogram.shape[0]
y_threshold = (y_min + y_average) / 5 # U和0要求阈值偏小，否则U和0会被分成两半
wave_peaks = img_math.find_waves(y_threshold, y_histogram)
if len(wave_peaks) <= 6:
print("peak less 1:", len(wave_peaks))
continue
wave = max(wave_peaks, key=lambda x: x[1] - x[0])
max_wave_dis = wave[1] - wave[0]
# 判断是否是左侧车牌边缘
if wave_peaks[0][1] - wave_peaks[0][
0] < max_wave_dis / 3 and wave_peaks[0][0] == 0:
wave_peaks.pop(0)
# 组合分离汉字
cur_dis = 0
for i, wave in enumerate(wave_peaks):
if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:
break
else:
cur_dis += wave[1] - wave[0]
if i > 0:
wave = (wave_peaks[0][0], wave_peaks[i][1])
wave_peaks = wave_peaks[i + 1:]
wave_peaks.insert(0, wave)
point = wave_peaks[2]
point_img = gray_img[:, point[0]:point[1]]
if np.mean(point_img) < 255 / 5:
wave_peaks.pop(2)
if len(wave_peaks) <= 6:
print("peak less 2:", len(wave_peaks))
continue
part_cards = img_math.seperate_card(gray_img, wave_peaks)
for i, part_card in enumerate(part_cards):
# 可能是固定车牌的铆钉
if np.mean(part_card) < 255 / 5:
print("a point")
continue
part_card_old = part_card
w = abs(part_card.shape[1] - SZ) // 2
part_card = cv2.copyMakeBorder(part_card,
0,
0,
w,
w,
cv2.BORDER_CONSTANT,
value=[0, 0, 0])
part_card = cv2.resize(part_card, (SZ, SZ),
interpolation=cv2.INTER_AREA)
part_card = img_recognition.preprocess_hog([part_card])
if i == 0:
resp = self.modelchinese.predict(part_card)
charactor = img_recognition.provinces[int(resp[0]) -
PROVINCE_START]
else:
resp = self.model.predict(part_card)
charactor = chr(resp[0])
# 判断最后一个数是否是车牌边缘，假设车牌边缘被认为是1
if charactor == "1" and i == len(part_cards) - 1:
if part_card_old.shape[0] / part_card_old.shape[
1] >= 7: # 1太细，认为是边缘
continue
predict_result.append(charactor)
roi = card_img
card_color = color
break
return predict_result, roi, card_color # 识别到的字符、定位的车牌图像、车牌颜色
def img_color_contours(self, img_contours, oldimg):
"""
:param img_contours: 预处理好的图像
:param oldimg: 原图像
:return: 已经定位好的车牌
"""
# cv_show("s",img_contours)
if img_contours.any():
config.set_name(img_contours)
pic_hight, pic_width = img_contours.shape[:2]
card_contours = img_math.img_findContours(img_contours)
# 进行校正 仿射
card_imgs = img_math.img_Transform(card_contours, oldimg, pic_width,
pic_hight)
colors, car_imgs = img_math.img_color(card_imgs)
predict_result = []
card_color = None
# 通过波峰找到文字区
for index, color in enumerate(colors):
if color in ("blue", "yello", "green"):
card_img = card_imgs[index]
gray_img = cv2.cvtColor(card_img, cv2.COLOR_BGR2GRAY)
if color == "green" or color == "yello":
gray_img = cv2.bitwise_not(gray_img)
ret, gray_img = cv2.threshold(
gray_img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
x_histogram = np.sum(gray_img, axis=1)
# axis=1列压缩
x_min = np.min(x_histogram)
x_average = np.sum(x_histogram) / x_histogram.shape[0]
x_threshold = (x_min + x_average) / 2
wave_peaks = img_math.find_waves(x_threshold, x_histogram)
if len(wave_peaks) == 0:
print("peak less 0:")
continue
# 认为水平方向，最大的波峰为车牌区域
wave = max(wave_peaks, key=lambda x: x[1] - x[0])
gray_img = gray_img[wave[0]:wave[1]]
# cv_show('截取图片', gray_img)
row_num, col_num = gray_img.shape[:2]
gray_img = gray_img[1:row_num - 1]
y_histogram = np.sum(gray_img, axis=0)
y_min = np.min(y_histogram)
y_average = np.sum(y_histogram) / y_histogram.shape[0]
y_threshold = (y_min + y_average) / 5 # U和0要求阈值偏小，否则U和0会被分成两半
wave_peaks = img_math.find_waves(y_threshold, y_histogram)
if len(wave_peaks) <= 6:
print("peak less 1:", len(wave_peaks))
continue
wave = max(wave_peaks, key=lambda x: x[1] - x[0])
max_wave_dis = wave[1] - wave[0]
# 判断是否是左侧车牌边缘
if wave_peaks[0][1] - wave_peaks[0][
0] < max_wave_dis / 3 or wave_peaks[0][0] == 0:
wave_peaks.pop(0)
# 组合分离汉字
cur_dis = 0 #分割距离
for index, wave in enumerate(wave_peaks):
if wave[1] - wave[0] + cur_dis > max_wave_dis * 0.6:
break
else:
cur_dis += wave[1] - wave[0]
if index > 0:
wave = (wave_peaks[0][0], wave_peaks[index][1])
wave_peaks = wave_peaks[index + 1:]
wave_peaks.insert(0, wave)
point = wave_peaks[2]
# 车牌前两位 和后面之间的点
point_img = gray_img[:, point[0]:point[1]]
# 截取一部分
if np.mean(point_img) < 255 / 5:
wave_peaks.pop(2)
# 去除圆点
# print("wave_peak", wave_peaks)
if len(wave_peaks) <= 6:
print("peak less 2:", len(wave_peaks))
continue
if len(wave_peaks) > 8:
wave_peaks.pop(len(wave_peaks) - 1)
# 分离车牌字符
part_cards = img_math.seperate_card(gray_img, wave_peaks)
for i, part_card in enumerate(part_cards):
# enumerate 遍历数据对象组成索引序列
# 可能是固定车牌的铆钉
if np.mean(part_card) < 255 / 5:
print("a point")
continue
part_card_old = part_card
w = abs(part_card.shape[1] - SZ) // 2
# 训练图片长宽
part_card = cv2.copyMakeBorder(part_card,
0,
0,
w,
w,
cv2.BORDER_CONSTANT,
value=[0, 0, 0])
# 添加边框 参数：1 要处理的原图 2 上下左右要扩展的像素 6 边框类型 通过给定的属性添加颜色边框
part_card = cv2.resize(part_card, (SZ, SZ),
interpolation=cv2.INTER_AREA)
# 图片缩小的方式
part_card = img_recognition.preprocess_hog([part_card])
if i == 0:
# 模型预测,输入测试集,输出预测结果
resp = self.modelchinese.predict(part_card)
print("chinese", resp)
charactor = img_recognition.provinces[int(resp[0]) -
PROVINCE_START]
print("汉字", charactor)
else:
resp = self.model.predict(part_card)
charactor = chr(resp[0])
print("字母数字", charactor)
# chr 返回对应字符
# 判断最后一个数是否是车牌边缘，假设车牌边缘被认为是1
if charactor == "1" and i == len(part_cards) - 1:
if part_card_old.shape[0] / part_card_old.shape[
1] >= 9: # 1太细，有可能认为是边缘
continue
predict_result.append(charactor)
# 车牌信息
roi = card_img
# 车牌图片
card_color = color
# 颜色
break
return predict_result, roi, card_color # 识别到的字符、定位的车牌图像、车牌颜色
