python 图像识别 定位_OpenCV图像识别：车牌定位算法源码，Python语言实现
#-*-coding: utf-8-*-
import cv2
import numpy as np
import math
def stretch(img):
max = float(img.max())
min = float(img.min())
for i in range(img.shape[0]):
for j in range(img.shape[1]):
img[i, j] = (255/(max-min))*img[i,j]-(255*min)/(max-min)
return img
def dobinaryzation(img):
max = float(img.max())
min = float(img.min())
x = max - ((max-min) / 2)
ret, threshedimg = cv2.threshold(img, x, 255, cv2.THRESH_BINARY)
return threshedimg
def find_retangle(contour):
y, x = [], []
for p in contour:
y.append(p[0][0])
x.append(p[0][1])
return [min(y), min(x), max(y), max(x)]
def locate_license(img, orgimg):
img, contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# 找出最大的三个区域
blocks = []
for c in contours:
# 找出轮廓的左上点和右下点，由此计算它的面积和长宽比
r = find_retangle(c)
a = (r[2]-r[0]) * (r[3]-r[1])
s = (r[2]-r[0]) / (r[3]-r[1])
blocks.append([r, a, s])
# 选出面积最大的3个区域
blocks = sorted(blocks, key=lambda b: b[2])[-3:]
# 使用颜色识别判断找出最像车牌的区域
maxweight, maxinedx = 0, -1
for i in xrange(len(blocks)):
b = orgimg[blocks[i][0][1]:blocks[i][0][3], blocks[i][0][0]:blocks[i][0][2]]
# RGB转HSV
hsv = cv2.cvtColor(b, cv2.COLOR_BGR2HSV)
# 蓝色车牌范围
lower = np.array([100,50,50])
upper = np.array([140,255,255])
# 根据阈值构建掩模
mask = cv2.inRange(hsv, lower, upper)
# 统计权值
w1 = 0
for m in mask:
w1 += m / 255
w2 = 0
for w in w1:
w2 += w
# 选出最大权值的区域
if w2 > maxweight:
maxindex = i
maxweight = w2
return blocks[maxindex][0]
def find_license(img):
'''预处理'''
# 压缩图像
img = cv2.resize(img, (400, 400*img.shape[0]/img.shape[1]))
# RGB转灰色
grayimg = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# 灰度拉伸
stretchedimg = stretch(grayimg)
# 进行开运算，用来去噪声
r = 16
h = w = r * 2 + 1
kernel = np.zeros((h, w), dtype=np.uint8)
cv2.circle(kernel, (r, r), r, 1, -1)
openingimg = cv2.morphologyEx(stretchedimg, cv2.MORPH_OPEN, kernel)
strtimg = cv2.absdiff(stretchedimg,openingimg)
# 图像二值化
binaryimg = dobinaryzation(strtimg)
# 使用Canny函数做边缘检测
cannyimg = cv2.Canny(binaryimg, binaryimg.shape[0], binaryimg.shape[1])
''' 消除小区域，保留大块区域，从而定位车牌'''
# 进行闭运算
kernel = np.ones((5,19), np.uint8)
closingimg = cv2.morphologyEx(cannyimg, cv2.MORPH_CLOSE, kernel)
# 进行开运算
openingimg = cv2.morphologyEx(closingimg, cv2.MORPH_OPEN, kernel)
# 再次进行开运算
kernel = np.ones((11,5), np.uint8)
openingimg = cv2.morphologyEx(openingimg, cv2.MORPH_OPEN, kernel)
# 消除小区域，定位车牌位置
rect = locate_license(openingimg, img)
return rect, img
if __name__ == '__main__':
# 读取图片
orgimg = cv2.imread('car.jpg')
rect, img = find_license(orgimg)
# 框出车牌
cv2.rectangle(img, (rect[0], rect[1]), (rect[2], rect[3]), (0,255,0),2)
cv2.imshow('img', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
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