detect.py

#coding=utf-8
import sys
import cv2
import numpy as np
import glob
import os
def preprocess(gray,rates):

    # # 直方图均衡化
    # equ = cv2.equalizeHist(gray)
    # 高斯平滑
    gaussian = cv2.GaussianBlur(gray, (3, 3), 0, 0, cv2.BORDER_DEFAULT)
    # 中值滤波
    median = cv2.medianBlur(gaussian, 5)
    # Sobel算子，X方向求梯度
    sobel = cv2.Sobel(median, cv2.CV_8U, 1, 0, ksize=3)
    # 二值化

    ret, binary = cv2.threshold(sobel, 170, 255, cv2.THRESH_BINARY)
    #binary=Threshold(sobel)
    # 膨胀和腐蚀操作的核函数
    element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (rates, 1))
    element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (rates, 7))
    # 膨胀一次，让轮廓突出
    dilation = cv2.dilate(binary, element2, iterations=1)
    # 腐蚀一次，去掉细节
    erosion = cv2.erode(dilation, element1, iterations=1)
    # 再次膨胀，让轮廓明显一些
    dilation2 = cv2.dilate(erosion, element2, iterations=3)
    return dilation2

def preprocess2(gray,rates):
    # # 直方图均衡化
    # equ = cv2.equalizeHist(gray)
    # 高斯平滑
    gaussian = cv2.GaussianBlur(gray, (3, 3), 0, 0, cv2.BORDER_DEFAULT)
    # 中值滤波
    median = cv2.medianBlur(gaussian, 5)
    # Sobel算子，X方向求梯度
    sobel = cv2.Sobel(median, cv2.CV_8U, 1, 0, ksize=3)
    # 二值化
    ret, binary = cv2.threshold(sobel, 170, 255, cv2.THRESH_BINARY)
    # 膨胀和腐蚀操作的核函数
    element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (rates, 1))
    element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (rates, 7))
    # 膨胀一次，让轮廓突出
    dilation = cv2.dilate(binary, element2, iterations=1)
    # 腐蚀一次，去掉细节
    erosion = cv2.erode(dilation, element1, iterations=1)
    # 再次膨胀，让轮廓明显一些
    dilation2 = cv2.dilate(erosion, element2, iterations=3)
    return dilation2





def findPlateNumberRegion(img):
    region = []
    # 查找轮廓
    t,contours, hierarchy =  cv2.findContours(img,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    # 筛选面积小的
    #t接空值
    for i in range(len(contours)):
        cnt = contours[i]
        # 计算该轮廓的面积
        area = cv2.contourArea(cnt)

        # 面积小的都筛选掉
        if (area < 2000):
            continue

        # 轮廓近似，作用很小
        epsilon = 0.001 * cv2.arcLength(cnt, True)
        approx = cv2.approxPolyDP(cnt, epsilon, True)

        # 找到最小的矩形，该矩形可能有方向
        rect = cv2.minAreaRect(cnt)
        #print "rect is: "
        #print rect

        # box是四个点的坐标
        box = cv2.boxPoints(rect)
        box = np.int0(box)

        # 计算高和宽
        height = abs(box[0][1] - box[2][1])
        width = abs(box[0][0] - box[2][0])
        # 车牌正常情况下长高比在2.7-5之间
        ratio = float(width) / float(height)
        #print ratio
        if (ratio > 5 or ratio < 2.7):
            continue
        region.append(box)

    return region


def detect(img,i):
        # 转化成灰度图
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        # 形态学变换的预处理
    dilation = preprocess(gray,9)

    # 查找车牌区域
    region = findPlateNumberRegion(dilation)
    rates=9
    # 用绿线画出这些找到的轮廓
    while(not region):
     rates+=6
     dilation = preprocess(gray, rates)
     region = findPlateNumberRegion(dilation)



    for box in region:
        cv2.drawContours(img, [box], 0, (0, 255, 0), 2)
    ys = [box[0, 1], box[1, 1], box[2, 1], box[3, 1]]
    xs = [box[0, 0], box[1, 0], box[2, 0], box[3, 0]]
    ys_sorted_index = np.argsort(ys)
    xs_sorted_index = np.argsort(xs)

    x1 = box[xs_sorted_index[0], 0]
    x2 = box[xs_sorted_index[3], 0]

    y1 = box[ys_sorted_index[0], 1]
    y2 = box[ys_sorted_index[3], 1]

    img_org2 = img.copy()
    img_plate = img_org2[y1:y2, x1:x2]
    cv2.imwrite('./de/'+i, img_plate)

    cv2.imwrite('contours.png', img)


if __name__ == '__main__':

    for files in glob.glob('./sample_images/*.jpg'):

        filepath, filename = os.path.split(files)
        imagePath = filepath + '/' + filename
        print (filename)
        img = cv2.imread(imagePath)
        detect(img,filename)