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`‎opencv/04.py‎`

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+236Lines changed: 236 additions & 0 deletions

Original file line number	Diff line number	Diff line change
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	`1`	`+#!/usr/bin/env python`
	`2`	`+# -- coding: utf-8 --`
	`3`	`+`
	`4`	`+# @Time : 2017/10/14 0014 上午 10:18`
	`5`	`+# @Author : 李雪洋`
	`6`	`+# @File : 04.py`
	`7`	`+# @Software: PyCharm`
	`8`	`+`
	`9`	`+import cv2`
	`10`	`+import numpy as np`
	`11`	`+`
	`12`	`+`
	`13`	`+# 获取检测到的最小矩形坐标`
	`14`	`+def getRectangularCoordinate(box):`
	`15`	`+ ys = [box[0, 1], box[1, 1], box[2, 1], box[3, 1]]`
	`16`	`+ xs = [box[0, 0], box[1, 0], box[2, 0], box[3, 0]]`
	`17`	`+ ys_sorted_index = np.argsort(ys)`
	`18`	`+ xs_sorted_index = np.argsort(xs)`
	`19`	`+`
	`20`	`+ x1 = box[xs_sorted_index[0], 0]`
	`21`	`+ x2 = box[xs_sorted_index[3], 0]`
	`22`	`+`
	`23`	`+ y1 = box[ys_sorted_index[0], 1]`
	`24`	`+ y2 = box[ys_sorted_index[3], 1]`
	`25`	`+`
	`26`	`+ return [x1, x2, y1, y2]`
	`27`	`+`
	`28`	`+`
	`29`	`+def findPlateNumberRegion2(img):`
	`30`	`+ region = []`
	`31`	`+ # 查找轮廓`
	`32`	`+ (_, contours, _) = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)`
	`33`	`+`
	`34`	`+ # 筛选面积小的`
	`35`	`+ for i in contours:`
	`36`	`+ # 计算该轮廓的面积`
	`37`	`+ area = cv2.contourArea(i)`
	`38`	`+`
	`39`	`+ # 面积小的都筛选掉`
	`40`	`+ if area < 70:`
	`41`	`+ continue`
	`42`	`+`
	`43`	`+ # 找到最小的矩形，该矩形可能有方向`
	`44`	`+ rect = cv2.minAreaRect(i)`
	`45`	`+ print("rect is: " + str(rect))`
	`46`	`+`
	`47`	`+ # box是四个点的坐标`
	`48`	`+ box = cv2.boxPoints(rect)`
	`49`	`+ box = np.int0(box)`
	`50`	`+`
	`51`	`+ print("box:" + str(box))`
	`52`	`+`
	`53`	`+ # 计算高和宽`
	`54`	`+ height = abs(box[0][1] - box[2][1])`
	`55`	`+ width = abs(box[0][0] - box[2][0])`
	`56`	`+ # 身份证号码正常情况下长高比在6.8-7.8之间`
	`57`	`+ ratio = float(width) / float(height)`
	`58`	`+ print("ratio:" + str(ratio))`
	`59`	`+ if 0 < ratio < 1:`
	`60`	`+ region.append(box)`
	`61`	`+ # 该列表为无序列表，要想得到正确的身份证顺序，需要对其进行排序，排序的规则就是以轮廓的左上角x坐标为准`
	`62`	`+ return sorted(region, key=lambda a_list: a_list[1, 0])`
	`63`	`+`
	`64`	`+`
	`65`	`+def findPlateNumberRegion(img, large, small):`
	`66`	`+ region = []`
	`67`	`+ # 查找轮廓`
	`68`	`+ (_, contours, _) = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)`
	`69`	`+`
	`70`	`+ # 筛选面积小的`
	`71`	`+ for i in range(len(contours)):`
	`72`	`+ cnt = contours[i]`
	`73`	`+ # 计算该轮廓的面积`
	`74`	`+ area = cv2.contourArea(cnt)`
	`75`	`+ #`
	`76`	`+ # 面积小的都筛选掉`
	`77`	`+ # if area < 2000:`
	`78`	`+ # continue`
	`79`	`+`
	`80`	`+ # 找到最小的矩形，该矩形可能有方向`
	`81`	`+ rect = cv2.minAreaRect(cnt)`
	`82`	`+ print("rect is: " + str(rect))`
	`83`	`+`
	`84`	`+ # box是四个点的坐标`
	`85`	`+ box = cv2.boxPoints(rect)`
	`86`	`+ box = np.int0(box)`
	`87`	`+`
	`88`	`+ print("box:" + str(box))`
	`89`	`+`
	`90`	`+ # 计算高和宽`
	`91`	`+ height = abs(box[0][1] - box[2][1])`
	`92`	`+ width = abs(box[0][0] - box[2][0])`
	`93`	`+ # 身份证号码正常情况下长高比在6.8-7.8之间`
	`94`	`+ ratio = float(width) / float(height)`
	`95`	`+ print("ratio:" + str(ratio))`
	`96`	`+ if small < ratio < large:`
	`97`	`+ return box, rect[2]`
	`98`	`+`
	`99`	`+`
	`100`	`+def getEffectiveArea(img, srcImg):`
	`101`	`+ [ret3, th3] = cv2.threshold(img, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)`
	`102`	`+ cv2.imshow('th3', th3)`
	`103`	`+`
	`104`	`+ kernel = np.ones((10, 10), np.uint8)`
	`105`	`+ erosion = cv2.erode(th3, kernel, iterations=1)`
	`106`	`+ # cv2.imshow('th3-erosion', erosion)`
	`107`	`+`
	`108`	`+ kernel = np.ones((10, 10), np.uint8)`
	`109`	`+ dilation = cv2.dilate(th3, kernel, iterations=1)`
	`110`	`+ # cv2.imshow('th3-dilation', dilation)`
	`111`	`+`
	`112`	`+ box, angel = findPlateNumberRegion(dilation, 2, 1)`
	`113`	`+`
	`114`	`+ rows, cols, _ = srcImg.shape`
	`115`	`+ # 这里的第一个参数为旋转中心，第二个为旋转角度，第三个为旋转后的缩放因子`
	`116`	`+ # 可以通过设置旋转中心，缩放因子，以及窗口大小来防止旋转后超出边界的问题`
	`117`	`+ M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angel, 1)`
	`118`	`+ # 第三个参数是输出图像的尺寸中心`
	`119`	`+ srcImg = cv2.warpAffine(srcImg, M, (cols, rows))`
	`120`	`+ dilation = cv2.warpAffine(dilation, M, (cols, rows))`
	`121`	`+ img = cv2.warpAffine(img, M, (cols, rows))`
	`122`	`+`
	`123`	`+ box, angel = findPlateNumberRegion(dilation, 2, 1)`
	`124`	`+`
	`125`	`+ cv2.drawContours(srcImg, [box], 0, (0, 255, 0), 2)`
	`126`	`+`
	`127`	`+ coordinate = getRectangularCoordinate(box)`
	`128`	`+`
	`129`	`+ img_org2 = srcImg.copy()`
	`130`	`+`
	`131`	`+ img_plate = img_org2[coordinate[2]:coordinate[3], coordinate[0]:coordinate[1]]`
	`132`	`+ img = img[coordinate[2]:coordinate[3], coordinate[0]:coordinate[1]]`
	`133`	`+ cv2.imshow('number plate', img_plate)`
	`134`	`+ cv2.waitKey(0)`
	`135`	`+`
	`136`	`+ # (530, 340)为正常可辨识范围取的基本值，可浮动`
	`137`	`+ img = cv2.resize(img, (530, 340), interpolation=cv2.INTER_CUBIC)`
	`138`	`+ img_plate = cv2.resize(img_plate, (530, 340), interpolation=cv2.INTER_CUBIC)`
	`139`	`+`
	`140`	`+ cv2.imshow('img_plate', img_plate)`
	`141`	`+ cv2.imshow('img-00', img)`
	`142`	`+ cv2.waitKey(0)`
	`143`	`+`
	`144`	`+ return img, img_plate`
	`145`	`+`
	`146`	`+`
	`147`	`+img = cv2.imread('./img/01.bmp')`
	`148`	`+cv2.imshow('src-img', img)`
	`149`	`+`
	`150`	`+gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)`
	`151`	`+`
	`152`	`+blur = cv2.GaussianBlur(gray, (5, 5), 0)`
	`153`	`+`
	`154`	`+########################取有效区域########################`
	`155`	`+effectiveArea, srcImg = getEffectiveArea(blur, img)`
	`156`	`+########################取有效区域########################`
	`157`	`+`
	`158`	`+# 阈值一定要设为 0！`
	`159`	`+ret3, th3 = cv2.threshold(effectiveArea, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)`
	`160`	`+cv2.imshow('th3', th3)`
	`161`	`+`
	`162`	`+# 腐蚀操作`
	`163`	`+# 去掉部分噪点`
	`164`	`+kernel = np.ones((3, 3), np.uint8)`
	`165`	`+erosion = cv2.erode(th3, kernel, iterations=1)`
	`166`	`+cv2.imshow('th3-erosion', erosion)`
	`167`	`+`
	`168`	`+# 膨胀的核函数(9, 7)`
	`169`	`+# element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (12, 15))`
	`170`	`+# 膨胀一次，让轮廓突出`
	`171`	`+kernel = np.ones((15, 15), np.uint8)`
	`172`	`+dilation = cv2.dilate(erosion, kernel, iterations=1)`
	`173`	`+cv2.imshow('th3-dilation', dilation)`
	`174`	`+cv2.waitKey(0)`
	`175`	`+`
	`176`	`+box, angel = findPlateNumberRegion(dilation, 10, 6)`
	`177`	`+`
	`178`	`+# cv2.drawContours(srcImg, [box], 0, (0, 255, 0), 2)`
	`179`	`+`
	`180`	`+coordinate = getRectangularCoordinate(box)`
	`181`	`+`
	`182`	`+img_org2 = srcImg.copy()`
	`183`	`+`
	`184`	`+img_plate = img_org2[coordinate[2]:coordinate[3], coordinate[0]:coordinate[1]]`
	`185`	`+cv2.imshow('number plate', img_plate)`
	`186`	`+`
	`187`	`+rows, cols, _ = img_plate.shape`
	`188`	`+# 这里的第一个参数为旋转中心，第二个为旋转角度，第三个为旋转后的缩放因子`
	`189`	`+# 可以通过设置旋转中心，缩放因子，以及窗口大小来防止旋转后超出边界的问题`
	`190`	`+M = cv2.getRotationMatrix2D((cols / 2, rows / 2), angel, 1)`
	`191`	`+# 第三个参数是输出图像的尺寸中心`
	`192`	`+dst = cv2.warpAffine(img_plate, M, (cols, rows))`
	`193`	`+`
	`194`	`+cv2.imshow('dst', dst)`
	`195`	`+cv2.waitKey(0)`
	`196`	`+`
	`197`	`+gray = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)`
	`198`	`+`
	`199`	`+blur = cv2.GaussianBlur(gray, (5, 5), 0)`
	`200`	`+`
	`201`	`+ret3, th3 = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)`
	`202`	`+`
	`203`	`+cv2.imshow('th3-eff', th3)`
	`204`	`+cv2.waitKey(0)`
	`205`	`+`
	`206`	`+kernel = np.ones((4, 3), np.uint8)`
	`207`	`+dilation = cv2.dilate(th3, kernel, iterations=1)`
	`208`	`+`
	`209`	`+cv2.imshow('end-dilation', dilation)`
	`210`	`+cv2.waitKey(0)`
	`211`	`+`
	`212`	`+# 分割字符`
	`213`	`+boxList = findPlateNumberRegion2(dilation)`
	`214`	`+`
	`215`	`+imgList = []`
	`216`	`+for box in boxList:`
	`217`	`+ cv2.drawContours(dst, [box], 0, (0, 255, 0), 1)`
	`218`	`+ coordinate = getRectangularCoordinate(box)`
	`219`	`+ img_org2 = dst.copy()`
	`220`	`+ img_plate = img_org2[coordinate[2]:coordinate[3], coordinate[0]:coordinate[1]]`
	`221`	`+ imgList.append(img_plate)`
	`222`	`+`
	`223`	`+cv2.imshow('endImg', dst)`
	`224`	`+cv2.waitKey(0)`
	`225`	`+`
	`226`	`+i = 0`
	`227`	`+for img in imgList:`
	`228`	`+ h, w, _ = img.shape`
	`229`	`+ img = cv2.resize(img, (w * 10, h * 10), interpolation=cv2.INTER_CUBIC)`
	`230`	`+ gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)`
	`231`	`+ cv2.imshow(str(i), gray)`
	`232`	`+ cv2.waitKey(0)`
	`233`	`+ i += 1`
	`234`	`+cv2.waitKey(0)`
	`235`	`+`
	`236`	`+cv2.destroyAllWindows()`

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