def fushiyupengzhang(pathtoimage):
"""
腐蚀与膨胀动态取值预览
:param pathtoimage:
:return:
"""
img = cv2.imread(pathtoimage)
im_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#sobel = cv2.Sobel(im_gray, cv2.CV_8U, 1, 0, ksize=3)
# 获取二值化阈值
thr = bz.myThreshold()
threshold = thr.getMinimumThreshold(im_gray)
if threshold <= 0:
raise Exception("获取二值化阈值失败")
retval, img = cv2.threshold(im_gray, threshold, 255, cv2.THRESH_BINARY)
cv2.namedWindow('image', cv2.WINDOW_NORMAL)
cv2.imshow('image', img)
cv2.createTrackbar('Er/Di', 'image', 0, 1, nothing)
# 创建腐蚀或膨胀选择滚动条，只有两个值
cv2.createTrackbar('x', 'image', 0, 100, nothing)
# 创建卷积核大小滚动条
cv2.createTrackbar('y', 'image', 0, 100, nothing)
while (1):
s = cv2.getTrackbarPos('Er/Di', 'image')
x = cv2.getTrackbarPos('x', 'image')
y = cv2.getTrackbarPos('y', 'image')
# 分别接收两个滚动条的数据
if x == 0:
x = 1
if y == 0:
y = 1
k = cv2.waitKey(1)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (x, y))
# 根据滚动条数据确定卷积核大小
erroding = cv2.erode(img, kernel, iterations=1)
dilation = cv2.dilate(img, kernel, iterations=1)
if k == 27:
break
# esc键退出
if s == 0:
cv2.imshow('image', erroding)
else:
cv2.imshow('image', dilation)
def preprocess(dirUuid, gray, algoFunc):
# 1. Sobel算子，x方向求梯度
#sobel = cv2.Sobel(gray, cv2.CV_8U, 1, 0, ksize = 3)
#获取二值化阈值
thr = bz.myThreshold()
#threshold = thr.get1DMaxEntropyThreshold(gray)
threshold = getattr(thr, algoFunc)(gray)
if threshold <= 0:
raise Exception("获取二值化阈值失败")
# 2. 二值化
ret, binary = cv2.threshold(gray, threshold, 255, cv2.THRESH_BINARY)
# func.showImg(binary, 'binary')
func.storePic(dirUuid, 'binary', binary)
#获取核大小
calculateElement(gray)
# 3. 膨胀和腐蚀操作的核函数
element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
element2 = cv2.getStructuringElement(cv2.MORPH_RECT, calculateElement(gray))
#微处理去掉小的噪点
dilation = cv2.dilate(binary, element1, iterations=1)
binary = cv2.erode(dilation, element1, iterations=1)
#文字膨胀与腐蚀使其连成一个整体
erosion = cv2.erode(binary, element2, iterations=1)
dilation = cv2.dilate(erosion, element1, iterations=1)
# 7. 存储中间图片
# cv2.namedWindow("binary", cv2.WINDOW_NORMAL)
# cv2.imshow("binary", binary)
# cv2.waitKey(0)
#
# cv2.namedWindow("dilation2", cv2.WINDOW_NORMAL)
# cv2.imshow("dilation2", erosion)
# cv2.waitKey(0)
#
# cv2.namedWindow("dilation2", cv2.WINDOW_NORMAL)
# cv2.imshow("dilation2", dilation)
# cv2.waitKey(0)
cv2.destroyAllWindows()
#sys.exit(0)
return dilation
def getCardNum(num, dirUuid, img, kenalRect):
"""
识别并提取身份证号码
:return:
"""
gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
thr = bz.myThreshold().getMinimumThreshold(gray)
ret, binary = cv2.threshold(gray, thr, 255, cv2.THRESH_BINARY)
# 3. 膨胀和腐蚀操作的核函数
kenal = cv2.getStructuringElement(cv2.MORPH_RECT,
(kenalRect[0], kenalRect[1]))
dilation = cv2.dilate(binary, kenal, iterations=1)
erosion = cv2.erode(dilation, kenal, iterations=1)
#OCR识别
cardNum = func.ocr(erosion)
cardNum = func.is_identi_number(cardNum)
if cardNum:
return cardNum
return False
def getChineseChar(dirUuid, img, kenalRect):
"""
分析汉字区域，并识别提取
:return:
"""
global CARD_NAME
global CARD_SEX
global CARD_ETHNIC
global CARD_YEAR
global CARD_MON
global CARD_DAY
global CARD_ADDR
global CARD_NUM
CARD_NAME = CARD_SEX = CARD_ETHNIC = CARD_YEAR = CARD_MON = CARD_DAY = CARD_ADDR = ''
# 图片大小比例缩小处理，为了加快性能
h, w, _ = img.shape
min_w = 200
scale = 1 #min(1., w * 1. / min_w)
h_proc = int(h * 1. / scale)
w_proc = int(w * 1. / scale)
im_dis = cv2.resize(img, (w_proc, h_proc))
# 灰度处理
gray = cv2.cvtColor(im_dis, cv2.COLOR_BGR2GRAY)
# 2. 形态学变换的预处理，得到可以查找矩形的图片
mybz = bz.myThreshold()
algos = mybz.getAlgos()
for i in algos:
#获取阈值
thr = getattr(mybz, algos[i])(gray)
#thr = mybz.getMinimumThreshold(gray)
#func.showImg(gray, 'gray')
# 膨胀和腐蚀
ret, binary = cv2.threshold(gray, thr, 255, cv2.THRESH_BINARY)
#获取行起始坐标
boundaryCoors = func.horizontalProjection(binary)
if not boundaryCoors:
continue
#垂直投影对行内字符进行切割
erosion = cb = copy.copy(binary)
#func.showImg(binary, 'binary')
coors = {} #信息所对应的坐标
textLine = 0 #有效文本行序号
for LineNum, boundaryCoor in enumerate(boundaryCoors):
if textLine == 2:
kenal1 = cv2.getStructuringElement(cv2.MORPH_RECT, (1, 1))
kenal2 = cv2.getStructuringElement(cv2.MORPH_RECT, (2, 2))
dilation = cv2.dilate(cb, kenal1, iterations=1)
erosion = cv2.erode(dilation, kenal2, iterations=1)
# func.showImg(erosion, 'erosion')
func.storePic(dirUuid, 'erosion', erosion)
# cv2.imwrite('E:\\Test\\0.png', erosion)
# vertiCoors, text = func.verticalProjection(erosion, boundaryCoor, textLine, img)
vertiCoors, text = func.verticalProjection(erosion, boundaryCoor,
textLine, erosion)
if len(vertiCoors) == 0:
continue
if textLine == 0:
CARD_NAME = text
elif textLine == 1:
if text[0] != '男' and text[0] != '女':
CARD_SEX = func.getSexByCardNum(CARD_NUM)
else:
CARD_SEX = text[0]
CARD_ETHNIC = text[1]
elif textLine == 2:
#为了获取更加精准的值，通过身份证号码规则直接取得出生年月
# CARD_YEAR = text[0]
# CARD_MON = text[1]
# CARD_DAY = text[2]
pass
else:
CARD_ADDR += text
if DEBUG:
fator = 2
for verticoo in vertiCoors:
box = [
[
verticoo[0] * scale - fator,
boundaryCoor[0] * scale - fator
],
[
verticoo[1] * scale + fator,
boundaryCoor[0] * scale - fator
],
[
verticoo[1] * scale + fator,
boundaryCoor[1] * scale + fator
],
[
verticoo[0] * scale - fator,
boundaryCoor[1] * scale + fator
],
]
cv2.drawContours(img, [np.int0(box)], 0, (0, 255, 0), 2)
textLine += 1
return
return False
def detect(dirUuid, img):
global CARD_NUM
CARD_NUM = ''
notFound = True
# 1. 转化成灰度图
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# func.showImg(gray, 'gray')
func.storePic(dirUuid, 'gray', gray)
# 2. 遍历二值化阈值算法
algos = bz.myThreshold().getAlgos()
# for i in algos:
#
# #形态学变换的预处理，得到可以查找矩形的图片
# dilation = preprocess(gray, algos[i])
#
# # 3. 查找和筛选文字区域
# region = findTextRegion(dilation)
#
# # 4. 用绿线画出这些找到的轮廓
# angle = 0
# for rect in region:
#
# angle = rect[2]
#
# #识别身份证号码
# a, b = rect[1]
# if a > b:
# width = a
# hight = b
# pts2 = np.float32([[0, hight], [0, 0], [width, 0], [width, hight]])
# else:
# width = b
# hight = a
# angle = 90 + angle
# pts2 = np.float32([[width, hight], [0, hight], [0, 0], [width, 0]])
#
# #透视变换
# box = cv2.cv.BoxPoints(rect)
# pts1 = np.float32(box)
# M = cv2.getPerspectiveTransform(pts1, pts2)
# cropImg = cv2.warpPerspective(img, M, (int(width), int(hight)))
#
# # 计算核大小
# kenalx = kenaly = int(math.ceil((hight / 100.0)))
# CARD_NUM = getCardNum(cropImg, (kenalx, kenaly))
# if CARD_NUM:
# notFound = False
# #找到身份证号码，然后根据号码区域的倾斜角度，对原图进行旋转变换
#
# if abs(angle) > 10:
# sp = img.shape
# H = sp[0]
# W = sp[1]
# M = cv2.getRotationMatrix2D((W/2, H/2), angle, 1)
# cropImg = cv2.warpAffine(img, M, (W, H))
# # cv2.namedWindow("倾斜矫正", cv2.WINDOW_NORMAL)
# # cv2.imshow("倾斜矫正", cropImg)
# # cv2.waitKey(0)
# #矫正图片地址
# global curpath
# path = 'tilt_correction.jpg'
# newFile = os.path.join(curpath, path)
# cv2.imwrite(newFile, cropImg)
#
# return True, '', newFile
#
# # 画图
# if DEBUG:
# cv2.drawContours(img, [np.int0(box)], 0, (0, 255, 0), 3)
#
# # 寻找汉字区域
# # 裁剪后的图片
# box = cv2.cv.BoxPoints(rect)
# box = np.int0(box)
# cropImg, point, width, hight = func.cropImgByBox(img, box)
# box = findChineseCharArea(point, width, hight)
# #cv2.drawContours(img, [box], 0, (0, 255, 0), 3)
#
# chiCharArea, point, width, hight = func.cropImgByBox(img, box)
# getChineseChar(chiCharArea, (kenalx, kenaly))
#
# # winname = "身份证号码： %s" % (CARD_NUM)
# # cv2.namedWindow(winname, cv2.WINDOW_NORMAL)
# # cv2.imshow(winname, cropImg)
# # cv2.waitKey(0)
#
# break
#
# if notFound:
# continue
# else:
# break
i = 1
#形态学变换的预处理，得到可以查找矩形的图片
dilation = preprocess(dirUuid, gray, algos[i])
# func.showImg(dilation, 'dilation')
func.storePic(dirUuid, 'dilation', dilation)
# 3. 查找和筛选文字区域
region = findTextRegion(dilation)
# 4. 用绿线画出这些找到的轮廓
angle = 0
num = 0
for rect in region:
angle = rect[2]
#识别身份证号码
a, b = rect[1]
if a > b:
width = a
hight = b
pts2 = np.float32([[0, hight], [0, 0], [width, 0], [width, hight]])
else:
width = b
hight = a
angle = 90 + angle
pts2 = np.float32([[width, hight], [0, hight], [0, 0], [width, 0]])
#透视变换
# box = cv2.cv.BoxPoints(rect)
box = cv2.boxPoints(rect)
pts1 = np.float32(box)
M = cv2.getPerspectiveTransform(pts1, pts2)
cropImg = cv2.warpPerspective(img, M, (int(width), int(hight)))
# 计算核大小
kenalx = kenaly = int(math.ceil((hight / 100.0)))
CARD_NUM = getCardNum(num, dirUuid, cropImg, (kenalx, kenaly))
num = num + 1
if CARD_NUM:
notFound = False
#找到身份证号码，然后根据号码区域的倾斜角度，对原图进行旋转变换
if abs(angle) > 10:
sp = img.shape
H = sp[0]
W = sp[1]
M = cv2.getRotationMatrix2D((W / 2, H / 2), angle, 1)
cropImg = cv2.warpAffine(img, M, (W, H))
# cv2.namedWindow("倾斜矫正", cv2.WINDOW_NORMAL)
# cv2.imshow("倾斜矫正", cropImg)
# cv2.waitKey(0)
#矫正图片地址
global curpath
path = 'tilt_correction.jpg'
newFile = os.path.join(curpath, path)
cv2.imwrite(newFile, cropImg)
return True, '', newFile, ''
# 画图
if DEBUG:
cv2.drawContours(img, [np.int0(box)], 0, (0, 255, 0), 3)
# 寻找汉字区域
# 裁剪后的图片5
box = cv2.boxPoints(rect)
box = np.int0(box)
# 寻找人脸区域
faceBox = cv2.boxPoints(rect)
faceBox = np.int0(faceBox)
cropImg, point, width, hight = func.cropImgByBox(img, box)
box = findChineseCharArea(point, width, hight)
faceBox = findFaceArea(point, width, hight)
#cv2.drawContours(img, [box], 0, (0, 255, 0), 3)
chiCharArea, point, width, hight = func.cropImgByBox(img, box)
# func.showImg(chiCharArea, 'chiCharArea')
getChineseChar(dirUuid, chiCharArea, (kenalx, kenaly))
faceArea, _, _, _ = func.cropImgByBox(img, faceBox)
# func.showImg(faceArea, 'faceArea')
func.storePic(dirUuid, 'faceArea', faceArea)
# winname = "身份证号码： %s" % (CARD_NUM)
# cv2.namedWindow(winname, cv2.WINDOW_NORMAL)
# cv2.imshow(winname, cropImg)
# cv2.waitKey(0)
break
if notFound:
continue
else:
break
if notFound:
#win32api.MessageBox(0, "无法识别，请换一个分辨率高点的照片~", "错误提示")
return False, '无法识别，请换一个分辨率高点的照片~", "错误提示', '', ''
# 带轮廓的图片
if DEBUG:
cv2.namedWindow("img", cv2.WINDOW_NORMAL)
cv2.imshow("img", img)
key = cv2.waitKey(0)
cv2.destroyAllWindows()
if key != 32:
sys.exit()
if CARD_NUM != '':
# 为了获取更加精准的值，通过身份证号码规则直接取得出生年月
CARD_YEAR, CARD_MON, CARD_DAY = func.getBirthByCardNum(CARD_NUM)
info = """
姓名：%s
性别：%s 民族：%s
出生：%s 年 %s 月 %s 日
住址：%s
公民身份号码：%s
""" % (CARD_NAME, CARD_SEX, CARD_ETHNIC, CARD_YEAR, CARD_MON, CARD_DAY,
CARD_ADDR, CARD_NUM)
ret = [
CARD_NAME, CARD_SEX, CARD_ETHNIC, CARD_YEAR, CARD_MON, CARD_DAY,
CARD_ADDR, CARD_NUM
]
if DEBUG:
print info
return True, ret, '', faceArea
else:
return True, '', '', ''