scbc_repos/ssat_sdk/picture/image_util.py

# -*- coding:utf-8 -*-
import os, sys, time
import inspect
import cv2 as cv
import numpy as np
from ssat_sdk.utils import LoggingUtil

pyFileName = os.path.split(__file__)[-1]

def get_current_function_name():
    return inspect.stack()[1][3]

'''
按照坐标系调整区域数值
:param areaRXDY:二维区域。坐标系：x轴：水平向右，y轴：纵向向下
:return areaDXRY：二维区域。坐标系：x轴：纵向向下，y轴：水平向右
'''
def coordinateRXDY2DXRY(areaRXDY):
    areaDXRY = []
    areaDXRY.append(areaRXDY[1])
    areaDXRY.append(areaRXDY[0])
    areaDXRY.append(areaRXDY[3])
    areaDXRY.append(areaRXDY[2])
    return areaDXRY
'''
截取图片区域。
:param area:二维区域。area按照常规x,y坐标描述。示例：[x1,y1,x2,y2]. 点(x1,y1)为矩形左上角，点(x2,y2)为矩形右下角。
'''
def cutMat(srcimg, area):
    try:
        mat_area = coordinateRXDY2DXRY(area)
        # print "cutMat:mat_area:", mat_area
        imgArr = np.array(srcimg)
        return imgArr[int(mat_area[0]):int(mat_area[2]),int(mat_area[1]):int(mat_area[3])]
    except Exception, e:
        LoggingUtil.getDebugLogger().info(pyFileName, "image_util", get_current_function_name(), 'ERROR:截图异常:'+str(e))
        return None

'''
    # 描述：裁剪图片，返回指定裁剪区域的图片;
    # 参数：
    #   srcimg：opencv.imread打开的对象。
    #   area: 要裁剪的区域，示例：[x1,y1,x2,y2]. 点(x1,y1)为矩形左上角，点(x2,y2)为矩形右下角。
    #
    # 返回：成功返回裁剪区域的图片对象，否则返回None
    #
    # 注意：相对cutMat来说，少一步坐标转换。
    # '''
def cutImage(srcimg, area):
    try:
        return np.array(srcimg[area[1]:area[3]+1, area[0]:area[2]+1])
    except Exception,e:
        LoggingUtil.getDebugLogger().info(pyFileName, "image_util", get_current_function_name(), 'ERROR:裁剪图片异常:'+str(e))
        return None

'''
    # 描述：裁剪图片，返回指定裁剪区域的图片;
    # 参数：
    #   imgPath:图片路径
    #   area: 要裁剪的区域，示例：[x1,y1,x2,y2]. 点(x1,y1)为矩形左上角，点(x2,y2)为矩形右下角。
    #
    # 返回：成功返回裁剪区域的图片对象，否则返回None
    #
    # 注意：相对cutMat来说，少一步坐标转换。
    # '''
def cutImageByPath(imgPath, area):
    srcimg = cv.imread(imgPath)
    try:
        return np.array(srcimg[area[1]:area[3], area[0]:area[2]])
    except Exception,e:
        LoggingUtil.getDebugLogger().info(pyFileName, "image_util", get_current_function_name(), 'ERROR:裁剪图片异常:'+str(e))
        return None

def saveCropPic(srcPic, destPic, area):
    srcImg = cv.imread(srcPic)
    if srcImg is None:
        LoggingUtil.getDebugLogger().info(pyFileName, "image_util", get_current_function_name(), 'SDK:打开图像返回空对象，文件可能不存在或无权限打开')
        return False
    if len(area) != 4:
        LoggingUtil.getDebugLogger().info(pyFileName, "image_util", get_current_function_name(), 'SDK:给定的区域坐标无效:'+str(area))
        return False
    destImg = cutMat(srcImg, area)
    if destImg is None:
        LoggingUtil.getDebugLogger().info(pyFileName, "image_util", get_current_function_name(), 'SDK:截图失败:')
        return False
    cv.imwrite(destPic,destImg)
    return True

def saveCropImg(srcImg, destPic, area):
    destImg = cutMat(srcImg, area)
    cv.imwrite(destPic,destImg)
    return True

def cutImgWithRate(srcImg, destPic, rateArea):
    height, width, dim = srcImg.shape
    print "srcImg: height, width:", height, width, rateArea
    dest_x1 = int(width * rateArea[0])
    dest_x2 = int(width * rateArea[2])
    dest_y1 = int(height * rateArea[1])
    dest_y2 = int(height * rateArea[3])
    print "dest: dest_x1,dest_y1, dest_x2, dest_y2:", dest_x1,dest_y1, dest_x2, dest_y2
    destImg = cutMat(srcImg, (dest_x1,dest_y1, dest_x2, dest_y2))
    cv.imwrite(destPic, destImg)

def cutPicWithRate(srcPic, destPic, rateArea):
    srcImg = cv.imread(srcPic)
    cutImgWithRate(srcImg, destPic, rateArea)

'''
截取选中区域以外的图片
'''
def saveRetCropPic(srcPic, area):
    srcImg = cv.imread(srcPic)
    return saveRetCropImg(srcImg, area)

def saveRetCropImg(srcImg, area):
    # print "saveRetCropImg,area:",area
    height,width,chNum = srcImg.shape
    area1 = [0,0,area[0], height]
    area2 = [area[0],0,area[2], area[1]]
    area3 = [area[0], area[3],area[2], height]
    area4 = [area[2],0,width, height]

    rectList = []
    addRect2List(rectList, area1)
    addRect2List(rectList, area2)
    addRect2List(rectList, area3)
    addRect2List(rectList, area4)

    imgList = []
    for rect in rectList:
        img = cutMat(srcImg, rect)
        imgList.append(img)

    return imgList

'''
根据设定的灰阶阈值，保存一张新图片，并返回img对象。
原图像是BGR模式
'''
def saveThresholdPicImg(srcImg,threshLow,threshTop, thresholdType):
    # print u"转成灰阶图;"
    img_gray = cv.cvtColor(srcImg, cv.COLOR_BGR2GRAY)
    # print u"转成二值化;"
    ret, img_threshold = cv.threshold(img_gray, threshLow, threshTop, thresholdType)
    return img_threshold

'''
检测rect是否正确，正确才添加到rectList
'''
def addRect2List(rectList, rect):
    # print "addRect2List,rect:",rect
    if (rect[0] <> rect[2]) and (rect[1] <> rect[3]):
        rectList.append(rect)

def caculateLinLen(line):
    dx = abs(line[0] - line[2])
    dy = abs(line[1]-line[3])
    # print "caculateLinLen:",dx,dy
    lineLen = np.sqrt(np.square(dx) + np.square(dy))
    return lineLen


class ImageUtil():
    def __init__(self):
        pass

    '''
    根据传入的图像对象，逆时针旋转角度
    '''
    def rotaImg(self, srcImg, angle):
        height, width, color = srcImg.shape
        M = cv.getRotationMatrix2D(((height-1)/2.0,(width-1)/2.0),angle,1)
        print M
        dstImg = cv.warpAffine(srcImg, M, (width,height))
        return dstImg

    def rotaImg180(self, srcImg):
        dstImg = cv.flip(srcImg,0) #根据x轴翻转
        dstImg = cv.flip(dstImg,1) #根据y轴翻转
        return dstImg

    '''
    根据传入的图片和高宽差参数，移动图片位置
    '''
    def transferImg(self, srcImg, dHeight, dWidth):
        height,width,color = srcImg.shape
        M = np.float32([[1,0,dWidth],[0,1,dHeight]])
        dstImg = cv.warpAffine(srcImg,M,(width,height))
        return dstImg
    '''
    根据传入的图片mat和坐标点，计算灰阶中间值
    '''
    def calGrayMid(self, img, area):
        # cv.line(img,(area[0],area[1]),(area[2],area[3]),(0,255,0))
        # cv.imshow("img",img)
        # cv.waitKey(0)
        # cv.destroyAllWindows()
        dstArea = list(area)
        if area[0] + area[1] > area[2]+area[3]:
            dstArea[0] = area[2]
            dstArea[1] = area[3]
            dstArea[2] = area[0]
            dstArea[3] = area[1]
        dstImg = cutImage(img, dstArea)
        # print "ImageUtil.calGrayAvg: dstImg:",dstImg
        return (np.max(dstImg) + np.min(dstImg))/2

    '''
    根据传入的图片mat和坐标点，计算灰阶平均值
    '''
    def calGrayAvg(self, img, area):
        # cv.line(img,(area[0],area[1]),(area[2],area[3]),(0,255,0))
        # cv.imshow("img",img)
        # cv.waitKey(0)
        # cv.destroyAllWindows()
        dstArea = list(area)
        if area[0] + area[1] > area[2]+area[3]:
            dstArea[0] = area[2]
            dstArea[1] = area[3]
            dstArea[2] = area[0]
            dstArea[3] = area[1]
        dstImg = cutImage(img, dstArea)
        # print "ImageUtil.calGrayAvg: dstImg:",dstImg
        return np.average(dstImg)