scbc_repos/ssat_sdk/pic_tool.py

#-*- coding:utf-8 -*-
import sys
reload(sys)
sys.setdefaultencoding('utf-8')
from PIL import Image
from PIL import ImageChops
from PIL import ImageDraw

from picture import PreImageJudge

from picture.DoubleImage import DoubleImage
from picture import CrossColor
from picture import RGB
from picture.feature_detect import *
from ssat_sdk.picture.color_space import *
from ssat_sdk.picture.pq_detect import PQDetect
import ssat_sdk.picture.image_util

import cv2 as cv
import numpy as np

'''
用于向脚本提供图片、图像内容处理接口，提供图像检测接口、图像比对接口
'''
class ImageCMP():
    def __init__(self):
        print "Init Image Compare tool"
        self.featureDetect = FeatureDetect()
        self.CIECaculator = CIEluvCaculator()
        self.doubleImg = DoubleImage()
        self.pq = PQDetect()



    def getBlendImageCount(self, img):
        print "getBlendImageCount,img1.size", img.size, ";format:", img.format, ";model:", img.mode
        imgL = img.convert("L")
        pixSum = imgL.histogram()
        nonZeroCount = 0
        for i in range(pixSum.__len__()):
            if (pixSum[i] <> 0):
                nonZeroCount = nonZeroCount + 1

        print "getImageLArr,nonZeroCount=", nonZeroCount
        print pixSum
        return nonZeroCount

    def checkLikePic(self, im1, im2):
        im3 = ImageChops.invert(im2)
        im4 = Image.blend(im1, im3, 0.5)
        return self.getBlendImageCount(im4)

    '''
    参数，传入两张需要比对的图片的地址
    返回值：True代表图片一致，False代表图片不一致
    '''
    def cmpPicTotal(self, picPath1, picPath2, threshold=20, expDotRate=0.001):
        return self.doubleImg.cmpPicTotal(picPath1,picPath2, threshold, expDotRate)

    '''
    参数，传入两张需要比对的图片的对象
    返回值：True代表图片一致，False代表图片不一致
    '''
    def cmpImgTotal(self, img1, img2, threshold=20, expDotRate=0.001):
        print "cmpImgTotal,",img1.shape, img2.shape
        return self.doubleImg.cmpImgTotal(img1, img2, threshold, expDotRate)

    '''
    # 参数，传入两张需要比对的图片的地址，采用颜色空间LUV进行颜色判断。
    :param picPath1:第一张图片路径
    :param picPath2:第二张图片路径
    :param lineStd:luv差距值，相似度的阈值
    :return 判断颜色是否一样，True 或者False
    '''
    def cmpPicLuv(self, picPath1, picPath2, lineStd = 5):
        img1 = cv.imread(picPath1)
        luv1 = self.CIECaculator.getAverageLUV(cv.cvtColor(img1, cv.COLOR_BGR2LUV))
        img2 = cv.imread(picPath2)
        luv2 = self.CIECaculator.getAverageLUV(cv.cvtColor(img2, cv.COLOR_BGR2LUV))
        L, U, diff = self.CIECaculator.getDiffLevel(luv1,luv2)
        print L, U, diff
        if diff <= lineStd:
            return True
        else:
            return False

    '''
    #裁剪原图片文件的area区域，并保存到destPath文件中
    :param srcPic:图片文件路径
    :param destPath:裁剪图片保存路径
    :param area:(start_pointX,start_pointY,end_pointX, end_pointY). 原点：左上角，高为y轴，宽为x轴
    :return True/False
    '''
    def saveCropPic(self,srcPic, destPath, area):
        return image_util.saveCropPic(srcPic, destPath, area)

    '''
    #解决image的area区域，并保存到destPath文件中。srcImage是PIL里的Image对象。
    :param srcImg:Opencv图片对象
    :param area:(start_pointX,start_pointY,end_pointX, end_pointY). 原点：左上角，高为y轴，宽为x轴
    :return True/False
    '''
    def saveCropImage(self, srcImg, destPath, area):
        return image_util.saveCropImg(srcImg, destPath, area)


    '''
    裁剪非选中区域，返回image对象列表
    :param srcPic:图片文件路径
    :param area:选中区域
    :return 列表：OpenCV图像对象，BGR颜色空间
    '''
    def saveRetPic(self,srcPic, area):
        return image_util.saveRetCropPic(srcPic,area)

    """
    参数：
    cv_img：数组，OpenCV常用的图像3维数组。例如：imread返回的值
    area：元组，被检测的图片区域，例如：(start_pointX,start_pointY,end_pointX, end_pointY)
    targetBGR：元组，应该达成的BGR数值。例如：（Blue，Green，Red）
    返回值：
    True：代表直线与目标BGR值符合。
    False：代表直线与目标BGR值不符合。
    """
    def checkLineByImg(self, cv_img, area, targetBGR):
        if cv_img == None:
            return False
        for x in range(area[0],area[2]):
            for y in range(area[1], area[3]):
                print x,y, cv_img[x,y]
                if (abs(cv_img[x,y][0] - targetBGR[0]) > 20) \
                        or (abs(cv_img[x,y][1] - targetBGR[1]) > 20) \
                        or (abs(cv_img[x,y][2] <> targetBGR[3]) > 20):
                    return False
        return True
    """
    参数：
    picpath  字符串，图片文件的路径
    area：元组，被检测的图片区域，例如：(start_pointX,start_pointY,end_pointX, end_pointY)
    targetBGR：元组，应该达成的BGR数值。例如：（Blue，Green，Red）
    返回值：
    True：代表直线与目标BGR值符合。
    False：代表直线与目标BGR值不符合。
    """
    def checkLineByPath(self, picpath, area, targetBGR):
        areaDXRY = self.coordinateRXDY2DXRY(area)
        return self.checkLineByImg(cv.imread(picpath), areaDXRY, targetBGR)



    def coordinateRXDY2DXRY(self, areaRXDY):
        areaDXRY = []
        areaDXRY.append(areaRXDY[1])
        areaDXRY.append(areaRXDY[0])
        areaDXRY.append(areaRXDY[3])
        areaDXRY.append(areaRXDY[2])
        return areaDXRY

    def isDoubleImage(self,std_picpath,test_picpath):
        return not (DoubleImage.cmpPicTotal(std_picpath,test_picpath))

    '''
    ::接口异常
    '''
    def isCrossColor(self,picpath):
        return not (CrossColor.cmpPicTotal(picpath))

    '''
    判断一张图片是否是纯黑图片。
    :param picpath:需要判断的图片路径
    :param grayTH : 黑色判断的灰阶阈值
    :param splitCount: 图片拆分的行列次数
    :param expTh：黑色计算的容错区域比例。
    '''
    def isBlack(self,picpath, black_th=15,splitCount=10, expTh = 0.01):
        #print "pic_tool.isBlack, picpath=", picpath
        rgb = RGB.RGBColor()
        img = cv.imread(picpath)
        return rgb.isBlack(img, black_th, splitCount, expTh)

    '''
    获取区域的最大RGB值
    :param picpath:图片路径。 area：图片区域坐标。
    '''
    def getMaxRGB(self,picpath,area):
        return PreImageJudge.getMaxRGB(picpath,area)

    '''
    获取区域的BGR平均值
    :param picpath:图片路径。 area：图片区域坐标。
    '''
    def getAverageRGB(self,picpath,area):
        return PreImageJudge.getAverageRGB(picpath,area)

    '''
   获取区域的平均灰阶值
   :param picpath:图片路径。 area：图片区域坐标。
   '''
    def getAvgGray(self, picpath, area):
        return self.CIECaculator.getAvgGray(picpath, area)

    '''
    返回图片指定坐标的BGR颜色值
    :param img: cv.imread()返回的图片对象
    :param point坐标：(高，宽)
    :return BGR颜色值：(blue,green,red)
    '''
    def getDotBGR(self,img, point):
        return img[point[0], point[1]]

    '''
    检测图像是否有马赛克，准确度不够
    ::接口废弃
    '''
    def hasMosaic(self,std_pic,test_pic):
        return PreImageJudge.hasMosaic(std_pic,test_pic)

    '''
    ::接口废弃
    '''
    def cmpPicWithRGB(self,std_pic,test_pic):
        return RGB.cmpPicWithRGB(std_pic,test_pic)

    '''
    用途：加载一张图片文件，获取指定长度的直线。可以指定图片区域，获取指定区域的直线
    :param file:文件路径
    :param lineMinLen:直线最短界限
    :param lineMaxLen：直线最长界限
    :return line数组：[[x1,y1,x2,y2],[...],...]
    '''
    def getLinesByFile(self, file, lineMinLen=20, lineMaxLen=1000, ableArea=None, threshold1=50, threshold2=100,
                       apertureSize=5):
        return self.featureDetect.getLinesByFile(file,lineMinLen,lineMaxLen,ableArea,threshold1,threshold2,apertureSize)

    '''
        用途：传入OpenCV读取的image图片对象，获取指定长度的直线。可以指定图片区域，获取指定区域的直线
        :param file:文件路径
        :param lineMinLen:直线最短界限
        :param lineMaxLen：直线最长界限
        :return line数组：[[x1,y1,x2,y2],[...],...]
        '''
    def getLinesByImg(self, img, lineMinLen=20, lineMaxLen=1000, ableArea=None, threshold1=50, threshold2=100,
                 apertureSize=5):
        return self.featureDetect.getLines(img,lineMinLen,lineMaxLen,ableArea,threshold1,threshold2,apertureSize)

    '''
    读取传入的图片路径，加载图片。
    检测中心区域外的四周颜色是否为纯色图片,且四周纯色一致。
    :param picPath:图片路径
    :rate ：四周在宽、高上的比例。
    :return :检测结果。Ture/False
    '''
    def checkAroundSSColor(self, picPath, rate=0.2):
        srcImg = cv.imread(picPath)
        height,width,color = srcImg.shape
        # print "checkAroundSSColor,height,width,color:",height,width,color
        centerArea = [int(width*rate), int(height*rate), int(width*(1-rate)), int(height*(1-rate))]
        imgList = image_util.saveRetCropImg(srcImg, centerArea)
        for index in range(1,imgList.__len__()):
            prevImg = imgList[index-1]
            currentImg = imgList[index]
            prevSingle = self.isSingleColor(prevImg)
            currentSingle = self.isSingleColor(currentImg)
            isSimilar = self.rgbColor.isColorSimilar(prevSingle[1],currentSingle[1])
            # print "checkAroundSSColor:",prevSingle,currentSingle,isSimilar
            self.rgbColor = None
            if prevSingle[0] and currentSingle[0] and isSimilar:
                continue
            else:
                return False
        return True

    '''
    比较bgr1和bgr2颜色是否一样
    '''
    def isColorSimilar(self, bgr1, bgr2):
        rgbColor = RGB.RGBColor()
        return rgbColor.isColorSimilar(bgr1,bgr2)

    '''
    判断图片对象是否为纯色图片
    :param img: OpenCV的mat对象
    :return 检测结果：[Ture/False, [BGR平均值]]
    '''
    def isSingleColor(self, img):
        self.rgbColor = RGB.RGBColor()
        return self.rgbColor.isSingleColor(img)

    '''
    根据传入的图片路径，计算图片色温.图片必须是单色图片
    :param picPath,图片路径
    '''
    def calculateCCT(self, picPath):
        return self.pq.calculateCCT(picPath)

    '''
    根据传入的图片路径，计算图片清晰度。
    :param picPath,图片路径
    '''
    def detectSharpness(self, picPath):
        return self.pq.detSharpTenengrad(picPath)

if __name__ == "__main__":
    imgCmp = ImageCMP()
    picStandard = r"E:\0year2020\pic\standard.jpg"
    picDynamic = r"E:\0year2020\pic\dynamic.jpg"
    picMovie = r"E:\0year2020\pic\movie.jpg"

    cctM = imgCmp.calculateCCT(picMovie)
    cctS = imgCmp.calculateCCT(picStandard)
    cctD = imgCmp.calculateCCT(picDynamic)
     
    print "cctM", cctM
    print "cctS",cctS
    print "cctD", cctD

    #bled = r"D:\TST\test1\blend.jpg"
    #diffPath = r"D:\TST\test1\diff.jpg"
    #pic1 = r"D:\TST\test1\contrast-10.jpg"
    #pic2 = r"D:\TST\test1\brightness+10.jpg"

    #imgCmp.cmpPicTotal(pic1,pic2)

    #pic3= r"D:\TST\image\65-601.jpg"
    #pic4= r"D:\TST\image\65-603.jpg"
    #imgCmp.cmpPicTotal(pic3,pic4)

    # picPath = r"D:\1.png"


    # mat = np.array(cv.imread(picPath))
    # print mat
    # PreImageJudge.getMaxRGB(picPath,(0,0,100,200))
    # cv.imshow("test",np.array(mat))
    # rxdyArea = imgCmp.coordinateRXDY2DXRY([0,0,200,300])
    # mat5 = mat[rxdyArea[0]:rxdyArea[2],rxdyArea[1]:rxdyArea[3]]
    # cv.imshow("test5",np.array(mat5))
    # cv.waitKey(0)
    # cv.destroyAllWindows()
    # print imgCmp.checkLineByPath(mat5, (507,200,798,200), (255,0,0))