Imagehandler.py

# -*- coding: utf-8 -*-
"""
Created on  Jun 16 14:28:26 2016

@author: Ankit Singh
"""
from PatternFinding import PatternFinding
from FindingOrientationOfContours import FindingOrientationOfContours
from AffineTransformation import AffineTransformation,PerspectiveTransformation
#import numpy as np

import cv2 as cv
import os.path


class Imagehandler(object):
    def __init__(self, path):
        if os.path.exists(path) and os.path.isfile(path):
            self.ImagePath = path
        else:
            raise OSError('Invalid path specified in `config.yml`.')

    def __convertImagetoBlackWhite(self):
        self.Image = cv.imread(self.ImagePath, cv.IMREAD_COLOR)
        self.imageOriginal = self.Image
        if self.Image is None:
            print ('some problem with the image')
        else:
            print ('Image Loaded')

        self.Image = cv.cvtColor(self.Image, cv.COLOR_BGR2GRAY)
        self.Image = cv.adaptiveThreshold(
            self.Image,
            255,                    # Value to assign
            cv.ADAPTIVE_THRESH_MEAN_C,# Mean threshold
            cv.THRESH_BINARY,
            11,                     # Block size of small area
            2,                      # Const to substract
        )

        return self.Image

    def WritingImage(self, image, path, imageName):
        if image is None:
            print ('Image is not valid.Please select some other image')
        else:
            image = cv.cvtColor(image, cv.COLOR_BGR2RGB)
            print (path + imageName)
            #why would you want to imshow your images. Let them be there in Results folder
            cv.imwrite(path + imageName, image)
            # cv.imshow(imageName, image)
            # cv.waitKey(0)ž
            # cv.destroyAllWindows()

    def GetImageContour(self):
        thresholdImage = self.__convertImagetoBlackWhite()  #B & W with adaptive threshold
        thresholdImage = cv.Canny(thresholdImage, 100, 200) #Edges by canny edge detection
        thresholdImage, contours, hierarchy = cv.findContours(
            thresholdImage, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
        self.Contours = contours
        # uncomment this to see the contours on the image
        # cv2.drawContours(thresholdImage, contours, -1, (0,255,0), 3)
        # patternFindingObj=PatternFinding()
        # areas= [cv.contourArea(contour) for contour in contours]
        # for index in range(len(contours)):
        #     IsPattern=self.IsPossibleQRContour(index)
        #     if IsPattern is True:
        #         x,y,w,h=cv.boundingRect(contours[index])
        #         cv.rectangle(self.imageOriginal,(x,y),(x+w,y+h),(0,0,255),2)
        #         cv.imshow("hello",self.imageOriginal)
        # maxAreaIndex=np.argmax(areas)
        # x,y,w,h=cv.boundingRect(contours[maxAreaIndex])
        # cv.rectangle(self.image2,(x,y),(x+w,y+h),(0,255,0),2)
        # cv.imshow("hello",self.imageOriginal)
        # cv.waitKey(0)
        #cv.destroyAllWindows()
        contour_group = (thresholdImage, contours, hierarchy)
        return contour_group

    def QRCodeInImage(self):
        patternFindingObj = PatternFinding(self.GetImageContour(), self.imageOriginal)
        patterns = patternFindingObj.FindingQRPatterns(3)
        if len(patterns) == 0:
            print ('patterns unable to find')
        contourA = patterns[0]
        contourB = patterns[1]
        contourC = patterns[2]
        orientationObj = FindingOrientationOfContours()
        massQuad , ORIENTATION= orientationObj.FindOrientation(contourA, contourB, contourC)
        tl=massQuad.tl
        tr=massQuad.tr
        bl=massQuad.bl
        #You could choose the method you like
        if(True):
            affineTransformObj = AffineTransformation(self.imageOriginal, ORIENTATION)
            self.TransformImage = affineTransformObj.transform(tl, tr, bl)
        else:
            pspTransforObj = PerspectiveTransformation(self.imageOriginal, ORIENTATION)
            self.TransformImage = pspTransforObj.transform(massQuad)
        return self.TransformImage



if __name__ == '__main__':
    pass
    # hdl=Imagehandler("C:\Python35\PYwork\opencv\image\QR.png")
    # hdl.QRCodeInImage()
    # cv.imshow("test",hdl.TransformImage)
    # cv.waitKey(0)