Applies operations to all images neow. Last step is template matching

voyante.py

@@ -1,52 +1,33 @@
 import cv2
 import numpy as np
 
-img1_location="data/GetPicture1.jpeg"
-img2_location="data/GetPicture2.jpeg"
-img3_location="data/GetPicture3.jpeg"
-
-img1g = cv2.imread(img1_location,0) # cv2.IMREAD_UNCHANGED
-img2g = cv2.imread(img2_location,0)
-img3g = cv2.imread(img3_location,0)
+img_locations=["data/GetPicture1.jpeg", "data/GetPicture2.jpeg", "data/GetPicture3.jpeg"]
 
 # Cropping the image to get only the cup. Could be automated, or simply ask the user to crop himself
-height1, width1 = img1g.shape
-cropimg1 = img1g[0:width1-150, 0:height1]
-
-height2, width2 = img2g.shape
-cropimg2 = img2g[0:width2-150, 0:height2]
-
-height3, width3 = img3g.shape
-cropimg3 = img3g[0:width3-150, 0:height3]
-
+def crop(img, width_start, width_end, height_start, height_end):
+    return img[width_start:width_end, height_start:height_end]
 
 # Binarization of the image, to get only black and white (2 colors)
-blur1 = cv2.GaussianBlur(cropimg1,(5,5),0)
-ret1,th1 = cv2.threshold(blur1,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+def binarize(img):
+    blur = cv2.GaussianBlur(img,(5,5),0)
+    return cv2.threshold(blur,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
 
-blur2 = cv2.GaussianBlur(cropimg2,(5,5),0)
-ret2,th2 = cv2.threshold(blur2,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
-
-blur3 = cv2.GaussianBlur(cropimg3,(5,5),0)
-ret3,th3 = cv2.threshold(blur3,0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)
+res_img = []
+for img_loc in img_locations:
+    img = cv2.imread(img_loc,0) # cv2.IMREAD_UNCHANGED
+    h1, w1 = img.shape
+    crop_img = crop(img, 0, w1-150, 0, h1)
+    thresh, bin_img = binarize(crop_img)
+    res_img.append(bin_img)
 
 # Now we want to search similarities between pictures.
-res = cv2.matchTemplate(th3, th3, cv2.TM_SQDIFF)
+res = cv2.matchTemplate(res_img[0], res_img[0], cv2.TM_SQDIFF)
 min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
 print max_val
 
-cv2.imshow('imageg',img1g)
-cv2.imshow('imageg2',img2g)
-cv2.imshow('imageg3',img3g)
-cv2.imshow('imagecrop',cropimg1)
-cv2.imshow('imagecrop2',cropimg2)
-cv2.imshow('imagecrop3',cropimg3)
-
-cv2.imshow('imageout3',th3)
-cv2.imshow('imageout2',th2)
-cv2.imshow('imageout1',th1)
-
-
+cv2.imshow('img1',res_img[0])
+cv2.imshow('img2',res_img[1])
+cv2.imshow('img3',res_img[2])
 
 
 cv2.waitKey(0)