Some simple templatematching

templatematching.py

import sys
import cv2
import numpy as np

FULL = "full.jpg"
CROP = "crop.jpg"

def template_matching(full, template, start=0.1, stop=1.0, step=0.1):
    
    crop_full = cv2.resize(template, tuple(np.roll(full.shape, 1)))

    best_val = 0
    best_scale = 0
    best_shape = (0, 0)
    best_loc = (0, 0)
    
    for s in np.arange(start, stop, step):
        tmp = cv2.resize(crop_full, (0, 0), fx=s, fy=s)
        
        res = cv2.matchTemplate(full_gray, tmp, cv2.TM_CCOEFF_NORMED)
        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
        
        #top_left = max_loc
        #bottom_right = (top_left[0] + w, top_left[1] + h)
        #factor = max_val / (tmp.shape[0]*tmp.shape[1])
        print "trying scale ", s, max_val, 1.0/s#, "factor", factor
        if max_val > best_val:
            best_val = max_val
            best_scale = s
            best_shape = tmp.shape
            best_loc = max_loc
        
        #cv2.imshow("image", tmp)
        #cv2.imshow("template", cv2.convertScaleAbs(res))
        #cv2.waitKey(1)
        
    return best_val, best_scale, best_shape, best_loc

if __name__ == '__main__':
    
    cv2.namedWindow("image", cv2.WINDOW_NORMAL)
    cv2.namedWindow("template", cv2.WINDOW_NORMAL)
    
    full = cv2.imread(FULL)
    crop = cv2.imread(CROP)
    
    if crop.shape[0] > full.shape[0] or crop.shape[1] > full.shape[1]:
        print "crop is bigger than full image...abort"
        print "crop", crop.shape
        print "full", full.shape
        sys.exit(-1)
    
    full_gray = cv2.cvtColor(full, cv2.COLOR_BGR2GRAY)
    crop_gray = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)
    
    
    best_val, best_scale, best_shape, best_loc = template_matching(full_gray, crop_gray)
    # get better result
    best_val, best_scale, best_shape, best_loc = template_matching(full_gray, crop_gray,
                                                                   best_scale-0.15, best_scale+0.15, 0.001)
    
    print "upper left corner:", best_loc
    print "width, height:", np.roll(best_shape, 1)
    print "middlepoint:", (best_loc[0]+best_shape[1]/2, best_loc[1]+best_shape[0]/2)
    print "zoom:", 1.0/best_scale
    cv2.rectangle(full, best_loc, (best_loc[0]+best_shape[1], best_loc[1]+best_shape[0]), (255, 255, 255), 5)
    cv2.imshow("image", full)
    cv2.waitKey(0)