19.template_match.py

#finding the location of a template image in a larger image. OpenCV
#comes with a function cv2.matchTemplate() for this purpose. It simply slides the template image over the input
#image (as in 2D convolution) and compares the template and patch of input image under the template image. Several
#comparison methods are implemented in OpenCV.

#If input image is of size (WxH) and template image is of size (wxh), output image will have a size of (W-w+1, H-h+1).
#Once you got the result, you can use cv2.minMaxLoc() function to find where is the maximum/minimum value. Take
#it as the top-left corner of rectangle and take (w,h) as width and height of the rectangle
# This box defines your template

import cv2
import numpy as np
from matplotlib import pyplot as plt
img = cv2.imread('messi5.jpg',0)
img2 = img.copy()
template = cv2.imread('template.jpg',0)
w, h = template.shape[::-1]
# All the 6 methods for comparison in a list
methods = ['cv2.TM_CCOEFF', 'cv2.TM_CCOEFF_NORMED', 'cv2.TM_CCORR',
'cv2.TM_CCORR_NORMED', 'cv2.TM_SQDIFF', 'cv2.TM_SQDIFF_NORMED']
for meth in methods:
	img = img2.copy()
	method = eval(meth)
	# Apply template Matching
	res = cv2.matchTemplate(img,template,method)
	min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)
	# If the method is TM_SQDIFF or TM_SQDIFF_NORMED, take 	minimum
	if method in [cv2.TM_SQDIFF, cv2.TM_SQDIFF_NORMED]:
		top_left = min_loc
	else:
		top_left = max_loc
	bottom_right = (top_left[0] + w, top_left[1] + h)
	cv2.rectangle(img,top_left, bottom_right, 255, 2)
	plt.subplot(121),plt.imshow(res,cmap = 'gray')
	plt.title('Matching Result'), plt.xticks([]), plt.yticks([])
	plt.subplot(122),plt.imshow(img,cmap = 'gray')
	plt.title('Detected Point'), plt.xticks([]), plt.yticks([])
	plt.suptitle(meth)
plt.show()



##
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#Template Matching with Multiple Objects

import cv2
import numpy as np
from matplotlib import pyplot as plt
img_rgb = cv2.imread('mario.png')
img_gray = cv2.cvtColor(img_rgb, cv2.COLOR_BGR2GRAY)
template = cv2.imread('mario_coin.png',0)
w, h = template.shape[::-1]
res = cv2.matchTemplate(img_gray,template,cv2.TM_CCOEFF_NORMED)
threshold = 0.8
loc = np.where( res >= threshold)
for pt in zip(*loc[::-1]):
	cv2.rectangle(img_rgb, pt, (pt[0] + w, pt[1] + h), (0,0,255), 2)
cv2.imwrite('res.png',img_rgb)