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Don't used output of 1.jpeg. Use original w/out rectangle. Try this with images and see what happend. image of 1.jpeg. And template too: And output:

Don't used output of 1.jpeg. Use original w/out rectangle. Try this with images and see what happend. image of 1.jpeg. 1.jpeg:

image description And template too: And output:

Don't used output of 1.jpeg. Use original w/out rectangle. Try this with images and see what happend. image of 1.jpeg:

image description And template too: And output:

Don't used output of 1.jpeg. Use original w/out rectangle. Try this with images and see what happend. image of 1.jpeg:happend:

image of 1.jpeg: And template too: And output:

Don't used output of 1.jpeg. Use original w/out rectangle. Try this with images and see what happend:

I also removed threshold too. Here is code: import cv2 import numpy as np

img = cv2.imread("simpsons.jpg") gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) template = cv2.imread("barts_face.jpg", 0) w, h = template.shape[::-1]

result = cv2.matchTemplate(gray_img, template, cv2.TM_CCOEFF_NORMED) loc = np.where(result >= 0.4)

for pt in zip(*loc[::-1]): cv2.rectangle(img, pt, (pt[0] + w, pt[1] + h), (0, 255, 0), 3)

cv2.imwrite('Result1.jpg', img) cv2.imshow("img", img)

cv2.waitKey(0) cv2.destroyAllWindows()

image of 1.jpeg: And template too: And output:

Don't used output of 1.jpeg. Use original w/out rectangle. Try this with images and see what happend:

I also removed threshold too. Here is code: code:

import cv2
import numpy as np
np
  
img = cv2.imread("simpsons.jpg")
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
template = cv2.imread("barts_face.jpg", 0)
w, h = template.shape[::-1]template.shape[::-1]
  
result = cv2.matchTemplate(gray_img, template, cv2.TM_CCOEFF_NORMED)
loc = np.where(result >= 0.4)0.4)
  
for pt in zip(*loc[::-1]):
    cv2.rectangle(img, pt, (pt[0] + w, pt[1] + h), (0, 255, 0), 3)3)
  
cv2.imwrite('Result1.jpg', img)
cv2.imshow("img", img)img)
  
cv2.waitKey(0)
cv2.destroyAllWindows()cv2.destroyAllWindows()

image of 1.jpeg: And template too: And output:

Don't used output of 1.jpeg. Use original w/out rectangle. Try this with images and see what happend:

I also removed threshold threshold too. Here is code:

import cv2
import numpy as np

img = cv2.imread("simpsons.jpg")
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
template = cv2.imread("barts_face.jpg", 0)
w, h = template.shape[::-1]

result = cv2.matchTemplate(gray_img, template, cv2.TM_CCOEFF_NORMED)
loc = np.where(result >= 0.4)

for pt in zip(*loc[::-1]):
    cv2.rectangle(img, pt, (pt[0] + w, pt[1] + h), (0, 255, 0), 3)

cv2.imwrite('Result1.jpg', img)
cv2.imshow("img", img)

cv2.waitKey(0)
cv2.destroyAllWindows()

image of 1.jpeg: And template too: And output:

Don't used output of 1.jpeg. Use original w/out rectangle. Try this with images and see what happend:

I also removed threshold too. Here is code:

import cv2
import numpy as np

img = cv2.imread("simpsons.jpg")
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
template = cv2.imread("barts_face.jpg", 0)
w, h = template.shape[::-1]

result = cv2.matchTemplate(gray_img, template, cv2.TM_CCOEFF_NORMED)
loc = np.where(result >= 0.4)

for pt in zip(*loc[::-1]):
    cv2.rectangle(img, pt, (pt[0] + w, pt[1] + h), (0, 255, 0), 3)

cv2.imwrite('Result1.jpg', img)
cv2.imshow("img", img)

cv2.waitKey(0)
cv2.destroyAllWindows()

image of 1.jpeg: