Today I want to show you a sweet algorithm with which you can remove objects from the picture. For example, if we have thousands of images where we have some objects that we want to delete, this algorithm can help us complete this task.

We will be using modified Template Matching approach.
We will load the template, convert to grayscale, perform canny edge detection, after that we do load the original image, convert to grayscale
Continuously rescale the image, apply template matching using edges, and keep track of the correlation coefficient (higher value means better match)
Find coordinates of best-fit bounding box then erase unwanted ROI
We'll use the cv2 module and NumPy. You can read about them on these URLs.

We'll use the cv2 module and NumPy. You can read about them on these URLs, CV2, and Numpy.

Let's start coding
First we will import a module



import cv2
import numpy as np

After that we do resize a image and maintain aspect ratio



def  maintain_aspect_ratio_resize(image,  width=None,  height=None,  inter=cv2.INTER_AREA):

then we grab the image size and initialize dimensions



dim =  None
(h, w)  = image.shape[:2]

then we return original image if no need to resize:



if width is  None  and height is  None:
    return image

We are resizing height if width is none



if width is  None:
    r = height /  float(h)
    dim =  (int(w * r), height)

We are resizing width if height is none



else:
    r = width /  float(w)
    dim =  (width,  int(h * r))

Return the resized image



return cv2.resize(image, dim,  interpolation=inter)

Load template, convert to grayscale, perform canny edge detection



template = cv2.imread('template.png')
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template,  50,  200)
(tH, tW)  = template.shape[:2]
cv2.imshow("template", template)

Load original image, convert to grayscale



original_image = cv2.imread('test.png')
final = original_image.copy()
gray = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
found =  None

Dynamically rescale image for better template matching



for scale in np.linspace(0.2,  1.0,  20)[::-1]:
      resized = maintain_aspect_ratio_resize(gray,  width=int(gray.shape[1]  * scale))
      r = gray.shape[1]  /  float(resized.shape[1])

      if resized.shape[0]  < tH or resized.shape[1]  < tW:
          break

      canny = cv2.Canny(resized,  50,  200)
      detected = cv2.matchTemplate(canny, template, cv2.TM_CCOEFF)
      (_, max_val, _, max_loc)  = cv2.minMaxLoc(detected)
      if found is  None  or max_val > found[0]:
         found =  (max_val, max_loc, r)

Compute coordinates of bounding box



(_, max_loc, r)  = found
(start_x, start_y)  =  (int(max_loc[0]  * r),  int(max_loc[1]  * r))
(end_x, end_y)  =  (int((max_loc[0]  + tW)  * r),  int((max_loc[1]  + tH)  * r))

Draw bounding box on ROI to remove



cv2.rectangle(original_image,  (start_x, start_y),  (end_x, end_y),  (0,255,0),  2)
cv2.imshow('detected', original_image)

Erase unwanted ROI (Fill ROI with white)



cv2.rectangle(final,  (start_x, start_y),  (end_x, end_y),  (255,255,255),  -1)
cv2.imwrite('final.png', final)
cv2.waitKey(0)

Original image:

When we run the script, we get this result

Whole code:



import cv2
import numpy as np

def  maintain_aspect_ratio_resize(image,  width=None,  height=None,  inter=cv2.INTER_AREA):
     dim =  None
     (h, w)  = image.shape[:2]
     if width is  None  and height is  None:
        return image

     if width is  None:
        r = height /  float(h)
        dim =  (int(w * r), height)
      else:
          r = width /  float(w)
          dim =  (width,  int(h * r))

       return cv2.resize(image, dim,  interpolation=inter)


template = cv2.imread('template.png')
template = cv2.cvtColor(template, cv2.COLOR_BGR2GRAY)
template = cv2.Canny(template,  50,  200)
(tH, tW)  = template.shape[:2]
cv2.imshow("template", template)

original_image = cv2.imread('test.png')
final = original_image.copy()
gray = cv2.cvtColor(original_image, cv2.COLOR_BGR2GRAY)
found =  None

for scale in np.linspace(0.2,  1.0,  20)[::-1]:
     resized = maintain_aspect_ratio_resize(gray,  width=int(gray.shape[1]  * scale))
     r = gray.shape[1]  /  float(resized.shape[1])

     if resized.shape[0]  < tH or resized.shape[1]  < tW:
        break
     canny = cv2.Canny(resized,  50,  200)
     detected = cv2.matchTemplate(canny, template, cv2.TM_CCOEFF)
     (_, max_val, _, max_loc)  = cv2.minMaxLoc(detected)

     if found is  None  or max_val > found[0]:
        found =  (max_val, max_loc, r)

(_, max_loc, r)  = found
(start_x, start_y)  =  (int(max_loc[0]  * r),  int(max_loc[1]  * r))
(end_x, end_y)  =  (int((max_loc[0]  + tW)  * r),  int((max_loc[1]  + tH)  * r))


cv2.rectangle(original_image,  (start_x, start_y),  (end_x, end_y),  (0,255,0),  2)
cv2.imshow('detected', original_image)

cv2.rectangle(final,  (start_x, start_y),  (end_x, end_y),  (255,255,255),  -1)
cv2.imwrite('final.png', final)
cv2.waitKey(0)

Thank you all

How to remove an object from an image with Python