Detect and replace region from one image onto another image with OpenCV

Question

I have two pictures of the same dimension and I want to detect and replace the white region in the first picture (black image) at the same location in the second picture. Is there any way to do this using OpenCV ? I want to replace the blue region in the original image with the white region in the first picture.

First picture

Original image

Answer 1

If I'm understanding you correctly, you want to replace the white ROI on the black image onto the original image. Here's a simple approach:

1. Obtain binary image. Load image, grayscale, Gaussian blur , then Otsu's threshold

2. Extract ROI and replace. Find contours with cv2.findContours then filter using contour approximation with cv2.arcLength and cv2.approxPolyDP . With the assumption that the region is a rectangle, if the contour approximation result is 4 then we have found our desired region. In addition, we filter using cv2.contourArea to ensure that we don't include noise. Finally we obtain the bounding box coordinates with cv2.boundingRect and extract the ROI with Numpy slicing. Finally we replace the ROI into the original image.

---

Detected region to extract/replace highlighted in green

Extracted ROI

Result

Code

import cv2

# Load images, grayscale, Gaussian blur, Otsu's threshold
original = cv2.imread('1.jpg')
image = cv2.imread('2.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Find contours, filter using contour approximation + area, then extract
# ROI using Numpy slicing and replace into original image
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.015 * peri, True)
    area = cv2.contourArea(c)
    if len(approx) == 4 and area > 1000:
        x,y,w,h = cv2.boundingRect(c)
        ROI = image[y:y+h,x:x+w]
        original[y:y+h, x:x+w] = ROI

cv2.imshow('thresh', thresh)
cv2.imshow('ROI', ROI)
cv2.imshow('original', original)
cv2.waitKey()