window_detect.py

import cv2

# Read the image
img = cv2.imread('img5.png')
height, width = img.shape[:2]

# Convert the image to grayscale
gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Apply Gaussian blur to reduce noise
blur_img = cv2.GaussianBlur(gray_img, (5, 5), 0)

# Perform edge detection using Canny
edge_threshold = 10
edges = cv2.Canny(blur_img, threshold1=edge_threshold, threshold2=edge_threshold*3)

# Find contours
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# Filter and find contours based on area
min_area = (height / 5) * (width / 7)
max_area = (height / 2) * (width / 2)

contour_list = []
offset_pixel = 50
for contour in contours:
    x, y, w, h = cv2.boundingRect(contour)
    area = w * h

    if area >= min_area and area <= max_area:
        new_contour = 1
        for item in contour_list:
            # Check if the current contour x and y is within any existing
            if (x + offset_pixel > item[0] and
                x + w - offset_pixel < item[2] and
                y + offset_pixel > item[1] and
                y + h - offset_pixel < item[3]):

                # Ignore contour
                new_contour = 0
                break
            # Check if current contour is bigger than existing contour
            elif (x - offset_pixel < item[0] and
                  x + w + offset_pixel > item[2] and
                  y - offset_pixel < item[1] and
                  y + h + offset_pixel > item[3]):
                
                # Remove the existing contour and add the new one
                contour_list.remove(item)

        # Store contour bottom left and top right coordinates
        if new_contour:
            contour_list.append((x, y, x + w, y + h))

for square in contour_list:
    cv2.rectangle(img, (square[0], square[1]), (square[2], square[3]), (0, 255, 0), 2)

# Display the image with potential windows
cv2.imshow('Potential Windows', img)
cv2.waitKey(0)
cv2.destroyAllWindows()