import cv2

# Load the image
image = cv2.imread("mid_image.jpg")

# Convert image to grayscale
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Resize the image to half its original size for display (pop up window)
scale_percent = 20 # percent of original size
width = int(gray.shape[1] * scale_percent / 100)
height = int(gray.shape[0] * scale_percent / 100)
dim = (width, height)

resized_gray = cv2.resize(gray, dim, interpolation=cv2.INTER_AREA)

# Show resized image
cv2.imshow("Detected Rectangle", resized_gray)
cv2.waitKey(0)
cv2.destroyAllWindows()


"""# Apply Gaussian blur
blur = cv2.GaussianBlur(gray, (5, 5), 0)

# Run Canny edge detector
edges = cv2.Canny(blur, 50, 150)

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

# Select the largest contour (presumed to be the rectangle)
contour = max(contours, key=cv2.contourArea)

# Draw the detected contour on a copy of the original image
output = image.copy()
cv2.drawContours(output, [contour], -1, (0, 255, 0), 3)

# Save or show the result
cv2.imwrite("rectangle_edges.jpg", output)
cv2.imshow("Detected Rectangle", output)
cv2.waitKey(0)
cv2.destroyAllWindows()"""