import cv2 
import numpy as np 

def pixel_to_sqft(pixel_area, resolution_cm=30):
    
    area_cm2 = pixel_area * (resolution_cm ** 2)
    area_m2 = area_cm2 / 10000.0
    area_ft2 = area_m2 * 10.7639
    return area_ft2

def process_and_overlay_image(original_image, mask_prediction, output_image_path = None, resolution_cm=30):
    
   
    # Load original image
    
    
    # Convert mask prediction to binary mask
    mask = mask_prediction.astype(np.uint8) * 255
    
    # Find contours in the mask
    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

    # List to hold areas in square feet
    areas_sqft = []

    for contour in contours:
        area_pixels = cv2.contourArea(contour)
        area_sqft = pixel_to_sqft(area_pixels, resolution_cm)
        areas_sqft.append(area_sqft)
        
        # Draw contours on the original image
        cv2.drawContours(original_image, [contour], -1, (0, 255, 0), int(0.5))  # Green color for contours

        # Calculate and draw centroid
        M = cv2.moments(contour)
        if M["m00"] != 0:  
            cX = int(M["m10"] / M["m00"])
            cY = int(M["m01"] / M["m00"])
        else:
            cX, cY = 0, 0

        cv2.putText(original_image, f'{area_sqft:.0f}', (cX, cY), 
                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0), 1) 

    # Save and display the image with contours
    #cv2.imwrite(output_image_path, cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))

    # Display the image using matplotlib
    #return original_image
    return (cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))