import cv2
import numpy as np
import os

def trim_walls_manual():
    src_path = '/Users/davidkotnik/.gemini/antigravity/brain/07019d04-a214-43ab-9565-86f4e8f17e5b/uploaded_media_1769607894587.jpg'
    
    print(f"Loading {src_path}")
    img = cv2.imread(src_path)
    if img is None: return

    # 1. Basic GrabCut to remove BG
    mask = np.zeros(img.shape[:2], np.uint8)
    bgdModel = np.zeros((1,65),np.float64)
    fgdModel = np.zeros((1,65),np.float64)
    h, w = img.shape[:2]
    cv2.grabCut(img, mask, (10, 10, w-20, h-20), bgdModel, fgdModel, 5, cv2.GC_INIT_WITH_RECT)
    mask_final = np.where((mask==2)|(mask==0),0,1).astype('uint8') 
    
    # 2. MANUAL TRIM
    # The "Wall" problem is mainly on the bottom edges because of the isometric height.
    # We can try to erode the mask from the bottom-left direction?
    # Or simpler: Detect the DARK BROWN/BLACK pixels near the boundary and make them transparent.
    
    # Convert to HSV
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    
    # Define "Wall Shadow" color
    # Walls are very dark. Value < 40 or so.
    is_dark = (hsv[:,:,2] < 50)
    
    # And we only care about dark pixels that are currently part of the mask.
    is_part_of_object = (mask_final == 1)
    
    # We want to remove dark pixels ONLY if they are on the "outside" or extending downwards.
    # Let's try to remove ALL "Very Dark" pixels that are connected to the mask boundary?
    # No, that might remove internal details.
    
    # Let's try aggressive erosion of dark areas.
    # Create a mask of "Walls to Remove" = Dark pixels.
    walls_mask = (is_dark & is_part_of_object).astype(np.uint8)
    
    # Morphological Opening to remove noise (thin lines might be outlines, we want blocks)
    walls_mask = cv2.morphologyEx(walls_mask, cv2.MORPH_OPEN, np.ones((5,5), np.uint8))
    
    # Subtract walls from main mask
    mask_trimmed = mask_final.copy()
    mask_trimmed[walls_mask == 1] = 0
    
    # Verify: Did we eat too much?
    # The pipes drain hole is dark! We must protect the top-right quadrant.
    # Let's define a safe zone (Top 40% of image height + Right 40% of width)
    safe_y = int(h * 0.4)
    # Actually, simpler: just don't touch the top-right area where the pipe is.
    # Let's mask out the top-right from the removal process.
    # Pipe bounding box approx: x > w*0.6, y < h*0.5
    walls_mask[0:int(h*0.5), int(w*0.6):w] = 0
    
    # Now re-apply subtraction
    mask_trimmed = mask_final.copy()
    mask_trimmed[walls_mask == 1] = 0
    
    # 3. Output
    b, g, r = cv2.split(img)
    alpha = mask_trimmed * 255
    img_rgba = cv2.merge([b, g, r, alpha])

    # Crop
    coords = cv2.findNonZero(alpha)
    if coords is not None:
        x, y, cw, ch = cv2.boundingRect(coords)
        img_rgba = img_rgba[y:y+ch, x:x+cw]
        
    targets = [
        '/Users/davidkotnik/repos/novafarma/main/assets/stream_pipe.png',
        '/Users/davidkotnik/repos/novafarma/assets/DEMO_FAZA1/Environment/stream_pipe.png'
    ]
    for t in targets:
        cv2.imwrite(t, img_rgba)
        print(f"Saved {t}")

if __name__ == "__main__":
    trim_walls_manual()