import cv2
import numpy as np
import os
import sys

def remove_white_bg_and_resize(path, target_width=None):
    if not os.path.exists(path):
        print(f"File not found: {path}")
        return

    img = cv2.imread(path)
    if img is None:
        print(f"Error loading {path}")
        return

    # Convert to RGBA
    img = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)

    # Threshold for white (allowing slight off-white/compression artifacts)
    # White is (255,255,255). Let's say anything > 240 in all channels.
    lower_white = np.array([240, 240, 240, 255]) 
    upper_white = np.array([255, 255, 255, 255])
    
    # Create mask: looking for white pixels
    # Note: cv2.inRange checks ranges. For BGRA, A is 255.
    # However, input usually doesn't have A yet if loaded as BGR. 
    # But we converted.
    
    # Alternative: Grayscale thresholding is often safer for "almost white" backgrounds in illustrations
    gray = cv2.cvtColor(img, cv2.COLOR_BGRA2GRAY)
    # Create binary mask where white is 255, else 0
    _, mask = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY)
    
    # Invert mask: We want to KEEP non-white. 
    # Non-white = 0 in mask? No, thresholded white became 255.
    # So we invert.
    mask_inv = cv2.bitwise_not(mask)
    
    # Morphological cleanup (remove small white specks inside object if necessary, or smooth edges)
    # For vector art, simple threshold usually works well.
    
    # Assign new alpha channel
    b, g, r, a = cv2.split(img)
    # Use mask_inv as alpha
    final_img = cv2.merge((b, g, r, mask_inv))
    
    # Crop to content (Trim transparent borders)
    coords = cv2.findNonZero(mask_inv)
    if coords is not None:
        x, y, w, h = cv2.boundingRect(coords)
        # Padding
        pad = 2
        x = max(0, x - pad)
        y = max(0, y - pad)
        w = min(final_img.shape[1] - x, w + 2*pad)
        h = min(final_img.shape[0] - y, h + 2*pad)
        final_img = final_img[y:y+h, x:x+w]

    # Resize if target_width provided
    if target_width:
        h_curr, w_curr = final_img.shape[:2]
        if w_curr > target_width: # Only scale down
            ratio = target_width / w_curr
            new_h = int(h_curr * ratio)
            final_img = cv2.resize(final_img, (target_width, new_h), interpolation=cv2.INTER_AREA)
            print(f"Resized to {target_width}x{new_h}")

    cv2.imwrite(path, final_img)
    print(f"Processed (Cleaned White BG + Trim): {path}")

TARGETS = {
    'assets/DEMO_FAZA1/UI/badge_purchase.png': 512,
    'assets/DEMO_FAZA1/UI/health_critical.png': 512,
    'assets/DEMO_FAZA1/VFX/toxic_fog.png': 512,
    'assets/DEMO_FAZA1/Environment/obstacle_thorns.png': 256,
    'assets/DEMO_FAZA1/Structures/foundation_concrete.png': 512,
    'assets/DEMO_FAZA1/Items/hay_drop_0.png': 128
}

if __name__ == "__main__":
    base = os.getcwd()
    for rel, w in TARGETS.items():
        remove_white_bg_and_resize(os.path.join(base, rel), w)