Update GrassScene: S1 Max Logic, Infinite Grass mode, new assets (visoka_trava_v2), physics enabled. Time: 15:15

scripts/clean_pink_grass.py

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+import cv2
+import numpy as np
+import os
+
+def remove_pink_bg():
+    input_path = '/Users/davidkotnik/.gemini/antigravity/brain/63340bd3-91a9-439d-b1d9-5692ce5adaea/visoka_trava_v2_pink_bg_1769436757738.png'
+    output_path = 'assets/DEMO_FAZA1/Vegetation/visoka_trava_v2.png'
+    
+    # Ensure directory exists
+    os.makedirs(os.path.dirname(output_path), exist_ok=True)
+
+    # Read image
+    img = cv2.imread(input_path)
+    if img is None:
+        print(f"Error: Could not read {input_path}")
+        return
+
+    # Convert to RGBA
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)
+
+    # Define Magenta range (B, G, R) - standard magenta is (255, 0, 255) in BGR
+    # We'll allow a small tolerance to catch anti-aliasing edges
+    sensitivity = 20
+    lower_magenta = np.array([255 - sensitivity, 0, 255 - sensitivity])
+    upper_magenta = np.array([255, sensitivity, 255])
+    
+    # Create mask
+    # Note: OpenCV reads as BGR, so Pink #FF00FF is (255, 0, 255)
+    # Using simple color thresholding
+    mask = cv2.inRange(img[:,:,:3], lower_magenta, upper_magenta)
+    
+    # Invert mask (we want to keep non-magenta)
+    mask_inv = cv2.bitwise_not(mask)
+    
+    # Apply mask to alpha channel
+    # Where mask is white (magenta pixels), alpha becomes 0
+    # But mask_inv is white for KEEP pixels. 
+    # So we want Alpha to be 0 where mask is 255.
+    
+    # Better approach:
+    # 1. Split channels
+    b, g, r, a = cv2.split(img)
+    
+    # 2. Update alpha channel using the inverted mask
+    # If mask pixel is 255 (magenta), mask_inv is 0. We want alpha 0 there.
+    # If mask pixel is 0 (not magenta), mask_inv is 255. We want alpha 255 there.
+    # However, original alpha is 255 everywhere inside the image bounds.
+    # So we can just take bitwise_and or just set alpha to mask_inv.
+    
+    img[:, :, 3] = mask_inv
+
+    # Optional: Basic edge smoothing/despiking if needed, but for pixel/vector art simple cut is often better.
+    
+    # Save
+    cv2.imwrite(output_path, img)
+    print(f"Successfully saved transparent image to {output_path}")
+
+if __name__ == "__main__":
+    remove_pink_bg()

scripts/process_hay.py

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+import cv2
+import numpy as np
+import os
+
+def process_hay():
+    # Paths
+    input_path = 'assets/references/proizvodnja_sena.png'
+    output_dir = 'assets/DEMO_FAZA1/Items/Hay'
+    
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # Load image
+    img = cv2.imread(input_path)
+    if img is None:
+        print(f"Error: Could not load {input_path}")
+        return
+
+    # Convert to RGBA
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)
+
+    # Define Green Key (assuming bright green background)
+    # Range for Chroma Key Green in HSV is best, but BGR is fine for simple flat green
+    # Pure Green in BGR is (0, 255, 0)
+    
+    # Using HSV for better masking
+    hsv = cv2.cvtColor(img[:,:,:3], cv2.COLOR_BGR2HSV)
+    
+    # Mask for Green (Hue 35-85 approx for standard green screen)
+    lower_green = np.array([35, 50, 50])
+    upper_green = np.array([85, 255, 255])
+    
+    mask = cv2.inRange(hsv, lower_green, upper_green)
+    
+    # Invert mask (we want non-green parts)
+    mask_inv = cv2.bitwise_not(mask)
+    
+    # Set Alpha channel based on mask
+    # Everything green becomes transparent
+    img[:, :, 3] = mask_inv
+
+    # Find contours of objects
+    # Use the mask_inv (where 255 is the object)
+    contours, _ = cv2.findContours(mask_inv, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    
+    print(f"Found {len(contours)} objects.")
+
+    count = 0
+    for cnt in contours:
+        x, y, w, h = cv2.boundingRect(cnt)
+        
+        # Filter noise
+        if w < 10 or h < 10:
+            continue
+            
+        # Crop
+        crop = img[y:y+h, x:x+w]
+        
+        # Determine if it's a small pile or big bale based on size
+        # This is a heuristic. We'll label them by size.
+        area = w * h
+        label = "hay_piece"
+        
+        # Save
+        output_filename = f"{output_dir}/hay_drop_{count}.png"
+        cv2.imwrite(output_filename, crop)
+        print(f"Saved {output_filename} (Size: {w}x{h})")
+        count += 1
+
+if __name__ == "__main__":
+    process_hay()