84 lines
2.9 KiB
Python
84 lines
2.9 KiB
Python
import cv2
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import numpy as np
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import os
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import sys
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def remove_white_bg_and_resize(path, target_width=None):
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if not os.path.exists(path):
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print(f"File not found: {path}")
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return
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img = cv2.imread(path)
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if img is None:
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print(f"Error loading {path}")
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return
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# Convert to RGBA
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img = cv2.cvtColor(img, cv2.COLOR_BGR2BGRA)
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# Threshold for white (allowing slight off-white/compression artifacts)
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# White is (255,255,255). Let's say anything > 240 in all channels.
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lower_white = np.array([240, 240, 240, 255])
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upper_white = np.array([255, 255, 255, 255])
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# Create mask: looking for white pixels
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# Note: cv2.inRange checks ranges. For BGRA, A is 255.
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# However, input usually doesn't have A yet if loaded as BGR.
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# But we converted.
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# Alternative: Grayscale thresholding is often safer for "almost white" backgrounds in illustrations
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gray = cv2.cvtColor(img, cv2.COLOR_BGRA2GRAY)
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# Create binary mask where white is 255, else 0
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_, mask = cv2.threshold(gray, 240, 255, cv2.THRESH_BINARY)
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# Invert mask: We want to KEEP non-white.
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# Non-white = 0 in mask? No, thresholded white became 255.
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# So we invert.
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mask_inv = cv2.bitwise_not(mask)
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# Morphological cleanup (remove small white specks inside object if necessary, or smooth edges)
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# For vector art, simple threshold usually works well.
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# Assign new alpha channel
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b, g, r, a = cv2.split(img)
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# Use mask_inv as alpha
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final_img = cv2.merge((b, g, r, mask_inv))
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# Crop to content (Trim transparent borders)
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coords = cv2.findNonZero(mask_inv)
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if coords is not None:
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x, y, w, h = cv2.boundingRect(coords)
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# Padding
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pad = 2
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x = max(0, x - pad)
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y = max(0, y - pad)
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w = min(final_img.shape[1] - x, w + 2*pad)
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h = min(final_img.shape[0] - y, h + 2*pad)
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final_img = final_img[y:y+h, x:x+w]
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# Resize if target_width provided
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if target_width:
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h_curr, w_curr = final_img.shape[:2]
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if w_curr > target_width: # Only scale down
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ratio = target_width / w_curr
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new_h = int(h_curr * ratio)
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final_img = cv2.resize(final_img, (target_width, new_h), interpolation=cv2.INTER_AREA)
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print(f"Resized to {target_width}x{new_h}")
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cv2.imwrite(path, final_img)
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print(f"Processed (Cleaned White BG + Trim): {path}")
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TARGETS = {
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'assets/DEMO_FAZA1/UI/badge_purchase.png': 512,
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'assets/DEMO_FAZA1/UI/health_critical.png': 512,
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'assets/DEMO_FAZA1/VFX/toxic_fog.png': 512,
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'assets/DEMO_FAZA1/Environment/obstacle_thorns.png': 256,
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'assets/DEMO_FAZA1/Structures/foundation_concrete.png': 512,
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'assets/DEMO_FAZA1/Items/hay_drop_0.png': 128
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}
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if __name__ == "__main__":
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base = os.getcwd()
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for rel, w in TARGETS.items():
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remove_white_bg_and_resize(os.path.join(base, rel), w)
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