Datasets:
File size: 3,353 Bytes
c83afe9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 | import json
import os
import numpy as np
from PIL import Image
# --- Configuration Paths ---
# Path to the source mapping file
JSON_PATH = os.path.join('metadata', 'mapping_file.json')
# Root directory for output masks
BASE_OUT_DIR = os.path.join('data', 'Task_Image_Mask_raw_image')
INPAINT_DIR = os.path.join(BASE_OUT_DIR, 'inpainting_mask')
OUTPAINT_DIR = os.path.join(BASE_OUT_DIR, 'outpainting_mask')
# Ensure output directories exist
os.makedirs(INPAINT_DIR, exist_ok=True)
os.makedirs(OUTPAINT_DIR, exist_ok=True)
def rle2mask(mask_rle, shape=(512, 512)):
"""
Convert Absolute RLE [start, length, start, length...] to a binary mask.
Args:
mask_rle (list): List of integers in [start, len, start, len] format.
shape (tuple): (height, width) of the image.
"""
# 1. Split the list into starts and lengths
# Starts are at even indices [0, 2, 4...], Lengths are at odd indices [1, 3, 5...]
starts = np.array(mask_rle[0::2], dtype=int)
lengths = np.array(mask_rle[1::2], dtype=int)
# 2. Adjust for 1-based indexing (convert to 0-based for Python)
starts -= 1
ends = starts + lengths
# 3. Create flat array and fill mask segments
total_pixels = shape[0] * shape[1]
binary_mask = np.zeros(total_pixels, dtype=np.uint8)
for lo, hi in zip(starts, ends):
# Safety check for corrupted indices
if lo < total_pixels:
binary_mask[lo : min(hi, total_pixels)] = 1
# 4. Reshape to 2D
# IMPORTANT: Try 'C' order first. If it's still skewed, use order='F'.
# In BrushBench, this is typically Row-major ('C').
return binary_mask.reshape(shape, order='C')
def save_mask_as_png(mask_array, save_path):
"""
Convert a 0/1 binary array to a 0/255 grayscale image and save as PNG.
"""
# Map 1 to 255 (white) for visibility in standard image viewers
img_array = (mask_array * 255).astype(np.uint8)
img = Image.fromarray(img_array)
img.save(save_path)
def main():
# Verify the existence of the mapping file
if not os.path.exists(JSON_PATH):
print(f"Error: Could not find {JSON_PATH}")
return
print(f"Loading metadata from {JSON_PATH}...")
with open(JSON_PATH, 'r', encoding='utf-8') as f:
data = json.load(f)
processed_count = 0
for img_id, info in data.items():
# 1. Process Inpainting Mask (targeted region modification)
if 'inpainting_mask' in info:
in_mask = rle2mask(info['inpainting_mask'])
save_path = os.path.join(INPAINT_DIR, f"{img_id}.png")
save_mask_as_png(in_mask, save_path)
# 2. Process Outpainting Mask (edge expansion)
if 'outpainting_mask' in info:
out_mask = rle2mask(info['outpainting_mask'])
save_path = os.path.join(OUTPAINT_DIR, f"{img_id}.png")
save_mask_as_png(out_mask, save_path)
processed_count += 1
# Log progress every 100 images
if processed_count % 100 == 0:
print(f"Progress: {processed_count} samples processed...")
print(f"\nSuccess! Total samples handled: {processed_count}")
print(f"Inpainting masks saved to: {INPAINT_DIR}")
print(f"Outpainting masks saved to: {OUTPAINT_DIR}")
if __name__ == "__main__":
main() |