Datasets:
Download scripts/generate_metadata_Task_Image_Mask.py from Obedience-Violin/Violin: direct link, hf CLI and curl.
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- Download file 2.74 kB
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https://huggingface.co/datasets/Obedience-Violin/Violin/resolve/main/scripts/generate_metadata_Task_Image_Mask.py
- Command line
-
hf download hf://datasets/Obedience-Violin/Violin/scripts/generate_metadata_Task_Image_Mask.py
-
curl -L -o generate_metadata_Task_Image_Mask.py https://huggingface.co/datasets/Obedience-Violin/Violin/resolve/main/scripts/generate_metadata_Task_Image_Mask.py
2.74 kB
| import csv | |
| import os | |
| IMAGE_EXTENSIONS = {".png", ".jpg", ".jpeg", ".webp", ".bmp"} | |
| RAW_IMAGE_DIR = "data/Task_Image_Mask_raw_image" | |
| MASK_TYPES = ["inpainting", "outpainting", "random"] | |
| def scan_image_pairs(mask_type): | |
| """Return sorted list of (image_path, mask_path, image_id) for a given mask type.""" | |
| images_dir = os.path.join(RAW_IMAGE_DIR, "images") | |
| mask_dir = os.path.join(RAW_IMAGE_DIR, f"{mask_type}_mask") | |
| if not os.path.exists(images_dir): | |
| return [] | |
| pairs = [] | |
| for fname in sorted(os.listdir(images_dir)): | |
| if os.path.splitext(fname)[1].lower() not in IMAGE_EXTENSIONS: | |
| continue | |
| image_id = os.path.splitext(fname)[0] | |
| image_path = os.path.join(images_dir, fname) | |
| mask_path = os.path.join(mask_dir, f"{image_id}.png") | |
| if not os.path.exists(mask_path): | |
| print(f"Warning: mask not found for {fname} ({mask_type}), skipping.") | |
| continue | |
| DATA_ROOT = "data" | |
| rel_image_path = os.path.relpath(image_path, DATA_ROOT) | |
| rel_mask_path = os.path.relpath(mask_path, DATA_ROOT) | |
| pairs.append((rel_image_path, rel_mask_path, image_id)) | |
| # pairs.append((image_path, mask_path, image_id)) | |
| return pairs | |
| def generate_metadata(prompt_file, mask_type, output_file): | |
| with open(prompt_file, "r", encoding="utf-8") as f: | |
| prompts = list(csv.DictReader(f)) | |
| pairs = scan_image_pairs(mask_type) | |
| if not pairs: | |
| print(f"Warning: no image-mask pairs found for {mask_type}, skipping.") | |
| return | |
| rows = [] | |
| id_counter = 1 | |
| for image1_path, image2_path, image_id in pairs: | |
| for prompt in prompts: | |
| rows.append({ | |
| "id": id_counter, | |
| "prompt": prompt["prompt"], | |
| "mask_type": mask_type, | |
| "task": 4, | |
| "image_id": image_id, | |
| "image1_path": image1_path, | |
| "image2_path": image2_path, | |
| }) | |
| id_counter += 1 | |
| output_dir = os.path.dirname(output_file) | |
| if output_dir and not os.path.exists(output_dir): | |
| os.makedirs(output_dir, exist_ok=True) | |
| with open(output_file, "w", newline="", encoding="utf-8") as f: | |
| writer = csv.DictWriter(f, fieldnames=["id", "prompt", "mask_type", "task", "image_id", "image1_path", "image2_path"]) | |
| writer.writeheader() | |
| writer.writerows(rows) | |
| print(f"Generation complete: {output_file} ({len(rows)} rows)") | |
| if __name__ == "__main__": | |
| for mask_type in MASK_TYPES: | |
| generate_metadata( | |
| "raw_config/Task_Image_Mask.csv", | |
| mask_type, | |
| f"metadata/Task_Image_Mask_{mask_type}_metadata.csv" | |
| ) |