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Caption images using Anthropic Claude Opus 4.6 API.
Generates detailed descriptions for fine-tuning Flux.
"""
import os
import json
import base64
import argparse
import time
from pathlib import Path
from concurrent.futures import ThreadPoolExecutor, as_completed
import anthropic
from tqdm import tqdm
INPUT_DIR = Path("/home/adminuser/chungcat/data/raw/unsplash")
OUTPUT_DIR = Path("/home/adminuser/chungcat/data/captions")
CAPTION_PROMPT = """Describe this image in detail for an AI image generation model. Include:
- Main subject and composition
- Colors, lighting, mood
- Style (photographic, artistic, etc.)
- Important details and textures
- Background elements
Write a single detailed paragraph, 2-4 sentences. Be specific and descriptive."""
def encode_image(image_path):
with open(image_path, "rb") as f:
return base64.standard_b64encode(f.read()).decode("utf-8")
def caption_image(client, image_path, model="claude-opus-4-6-20250219"):
img_data = encode_image(image_path)
suffix = image_path.suffix.lower()
media_type = "image/jpeg" if suffix in [".jpg", ".jpeg"] else "image/png"
response = client.messages.create(
model=model,
max_tokens=300,
messages=[
{
"role": "user",
"content": [
{
"type": "image",
"source": {
"type": "base64",
"media_type": media_type,
"data": img_data,
},
},
{"type": "text", "text": CAPTION_PROMPT},
],
}
],
)
return response.content[0].text
def process_batch(client, images, output_dir, model, max_retries=3):
results = []
for img_path in images:
output_path = output_dir / f"{img_path.stem}.json"
if output_path.exists():
continue
for attempt in range(max_retries):
try:
caption = caption_image(client, img_path, model)
result = {
"image": str(img_path),
"caption": caption,
"filename": img_path.name,
}
output_path.write_text(json.dumps(result, ensure_ascii=False))
results.append(result)
break
except anthropic.RateLimitError:
time.sleep(2 ** attempt)
except Exception as e:
print(f"Error {img_path.name}: {e}")
if attempt == max_retries - 1:
print(f" Skipping after {max_retries} retries")
time.sleep(1)
return results
def main():
parser = argparse.ArgumentParser(description="Caption images with Claude Opus")
parser.add_argument("--input-dir", type=Path, default=INPUT_DIR)
parser.add_argument("--output-dir", type=Path, default=OUTPUT_DIR)
parser.add_argument("--model", default="claude-opus-4-6-20250219")
parser.add_argument("--batch-size", type=int, default=10)
parser.add_argument("--workers", type=int, default=5)
parser.add_argument("--max-images", type=int, default=None)
args = parser.parse_args()
api_key = os.environ.get("ANTHROPIC_API_KEY")
if not api_key:
raise ValueError("Set ANTHROPIC_API_KEY environment variable")
client = anthropic.Anthropic(api_key=api_key)
args.output_dir.mkdir(parents=True, exist_ok=True)
images = sorted(args.input_dir.glob("*.jpg")) + sorted(args.input_dir.glob("*.png"))
if args.max_images:
images = images[:args.max_images]
already_done = len(list(args.output_dir.glob("*.json")))
images = [img for img in images if not (args.output_dir / f"{img.stem}.json").exists()]
print(f"Total images: {len(images) + already_done}")
print(f"Already captioned: {already_done}")
print(f"To caption: {len(images)}")
batches = [images[i:i+args.batch_size] for i in range(0, len(images), args.batch_size)]
total_captioned = 0
with ThreadPoolExecutor(max_workers=args.workers) as executor:
futures = [
executor.submit(process_batch, client, batch, args.output_dir, args.model)
for batch in batches
]
for future in tqdm(as_completed(futures), total=len(futures)):
results = future.result()
total_captioned += len(results)
print(f"\nDone! Captioned {total_captioned} new images")
print(f"Total captions: {already_done + total_captioned}")
if __name__ == "__main__":
main()
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