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import base64
import io
import json
import os
from openai import OpenAI
from together import Together
from dotenv import load_dotenv
from PIL import Image
load_dotenv()
OpenAI_API_KEY = os.getenv("OpenAI_API_KEY")
Together_API_KEY = os.getenv("Together_API_KEY")
PROMPT = "What is the primary object that is completely occluded in this image? Be as specific as you can."
JUDGE_MASK_PROMPT = (
"A vision model was asked to identify the primary occluded object in an image. "
"In the image, a black or dark filled shape (rectangle, polygon, etc.) with a yellow or colored border "
"is used as an annotation marker to hide (occlude) the real object underneath it. "
"Here is the model's response:\n\n\"{response}\"\n\n"
"Does this response identify the annotation marker itself — the black/dark shape, rectangle, or polygon "
"with the yellow/colored border or outline — as the occluded object, rather than correctly identifying "
"what real-world object is hidden underneath the marker? "
"Answer only 'yes' or 'no'."
)
MASK_FOLLOWUP_PROMPT = (
"The black shape with the yellow border in this image is just an annotation marker used to indicate "
"where an object has been hidden — it is not the occluded object itself. "
"Looking carefully at the surrounding context of the image, what specific real-world object do you "
"think is actually hidden underneath that marker?"
)
# Maps result filename stem -> full Together model ID
MODELS = {
"Llama-4-Maverick-17B-128E-Instruct-FP8": (
"meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"
),
"Qwen3-VL-8B-Instruct": "Qwen/Qwen3-VL-8B-Instruct",
}
def judge(response, judge_model):
client = OpenAI(api_key=OpenAI_API_KEY)
prompt = JUDGE_MASK_PROMPT.format(response=response)
verdict = client.chat.completions.create(
model=judge_model,
temperature=0,
messages=[{"role": "user", "content": prompt}]
).choices[0].message.content.strip().lower()
return verdict.startswith("yes")
def encode_image(image_path, max_bytes=3.5 * 1024 * 1024):
with open(image_path, "rb") as f:
data = f.read()
if len(data) <= max_bytes:
return base64.b64encode(data).decode("utf-8")
img = Image.open(io.BytesIO(data))
scale = 0.9
while True:
buf = io.BytesIO()
img.save(buf, format="PNG", optimize=True)
if buf.tell() <= max_bytes:
break
w, h = img.size
img = img.resize((int(w * scale), int(h * scale)), Image.LANCZOS)
return base64.b64encode(buf.getvalue()).decode("utf-8")
def mask_followup_completion(model_id, image_path, initial_response):
client = Together(api_key=Together_API_KEY)
b64 = encode_image(image_path)
media_type = "image/png"
return client.chat.completions.create(
model=model_id,
temperature=0,
messages=[
{
"role": "user",
"content": [
{"type": "image_url", "image_url": {"url": f"data:{media_type};base64,{b64}"}},
{"type": "text", "text": PROMPT}
]
},
{"role": "assistant", "content": initial_response},
{"role": "user", "content": MASK_FOLLOWUP_PROMPT}
]
).choices[0].message.content
def run_vetter(args):
with open(os.path.join(args.data_dir, "annotations.json")) as f:
annotations = json.load(f)
id_to_file = {e["id"]: e["original_file"] for e in annotations}
os.makedirs(args.output_dir, exist_ok=True)
for stem, model_id in MODELS.items():
input_path = os.path.join(args.results_dir, f"{stem}_results.json")
output_path = os.path.join(args.output_dir, f"{stem}_vetted_results.json")
if not os.path.exists(input_path):
print(f"[{stem}] Result file not found: {input_path}, skipping.")
continue
with open(input_path) as f:
results = json.load(f)
# Load existing vetted output for resume support
if os.path.exists(output_path):
with open(output_path) as f:
vetted = json.load(f)
vetted_by_id = {r["id"]: r for r in vetted}
print(f"[{stem}] Resuming: {len(vetted_by_id)} already vetted.")
else:
vetted_by_id = {}
updated = False
for result in results:
entry_id = result["id"]
# Skip immediately if already in vetted output
if entry_id in vetted_by_id:
continue
vetted_entry = dict(result)
if "mask_followup_response" not in vetted_entry:
vetted_entry["mask_followup_response"] = None
response = result.get("response", "")
print(f"[{stem}] Judging {entry_id}...")
describes_mask = judge(response, args.judge_model)
if describes_mask:
original_file = id_to_file.get(entry_id)
if not original_file:
print(f" [!] No image mapping for id {entry_id}, skipping follow-up.")
vetted_by_id[entry_id] = vetted_entry
continue
base = os.path.splitext(original_file)[0]
occluded_path = os.path.join(args.data_dir, f"{base}_occluded.png")
print(f" [Judge] Describes mask — sending follow-up...")
followup = mask_followup_completion(model_id, occluded_path, response)
print(f" Follow-up: {followup}")
vetted_entry["mask_followup_response"] = followup
else:
vetted_entry["mask_followup_response"] = None
vetted_by_id[entry_id] = vetted_entry
updated = True
# Save after each entry
with open(output_path, "w") as f:
json.dump(list(vetted_by_id.values()), f, indent=2)
if not updated:
# Write output even if nothing was updated (first run with all already done)
with open(output_path, "w") as f:
json.dump(list(vetted_by_id.values()), f, indent=2)
flagged = sum(
1 for r in vetted_by_id.values() if r.get("mask_followup_response") is not None
)
print(f"[{stem}] Done. {flagged}/{len(results)} entries had mask follow-ups.")
print(f" Output: {output_path}")
def parse_args():
parser = argparse.ArgumentParser(
description="Vetter: detect and follow up when a model describes the occlusion marker itself."
)
parser.add_argument("--data_dir", type=str, default="data/combined_output",
help="Directory containing annotations.json and occluded images.")
parser.add_argument("--results_dir", type=str, default="data/eval_results",
help="Directory containing the original *_results.json files.")
parser.add_argument("--output_dir", type=str, default="data/eval_results",
help="Directory to write *_vetted_results.json files.")
parser.add_argument("--judge_model", type=str, default="gpt-4o",
help="OpenAI model to use for judging responses.")
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
run_vetter(args)
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