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@@ -58,3 +58,11 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+test_Evalmi50k_1_5_sampled.json filter=lfs diff=lfs merge=lfs -text
+test_TAD66k_forDG.json filter=lfs diff=lfs merge=lfs -text
+test_agiqa3k.json filter=lfs diff=lfs merge=lfs -text
+test_ava.json filter=lfs diff=lfs merge=lfs -text
+test_evalmuse.json filter=lfs diff=lfs merge=lfs -text
+test_kadid.json filter=lfs diff=lfs merge=lfs -text
+test_koniq.json filter=lfs diff=lfs merge=lfs -text
+test_spaq.json filter=lfs diff=lfs merge=lfs -text

add_image_bytes.py

@@ -0,0 +1,244 @@
+#!/usr/bin/env python3
+import json
+import os
+import base64
+import logging
+import time
+import random
+import pandas as pd
+from typing import Optional
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+BASE_JSON_DIR = '/data1/luyt/code_omniquality/mnt/petrelfs/luyiting/MultiAgentEval/data_process_v1'
+OUTPUT_DIR = '/data1/luyt/code_omniquality/mnt/petrelfs/luyiting/MultiAgentEval/data_process_v1/test_json'
+
+DATASET_CONFIGS = {
+    "koniq": {
+        "json_file": "test_koniq.json",
+        "image_base": "/data1/datasets/IQA/koniq1",
+        "path_type": "relative",
+    },
+    "kadid": {
+        "json_file": "test_kadid.json",
+        "image_base": "/data1/datasets/IQA/kadid10k/distorted_images",
+        "path_type": "relative_basename",
+    },
+    "spaq": {
+        "json_file": "test_spaq.json",
+        "image_base": "/data1/datasets/IQA/SPAQ/512x384",
+        "path_type": "relative_basename",
+    },
+    "ava": {
+        "json_file": "test_ava.json",
+        "image_base": "/data2/datasets/AVA",
+        "path_type": "relative",
+        "sample_n": 2000,
+    },
+    "tad66k": {
+        "json_file": "test_TAD66k_forDG.json",
+        "image_base": "/data2/datasets/TAD66k",
+        "path_type": "absolute_remap",
+        "remap_prefix": "/mnt/petrelfs/luyiting/data/IQA/TAD66K/",
+        "remap_target": "/data2/datasets/TAD66k/",
+        "sample_n": 2000,
+    },
+    "evalmuse": {
+        "json_file": "train_evalmuse_llava_style.json",
+        "image_base": "/data2/datasets/EvalMuse/dataset/images",
+        "path_type": "absolute_remap",
+        "remap_prefix": "/mnt/petrelfs/luyiting/data/IQA/EvalMuse/dataset/images/",
+        "remap_target": "/data2/datasets/EvalMuse/dataset/images/",
+        "output_name": "test_evalmuse.json",
+        "sample_n": 2000,
+    },
+    "agiqa3k": {
+        "json_file": None,  # generated from CSV
+        "csv_file": "AGIQA3K_data.csv",
+        "image_base": "/data2/datasets/AGIQA-3k",
+        "path_type": "filename_only",
+    },
+    "evalmi": {
+        "json_file": "test_Evalmi50k_1_5_sampled.json",
+        "image_base": "/data2/datasets/EvalMi-50K",
+        "path_type": "absolute_remap",
+        "remap_prefix": "/mnt/petrelfs/luyiting/data/IQA/EvalMi-50K/",
+        "remap_target": "/data2/datasets/EvalMi-50K/",
+        "sample_n": 2000,
+        "must_find": True,
+    },
+}
+
+
+def load_image_as_base64(img_path: str) -> Optional[str]:
+    try:
+        if not os.path.exists(img_path):
+            return None
+        with open(img_path, 'rb') as f:
+            return base64.b64encode(f.read()).decode('utf-8')
+    except Exception as e:
+        logger.error(f"Error loading image {img_path}: {e}")
+        return None
+
+
+def resolve_image_path(image_field: str, config: dict) -> Optional[str]:
+    image_base = config["image_base"]
+    if image_base is None:
+        return None
+
+    path_type = config["path_type"]
+
+    if path_type == "relative":
+        return os.path.join(image_base, image_field)
+    elif path_type == "relative_basename":
+        return os.path.join(image_base, os.path.basename(image_field))
+    elif path_type == "filename_only":
+        return os.path.join(image_base, image_field)
+    elif path_type == "absolute":
+        if os.path.exists(image_field):
+            return image_field
+        return None
+    elif path_type == "absolute_remap":
+        remap_prefix = config["remap_prefix"]
+        remap_target = config["remap_target"]
+        if image_field.startswith(remap_prefix):
+            return remap_target + image_field[len(remap_prefix):]
+        return os.path.join(remap_target, os.path.basename(image_field))
+
+    return None
+
+
+def generate_agiqa3k_json(csv_path: str) -> list:
+    """Generate AGIQA-3k test JSON from CSV."""
+    df = pd.read_csv(csv_path)
+    logger.info(f"[agiqa3k] Loaded CSV: {len(df)} rows")
+
+    json_data = []
+    for _, row in df.iterrows():
+        if pd.isna(row.get('name')) or pd.isna(row.get('prompt')):
+            continue
+        json_data.append({
+            "image": str(row['name']),
+            "gt_score": float(row['mos_align']) if pd.notna(row.get('mos_align')) else 0.0,
+            "gt_score1": float(row['mos_quality']) if pd.notna(row.get('mos_quality')) else 0.0,
+            "gt_score2": float(row['std_align']) if pd.notna(row.get('std_align')) else 0.0,
+            "prompt": str(row['prompt']),
+            "conversations": [
+                {
+                    "from": "human",
+                    "value": f"Judge the image alignment with the prompt: \"{row['prompt']}\"\n"
+                             "Please evaluate how well the image matches each element of provided prompt.\n\n"
+                             "And answer with the final alignment rating.\n"
+                             "Pick from [bad, poor, fair, good, excellent]."
+                }
+            ]
+        })
+    return json_data
+
+
+def process_dataset(name: str, config: dict):
+    if config["image_base"] is not None and not os.path.exists(config["image_base"]):
+        logger.warning(f"[{name}] Image base dir not found: {config['image_base']}, skipping")
+        return
+    if config["image_base"] is None:
+        logger.warning(f"[{name}] No image base configured (images not available locally), skipping")
+        return
+
+    # Load data
+    if config.get("csv_file"):
+        csv_path = os.path.join(BASE_JSON_DIR, config["csv_file"])
+        if not os.path.exists(csv_path):
+            logger.warning(f"[{name}] CSV file not found: {csv_path}, skipping")
+            return
+        data = generate_agiqa3k_json(csv_path)
+    else:
+        json_path = os.path.join(BASE_JSON_DIR, config["json_file"])
+        if not os.path.exists(json_path):
+            logger.warning(f"[{name}] JSON file not found: {json_path}, skipping")
+            return
+        logger.info(f"[{name}] Loading JSON: {json_path}")
+        with open(json_path, 'r', encoding='utf-8') as f:
+            data = json.load(f)
+
+    # If must_find is set, pre-filter to items whose images exist locally
+    sample_n = config.get("sample_n")
+    if config.get("must_find") and sample_n:
+        logger.info(f"[{name}] Pre-filtering items with existing local images...")
+        valid_data = []
+        for item in data:
+            if 'image' not in item:
+                continue
+            img_path = resolve_image_path(item['image'], config)
+            if img_path and os.path.exists(img_path):
+                valid_data.append(item)
+        logger.info(f"[{name}] Found {len(valid_data)}/{len(data)} items with local images")
+        data = valid_data
+
+    # Random sampling
+    if sample_n and len(data) > sample_n:
+        logger.info(f"[{name}] Randomly sampling {sample_n} from {len(data)} items")
+        random.seed(42)
+        data = random.sample(data, sample_n)
+
+    total = len(data)
+    found = 0
+    not_found = 0
+    start_time = time.time()
+
+    for i, item in enumerate(data):
+        if 'image' not in item:
+            continue
+
+        img_path = resolve_image_path(item['image'], config)
+        if img_path and os.path.exists(img_path):
+            img_bytes = load_image_as_base64(img_path)
+            if img_bytes:
+                item['image_byte'] = img_bytes
+                found += 1
+            else:
+                not_found += 1
+        else:
+            not_found += 1
+
+        if (i + 1) % 500 == 0:
+            elapsed = time.time() - start_time
+            logger.info(f"[{name}] Progress: {i+1}/{total}, found: {found}, not_found: {not_found}, elapsed: {elapsed:.1f}s")
+
+    elapsed = time.time() - start_time
+    logger.info(f"[{name}] Done: total={total}, found={found}, not_found={not_found}, elapsed={elapsed:.1f}s")
+
+    output_name = config.get("output_name") or config.get("json_file") or f"test_{name}.json"
+    output_path = os.path.join(OUTPUT_DIR, output_name)
+    logger.info(f"[{name}] Saving to: {output_path}")
+    with open(output_path, 'w', encoding='utf-8') as f:
+        json.dump(data, f, ensure_ascii=False)
+    logger.info(f"[{name}] Saved successfully ({os.path.getsize(output_path) / 1024 / 1024:.1f} MB)")
+
+
+def main():
+    os.makedirs(OUTPUT_DIR, exist_ok=True)
+
+    # Only process datasets not already done
+    already_done = set()
+    for fname in os.listdir(OUTPUT_DIR):
+        if fname.endswith('.json'):
+            already_done.add(fname)
+
+    for name, config in DATASET_CONFIGS.items():
+        output_name = config.get("output_name") or config.get("json_file") or f"test_{name}.json"
+        if output_name in already_done:
+            logger.info(f"[{name}] Already processed ({output_name}), skipping. Delete to reprocess.")
+            continue
+        try:
+            process_dataset(name, config)
+        except Exception as e:
+            logger.error(f"[{name}] Failed: {e}")
+            import traceback
+            traceback.print_exc()
+
+    logger.info("All datasets processed.")
+
+
+if __name__ == "__main__":
+    main()

test_Evalmi50k_1_5_sampled.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:4654be03e20b755f68aaffc02101ae1e057d972136a0ad5935aea5d849f89e51
+size 2082644631

test_TAD66k_forDG.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:e82bc753966c685bac76ce7608a7b76e1a9b60d740ebc1c6a561dd6c64d0a22e
+size 419386635

test_agiqa3k.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d519a7d49cd82b1a63dd7394152f1b420a22bb8d719ee56331e49a2ef8330305
+size 133820434

test_ava.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0943a1ab25e82c324f840de3ab89db8946466276180402a6f4eb36eca434ca8e
+size 351586738

test_evalmuse.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:9cd37269cd9a55b5807f84d4d37d1dd521739d8de1d295065dea150c173300c2
+size 3387211109

test_kadid.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3a3ea832eb55b0a816522677b9cf83f5b7cadd983e60defaf8fc7dce5cb5bf8e
+size 828912501

test_koniq.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:533237469d002eab0c3095b1140d9a5a5ae076953ab85eeaa016b20f2406561f
+size 199817363

test_spaq.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:54be2eb6c19c03e41622599fd8b5e4944c5fe8f9b5ac92d95e42a260b957dc27
+size 76775104