File size: 8,993 Bytes
47bd6d0 | 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 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 | #!/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()
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