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import argparse
from pathlib import Path
from tqdm.auto import tqdm
import numpy as np
from PIL import Image
import json
from brisque import BRISQUE
import ipdb
st=ipdb.set_trace
if __name__ == "__main__":
input_dir = "/home/jiahao/workspace/LGM/outputs/director3d/prompt_single/A_sparkling_diamond_tiara"
obj = BRISQUE(url=False)
input_dir = Path(input_dir)
dir_list = [input_dir]
all_results = []
for video_dir in tqdm(dir_list):
if video_dir.is_dir():
if 'gaussiandreamer' in str(video_dir):
images_dir = video_dir / "save" / "it1200-test"
method = 'gaussiandreamer'
elif 'lgm' in str(video_dir):
images_dir = video_dir / video_dir.name
method = 'lgm'
elif 'director3d' in str(video_dir):
images_dir = video_dir / "0" / video_dir.name
method = 'director3d'
else:
raise ValueError(f"Unknown video directory: {video_dir}")
images_list = list(images_dir.glob('*'))
results = []
for image_path in tqdm(images_list, desc=f"Processing {video_dir.name}"):
try:
image = np.array(Image.open(image_path))
except:
continue
metric = obj.score(image)
if np.isnan(metric):
print(f"NaN found in {image_path}")
continue
results.append(metric)
all_results.append(np.mean(results))
average_niqe = np.mean(all_results)
print(f"{method} Average BRISQUE: {average_niqe}")
output_metrics = {'average_BRISQUE': average_niqe, 'all_results': all_results}
with open(input_dir / 'BRISQUE.json', 'w') as f:
json.dump(output_metrics, f, indent=4) |