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| import os
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| import sys
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| import argparse
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| import json
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| import datetime
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| import cv2
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| import torch
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| import lpips
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| from torchvision import transforms
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| import torch.nn.functional as F
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| from PIL import Image, UnidentifiedImageError
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|
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| def verify_image(path, exts=('.png','.jpg','.jpeg','.webp')):
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| """Check file existence, non-empty, valid extension, and PIL readability."""
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| if not os.path.isfile(path):
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| return False, f'File does not exist: {path}'
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| if os.path.getsize(path) == 0:
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| return False, f'File is empty: {path}'
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| if not path.lower().endswith(exts):
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| return False, f'Unsupported format: {path}'
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| try:
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| img = Image.open(path)
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| img.verify()
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| except (UnidentifiedImageError, Exception) as e:
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| return False, f'Failed to read image: {path} ({e})'
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| return True, ''
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|
|
| def load_tensor(path):
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| """Load and normalize Tensor to [-1,1] as per original script"""
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| img = cv2.imread(path, cv2.IMREAD_COLOR)
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| if img is None:
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| raise RuntimeError(f'cv2 read failed: {path}')
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| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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| t = transforms.ToTensor()(img) * 2 - 1
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| return t.unsqueeze(0)
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|
|
| def main():
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| p = argparse.ArgumentParser(description='Automated anime effect evaluation script')
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| p.add_argument('--groundtruth', required=True, help='Original image path')
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| p.add_argument('--output', required=True, help='Anime-styled output image path')
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| p.add_argument('--lpips-thresh', type=float, default=0.30,
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| help='LPIPS distance threshold (Pass if >= threshold)')
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| p.add_argument('--clip-thresh', type=float, default=0.25,
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| help='CLIP Hayao style similarity threshold (Pass if > threshold)')
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| p.add_argument('--result', required=True, help='Result JSONL file path (append mode)')
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| args = p.parse_args()
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|
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| process = True
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| comments = []
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|
|
|
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| for tag, path in [('input', args.groundtruth), ('output', args.output)]:
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| ok, msg = verify_image(path)
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| if not ok:
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| process = False
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| comments.append(f'[{tag}] {msg}')
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|
|
|
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| lpips_val = None
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| result_flag = False
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| if process:
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| try:
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| img0 = load_tensor(args.groundtruth)
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| img1 = load_tensor(args.output)
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|
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| _, _, h0, w0 = img0.shape
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| _, _, h1, w1 = img1.shape
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| nh, nw = min(h0,h1), min(w0,w1)
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| if (h0,w0) != (nh,nw):
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| img0 = F.interpolate(img0, size=(nh,nw), mode='bilinear', align_corners=False)
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| if (h1,w1) != (nh,nw):
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| img1 = F.interpolate(img1, size=(nh,nw), mode='bilinear', align_corners=False)
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|
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| loss_fn = lpips.LPIPS(net='vgg').to(torch.device('cpu'))
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| with torch.no_grad():
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| lpips_val = float(loss_fn(img0, img1).item())
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|
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| passed = lpips_val >= args.lpips_thresh
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| comments.append(f'LPIPS={lpips_val:.4f} (>= {args.lpips_thresh} → {"OK" if passed else "FAIL"})')
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| result_flag = passed
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|
|
| except Exception as e:
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| process = False
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| comments.append(f'Metric calculation error: {e}')
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|
|
|
|
| if process:
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| try:
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| import clip
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| import PIL.Image
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| device = "cuda" if torch.cuda.is_available() else "cpu"
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| clip_model, clip_preprocess = clip.load("ViT-B/32", device=device)
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|
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| image = clip_preprocess(PIL.Image.open(args.output)).unsqueeze(0).to(device)
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| hayao_tokens = clip.tokenize(["a landscape in Hayao Miyazaki anime style"]).to(device)
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|
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| with torch.no_grad():
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| image_features = clip_model.encode_image(image)
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| text_features = clip_model.encode_text(hayao_tokens)
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|
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| image_features /= image_features.norm(dim=-1, keepdim=True)
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| text_features /= text_features.norm(dim=-1, keepdim=True)
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|
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| hayao_score = (image_features @ text_features.T).item()
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|
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| passed = hayao_score > args.clip_thresh
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| comments.append(f"CLIP Hayao style score = {hayao_score:.3f} (threshold = {args.clip_thresh} → {'OK' if passed else 'FAIL'})")
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| result_flag = result_flag and passed
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|
|
| except Exception as e:
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| comments.append(f"CLIP style check failed: {e}")
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|
|
|
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| entry = {
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| "Process": process,
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| "Result": result_flag,
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| "TimePoint": datetime.datetime.now().isoformat(sep='T', timespec='seconds'),
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| "comments": "; ".join(comments)
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| }
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| os.makedirs(os.path.dirname(args.result) or '.', exist_ok=True)
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| with open(args.result, 'a', encoding='utf-8') as f:
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| f.write(json.dumps(entry, ensure_ascii=False, default=str) + "\n")
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|
|
| if __name__ == "__main__":
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| main()
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|
|
