|
|
| 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 numpy as np
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| import torch
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| import lpips
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| from torchvision import transforms
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| from PIL import Image, UnidentifiedImageError
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|
|
| def verify_image(path, exts=('.png', '.jpg', '.jpeg', '.webp')):
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| """Check if file exists, is not empty, has valid extension, and can be opened by PIL."""
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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'Cannot 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 to [-1,1] Tensor as in 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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|
|
| if __name__ == "__main__":
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| p = argparse.ArgumentParser(description='Automated image quality evaluation script')
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| p.add_argument('--groundtruth', required=True, help='Path to original content image')
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| p.add_argument('--output', required=True, help='Path to stylized output image')
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| p.add_argument('--lpips-thresh', type=float, default=0.5, help='LPIPS threshold (>= passes)')
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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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|
|
| process = True
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| comments = []
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|
|
|
|
| for tag, path in [('groundtruth', 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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|
|
|
|
| lpips_pass = False
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| lpips_val = None
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| if process:
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| try:
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|
|
| img_c = load_tensor(args.groundtruth)
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| img_o = load_tensor(args.output)
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|
|
|
|
| _, _, h0, w0 = img_c.shape
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| _, _, h1, w1 = img_o.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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| img_c = torch.nn.functional.interpolate(img_c, size=(nh,nw), mode='bilinear', align_corners=False)
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| if (h1,w1)!=(nh,nw):
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| img_o = torch.nn.functional.interpolate(img_o, size=(nh,nw), mode='bilinear', align_corners=False)
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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(img_c, img_o).item())
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| lpips_pass = lpips_val >= args.lpips_thresh
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|
|
| comments.append(f'LPIPS={lpips_val:.4f} (>= {args.lpips_thresh} → {"OK" if lpips_pass else "FAIL"})')
|
|
|
| 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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|
|
|
|
| result_flag = (process and lpips_pass)
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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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|
|
|
|
| print("\nTest complete - Final status: " + ("PASS" if result_flag else "FAIL")) |
