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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 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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| 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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| """Verify file exists, is non-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 not found: {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 image to [-1,1] range Tensor"""
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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 histogram_intersection(a, b, bins=256):
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| """Calculate average intersection ratio of RGB channel histograms between two images"""
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| inters = []
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| for ch in range(3):
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| h1 = cv2.calcHist([a], [ch], None, [bins], [0,256]).ravel()
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| h2 = cv2.calcHist([b], [ch], None, [bins], [0,256]).ravel()
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| if h1.sum() > 0:
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| h1 = h1 / h1.sum()
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| if h2.sum() > 0:
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| h2 = h2 / h2.sum()
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| inters.append(np.minimum(h1, h2).sum())
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| return float(np.mean(inters))
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|
|
| if __name__ == "__main__":
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| p = argparse.ArgumentParser(description='Automated style transfer evaluation script')
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| p.add_argument('--groundtruth', required=True, help='Ground truth directory path')
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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('--hi-thresh', type=float, default=0.7, help='HI (Histogram Intersection) 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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|
|
|
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| content_path = os.path.join(args.groundtruth, 'gt_01.jpg')
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| style_path = os.path.join(args.groundtruth, 'gt_02.jpg')
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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 [('content', content_path), ('style', style_path), ('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 = hi_pass = False
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| lpips_val = hi_val = None
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| if process:
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| try:
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|
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| img_c = load_tensor(content_path)
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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 = F.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 = F.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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|
|
|
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| img_s = cv2.imread(style_path, cv2.IMREAD_COLOR)
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| img_o_cv = cv2.imread(args.output, cv2.IMREAD_COLOR)
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| hi_val = histogram_intersection(img_s, img_o_cv)
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| hi_pass = hi_val >= args.hi_thresh
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|
|
| comments.append(f'LPIPS={lpips_val:.4f} (>= {args.lpips_thresh} → {"OK" if lpips_pass else "FAIL"})')
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| comments.append(f'HI={hi_val:.4f} (>= {args.hi_thresh} → {"OK" if hi_pass else "FAIL"})')
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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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|
|
|
|
| result_flag = (process and lpips_pass and hi_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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| print(entry["comments"])
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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("\nEvaluation complete - Final status: " + ("PASSED" if result_flag else "FAILED")) |
