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Author: FanChiMao

evaluation.py

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+import glob
+import os
+import time
+from collections import OrderedDict
+
+import numpy as np
+import torch
+import cv2
+import argparse
+
+from natsort import natsort
+from skimage.metrics import structural_similarity as ssim
+from skimage.metrics import peak_signal_noise_ratio as psnr
+import lpips
+
+
+class Measure():
+    def __init__(self, net='alex', use_gpu=False):
+        self.device = 'cuda' if use_gpu else 'cpu'
+        self.model = lpips.LPIPS(net=net)
+        self.model.to(self.device)
+
+    def measure(self, imgA, imgB):
+        return [float(f(imgA, imgB)) for f in [self.psnr, self.ssim, self.lpips]]
+
+    def lpips(self, imgA, imgB, model=None):
+        tA = t(imgA).to(self.device)
+        tB = t(imgB).to(self.device)
+        dist01 = self.model.forward(tA, tB).item()
+        return dist01
+
+    def ssim(self, imgA, imgB):
+        # multichannel: If True, treat the last dimension of the array as channels. Similarity calculations are done independently for each channel then averaged.
+        score, diff = ssim(imgA, imgB, full=True, multichannel=True)
+        return score
+
+    def psnr(self, imgA, imgB):
+        psnr_val = psnr(imgA, imgB)
+        return psnr_val
+
+
+def t(img):
+    def to_4d(img):
+        assert len(img.shape) == 3
+        assert img.dtype == np.uint8
+        img_new = np.expand_dims(img, axis=0)
+        assert len(img_new.shape) == 4
+        return img_new
+
+    def to_CHW(img):
+        return np.transpose(img, [2, 0, 1])
+
+    def to_tensor(img):
+        return torch.Tensor(img)
+
+    return to_tensor(to_4d(to_CHW(img))) / 127.5 - 1
+
+
+def fiFindByWildcard(wildcard):
+    return natsort.natsorted(glob.glob(wildcard, recursive=True))
+
+
+def imread(path):
+    return cv2.imread(path)[:, :, [2, 1, 0]]
+
+
+def format_result(psnr, ssim, lpips):
+    return f'{psnr:0.2f}, {ssim:0.3f}, {lpips:0.3f}'
+
+def measure_dirs(dirA, dirB, use_gpu, verbose=False):
+    if verbose:
+        vprint = lambda x: print(x)
+    else:
+        vprint = lambda x: None
+
+
+    t_init = time.time()
+
+    paths_A = fiFindByWildcard(os.path.join(dirA, f'*.{type}'))
+    paths_B = fiFindByWildcard(os.path.join(dirB, f'*.{type}'))
+
+    vprint("Comparing: ")
+    vprint(dirA)
+    vprint(dirB)
+
+    measure = Measure(use_gpu=use_gpu)
+
+    results = []
+    for pathA, pathB in zip(paths_A, paths_B):
+        result = OrderedDict()
+
+        t = time.time()
+        result['psnr'], result['ssim'], result['lpips'] = measure.measure(imread(pathA), imread(pathB))
+        d = time.time() - t
+        vprint(f"{pathA.split('/')[-1]}, {pathB.split('/')[-1]}, {format_result(**result)}, {d:0.1f}")
+
+        results.append(result)
+
+    psnr = np.mean([result['psnr'] for result in results])
+    ssim = np.mean([result['ssim'] for result in results])
+    lpips = np.mean([result['lpips'] for result in results])
+
+    vprint(f"Final Result: {format_result(psnr, ssim, lpips)}, {time.time() - t_init:0.1f}s")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-dirA', default='D:/NCHU/paper submit/ICIP 2022/evaluation/gt_mit', type=str)
+    parser.add_argument('-dirB', default='D:/NCHU/paper submit/ICIP 2022/evaluation/enhanced_mit', type=str)
+    parser.add_argument('-type', default='png')
+    parser.add_argument('--use_gpu', default=True)
+    args = parser.parse_args()
+
+    dirA = args.dirA
+    dirB = args.dirB
+    type = args.type
+    use_gpu = args.use_gpu
+
+    if len(dirA) > 0 and len(dirB) > 0:
+        measure_dirs(dirA, dirB, use_gpu=use_gpu, verbose=True)