Datasets:
File size: 4,823 Bytes
83baa54 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 | import pyiqa
import os
import cv2
import glob
import torch
import numpy as np
def ls(filename):
return sorted(glob.glob(filename))
class NTIRE_evaluation():
def __init__(self):
# self.psnr_range = [0,50]
# self.ssim_range = [0.5,1]
# self.lpips_range = [0,1]
# self.dists_range = [0,1]
# self.niqe_range = [0,1]
# self.device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
# self.iqa_psnr = pyiqa.create_metric('psnr', test_y_channel=True, color_space='ycbcr')
# self.iqa_ssim = pyiqa.create_metric('ssim', test_y_channel=True, color_space='ycbcr')
self.iqa_psnr = pyiqa.create_metric('psnr', test_y_channel=False)
self.iqa_ssim = pyiqa.create_metric('ssim', test_y_channel=False)
self.iqa_lpips = pyiqa.create_metric('lpips')
self.iqa_dists = pyiqa.create_metric('dists')
self.iqa_niqe = pyiqa.create_metric('niqe')
def img2tensor(self, img, bgr2rgb, float32):
'''
Numpy array to tensor.
Args:
imgs (list[ndarray] | ndarray): Input images.
bgr2rgb (bool): Whether to change bgr to rgb.
float32 (bool): Whether to change to float32.
'''
if img.shape[2] == 3 and bgr2rgb:
if img.dtype == 'float64':
img = img.astype('float32')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = torch.from_numpy(img.transpose(2, 0, 1))
if float32:
img = img.float()
return img
def single_image_eval(self, in_path, ref_path):
lr = cv2.imread(in_path, cv2.IMREAD_COLOR)
lr = self.img2tensor(lr, bgr2rgb=True, float32=True).unsqueeze(0).contiguous()
hr = cv2.imread(ref_path, cv2.IMREAD_COLOR)
hr = self.img2tensor(hr, bgr2rgb=True, float32=True).unsqueeze(0).contiguous()
if (lr.shape != hr.shape): raise ValueError("Bad prediction shape. Prediction shape: {}\nSolution shape:{}".format(lr.shape, hr.shape))
hr = hr[..., 4:-4, 4:-4]/255.
lr = lr[..., 4:-4, 4:-4]/255.
PSNR = self.iqa_psnr(lr, hr).item()
SSIM = self.iqa_ssim(lr, hr).item()
LPIPS = self.iqa_lpips(lr, hr).item()
DISTS = self.iqa_dists(lr, hr).item()
NIQE = self.iqa_niqe(lr).item()
return {'psnr':PSNR, 'ssim':SSIM, 'lpips':LPIPS, 'dists':DISTS, 'niqe':NIQE}
def folder_score(self, lr_list, gt_list):
psnr_list = []
ssim_list = []
lpips_list = []
dists_list = []
niqe_list = []
for p in list(zip(lr_list, gt_list)):
lr_path = p[0]
hr_path = p[1]
score_dict = self.single_image_eval(lr_path, hr_path)
psnr_list.append(score_dict['psnr'])
ssim_list.append(score_dict['ssim'])
lpips_list.append(score_dict['lpips'])
dists_list.append(score_dict['dists'])
niqe_list.append(score_dict['niqe'])
psnr_mean = np.array(psnr_list).mean()
ssim_mean = np.array(ssim_list).mean()
lpips_mean = np.array(lpips_list).mean()
dists_mean = np.array(dists_list).mean()
niqe_mean = np.array(niqe_list).mean()
return psnr_mean, ssim_mean, lpips_mean, dists_mean, niqe_mean
# Default I/O directories:
default_result_dir = './OpenRR-5k_val/output'
default_GT_dir = './OpenRR-5k_val/transmission_layer'
output_dir = "./"
if __name__ == '__main__':
# Create the output directory, if it does not already exist and open output files
if not os.path.exists(output_dir):
os.makedirs(output_dir)
score_file = open(os.path.join(output_dir, 'scores.txt'), 'w')
# Get all the solution files from the solution directory
hr_list = sorted(ls(os.path.join(default_GT_dir, '*.jpg'))) # GT
sr_list = sorted(ls(os.path.join(default_result_dir, '*.jpg'))) # model output
if (len(sr_list) != len(hr_list)): raise ValueError("Bad number of predictions. # of predictions: {}\n # of solutions:{}".format(len(sr_list), len(hr_list)))
# Define the evaluation
EvalScheme = NTIRE_evaluation()
score = EvalScheme.folder_score(sr_list, hr_list)
# Write score corresponding to selected task and metric to the output file
score_file.write("PSNR: %0.4f\n" % float(score[0]))
score_file.write("SSIM: %0.4f\n" % float(score[1]))
score_file.write("LPIPS: %0.4f\n" % float(score[2]))
score_file.write("DISTS: %0.4f\n" % float(score[3]))
score_file.write("NIQE: %0.4f\n" % float(score[4]))
# score_file.write("ExtraRuntime: %0.4f\n" % float(score))
# score_file.write("ExtraPlatform: %0.4f\n" % float(score))
# score_file.write("ExtraData: %0.4f\n" % float(score))
score_file.close()
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