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import cv2
import numpy as np
import math
# from skimage.metrics import structural_similarity as ssim
from skimage.measure import compare_ssim
from scipy.misc import imread
from glob import glob
import argparse
parser = argparse.ArgumentParser(description="evaluation codes")
parser.add_argument("--path", type=str, help="Path to evaluate images.")
args = parser.parse_args()
def psnr(img1, img2):
mse = np.mean((img1 / 255.0 - img2 / 255.0) ** 2)
if mse < 1.0e-10:
return 100
PIXEL_MAX = 1
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
def psnr_raw(img1, img2):
mse = np.mean((img1 - img2) ** 2)
if mse < 1.0e-10:
return 100
PIXEL_MAX = 1
return 20 * math.log10(PIXEL_MAX / math.sqrt(mse))
def my_ssim(img1, img2):
return compare_ssim(
img1, img2, data_range=img1.max() - img1.min(), multichannel=True
)
def quan_eval(path, suffix="jpg"):
# path: /disk2/yazhou/projects/IISP/exps/test_final_unet_globalEDV2/
# ours
gt_imgs = sorted(glob(path + "tar*.%s" % suffix))
pred_imgs = sorted(glob(path + "pred*.%s" % suffix))
# with open(split_path + "test_gt.txt", 'r') as f_gt, open(split_path+"test_rgb.txt","r") as f_rgb:
# gt_imgs = [line.rstrip() for line in f_gt.readlines()]
# pred_imgs = [line.rstrip() for line in f_rgb.readlines()]
assert len(gt_imgs) == len(pred_imgs)
psnr_avg = 0.0
ssim_avg = 0.0
for i in range(len(gt_imgs)):
gt = imread(gt_imgs[i])
pred = imread(pred_imgs[i])
psnr_temp = psnr(gt, pred)
psnr_avg += psnr_temp
ssim_temp = my_ssim(gt, pred)
ssim_avg += ssim_temp
print("psnr: ", psnr_temp)
print("ssim: ", ssim_temp)
psnr_avg /= float(len(gt_imgs))
ssim_avg /= float(len(gt_imgs))
print("psnr_avg: ", psnr_avg)
print("ssim_avg: ", ssim_avg)
return psnr_avg, ssim_avg
def mse(gt, pred):
return np.mean((gt - pred) ** 2)
def mse_raw(path, suffix="npy"):
gt_imgs = sorted(glob(path + "raw_tar*.%s" % suffix))
pred_imgs = sorted(glob(path + "raw_pred*.%s" % suffix))
# with open(split_path + "test_gt.txt", 'r') as f_gt, open(split_path+"test_rgb.txt","r") as f_rgb:
# gt_imgs = [line.rstrip() for line in f_gt.readlines()]
# pred_imgs = [line.rstrip() for line in f_rgb.readlines()]
assert len(gt_imgs) == len(pred_imgs)
mse_avg = 0.0
psnr_avg = 0.0
for i in range(len(gt_imgs)):
gt = np.load(gt_imgs[i])
pred = np.load(pred_imgs[i])
mse_temp = mse(gt, pred)
mse_avg += mse_temp
psnr_temp = psnr_raw(gt, pred)
psnr_avg += psnr_temp
print("mse: ", mse_temp)
print("psnr: ", psnr_temp)
mse_avg /= float(len(gt_imgs))
psnr_avg /= float(len(gt_imgs))
print("mse_avg: ", mse_avg)
print("psnr_avg: ", psnr_avg)
return mse_avg, psnr_avg
test_full = False
# if test_full:
# psnr_avg, ssim_avg = quan_eval(ROOT_PATH+"%s/vis_%s_full/"%(args.task, args.ckpt), "jpeg")
# mse_avg, psnr_avg_raw = mse_raw(ROOT_PATH+"%s/vis_%s_full/"%(args.task, args.ckpt))
# else:
psnr_avg, ssim_avg = quan_eval(args.path, "jpg")
mse_avg, psnr_avg_raw = mse_raw(args.path)
print(
"pnsr: {}, ssim: {}, mse: {}, psnr raw: {}".format(
psnr_avg, ssim_avg, mse_avg, psnr_avg_raw
)
)
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