File size: 6,007 Bytes
6107278 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | import cv2 as cv
import lpips
import numpy as np
import os
from config import config
from openpyxl import Workbook
import torch
from skimage.metrics import structural_similarity as ssim
from math import log10, sqrt
import torchvision.transforms as transforms
from decimal import Decimal
lplps_model = lpips.LPIPS()
def cal_BSR(cxr_path, gt_path, bs_path):
cxr = cv.imread(cxr_path, 0)
gt = cv.imread(gt_path, 0)
bs = cv.imread(bs_path, 0)
cxr = cxr / 255
gt = gt / 255
bs = bs / 255
bone = cv.subtract(cxr, gt)
gt = cv.resize(gt, (config.image_size, config.image_size))
bs = cv.resize(bs, (config.image_size, config.image_size))
bone = cv.resize(bone, (config.image_size, config.image_size))
bs += np.average(cv.subtract(gt, bs))
bias = cv.subtract(gt, bs)
bias[bias < 0] = 0
BSR = 1 - np.sum(bias ** 2) / np.sum(bone ** 2)
return BSR
def cal_MSE(gt_path, bs_path):
gt = cv.imread(gt_path, 0)
bs = cv.imread(bs_path, 0)
gt = cv.resize(gt, (config.image_size, config.image_size))
bs = cv.resize(bs, (config.image_size, config.image_size))
# MSE = np.mean((gt - bs) ** 2)
MSE = 2 * lpips.l2(gt, bs)
return MSE
def cal_SSIM(gt_path, bs_path):
gt = cv.imread(gt_path, 0)
bs = cv.imread(bs_path, 0)
gt = gt / 255
bs = bs / 255
gt = cv.resize(gt, (config.image_size, config.image_size))
bs = cv.resize(bs, (config.image_size, config.image_size))
SSIM = ssim(gt, bs, channel_axis=None, data_range=1)
return SSIM
def cal_PSNR(gt_path, bs_path):
mse = cal_MSE(gt_path, bs_path)
max_pixel = 1
PSNR = 20 * log10(max_pixel / sqrt(mse))
return PSNR
def cal_LPIPS(gt_path, bs_path):
gt = cv.imread(gt_path, 0)
bs = cv.imread(bs_path, 0)
gt = cv.resize(gt, (config.image_size, config.image_size))
bs = cv.resize(bs, (config.image_size, config.image_size))
gt = transforms.ToTensor()(gt)
bs = transforms.ToTensor()(bs)
gt = torch.unsqueeze(gt, dim=0)
bs = torch.unsqueeze(bs, dim=0)
LPIPS = lplps_model(gt, bs).item()
return LPIPS
if __name__ == "__main__":
wb = Workbook()
ws = wb.active
CXR_path = "SZCH-X-Rays-741/CXR"
GT_path = "SZCH-X-Rays-741/BS"
BS_path = "YOUR BONE SUPPRESSION RESULT"
BSR_list = []
MSE_list = []
SSIM_list = []
PSNR_list = []
LPIPS_list = []
# ws.append(["Filename", "BSR", "MSE", "SSIM", "PSNR", "LPIPS"])
txt = 'SZCH-X-Rays_testset.txt'
with open(txt, 'r', encoding='utf-8') as file:
lines = file.readlines()
file_names = [line.strip() for line in lines]
for filename in os.listdir(BS_path):
if filename in file_names:
pass
else:
continue
cxr_path = os.path.join(CXR_path, filename)
gt_path = os.path.join(GT_path, filename)
bs_path = os.path.join(BS_path, filename)
BSR = cal_BSR(cxr_path, gt_path, bs_path)
MSE = cal_MSE(gt_path, bs_path)
SSIM = cal_SSIM(gt_path, bs_path)
PSNR = cal_PSNR(gt_path, bs_path)
LPIPS = cal_LPIPS(gt_path, bs_path)
BSR_list.append(BSR)
MSE_list.append(MSE)
SSIM_list.append(SSIM)
PSNR_list.append(PSNR)
LPIPS_list.append(LPIPS)
bs = cv.imread(bs_path, 0)
bs = cv.resize(bs, (config.image_size, config.image_size))
bs = transforms.ToTensor()(bs)
bs = transforms.Normalize([0.5], [0.5])(bs)
print(f"{filename} LPIPS:{LPIPS} MSE: {MSE} PSNR:{PSNR} SSIM:{SSIM} BSR: {BSR}")
ws.append([filename, LPIPS, MSE, PSNR, SSIM, BSR])
ws.append(["Mean",
np.mean(np.array(LPIPS_list)),
np.mean(np.array(MSE_list)),
np.mean(np.array(PSNR_list)),
np.mean(np.array(SSIM_list)),
np.mean(np.array(BSR_list))])
ws.append(["Std",
np.std(np.array(LPIPS_list)),
np.std(np.array(MSE_list)),
np.std(np.array(PSNR_list)),
np.std(np.array(SSIM_list)),
np.std(np.array(BSR_list))])
print("Average LPIPS:", np.mean(np.array(LPIPS_list)), "Std:", np.std(np.array(LPIPS_list)))
print("Average MSE:", np.mean(np.array(MSE_list)), "Std:", np.std(np.array(MSE_list)))
print("Average PSNR:", np.mean(np.array(PSNR_list)), "Std:", np.std(np.array(PSNR_list)))
print("Average SSIM:", np.mean(np.array(SSIM_list)), "Std:", np.std(np.array(SSIM_list)))
print("Average BSR:", np.mean(np.array(BSR_list)), "Std:", np.std(np.array(BSR_list)))
print("LPIPS:", Decimal(str(np.mean(np.array(LPIPS_list)))).quantize(Decimal("0.001"), rounding="ROUND_HALF_UP"),
"$\pm$", Decimal(str(np.std(np.array(LPIPS_list)))).quantize(Decimal("0.001"), rounding="ROUND_HALF_UP"))
print("MSE:", Decimal(str(np.mean(np.array(MSE_list)) * 1000)).quantize(Decimal("0.001"), rounding="ROUND_HALF_UP"),
"$\pm$", Decimal(str(np.std(np.array(MSE_list)) * 1000)).quantize(Decimal("0.001"), rounding="ROUND_HALF_UP"))
print("PSNR:", Decimal(str(np.mean(np.array(PSNR_list)))).quantize(Decimal("0.001"), rounding="ROUND_HALF_UP"),
"$\pm$", Decimal(str(np.std(np.array(PSNR_list)))).quantize(Decimal("0.001"), rounding="ROUND_HALF_UP"))
print("SSIM:",
Decimal(str(np.mean(np.array(SSIM_list)) * 100)).quantize(Decimal("0.001"), rounding="ROUND_HALF_UP"),
"$\pm$", Decimal(str(np.std(np.array(SSIM_list)) * 100)).quantize(Decimal("0.001"), rounding="ROUND_HALF_UP"))
print("BSR:", Decimal(str(np.mean(np.array(BSR_list)) * 100)).quantize(Decimal("0.001"), rounding="ROUND_HALF_UP"),
"$\pm$", Decimal(str(np.std(np.array(BSR_list)) * 100)).quantize(Decimal("0.001"), rounding="ROUND_HALF_UP"))
wb.save("sample_m.xlsx")
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