| 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 = 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 = []
|
|
|
| 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")
|
|
|
