| import cv2 as cv
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| import lpips
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| import numpy as np
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| import os
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| from config import config
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| from openpyxl import Workbook
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|
|
| import torch
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| from math import log10, sqrt
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| import torchvision.transforms as transforms
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|
|
| def cal_BSR(cxr_path, gt_path, bs_path):
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| cxr = cv.imread(cxr_path, 0)
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| gt = cv.imread(gt_path, 0)
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| bs = cv.imread(bs_path, 0)
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|
|
| cxr = cxr / 255
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| gt = gt / 255
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| bs = bs / 255
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|
|
| bone = cv.subtract(cxr, gt)
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|
|
|
|
| gt = cv.resize(gt, (config.image_size, config.image_size))
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| bs = cv.resize(bs, (config.image_size, config.image_size))
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| bone = cv.resize(bone, (config.image_size, config.image_size))
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|
|
| bs += np.average(cv.subtract(gt, bs))
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|
|
| bias = cv.subtract(gt, bs)
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| bias[bias < 0] = 0
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|
|
| BSR = 1 - np.sum(bias ** 2) / np.sum(bone ** 2)
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| return BSR
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|
|
|
|
| def cal_MSE(gt_path, bs_path):
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| gt = cv.imread(gt_path, 0)
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| bs = cv.imread(bs_path, 0)
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|
|
|
|
|
|
| gt = cv.resize(gt, (config.image_size, config.image_size))
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| bs = cv.resize(bs, (config.image_size, config.image_size))
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|
|
| MSE = np.mean((gt - bs) ** 2)
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| MSE = 2*lpips.l2(gt,bs)
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| return MSE
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|
|
|
|
|
|
|
|
| def cal_PSNR(gt_path, bs_path):
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| mse = cal_MSE(gt_path,bs_path)
|
| max_pixel = 1
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|
|
| PSNR = 20 * log10(max_pixel / sqrt(mse))
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| return PSNR
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|
|
|
|
|
|
|
|
| def cal_LPIPS(gt_path, bs_path):
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| lplps_model = lpips.LPIPS()
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|
|
| gt = cv.imread(gt_path, 0)
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| bs = cv.imread(bs_path, 0)
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|
|
|
|
| gt = cv.resize(gt, (config.image_size, config.image_size))
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| bs = cv.resize(bs, (config.image_size, config.image_size))
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|
|
| gt = transforms.ToTensor()(gt)
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| bs = transforms.ToTensor()(bs)
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|
|
| gt = torch.unsqueeze(gt, dim=0)
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| bs = torch.unsqueeze(bs, dim=0)
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|
|
| LPIPS = lplps_model(gt, bs).item()
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|
|
| return LPIPS
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|
|
|
|
| if __name__ == "__main__":
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| wb = Workbook()
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|
|
| ws = wb.active
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|
|
| CXR_path = "SZCH-X-Rays/CXR"
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| GT_path = "SZCH-X-Rays/BS"
|
| BS_path = "lcm_output_bs/Fusion_BS"
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|
|
| BSR_list = []
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| MSE_list = []
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| PSNR_list = []
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| LPIPS_list = []
|
| ws.append(["Filename", "BSR", "MSE", "PSNR", "LPIPS"])
|
| txt = 'SZCH_testset.txt'
|
| with open(txt, 'r', encoding='utf-8') as file:
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| lines = file.readlines()
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| file_names = [line.strip() for line in lines]
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|
|
| for filename in os.listdir(BS_path):
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| if filename in file_names:
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| 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)
|
| PSNR = cal_PSNR(gt_path, bs_path)
|
| LPIPS = cal_LPIPS(gt_path, bs_path)
|
|
|
| BSR_list.append(BSR)
|
| MSE_list.append(MSE)
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| PSNR_list.append(PSNR)
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| LPIPS_list.append(LPIPS)
|
| print(f"{filename} BSR: {BSR} MSE: {MSE} PSNR:{PSNR} LPIPS:{LPIPS}")
|
| ws.append([filename, BSR, MSE, PSNR, LPIPS])
|
|
|
| ws.append(["Mean",
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| np.mean(np.array(BSR_list)),
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| np.mean(np.array(MSE_list)),
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| np.mean(np.array(PSNR_list)),
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| np.mean(np.array(LPIPS_list))])
|
| ws.append(["Std",
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| np.std(np.array(BSR_list)),
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| np.std(np.array(MSE_list)),
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| np.std(np.array(PSNR_list)),
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| np.std(np.array(LPIPS_list))])
|
| print("Average BSR:", np.mean(np.array(BSR_list)), "Std:", np.std(np.array(BSR_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 LPIPS:", np.mean(np.array(LPIPS_list)), "Std:", np.std(np.array(LPIPS_list)))
|
|
|
|
|
| wb.save("sample.xlsx")
|
|
|
