IS_Net/basics.py

import os
# os.environ['CUDA_VISIBLE_DEVICES'] = '2'
from skimage import io, transform
import torch
import torchvision
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms, utils
import torch.optim as optim
from skimage.metrics import structural_similarity as ssim
import matplotlib.pyplot as plt
import numpy as np
from PIL import Image
import glob
import cv2
from scipy.stats import pearsonr

def mae_torch(pred,gt):

	h,w = gt.shape[0:2]
	sumError = torch.sum(torch.absolute(torch.sub(pred.float(), gt.float())))
	maeError = torch.divide(sumError,float(h)*float(w)*255.0+1e-4)

	return maeError

import torch

def maximal_f_measure_torch(pd, gt):
    gtNum = torch.sum((gt > 128).float() * 1)  # 计算真实标签中像素值大于128的数量

    # 从预测张量中提取正例和负例
    pp = pd[gt > 128]
    nn = pd[gt <= 128]

    # 计算正例和负例的直方图
    pp_hist = torch.histc(pp, bins=255, min=0, max=255)
    nn_hist = torch.histc(nn, bins=255, min=0, max=255)

    # 反转直方图并计算累积和
    pp_hist_flip = torch.flipud(pp_hist)
    nn_hist_flip = torch.flipud(nn_hist)

    pp_hist_flip_cum = torch.cumsum(pp_hist_flip, dim=0)
    nn_hist_flip_cum = torch.cumsum(nn_hist_flip, dim=0)

    # 计算Precision、Recall 和 F-measure
    precision = (pp_hist_flip_cum) / (pp_hist_flip_cum + nn_hist_flip_cum + 1e-4)
    recall = (pp_hist_flip_cum) / (gtNum + 1e-4)
    f_measure = (2 * precision * recall) / (precision + recall + 1e-4)

    # 找到最大F-measure及其对应的阈值
    max_f_measure, threshold = torch.max(f_measure, dim=0)

    return max_f_measure.item(), threshold.item()

def calculate_meam(image1, image2):
    # 直方图均衡化
    image1_equalized = cv2.equalizeHist(image1)
    image2_equalized = cv2.equalizeHist(image2)

    # 计算Pearson相关系数
    correlation_coefficient, _ = pearsonr(image1_equalized.flatten(), image2_equalized.flatten())

    # 计算MEAM值
    meam_value = correlation_coefficient * np.mean(np.minimum(image1_equalized, image2_equalized))

    return meam_value

def f1score_torch(pd,gt):

	# print(gt.shape)
	gtNum = torch.sum((gt>128).float()*1) ## number of ground truth pixels

	pp = pd[gt>128]
	nn = pd[gt<=128]

	pp_hist =torch.histc(pp,bins=255,min=0,max=255)
	nn_hist = torch.histc(nn,bins=255,min=0,max=255)


	pp_hist_flip = torch.flipud(pp_hist)
	nn_hist_flip = torch.flipud(nn_hist)

	pp_hist_flip_cum = torch.cumsum(pp_hist_flip, dim=0)
	nn_hist_flip_cum = torch.cumsum(nn_hist_flip, dim=0)

	precision = (pp_hist_flip_cum)/(pp_hist_flip_cum + nn_hist_flip_cum + 1e-4)#torch.divide(pp_hist_flip_cum,torch.sum(torch.sum(pp_hist_flip_cum, nn_hist_flip_cum), 1e-4))
	recall = (pp_hist_flip_cum)/(gtNum + 1e-4)
	f1 = (1+0.3)*precision*recall/(0.3*precision+recall + 1e-4)

	return torch.reshape(precision,(1,precision.shape[0])),torch.reshape(recall,(1,recall.shape[0])),torch.reshape(f1,(1,f1.shape[0]))


def f1_mae_torch(pred, gt, valid_dataset, idx, mybins, hypar):

	import time
	tic = time.time()

	if(len(gt.shape)>2):
		gt = gt[:,:,0]
	# if pred.shape != gt.shape:
	# 	plt.imshow(pred.cpu().detach().numpy())
	# 	plt.show()
	# 	plt.imshow(gt.cpu().detach().numpy())
	# 	plt.show()
		# pred = pred.transpose(1,0)
	# print(pred.shape,gt.shape)
	# print(valid_dataset.dataset["im_name"][idx]+".png")
	pre, rec, f1 = f1score_torch(pred,gt)
	mae = mae_torch(pred,gt)

	# hypar["valid_out_dir"] = hypar["valid_out_dir"]+"-eval" ###
	if(hypar["valid_out_dir"]!=""):
		if(not os.path.exists(hypar["valid_out_dir"])):
			os.mkdir(hypar["valid_out_dir"])
		dataset_folder = os.path.join(hypar["valid_out_dir"],valid_dataset.dataset["data_name"][idx])
		if(not os.path.exists(dataset_folder)):
			os.mkdir(dataset_folder)
		io.imsave(os.path.join(dataset_folder,valid_dataset.dataset["im_name"][idx]+".png"),pred.cpu().data.numpy().astype(np.uint8))
	# print(valid_dataset.dataset["im_name"][idx]+".png")
	# print("time for evaluation : ", time.time()-tic)

	return pre.cpu().data.numpy(), rec.cpu().data.numpy(), f1.cpu().data.numpy(), mae.cpu().data.numpy()