utils.py

import random
import numpy as np
import torch
from args import Args as args
import scipy.io as sio
import matplotlib.pyplot as plt
import seaborn as sns
from os import listdir
from os.path import join
import cv2
from torch import nn
import torch.nn.functional as F

EPSILON = 1e-5

def normalize_tensor(tensor):
	(b, ch, h, w) = tensor.size()

	tensor_v = tensor.view(b, -1)
	t_min = torch.min(tensor_v, 1)[0]
	t_max = torch.max(tensor_v, 1)[0]

	t_min = t_min.view(b, 1, 1, 1)
	t_min = t_min.repeat(1, ch, h, w)
	t_max = t_max.view(b, 1, 1, 1)
	t_max = t_max.repeat(1, ch, h, w)
	tensor = (tensor - t_min) / (t_max - t_min + EPSILON)
	return tensor

def list_images(directory):
	images = []
	names = []
	dir = listdir(directory)
	dir.sort()
	for file in dir:
		# name = file.lower()
		name = file
		if name.endswith('.png'):
			images.append(join(directory, file))
		elif name.endswith('.jpg'):
			images.append(join(directory, file))
		elif name.endswith('.jpeg'):
			images.append(join(directory, file))
		elif name.endswith('.bmp'):
			images.append(join(directory, file))
		elif name.endswith('.tif'):
			images.append(join(directory, file))
		name1 = name.split('.')
		names.append(name1[0])
	return images, names

def gradient(x):
	dim = x.shape;
	if (args.cuda):
		x = x.cuda(int(args.device));
	#kernel = [[0.,1.,0.],[1.,-4.,1.],[0.,1.,0.]];
	kernel = [[1 / 8, 1 / 8, 1 / 8], [1 / 8, -1, 1 / 8], [1 / 8, 1 / 8, 1 / 8]];
	kernel = torch.FloatTensor(kernel).unsqueeze(0).unsqueeze(0)
	kernel = kernel.repeat(dim[1],dim[1],1,1);
	weight = nn.Parameter(data=kernel,requires_grad=False);
	if (args.cuda):
		weight = weight.cuda(int(args.device));
	pad = nn.ReflectionPad2d(1);		
	gradMap = F.conv2d(pad(x),weight=weight,stride=1,padding=0);
	#showTensor(gradMap);
	return gradMap; 

# load training images
def load_dataset(image_path, BATCH_SIZE, num_imgs=None):
	if num_imgs is None:
		num_imgs = len(image_path)
	original_imgs_path = image_path[:num_imgs]
	# random
	random.shuffle(original_imgs_path)
	mod = num_imgs % BATCH_SIZE
	print('BATCH SIZE %d.' % BATCH_SIZE)
	print('Train images number %d.' % num_imgs)
	print('Train images samples %s.' % str(num_imgs / BATCH_SIZE))

	if mod > 0:
		print('Train set has been trimmed %d samples...\n' % mod)
		original_imgs_path = original_imgs_path[:-mod]
	batches = int(len(original_imgs_path) // BATCH_SIZE)
	return original_imgs_path, batches


def save_mat(out, path):
	if args.cuda:
		out = out.cpu().data[0].numpy()
	else:
		out = out.data[0].numpy()
	out = np.squeeze(out)
	out = out.transpose((2, 1, 0))
	sio.savemat(path, {'img': out})


def get_image(path, height=256, width=256, flag=False):
	if flag is True:
		mode = cv2.IMREAD_COLOR
	else:
		mode = cv2.IMREAD_GRAYSCALE
	# image = Image.open(path).convert(mode)
	image = cv2.imread(path, mode)
	if height is not None and width is not None:
		# image = image.resize((height, width), Image.ANTIALIAS)
		# image = image.resize((height, width))
		image = cv2.resize(image,(height, width))
	return image


def get_train_images(paths, height=256, width=256, flag=False):
	if isinstance(paths, str):
		paths = [paths]
	images = []
	for path in paths:
		image = get_image(path, height, width, flag)
		if flag is True:
			image = np.transpose(image, (2, 0, 1))
		else:
			image = np.reshape(image, [1, image.shape[0], image.shape[1]])
		images.append(image)

	images = np.stack(images, axis=0)
	images = torch.from_numpy(images).float()
	return images

def calculate_entropy(image):
	flat_image = image.flatten()
	probabilities = np.histogram(flat_image, bins=256, range=[0, 256], density=True)[0]
	entropy = -np.sum(probabilities * np.log2(probabilities + 1e-10))
	return entropy

def calculate_sd(image):
	variance = np.std(image)
	return variance

def save_metrics_to_txt(output_path, entropy, variance):
	metrics = f"{entropy}\n{variance}"

	with open(output_path[:-4]+".txt", "w") as file:
		file.write(metrics)	

def save_image(img_fusion, output_path):
	img_fusion = img_fusion.float()
	#if args.cuda:
	img_fusion = img_fusion.cpu().data[0].numpy()
	#else:
		#img_fusion = img_fusion.clamp(0, 255).data[0].numpy()

	img_fusion = img_fusion * 255
	img_fusion = img_fusion.transpose(1, 2, 0).astype('uint8')
	if img_fusion.shape[2] == 1:
		img_fusion = img_fusion.reshape([img_fusion.shape[0], img_fusion.shape[1]])


	cv2.imwrite(output_path, img_fusion)


def show_heatmap(feature, output_path):
	sns.set()
	feature = feature.float()
	if args.cuda:
		feature = feature.cpu().data[0].numpy()
	else:
		feature = feature.clamp(0, 255).data[0].numpy()

	feature = (feature - np.min(feature)) / (np.max(feature) - np.min(feature) + EPSILON)
	feature = feature * 255
	feature = feature.transpose(1, 2, 0).astype('uint8')
	if feature.shape[2] == 1:
		feature = feature.reshape([feature.shape[0], feature.shape[1]])

	fig = plt.figure()
	# sns.heatmap(feature, cmap='YlGnBu', xticklabels=50, yticklabels=50)
	sns.heatmap(feature, xticklabels=50, yticklabels=50)
	fig.savefig(output_path, bbox_inches='tight')
	# plt.show()


def gram_matrix(y):
	(b, ch, h, w) = y.size()
	features = y.view(b, ch, w * h)
	features_t = features.transpose(1, 2)
	gram = features.bmm(features_t) / (ch * h * w)
	return gram


def normalize_tensor(tensor):
	(b, ch, h, w) = tensor.size()

	tensor_v = tensor.view(b, -1)
	t_min = torch.min(tensor_v, 1)[0]
	t_max = torch.max(tensor_v, 1)[0]

	t_min = t_min.view(b, 1, 1, 1)
	t_min = t_min.repeat(1, ch, h, w)
	t_max = t_max.view(b, 1, 1, 1)
	t_max = t_max.repeat(1, ch, h, w)
	tensor = (tensor - t_min) / (t_max - t_min + EPSILON)
	return tensor