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U2NetSegmentationBackgroundRemoval
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import torch
import os 
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 

import numpy as np 
from PIL import Image 
import glob 


def normPRED(d):
	ma = torch.max(d)
	mi = torch.min(d)

	dn = (d - mi)/(ma - mi)

	return dn 

def save_output(image_name, pred, d_dir):

	predict = pred 
	predict = predict.squeeze()
	predict_np = predict.cpu().data.numpy()

	im = Image.fromarray(predict_np * 255).convert('RGB')
	img_name = image_name.split(os.sep)[-1]

	image = io.imread(image_name)
	imo = im.resize((image.shape[1], image.shape[0]), resample = Image.BILINEAR)

	pb_np = np.array(imo)

	aaa = img_name.split(".")
	bbb = aaa[0:-1]
	imidx = bbb[0]

	for i in range(1, len(bbb)):
		imidx = imidx + "." + bbb[i]

	imo.save(d_dir + "/" + imidx + '.png')



#image_dir = "./test_data/"
#prediction_dir = './outputs_pred/'

#model_dir = 'quant_model_u2net.pth'#'u2net.pth'

#img_name_list = glob.glob(image_dir + "/*")

#print("Number of images : ", len(img_name_list))


### Make test dataset 

class RescaleT(object):

	def __init__(self,output_size):
		assert isinstance(output_size,(int,tuple))
		self.output_size = output_size

	def __call__(self,sample):
		imidx, image, label = sample['imidx'], sample['image'],sample['label']

		h, w = image.shape[:2]

		if isinstance(self.output_size,int):
			if h > w:
				new_h, new_w = self.output_size*h/w,self.output_size
			else:
				new_h, new_w = self.output_size,self.output_size*w/h
		else:
			new_h, new_w = self.output_size

		new_h, new_w = int(new_h), int(new_w)

		# #resize the image to new_h x new_w and convert image from range [0,255] to [0,1]
		# img = transform.resize(image,(new_h,new_w),mode='constant')
		# lbl = transform.resize(label,(new_h,new_w),mode='constant', order=0, preserve_range=True)

		img = transform.resize(image,(self.output_size,self.output_size),mode='constant')
		lbl = transform.resize(label,(self.output_size,self.output_size),mode='constant', order=0, preserve_range=True)

		return {'imidx':imidx, 'image':img,'label':lbl}

class ToTensorLab(object):
	"""Convert ndarrays in sample to Tensors."""
	def __init__(self,flag=0):
		self.flag = flag

	def __call__(self, sample):

		imidx, image, label =sample['imidx'], sample['image'], sample['label']

		tmpLbl = np.zeros(label.shape)

		if(np.max(label)<1e-6):
			label = label
		else:
			label = label/np.max(label)

		# change the color space
		if self.flag == 2: # with rgb and Lab colors
			tmpImg = np.zeros((image.shape[0],image.shape[1],6))
			tmpImgt = np.zeros((image.shape[0],image.shape[1],3))
			if image.shape[2]==1:
				tmpImgt[:,:,0] = image[:,:,0]
				tmpImgt[:,:,1] = image[:,:,0]
				tmpImgt[:,:,2] = image[:,:,0]
			else:
				tmpImgt = image
			tmpImgtl = color.rgb2lab(tmpImgt)

			# nomalize image to range [0,1]
			tmpImg[:,:,0] = (tmpImgt[:,:,0]-np.min(tmpImgt[:,:,0]))/(np.max(tmpImgt[:,:,0])-np.min(tmpImgt[:,:,0]))
			tmpImg[:,:,1] = (tmpImgt[:,:,1]-np.min(tmpImgt[:,:,1]))/(np.max(tmpImgt[:,:,1])-np.min(tmpImgt[:,:,1]))
			tmpImg[:,:,2] = (tmpImgt[:,:,2]-np.min(tmpImgt[:,:,2]))/(np.max(tmpImgt[:,:,2])-np.min(tmpImgt[:,:,2]))
			tmpImg[:,:,3] = (tmpImgtl[:,:,0]-np.min(tmpImgtl[:,:,0]))/(np.max(tmpImgtl[:,:,0])-np.min(tmpImgtl[:,:,0]))
			tmpImg[:,:,4] = (tmpImgtl[:,:,1]-np.min(tmpImgtl[:,:,1]))/(np.max(tmpImgtl[:,:,1])-np.min(tmpImgtl[:,:,1]))
			tmpImg[:,:,5] = (tmpImgtl[:,:,2]-np.min(tmpImgtl[:,:,2]))/(np.max(tmpImgtl[:,:,2])-np.min(tmpImgtl[:,:,2]))

			# tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg))

			tmpImg[:,:,0] = (tmpImg[:,:,0]-np.mean(tmpImg[:,:,0]))/np.std(tmpImg[:,:,0])
			tmpImg[:,:,1] = (tmpImg[:,:,1]-np.mean(tmpImg[:,:,1]))/np.std(tmpImg[:,:,1])
			tmpImg[:,:,2] = (tmpImg[:,:,2]-np.mean(tmpImg[:,:,2]))/np.std(tmpImg[:,:,2])
			tmpImg[:,:,3] = (tmpImg[:,:,3]-np.mean(tmpImg[:,:,3]))/np.std(tmpImg[:,:,3])
			tmpImg[:,:,4] = (tmpImg[:,:,4]-np.mean(tmpImg[:,:,4]))/np.std(tmpImg[:,:,4])
			tmpImg[:,:,5] = (tmpImg[:,:,5]-np.mean(tmpImg[:,:,5]))/np.std(tmpImg[:,:,5])

		elif self.flag == 1: #with Lab color
			tmpImg = np.zeros((image.shape[0],image.shape[1],3))

			if image.shape[2]==1:
				tmpImg[:,:,0] = image[:,:,0]
				tmpImg[:,:,1] = image[:,:,0]
				tmpImg[:,:,2] = image[:,:,0]
			else:
				tmpImg = image

			tmpImg = color.rgb2lab(tmpImg)

			# tmpImg = tmpImg/(np.max(tmpImg)-np.min(tmpImg))

			tmpImg[:,:,0] = (tmpImg[:,:,0]-np.min(tmpImg[:,:,0]))/(np.max(tmpImg[:,:,0])-np.min(tmpImg[:,:,0]))
			tmpImg[:,:,1] = (tmpImg[:,:,1]-np.min(tmpImg[:,:,1]))/(np.max(tmpImg[:,:,1])-np.min(tmpImg[:,:,1]))
			tmpImg[:,:,2] = (tmpImg[:,:,2]-np.min(tmpImg[:,:,2]))/(np.max(tmpImg[:,:,2])-np.min(tmpImg[:,:,2]))

			tmpImg[:,:,0] = (tmpImg[:,:,0]-np.mean(tmpImg[:,:,0]))/np.std(tmpImg[:,:,0])
			tmpImg[:,:,1] = (tmpImg[:,:,1]-np.mean(tmpImg[:,:,1]))/np.std(tmpImg[:,:,1])
			tmpImg[:,:,2] = (tmpImg[:,:,2]-np.mean(tmpImg[:,:,2]))/np.std(tmpImg[:,:,2])

		else: # with rgb color
			tmpImg = np.zeros((image.shape[0],image.shape[1],3))
			image = image/np.max(image)
			if image.shape[2]==1:
				tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
				tmpImg[:,:,1] = (image[:,:,0]-0.485)/0.229
				tmpImg[:,:,2] = (image[:,:,0]-0.485)/0.229
			else:
				tmpImg[:,:,0] = (image[:,:,0]-0.485)/0.229
				tmpImg[:,:,1] = (image[:,:,1]-0.456)/0.224
				tmpImg[:,:,2] = (image[:,:,2]-0.406)/0.225

		tmpLbl[:,:,0] = label[:,:,0]


		tmpImg = tmpImg.transpose((2, 0, 1))
		tmpLbl = label.transpose((2, 0, 1))

		return {'imidx':torch.from_numpy(imidx), 'image': torch.from_numpy(tmpImg), 'label': torch.from_numpy(tmpLbl)}

class TestData(Dataset):

	def __init__(self, img_name_list, lbl_name_list, transform = None):

		self.img_list = img_name_list
		self.label_name_list = lbl_name_list
		self.transform = transform

	def __len__(self):

		return len(self.img_list) 

	def __getitem__(self, idx):

		#image = io.imread(self.img_list[idx])
		image = self.img_list[idx]
		imname = self.img_list[idx]
		imidx = np.array([idx])

		if (0 == len(self.label_name_list)):
			label_3 = np.zeros(image.shape)

		else:
			label_3 = io.imread(self.label_name_list[idx])

		label = np.zeros(label_3.shape[0:2])

		if(3==len(label_3.shape)):
			label = label_3[:,:,0]
		elif(2==len(label_3.shape)):
			label = label_3

		if(3==len(image.shape) and 2==len(label.shape)):
			label = label[:,:,np.newaxis]
		elif(2==len(image.shape) and 2==len(label.shape)):
			image = image[:,:,np.newaxis]
			label = label[:,:,np.newaxis]

		sample = {'imidx':imidx, 'image':image, 'label':label}

		if self.transform:
			sample = self.transform(sample)

		return sample



#test_dataset = TestData(img_name_list = img_name_list, lbl_name_list = [], 
#						transform = transforms.Compose([RescaleT(512), ToTensorLab(flag = 0)]))


### Make test dataloader 


#test_dataloader = DataLoader(test_dataset, batch_size = 1, shuffle = False, num_workers = 1)


#net = U2Net(3,1)

#net = torch.jit.load('quant_model_u2net.pth')
#net.load_state_dict(torch.load(model_dir))


"""net.eval()


for i_test, data_test in enumerate(test_dataloader):

	print("Inferencing : ", img_name_list[i_test].split(os.sep)[-1])

	inputs_test = data_test['image']


	inputs_test = inputs_test.type(torch.FloatTensor)

	inputs_test = Variable(inputs_test)

	d1, d2, d3, d4, d5, d6, d7 = net(inputs_test)

	pred = 1.0 - d1[:,0,:,:]
	pred = normPRED(pred)

	#save_output(img_name_list[i_test], pred, prediction_dir)

	#del d1, d2, d3, d4, d5, d6, d7"""