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from gui.ui_win import Ui_Form
from gui.ui_draw import *
from PIL import Image, ImageQt
import numpy as np
import random, io, os
import torch
import torch.nn.functional as F
import torchvision.transforms as transforms
from util import task, util
from dataloader.image_folder import make_dataset
from dataloader.data_loader import get_transform
from model import create_model
class ui_model(QtWidgets.QWidget, Ui_Form):
"""define the class of UI"""
shape = 'line'
CurrentWidth = 1
def __init__(self, opt):
super(ui_model, self).__init__()
self.setupUi(self)
self.opt = opt
self.show_result_flag = False
self.mask_type = None
self.img_power = None
self.model_names = ['celeba', 'ffhq', 'imagenet', 'places2']
self.img_root = './examples/'
self.img_files = ['celeba/img', 'ffhq/img', 'imagenet/img', 'places2/img']
self.show_logo()
self.comboBox.activated.connect(self.load_model) # select model
self.pushButton_2.clicked.connect(self.select_image) # manually select an image
self.pushButton_3.clicked.connect(self.random_image) # randomly select an image
self.pushButton_4.clicked.connect(self.load_mask) # manually select a mask
self.pushButton_5.clicked.connect(self.random_mask) # randomly select a mask
# draw/erasure the mask
self.radioButton.toggled.connect(lambda: self.draw_mask('line')) # draw the line
self.radioButton_2.toggled.connect(lambda: self.draw_mask('rectangle')) # draw the rectangle
self.radioButton_3.toggled.connect(lambda: self.draw_mask('center')) # center mask
self.spinBox.valueChanged.connect(self.change_thickness)
self.pushButton.clicked.connect(self.clear_mask)
# fill image
self.pushButton_6.clicked.connect(self.fill_image)
self.comboBox_2.activated.connect(self.show_result)
self.pushButton_7.clicked.connect(self.save_result)
opt.preprocess = 'scale_shortside'
self.transform_o = get_transform(opt, convert=False, augment=False)
self.pil2tensor = transforms.ToTensor()
def show_logo(self):
"""Show the logo of NTU and BTC"""
img = QtWidgets.QLabel(self)
img.setGeometry(1000, 10, 140, 50)
pixmap = QtGui.QPixmap("./gui/logo/NTU_logo.jpg") # read examples
pixmap = pixmap.scaled(140, 140, QtCore.Qt.KeepAspectRatio, QtCore.Qt.SmoothTransformation)
img.setPixmap(pixmap)
img.show()
img1 = QtWidgets.QLabel(self)
img1.setGeometry(1200, 10, 70, 50)
pixmap1 = QtGui.QPixmap("./gui/logo/BTC_logo.png") # read examples
pixmap1 = pixmap1.scaled(70, 70, QtCore.Qt.KeepAspectRatio, QtCore.Qt.SmoothTransformation)
img1.setPixmap(pixmap1)
img1.show()
def show_image(self, img):
"""Show the masked examples"""
show_img = img.copy()
if self.mask_type == 'center':
sub_img = Image.fromarray(np.uint8(255 * np.ones((int(self.pw/2), int(self.pw/2), 3))))
mask = Image.fromarray(np.uint8(255 * np.ones((int(self.pw/2), int(self.pw/2)))))
show_img.paste(sub_img, box=(int(self.pw/4), int(self.pw/4)), mask=mask)
elif self.mask_type == 'external':
mask = Image.open(self.mname).resize(self.img_power.size).convert('RGB')
mask_L = Image.open(self.mname).resize(self.img_power.size).convert('L')
show_img = Image.composite(mask, show_img, mask_L)
self.new_painter(ImageQt.ImageQt(show_img))
def show_result(self):
"""Show different kind examples"""
value = self.comboBox_2.currentIndex()
if value == 0:
self.new_painter(ImageQt.ImageQt(self.img_power))
elif value == 1:
masked_img = torch.where(self.mask > 0, self.img_m, torch.ones_like(self.img_m))
masked_img = Image.fromarray(util.tensor2im(masked_img.detach()))
self.new_painter(ImageQt.ImageQt(masked_img))
elif value == 2:
if 'refine' in self.opt.coarse_or_refine:
img_out = Image.fromarray(util.tensor2im(self.img_ref_out.detach()))
else:
img_out = Image.fromarray(util.tensor2im(self.img_out.detach()))
self.new_painter(ImageQt.ImageQt(img_out))
def save_result(self):
"""Save the results to the disk"""
util.mkdir(self.opt.results_dir)
img_name = self.fname.split('/')[-1]
data_name = self.opt.img_file.split('/')[-1].split('.')[0]
original_name = '%s_%s_%s' % ('original', data_name, img_name) # save the original image
original_path = os.path.join(self.opt.results_dir, original_name)
img_original = util.tensor2im(self.img_truth)
util.save_image(img_original, original_path)
mask_name = '%s_%s_%d_%s' % ('mask', data_name, self.PaintPanel.iteration, img_name)
mask_path = os.path.join(self.opt.results_dir, mask_name)
mask = self.mask.repeat(1, 3, 1, 1)
img_mask = util.tensor2im(1-mask)
util.save_image(img_mask, mask_path)
#save masked image
masked_img_name = '%s_%s_%d_%s' % ('masked_img', data_name, self.PaintPanel.iteration, img_name)
img_path = os.path.join(self.opt.results_dir, masked_img_name)
img = torch.where(self.mask < 0.2, torch.ones_like(self.img_truth), self.img_truth)
masked_img = util.tensor2im(img)
util.save_image(masked_img, img_path)
# save the generated results
img_g_name = '%s_%s_%d_%s' % ('g', data_name, self.PaintPanel.iteration, img_name)
img_path = os.path.join(self.opt.results_dir, img_g_name)
img_g = util.tensor2im(self.img_g)
util.save_image(img_g, img_path)
# save the results
result_name = '%s_%s_%d_%s' % ('out', data_name, self.PaintPanel.iteration, img_name)
result_path = os.path.join(self.opt.results_dir, result_name)
img_result = util.tensor2im(self.img_out)
util.save_image(img_result, result_path)
# save the refined results
if 'tc' in self.opt.model and 'refine' in self.opt.coarse_or_refine:
result_name = '%s_%s_%d_%s' % ('ref', data_name, self.PaintPanel.iteration, img_name)
result_path = os.path.join(self.opt.results_dir, result_name)
img_result = util.tensor2im(self.img_ref_out)
util.save_image(img_result, result_path)
def load_model(self):
"""Load different kind models"""
value = self.comboBox.currentIndex()
if value == 0:
raise NotImplementedError("Please choose a model")
else:
index = value-1 # define the model type and dataset type
self.opt.name = self.model_names[index]
self.opt.img_file = self.img_root + self.img_files[index % len(self.img_files)]
self.model = create_model(self.opt)
self.model.setup(self.opt)
def load_image(self, fname):
"""Load the image"""
self.img_o = Image.open(fname).convert('RGB')
self.ow, self.oh = self.img_o.size
self.img_power = self.transform_o(self.img_o)
self.pw, self.ph = self.img_power.size
return self.img_power
def select_image(self):
"""Load the image"""
self.fname, _ = QtWidgets.QFileDialog.getOpenFileName(self, 'select the image', self.opt.img_file, '*')
img = self.load_image(self.fname)
self.mask_type = 'none'
self.show_image(img)
def random_image(self):
"""Random load the test image"""
image_paths, image_size = make_dataset(self.opt.img_file)
item = random.randint(0, image_size-1)
self.fname = image_paths[item]
img = self.load_image(self.fname)
self.mask_type = 'none'
self.show_image(img)
def load_mask(self):
"""Load a mask"""
self.mask_type = 'external'
self.mname, _ = QtWidgets.QFileDialog.getOpenFileName(self, 'select the mask', self.opt.mask_file,'*')
self.show_image(self.img_power)
def random_mask(self):
"""Random load the test mask"""
if self.opt.mask_file == 'none':
raise NotImplementedError("Please input the mask path")
self.mask_type = 'external'
mask_paths, mask_size = make_dataset(self.opt.mask_file)
item = random.randint(0, mask_size - 1)
self.mname = mask_paths[item]
self.show_image(self.img_power)
def read_mask(self):
"""Read the mask from the painted plain"""
self.PaintPanel.saveDraw()
buffer = QtCore.QBuffer()
buffer.open(QtCore.QBuffer.ReadWrite)
self.PaintPanel.map.save(buffer, 'PNG')
pil_im = Image.open(io.BytesIO(buffer.data()))
return pil_im
def new_painter(self, image=None):
"""Build a painter to load and process the image"""
# painter
self.PaintPanel = painter(self, image)
self.PaintPanel.close()
if image is not None:
w, h = image.size().width(), image.size().height()
self.stackedWidget.setGeometry(QtCore.QRect(250+int(512-w/2), 100+int(128-h/8), w, h))
self.stackedWidget.insertWidget(0, self.PaintPanel)
self.stackedWidget.setCurrentWidget(self.PaintPanel)
def change_thickness(self, num):
"""Change the width of the painter"""
self.CurrentWidth = num
self.PaintPanel.CurrentWidth = num
def draw_mask(self, masktype):
"""Draw the mask"""
if masktype == 'center':
self.mask_type = 'center'
if self.img_power is not None:
self.show_image(self.img_power)
else:
self.mask_type = 'draw'
self.shape = masktype
self.PaintPanel.shape = masktype
def clear_mask(self):
"""Clear the mask"""
self.mask_type = 'draw'
if self.PaintPanel.Brush:
self.PaintPanel.Brush = False
else:
self.PaintPanel.Brush = True
def set_input(self):
"""Set the input for the network"""
img_o = self.pil2tensor(self.img_o).unsqueeze(0)
img = self.pil2tensor(self.img_power).unsqueeze(0)
if self.mask_type == 'draw':
# get the test mask from painter
mask = self.read_mask()
mask = torch.autograd.Variable(self.pil2tensor(mask)).unsqueeze(0)[:, 0:1, :, :]
elif self.mask_type == 'center':
mask = torch.zeros_like(img)[:, 0:1, :, :]
mask[:, :, int(self.pw/4):int(3*self.pw/4), int(self.ph/4):int(3*self.ph/4)] = 1
elif self.mask_type == 'external':
mask = self.pil2tensor(Image.open(self.mname).resize((self.pw, self.ph)).convert('L')).unsqueeze(0)
mask = (mask < 0.5).float()
if len(self.opt.gpu_ids) > 0:
img = img.cuda(self.opt.gpu_ids[0])
mask = mask.cuda(self.opt.gpu_ids[0])
img_o = img_o.cuda(self.opt.gpu_ids[0])
self.mask = mask
self.img_org = img_o * 2 - 1
self.img_truth = img * 2 - 1
self.img_m = self.mask * self.img_truth
def fill_image(self):
"""Forward to get the completed results"""
self.set_input()
if self.PaintPanel.iteration < 1:
with torch.no_grad():
fixed_img = F.interpolate(self.img_m, size=[self.opt.fixed_size, self.opt.fixed_size], mode='bicubic', align_corners=True).clamp(-1, 1)
fixed_mask = (F.interpolate(self.mask, size=[self.opt.fixed_size, self.opt.fixed_size], mode='bicubic', align_corners=True) > 0.9).type_as(fixed_img)
out, mask = self.model.netE(fixed_img, mask=fixed_mask, return_mask=True)
out = self.model.netT(out, mask, bool_mask=False)
self.img_g = self.model.netG(out)
img_g_org = F.interpolate(self.img_g, size=self.img_truth.size()[2:], mode='bicubic', align_corners=True).clamp(-1, 1)
self.img_out = self.mask * self.img_truth + (1 - self.mask) * img_g_org
if 'refine' in self.opt.coarse_or_refine:
img_ref = self.model.netG_Ref(self.img_out, mask=self.mask)
self.img_ref_out = self.mask * self.img_truth + (1 - self.mask) * img_ref
print('finish the completion')
self.show_result_flag = True
self.show_result() |