def save_result(self, results, **kwargs):
visuals_np = Visualizer.convert_visuals_to_numpy(
results, batchSize=1, label_nc=self.opt.label_nc)
# We only run the demo with batch Size 1, so let's remove the first dimension.
visuals_np = OrderedDict([(k, v[0]) for k, v in visuals_np.items()])
filename = self._get_filename(kwargs)
save_path = os.path.join(self.save_dir, filename)
save_image(visuals_np["fake_image"], save_path, create_dir=True)
save_style_matrix(results["encoded_style"][0],
"{}.csv".format(save_path[:-4]))
return self.save_dir
save_path = os.path.join(opt.results_dir, opt.name)
#save_path = os.path.join(opt.results_dir, 'debug_mixed_train')
os.makedirs(save_path, exist_ok=True)
# test
for i, data_i in tqdm(enumerate(dataloader)):
if i * opt.batchSize >= opt.how_many:
break
generated = model(data_i, mode='inference2')
img_path = data_i['path']
for b in range(generated.shape[0]):
visuals = OrderedDict([
('input_label', data_i['label'][b]),
('synthesized_image', generated[b]),
])
visuals_rgb = visualizer.convert_visuals_to_numpy(visuals)
name = os.path.splitext(os.path.basename(img_path[b]))[0]
im1 = visuals_rgb['input_label']
im2 = visuals_rgb['synthesized_image']
h = im1.shape[0]
im = np.zeros((h, h * 2, 3))
im[:, :h] = im1
im[:, h:2 * h] = im2
cv2.imwrite(os.path.join(save_path, name + '.png'), im[:, :, ::-1])
'Experiment = %s, Phase = %s, Epoch = %s' %
(opt.name, opt.phase, opt.which_epoch))
# test
for i, data_i in enumerate(dataloader):
if i * opt.batchSize >= opt.how_many:
break
generated = model(data_i, mode='inference')
img_path = data_i['path']
for b in range(generated.shape[0]):
if opt.dataset_mode == 'brats':
visuals = OrderedDict([('input_label', data_i['label'][b]),
('synthesized_image', generated[b])])
visuals = visualizer.convert_visuals_to_numpy(visuals)
image_numpy = visuals["synthesized_image"]
util.save_image(image_numpy[:, :, 0], opt.results_dir + str(opt.condition_class) + '/train_t1ce_img_full'
+ '/{}.png'.format(i * opt.batchSize + b),
create_dir=True)
util.save_image(image_numpy[:, :, 1], opt.results_dir + str(opt.condition_class) + '/train_flair_img_full'
+ '/{}.png'.format(i * opt.batchSize + b),
create_dir=True)
util.save_image(image_numpy[:, :, 2], opt.results_dir + str(opt.condition_class) + '/train_t2_img_full'
+ '/{}.png'.format(i * opt.batchSize + b),
create_dir=True)
util.save_image(image_numpy[:, :, 3], opt.results_dir + str(opt.condition_class) + '/train_t1_img_full'
+ '/{}.png'.format(i * opt.batchSize + b),
create_dir=True)
print('processing t1ce, flair, t2, t1 modalities of index {}'.format(i * opt.batchSize + b))
else:
def main():
global mousePressed, mousePressedLocation, mousePressedOffset, lastMouseLocation, brushSize, selectedSemantic, semantics
# Create a figure to draw to and connect the mouse functions
fig = plt.figure()
fig.canvas.mpl_connect('button_press_event', onpress)
fig.canvas.mpl_connect('button_release_event', onrelease)
fig.canvas.mpl_connect('motion_notify_event', mousemove)
fig.canvas.mpl_connect('scroll_event', scrollevent)
fig.canvas.mpl_connect('key_press_event', onkey)
# Create the image that is drawn to the figure
imgData=np.zeros((256, 256))
img = plt.imshow(imgData);
plt.pause(0.05)
# First, load the model
opt = TestOptions().parse()
dataloader = data.create_dataloader(opt)
model = Pix2PixModel(opt)
model.eval()
visualizer = Visualizer(opt)
# Create a webpage that summarizes the all results
web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
# Get the format of the input data
inData = None
for i, data_i in enumerate(dataloader):
inData = data_i
break
# Initially, Set the input to zero
inData['image'][:,:,:,:] = 0.0;
# initially, set the labels to zero
inData['label'][0,0,:,:] = 0
tmpImg = np.zeros((1,1,256,256), dtype=np.uint8);
# While the program is running, generate imagery from input
while True:
# Update the mouse location
if mousePressed:
# Doesn't work:
# Get current location
# cursorPos = win32gui.GetCursorPos()
# mouseLocation = [cursorPos[0] - mousePressedLocation[0] + mousePressedOffset[0], cursorPos[1] - mousePressedLocation[1] + mousePressedOffset[1]]
# Print the last mouse location
# print(lastMouseLocation)
# Draw any mouse movements to the input image
# inData['label'][0,0] = 0
cv2.circle(tmpImg[0,0], (int(lastMouseLocation[0]),int(lastMouseLocation[1])), brushSize, (semantics[selectedSemantic][0]-1), -1)
inData['label'] = torch.tensor(tmpImg)
# data_i['label']
# Run a forward pass through the model to "infer" the output image
generated = model(inData, mode='inference')
img_path = inData['path']
for b in range(generated.shape[0]):
# Extract the visuals
# print('process image... %s' % img_path[b])
visuals = OrderedDict([('input_label', data_i['label'][b]),('synthesized_image', generated[b])])
# Draw the image
imgData = visualizer.convert_visuals_to_numpy(visuals)['synthesized_image'];
# Draw the cursor location and size
cv2.circle(imgData, (int(lastMouseLocation[0]),int(lastMouseLocation[1])), brushSize, (255, 0, 0), 2)
img.set_data(imgData);
# img.set_data(tmpImg[0,0])
plt.pause(0.05)
