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import numpy as np
import os
import ntpath
import time
from . import util
from . import html
# from scipy.misc import imresize
# save image to the disk
def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
image_dir = webpage.get_image_dir()
if isinstance(image_path,list):
image_path = image_path[0]
short_path = ntpath.basename(image_path)
name = os.path.splitext(short_path)[0]
webpage.add_header(name)
ims, txts, links = [], [], []
for label, im_data in visuals.items():
im = util.tensor2im(im_data)
# image_name = '%s_%s.png' % (name, label)
# save_path = os.path.join(image_dir, image_name)
image_name = '%s.png' % (name)
util.mkdirs(os.path.join(image_dir, label))
save_path = os.path.join(image_dir, label, image_name)
h, w, _ = im.shape
# if aspect_ratio > 1.0:
# im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic')
# if aspect_ratio < 1.0:
# im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic')
util.save_image(im, save_path)
link_name = os.path.join(label,image_name)
ims.append(link_name)
txts.append(label)
links.append(link_name)
webpage.add_images(ims, txts, links, width=width)
def save_crop(visuals, save_path):
im_data = visuals['fake_A']
im = util.tensor2im(im_data)
util.save_image(im, save_path)
# save image to the disk
def save_images_test(webpage, visuals, image_dir, image_path, aspect_ratio=1.0, width=256):
# image_dir = webpage.get_image_dir()
if isinstance(image_path,list):
image_path = image_path[0]
short_path = ntpath.basename(image_path)
name = os.path.splitext(short_path)[0]
webpage.add_header(name)
ims, txts, links = [], [], []
for label, im_data in visuals.items():
im = util.tensor2im(im_data)
# image_name = '%s_%s.png' % (name, label)
# save_path = os.path.join(image_dir, image_name)
image_name = '%s.png' % (name)
util.mkdirs(os.path.join(image_dir, label))
save_path = os.path.join(image_dir, label, image_name)
exit('wwwwwwwwwwwww')
h, w, _ = im.shape
# if aspect_ratio > 1.0:
# im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic')
# if aspect_ratio < 1.0:
# im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic')
util.save_image(im, save_path)
link_name = os.path.join(label,image_name)
ims.append(link_name)
txts.append(label)
links.append(link_name)
webpage.add_images(ims, txts, links, width=width)
# save image to the disk
# Original Version
# def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
# image_dir = webpage.get_image_dir()
# short_path = ntpath.basename(image_path[0])
# name = os.path.splitext(short_path)[0]
# webpage.add_header(name)
# ims, txts, links = [], [], []
# for label, im_data in visuals.items():
# im = util.tensor2im(im_data)
# image_name = '%s_%s.png' % (name, label)
# save_path = os.path.join(image_dir, image_name)
# h, w, _ = im.shape
# if aspect_ratio > 1.0:
# im = imresize(im, (h, int(w * aspect_ratio)), interp='bicubic')
# if aspect_ratio < 1.0:
# im = imresize(im, (int(h / aspect_ratio), w), interp='bicubic')
# util.save_image(im, save_path)
# ims.append(image_name)
# txts.append(label)
# links.append(image_name)
# webpage.add_images(ims, txts, links, width=width)
class Visualizer():
def __init__(self, opt):
self.display_id = opt.display_id
self.use_html = opt.isTrain and not opt.no_html
self.win_size = opt.display_winsize
self.name = opt.name
self.opt = opt
self.saved = False
if self.display_id > 0:
import visdom
self.ncols = opt.display_ncols
self.vis = visdom.Visdom(server=opt.display_server, port=opt.display_port, env=opt.display_env, raise_exceptions=True)
if self.use_html:
self.web_dir = os.path.join(opt.checkpoints_dir, opt.name, 'web')
self.img_dir = os.path.join(self.web_dir, 'images')
print('create web directory %s...' % self.web_dir)
util.mkdirs([self.web_dir, self.img_dir])
self.log_name = os.path.join(opt.checkpoints_dir, opt.name, 'loss_log.txt')
with open(self.log_name, "a") as log_file:
now = time.strftime("%c")
log_file.write('================ Training Loss (%s) ================\n' % now)
def reset(self):
self.saved = False
def throw_visdom_connection_error(self):
print('\n\nCould not connect to Visdom server (https://github.com/facebookresearch/visdom) for displaying training progress.\nYou can suppress connection to Visdom using the option --display_id -1. To install visdom, run \n$ pip install visdom\n, and start the server by \n$ python -m visdom.server.\n\n')
exit(1)
# |visuals|: dictionary of images to display or save
def display_current_results(self, visuals, epoch, save_result):
if self.display_id > 0: # show images in the browser
ncols = self.ncols
if ncols > 0:
ncols = min(ncols, len(visuals))
h, w = next(iter(visuals.values())).shape[:2]
table_css = """<style>
table {border-collapse: separate; border-spacing:4px; white-space:nowrap; text-align:center}
table td {width: %dpx; height: %dpx; padding: 4px; outline: 4px solid black}
</style>""" % (w, h)
title = self.name
label_html = ''
label_html_row = ''
images = []
idx = 0
for label, image in visuals.items():
image_numpy = util.tensor2im(image)
label_html_row += '<td>%s</td>' % label
images.append(image_numpy.transpose([2, 0, 1]))
idx += 1
if idx % ncols == 0:
label_html += '<tr>%s</tr>' % label_html_row
label_html_row = ''
white_image = np.ones_like(image_numpy.transpose([2, 0, 1])) * 255
while idx % ncols != 0:
images.append(white_image)
label_html_row += '<td></td>'
idx += 1
if label_html_row != '':
label_html += '<tr>%s</tr>' % label_html_row
# pane col = image row
try:
self.vis.images(images, nrow=ncols, win=self.display_id + 1,
padding=2, opts=dict(title=title + ' images'))
label_html = '<table>%s</table>' % label_html
self.vis.text(table_css + label_html, win=self.display_id + 2,
opts=dict(title=title + ' labels'))
except ConnectionError:
self.throw_visdom_connection_error()
else:
idx = 1
for label, image in visuals.items():
image_numpy = util.tensor2im(image)
self.vis.image(image_numpy.transpose([2, 0, 1]), opts=dict(title=label),
win=self.display_id + idx)
idx += 1
if self.use_html and (save_result or not self.saved): # save images to a html file
self.saved = True
for label, image in visuals.items():
image_numpy = util.tensor2im(image)
img_path = os.path.join(self.img_dir, 'epoch%.3d_%s.png' % (epoch, label))
util.save_image(image_numpy, img_path)
# update website
webpage = html.HTML(self.web_dir, 'Experiment name = %s' % self.name, reflesh=1)
for n in range(epoch, 0, -1):
webpage.add_header('epoch [%d]' % n)
ims, txts, links = [], [], []
for label, image_numpy in visuals.items():
image_numpy = util.tensor2im(image)
img_path = 'epoch%.3d_%s.png' % (n, label)
ims.append(img_path)
txts.append(label)
links.append(img_path)
webpage.add_images(ims, txts, links, width=self.win_size)
webpage.save()
# losses: dictionary of error labels and values
def plot_current_losses(self, epoch, counter_ratio, opt, losses):
if not hasattr(self, 'plot_data'):
self.plot_data = {'X': [], 'Y': [], 'legend': list(losses.keys())}
self.plot_data['X'].append(epoch + counter_ratio)
self.plot_data['Y'].append([losses[k] for k in self.plot_data['legend']])
try:
self.vis.line(
X=np.stack([np.array(self.plot_data['X'])] * len(self.plot_data['legend']), 1),
Y=np.array(self.plot_data['Y']),
opts={
'title': self.name + ' loss over time',
'legend': self.plot_data['legend'],
'xlabel': 'epoch',
'ylabel': 'loss'},
win=self.display_id)
except ConnectionError:
self.throw_visdom_connection_error()
# losses: same format as |losses| of plot_current_losses
def print_current_losses(self, epoch, i, losses, t, t_data):
message = '(epoch: %d, iters: %d, time: %.3f, data: %.3f) ' % (epoch, i, t, t_data)
for k, v in losses.items():
message += '%s: %.3f ' % (k, v)
print(message)
with open(self.log_name, "a") as log_file:
log_file.write('%s\n' % message)
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