"""
Copyright (C) 2019 NVIDIA Corporation. All rights reserved.
Licensed under the CC BY-NC-SA 4.0 license (https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode).
"""
import re
import importlib
import torch
from argparse import Namespace
import numpy as np
from PIL import Image
import os
import argparse
import dill as pickle
def save_obj(obj, name):
with open(name, 'wb') as f:
pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)
def load_obj(name):
with open(name, 'rb') as f:
return pickle.load(f)
def copyconf(default_opt, **kwargs):
conf = argparse.Namespace(**vars(default_opt))
for key in kwargs:
print(key, kwargs[key])
setattr(conf, key, kwargs[key])
return conf
def tile_images(imgs, picturesPerRow=4):
""" Code borrowed from
https://stackoverflow.com/questions/26521365/cleanly-tile-numpy-array-of-images-stored-in-a-flattened-1d-format/26521997
"""
if imgs.shape[0] % picturesPerRow == 0:
rowPadding = 0
else:
rowPadding = picturesPerRow - imgs.shape[0] % picturesPerRow
if rowPadding > 0:
imgs = np.concatenate([imgs, np.zeros((rowPadding, *imgs.shape[1:]), dtype=imgs.dtype)], axis=0)
tiled = []
for i in range(0, imgs.shape[0], picturesPerRow):
tiled.append(np.concatenate([imgs[j] for j in range(i, i + picturesPerRow)], axis=1))
tiled = np.concatenate(tiled, axis=0)
return tiled
def tensor2im(image_tensor, imtype=np.uint8, normalize=True, tile=False):
if isinstance(image_tensor, list):
image_numpy = []
for i in range(len(image_tensor)):
image_numpy.append(tensor2im(image_tensor[i], imtype, normalize))
return image_numpy
if image_tensor.dim() == 4:
images_np = []
for b in range(image_tensor.size(0)):
one_image = image_tensor[b]
one_image_np = tensor2im(one_image)
images_np.append(one_image_np.reshape(1, *one_image_np.shape))
images_np = np.concatenate(images_np, axis=0)
if tile:
images_tiled = tile_images(images_np)
return images_tiled
else:
return images_np
if image_tensor.dim() == 2:
image_tensor = image_tensor.unsqueeze(0)
image_numpy = image_tensor.detach().cpu().float().numpy()
if normalize:
image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
else:
image_numpy = np.transpose(image_numpy, (1, 2, 0)) * 255.0
image_numpy = np.clip(image_numpy, 0, 255)
if image_numpy.shape[2] == 1:
image_numpy = image_numpy[:, :, 0]
return image_numpy.astype(imtype)
def tensor2label(label_tensor, n_label, imtype=np.uint8, tile=False):
if label_tensor.dim() == 4:
images_np = []
for b in range(label_tensor.size(0)):
one_image = label_tensor[b]
one_image_np = tensor2label(one_image, n_label, imtype)
images_np.append(one_image_np.reshape(1, *one_image_np.shape))
images_np = np.concatenate(images_np, axis=0)
if tile:
images_tiled = tile_images(images_np)
return images_tiled
else:
images_np = images_np[0]
return images_np
if label_tensor.dim() == 1:
return np.zeros((64, 64, 3), dtype=np.uint8)
if n_label == 0:
return tensor2im(label_tensor, imtype)
label_tensor = label_tensor.cpu().float()
if label_tensor.size()[0] > 1:
label_tensor = label_tensor.max(0, keepdim=True)[1]
label_tensor = Colorize(n_label)(label_tensor)
label_numpy = np.transpose(label_tensor.numpy(), (1, 2, 0))
result = label_numpy.astype(imtype)
return result
def save_image(image_numpy, image_path, create_dir=False):
if create_dir:
os.makedirs(os.path.dirname(image_path), exist_ok=True)
if len(image_numpy.shape) == 2:
image_numpy = np.expand_dims(image_numpy, axis=2)
if image_numpy.shape[2] == 1:
image_numpy = np.repeat(image_numpy, 3, 2)
image_pil = Image.fromarray(image_numpy)
image_pil.save(image_path.replace('.jpg', '.png'))
def mkdirs(paths):
if isinstance(paths, list) and not isinstance(paths, str):
for path in paths:
mkdir(path)
else:
mkdir(paths)
def mkdir(path):
if not os.path.exists(path):
os.makedirs(path)
def atoi(text):
return int(text) if text.isdigit() else text
def natural_keys(text):
'''
alist.sort(key=natural_keys) sorts in human order
http://nedbatchelder.com/blog/200712/human_sorting.html
(See Toothy's implementation in the comments)
'''
return [atoi(c) for c in re.split('(\d+)', text)]
def natural_sort(items):
items.sort(key=natural_keys)
def str2bool(v):
if v.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif v.lower() in ('no', 'false', 'f', 'n', '0'):
return False
else:
raise argparse.ArgumentTypeError('Boolean value expected.')
def find_class_in_module(target_cls_name, module):
target_cls_name = target_cls_name.replace('_', '').lower()
clslib = importlib.import_module(module)
cls = None
for name, clsobj in clslib.__dict__.items():
if name.lower() == target_cls_name:
cls = clsobj
if cls is None:
print("In %s, there should be a class whose name matches %s in lowercase without underscore(_)" % (module, target_cls_name))
exit(0)
return cls
def save_network(net, label, epoch, opt):
save_filename = '%s_net_%s.pth' % (epoch, label)
save_path = os.path.join(opt.checkpoints_dir, opt.name, save_filename)
torch.save(net.cpu().state_dict(), save_path)
if len(opt.gpu_ids) and torch.cuda.is_available():
net.cuda()
def load_network(net, label, epoch, opt):
save_filename = '%s_net_%s.pth' % (epoch, label)
save_dir = os.path.join(opt.checkpoints_dir, opt.name)
save_path = os.path.join(save_dir, save_filename)
weights = torch.load(save_path)
net.load_state_dict(weights)
print('Load checkpoint from path: ', save_path)
return net
def uint82bin(n, count=8):
"""returns the binary of integer n, count refers to amount of bits"""
return ''.join([str((n >> y) & 1) for y in range(count - 1, -1, -1)])
def labelcolormap(N):
if N == 35:
cmap = np.array([(0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (0, 0, 0), (111, 74, 0), (81, 0, 81),
(128, 64, 128), (244, 35, 232), (250, 170, 160), (230, 150, 140), (70, 70, 70), (102, 102, 156), (190, 153, 153),
(180, 165, 180), (150, 100, 100), (150, 120, 90), (153, 153, 153), (153, 153, 153), (250, 170, 30), (220, 220, 0),
(107, 142, 35), (152, 251, 152), (70, 130, 180), (220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70),
(0, 60, 100), (0, 0, 90), (0, 0, 110), (0, 80, 100), (0, 0, 230), (119, 11, 32), (0, 0, 142)],
dtype=np.uint8)
else:
cmap = np.zeros((N, 3), dtype=np.uint8)
for i in range(N):
r, g, b = 0, 0, 0
id = i + 1
for j in range(7):
str_id = uint82bin(id)
r = r ^ (np.uint8(str_id[-1]) << (7 - j))
g = g ^ (np.uint8(str_id[-2]) << (7 - j))
b = b ^ (np.uint8(str_id[-3]) << (7 - j))
id = id >> 3
cmap[i, 0] = r
cmap[i, 1] = g
cmap[i, 2] = b
if N == 182:
important_colors = {
'sea': (54, 62, 167),
'sky-other': (95, 219, 255),
'tree': (140, 104, 47),
'clouds': (170, 170, 170),
'grass': (29, 195, 49)
}
for i in range(N):
name = util.coco.id2label(i)
if name in important_colors:
color = important_colors[name]
cmap[i] = np.array(list(color))
return cmap
class Colorize(object):
def __init__(self, n=35):
self.cmap = labelcolormap(n)
self.cmap = torch.from_numpy(self.cmap[:n])
def __call__(self, gray_image):
size = gray_image.size()
color_image = torch.ByteTensor(3, size[1], size[2]).fill_(0)
for label in range(0, len(self.cmap)):
mask = (label == gray_image[0]).cpu()
color_image[0][mask] = self.cmap[label][0]
color_image[1][mask] = self.cmap[label][1]
color_image[2][mask] = self.cmap[label][2]
return color_image