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import json
from os.path import join
import numpy as np
from PIL import Image
from utils.isoutil import *
import torch
import torchvision
import sys
class MeanTracker(object):
def __init__(self, name):
self.values = []
self.name = name
def add(self, val):
self.values.append(float(val))
def mean(self):
return np.mean(self.values)
def flush(self):
mean = self.mean()
self.values = []
return self.name, mean
def save_params(config, training_path):
save_dict_path = join(training_path, 'param.json')
with open(save_dict_path, 'w') as outfile:
json.dump(config,
outfile,
sort_keys=False,
indent=4,
separators=(',', ': '))
def load_params(config_file):
with open(config_file, 'r') as f:
data = json.load(f)
return data
def save_images(isovists, iter_num, title, sample_folder):
figs=[]
for i, x_ in enumerate(isovists):
x_ = np.squeeze(x_)
figs.append(plot_isovist_numpy(x_, figsize=(1,1)))
figs = torch.tensor(figs, dtype=torch.float)
nrow = int(np.sqrt(isovists.shape[0]))
im = torchvision.utils.make_grid(figs, normalize=True, range=(0, 255), nrow=nrow)
im = Image.fromarray(np.uint8(np.transpose(im.numpy(), (1, 2, 0))*255))
im.save(join(sample_folder, f'{title}_{iter_num:06}.jpg'))
def imshow(img):
npimg = img.numpy()
plt.figure(figsize = (30,30))
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.axis('off')
plt.show()
def write(text):
sys.stdout.write('\n' + text)
if hasattr(sys.stdout, 'flush'):
sys.stdout.flush()
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