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### Copyright (C) 2017 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 os
import sys
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), '../live-cortex/rpc/'))
from collections import OrderedDict
from options.test_options import TestOptions
from options.dataset_options import DatasetOptions
from data.data_loader import CreateDataLoader
from models.models import create_model
import util.util as util
from util.visualizer import Visualizer
from util import html
import torch
import numpy as np
from run_engine import run_trt_engine, run_onnx
from datetime import datetime
from PIL import Image, ImageOps
from shutil import copyfile, rmtree
from random import randint
from img_ops import read_sequence
import torch.utils.data as data
from PIL import Image
import torchvision.transforms as transforms
def get_transform(opt, method=Image.BICUBIC, normalize=True):
transform_list = []
base = float(2 ** opt.n_downsample_global)
if opt.netG == 'local':
base *= (2 ** opt.n_local_enhancers)
transform_list.append(transforms.Lambda(lambda img: __make_power_2(img, base, method)))
transform_list += [transforms.ToTensor()]
if normalize:
transform_list += [transforms.Normalize((0.5, 0.5, 0.5),
(0.5, 0.5, 0.5))]
return transforms.Compose(transform_list)
def normalize():
return transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
def __make_power_2(img, base, method=Image.BICUBIC):
ow, oh = img.size
h = int(round(oh / base) * base)
w = int(round(ow / base) * base)
if (h == oh) and (w == ow):
return img
return img.resize((w, h), method)
opt = TestOptions().parse(save=False)
data_opt = DatasetOptions().parse(opt.unknown)
opt.nThreads = 1 # test code only supports nThreads = 1
opt.batchSize = 1 # test code only supports batchSize = 1
opt.serial_batches = True # no shuffle
opt.no_flip = True # no flip
if data_opt.tag == '':
d = datetime.now()
tag = data_opt.tag = "{}_{}".format(
opt.name,
# opt.experiment,
d.strftime('%Y%m%d%H%M')
)
else:
tag = data_opt.tag
if opt.which_epoch == 'latest':
iter_path = os.path.join(opt.checkpoints_dir, opt.name, 'iter.txt')
if os.path.exists(iter_path):
try:
current_epoch, epoch_iter = np.loadtxt(iter_path , delimiter=',', dtype=int)
except:
current_epoch, epoch_iter = 1, 0
print('Resuming from epoch %d at iteration %d' % (current_epoch, epoch_iter))
else:
current_epoch, epoch_iter = 1, 0
else:
current_epoch = opt.which_epoch
epoch_id = "{:02d}_{:04d}_{:04d}".format(current_epoch, data_opt.augment_take, data_opt.augment_make)
opt.render_dir = os.path.join(opt.results_dir, opt.name, epoch_id)
if not opt.engine and not opt.onnx:
model = create_model(opt)
if opt.data_type == 16:
model.half()
elif opt.data_type == 8:
model.type(torch.uint8)
if opt.verbose:
print(model)
sequence = read_sequence(data_opt.sequence_name, '')
print("Got sequence {}, {} images".format(data_opt.sequence_name, len(sequence)))
_len = len(sequence) - data_opt.augment_take - 2
if _len <= 0:
print("Got empty sequence...")
data_opt.processing = False
rpc_client.send_status('processing', False)
sys.exit(1)
transform = get_transform(opt)
print('tag:', tag)
print('render_dir:', opt.render_dir)
util.mkdir(opt.render_dir)
# add augment name
for m in range(data_opt.augment_take):
i = randint(0, _len)
index = i
for n in range(data_opt.augment_make):
index = i + n
if n == 0:
A_path = sequence[index]
if opt.verbose:
print(A_path)
A = Image.open(A_path)
A_tensor = transform(A.convert('RGB'))
else:
if opt.verbose:
print(A_path)
A_path = os.path.join(opt.render_dir, "recur_{}_{:05d}_{:05d}.png".format(epoch_id, m, index))
A = Image.open(A_path)
A_tensor = transform(A.convert('RGB'))
inst_tensor = torch.LongTensor([0])
if opt.verbose:
print(A_tensor, inst_tensor)
data = {'label': A_tensor.unsqueeze(0), 'inst': inst_tensor}
if opt.data_type == 16:
data['label'] = data['label'].half()
data['inst'] = data['inst'].half()
elif opt.data_type == 8:
data['label'] = data['label'].uint8()
data['inst'] = data['inst'].uint8()
minibatch = 1
generated = model.inference(data['label'], data['inst'])
tmp_path = os.path.join(opt.render_dir, "recur_{}_{:05d}_{:05d}_tmp.png".format(epoch_id, m, index+1))
next_path = os.path.join(opt.render_dir, "recur_{}_{:05d}_{:05d}.png".format(epoch_id, m, index+1))
print('process image... %i' % index)
im = util.tensor2im(generated.data[0])
image_pil = Image.fromarray(im, mode='RGB')
image_pil.save(tmp_path)
os.rename(tmp_path, next_path)
frame_A = os.path.join("./datasets/", data_opt.sequence_name, "train_A", "recur_{}_{:05d}_{:05d}.png".format(epoch_id, m, index+1))
frame_B = os.path.join("./datasets/", data_opt.sequence_name, "train_B", "recur_{}_{:05d}_{:05d}.png".format(epoch_id, m, index+1))
if os.path.exists(frame_A):
os.unlink(frame_A)
if os.path.exists(frame_B):
os.unlink(frame_B)
os.symlink(os.path.abspath(next_path), os.path.abspath(frame_A))
os.symlink(os.path.abspath(sequence[index+2]), os.path.abspath(frame_B))
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