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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
from run_engine import run_trt_engine, run_onnx
from datetime import datetime
from PIL import Image, ImageOps
from skimage.transform import resize
from scipy.misc import imresize
import numpy as np
import cv2
import math
import subprocess
import glob
import gevent
from time import sleep
from shutil import copyfile, rmtree
from img_ops import process_image, mix_next_image
from listener import Listener
module_name = 'pix2pixhd'
opt = TestOptions().parse(save=False)
data_opt_parser = DatasetOptions()
data_opt = data_opt_parser.parse(opt.unknown)
data_opt.resize_before_sending = True
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
opt.render_dir = render_dir = opt.results_dir + opt.name + "/" + tag + "/"
print('tag:', tag)
print('render_dir:', render_dir)
util.mkdir(render_dir)
def process_live_input(opt, data_opt, rpc_client):
print(">>> Process live HD input")
if data_opt.processing:
print("Already processing...")
data_opt.processing = True
data_loader = CreateDataLoader(opt)
dataset = data_loader.load_data()
create_render_dir(opt)
sequence = read_sequence(data_opt.sequence_name, '')
print("Got sequence {}, {} images".format(data_opt.sequence, len(sequence)))
if len(sequence) == 0:
print("Got empty sequence...")
data_opt.processing = False
rpc_client.send_status('processing', False)
return
print("First image: {}".format(sequence[0]))
rpc_client.send_status('processing', True)
start_img_path = os.path.join(opt.render_dir, "frame_{:05d}.png".format(0))
copyfile(sequence[0], start_img_path)
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_i = 1
print("generating...")
for i, data in enumerate(data_loader):
if i >= opt.how_many:
print("generated {} images, exiting".format(i))
break
if data_opt.load_checkpoint is True:
checkpoint_fn = "{}_net_{}.pth".format(data_opt.epoch, 'G')
checkpoint_path = os.path.join(opt.checkpoints_dir, '', data_opt.checkpoint_name)
checkpoint_fn_path = os.path.join(checkpoint_path, checkpoint_fn)
if os.path.exists(checkpoint_fn_path):
print("Load checkpoint: {}".format(checkpoint_fn_path))
model.load_network(model.netG, 'G', data_opt.epoch, checkpoint_path)
else:
print("Checkpoint not found: {}".format(checkpoint_fn_path))
data_opt.load_checkpoint = False
if data_opt.load_sequence is True:
data_opt.load_sequence = False
new_sequence = read_sequence(data_opt.sequence_name, '')
if len(new_sequence) != 0:
print("Got sequence {}, {} images, first: {}".format(data_opt.sequence_name, len(new_sequence), new_sequence[0]))
sequence = new_sequence
sequence_i = 1
else:
print("Sequence not found")
if data_opt.seek_to != 1:
if data_opt.seek_to > 0 and data_opt.seek_to < len(sequence):
sequence_i = data_opt.seek_to
data_opt.seek_to = 1
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'])
im = util.tensor2im(generated.data[0])
sequence_i = mix_next_image(opt, data_opt, rpc_client, im, i, sequence, sequence_i)
if data_opt.pause:
data_opt.pause = False
break
gevent.sleep(data_opt.frame_delay)
data_opt.processing = False
rpc_client.send_status('processing', False)
if __name__ == '__main__':
listener = Listener(opt, data_opt, data_opt_parser, process_live_input)
listener.connect()
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