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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 options.test_options import TestOptions
from options.dataset_options import DatasetOptions
from data import CreateRecursiveDataLoader
from models import create_model
# from util.visualizer import Visualizer
from util.util import mkdirs, crop_image
from util import html
from shutil import move, copyfile
from PIL import Image, ImageOps
from skimage.transform import resize
from scipy.misc import imresize
from shutil import copyfile, rmtree
import numpy as np
import cv2
from datetime import datetime
import re
import sys
import math
import subprocess
from time import sleep
from rpc import CortexRPC
def clamp(n,a,b):
return max(a, min(n, b))
def lerp(n,a,b):
return (b-a)*n+a
def load_opt():
opt_parser = TestOptions()
opt = opt_parser.parse()
data_opt_parser = DatasetOptions()
data_opt = data_opt_parser.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
data_opt.tag = get_tag(opt, data_opt)
opt.render_dir = opt.results_dir + opt.name + "/" + data_opt.tag + "/"
return opt, data_opt, data_opt_parser
def get_tag(opt, data_opt):
if data_opt.tag == '':
d = datetime.now()
tag = data_opt.tag = "{}_{}_{}".format(
opt.name,
'live',
d.strftime('%Y%m%d%H%M')
)
else:
tag = data_opt.tag
return tag
def load_first_frame(opt, data_opt):
print("create render_dir: {}".format(opt.render_dir))
if os.path.exists(opt.render_dir):
rmtree(opt.render_dir)
mkdirs(opt.render_dir)
start_img_path = os.path.join(opt.render_dir, "frame_00000.png")
if data_opt.just_copy:
copyfile(opt.start_img, start_img_path)
A_im = None
A_img = None
A_offset = 0
else:
print("preload {}".format(opt.start_img))
A_img = Image.open(opt.start_img).convert('RGB')
A_im = np.asarray(A_img)
A = process_image(opt, data_opt, A_im)
cv2.imwrite(start_img_path, A)
numz = re.findall(r'\d+', os.path.basename(opt.start_img))
print(numz)
if len(numz) > 0:
A_offset = int(numz[0])
print(A_offset)
if A_offset:
print(">> starting offset: {}".format(A_offset))
A_dir = opt.start_img.replace(numz[0], "{:05d}")
print(A_dir)
else:
print("Sequence not found")
return A_offset, A_im, A_dir
def process_image(opt, data_opt, im):
img = im[:, :, ::-1].copy()
processed = False
if data_opt.clahe is True:
processed = True
lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
l, a, b = cv2.split(lab)
clahe = cv2.createCLAHE(clipLimit=data_opt.clip_limit, tileGridSize=(8,8))
l = clahe.apply(l)
limg = cv2.merge((l,a,b))
img = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
if data_opt.posterize is True:
processed = True
img = cv2.pyrMeanShiftFiltering(img, data_opt.spatial_window, data_opt.color_window)
if data_opt.grayscale is True:
processed = True
img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
if data_opt.blur is True:
processed = True
img = cv2.GaussianBlur(img, (data_opt.blur_radius, data_opt.blur_radius), data_opt.blur_sigma)
if data_opt.canny is True:
processed = True
img = cv2.Canny(img, data_opt.canny_lo, data_opt.canny_hi)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if processed is False or data_opt.process_frac == 0:
return img
src_img = im[:, :, ::-1].copy()
frac_a = data_opt.process_frac
frac_b = 1.0 - frac_a
array_a = np.multiply(src_img.astype('float64'), frac_a)
array_b = np.multiply(img.astype('float64'), frac_b)
img = np.add(array_a, array_b).astype('uint8')
return img
class Listener():
def __init__(self):
opt, data_opt, data_opt_parser = load_opt()
self.opt = opt
self.data_opt = data_opt
self.data_opt_parser = data_opt_parser.parser
self.working = False
def _set_fn(self, key, value):
if key == 'pause' and self.data_opt.processing is False:
process_live_input(self.opt, self.data_opt, self.rpc_client)
if hasattr(self.data_opt, key):
try:
if str(value) == 'True':
setattr(self.data_opt, key, True)
print('set {} {}: {}'.format('bool', key, True))
elif str(value) == 'False':
setattr(self.data_opt, key, False)
print('set {} {}: {}'.format('bool', key, False))
else:
new_opt, misc = self.data_opt_parser.parse_known_args([ '--' + key.replace('_', '-'), str(value) ])
new_value = getattr(new_opt, key)
setattr(self.data_opt, key, new_value)
print('set {} {}: {}'.format(type(new_value), key, new_value))
except Exception as e:
print('error {} - cant set value {}: {}'.format(e, key, value))
def _get_fn(self):
return vars(self.data_opt)
def _cmd_fn(self, cmd, payload):
print("got command {}".format(cmd))
return 'yes'
def _ready_fn(self, rpc_client):
# self.connect()
process_live_input(self.opt, self.data_opt, rpc_client)
def connect(self):
self.rpc_client = CortexRPC(self._get_fn, self._set_fn, self._ready_fn, self._cmd_fn)
def process_live_input(opt, data_opt, rpc_client):
if data_opt.processing:
print("Already processing...")
data_opt.processing = True
data_loader = CreateRecursiveDataLoader(opt)
dataset = data_loader.load_data()
model = create_model(opt)
print("generating...")
A_offset, A_im, A_dir = load_first_frame(opt, data_opt)
last_im = None
for i, data in enumerate(data_loader):
if i >= opt.how_many:
break
model.set_input(data)
model.test()
visuals = model.get_current_visuals()
img_path = model.get_image_paths()
if (i % 100) == 0:
print('%04d: process image...' % (i))
im = visuals['fake_B']
last_path = opt.render_dir + "frame_{:05d}.png".format(i)
tmp_path = opt.render_dir + "frame_{:05d}_tmp.png".format(i+1)
next_path = opt.render_dir + "frame_{:05d}.png".format(i+1)
current_path = opt.render_dir + "ren_{:05d}.png".format(i+1)
if A_dir is not None:
sequence_path = A_dir.format(A_offset+i+1)
if data_opt.send_image == 'b':
image_pil = Image.fromarray(im, mode='RGB')
rpc_client.send_pil_image("frame_{:05d}.png".format(i+1), image_pil)
if data_opt.store_a is not True:
os.remove(last_path)
if data_opt.store_b is True:
image_pil = Image.fromarray(im, mode='RGB')
image_pil.save(tmp_path)
os.rename(tmp_path, current_path)
if data_opt.recursive and last_im is not None:
if data_opt.sequence and A_dir is not None:
A_img = Image.open(sequence_path).convert('RGB')
A_im = np.asarray(A_img)
frac_a = data_opt.recursive_frac
frac_b = data_opt.sequence_frac
frac_sum = frac_a + frac_b
if frac_sum > 1.0:
frac_a = frac_a / frac_sum
frac_b = frac_b / frac_sum
if data_opt.transition:
t = lerp(math.sin(i / data_opt.transition_period * math.pi * 2.0 ) / 2.0 + 0.5, data_opt.transition_min, data_opt.transition_max)
frac_a *= 1.0 - t
frac_b *= 1.0 - t
frac_c = 1.0 - frac_a - frac_b
array_a = np.multiply(last_im.astype('float64'), frac_a)
array_b = np.multiply(A_im.astype('float64'), frac_b)
array_c = np.multiply(im.astype('float64'), frac_c)
array_ab = np.add(array_a, array_b)
array_abc = np.add(array_ab, array_c)
next_im = array_abc.astype('uint8')
else:
frac_a = data_opt.recursive_frac
frac_b = 1.0 - frac_a
array_a = np.multiply(last_im.astype('float64'), frac_a)
array_b = np.multiply(im.astype('float64'), frac_b)
next_im = np.add(array_a, array_b).astype('uint8')
if data_opt.recurse_roll != 0:
last_im = np.roll(im, data_opt.recurse_roll, axis=data_opt.recurse_roll_axis)
else:
last_im = next_im.copy().astype('uint8')
else:
last_im = im.copy().astype('uint8')
next_im = im
next_img = process_image(opt, data_opt, next_im)
if data_opt.send_image == 'sequence':
rpc_client.send_pil_image("frame_{:05d}.png".format(i+1), A_img)
if data_opt.send_image == 'recursive':
pil_im = Image.fromarray(next_im)
rpc_client.send_pil_image("frame_{:05d}.png".format(i+1), pil_im)
if data_opt.send_image == 'a':
rgb_im = cv2.cvtColor(next_img, cv2.COLOR_BGR2RGB)
pil_im = Image.fromarray(rgb_im)
rpc_client.send_pil_image("frame_{:05d}.png".format(i+1), pil_im)
cv2.imwrite(tmp_path, next_img)
os.rename(tmp_path, next_path)
if data_opt.exit:
sys.exit(0)
if data_opt.pause:
data_opt.pause = False
break
data_opt.processing = False
if __name__ == '__main__':
listener = Listener()
listener.connect()
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