def render(self):
super(SaveGameFrame, self).render()
if bimpy.begin(self.name,
self.opened,
flags=bimpy.WindowFlags.NoCollapse
| bimpy.WindowFlags.MenuBar):
if bimpy.begin_menu_bar():
bimpy.menu_item('Save', 'Cmd+S', self.click_states['save'])
bimpy.menu_item('Reload', 'Cmd+R', self.click_states['reload'])
bimpy.menu_item('Export', 'Cmd+E', self.click_states['export'])
bimpy.menu_item('Reload & Diff', 'Cmd+D',
self.click_states['reload_and_diff'])
bimpy.end_menu_bar()
if self.diff_string:
bimpy.columns(2, "hex split")
bimpy.text('Game: ')
bimpy.same_line()
bimpy.text(self.backend.game.value)
for section_name, section_items in self.items.items():
if bimpy.collapsing_header(section_name):
for item in section_items:
item.render_widget()
if self.diff_string:
bimpy.next_column()
for line in self.diff_string.splitlines():
bimpy.text(line)
bimpy.end()
def drawPathFinder(columns_count=7, lines_count=5):
bimpy.columns(columns_count, None, borders_vertical)
i = 0
while i != columns_count:
i += 1
if borders_horizontal and bimpy.get_column_index == 0:
bimpy.separator()
#bimpy.text("{}".format("a"))
bimpy.button("Auto Path", bimpy.Vec2(100,100))
bimpy.next_column()
bimpy.columns(1)
if borders_horizontal:
bimpy.separator()
def main():
selected_compiler = bimpy.Int()
ctx = bimpy.Context()
ctx.init(WIDTH, HEIGHT, "Virtual enviroment manager")
environments = getAvailableEnviroments()
compilers_list = list(data.compilers.keys())
show_new_env_menu = False
while (not ctx.should_close()):
with ctx:
bimpy.set_next_window_pos(bimpy.Vec2(0, 0), bimpy.Condition.Once)
bimpy.set_next_window_size(bimpy.Vec2(WIDTH, HEIGHT),
bimpy.Condition.Once)
bimpy.begin("Enviroments",bimpy.Bool(True), \
bimpy.WindowFlags.NoCollapse and bimpy.WindowFlags.NoResize)
bimpy.text(sys.version)
bimpy.columns(2)
for enviroment in environments:
if bimpy.button(enviroment):
compiler = list(data.compilers.values())[
selected_compiler.
value] if selected_compiler.value != 0 else ""
subprocess.call(
['start', environments[enviroment], compiler],
shell=True)
bimpy.next_column()
if bimpy.button("O##" + enviroment):
subprocess.Popen(r'explorer /select,' +
os.path.dirname(environments[enviroment]))
#os.startfile(os.path.realpath(os.path.dirname(environments[enviroment])))
bimpy.next_column()
bimpy.columns(1)
if bimpy.combo("Compiler", selected_compiler, compilers_list):
pass
# if bimpy.button("Add new enviroment"):
# new_env_ctx = BimpyContext(WIDTH, HEIGHT, "New enviroment menu")
# while(not new_env_ctx.ctx.should_close()):
# with new_env_ctx.ctx:
# bimpy.begin("dsad")
# bimpy.text("d")
# bimpy.end()
# if bimpy.button("Create new enviroment"):
bimpy.end()
def on_update(self):
y = 30
self.text('Window size: %dx%d' % (self.width, self.height), 10, y)
y += 30
self.text('World size: %dx%d' % (self.world_width, self.world_height),
10, y)
y += 30
self.text('Scale %f' % self.scale, 10, y)
y += 30
self.text('%f x %f' % self.cursor_pos_world, 10, y)
y += 30
self.point(*self.cursor_pos_world, (230, 10, 10, 240), radius=2.)
plt = vec2(-0.035, -0.045)
pmt = vec2(0.56, 0.31)
prt = vec2(0.862, 0.0421)
plm = vec2(0.039, -0.3)
prm = vec2(0.87, -0.088)
plb = vec2(0.31, -0.35)
pmb = vec2(0.55, -0.35)
prb = vec2(0.84, -0.25)
self._ctx.nvgBeginPath()
self._ctx.nvgScale(vec2(1000.))
self._ctx.nvgTranslate(vec2(1.))
self._ctx.nvgScale(vec2(1., -1.))
self._ctx.nvgMoveTo(plt)
self._ctx.nvgQuadTo(plm, plb)
self._ctx.nvgQuadTo(pmb, prb)
self._ctx.nvgQuadTo(prm, prt)
self._ctx.nvgQuadTo(pmt, plt)
self._ctx.nvgClosePath()
self._ctx.nvgFillColor(vec4(0.73, 0.78, 0.83, 1.))
self._ctx.nvgFill()
bimpy.begin('Editor')
bimpy.columns(2)
bimpy.begin_child('Scene', bimpy.Vec2(0, 300))
self.scene_tree()
bimpy.end_child()
bimpy.next_column()
self.object_inspector()
self.draw()
bimpy.end()
def drawMainTab(columns_count=7, lines_count=5):
bimpy.columns(columns_count, None, borders_vertical)
i = 0
col_c = 0
while col_c > lines_count:
col_c += 1
while i != columns_count:
if borders_horizontal and bimpy.get_column_index == 0:
bimpy.separator()
#bimpy.text("{}".format("a"))
bimpy.button(field_names[i], bimpy.Vec2(100,100))
bimpy.next_column()
i += 1
columns_count = 0
bimpy.columns(1)
if borders_horizontal:
bimpy.separator()
opened = bimpy.Bool(True)
a = 0.0
mylist = ["aaa", "bbb", "ccc"]
selectedItem = bimpy.Int()
vals = [0., 0.1, 0.2, 0.1, 0.4, 0.2]
while (not ctx.should_close()):
ctx.new_frame()
if opened.value:
if bimpy.begin("Hello!", opened=opened):
bimpy.columns(4, "mycolumns")
bimpy.separator()
bimpy.text("Some text")
bimpy.next_column()
bimpy.text("Some text")
bimpy.next_column()
bimpy.text("Some text")
bimpy.next_column()
bimpy.text("Some text")
bimpy.separator()
bimpy.columns(1)
if bimpy.button("Some button"):
a = 0
print("!!!")
def sample(cfg, logger):
torch.cuda.set_device(0)
model = Model(startf=cfg.MODEL.START_CHANNEL_COUNT,
layer_count=cfg.MODEL.LAYER_COUNT,
maxf=cfg.MODEL.MAX_CHANNEL_COUNT,
latent_size=cfg.MODEL.LATENT_SPACE_SIZE,
truncation_psi=cfg.MODEL.TRUNCATIOM_PSI,
truncation_cutoff=cfg.MODEL.TRUNCATIOM_CUTOFF,
mapping_layers=cfg.MODEL.MAPPING_LAYERS,
channels=cfg.MODEL.CHANNELS,
generator=cfg.MODEL.GENERATOR,
encoder=cfg.MODEL.ENCODER)
model.cuda(0)
model.eval()
model.requires_grad_(False)
decoder = model.decoder
encoder = model.encoder
mapping_tl = model.mapping_d
mapping_fl = model.mapping_f
dlatent_avg = model.dlatent_avg
logger.info("Trainable parameters generator:")
count_parameters(decoder)
logger.info("Trainable parameters discriminator:")
count_parameters(encoder)
arguments = dict()
arguments["iteration"] = 0
model_dict = {
'discriminator_s': encoder,
'generator_s': decoder,
'mapping_tl_s': mapping_tl,
'mapping_fl_s': mapping_fl,
'dlatent_avg': dlatent_avg
}
checkpointer = Checkpointer(cfg, model_dict, {}, logger=logger, save=False)
extra_checkpoint_data = checkpointer.load()
model.eval()
layer_count = cfg.MODEL.LAYER_COUNT
def encode(x):
Z, _ = model.encode(x, layer_count - 1, 1)
Z = Z.repeat(1, model.mapping_f.num_layers, 1)
return Z
def decode(x):
layer_idx = torch.arange(2 * layer_count)[np.newaxis, :, np.newaxis]
ones = torch.ones(layer_idx.shape, dtype=torch.float32)
coefs = torch.where(layer_idx < model.truncation_cutoff, ones, ones)
# x = torch.lerp(model.dlatent_avg.buff.data, x, coefs)
return model.decoder(x, layer_count - 1, 1, noise=True)
path = 'dataset_samples/faces/realign1024x1024'
paths = list(os.listdir(path))
paths.sort()
paths_backup = paths[:]
randomize = bimpy.Bool(True)
current_file = bimpy.String("")
ctx = bimpy.Context()
attribute_values = [bimpy.Float(0) for i in indices]
W = [
torch.tensor(np.load("principal_directions/direction_%d.npy" % i),
dtype=torch.float32) for i in indices
]
rnd = np.random.RandomState(5)
def loadNext():
img = np.asarray(Image.open(path + '/' + paths[0]))
current_file.value = paths[0]
paths.pop(0)
if len(paths) == 0:
paths.extend(paths_backup)
if img.shape[2] == 4:
img = img[:, :, :3]
im = img.transpose((2, 0, 1))
x = torch.tensor(np.asarray(im, dtype=np.float32),
device='cpu',
requires_grad=True).cuda() / 127.5 - 1.
if x.shape[0] == 4:
x = x[:3]
needed_resolution = model.decoder.layer_to_resolution[-1]
while x.shape[2] > needed_resolution:
x = F.avg_pool2d(x, 2, 2)
if x.shape[2] != needed_resolution:
x = F.adaptive_avg_pool2d(x,
(needed_resolution, needed_resolution))
img_src = ((x * 0.5 + 0.5) * 255).type(torch.long).clamp(
0, 255).cpu().type(torch.uint8).transpose(0,
2).transpose(0,
1).numpy()
latents_original = encode(x[None, ...].cuda())
latents = latents_original[0, 0].clone()
latents -= model.dlatent_avg.buff.data[0]
for v, w in zip(attribute_values, W):
v.value = (latents * w).sum()
for v, w in zip(attribute_values, W):
latents = latents - v.value * w
return latents, latents_original, img_src
def loadRandom():
latents = rnd.randn(1, cfg.MODEL.LATENT_SPACE_SIZE)
lat = torch.tensor(latents).float().cuda()
dlat = mapping_fl(lat)
layer_idx = torch.arange(2 * layer_count)[np.newaxis, :, np.newaxis]
ones = torch.ones(layer_idx.shape, dtype=torch.float32)
coefs = torch.where(layer_idx < model.truncation_cutoff, ones, ones)
dlat = torch.lerp(model.dlatent_avg.buff.data, dlat, coefs)
x = decode(dlat)[0]
img_src = ((x * 0.5 + 0.5) * 255).type(torch.long).clamp(
0, 255).cpu().type(torch.uint8).transpose(0,
2).transpose(0,
1).numpy()
latents_original = dlat
latents = latents_original[0, 0].clone()
latents -= model.dlatent_avg.buff.data[0]
for v, w in zip(attribute_values, W):
v.value = (latents * w).sum()
for v, w in zip(attribute_values, W):
latents = latents - v.value * w
return latents, latents_original, img_src
latents, latents_original, img_src = loadNext()
ctx.init(1800, 1600, "Styles")
def update_image(w, latents_original):
with torch.no_grad():
w = w + model.dlatent_avg.buff.data[0]
w = w[None, None, ...].repeat(1, model.mapping_f.num_layers, 1)
layer_idx = torch.arange(model.mapping_f.num_layers)[np.newaxis, :,
np.newaxis]
cur_layers = (7 + 1) * 2
mixing_cutoff = cur_layers
styles = torch.where(layer_idx < mixing_cutoff, w,
latents_original)
x_rec = decode(styles)
resultsample = ((x_rec * 0.5 + 0.5) * 255).type(torch.long).clamp(
0, 255)
resultsample = resultsample.cpu()[0, :, :, :]
return resultsample.type(torch.uint8).transpose(0,
2).transpose(0, 1)
im_size = 2**(cfg.MODEL.LAYER_COUNT + 1)
im = update_image(latents, latents_original)
print(im.shape)
im = bimpy.Image(im)
display_original = True
seed = 0
while not ctx.should_close():
with ctx:
new_latents = latents + sum(
[v.value * w for v, w in zip(attribute_values, W)])
if display_original:
im = bimpy.Image(img_src)
else:
im = bimpy.Image(update_image(new_latents, latents_original))
bimpy.begin("Principal directions")
bimpy.columns(2)
bimpy.set_column_width(0, im_size + 20)
bimpy.image(im)
bimpy.next_column()
for v, label in zip(attribute_values, labels):
bimpy.slider_float(label, v, -40.0, 40.0)
bimpy.checkbox("Randomize noise", randomize)
if randomize.value:
seed += 1
torch.manual_seed(seed)
if bimpy.button('Next'):
latents, latents_original, img_src = loadNext()
display_original = True
if bimpy.button('Display Reconstruction'):
display_original = False
if bimpy.button('Generate random'):
latents, latents_original, img_src = loadRandom()
display_original = False
if bimpy.input_text(
"Current file", current_file,
64) and os.path.exists(path + '/' + current_file.value):
paths.insert(0, current_file.value)
latents, latents_original, img_src = loadNext()
bimpy.end()
def edit_config(cfg):
bimpy.begin("Parameters")
ret = False
bimpy.columns(2)
bimpy.text("Stretch")
bimpy.next_column()
if edit_autofloat(cfg.stretch, "stretch", ps.ConfigurationInfo.stretch):
ret = True
bimpy.next_column()
bimpy.separator()
bimpy.text("Window Size")
bimpy.next_column()
r = ranged_slider("windowSize", cfg.windowSize,
ps.ConfigurationInfo.windowSize)
if r != None:
cfg.windowSize = r
ret = True
bimpy.next_column()
bimpy.separator()
bimpy.text("Onset Sensitivity")
bimpy.next_column()
r = ranged_slider("onsetSensitivity", cfg.onsetSensitivity,
ps.ConfigurationInfo.onsetSensitivity)
if r != None:
cfg.onsetSensitivity = r
ret = True
bimpy.next_column()
bimpy.separator()
bimpy.separator()
bimpy.text("Harmonics:")
#bimpy.separator()
bimpy.next_column()
b = bimpy.Bool(cfg.harmonics)
if bimpy.checkbox("##harmonics", b):
cfg.harmonics = b.value
bimpy.next_column()
bimpy.separator()
if b.value:
bimpy.text("Freq")
bimpy.next_column()
r = ranged_slider("hFreq", cfg.hFreq, ps.ConfigurationInfo.hFreq)
if r != None:
cfg.hFreq = r
ret = True
bimpy.next_column()
bimpy.separator()
bimpy.text("Bandwidth")
bimpy.next_column()
r = ranged_slider("hBandwidth", cfg.hBandwidth,
ps.ConfigurationInfo.hBandwidth)
if r != None:
cfg.hBandwidth = r
ret = True
bimpy.next_column()
bimpy.separator()
bimpy.text("Count")
#bimpy.separator()
bimpy.next_column()
# bimpy.text(str(cfg.hNumberHarm))
r = ranged_slider("hNumberHarm", cfg.hNumberHarm,
ps.ConfigurationInfo.hNumberHarm)
if r != None:
cfg.hNumberHarm = r
ret = True
bimpy.next_column()
bimpy.separator()
bimpy.text("Gaussian")
#bimpy.separator()
bimpy.next_column()
bimpy.text(str(cfg.hGauss))
# r = ranged_slider("hGauss",cfg.hGauss, ps.ConfigurationInfo.hGauss)
# if r != None:
# cfg.hGauss = r
# ret = True
bimpy.next_column()
bimpy.separator()
bimpy.separator()
bimpy.text("Pitch Shift")
bimpy.next_column()
b = bimpy.Bool(cfg.pitchShift)
if bimpy.checkbox("##pitchShift", b):
cfg.pitchShift = b.value
bimpy.next_column()
bimpy.separator()
if b.value:
bimpy.text("Cents")
#bimpy.separator()
bimpy.next_column()
# bimpy.text(str(cfg.psCents))
r = ranged_slider("psCents", cfg.psCents, ps.ConfigurationInfo.psCents)
if r != None:
cfg.psCents = r
ret = True
bimpy.next_column()
bimpy.separator()
bimpy.separator()
bimpy.text("Freq Shift")
#bimpy.separator()
bimpy.next_column()
b = bimpy.Bool(cfg.freqShift)
if bimpy.checkbox("##freqShift", b):
cfg.freqShift = b.value
bimpy.next_column()
bimpy.separator()
if b.value:
bimpy.text("Freq")
#bimpy.separator()
bimpy.next_column()
# bimpy.text(str(cfg.fsFreq))
r = ranged_slider("fsFreq", cfg.fsFreq, ps.ConfigurationInfo.fsFreq)
if r != None:
cfg.fsFreq = r
ret = True
bimpy.next_column()
bimpy.separator()
bimpy.separator()
bimpy.text("Filter")
#bimpy.separator()
bimpy.next_column()
b = bimpy.Bool(cfg.filter)
if bimpy.checkbox("##filter", b):
cfg.filter = b.value
bimpy.next_column()
bimpy.separator()
if b.value:
bimpy.text("Freq 1")
#bimpy.separator()
bimpy.next_column()
# bimpy.text(str(cfg.fFreq1))
r = ranged_slider("fFreq1", cfg.fFreq1, ps.ConfigurationInfo.fFreq1)
if r != None:
cfg.fFreq1 = r
ret = True
bimpy.next_column()
bimpy.separator()
bimpy.text("Freq 2")
#bimpy.separator()
bimpy.next_column()
# bimpy.text(str(cfg.fFreq2))
r = ranged_slider("fFreq2", cfg.fFreq2, ps.ConfigurationInfo.fFreq2)
if r != None:
cfg.fFreq2 = r
ret = True
bimpy.next_column()
bimpy.separator()
bimpy.text("Arbitrary Freq")
#bimpy.separator()
bimpy.next_column()
edit_autofloat(cfg.fFreqArbitrary, "fFreqArbitrary",
ps.ConfigurationInfo.fFreqArbitrary)
bimpy.next_column()
bimpy.separator()
bimpy.text("Bandstop")
#bimpy.separator()
bimpy.next_column()
bimpy.text(str(cfg.fBandstop))
bimpy.next_column()
bimpy.separator()
bimpy.separator()
bimpy.text("Tonal / Noise")
#bimpy.separator()
bimpy.next_column()
b = bimpy.Bool(cfg.tonalNoise)
if bimpy.checkbox("##tonalNoise", b):
cfg.tonalNoise = b.value
#bimpy.text(str(cfg.tonalNoise))
bimpy.next_column()
bimpy.separator()
if b.value:
bimpy.text("Amount")
#bimpy.separator()
bimpy.next_column()
# bimpy.text(str(cfg.tnAmount))
r = ranged_slider("tnAmount", cfg.tnAmount,
ps.ConfigurationInfo.tnAmount)
if r != None:
cfg.tnAmount = r
ret = True
bimpy.next_column()
bimpy.separator()
bimpy.text("Bandwidth")
#bimpy.separator()
bimpy.next_column()
# bimpy.text(str(cfg.tnBandwidth))
r = ranged_slider("tnBandwidth", cfg.tnBandwidth,
ps.ConfigurationInfo.tnBandwidth)
if r != None:
cfg.tnBandwidth = r
ret = True
bimpy.next_column()
bimpy.separator()
bimpy.separator()
bimpy.text("Compress")
#bimpy.separator()
bimpy.next_column()
# bimpy.text(str(cfg.compress))
b = bimpy.Bool(cfg.compress)
if bimpy.checkbox("##compress", b):
cfg.compress = b.value
bimpy.next_column()
bimpy.separator()
bimpy.separator()
bimpy.text("Binaural")
#bimpy.separator()
bimpy.next_column()
edit_autofloat(cfg.binaural, "binaural", ps.ConfigurationInfo.binaural)
bimpy.next_column()
bimpy.separator()
bimpy.end()
return ret
bimpy.same_line()
if bimpy.button("Path Finder", bimpy.Vec2(150, 25)):
menu_tab = 2
if menu_tab == 1:
drawMainTab()
elif menu_tab == 2:
drawPathFinder()
# end the window
bimpy.end()
# setup the 2nd window
bimpy.set_next_window_pos(bimpy.Vec2(800, 20), bimpy.Condition.Once)
bimpy.set_next_window_size(bimpy.Vec2(winWidth - 820, winHeight-300), bimpy.Condition.Once)
bimpy.begin("Control Board", flags=bimpy.NoMove | bimpy.NoCollapse)
bimpy.columns(3, None, False)
bimpy.next_column()
if bimpy.button("Forward", bimpy.Vec2(140,80)):
move_forward()
bimpy.next_column()
bimpy.next_column()
if bimpy.button("Left", bimpy.Vec2(140,80)):
turn_left()
bimpy.next_column()
bimpy.next_column()
if bimpy.button("Right", bimpy.Vec2(140,80)):
turn_right()
bimpy.next_column()
bimpy.next_column()
if bimpy.button("Back", bimpy.Vec2(140,80)):
move_backwards()
bimpy.text('Spotify Playlist URI')
bimpy.same_line()
bimpy.input_text('', playlist_url, 255)
if not playlist_downloading and not imgs_downloading:
if bimpy.button("Fetch##Fetcher"):
# data = fetch_playlist() # this blocks so let's use a thread
thread = threading.Thread(target=fetch_playlist,
args=(playlist_url.value.strip(), ))
thread.start()
elif imgs_downloading:
bimpy.button("Fetching album covers...")
elif playlist_downloading:
bimpy.button("Fetching...")
if data:
bimpy.columns(2, "tracks")
bimpy.text("Track")
bimpy.next_column()
bimpy.text("Album")
bimpy.separator()
bimpy.next_column()
count = 1
for item in data['items']:
# bimpy.text(f"{count}")
# bimpy.next_column()
bimpy.text(item["track"]["name"])
bimpy.next_column()
bimpy.text(item["track"]["album"]["name"])
bimpy.next_column()
count += 1
bimpy.columns(1)