def render_widget(self):
for key, pos in self.bitmap.items():
if bimpy.checkbox(key, self.bitmap_values[key]):
if self.bitmap_values[key]:
self._set_bit(pos)
else:
self._clear_bit(pos)
def render(self, is_lock):
bimpy.indent(10)
bimpy.text('- Adam')
bimpy.same_line()
bimpy_tools.help_marker('torch.optim.Adam')
flags = bimpy.InputTextFlags.EnterReturnsTrue
if is_lock:
flags |= bimpy.InputTextFlags.ReadOnly
bimpy.push_item_width(140)
if bimpy.input_float('lr##adam_optimizer', self._lr, flags=flags):
self._lr.value = max(0.0, self._lr.value)
if bimpy.input_float2('momentum##adam_optimizer',
self._betas_first,
self._betas_second,
flags=flags):
self._betas_first.value = max(0.0, self._betas_first.value)
self._betas_second.value = max(0.0, self._betas_second.value)
if bimpy.input_float('eps##adam_optimizer',
self._eps,
decimal_precision=8,
flags=flags):
self._dampening.value = max(0.0, self._eps.value)
if bimpy.input_float('weight_decay##adam_optimizer',
self._weight_decay,
flags=flags):
self._weight_decay.value = max(0.0, self._weight_decay.value)
bimpy.checkbox('amsgrad##adam_optimizer', self._amsgrad)
bimpy.pop_item_width()
bimpy.unindent(10)
def render(self, is_lock):
bimpy.indent(10)
bimpy.text('- SGD')
bimpy.same_line()
bimpy_tools.help_marker('torch.optim.SGD')
flags = bimpy.InputTextFlags.EnterReturnsTrue
if is_lock:
flags |= bimpy.InputTextFlags.ReadOnly
bimpy.push_item_width(120)
if bimpy.input_float('lr##sgd_optimizer', self._lr, flags=flags):
self._lr.value = max(0.0, self._lr.value)
if bimpy.input_float('momentum##sgd_optimizer',
self._momentum,
flags=flags):
self._momentum.value = max(0.0, self._momentum.value)
if bimpy.input_float('dampening##sgd_optimizer',
self._dampening,
flags=flags):
self._dampening.value = max(0.0, self._dampening.value)
if bimpy.input_float('weight_decay##sgd_optimizer',
self._weight_decay,
flags=flags):
self._weight_decay.value = max(0.0, self._weight_decay.value)
if bimpy.checkbox('nesterov##sgd_optimizer', self._nesterov):
self._hint_nesterov = False
if self._nesterov.value:
if self._momentum.value == 0 or self._dampening.value > 0:
self._nesterov.value = False
self._hint_nesterov = True
bimpy.same_line()
bimpy_tools.help_marker(
'Nesterov momentum requires a momentum and zero dampening',
self._hint_nesterov)
bimpy.pop_item_width()
bimpy.unindent(10)
def render_widget(self):
if bimpy.checkbox(self.bimpy_name, self._bimpy_value):
self._value = self._bimpy_value.value
save_data(args.save_path)
if bimpy.menu_item('Load', ''):
load_data(args.save_path)
bimpy.end_main_menu_bar(
) # According to bimpy docs, this is a special case where end is called inside the if.
if bimpy.begin("Video", opened=tab_video_view):
is_placing_rect = True
s = bimpy.text(args.base_path_video)
b_i = bimpy.Int(display_frame)
bimpy.slider_int("Frame", b_i, 0, video_len, "%d")
if bimpy.button(" < Prev (z) ") or bimpy.is_key_released(ord('Z')):
b_i.value -= 1
bimpy.same_line()
bimpy.checkbox("Autoplay (c to stop)", is_autoplay)
if bimpy.is_key_down(ord('C')):
is_autoplay.value = False
bimpy.same_line()
if bimpy.button(" Next > (x) ") or bimpy.is_key_released(
ord('X')) or is_autoplay.value:
b_i.value += 1
if display_frame != b_i.value:
simulate_to_frame(b_i.value)
bimpy.combo('Label used for annotation', current_label_idx, all_labels)
img_display_w = bimpy.get_window_content_region_width()
img_display_h = img_display_w * video_h / video_w
def sample(cfg, logger):
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=3)
model.eval()
logger.info("Trainable parameters generator:")
count_parameters(model.generator)
model_dict = {
'generator_s': model.generator,
'mapping_fl_s': model.mapping,
'dlatent_avg': model.dlatent_avg,
}
checkpointer = Checkpointer(cfg,
model_dict,
logger=logger,
save=True)
checkpointer.load()
ctx = bimpy.Context()
remove = bimpy.Bool(False)
layers = bimpy.Int(8)
ctx.init(1800, 1600, "Styles")
rnd = np.random.RandomState(5)
latents = rnd.randn(1, cfg.MODEL.LATENT_SPACE_SIZE)
sample = torch.tensor(latents).float().cuda()
def update_image(sample):
with torch.no_grad():
torch.manual_seed(0)
model.eval()
x_rec = model.generate(layers.value, remove.value, z=sample)
#model.generator.set(l.value, c.value)
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)
with torch.no_grad():
save_image(model.generate(8, True, z=sample) * 0.5 + 0.5, 'sample.png')
im = bimpy.Image(update_image(sample))
while(not ctx.should_close()):
with ctx:
bimpy.set_window_font_scale(2.0)
if bimpy.checkbox('REMOVE BLOB', remove):
im = bimpy.Image(update_image(sample))
if bimpy.button('NEXT'):
latents = rnd.randn(1, cfg.MODEL.LATENT_SPACE_SIZE)
sample = torch.tensor(latents).float().cuda()
im = bimpy.Image(update_image(sample))
if bimpy.slider_int("Layers", layers, 0, 8):
im = bimpy.Image(update_image(sample))
bimpy.image(im, bimpy.Vec2(1024, 1024))
bimpy.color_edit("Hovered Header", HeaderHoverColor)
bimpy.color_edit("Text", TextColor)
bimpy.color_edit("Slider Thumbs", SliderColor)
bimpy.color_edit("Activated Thumbs", SliderActiveColor)
bimpy.color_edit("CheckBox Marks", CheckMarkColor)
bimpy.push_style_color(bimpy.Colors.TitleBgActive, BarColor)
bimpy.push_style_color(bimpy.Colors.Header, HeaderColor)
bimpy.push_style_color(bimpy.Colors.HeaderHovered,
HeaderHoverColor)
bimpy.push_style_color(bimpy.Colors.Text, TextColor)
bimpy.push_style_color(bimpy.Colors.SliderGrab, SliderColor)
bimpy.push_style_color(bimpy.Colors.SliderGrabActive,
SliderActiveColor)
bimpy.push_style_color(bimpy.Colors.CheckMark, CheckMarkColor)
if bimpy.collapsing_header("Options"):
if bimpy.checkbox("Option 1", OnlyOneHeader):
if OnlyOneHeader.value:
print("option 1 toggled")
if bimpy.collapsing_header("Delete"):
bimpy.text(
"This button will delete all traces of this software.")
bimpy.text("")
if bimpy.button("Destruct"):
print("destructed")
bimpy.end()
special_keys = [0x06]
for i in range(1, 256):
if win32api.GetAsyncKeyState(i):
if i in special_keys:
if Toggle == True:
mouseclick.click(Button.left, 1)
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 show_demo_window():
bp.begin_root(menu=True)
# Menu Bar
if bp.begin_menu_bar():
if bp.begin_menu("Menu"):
bp.end_menu()
if bp.begin_menu("Examples"):
bp.end_menu()
if bp.begin_menu("Tools"):
bp.end_menu()
bp.end_menu_bar()
global clicked
if bp.button("Button"):
clicked += 1
if clicked & 1:
bp.same_line()
bp.text("Thanks for clicking me!")
bp.checkbox("checkbox", check)
bp.radio_button("radio a", e, 0)
bp.same_line()
bp.radio_button("radio b", e, 1)
bp.same_line()
bp.radio_button("radio c", e, 2)
# Color buttons, demonstrate using PushID() to add unique identifier in the ID stack, and changing style.
for i in range(7):
if i > 0:
bp.same_line()
bp.push_id_int(i)
bp.push_style_color(bp.Colors.Button, bp.Vec4(i / 7.0, 0.6, 0.6, 1.0))
bp.push_style_color(bp.Colors.ButtonHovered,
bp.Vec4(i / 7.0, 0.7, 0.7, 1.0))
bp.push_style_color(bp.Colors.ButtonActive,
bp.Vec4(i / 7.0, 0.8, 0.8, 1.0))
bp.button("Click")
bp.pop_style_color(3)
bp.pop_id()
# Use AlignTextToFramePadding() to align text baseline to the baseline of framed elements (otherwise a Text+SameLine+Button sequence will have the text a little too high by default)
bp.align_text_to_frame_padding()
bp.text("Hold to repeat:")
bp.same_line()
# Arrow buttons with Repeater
spacing = bp.get_style().item_inner_spacing.x
bp.push_button_repeat(True)
global counter
if bp.arrow_button("##left", bp.Direction.Left):
counter -= 1
bp.same_line(0.0, spacing)
if bp.arrow_button("##right", bp.Direction.Right):
counter += 1
bp.pop_button_repeat()
bp.same_line()
bp.text("%d" % counter)
bp.text("Hover over me")
if bp.is_item_hovered():
bp.set_tooltip("I am a tooltip")
bp.same_line()
bp.text("- or me")
if bp.is_item_hovered():
bp.begin_tooltip()
bp.text("I am a fancy tooltip")
arr = [0.6, 0.1, 1.0, 0.5, 0.92, 0.1, 0.2]
bp.plot_lines("Curve", arr)
bp.end_tooltip()
bp.separator()
bp.label_text("label", "Value")
# Using the _simplified_ one-liner Combo() api here
# See "Combo" section for examples of how to use the more complete BeginCombo()/EndCombo() api.
items = [
"AAAA", "BBBB", "CCCC", "DDDD", "EEEE", "FFFF", "GGGG", "HHHH", "IIII",
"JJJJ", "KKKK", "LLLLLLL", "MMMM", "OOOOOOO"
]
bp.combo("combo", item_current, items)
bp.same_line()
help_marker(
"Refer to the \"Combo\" section below for an explanation of the full BeginCombo/EndCombo API, and demonstration of various flags.\n"
)
# To wire InputText() with std::string or any other custom string type,
# see the "Text Input > Resize Callback" section of this demo, and the misc/cpp/imgui_stdlib.h file.
bp.input_text("input text", str0, 128)
bp.same_line()
help_marker(
"USER:\nHold SHIFT or use mouse to select text.\n"
"CTRL+Left/Right to word jump.\n"
"CTRL+A or double-click to select all.\n"
"CTRL+X,CTRL+C,CTRL+V clipboard.\n"
"CTRL+Z,CTRL+Y undo/redo.\n"
"ESCAPE to revert.\n\nPROGRAMMER:\nYou can use the ImGuiInputTextFlags_CallbackResize facility if you need to wire InputText() to a dynamic string type. See misc/cpp/imgui_stdlib.h for an example (this is not demonstrated in imgui_demo.cpp)."
)
bp.end()
bimpy.input_text("Nom du fichier", nom, 15)
if bimpy.button("Visualisation"):
while (i < 1000000):
num = i / 1000000
bimpy.progress_bar(num)
bimpy.end()
ctx.render()
i = i + 1
if bimpy.button("Debut du Scan"):
a = 2
if bimpy.button("Visualisation du resultat"):
a = 3
bimpy.text("Choix des formats de sortie")
bimpy.checkbox("VTK", vtk)
bimpy.checkbox("STL", stl)
bimpy.checkbox("OBJ", obj)
bimpy.checkbox("PLY", ply)
bimpy.checkbox("PCD", pcd)
if bimpy.button("Conversion du fichier"):
a = 4
if bimpy.button("Fermer l'application"):
sys.exit(0)
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
def view_legacy_controller(pl):
bimpy.begin("Single File")
bimpy.text("No file" if not ViewConfig.fileName else ViewConfig.fileName)
if bimpy.button("Open File"):
fn = askopenfilename(filetypes=(("WAV files", "*.wav"), ("AIFF files",
"*.aif")))
if fn:
_lc.OpenFile(fn)
ViewConfig.fileName = fn
bimpy.text("No configuration file"
if not ViewConfig.configFileName else ViewConfig.configFileName)
if bimpy.button("Open Configuration"):
fn = askopenfilename(filetypes=(("JSON files", "*.ps.json"),
("All files", "*.*")))
if bimpy.button("Save Configuration"):
pass
bimpy.separator()
vcfg = bimpy.Bool(ViewConfig.showParameters)
if bimpy.checkbox("Edit Configuration", vcfg):
ViewConfig.showParameters = vcfg.value
bimpy.separator()
if (not _lc.IsPlaying()):
if bimpy.button("Play"):
_lc.Play()
bimpy.same_line()
if (_lc.IsPlaying()):
if bimpy.button("Stop"):
_lc.Stop()
bimpy.same_line()
f = bimpy.Float(_lc.GetSeek())
if bimpy.slider_float("##seek", f, 0, 1):
if (not _lc.IsPlaying()):
_lc.Seek(f.value)
#bimpy.text(str(_lc.GetSeek()));
bimpy.separator()
bimpy.slider_float("Start##region_start", bimpy.Float(0), 0, 1)
bimpy.slider_float("End##region_end", bimpy.Float(1), 0, 1)
if bimpy.button("Render file (default)"):
path = os.path.abspath(__file__)
output_file = os.path.dirname(path) + "/render.wav"
_lc.RenderToFile(output_file)
# bimpy.text("render: "+str(_lc.GetRenderPercent()))
# if bimpy.button("Cancel"):
# _lc.CancelRender()
# if bimpy.button("Render async (default)"):
# path = os.path.abspath(__file__)
# output_file = os.path.dirname(path)+"/render_async.wav"
# _lc.RenderToFileAsync(output_file)
# bimpy.text(str(_lc.GetRenderPercent()))
# bimpy.button("Cancel Render")
#
# if bimpy.button("Render Task"):
# if not ViewConfig.renderTask:
# path = os.path.abspath(__file__)
# input_file = os.path.dirname(path)+"/test_file.wav"
# output_file = os.path.dirname(path)+"/render-task.wav"
#
# ViewConfig.renderTask = ps.LegacyRenderTask(input_file, output_file, _lc.RenderRange(), _lc.Parameters());
# ViewConfig.renderTask.StartRender()
# if ViewConfig.renderTask:
# bimpy.text(str(ViewConfig.renderTask.GetRenderPercent()))
# bimpy.button("Cancel Render")
# bimpy.end()
#
if ViewConfig.showParameters:
#cfg = _lc.Parameters()
if edit_config(_parameters) == True:
_lc.SetParameters(_parameters)
print("edit")
pass
def render(self, ctx, windows_info):
# calculate autoly
self.pos = bimpy.Vec2(
windows_info['file_brewswer_ui']['x'] +
windows_info['file_brewswer_ui']['w'] + conf.margin, conf.margin)
self.size = bimpy.Vec2(
ctx.width() - self.pos.x - conf.margin,
ctx.height() - 3 * conf.margin - conf.meta_info_height)
bimpy.set_next_window_pos(self.pos, bimpy.Condition.Always)
bimpy.set_next_window_size(self.size, bimpy.Condition.Always)
bimpy.begin(
LANG.image_shower_ui_title, bimpy.Bool(True),
bimpy.WindowFlags.NoCollapse | bimpy.WindowFlags.NoMove
| bimpy.WindowFlags.NoResize
| bimpy.WindowFlags.HorizontalScrollbar)
###########UI###########
# modal part
if self.im is not None:
bimpy.set_cursor_pos(bimpy.Vec2(0.0, conf.margin * 3))
bimpy.image(self.im)
# if image is loaded
if self.labels is not None:
for i, label in enumerate(self.labels):
color = self.COLORS[self.classes.index(label)]
# print((self.bbox[i][0], self.bbox[i][1] - 10))
# show on the left bottom of the picture
bimpy.set_cursor_pos(
bimpy.Vec2(self.bbox[i][0] + 10, self.bbox[i][3] + 10))
# set style
bimpy.push_id_int(i)
if conf.show_yolo_confience:
bimpy.button(
label + ' ' +
str(format(self.confidence[i] * 100, '.2f')) + '%')
else:
bimpy.button(label)
if bimpy.is_item_hovered(i):
s = "{} ({})\n{}"
label = label[0].upper() + label[1:]
s = s.format(
label,
str(format(self.confidence[i] * 100, '.2f')) + '%',
LANG.click_to_view_more)
bimpy.set_tooltip(s)
if bimpy.is_item_active():
self.select_label = label
bimpy.pop_id()
# bimpy.set_cursor_pos(bimpy.Vec2(conf.margin, self.size.y - conf.margin * 2))
bimpy.set_cursor_pos(bimpy.Vec2(conf.margin, conf.margin * 1.5))
if bimpy.button(LANG.smart_analyse) == True:
self.object_detection()
bimpy.same_line()
bimpy.checkbox(LANG.auto, self.auto)
### Resize ###
bimpy.same_line()
bimpy.push_item_width(150)
bimpy.drag_float(LANG.drag, self.scale, 1.0, 10, 1000)
bimpy.pop_item_width()
if abs(self.last_scale - self.scale.value) > 4.:
xx = self.size.x * self.scale.value / 100.
yy = (self.size.y - 45 - 40) * self.scale.value / 100.
im = self.i_s.resize(self.raw_im, xx, yy)
self.now_im = im
self.set_im(im)
# set to save computation
self.last_scale = self.scale.value
# if selected obj
if self.select_label != '':
# print(self.select_label)
windows_info['retrival_ui'][
'self'].select_label = self.select_label
bimpy.open_popup('{}: {}'.format(LANG.retrieve, self.select_label))
# reset
self.select_label = ''
windows_info['retrival_ui']['self'].retrival()
########################
t = {
'x': bimpy.get_window_pos().x,
'y': bimpy.get_window_pos().y,
'w': bimpy.get_window_size().x,
'h': bimpy.get_window_size().y,
'self': self,
}
bimpy.end()
return t
