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Talkinghead performance improvements and refactoring (#207)

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* talkinghead: fix and improve THA3 manual poser

* server.py: no, don't yet use fp16 for talkinghead

* talkinghead: remove wxPython dependency from live mode

* comment out unused functions

* add TODO list

* coding style

* remove unused import

* add TODO marker

* message wordings

* fix typos in variable names

* talkinghead updates

* talkinghead updates

* Empty commit

* presentation order, sectioning

* fix the inMotion flag update

* mark a TODO

* refactor

* remove done TODO items

* mark a TODO

* comment wording

* pause animation while loading a new image

* parser doesn't belong here, not a command-line app anymore

* message wording

* use finally

* remove superfluous "global" declarations

* lots of cleanup

* remove silly sys.path entry

* improve docstring

* oops

* app.py now only serves the live mode for the plugin

* talkinghead live mode: remove ifacialmocap stuff (unused)

* improve comment

* list walking is so 1990s

* use double quotes consistently

* remove now-unused ifacialmocap-related files from the repo

* remove done TODO item

* improve docstring

* update comment

* remove now-unused function

* update comment

* improve docstring

* add TODO marker

* oops, typo

* add --talkinghead-model command-line option to server.py

Default is 'auto': float16 on GPU, float32 on CPU.

* talkinghead: auto-install THA3 models if needed

* remove tha3/models from git repo (have autodownload now)

* Add hf-hub as explicit dependency

* Add THA models to gitignore

---------

Co-authored-by: Cohee <18619528+Cohee1207@users.noreply.github.com>
This commit is contained in:
Juha Jeronen
2023-12-21 23:48:25 +02:00
committed by GitHub
parent 47a5489142
commit 4c6f843ff9
22 changed files with 525 additions and 2363 deletions
Download Patch File Download Diff File Expand all files Collapse all files

1
.gitignore vendored
View File

@@ -139,3 +139,4 @@ model.pt
api_key.txt
.vscode
stt_test.wav
talkinghead/tha3/models

1
requirements-rocm.txt
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@@ -24,3 +24,4 @@ vosk
sounddevice
openai-whisper
selenium
huggingface-hub

1
requirements-silicon.txt
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@@ -23,3 +23,4 @@ vosk
sounddevice
openai-whisper
selenium
huggingface-hub

2
requirements.txt
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@@ -23,6 +23,6 @@ vosk
sounddevice
openai-whisper
selenium
huggingface-hub
fastapi
wxpython; sys_platform == 'win32' or sys_platform == 'darwin'

43
server.py
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@@ -86,6 +86,15 @@ parser.add_argument(
"--secure", action="store_true", help="Enforces the use of an API key"
)
parser.add_argument("--talkinghead-gpu", action="store_true", help="Run the talkinghead animation on the GPU (CPU is default)")
parser.add_argument(
"--talkinghead-model", type=str, help="The THA3 model to use. 'float' models are fp32, 'half' are fp16. 'auto' (default) picks fp16 for GPU and fp32 for CPU.",
required=False, default="auto",
choices=["auto", "standard_float", "separable_float", "standard_half", "separable_half"],
)
parser.add_argument(
"--talkinghead-models", type=str, help="If THA3 models are not yet installed, use the given HuggingFace repository to install them.",
default="OktayAlpk/talking-head-anime-3"
)
parser.add_argument("--coqui-gpu", action="store_true", help="Run the voice models on the GPU (CPU is default)")
parser.add_argument("--coqui-models", help="Install given Coqui-api TTS model at launch (comma separated list, last one will be loaded at start)")
@@ -180,21 +189,41 @@ if not torch.cuda.is_available() and not args.cpu:
print(f"{Fore.GREEN}{Style.BRIGHT}Using torch device: {device_string}{Style.RESET_ALL}")
if "talkinghead" in modules:
# Install the THA3 models if needed
talkinghead_models_dir = os.path.join(os.getcwd(), "talkinghead", "tha3", "models")
if not os.path.exists(talkinghead_models_dir):
# API:
# https://huggingface.co/docs/huggingface_hub/en/guides/download
try:
from huggingface_hub import snapshot_download
except ImportError:
raise ImportError(
"You need to install huggingface_hub to install talkinghead models automatically. "
"See https://pypi.org/project/huggingface-hub/ for installation."
)
os.makedirs(talkinghead_models_dir, exist_ok=True)
print(f"THA3 models not yet installed. Installing from {args.talkinghead_models} into talkinghead/tha3/models.")
# TODO: I'd prefer to install with symlinks, but how about Windows users?
snapshot_download(repo_id=args.talkinghead_models, local_dir=talkinghead_models_dir, local_dir_use_symlinks=False)
import sys
import threading
mode = "cuda" if args.talkinghead_gpu else "cpu"
print("Initializing talkinghead pipeline in " + mode + " mode....")
model = args.talkinghead_model
if model == "auto": # default
# FP16 boosts the rendering performance by ~1.5x, but is only supported on GPU.
model = "separable_half" if args.talkinghead_gpu else "separable_float"
print(f"Initializing talkinghead pipeline in {mode} mode with model {model}....")
talkinghead_path = os.path.abspath(os.path.join(os.getcwd(), "talkinghead"))
sys.path.append(talkinghead_path) # Add the path to the 'tha3' module to the sys.path list
try:
import talkinghead.tha3.app.app as talkinghead
from talkinghead import *
def launch_talkinghead_gui():
talkinghead.launch_gui(mode, "separable_float")
#choices=['standard_float', 'separable_float', 'standard_half', 'separable_half'],
#choices='The device to use for PyTorch ("cuda" for GPU, "cpu" for CPU).'
talkinghead_thread = threading.Thread(target=launch_talkinghead_gui)
def launch_talkinghead():
# mode: choices='The device to use for PyTorch ("cuda" for GPU, "cpu" for CPU).'
# model: choices=['standard_float', 'separable_float', 'standard_half', 'separable_half'],
talkinghead.launch(mode, model)
talkinghead_thread = threading.Thread(target=launch_talkinghead)
talkinghead_thread.daemon = True # Set the thread as a daemon thread
talkinghead_thread.start()

20
talkinghead/start_standalone_app.sh
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@@ -1,20 +0,0 @@
#!/bin/bash
#
# Launch THA3 in standalone app mode.
#
# This standalone app mode does not interact with SillyTavern.
#
# The usual way to run this fork of THA3 is as a SillyTavern-extras plugin.
# The standalone app mode comes from the original THA3 code, and is included
# for testing and debugging.
#
# If you want to manually pose a character (to generate static expression images),
# use `start_manual_poser.sh` instead.
#
# This must run in the "extras" conda venv!
# Do this first:
# conda activate extras
#
# The `--char=...` flag can be used to specify which image to load under "tha3/images".
#
python -m tha3.app.app --char=example.png $@

1200
talkinghead/tha3/app/app.py
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File diff suppressed because it is too large Load Diff

171
talkinghead/tha3/app/manual_poser.py
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@@ -59,7 +59,7 @@ import os
import pathlib
import sys
import time
from typing import Dict, List, Tuple
from typing import List
import PIL.Image
@@ -71,8 +71,8 @@ import wx
from tha3.poser.modes.load_poser import load_poser
from tha3.poser.poser import Poser, PoseParameterCategory, PoseParameterGroup
from tha3.util import rgba_to_numpy_image, grid_change_to_numpy_image, \
rgb_to_numpy_image, resize_PIL_image, extract_PIL_image_from_filelike, extract_pytorch_image_from_PIL_image
from tha3.util import resize_PIL_image, extract_PIL_image_from_filelike, extract_pytorch_image_from_PIL_image
from tha3.app.util import load_emotion_presets, posedict_to_pose, pose_to_posedict, torch_image_to_numpy, FpsStatistics
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
@@ -95,97 +95,6 @@ logger = logging.getLogger(__name__)
# input_exts_and_descs_str = "|".join(format_fileformat_list(PIL_supported_input_formats)) # filter-spec accepted by `wx.FileDialog`
# output_exts_and_descs_str = "|".join(format_fileformat_list(PIL_supported_output_formats))
# The keys for a pose in the emotion JSON files.
#
# TODO: "eye_unimpressed" is arity-2, but has only one entry in the JSON. The current implementation smashes both into one,
# letting the second one (right slider) win. Maybe the two values should be saved separately, but we have to avoid
# breaking the live mode served by `app.py`.
posedict_keys = ["eyebrow_troubled_left_index", "eyebrow_troubled_right_index",
"eyebrow_angry_left_index", "eyebrow_angry_right_index",
"eyebrow_lowered_left_index", "eyebrow_lowered_right_index",
"eyebrow_raised_left_index", "eyebrow_raised_right_index",
"eyebrow_happy_left_index", "eyebrow_happy_right_index",
"eyebrow_serious_left_index", "eyebrow_serious_right_index",
"eye_wink_left_index", "eye_wink_right_index",
"eye_happy_wink_left_index", "eye_happy_wink_right_index",
"eye_surprised_left_index", "eye_surprised_right_index",
"eye_relaxed_left_index", "eye_relaxed_right_index",
"eye_unimpressed", "eye_unimpressed",
"eye_raised_lower_eyelid_left_index", "eye_raised_lower_eyelid_right_index",
"iris_small_left_index", "iris_small_right_index",
"mouth_aaa_index",
"mouth_iii_index",
"mouth_uuu_index",
"mouth_eee_index",
"mouth_ooo_index",
"mouth_delta",
"mouth_lowered_corner_left_index", "mouth_lowered_corner_right_index",
"mouth_raised_corner_left_index", "mouth_raised_corner_right_index",
"mouth_smirk",
"iris_rotation_x_index", "iris_rotation_y_index",
"head_x_index", "head_y_index",
"neck_z_index",
"body_y_index", "body_z_index",
"breathing_index"]
assert len(posedict_keys) == 45
def load_emotion_presets() -> Tuple[Dict[str, Dict[str, float]], List[str]]:
"""Load emotion presets from disk.
These are JSON files in "talkinghead/emotions".
Returns the tuple `(emotions, emotion_names)`, where::
emotions = {emotion0_name: posedict0, ...}
emotion_names = [emotion0_name, emotion1_name, ...]
The dict contains the actual pose data. The list is a sorted list of emotion names
that can be used to map a linear index (e.g. the choice index in a GUI dropdown)
to the corresponding key of `emotions`.
The directory "talkinghead/emotions" must also contain a "_defaults.json" file,
containing factory defaults (as a fallback) for the 28 standard emotions
(as recognized by distilbert), as well as a hidden "zero" preset that represents
a neutral pose. (This is separate from the "neutral" emotion, which is allowed
to be "non-zero".)
"""
emotion_names = []
for root, dirs, files in os.walk("emotions", topdown=True):
for filename in files:
if filename == "_defaults.json": # skip the repository containing the default fallbacks
continue
if filename.lower().endswith(".json"):
emotion_names.append(filename[:-5]) # drop the ".json"
emotion_names.sort() # the 28 actual emotions
# TODO: Note that currently, we build the list of emotion names from JSON filenames,
# and then check whether each JSON implements the emotion matching its filename.
# On second thought, I'm not sure whether that makes much sense. Maybe rethink the design.
# - We *do* want custom JSON files to show up in the list, if those are placed in "tha3/emotions". So the list of emotions shouldn't be hardcoded.
# - *Having* a fallback repository with factory defaults (and a hidden "zero" preset) is useful.
# But we are currently missing a way to reset an emotion to its factory default.
def load_emotion_with_fallback(emotion_name: str) -> Dict[str, float]:
try:
with open(os.path.join("emotions", f"{emotion_name}.json"), "r") as json_file:
emotions_from_json = json.load(json_file) # A single json file may contain presets for multiple emotions.
posedict = emotions_from_json[emotion_name]
except (FileNotFoundError, KeyError): # If no separate json exists for the specified emotion, load the default (all 28 emotions have a default).
with open(os.path.join("emotions", "_defaults.json"), "r") as json_file:
emotions_from_json = json.load(json_file)
posedict = emotions_from_json[emotion_name]
# If still not found, it's an error, so fail-fast: let the app exit with an informative exception message.
return posedict
# Dict keeps its keys in insertion order, so define some special states before inserting the actual emotions.
emotions = {"[custom]": {}, # custom = the user has changed at least one value manually after last loading a preset
"[reset]": load_emotion_with_fallback("zero")} # reset = a preset with all sliders in their default positions. Found in "_defaults.json".
for emotion_name in emotion_names:
emotions[emotion_name] = load_emotion_with_fallback(emotion_name)
emotion_names = list(emotions.keys())
return emotions, emotion_names
class SimpleParamGroupsControlPanel(wx.Panel):
"""A simple control panel for groups of arity-1 continuous parameters (i.e. float value, and no separate left/right controls).
@@ -404,43 +313,6 @@ class MorphCategoryControlPanel(wx.Panel):
self.update_ui()
def convert_output_image_from_torch_to_numpy(output_image):
if output_image.shape[2] == 2:
h, w, c = output_image.shape
numpy_image = torch.transpose(output_image.reshape(h * w, c), 0, 1).reshape(c, h, w)
elif output_image.shape[0] == 4:
numpy_image = rgba_to_numpy_image(output_image)
elif output_image.shape[0] == 3:
numpy_image = rgb_to_numpy_image(output_image)
elif output_image.shape[0] == 1:
c, h, w = output_image.shape
alpha_image = torch.cat([output_image.repeat(3, 1, 1) * 2.0 - 1.0, torch.ones(1, h, w)], dim=0)
numpy_image = rgba_to_numpy_image(alpha_image)
elif output_image.shape[0] == 2:
numpy_image = grid_change_to_numpy_image(output_image, num_channels=4)
else:
raise RuntimeError(f"Unsupported # image channels: {output_image.shape[0]}")
numpy_image = numpy.uint8(numpy.rint(numpy_image * 255.0))
return numpy_image
class FpsStatistics:
def __init__(self):
self.count = 100
self.fps = []
def add_fps(self, fps: float) -> None:
self.fps.append(fps)
while len(self.fps) > self.count:
del self.fps[0]
def get_average_fps(self) -> float:
if len(self.fps) == 0:
return 0.0
else:
return sum(self.fps) / len(self.fps)
class MyFileDropTarget(wx.FileDropTarget):
def OnDropFiles(self, x, y, filenames):
if len(filenames) > 1:
@@ -571,7 +443,7 @@ class MainFrame(wx.Frame):
self.left_panel_sizer.Add(self.source_image_panel, 0, wx.FIXED_MINSIZE)
# Emotion picker.
self.emotions, self.emotion_names = load_emotion_presets()
self.emotions, self.emotion_names = load_emotion_presets("emotions")
# # Horizontal emotion picker layout; looks bad, text label vertical alignment is wrong.
# self.emotion_panel = wx.Panel(self.left_panel, style=wx.SIMPLE_BORDER, size=(-1, -1))
@@ -767,7 +639,7 @@ class MainFrame(wx.Frame):
if len(emotions_from_json) > 1:
logger.warning(f"File {json_file_name} contains multiple emotions, loading the first one '{first_emotion_name}'.")
posedict = emotions_from_json[first_emotion_name]
pose = self.posedict_to_pose(posedict)
pose = posedict_to_pose(posedict)
# Apply loaded emotion
self.set_current_pose(pose)
@@ -863,7 +735,7 @@ class MainFrame(wx.Frame):
emotion_name = self.emotion_choice.GetString(current_emotion_index)
logger.info(f"Loading emotion preset {emotion_name}")
posedict = self.emotions[emotion_name]
pose = self.posedict_to_pose(posedict)
pose = posedict_to_pose(posedict)
self.set_current_pose(pose)
current_pose = pose
else:
@@ -913,7 +785,7 @@ class MainFrame(wx.Frame):
with torch.no_grad():
output_image = self.poser.pose(self.torch_source_image, pose, output_index)[0].detach().cpu()
numpy_image = convert_output_image_from_torch_to_numpy(output_image)
numpy_image = torch_image_to_numpy(output_image)
self.last_output_numpy_image = numpy_image
wx_image = wx.ImageFromBuffer(
numpy_image.shape[0],
@@ -963,25 +835,6 @@ class MainFrame(wx.Frame):
wx.CallAfter(update_images_cont2)
wx.CallAfter(update_images_cont)
def current_pose_to_posedict(self) -> Dict[str, float]:
"""Convert the character's current pose into a posedict for saving into an emotion JSON."""
current_pose_values = self.get_current_pose()
current_pose_dict = dict(zip(posedict_keys, current_pose_values))
return current_pose_dict
def posedict_to_pose(self, posedict: Dict[str, float]) -> List[float]:
"""Convert a posedict (from an emotion JSON) into a list of morph values (in the order the models expect them)."""
# sanity check
unrecognized_keys = set(posedict.keys()) - set(posedict_keys)
if unrecognized_keys:
logger.warning(f"Ignoring unrecognized keys in posedict: {unrecognized_keys}")
# Missing keys are fine - keys for zero values can simply be omitted.
pose = [0.0 for i in range(self.poser.get_num_parameters())]
for idx, key in enumerate(posedict_keys):
pose[idx] = posedict.get(key, 0.0)
return pose
def on_save_image(self, event: wx.Event) -> None:
"""Ask the user for destination and save the output image.
@@ -1036,7 +889,7 @@ class MainFrame(wx.Frame):
current_emotion_old_index = self.emotion_choice.GetSelection()
current_emotion_name = self.emotion_choice.GetString(current_emotion_old_index)
self.emotions, self.emotion_names = load_emotion_presets()
self.emotions, self.emotion_names = load_emotion_presets("emotions")
self.emotion_choice.SetItems(self.emotion_names)
current_emotion_new_index = self.emotion_choice.FindString(current_emotion_name)
@@ -1076,13 +929,13 @@ class MainFrame(wx.Frame):
if emotion_name.startswith("[") and emotion_name.endswith("]"):
continue # skip "[custom]" and "[reset]"
try:
pose = self.posedict_to_pose(posedict)
pose = posedict_to_pose(posedict)
posetensor = torch.tensor(pose, device=self.device, dtype=self.dtype)
output_index = self.output_index_choice.GetSelection()
with torch.no_grad():
output_image = self.poser.pose(self.torch_source_image, posetensor, output_index)[0].detach().cpu()
numpy_image = convert_output_image_from_torch_to_numpy(output_image)
numpy_image = torch_image_to_numpy(output_image)
image_file_name = os.path.join(dir_name, f"{emotion_name}.png")
self.save_numpy_image(numpy_image, image_file_name)
@@ -1107,11 +960,11 @@ class MainFrame(wx.Frame):
os.makedirs(os.path.dirname(image_file_name), exist_ok=True)
pil_image.save(image_file_name)
data_dict = self.current_pose_to_posedict()
pose_dict = pose_to_posedict(self.get_current_pose())
json_file_path = os.path.splitext(image_file_name)[0] + ".json"
filename_without_extension = os.path.splitext(os.path.basename(image_file_name))[0]
data_dict_with_filename = {filename_without_extension: data_dict} # Create a new dict with the filename as the key
data_dict_with_filename = {filename_without_extension: pose_dict} # JSON structure: {emotion_name0: posedict0, ...}
try:
with open(json_file_path, "w") as file:

194
talkinghead/tha3/app/util.py Normal file
View File

@@ -0,0 +1,194 @@
"""App-level utilities."""
__all__ = ["posedict_keys", "posedict_key_to_index",
"load_emotion_presets",
"posedict_to_pose", "pose_to_posedict",
"torch_image_to_numpy", "to_talkinghead_image",
"FpsStatistics"]
import logging
import json
import os
from typing import Dict, List, Tuple
import PIL
import numpy
import torch
from tha3.util import rgba_to_numpy_image, rgb_to_numpy_image, grid_change_to_numpy_image
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# The keys for a pose in the emotion JSON files.
#
# TODO: "eye_unimpressed" is arity-2, but has only one entry in the JSON. The current implementation smashes both into one,
# letting the second one (right slider) win. Maybe the two values should be saved separately, but we have to avoid
# breaking the live mode served by `app.py`.
posedict_keys = ["eyebrow_troubled_left_index", "eyebrow_troubled_right_index",
"eyebrow_angry_left_index", "eyebrow_angry_right_index",
"eyebrow_lowered_left_index", "eyebrow_lowered_right_index",
"eyebrow_raised_left_index", "eyebrow_raised_right_index",
"eyebrow_happy_left_index", "eyebrow_happy_right_index",
"eyebrow_serious_left_index", "eyebrow_serious_right_index",
"eye_wink_left_index", "eye_wink_right_index",
"eye_happy_wink_left_index", "eye_happy_wink_right_index",
"eye_surprised_left_index", "eye_surprised_right_index",
"eye_relaxed_left_index", "eye_relaxed_right_index",
"eye_unimpressed", "eye_unimpressed",
"eye_raised_lower_eyelid_left_index", "eye_raised_lower_eyelid_right_index",
"iris_small_left_index", "iris_small_right_index",
"mouth_aaa_index",
"mouth_iii_index",
"mouth_uuu_index",
"mouth_eee_index",
"mouth_ooo_index",
"mouth_delta",
"mouth_lowered_corner_left_index", "mouth_lowered_corner_right_index",
"mouth_raised_corner_left_index", "mouth_raised_corner_right_index",
"mouth_smirk",
"iris_rotation_x_index", "iris_rotation_y_index",
"head_x_index", "head_y_index",
"neck_z_index",
"body_y_index", "body_z_index",
"breathing_index"]
assert len(posedict_keys) == 45
# posedict_keys gives us index->key; make an inverse mapping.
# Note this doesn't work for "eye_unimpressed", because it's not unique. (All the more reason to fix that.)
posedict_key_to_index = {key: idx for idx, key in enumerate(posedict_keys)}
def load_emotion_presets(directory: str) -> Tuple[Dict[str, Dict[str, float]], List[str]]:
"""Load emotion presets from disk.
Returns the tuple `(emotions, emotion_names)`, where::
emotions = {emotion0_name: posedict0, ...}
emotion_names = [emotion0_name, emotion1_name, ...]
The dict contains the actual pose data. The list is a sorted list of emotion names
that can be used to map a linear index (e.g. the choice index in a GUI dropdown)
to the corresponding key of `emotions`.
The directory "talkinghead/emotions" must also contain a "_defaults.json" file,
containing factory defaults (as a fallback) for the 28 standard emotions
(as recognized by distilbert), as well as a hidden "zero" preset that represents
a neutral pose. (This is separate from the "neutral" emotion, which is allowed
to be "non-zero".)
"""
emotion_names = []
for root, dirs, files in os.walk(directory, topdown=True):
for filename in files:
if filename == "_defaults.json": # skip the repository containing the default fallbacks
continue
if filename.lower().endswith(".json"):
emotion_names.append(filename[:-5]) # drop the ".json"
emotion_names.sort() # the 28 actual emotions
# TODO: Note that currently, we build the list of emotion names from JSON filenames,
# and then check whether each JSON implements the emotion matching its filename.
# On second thought, I'm not sure whether that makes much sense. Maybe rethink the design.
# - We *do* want custom JSON files to show up in the list, if those are placed in "tha3/emotions". So the list of emotions shouldn't be hardcoded.
# - *Having* a fallback repository with factory defaults (and a hidden "zero" preset) is useful.
# But we are currently missing a way to reset an emotion to its factory default.
def load_emotion_with_fallback(emotion_name: str) -> Dict[str, float]:
try:
with open(os.path.join(directory, f"{emotion_name}.json"), "r") as json_file:
emotions_from_json = json.load(json_file) # A single json file may contain presets for multiple emotions.
posedict = emotions_from_json[emotion_name]
except (FileNotFoundError, KeyError): # If no separate json exists for the specified emotion, load the default (all 28 emotions have a default).
with open(os.path.join(directory, "_defaults.json"), "r") as json_file:
emotions_from_json = json.load(json_file)
posedict = emotions_from_json[emotion_name]
# If still not found, it's an error, so fail-fast: let the app exit with an informative exception message.
return posedict
# Dict keeps its keys in insertion order, so define some special states before inserting the actual emotions.
emotions = {"[custom]": {}, # custom = the user has changed at least one value manually after last loading a preset
"[reset]": load_emotion_with_fallback("zero")} # reset = a preset with all sliders in their default positions. Found in "_defaults.json".
for emotion_name in emotion_names:
emotions[emotion_name] = load_emotion_with_fallback(emotion_name)
emotion_names = list(emotions.keys())
return emotions, emotion_names
def posedict_to_pose(posedict: Dict[str, float]) -> List[float]:
"""Convert a posedict (from an emotion JSON) into a list of morph values (in the order the models expect them)."""
# sanity check
unrecognized_keys = set(posedict.keys()) - set(posedict_keys)
if unrecognized_keys:
logger.warning(f"posedict_to_pose: ignoring unrecognized keys in posedict: {unrecognized_keys}")
# Missing keys are fine - keys for zero values can simply be omitted.
pose = [0.0 for i in range(len(posedict_keys))]
for idx, key in enumerate(posedict_keys):
pose[idx] = posedict.get(key, 0.0)
return pose
def pose_to_posedict(pose: List[float]) -> Dict[str, float]:
"""Convert `pose` into a posedict for saving into an emotion JSON."""
return dict(zip(posedict_keys, pose))
# --------------------------------------------------------------------------------
# TODO: move the image utils to the lower-level `tha3.util`?
def torch_image_to_numpy(image: torch.tensor) -> numpy.array:
if image.shape[2] == 2:
h, w, c = image.shape
numpy_image = torch.transpose(image.reshape(h * w, c), 0, 1).reshape(c, h, w)
elif image.shape[0] == 4:
numpy_image = rgba_to_numpy_image(image)
elif image.shape[0] == 3:
numpy_image = rgb_to_numpy_image(image)
elif image.shape[0] == 1:
c, h, w = image.shape
alpha_image = torch.cat([image.repeat(3, 1, 1) * 2.0 - 1.0, torch.ones(1, h, w)], dim=0)
numpy_image = rgba_to_numpy_image(alpha_image)
elif image.shape[0] == 2:
numpy_image = grid_change_to_numpy_image(image, num_channels=4)
else:
msg = f"torch_image_to_numpy: unsupported # image channels: {image.shape[0]}"
logger.error(msg)
raise RuntimeError(msg)
numpy_image = numpy.uint8(numpy.rint(numpy_image * 255.0))
return numpy_image
def to_talkinghead_image(image: PIL.Image, new_size: Tuple[int] = (512, 512)) -> PIL.Image:
"""Resize image to `new_size`, add alpha channel, and center.
With default `new_size`:
- Step 1: Resize (Lanczos) the image to maintain the aspect ratio with the larger dimension being 512 pixels.
- Step 2: Create a new image of size 512x512 with transparency.
- Step 3: Paste the resized image into the new image, centered.
"""
image.thumbnail(new_size, PIL.Image.LANCZOS)
new_image = PIL.Image.new("RGBA", new_size)
new_image.paste(image, ((new_size[0] - image.size[0]) // 2,
(new_size[1] - image.size[1]) // 2))
return new_image
# --------------------------------------------------------------------------------
class FpsStatistics:
"""A simple average FPS (frames per second) counter."""
def __init__(self):
self.count = 100
self.fps = []
def add_fps(self, fps: float) -> None:
self.fps.append(fps)
while len(self.fps) > self.count:
del self.fps[0]
def get_average_fps(self) -> float:
if len(self.fps) == 0:
return 0.0
else:
return sum(self.fps) / len(self.fps)

0
talkinghead/tha3/mocap/__init__.py
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239
talkinghead/tha3/mocap/ifacialmocap_constants.py
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@@ -1,239 +0,0 @@
EYE_LOOK_IN_LEFT = "eyeLookInLeft"
EYE_LOOK_OUT_LEFT = "eyeLookOutLeft"
EYE_LOOK_DOWN_LEFT = "eyeLookDownLeft"
EYE_LOOK_UP_LEFT = "eyeLookUpLeft"
EYE_BLINK_LEFT = "eyeBlinkLeft"
EYE_SQUINT_LEFT = "eyeSquintLeft"
EYE_WIDE_LEFT = "eyeWideLeft"
EYE_LOOK_IN_RIGHT = "eyeLookInRight"
EYE_LOOK_OUT_RIGHT = "eyeLookOutRight"
EYE_LOOK_DOWN_RIGHT = "eyeLookDownRight"
EYE_LOOK_UP_RIGHT = "eyeLookUpRight"
EYE_BLINK_RIGHT = "eyeBlinkRight"
EYE_SQUINT_RIGHT = "eyeSquintRight"
EYE_WIDE_RIGHT = "eyeWideRight"
BROW_DOWN_LEFT = "browDownLeft"
BROW_OUTER_UP_LEFT = "browOuterUpLeft"
BROW_DOWN_RIGHT = "browDownRight"
BROW_OUTER_UP_RIGHT = "browOuterUpRight"
BROW_INNER_UP = "browInnerUp"
NOSE_SNEER_LEFT = "noseSneerLeft"
NOSE_SNEER_RIGHT = "noseSneerRight"
CHEEK_SQUINT_LEFT = "cheekSquintLeft"
CHEEK_SQUINT_RIGHT = "cheekSquintRight"
CHEEK_PUFF = "cheekPuff"
MOUTH_LEFT = "mouthLeft"
MOUTH_DIMPLE_LEFT = "mouthDimpleLeft"
MOUTH_FROWN_LEFT = "mouthFrownLeft"
MOUTH_LOWER_DOWN_LEFT = "mouthLowerDownLeft"
MOUTH_PRESS_LEFT = "mouthPressLeft"
MOUTH_SMILE_LEFT = "mouthSmileLeft"
MOUTH_STRETCH_LEFT = "mouthStretchLeft"
MOUTH_UPPER_UP_LEFT = "mouthUpperUpLeft"
MOUTH_RIGHT = "mouthRight"
MOUTH_DIMPLE_RIGHT = "mouthDimpleRight"
MOUTH_FROWN_RIGHT = "mouthFrownRight"
MOUTH_LOWER_DOWN_RIGHT = "mouthLowerDownRight"
MOUTH_PRESS_RIGHT = "mouthPressRight"
MOUTH_SMILE_RIGHT = "mouthSmileRight"
MOUTH_STRETCH_RIGHT = "mouthStretchRight"
MOUTH_UPPER_UP_RIGHT = "mouthUpperUpRight"
MOUTH_CLOSE = "mouthClose"
MOUTH_FUNNEL = "mouthFunnel"
MOUTH_PUCKER = "mouthPucker"
MOUTH_ROLL_LOWER = "mouthRollLower"
MOUTH_ROLL_UPPER = "mouthRollUpper"
MOUTH_SHRUG_LOWER = "mouthShrugLower"
MOUTH_SHRUG_UPPER = "mouthShrugUpper"
JAW_LEFT = "jawLeft"
JAW_RIGHT = "jawRight"
JAW_FORWARD = "jawForward"
JAW_OPEN = "jawOpen"
TONGUE_OUT = "tongueOut"
BLENDSHAPE_NAMES = [
EYE_LOOK_IN_LEFT, # 0
EYE_LOOK_OUT_LEFT, # 1
EYE_LOOK_DOWN_LEFT, # 2
EYE_LOOK_UP_LEFT, # 3
EYE_BLINK_LEFT, # 4
EYE_SQUINT_LEFT, # 5
EYE_WIDE_LEFT, # 6
EYE_LOOK_IN_RIGHT, # 7
EYE_LOOK_OUT_RIGHT, # 8
EYE_LOOK_DOWN_RIGHT, # 9
EYE_LOOK_UP_RIGHT, # 10
EYE_BLINK_RIGHT, # 11
EYE_SQUINT_RIGHT, # 12
EYE_WIDE_RIGHT, # 13
BROW_DOWN_LEFT, # 14
BROW_OUTER_UP_LEFT, # 15
BROW_DOWN_RIGHT, # 16
BROW_OUTER_UP_RIGHT, # 17
BROW_INNER_UP, # 18
NOSE_SNEER_LEFT, # 19
NOSE_SNEER_RIGHT, # 20
CHEEK_SQUINT_LEFT, # 21
CHEEK_SQUINT_RIGHT, # 22
CHEEK_PUFF, # 23
MOUTH_LEFT, # 24
MOUTH_DIMPLE_LEFT, # 25
MOUTH_FROWN_LEFT, # 26
MOUTH_LOWER_DOWN_LEFT, # 27
MOUTH_PRESS_LEFT, # 28
MOUTH_SMILE_LEFT, # 29
MOUTH_STRETCH_LEFT, # 30
MOUTH_UPPER_UP_LEFT, # 31
MOUTH_RIGHT, # 32
MOUTH_DIMPLE_RIGHT, # 33
MOUTH_FROWN_RIGHT, # 34
MOUTH_LOWER_DOWN_RIGHT, # 35
MOUTH_PRESS_RIGHT, # 36
MOUTH_SMILE_RIGHT, # 37
MOUTH_STRETCH_RIGHT, # 38
MOUTH_UPPER_UP_RIGHT, # 39
MOUTH_CLOSE, # 40
MOUTH_FUNNEL, # 41
MOUTH_PUCKER, # 42
MOUTH_ROLL_LOWER, # 43
MOUTH_ROLL_UPPER, # 44
MOUTH_SHRUG_LOWER, # 45
MOUTH_SHRUG_UPPER, # 46
JAW_LEFT, # 47
JAW_RIGHT, # 48
JAW_FORWARD, # 49
JAW_OPEN, # 50
TONGUE_OUT, # 51
]
EYE_LEFT_BLENDSHAPES = [
EYE_LOOK_IN_LEFT, # 0
EYE_LOOK_OUT_LEFT, # 1
EYE_LOOK_DOWN_LEFT, # 2
EYE_LOOK_UP_LEFT, # 3
EYE_BLINK_LEFT, # 4
EYE_SQUINT_LEFT, # 5
EYE_WIDE_LEFT, # 6
]
EYE_RIGHT_BLENDSHAPES = [
EYE_LOOK_IN_RIGHT, # 7
EYE_LOOK_OUT_RIGHT, # 8
EYE_LOOK_DOWN_RIGHT, # 9
EYE_LOOK_UP_RIGHT, # 10
EYE_BLINK_RIGHT, # 11
EYE_SQUINT_RIGHT, # 12
EYE_WIDE_RIGHT, # 13
]
BROW_LEFT_BLENDSHAPES = [
BROW_DOWN_LEFT, # 14
BROW_OUTER_UP_LEFT, # 15
]
BROW_RIGHT_BLENDSHAPES = [
BROW_DOWN_RIGHT, # 16
BROW_OUTER_UP_RIGHT, # 17
]
BROW_BOTH_BLENDSHAPES = [
BROW_INNER_UP, # 18
]
NOSE_BLENDSHAPES = [
NOSE_SNEER_LEFT, # 19
NOSE_SNEER_RIGHT, # 20
]
CHECK_BLENDSHAPES = [
CHEEK_SQUINT_LEFT, # 21
CHEEK_SQUINT_RIGHT, # 22
CHEEK_PUFF, # 23
]
MOUTH_LEFT_BLENDSHAPES = [
MOUTH_LEFT, # 24
MOUTH_DIMPLE_LEFT, # 25
MOUTH_FROWN_LEFT, # 26
MOUTH_LOWER_DOWN_LEFT, # 27
MOUTH_PRESS_LEFT, # 28
MOUTH_SMILE_LEFT, # 29
MOUTH_STRETCH_LEFT, # 30
MOUTH_UPPER_UP_LEFT, # 31
]
MOUTH_RIGHT_BLENDSHAPES = [
MOUTH_RIGHT, # 32
MOUTH_DIMPLE_RIGHT, # 33
MOUTH_FROWN_RIGHT, # 34
MOUTH_LOWER_DOWN_RIGHT, # 35
MOUTH_PRESS_RIGHT, # 36
MOUTH_SMILE_RIGHT, # 37
MOUTH_STRETCH_RIGHT, # 38
MOUTH_UPPER_UP_RIGHT, # 39
]
MOUTH_BOTH_BLENDSHAPES = [
MOUTH_CLOSE, # 40
MOUTH_FUNNEL, # 41
MOUTH_PUCKER, # 42
MOUTH_ROLL_LOWER, # 43
MOUTH_ROLL_UPPER, # 44
MOUTH_SHRUG_LOWER, # 45
MOUTH_SHRUG_UPPER, # 46
]
JAW_BLENDSHAPES = [
JAW_LEFT, # 47
JAW_RIGHT, # 48
JAW_FORWARD, # 49
JAW_OPEN, # 50
]
TONGUE_BLENDSHAPES = [
TONGUE_OUT, # 51
]
COLUMN_0_BLENDSHAPES = EYE_RIGHT_BLENDSHAPES + BROW_RIGHT_BLENDSHAPES + [NOSE_SNEER_RIGHT, CHEEK_SQUINT_RIGHT]
COLUMN_1_BLENDSHAPES = EYE_LEFT_BLENDSHAPES + BROW_LEFT_BLENDSHAPES + [NOSE_SNEER_LEFT, CHEEK_SQUINT_LEFT]
COLUMN_2_BLENDSHAPES = MOUTH_RIGHT_BLENDSHAPES + [JAW_RIGHT]
COLUMN_3_BLENDSHAPES = MOUTH_LEFT_BLENDSHAPES + [JAW_LEFT]
COLUMN_4_BLENDSHAPES = [BROW_INNER_UP, CHEEK_PUFF] + MOUTH_BOTH_BLENDSHAPES + [JAW_FORWARD, JAW_OPEN, TONGUE_OUT]
BLENDSHAPE_COLUMNS = [
COLUMN_0_BLENDSHAPES,
COLUMN_1_BLENDSHAPES,
COLUMN_2_BLENDSHAPES,
COLUMN_3_BLENDSHAPES,
COLUMN_4_BLENDSHAPES,
]
RIGHT_EYE_BONE_X = "rightEyeBoneX"
RIGHT_EYE_BONE_Y = "rightEyeBoneY"
RIGHT_EYE_BONE_Z = "rightEyeBoneZ"
RIGHT_EYE_BONE_ROTATIONS = [RIGHT_EYE_BONE_X, RIGHT_EYE_BONE_Y, RIGHT_EYE_BONE_Z]
LEFT_EYE_BONE_X = "leftEyeBoneX"
LEFT_EYE_BONE_Y = "leftEyeBoneY"
LEFT_EYE_BONE_Z = "leftEyeBoneZ"
LEFT_EYE_BONE_ROTATIONS = [LEFT_EYE_BONE_X, LEFT_EYE_BONE_Y, LEFT_EYE_BONE_Z]
HEAD_BONE_X = "headBoneX"
HEAD_BONE_Y = "headBoneY"
HEAD_BONE_Z = "headBoneZ"
HEAD_BONE_ROTATIONS = [HEAD_BONE_X, HEAD_BONE_Y, HEAD_BONE_Z]
ROTATION_NAMES = RIGHT_EYE_BONE_ROTATIONS + LEFT_EYE_BONE_ROTATIONS + HEAD_BONE_ROTATIONS
RIGHT_EYE_BONE_QUAT = "rightEyeBoneQuat"
LEFT_EYE_BONE_QUAT = "leftEyeBoneQuat"
HEAD_BONE_QUAT = "headBoneQuat"
QUATERNION_NAMES = [
RIGHT_EYE_BONE_QUAT,
LEFT_EYE_BONE_QUAT,
HEAD_BONE_QUAT
]
IFACIALMOCAP_DATETIME_FORMAT = "%Y/%m/%d-%H:%M:%S.%f"

27
talkinghead/tha3/mocap/ifacialmocap_pose.py
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@@ -1,27 +0,0 @@
from tha3.mocap.ifacialmocap_constants import BLENDSHAPE_NAMES, HEAD_BONE_X, HEAD_BONE_Y, HEAD_BONE_Z, \
HEAD_BONE_QUAT, LEFT_EYE_BONE_X, LEFT_EYE_BONE_Y, LEFT_EYE_BONE_Z, LEFT_EYE_BONE_QUAT, RIGHT_EYE_BONE_X, \
RIGHT_EYE_BONE_Y, RIGHT_EYE_BONE_Z, RIGHT_EYE_BONE_QUAT
def create_default_ifacialmocap_pose():
data = {}
for blendshape_name in BLENDSHAPE_NAMES:
data[blendshape_name] = 0.0
data[HEAD_BONE_X] = 0.0
data[HEAD_BONE_Y] = 0.0
data[HEAD_BONE_Z] = 0.0
data[HEAD_BONE_QUAT] = [0.0, 0.0, 0.0, 1.0]
data[LEFT_EYE_BONE_X] = 0.0
data[LEFT_EYE_BONE_Y] = 0.0
data[LEFT_EYE_BONE_Z] = 0.0
data[LEFT_EYE_BONE_QUAT] = [0.0, 0.0, 0.0, 1.0]
data[RIGHT_EYE_BONE_X] = 0.0
data[RIGHT_EYE_BONE_Y] = 0.0
data[RIGHT_EYE_BONE_Z] = 0.0
data[RIGHT_EYE_BONE_QUAT] = [0.0, 0.0, 0.0, 1.0]
return data

12
talkinghead/tha3/mocap/ifacialmocap_pose_converter.py
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@@ -1,12 +0,0 @@
from abc import ABC, abstractmethod
from typing import Dict, List
class IFacialMocapPoseConverter(ABC):
@abstractmethod
def convert(self, ifacialmocap_pose: Dict[str, float]) -> List[float]:
pass
@abstractmethod
def init_pose_converter_panel(self, parent):
pass

491
talkinghead/tha3/mocap/ifacialmocap_poser_converter_25.py
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@@ -1,491 +0,0 @@
import math
import time
from enum import Enum
from typing import Optional, Dict, List
import numpy
import scipy.optimize
import wx
from tha3.mocap.ifacialmocap_constants import MOUTH_SMILE_LEFT, MOUTH_SHRUG_UPPER, MOUTH_SMILE_RIGHT, \
BROW_INNER_UP, BROW_OUTER_UP_RIGHT, BROW_OUTER_UP_LEFT, BROW_DOWN_LEFT, BROW_DOWN_RIGHT, EYE_WIDE_LEFT, \
EYE_WIDE_RIGHT, EYE_BLINK_LEFT, EYE_BLINK_RIGHT, CHEEK_SQUINT_LEFT, CHEEK_SQUINT_RIGHT, EYE_LOOK_IN_LEFT, \
EYE_LOOK_OUT_LEFT, EYE_LOOK_IN_RIGHT, EYE_LOOK_OUT_RIGHT, EYE_LOOK_UP_LEFT, EYE_LOOK_UP_RIGHT, EYE_LOOK_DOWN_RIGHT, \
EYE_LOOK_DOWN_LEFT, HEAD_BONE_X, HEAD_BONE_Y, HEAD_BONE_Z, JAW_OPEN, MOUTH_FROWN_LEFT, MOUTH_FROWN_RIGHT, \
MOUTH_LOWER_DOWN_LEFT, MOUTH_LOWER_DOWN_RIGHT, MOUTH_FUNNEL, MOUTH_PUCKER
from tha3.mocap.ifacialmocap_pose_converter import IFacialMocapPoseConverter
from tha3.poser.modes.pose_parameters import get_pose_parameters
class EyebrowDownMode(Enum):
TROUBLED = 1
ANGRY = 2
LOWERED = 3
SERIOUS = 4
class WinkMode(Enum):
NORMAL = 1
RELAXED = 2
def rad_to_deg(rad):
return rad * 180.0 / math.pi
def deg_to_rad(deg):
return deg * math.pi / 180.0
def clamp(x, min_value, max_value):
return max(min_value, min(max_value, x))
class IFacialMocapPoseConverter25Args:
def __init__(self,
lower_smile_threshold: float = 0.4,
upper_smile_threshold: float = 0.6,
eyebrow_down_mode: EyebrowDownMode = EyebrowDownMode.ANGRY,
wink_mode: WinkMode = WinkMode.NORMAL,
eye_surprised_max_value: float = 0.5,
eye_wink_max_value: float = 0.8,
eyebrow_down_max_value: float = 0.4,
cheek_squint_min_value: float = 0.1,
cheek_squint_max_value: float = 0.7,
eye_rotation_factor: float = 1.0 / 0.75,
jaw_open_min_value: float = 0.1,
jaw_open_max_value: float = 0.4,
mouth_frown_max_value: float = 0.6,
mouth_funnel_min_value: float = 0.25,
mouth_funnel_max_value: float = 0.5,
iris_small_left=0.0,
iris_small_right=0.0):
self.iris_small_right = iris_small_left
self.iris_small_left = iris_small_right
self.wink_mode = wink_mode
self.mouth_funnel_max_value = mouth_funnel_max_value
self.mouth_funnel_min_value = mouth_funnel_min_value
self.mouth_frown_max_value = mouth_frown_max_value
self.jaw_open_max_value = jaw_open_max_value
self.jaw_open_min_value = jaw_open_min_value
self.eye_rotation_factor = eye_rotation_factor
self.cheek_squint_max_value = cheek_squint_max_value
self.cheek_squint_min_value = cheek_squint_min_value
self.eyebrow_down_max_value = eyebrow_down_max_value
self.eye_blink_max_value = eye_wink_max_value
self.eye_wide_max_value = eye_surprised_max_value
self.eyebrow_down_mode = eyebrow_down_mode
self.lower_smile_threshold = lower_smile_threshold
self.upper_smile_threshold = upper_smile_threshold
class IFacialMocapPoseConverter25(IFacialMocapPoseConverter):
def __init__(self, args: Optional[IFacialMocapPoseConverter25Args] = None):
super().__init__()
if args is None:
args = IFacialMocapPoseConverter25Args()
self.args = args
pose_parameters = get_pose_parameters()
self.pose_size = 45
self.eyebrow_troubled_left_index = pose_parameters.get_parameter_index("eyebrow_troubled_left")
self.eyebrow_troubled_right_index = pose_parameters.get_parameter_index("eyebrow_troubled_right")
self.eyebrow_angry_left_index = pose_parameters.get_parameter_index("eyebrow_angry_left")
self.eyebrow_angry_right_index = pose_parameters.get_parameter_index("eyebrow_angry_right")
self.eyebrow_happy_left_index = pose_parameters.get_parameter_index("eyebrow_happy_left")
self.eyebrow_happy_right_index = pose_parameters.get_parameter_index("eyebrow_happy_right")
self.eyebrow_raised_left_index = pose_parameters.get_parameter_index("eyebrow_raised_left")
self.eyebrow_raised_right_index = pose_parameters.get_parameter_index("eyebrow_raised_right")
self.eyebrow_lowered_left_index = pose_parameters.get_parameter_index("eyebrow_lowered_left")
self.eyebrow_lowered_right_index = pose_parameters.get_parameter_index("eyebrow_lowered_right")
self.eyebrow_serious_left_index = pose_parameters.get_parameter_index("eyebrow_serious_left")
self.eyebrow_serious_right_index = pose_parameters.get_parameter_index("eyebrow_serious_right")
self.eye_surprised_left_index = pose_parameters.get_parameter_index("eye_surprised_left")
self.eye_surprised_right_index = pose_parameters.get_parameter_index("eye_surprised_right")
self.eye_wink_left_index = pose_parameters.get_parameter_index("eye_wink_left")
self.eye_wink_right_index = pose_parameters.get_parameter_index("eye_wink_right")
self.eye_happy_wink_left_index = pose_parameters.get_parameter_index("eye_happy_wink_left")
self.eye_happy_wink_right_index = pose_parameters.get_parameter_index("eye_happy_wink_right")
self.eye_relaxed_left_index = pose_parameters.get_parameter_index("eye_relaxed_left")
self.eye_relaxed_right_index = pose_parameters.get_parameter_index("eye_relaxed_right")
self.eye_raised_lower_eyelid_left_index = pose_parameters.get_parameter_index("eye_raised_lower_eyelid_left")
self.eye_raised_lower_eyelid_right_index = pose_parameters.get_parameter_index("eye_raised_lower_eyelid_right")
self.iris_small_left_index = pose_parameters.get_parameter_index("iris_small_left")
self.iris_small_right_index = pose_parameters.get_parameter_index("iris_small_right")
self.iris_rotation_x_index = pose_parameters.get_parameter_index("iris_rotation_x")
self.iris_rotation_y_index = pose_parameters.get_parameter_index("iris_rotation_y")
self.head_x_index = pose_parameters.get_parameter_index("head_x")
self.head_y_index = pose_parameters.get_parameter_index("head_y")
self.neck_z_index = pose_parameters.get_parameter_index("neck_z")
self.mouth_aaa_index = pose_parameters.get_parameter_index("mouth_aaa")
self.mouth_iii_index = pose_parameters.get_parameter_index("mouth_iii")
self.mouth_uuu_index = pose_parameters.get_parameter_index("mouth_uuu")
self.mouth_eee_index = pose_parameters.get_parameter_index("mouth_eee")
self.mouth_ooo_index = pose_parameters.get_parameter_index("mouth_ooo")
self.mouth_lowered_corner_left_index = pose_parameters.get_parameter_index("mouth_lowered_corner_left")
self.mouth_lowered_corner_right_index = pose_parameters.get_parameter_index("mouth_lowered_corner_right")
self.mouth_raised_corner_left_index = pose_parameters.get_parameter_index("mouth_raised_corner_left")
self.mouth_raised_corner_right_index = pose_parameters.get_parameter_index("mouth_raised_corner_right")
self.body_y_index = pose_parameters.get_parameter_index("body_y")
self.body_z_index = pose_parameters.get_parameter_index("body_z")
self.breathing_index = pose_parameters.get_parameter_index("breathing")
self.breathing_start_time = time.time()
self.panel = None
def init_pose_converter_panel(self, parent):
self.panel = wx.Panel(parent, style=wx.SIMPLE_BORDER)
self.panel_sizer = wx.BoxSizer(wx.VERTICAL)
self.panel.SetSizer(self.panel_sizer)
self.panel.SetAutoLayout(1)
parent.GetSizer().Add(self.panel, 0, wx.EXPAND)
if True:
eyebrow_down_mode_text = wx.StaticText(self.panel, label=" --- Eyebrow Down Mode --- ",
style=wx.ALIGN_CENTER)
self.panel_sizer.Add(eyebrow_down_mode_text, 0, wx.EXPAND)
self.eyebrow_down_mode_choice = wx.Choice(
self.panel,
choices=[
"ANGRY",
"TROUBLED",
"SERIOUS",
"LOWERED",
])
self.eyebrow_down_mode_choice.SetSelection(0)
self.panel_sizer.Add(self.eyebrow_down_mode_choice, 0, wx.EXPAND)
self.eyebrow_down_mode_choice.Bind(wx.EVT_CHOICE, self.change_eyebrow_down_mode)
separator = wx.StaticLine(self.panel, -1, size=(256, 5))
self.panel_sizer.Add(separator, 0, wx.EXPAND)
if True:
wink_mode_text = wx.StaticText(self.panel, label=" --- Wink Mode --- ", style=wx.ALIGN_CENTER)
self.panel_sizer.Add(wink_mode_text, 0, wx.EXPAND)
self.wink_mode_choice = wx.Choice(
self.panel,
choices=[
"NORMAL",
"RELAXED",
])
self.wink_mode_choice.SetSelection(0)
self.panel_sizer.Add(self.wink_mode_choice, 0, wx.EXPAND)
self.wink_mode_choice.Bind(wx.EVT_CHOICE, self.change_wink_mode)
separator = wx.StaticLine(self.panel, -1, size=(256, 5))
self.panel_sizer.Add(separator, 0, wx.EXPAND)
if True:
iris_size_text = wx.StaticText(self.panel, label=" --- Iris Size --- ", style=wx.ALIGN_CENTER)
self.panel_sizer.Add(iris_size_text, 0, wx.EXPAND)
self.iris_left_slider = wx.Slider(self.panel, minValue=0, maxValue=1000, value=0, style=wx.HORIZONTAL)
self.panel_sizer.Add(self.iris_left_slider, 0, wx.EXPAND)
self.iris_left_slider.Bind(wx.EVT_SLIDER, self.change_iris_size)
self.iris_right_slider = wx.Slider(self.panel, minValue=0, maxValue=1000, value=0, style=wx.HORIZONTAL)
self.panel_sizer.Add(self.iris_right_slider, 0, wx.EXPAND)
self.iris_right_slider.Bind(wx.EVT_SLIDER, self.change_iris_size)
self.iris_right_slider.Enable(False)
self.link_left_right_irises = wx.CheckBox(
self.panel, label="Use same value for both sides")
self.link_left_right_irises.SetValue(True)
self.panel_sizer.Add(self.link_left_right_irises, wx.SizerFlags().CenterHorizontal().Border())
self.link_left_right_irises.Bind(wx.EVT_CHECKBOX, self.link_left_right_irises_clicked)
separator = wx.StaticLine(self.panel, -1, size=(256, 5))
self.panel_sizer.Add(separator, 0, wx.EXPAND)
if True:
iris_size_text = wx.StaticText(self.panel, label=" --- Iris Size --- ", style=wx.ALIGN_CENTER)
self.panel_sizer.Add(iris_size_text, 0, wx.EXPAND)
self.iris_left_slider = wx.Slider(self.panel, minValue=0, maxValue=1000, value=0, style=wx.HORIZONTAL)
self.panel_sizer.Add(self.iris_left_slider, 0, wx.EXPAND)
self.iris_left_slider.Bind(wx.EVT_SLIDER, self.change_iris_size)
self.iris_right_slider = wx.Slider(self.panel, minValue=0, maxValue=1000, value=0, style=wx.HORIZONTAL)
self.panel_sizer.Add(self.iris_right_slider, 0, wx.EXPAND)
self.iris_right_slider.Bind(wx.EVT_SLIDER, self.change_iris_size)
self.iris_right_slider.Enable(False)
self.link_left_right_irises = wx.CheckBox(
self.panel, label="Use same value for both sides")
self.link_left_right_irises.SetValue(True)
self.panel_sizer.Add(self.link_left_right_irises, wx.SizerFlags().CenterHorizontal().Border())
self.link_left_right_irises.Bind(wx.EVT_CHECKBOX, self.link_left_right_irises_clicked)
separator = wx.StaticLine(self.panel, -1, size=(256, 5))
self.panel_sizer.Add(separator, 0, wx.EXPAND)
if True:
breathing_frequency_text = wx.StaticText(
self.panel, label=" --- Breathing --- ", style=wx.ALIGN_CENTER)
self.panel_sizer.Add(breathing_frequency_text, 0, wx.EXPAND)
self.restart_breathing_cycle_button = wx.Button(self.panel, label="Restart Breathing Cycle")
self.restart_breathing_cycle_button.Bind(wx.EVT_BUTTON, self.restart_breathing_cycle_clicked)
self.panel_sizer.Add(self.restart_breathing_cycle_button, 0, wx.EXPAND)
self.breathing_frequency_slider = wx.Slider(
self.panel, minValue=0, maxValue=60, value=20, style=wx.HORIZONTAL)
self.panel_sizer.Add(self.breathing_frequency_slider, 0, wx.EXPAND)
self.breathing_gauge = wx.Gauge(self.panel, style=wx.GA_HORIZONTAL, range=1000)
self.panel_sizer.Add(self.breathing_gauge, 0, wx.EXPAND)
self.panel_sizer.Fit(self.panel)
def restart_breathing_cycle_clicked(self, event: wx.Event):
self.breathing_start_time = time.time()
def change_eyebrow_down_mode(self, event: wx.Event):
selected_index = self.eyebrow_down_mode_choice.GetSelection()
if selected_index == 0:
self.args.eyebrow_down_mode = EyebrowDownMode.ANGRY
elif selected_index == 1:
self.args.eyebrow_down_mode = EyebrowDownMode.TROUBLED
elif selected_index == 2:
self.args.eyebrow_down_mode = EyebrowDownMode.SERIOUS
else:
self.args.eyebrow_down_mode = EyebrowDownMode.LOWERED
def change_wink_mode(self, event: wx.Event):
selected_index = self.wink_mode_choice.GetSelection()
if selected_index == 0:
self.args.wink_mode = WinkMode.NORMAL
else:
self.args.wink_mode = WinkMode.RELAXED
def change_iris_size(self, event: wx.Event):
if self.link_left_right_irises.GetValue():
left_value = self.iris_left_slider.GetValue()
right_value = self.iris_right_slider.GetValue()
if left_value != right_value:
self.iris_right_slider.SetValue(left_value)
self.args.iris_small_left = left_value / 1000.0
self.args.iris_small_right = left_value / 1000.0
else:
self.args.iris_small_left = self.iris_left_slider.GetValue() / 1000.0
self.args.iris_small_right = self.iris_right_slider.GetValue() / 1000.0
def link_left_right_irises_clicked(self, event: wx.Event):
if self.link_left_right_irises.GetValue():
self.iris_right_slider.Enable(False)
else:
self.iris_right_slider.Enable(True)
self.change_iris_size(event)
def decompose_head_body_param(self, param, threshold=2.0 / 3):
if abs(param) < threshold:
return (param, 0.0)
else:
if param < 0:
sign = -1.0
else:
sign = 1.0
return (threshold * sign, (abs(param) - threshold) * sign)
breathing_start_time = time.time()
def convert(self, ifacialmocap_pose: Dict[str, float]) -> List[float]:
pose = [0.0 for i in range(self.pose_size)]
smile_value = \
(ifacialmocap_pose[MOUTH_SMILE_LEFT] + ifacialmocap_pose[MOUTH_SMILE_RIGHT]) / 2.0 \
+ ifacialmocap_pose[MOUTH_SHRUG_UPPER]
if smile_value < self.args.lower_smile_threshold:
smile_degree = 0.0
elif smile_value > self.args.upper_smile_threshold:
smile_degree = 1.0
else:
smile_degree = (smile_value - self.args.lower_smile_threshold) / (
self.args.upper_smile_threshold - self.args.lower_smile_threshold)
# Eyebrow
if True:
brow_inner_up = ifacialmocap_pose[BROW_INNER_UP]
brow_outer_up_right = ifacialmocap_pose[BROW_OUTER_UP_RIGHT]
brow_outer_up_left = ifacialmocap_pose[BROW_OUTER_UP_LEFT]
brow_up_left = clamp(brow_inner_up + brow_outer_up_left, 0.0, 1.0)
brow_up_right = clamp(brow_inner_up + brow_outer_up_right, 0.0, 1.0)
pose[self.eyebrow_raised_left_index] = brow_up_left
pose[self.eyebrow_raised_right_index] = brow_up_right
brow_down_left = (1.0 - smile_degree) \
* clamp(ifacialmocap_pose[BROW_DOWN_LEFT] / self.args.eyebrow_down_max_value, 0.0, 1.0)
brow_down_right = (1.0 - smile_degree) \
* clamp(ifacialmocap_pose[BROW_DOWN_RIGHT] / self.args.eyebrow_down_max_value, 0.0, 1.0)
if self.args.eyebrow_down_mode == EyebrowDownMode.TROUBLED:
pose[self.eyebrow_troubled_left_index] = brow_down_left
pose[self.eyebrow_troubled_right_index] = brow_down_right
elif self.args.eyebrow_down_mode == EyebrowDownMode.ANGRY:
pose[self.eyebrow_angry_left_index] = brow_down_left
pose[self.eyebrow_angry_right_index] = brow_down_right
elif self.args.eyebrow_down_mode == EyebrowDownMode.LOWERED:
pose[self.eyebrow_lowered_left_index] = brow_down_left
pose[self.eyebrow_lowered_right_index] = brow_down_right
elif self.args.eyebrow_down_mode == EyebrowDownMode.SERIOUS:
pose[self.eyebrow_serious_left_index] = brow_down_left
pose[self.eyebrow_serious_right_index] = brow_down_right
brow_happy_value = clamp(smile_value, 0.0, 1.0) * smile_degree
pose[self.eyebrow_happy_left_index] = brow_happy_value
pose[self.eyebrow_happy_right_index] = brow_happy_value
# Eye
if True:
# Surprised
pose[self.eye_surprised_left_index] = clamp(
ifacialmocap_pose[EYE_WIDE_LEFT] / self.args.eye_wide_max_value, 0.0, 1.0)
pose[self.eye_surprised_right_index] = clamp(
ifacialmocap_pose[EYE_WIDE_RIGHT] / self.args.eye_wide_max_value, 0.0, 1.0)
# Wink
if self.args.wink_mode == WinkMode.NORMAL:
wink_left_index = self.eye_wink_left_index
wink_right_index = self.eye_wink_right_index
else:
wink_left_index = self.eye_relaxed_left_index
wink_right_index = self.eye_relaxed_right_index
pose[wink_left_index] = (1.0 - smile_degree) * clamp(
ifacialmocap_pose[EYE_BLINK_LEFT] / self.args.eye_blink_max_value, 0.0, 1.0)
pose[wink_right_index] = (1.0 - smile_degree) * clamp(
ifacialmocap_pose[EYE_BLINK_RIGHT] / self.args.eye_blink_max_value, 0.0, 1.0)
pose[self.eye_happy_wink_left_index] = smile_degree * clamp(
ifacialmocap_pose[EYE_BLINK_LEFT] / self.args.eye_blink_max_value, 0.0, 1.0)
pose[self.eye_happy_wink_right_index] = smile_degree * clamp(
ifacialmocap_pose[EYE_BLINK_RIGHT] / self.args.eye_blink_max_value, 0.0, 1.0)
# Lower eyelid
cheek_squint_denom = self.args.cheek_squint_max_value - self.args.cheek_squint_min_value
pose[self.eye_raised_lower_eyelid_left_index] = \
clamp(
(ifacialmocap_pose[CHEEK_SQUINT_LEFT] - self.args.cheek_squint_min_value) / cheek_squint_denom,
0.0, 1.0)
pose[self.eye_raised_lower_eyelid_right_index] = \
clamp(
(ifacialmocap_pose[CHEEK_SQUINT_RIGHT] - self.args.cheek_squint_min_value) / cheek_squint_denom,
0.0, 1.0)
# Iris rotation
if True:
eye_rotation_y = (ifacialmocap_pose[EYE_LOOK_IN_LEFT]
- ifacialmocap_pose[EYE_LOOK_OUT_LEFT]
- ifacialmocap_pose[EYE_LOOK_IN_RIGHT]
+ ifacialmocap_pose[EYE_LOOK_OUT_RIGHT]) / 2.0 * self.args.eye_rotation_factor
pose[self.iris_rotation_y_index] = clamp(eye_rotation_y, -1.0, 1.0)
eye_rotation_x = (ifacialmocap_pose[EYE_LOOK_UP_LEFT]
+ ifacialmocap_pose[EYE_LOOK_UP_RIGHT]
- ifacialmocap_pose[EYE_LOOK_DOWN_LEFT]
- ifacialmocap_pose[EYE_LOOK_DOWN_RIGHT]) / 2.0 * self.args.eye_rotation_factor
pose[self.iris_rotation_x_index] = clamp(eye_rotation_x, -1.0, 1.0)
# Iris size
if True:
pose[self.iris_small_left_index] = self.args.iris_small_left
pose[self.iris_small_right_index] = self.args.iris_small_right
# Head rotation
if True:
x_param = clamp(-ifacialmocap_pose[HEAD_BONE_X] * 180.0 / math.pi, -15.0, 15.0) / 15.0
pose[self.head_x_index] = x_param
y_param = clamp(-ifacialmocap_pose[HEAD_BONE_Y] * 180.0 / math.pi, -10.0, 10.0) / 10.0
pose[self.head_y_index] = y_param
pose[self.body_y_index] = y_param
z_param = clamp(ifacialmocap_pose[HEAD_BONE_Z] * 180.0 / math.pi, -15.0, 15.0) / 15.0
pose[self.neck_z_index] = z_param
pose[self.body_z_index] = z_param
# Mouth
if True:
jaw_open_denom = self.args.jaw_open_max_value - self.args.jaw_open_min_value
mouth_open = clamp((ifacialmocap_pose[JAW_OPEN] - self.args.jaw_open_min_value) / jaw_open_denom, 0.0, 1.0)
pose[self.mouth_aaa_index] = mouth_open
pose[self.mouth_raised_corner_left_index] = clamp(smile_value, 0.0, 1.0)
pose[self.mouth_raised_corner_right_index] = clamp(smile_value, 0.0, 1.0)
is_mouth_open = mouth_open > 0.0
if not is_mouth_open:
mouth_frown_value = clamp(
(ifacialmocap_pose[MOUTH_FROWN_LEFT] + ifacialmocap_pose[
MOUTH_FROWN_RIGHT]) / self.args.mouth_frown_max_value, 0.0, 1.0)
pose[self.mouth_lowered_corner_left_index] = mouth_frown_value
pose[self.mouth_lowered_corner_right_index] = mouth_frown_value
else:
mouth_lower_down = clamp(
ifacialmocap_pose[MOUTH_LOWER_DOWN_LEFT] + ifacialmocap_pose[MOUTH_LOWER_DOWN_RIGHT], 0.0, 1.0)
mouth_funnel = ifacialmocap_pose[MOUTH_FUNNEL]
mouth_pucker = ifacialmocap_pose[MOUTH_PUCKER]
mouth_point = [mouth_open, mouth_lower_down, mouth_funnel, mouth_pucker]
aaa_point = [1.0, 1.0, 0.0, 0.0]
iii_point = [0.0, 1.0, 0.0, 0.0]
uuu_point = [0.5, 0.3, 0.25, 0.75]
ooo_point = [1.0, 0.5, 0.5, 0.4]
decomp = numpy.array([0, 0, 0, 0])
M = numpy.array([
aaa_point,
iii_point,
uuu_point,
ooo_point
])
def loss(decomp):
return numpy.linalg.norm(numpy.matmul(decomp, M) - mouth_point) \
+ 0.01 * numpy.linalg.norm(decomp, ord=1)
opt_result = scipy.optimize.minimize(
loss, decomp, bounds=[(0.0, 1.0), (0.0, 1.0), (0.0, 1.0), (0.0, 1.0)])
decomp = opt_result["x"]
restricted_decomp = [decomp.item(0), decomp.item(1), decomp.item(2), decomp.item(3)]
pose[self.mouth_aaa_index] = restricted_decomp[0]
pose[self.mouth_iii_index] = restricted_decomp[1]
mouth_funnel_denom = self.args.mouth_funnel_max_value - self.args.mouth_funnel_min_value
ooo_alpha = clamp((mouth_funnel - self.args.mouth_funnel_min_value) / mouth_funnel_denom, 0.0, 1.0)
uo_value = clamp(restricted_decomp[2] + restricted_decomp[3], 0.0, 1.0)
pose[self.mouth_uuu_index] = uo_value * (1.0 - ooo_alpha)
pose[self.mouth_ooo_index] = uo_value * ooo_alpha
#if self.panel is not None:
#frequency = self.breathing_frequency_slider.GetValue()
frequency = 18 #breathing rate 10-50
if frequency == 0:
#value = 0.0
#pose[self.breathing_index] = value
self.breathing_start_time = time.time()
else:
period = 60.0 / frequency
now = time.time()
diff = now - self.breathing_start_time
frac = (diff % period) / period
value = (-math.cos(2 * math.pi * frac) + 1.0) / 2.0
pose[self.breathing_index] = value
#print("pose", pose[self.breathing_index])
#self.breathing_gauge.SetValue(int(1000 * value))
return pose
def create_ifacialmocap_pose_converter(
args: Optional[IFacialMocapPoseConverter25Args] = None) -> IFacialMocapPoseConverter:
return IFacialMocapPoseConverter25(args)

89
talkinghead/tha3/mocap/ifacialmocap_v2.py
View File

@@ -1,89 +0,0 @@
import math
from tha3.mocap.ifacialmocap_constants import BLENDSHAPE_NAMES, HEAD_BONE_X, HEAD_BONE_Y, HEAD_BONE_Z, \
RIGHT_EYE_BONE_X, RIGHT_EYE_BONE_Y, RIGHT_EYE_BONE_Z, LEFT_EYE_BONE_X, LEFT_EYE_BONE_Y, LEFT_EYE_BONE_Z, \
HEAD_BONE_QUAT, LEFT_EYE_BONE_QUAT, RIGHT_EYE_BONE_QUAT
IFACIALMOCAP_PORT = 49983
IFACIALMOCAP_START_STRING = "iFacialMocap_sahuasouryya9218sauhuiayeta91555dy3719|sendDataVersion=v2".encode('utf-8')
def parse_ifacialmocap_v2_pose(ifacialmocap_output):
output = {}
parts = ifacialmocap_output.split("|")
for part in parts:
part = part.strip()
if len(part) == 0:
continue
if "&" in part:
components = part.split("&")
assert len(components) == 2
key = components[0]
value = float(components[1]) / 100.0
if key.endswith("_L"):
key = key[:-2] + "Left"
elif key.endswith("_R"):
key = key[:-2] + "Right"
if key in BLENDSHAPE_NAMES:
output[key] = value
elif part.startswith("=head#"):
components = part[len("=head#"):].split(",")
assert len(components) == 6
output[HEAD_BONE_X] = float(components[0]) * math.pi / 180
output[HEAD_BONE_Y] = float(components[1]) * math.pi / 180
output[HEAD_BONE_Z] = float(components[2]) * math.pi / 180
elif part.startswith("rightEye#"):
components = part[len("rightEye#"):].split(",")
output[RIGHT_EYE_BONE_X] = float(components[0]) * math.pi / 180
output[RIGHT_EYE_BONE_Y] = float(components[1]) * math.pi / 180
output[RIGHT_EYE_BONE_Z] = float(components[2]) * math.pi / 180
elif part.startswith("leftEye#"):
components = part[len("leftEye#"):].split(",")
output[LEFT_EYE_BONE_X] = float(components[0]) * math.pi / 180
output[LEFT_EYE_BONE_Y] = float(components[1]) * math.pi / 180
output[LEFT_EYE_BONE_Z] = float(components[2]) * math.pi / 180
output[HEAD_BONE_QUAT] = [0.0, 0.0, 0.0, 1.0]
output[LEFT_EYE_BONE_QUAT] = [0.0, 0.0, 0.0, 1.0]
output[RIGHT_EYE_BONE_QUAT] = [0.0, 0.0, 0.0, 1.0]
return output
def parse_ifacialmocap_v1_pose(ifacialmocap_output):
output = {}
parts = ifacialmocap_output.split("|")
for part in parts:
part = part.strip()
if len(part) == 0:
continue
if part.startswith("=head#"):
components = part[len("=head#"):].split(",")
assert len(components) == 6
output[HEAD_BONE_X] = float(components[0]) * math.pi / 180
output[HEAD_BONE_Y] = float(components[1]) * math.pi / 180
output[HEAD_BONE_Z] = float(components[2]) * math.pi / 180
elif part.startswith("rightEye#"):
components = part[len("rightEye#"):].split(",")
output[RIGHT_EYE_BONE_X] = float(components[0]) * math.pi / 180
output[RIGHT_EYE_BONE_Y] = float(components[1]) * math.pi / 180
output[RIGHT_EYE_BONE_Z] = float(components[2]) * math.pi / 180
elif part.startswith("leftEye#"):
components = part[len("leftEye#"):].split(",")
output[LEFT_EYE_BONE_X] = float(components[0]) * math.pi / 180
output[LEFT_EYE_BONE_Y] = float(components[1]) * math.pi / 180
output[LEFT_EYE_BONE_Z] = float(components[2]) * math.pi / 180
else:
components = part.split("-")
assert len(components) == 2
key = components[0]
value = float(components[1]) / 100.0
if key.endswith("_L"):
key = key[:-2] + "Left"
elif key.endswith("_R"):
key = key[:-2] + "Right"
if key in BLENDSHAPE_NAMES:
output[key] = value
output[HEAD_BONE_QUAT] = [0.0, 0.0, 0.0, 1.0]
output[LEFT_EYE_BONE_QUAT] = [0.0, 0.0, 0.0, 1.0]
output[RIGHT_EYE_BONE_QUAT] = [0.0, 0.0, 0.0, 1.0]
return output

396
talkinghead/tha3/models/LICENSE.txt
View File

@@ -1,396 +0,0 @@
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This is the folder to extract the models to.

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