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main.py
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main.py
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import struct
import cv2
import torch
import pyvirtualcam
import numpy as np
import mediapipe as mp
from PIL import Image
import tha2.poser.modes.mode_20_wx
from models import TalkingAnimeLight, TalkingAnime3
from pose import get_pose
from utils import preprocessing_image, postprocessing_image
import errno
import json
import os
import queue
import socket
import time
import math
from pynput.mouse import Button, Controller
import re
from collections import OrderedDict
from multiprocessing import Value, Process, Queue
from pyanime4k import ac
from tha2.mocap.ifacialmocap_constants import *
from args import args
from tha3.util import torch_linear_to_srgb, resize_PIL_image, extract_PIL_image_from_filelike, \
extract_pytorch_image_from_PIL_image
import collections
def convert_linear_to_srgb(image: torch.Tensor) -> torch.Tensor:
rgb_image = torch_linear_to_srgb(image[0:3, :, :])
return torch.cat([rgb_image, image[3:4, :, :]], dim=0)
class FPS:
def __init__(self, avarageof=50):
self.frametimestamps = collections.deque(maxlen=avarageof)
def __call__(self):
self.frametimestamps.append(time.time())
if len(self.frametimestamps) > 1:
return len(self.frametimestamps) / (self.frametimestamps[-1] - self.frametimestamps[0])
else:
return 0.0
device = torch.device('cuda') if torch.cuda.is_available() and not args.skip_model else torch.device('cpu')
def create_default_blender_data():
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
class OSFClientProcess(Process):
def __init__(self):
super().__init__()
self.queue = Queue()
self.should_terminate = Value('b', False)
self.address = args.osf.split(':')[0]
self.port = int(args.osf.split(':')[1])
self.ifm_fps_number = Value('f', 0.0)
self.perf_time = 0
def run(self):
self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.socket.setblocking(False)
self.socket.bind(("", self.port))
self.socket.settimeout(0.1)
ifm_fps = FPS()
while True:
if self.should_terminate.value:
break
try:
socket_bytes = self.socket.recv(8192)
except socket.error as e:
err = e.args[0]
if err == errno.EAGAIN or err == errno.EWOULDBLOCK or err == 'timed out':
continue
else:
raise e
# socket_string = socket_bytes.decode("utf-8")
osf_raw = (struct.unpack('=di2f2fB1f4f3f3f68f136f210f14f', socket_bytes))
# print(osf_raw[432:])
data = {}
OpenSeeDataIndex = [
'time',
'id',
'cameraResolutionW',
'cameraResolutionH',
'rightEyeOpen',
'leftEyeOpen',
'got3DPoints',
'fit3DError',
'rawQuaternionX',
'rawQuaternionY',
'rawQuaternionZ',
'rawQuaternionW',
'rawEulerX',
'rawEulerY',
'rawEulerZ',
'translationY',
'translationX',
'translationZ',
]
for i in range(len(OpenSeeDataIndex)):
data[OpenSeeDataIndex[i]] = osf_raw[i]
data['translationY'] *= -1
data['translationZ'] *= -1
data['rotationY'] = data['rawEulerY']-10
data['rotationX'] = (-data['rawEulerX'] + 360)%360-180
data['rotationZ'] = (data['rawEulerZ'] - 90)
OpenSeeFeatureIndex = [
'EyeLeft',
'EyeRight',
'EyebrowSteepnessLeft',
'EyebrowUpDownLeft',
'EyebrowQuirkLeft',
'EyebrowSteepnessRight',
'EyebrowUpDownRight',
'EyebrowQuirkRight',
'MouthCornerUpDownLeft',
'MouthCornerInOutLeft',
'MouthCornerUpDownRight',
'MouthCornerInOutRight',
'MouthOpen',
'MouthWide'
]
for i in range(68):
data['confidence' + str(i)] = osf_raw[i + 18]
for i in range(68):
data['pointsX' + str(i)] = osf_raw[i * 2 + 18 + 68]
data['pointsY' + str(i)] = osf_raw[i * 2 + 18 + 68 + 1]
for i in range(70):
data['points3DX' + str(i)] = osf_raw[i * 3 + 18 + 68 + 68 * 2]
data['points3DY' + str(i)] = osf_raw[i * 3 + 18 + 68 + 68 * 2 + 1]
data['points3DZ' + str(i)] = osf_raw[i * 3 + 18 + 68 + 68 * 2 + 2]
for i in range(len(OpenSeeFeatureIndex)):
data[OpenSeeFeatureIndex[i]] = osf_raw[i + 432]
# print(data['rotationX'],data['rotationY'],data['rotationZ'])
a = np.array([
data['points3DX66'] - data['points3DX68'] + data['points3DX67'] - data['points3DX69'],
data['points3DY66'] - data['points3DY68'] + data['points3DY67'] - data['points3DY69'],
data['points3DZ66'] - data['points3DZ68'] + data['points3DZ67'] - data['points3DZ69']
])
a = (a / np.linalg.norm(a))
data['eyeRotationX'] = a[0]
data['eyeRotationY'] = a[1]
try:
self.queue.put_nowait(data)
except queue.Full:
pass
self.queue.close()
self.socket.close()
ifm_converter = tha2.poser.modes.mode_20_wx.IFacialMocapPoseConverter20()
class IFMClientProcess(Process):
def __init__(self):
super().__init__()
self.queue = Queue()
self.should_terminate = Value('b', False)
self.address = args.ifm.split(':')[0]
self.port = int(args.ifm.split(':')[1])
self.ifm_fps_number = Value('f', 0.0)
self.perf_time = 0
def run(self):
udpClntSock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
data = "iFacialMocap_sahuasouryya9218sauhuiayeta91555dy3719"
data = data.encode('utf-8')
udpClntSock.sendto(data, (self.address, self.port))
self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.socket.setblocking(False)
self.socket.bind(("", self.port))
self.socket.settimeout(0.1)
ifm_fps = FPS()
pre_socket_string = ''
while True:
if self.should_terminate.value:
break
try:
socket_bytes = self.socket.recv(8192)
except socket.error as e:
err = e.args[0]
if err == errno.EAGAIN or err == errno.EWOULDBLOCK or err == 'timed out':
continue
else:
raise e
socket_string = socket_bytes.decode("utf-8")
if args.debug and pre_socket_string != socket_string:
self.ifm_fps_number.value = ifm_fps()
pre_socket_string = socket_string
# print(socket_string)
# blender_data = json.loads(socket_string)
data = self.convert_from_blender_data(socket_string)
try:
self.queue.put_nowait(data)
except queue.Full:
pass
self.queue.close()
self.socket.close()
@staticmethod
def convert_from_blender_data(blender_data):
data = {}
for item in blender_data.split('|'):
if item.find('#') != -1:
k, arr = item.split('#')
arr = [float(n) for n in arr.split(',')]
data[k.replace("_L", "Left").replace("_R", "Right")] = arr
elif item.find('-') != -1:
k, v = item.split("-")
data[k.replace("_L", "Left").replace("_R", "Right")] = float(v) / 100
to_rad = 57.3
data[HEAD_BONE_X] = data["=head"][0] / to_rad
data[HEAD_BONE_Y] = data["=head"][1] / to_rad
data[HEAD_BONE_Z] = data["=head"][2] / to_rad
data[HEAD_BONE_QUAT] = [data["=head"][3], data["=head"][4], data["=head"][5], 1]
# print(data[HEAD_BONE_QUAT][2],min(data[EYE_BLINK_LEFT],data[EYE_BLINK_RIGHT]))
data[RIGHT_EYE_BONE_X] = data["rightEye"][0] / to_rad
data[RIGHT_EYE_BONE_Y] = data["rightEye"][1] / to_rad
data[RIGHT_EYE_BONE_Z] = data["rightEye"][2] / to_rad
data[LEFT_EYE_BONE_X] = data["leftEye"][0] / to_rad
data[LEFT_EYE_BONE_Y] = data["leftEye"][1] / to_rad
data[LEFT_EYE_BONE_Z] = data["leftEye"][2] / to_rad
return data
class MouseClientProcess(Process):
def __init__(self):
super().__init__()
self.queue = Queue()
def run(self):
mouse = Controller()
posLimit = [int(x) for x in args.mouse_input.split(',')]
prev = {
'eye_l_h_temp': 0,
'eye_r_h_temp': 0,
'mouth_ratio': 0,
'eye_y_ratio': 0,
'eye_x_ratio': 0,
'x_angle': 0,
'y_angle': 0,
'z_angle': 0,
}
while True:
pos = mouse.position
# print(pos)
eye_limit = [0.8, 0.5]
head_eye_reduce = 0.6
head_slowness = 0.2
mouse_data = {
'eye_l_h_temp': 0,
'eye_r_h_temp': 0,
'mouth_ratio': 0,
'eye_y_ratio': np.interp(pos[1], [posLimit[1], posLimit[3]], [1, -1]) * eye_limit[1],
'eye_x_ratio': np.interp(pos[0], [posLimit[0], posLimit[2]], [1, -1]) * eye_limit[0],
'x_angle': np.interp(pos[1], [posLimit[1], posLimit[3]], [1, -1]),
'y_angle': np.interp(pos[0], [posLimit[0], posLimit[2]], [1, -1]),
'z_angle': 0,
}
mouse_data['x_angle'] = np.interp(head_slowness, [0, 1], [prev['x_angle'], mouse_data['x_angle']])
mouse_data['y_angle'] = np.interp(head_slowness, [0, 1], [prev['y_angle'], mouse_data['y_angle']])
mouse_data['eye_y_ratio'] -= mouse_data['x_angle'] * eye_limit[1] * head_eye_reduce
mouse_data['eye_x_ratio'] -= mouse_data['y_angle'] * eye_limit[0] * head_eye_reduce
if args.bongo:
mouse_data['y_angle'] += 0.05
mouse_data['x_angle'] += 0.05
prev = mouse_data
self.queue.put_nowait(mouse_data)
time.sleep(1 / 60)
class ModelClientProcess(Process):
def __init__(self, input_image):
super().__init__()
self.should_terminate = Value('b', False)
self.updated = Value('b', False)
self.data = None
self.input_image = input_image
self.output_queue = Queue()
self.input_queue = Queue()
self.model_fps_number = Value('f', 0.0)
self.gpu_fps_number = Value('f', 0.0)
self.cache_hit_ratio = Value('f', 0.0)
self.gpu_cache_hit_ratio = Value('f', 0.0)
def run(self):
model = None
if not args.skip_model:
model = TalkingAnime3().to(device)
model = model.eval()
model = model
print("Pretrained Model Loaded")
eyebrow_vector = torch.empty(1, 12, dtype=torch.half if args.model.endswith('half') else torch.float)
mouth_eye_vector = torch.empty(1, 27, dtype=torch.half if args.model.endswith('half') else torch.float)
pose_vector = torch.empty(1, 6, dtype=torch.half if args.model.endswith('half') else torch.float)
input_image = self.input_image.to(device)
eyebrow_vector = eyebrow_vector.to(device)
mouth_eye_vector = mouth_eye_vector.to(device)
pose_vector = pose_vector.to(device)
model_cache = OrderedDict()
tot = 0
hit = 0
hit_in_a_row = 0
model_fps = FPS()
gpu_fps = FPS()
while True:
model_input = None
try:
while not self.input_queue.empty():
model_input = self.input_queue.get_nowait()
except queue.Empty:
continue
if model_input is None: continue
simplify_arr = [1000] * ifm_converter.pose_size
if args.simplify >= 1:
simplify_arr = [200] * ifm_converter.pose_size
simplify_arr[ifm_converter.eye_wink_left_index] = 50
simplify_arr[ifm_converter.eye_wink_right_index] = 50
simplify_arr[ifm_converter.eye_happy_wink_left_index] = 50
simplify_arr[ifm_converter.eye_happy_wink_right_index] = 50
simplify_arr[ifm_converter.eye_surprised_left_index] = 30
simplify_arr[ifm_converter.eye_surprised_right_index] = 30
simplify_arr[ifm_converter.iris_rotation_x_index] = 25
simplify_arr[ifm_converter.iris_rotation_y_index] = 25
simplify_arr[ifm_converter.eye_raised_lower_eyelid_left_index] = 10
simplify_arr[ifm_converter.eye_raised_lower_eyelid_right_index] = 10
simplify_arr[ifm_converter.mouth_lowered_corner_left_index] = 5
simplify_arr[ifm_converter.mouth_lowered_corner_right_index] = 5
simplify_arr[ifm_converter.mouth_raised_corner_left_index] = 5
simplify_arr[ifm_converter.mouth_raised_corner_right_index] = 5
if args.simplify >= 2:
simplify_arr[ifm_converter.head_x_index] = 100
simplify_arr[ifm_converter.head_y_index] = 100
simplify_arr[ifm_converter.eye_surprised_left_index] = 10
simplify_arr[ifm_converter.eye_surprised_right_index] = 10
model_input[ifm_converter.eye_wink_left_index] += model_input[
ifm_converter.eye_happy_wink_left_index]
model_input[ifm_converter.eye_happy_wink_left_index] = model_input[
ifm_converter.eye_wink_left_index] / 2
model_input[ifm_converter.eye_wink_left_index] = model_input[
ifm_converter.eye_wink_left_index] / 2
model_input[ifm_converter.eye_wink_right_index] += model_input[
ifm_converter.eye_happy_wink_right_index]
model_input[ifm_converter.eye_happy_wink_right_index] = model_input[
ifm_converter.eye_wink_right_index] / 2
model_input[ifm_converter.eye_wink_right_index] = model_input[
ifm_converter.eye_wink_right_index] / 2
uosum = model_input[ifm_converter.mouth_uuu_index] + \
model_input[ifm_converter.mouth_ooo_index]
model_input[ifm_converter.mouth_ooo_index] = uosum
model_input[ifm_converter.mouth_uuu_index] = 0
is_open = (model_input[ifm_converter.mouth_aaa_index] + model_input[
ifm_converter.mouth_iii_index] + uosum) > 0
model_input[ifm_converter.mouth_lowered_corner_left_index] = 0
model_input[ifm_converter.mouth_lowered_corner_right_index] = 0
model_input[ifm_converter.mouth_raised_corner_left_index] = 0.5 if is_open else 0
model_input[ifm_converter.mouth_raised_corner_right_index] = 0.5 if is_open else 0
simplify_arr[ifm_converter.mouth_lowered_corner_left_index] = 0
simplify_arr[ifm_converter.mouth_lowered_corner_right_index] = 0
simplify_arr[ifm_converter.mouth_raised_corner_left_index] = 0
simplify_arr[ifm_converter.mouth_raised_corner_right_index] = 0
if args.simplify >= 3:
simplify_arr[ifm_converter.iris_rotation_x_index] = 20
simplify_arr[ifm_converter.iris_rotation_y_index] = 20
simplify_arr[ifm_converter.eye_wink_left_index] = 32
simplify_arr[ifm_converter.eye_wink_right_index] = 32
simplify_arr[ifm_converter.eye_happy_wink_left_index] = 32
simplify_arr[ifm_converter.eye_happy_wink_right_index] = 32
if args.simplify >= 4:
simplify_arr[ifm_converter.head_x_index] = 50
simplify_arr[ifm_converter.head_y_index] = 50
simplify_arr[ifm_converter.neck_z_index] = 100
model_input[ifm_converter.eye_raised_lower_eyelid_left_index] = 0
model_input[ifm_converter.eye_raised_lower_eyelid_right_index] = 0
simplify_arr[ifm_converter.iris_rotation_x_index] = 10
simplify_arr[ifm_converter.iris_rotation_y_index] = 10
simplify_arr[ifm_converter.eye_wink_left_index] = 24
simplify_arr[ifm_converter.eye_wink_right_index] = 24
simplify_arr[ifm_converter.eye_happy_wink_left_index] = 24
simplify_arr[ifm_converter.eye_happy_wink_right_index] = 24
simplify_arr[ifm_converter.eye_surprised_left_index] = 8
simplify_arr[ifm_converter.eye_surprised_right_index] = 8
model_input[ifm_converter.eye_wink_left_index] += model_input[
ifm_converter.eye_wink_right_index]
model_input[ifm_converter.eye_wink_right_index] = model_input[
ifm_converter.eye_wink_left_index] / 2
model_input[ifm_converter.eye_wink_left_index] = model_input[
ifm_converter.eye_wink_left_index] / 2
model_input[ifm_converter.eye_surprised_left_index] += model_input[
ifm_converter.eye_surprised_right_index]
model_input[ifm_converter.eye_surprised_right_index] = model_input[
ifm_converter.eye_surprised_left_index] / 2
model_input[ifm_converter.eye_surprised_left_index] = model_input[
ifm_converter.eye_surprised_left_index] / 2
model_input[ifm_converter.eye_happy_wink_left_index] += model_input[
ifm_converter.eye_happy_wink_right_index]
model_input[ifm_converter.eye_happy_wink_right_index] = model_input[
ifm_converter.eye_happy_wink_left_index] / 2
model_input[ifm_converter.eye_happy_wink_left_index] = model_input[
ifm_converter.eye_happy_wink_left_index] / 2
model_input[ifm_converter.mouth_aaa_index] = min(
model_input[ifm_converter.mouth_aaa_index] +
model_input[ifm_converter.mouth_ooo_index] / 2 +
model_input[ifm_converter.mouth_iii_index] / 2 +
model_input[ifm_converter.mouth_uuu_index] / 2, 1
)
model_input[ifm_converter.mouth_ooo_index] = 0
model_input[ifm_converter.mouth_iii_index] = 0
model_input[ifm_converter.mouth_uuu_index] = 0
for i in range(4, args.simplify):
simplify_arr = [max(math.ceil(x * 0.8), 5) for x in simplify_arr]
for i in range(0, len(simplify_arr)):
if simplify_arr[i] > 0:
model_input[i] = round(model_input[i] * simplify_arr[i]) / simplify_arr[i]
input_hash = hash(tuple(model_input))
cached = model_cache.get(input_hash)
tot += 1
eyebrow_vector_c = [0.0] * 12
mouth_eye_vector_c = [0.0] * 27
if cached is not None and hit_in_a_row < self.model_fps_number.value:
self.output_queue.put_nowait(cached)
model_cache.move_to_end(input_hash)
hit += 1
hit_in_a_row += 1
else:
hit_in_a_row = 0
if args.perf == 'model':
tic = time.perf_counter()
if args.eyebrow:
for i in range(12):
eyebrow_vector[0, i] = model_input[i]
eyebrow_vector_c[i] = model_input[i]
for i in range(27):
mouth_eye_vector[0, i] = model_input[i + 12]
mouth_eye_vector_c[i] = model_input[i + 12]
for i in range(6):
pose_vector[0, i] = model_input[i + 27 + 12]
if model is None:
output_image = input_image
else:
output_image = model(input_image, mouth_eye_vector, pose_vector, eyebrow_vector, mouth_eye_vector_c,
eyebrow_vector_c,
self.gpu_cache_hit_ratio)
if args.perf == 'model':
torch.cuda.synchronize()
print("model", (time.perf_counter() - tic) * 1000)
tic = time.perf_counter()
postprocessed_image = output_image[0].float()
if args.perf == 'model':
print("cpu()", (time.perf_counter() - tic) * 1000)
tic = time.perf_counter()
postprocessed_image = convert_linear_to_srgb((postprocessed_image + 1.0) / 2.0)
c, h, w = postprocessed_image.shape
postprocessed_image = 255.0 * torch.transpose(postprocessed_image.reshape(c, h * w), 0, 1).reshape(h, w,
c)
postprocessed_image = postprocessed_image.byte().detach().cpu().numpy()
if args.perf == 'model':
print("postprocess", (time.perf_counter() - tic) * 1000)
tic = time.perf_counter()
self.output_queue.put_nowait(postprocessed_image)
if args.debug:
self.gpu_fps_number.value = gpu_fps()
if args.max_cache_len > 0:
model_cache[input_hash] = postprocessed_image
if len(model_cache) > args.max_cache_len:
model_cache.popitem(last=False)
if args.debug:
self.model_fps_number.value = model_fps()
self.cache_hit_ratio.value = hit / tot
@torch.no_grad()
def main():
img = Image.open(f"data/images/{args.character}.png")
img = img.convert('RGBA')
IMG_WIDTH = 512
wRatio = img.size[0] / IMG_WIDTH
img = img.resize((IMG_WIDTH, int(img.size[1] / wRatio)))
for i, px in enumerate(img.getdata()):
if px[3] <= 0:
y = i // IMG_WIDTH
x = i % IMG_WIDTH
img.putpixel((x, y), (0, 0, 0, 0))
input_image = preprocessing_image(img.crop((0, 0, IMG_WIDTH, IMG_WIDTH)))
if args.model.endswith('half'):
input_image = torch.from_numpy(input_image).half() * 2.0 - 1
else:
input_image = torch.from_numpy(input_image).float() * 2.0 - 1
input_image = input_image.unsqueeze(0)
extra_image = None
if img.size[1] > IMG_WIDTH:
extra_image = np.array(img.crop((0, IMG_WIDTH, img.size[0], img.size[1])))
print("Character Image Loaded:", args.character)
cap = None
output_fps = FPS()
if not args.debug_input:
if args.ifm is not None:
client_process = IFMClientProcess()
client_process.daemon = True
client_process.start()
print("iFacialMocap Service Running:", args.ifm)
elif args.osf is not None:
client_process = OSFClientProcess()
client_process.daemon = True
client_process.start()
print("OpenSeeFace Service Running:", args.osf)
elif args.mouse_input is not None:
client_process = MouseClientProcess()
client_process.daemon = True
client_process.start()
print("Mouse Input Running")
else:
if args.input == 'cam':
cap = cv2.VideoCapture(0)
ret, frame = cap.read()
if ret is None:
raise Exception("Can't find Camera")
else:
cap = cv2.VideoCapture(args.input)
frame_count = 0
os.makedirs(os.path.join('dst', args.character, args.output_dir), exist_ok=True)
print("Webcam Input Running")
facemesh = mp.solutions.face_mesh.FaceMesh(refine_landmarks=True)
if args.output_webcam:
cam_scale = 1
cam_width_scale = 1
if args.anime4k:
cam_scale = 2
if args.alpha_split:
cam_width_scale = 2
cam = pyvirtualcam.Camera(width=args.output_w * cam_scale * cam_width_scale, height=args.output_h * cam_scale,
fps=60,
backend=args.output_webcam,
fmt=
{'unitycapture': pyvirtualcam.PixelFormat.RGBA, 'obs': pyvirtualcam.PixelFormat.RGB}[
args.output_webcam])
print(f'Using virtual camera: {cam.device}')
a = None
if args.anime4k:
parameters = ac.Parameters()
# enable HDN for ACNet
parameters.HDN = True
# a = ac.AC(
# managerList=ac.ManagerList([ac.CUDAManager(dID=0)]),
# type=ac.ProcessorType.Cuda_ACNet,
# )
a = ac.AC(
managerList=ac.ManagerList([ac.OpenCLACNetManager(pID=0, dID=0)]),
type=ac.ProcessorType.OpenCL_ACNet,
)
a.set_arguments(parameters)
print("Anime4K Loaded")
position_vector = [0, 0, 0, 1]
position_vector_0 = None
pose_vector_0 = None
pose_queue = []
blender_data={}
if(args.ifm):
blender_data = create_default_blender_data()
mouse_data = {
'eye_l_h_temp': 0,
'eye_r_h_temp': 0,
'mouth_ratio': 0,
'eye_y_ratio': 0,
'eye_x_ratio': 0,
'x_angle': 0,
'y_angle': 0,
'z_angle': 0,
}
model_output = None
model_process = ModelClientProcess(input_image)
model_process.daemon = True
model_process.start()
print("Ready. Close this console to exit.")
while True:
# ret, frame = cap.read()
# input_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# results = facemesh.process(input_frame)
if args.perf == 'main':
tic = time.perf_counter()
if args.debug_input:
eyebrow_vector_c = [0.0] * 12
mouth_eye_vector_c = [0.0] * 27
pose_vector_c = [0.0] * 6
mouth_eye_vector_c[2] = math.sin(time.perf_counter() * 3)
mouth_eye_vector_c[3] = math.sin(time.perf_counter() * 3)
mouth_eye_vector_c[14] = 0
mouth_eye_vector_c[25] = math.sin(time.perf_counter() * 2.2) * 0.2
mouth_eye_vector_c[26] = math.sin(time.perf_counter() * 3.5) * 0.8
pose_vector_c[0] = math.sin(time.perf_counter() * 1.1)
pose_vector_c[1] = math.sin(time.perf_counter() * 1.2)
pose_vector_c[2] = math.sin(time.perf_counter() * 1.5)
eyebrow_vector_c[6]=math.sin(time.perf_counter() * 1.1)
eyebrow_vector_c[7]=math.sin(time.perf_counter() * 1.1)
elif args.osf is not None:
try:
new_blender_data = blender_data
while not client_process.should_terminate.value and not client_process.queue.empty():
new_blender_data = client_process.queue.get_nowait()
blender_data = new_blender_data
except queue.Empty:
pass
eyebrow_vector_c = [0.0] * 12
mouth_eye_vector_c = [0.0] * 27
pose_vector_c = [0.0] * 6
if len(blender_data)!=0:
mouth_eye_vector_c[2] = 1-blender_data['leftEyeOpen']
mouth_eye_vector_c[3] = 1-blender_data['rightEyeOpen']
mouth_eye_vector_c[14] = max(blender_data['MouthOpen'],0)
# print(mouth_eye_vector_c[14])
mouth_eye_vector_c[25] = -blender_data['eyeRotationY']*3-(blender_data['rotationX'])/57.3*1.5
mouth_eye_vector_c[26] = blender_data['eyeRotationX']*3+(blender_data['rotationY'])/57.3
# print(mouth_eye_vector_c[25:27])
eyebrow_vector_c[6]=blender_data['EyebrowUpDownLeft']
eyebrow_vector_c[7]=blender_data['EyebrowUpDownRight']
# print(blender_data['EyebrowUpDownLeft'],blender_data['EyebrowUpDownRight'])
# if pose_vector_0==None:
# pose_vector_0=[0,0,0]
# pose_vector_0[0] = blender_data['rotationX']
# pose_vector_0[1] = blender_data['rotationY']
# pose_vector_0[2] = blender_data['rotationZ']
# pose_vector_c[0] = (blender_data['rotationX']-pose_vector_0[0])/57.3*3
# pose_vector_c[1] = -(blender_data['rotationY']-pose_vector_0[1])/57.3*3
# pose_vector_c[2] = (blender_data['rotationZ']-pose_vector_0[2])/57.3
pose_vector_c[0] = (blender_data['rotationX'])/57.3*3
pose_vector_c[1] = -(blender_data['rotationY'])/57.3*3
pose_vector_c[2] = (blender_data['rotationZ'])/57.3*2
# print(pose_vector_c)
if position_vector_0==None:
position_vector_0=[0,0,0,1]
position_vector_0[0] = blender_data['translationX']
position_vector_0[1] = blender_data['translationY']
position_vector_0[2] = blender_data['translationZ']
position_vector[0] = -(blender_data['translationX']-position_vector_0[0])*0.1
position_vector[1] = -(blender_data['translationY']-position_vector_0[1])*0.1
position_vector[2] = -(blender_data['translationZ']-position_vector_0[2])*0.1
elif args.ifm is not None:
# get pose from ifm
try:
new_blender_data = blender_data
while not client_process.should_terminate.value and not client_process.queue.empty():
new_blender_data = client_process.queue.get_nowait()
blender_data = new_blender_data
except queue.Empty:
pass
ifacialmocap_pose_converted = ifm_converter.convert(blender_data)
# ifacialmocap_pose = blender_data
#
# eye_l_h_temp = ifacialmocap_pose[EYE_BLINK_LEFT]
# eye_r_h_temp = ifacialmocap_pose[EYE_BLINK_RIGHT]
# mouth_ratio = (ifacialmocap_pose[JAW_OPEN] - 0.10)*1.3
# x_angle = -ifacialmocap_pose[HEAD_BONE_X] * 1.5 + 1.57
# y_angle = -ifacialmocap_pose[HEAD_BONE_Y]
# z_angle = ifacialmocap_pose[HEAD_BONE_Z] - 1.57
#
# eye_x_ratio = (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 / 0.75
#
# eye_y_ratio = (ifacialmocap_pose[EYE_LOOK_UP_LEFT]
# + ifacialmocap_pose[EYE_LOOK_UP_RIGHT]
# - ifacialmocap_pose[EYE_LOOK_DOWN_RIGHT]
# + ifacialmocap_pose[EYE_LOOK_DOWN_LEFT]) / 2.0 / 0.75
eyebrow_vector_c = [0.0] * 12
mouth_eye_vector_c = [0.0] * 27
pose_vector_c = [0.0] * 6
for i in range(0, 12):
eyebrow_vector_c[i] = ifacialmocap_pose_converted[i]
for i in range(12, 39):
mouth_eye_vector_c[i - 12] = ifacialmocap_pose_converted[i]
for i in range(39, 42):
pose_vector_c[i - 39] = ifacialmocap_pose_converted[i]
position_vector = blender_data[HEAD_BONE_QUAT]
elif args.mouse_input is not None:
try:
new_blender_data = mouse_data
while not client_process.queue.empty():
new_blender_data = client_process.queue.get_nowait()
mouse_data = new_blender_data
except queue.Empty:
pass
eye_l_h_temp = mouse_data['eye_l_h_temp']
eye_r_h_temp = mouse_data['eye_r_h_temp']
mouth_ratio = mouse_data['mouth_ratio']
eye_y_ratio = mouse_data['eye_y_ratio']
eye_x_ratio = mouse_data['eye_x_ratio']
x_angle = mouse_data['x_angle']
y_angle = mouse_data['y_angle']
z_angle = mouse_data['z_angle']
eyebrow_vector_c = [0.0] * 12
mouth_eye_vector_c = [0.0] * 27
pose_vector_c = [0.0] * 6
mouth_eye_vector_c[2] = eye_l_h_temp
mouth_eye_vector_c[3] = eye_r_h_temp
mouth_eye_vector_c[14] = mouth_ratio * 1.5
mouth_eye_vector_c[25] = eye_y_ratio
mouth_eye_vector_c[26] = eye_x_ratio
pose_vector_c[0] = x_angle
pose_vector_c[1] = y_angle
pose_vector_c[2] = z_angle
else:
ret, frame = cap.read()
input_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
results = facemesh.process(input_frame)
if results.multi_face_landmarks is None:
continue
facial_landmarks = results.multi_face_landmarks[0].landmark
if args.debug:
pose, debug_image = get_pose(facial_landmarks, frame)
else:
pose = get_pose(facial_landmarks)
if len(pose_queue) < 3:
pose_queue.append(pose)
pose_queue.append(pose)
pose_queue.append(pose)
else:
pose_queue.pop(0)
pose_queue.append(pose)
np_pose = np.average(np.array(pose_queue), axis=0, weights=[0.6, 0.3, 0.1])
eye_l_h_temp = np_pose[0]
eye_r_h_temp = np_pose[1]
mouth_ratio = np_pose[2]
eye_y_ratio = np_pose[3]
eye_x_ratio = np_pose[4]
x_angle = np_pose[5]
y_angle = np_pose[6]
z_angle = np_pose[7]
eyebrow_vector_c = [0.0] * 12
mouth_eye_vector_c = [0.0] * 27
pose_vector_c = [0.0] * 6
mouth_eye_vector_c[2] = eye_l_h_temp
mouth_eye_vector_c[3] = eye_r_h_temp
mouth_eye_vector_c[14] = mouth_ratio * 1.5
mouth_eye_vector_c[25] = eye_y_ratio
mouth_eye_vector_c[26] = eye_x_ratio
pose_vector_c[0] = (x_angle - 1.5) * 1.6
pose_vector_c[1] = y_angle * 2.0 # temp weight
pose_vector_c[2] = (z_angle + 1.5) * 2 # temp weight
pose_vector_c[3] = pose_vector_c[1]
pose_vector_c[4] = pose_vector_c[2]
model_input_arr = eyebrow_vector_c
model_input_arr.extend(mouth_eye_vector_c)
model_input_arr.extend(pose_vector_c)
model_process.input_queue.put_nowait(model_input_arr)
has_model_output = 0
try:
new_model_output = model_output
while not model_process.output_queue.empty():
has_model_output += 1
new_model_output = model_process.output_queue.get_nowait()
model_output = new_model_output
except queue.Empty:
pass
if model_output is None:
time.sleep(1)
continue
# print(has_model_output)
# should_output=should_output or has_model_output
# if not should_output:
# continue
postprocessed_image = model_output
if args.perf == 'main':
print('===')
print("input", time.perf_counter() - tic)
tic = time.perf_counter()
if extra_image is not None:
postprocessed_image = cv2.vconcat([postprocessed_image, extra_image])
k_scale = 1
rotate_angle = 0
dx = 0
dy = 0
if args.extend_movement:
k_scale = position_vector[2] * math.sqrt(args.extend_movement) + 1
rotate_angle = -position_vector[0] * 10 * args.extend_movement
dx = position_vector[0] * 400 * k_scale * args.extend_movement
dy = -position_vector[1] * 600 * k_scale * args.extend_movement
if args.bongo:
rotate_angle -= 5
rm = cv2.getRotationMatrix2D((IMG_WIDTH / 2, IMG_WIDTH / 2), rotate_angle, k_scale)
rm[0, 2] += dx + args.output_w / 2 - IMG_WIDTH / 2
rm[1, 2] += dy + args.output_h / 2 - IMG_WIDTH / 2
postprocessed_image = cv2.warpAffine(
postprocessed_image,
rm,
(args.output_w, args.output_h))
if args.perf == 'main':
print("extendmovement", (time.perf_counter() - tic) * 1000)
tic = time.perf_counter()
output_fps_number = output_fps()
if args.anime4k:
alpha_channel = postprocessed_image[:, :, 3]
alpha_channel = cv2.resize(alpha_channel, None, fx=2, fy=2)
# a.load_image_from_numpy(cv2.cvtColor(postprocessed_image, cv2.COLOR_RGBA2RGB), input_type=ac.AC_INPUT_RGB)
# img = cv2.imread("character/test41.png")
img1 = cv2.cvtColor(postprocessed_image, cv2.COLOR_RGBA2BGR)
# a.load_image_from_numpy(img, input_type=ac.AC_INPUT_BGR)
a.load_image_from_numpy(img1, input_type=ac.AC_INPUT_BGR)
a.process()
postprocessed_image = a.save_image_to_numpy()
postprocessed_image = cv2.merge((postprocessed_image, alpha_channel))
postprocessed_image = cv2.cvtColor(postprocessed_image, cv2.COLOR_BGRA2RGBA)
if args.perf == 'main':
print("anime4k", (time.perf_counter() - tic) * 1000)
tic = time.perf_counter()
if args.alpha_split:
alpha_image = cv2.merge(
[postprocessed_image[:, :, 3], postprocessed_image[:, :, 3], postprocessed_image[:, :, 3]])
alpha_image = cv2.cvtColor(alpha_image, cv2.COLOR_RGB2RGBA)
postprocessed_image = cv2.hconcat([postprocessed_image, alpha_image])
if args.debug:
output_frame = cv2.cvtColor(postprocessed_image, cv2.COLOR_RGBA2BGRA)
# resized_frame = cv2.resize(output_frame, (np.min(debug_image.shape[:2]), np.min(debug_image.shape[:2])))
# output_frame = np.concatenate([debug_image, resized_frame], axis=1)
cv2.putText(output_frame, str('OUT_FPS:%.1f' % output_fps_number), (0, 16), cv2.FONT_HERSHEY_PLAIN, 1,
(0, 255, 0), 1)
if args.max_cache_len > 0:
cv2.putText(output_frame, str(
'GPU_FPS:%.1f / %.1f' % (model_process.model_fps_number.value, model_process.gpu_fps_number.value)),
(0, 32),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0), 1)
else:
cv2.putText(output_frame, str(
'GPU_FPS:%.1f' % (model_process.model_fps_number.value)),
(0, 32),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0), 1)
if args.ifm is not None:
cv2.putText(output_frame, str('IFM_FPS:%.1f' % client_process.ifm_fps_number.value), (0, 48),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0), 1)
if args.max_cache_len > 0:
cv2.putText(output_frame, str('MEMCACHED:%.1f%%' % (model_process.cache_hit_ratio.value * 100)),
(0, 64),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0), 1)
if args.max_gpu_cache_len > 0:
cv2.putText(output_frame, str('GPUCACHED:%.1f%%' % (model_process.gpu_cache_hit_ratio.value * 100)),
(0, 80),
cv2.FONT_HERSHEY_PLAIN, 1, (0, 255, 0), 1)
cv2.imshow("frame", output_frame)
# cv2.imshow("camera", debug_image)
cv2.waitKey(1)
if args.output_webcam:
# result_image = np.zeros([720, 1280, 3], dtype=np.uint8)
# result_image[720 - 512:, 1280 // 2 - 256:1280 // 2 + 256] = cv2.resize(
# cv2.cvtColor(postprocessing_image(output_image.cpu()), cv2.COLOR_RGBA2RGB), (512, 512))
result_image = postprocessed_image
if args.output_webcam == 'obs':
result_image = cv2.cvtColor(result_image, cv2.COLOR_RGBA2RGB)
cam.send(result_image)
cam.sleep_until_next_frame()
if args.perf == 'main':
print("output", (time.perf_counter() - tic) * 1000)
tic = time.perf_counter()
if __name__ == '__main__':
main()