669 lines
28 KiB
Python
669 lines
28 KiB
Python
import cv2
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import torch
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import pyvirtualcam
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import numpy as np
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import mediapipe as mp
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from PIL import Image
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import tha2.poser.modes.mode_20_wx
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from models import TalkingAnimeLight
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from pose import get_pose
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from utils import preprocessing_image, postprocessing_image
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import errno
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import json
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import os
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import queue
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import socket
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import time
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import math
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import re
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from collections import OrderedDict
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from multiprocessing import Value, Process, Queue
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from pyanime4k import ac
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from tha2.mocap.ifacialmocap_constants import *
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from args import args
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import collections
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class FPS:
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def __init__(self, avarageof=50):
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self.frametimestamps = collections.deque(maxlen=avarageof)
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def __call__(self):
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self.frametimestamps.append(time.time())
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if len(self.frametimestamps) > 1:
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return len(self.frametimestamps) / (self.frametimestamps[-1] - self.frametimestamps[0])
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else:
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return 0.0
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device = torch.device('cuda') if torch.cuda.is_available() and not args.skip_model else torch.device('cpu')
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def create_default_blender_data():
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data = {}
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for blendshape_name in BLENDSHAPE_NAMES:
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data[blendshape_name] = 0.0
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data[HEAD_BONE_X] = 0.0
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data[HEAD_BONE_Y] = 0.0
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data[HEAD_BONE_Z] = 0.0
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data[HEAD_BONE_QUAT] = [0.0, 0.0, 0.0, 1.0]
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data[LEFT_EYE_BONE_X] = 0.0
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data[LEFT_EYE_BONE_Y] = 0.0
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data[LEFT_EYE_BONE_Z] = 0.0
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data[LEFT_EYE_BONE_QUAT] = [0.0, 0.0, 0.0, 1.0]
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data[RIGHT_EYE_BONE_X] = 0.0
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data[RIGHT_EYE_BONE_Y] = 0.0
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data[RIGHT_EYE_BONE_Z] = 0.0
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data[RIGHT_EYE_BONE_QUAT] = [0.0, 0.0, 0.0, 1.0]
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return data
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ifm_converter = tha2.poser.modes.mode_20_wx.IFacialMocapPoseConverter20()
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class IFMClientProcess(Process):
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def __init__(self):
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super().__init__()
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self.queue = Queue()
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self.should_terminate = Value('b', False)
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self.address = args.ifm.split(':')[0]
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self.port = int(args.ifm.split(':')[1])
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self.ifm_fps_number = Value('f', 0.0)
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self.perf_time = 0
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def run(self):
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udpClntSock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
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data = "iFacialMocap_sahuasouryya9218sauhuiayeta91555dy3719"
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data = data.encode('utf-8')
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udpClntSock.sendto(data, (self.address, self.port))
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self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
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self.socket.setblocking(False)
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self.socket.bind(("", self.port))
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self.socket.settimeout(0.1)
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ifm_fps = FPS()
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pre_socket_string = ''
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while True:
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if self.should_terminate.value:
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break
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try:
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socket_bytes = self.socket.recv(8192)
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except socket.error as e:
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err = e.args[0]
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if err == errno.EAGAIN or err == errno.EWOULDBLOCK or err == 'timed out':
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continue
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else:
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raise e
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socket_string = socket_bytes.decode("utf-8")
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if args.debug and pre_socket_string != socket_string:
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self.ifm_fps_number.value = ifm_fps()
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pre_socket_string = socket_string
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# print(socket_string)
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# blender_data = json.loads(socket_string)
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data = self.convert_from_blender_data(socket_string)
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try:
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self.queue.put_nowait(data)
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except queue.Full:
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pass
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self.queue.close()
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self.socket.close()
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@staticmethod
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def convert_from_blender_data(blender_data):
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data = {}
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for item in blender_data.split('|'):
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if item.find('#') != -1:
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k, arr = item.split('#')
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arr = [float(n) for n in arr.split(',')]
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data[k.replace("_L", "Left").replace("_R", "Right")] = arr
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elif item.find('-') != -1:
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k, v = item.split("-")
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data[k.replace("_L", "Left").replace("_R", "Right")] = float(v) / 100
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to_rad = 57.3
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data[HEAD_BONE_X] = data["=head"][0] / to_rad
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data[HEAD_BONE_Y] = data["=head"][1] / to_rad
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data[HEAD_BONE_Z] = data["=head"][2] / to_rad
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data[HEAD_BONE_QUAT] = [data["=head"][3], data["=head"][4], data["=head"][5], 1]
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# print(data[HEAD_BONE_QUAT][2],min(data[EYE_BLINK_LEFT],data[EYE_BLINK_RIGHT]))
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data[RIGHT_EYE_BONE_X] = data["rightEye"][0] / to_rad
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data[RIGHT_EYE_BONE_Y] = data["rightEye"][1] / to_rad
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data[RIGHT_EYE_BONE_Z] = data["rightEye"][2] / to_rad
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data[LEFT_EYE_BONE_X] = data["leftEye"][0] / to_rad
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data[LEFT_EYE_BONE_Y] = data["leftEye"][1] / to_rad
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data[LEFT_EYE_BONE_Z] = data["leftEye"][2] / to_rad
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return data
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class ModelClientProcess(Process):
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def __init__(self, input_image):
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super().__init__()
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self.should_terminate = Value('b', False)
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self.updated = Value('b', False)
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self.data = None
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self.input_image = input_image
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self.output_queue = Queue()
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self.input_queue = Queue()
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self.model_fps_number = Value('f', 0.0)
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self.gpu_fps_number = Value('f', 0.0)
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self.cache_hit_ratio = Value('f', 0.0)
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self.gpu_cache_hit_ratio= Value('f', 0.0)
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def run(self):
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model = None
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if not args.skip_model:
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model = TalkingAnimeLight().to(device)
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model = model.eval()
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model = model
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print("Pretrained Model Loaded")
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mouth_eye_vector = torch.empty(1, 27)
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pose_vector = torch.empty(1, 3)
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input_image = self.input_image.to(device)
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mouth_eye_vector = mouth_eye_vector.to(device)
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pose_vector = pose_vector.to(device)
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model_cache = OrderedDict()
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tot = 0
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hit = 0
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model_fps = FPS()
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gpu_fps = FPS()
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while True:
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model_input = None
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try:
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while not self.input_queue.empty():
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model_input = self.input_queue.get_nowait()
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except queue.Empty:
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continue
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if model_input is None: continue
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simplify_arr = [1000] * ifm_converter.pose_size
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if args.simplify >= 1:
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simplify_arr = [200] * ifm_converter.pose_size
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simplify_arr[ifm_converter.eye_wink_left_index] = 50
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simplify_arr[ifm_converter.eye_wink_right_index] = 50
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simplify_arr[ifm_converter.eye_happy_wink_left_index] = 50
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simplify_arr[ifm_converter.eye_happy_wink_right_index] = 50
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simplify_arr[ifm_converter.eye_surprised_left_index] = 30
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simplify_arr[ifm_converter.eye_surprised_right_index] = 30
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simplify_arr[ifm_converter.iris_rotation_x_index] = 25
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simplify_arr[ifm_converter.iris_rotation_y_index] = 25
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simplify_arr[ifm_converter.eye_raised_lower_eyelid_left_index] = 10
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simplify_arr[ifm_converter.eye_raised_lower_eyelid_right_index] = 10
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simplify_arr[ifm_converter.mouth_lowered_corner_left_index] = 5
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simplify_arr[ifm_converter.mouth_lowered_corner_right_index] = 5
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simplify_arr[ifm_converter.mouth_raised_corner_left_index] = 5
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simplify_arr[ifm_converter.mouth_raised_corner_right_index] = 5
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if args.simplify >= 2:
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simplify_arr[ifm_converter.head_x_index] = 100
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simplify_arr[ifm_converter.head_y_index] = 100
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simplify_arr[ifm_converter.eye_surprised_left_index] = 10
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simplify_arr[ifm_converter.eye_surprised_right_index] = 10
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model_input[ifm_converter.eye_wink_left_index - 12] += model_input[
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ifm_converter.eye_happy_wink_left_index - 12]
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model_input[ifm_converter.eye_happy_wink_left_index - 12] = model_input[
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ifm_converter.eye_wink_left_index - 12] / 2
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model_input[ifm_converter.eye_wink_left_index - 12] = model_input[
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ifm_converter.eye_wink_left_index - 12] / 2
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model_input[ifm_converter.eye_wink_right_index - 12] += model_input[
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ifm_converter.eye_happy_wink_right_index - 12]
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model_input[ifm_converter.eye_happy_wink_right_index - 12] = model_input[
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ifm_converter.eye_wink_right_index - 12] / 2
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model_input[ifm_converter.eye_wink_right_index - 12] = model_input[
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ifm_converter.eye_wink_right_index - 12] / 2
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uosum = model_input[ifm_converter.mouth_uuu_index - 12] + \
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model_input[ifm_converter.mouth_ooo_index - 12]
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model_input[ifm_converter.mouth_ooo_index - 12] = uosum
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model_input[ifm_converter.mouth_uuu_index - 12] = 0
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is_open = (model_input[ifm_converter.mouth_aaa_index - 12] + model_input[
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ifm_converter.mouth_iii_index - 12] + uosum) > 0
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model_input[ifm_converter.mouth_lowered_corner_left_index - 12] = 0
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model_input[ifm_converter.mouth_lowered_corner_right_index - 12] = 0
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model_input[ifm_converter.mouth_raised_corner_left_index - 12] = 0.5 if is_open else 0
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model_input[ifm_converter.mouth_raised_corner_right_index - 12] = 0.5 if is_open else 0
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simplify_arr[ifm_converter.mouth_lowered_corner_left_index] = 0
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simplify_arr[ifm_converter.mouth_lowered_corner_right_index] = 0
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simplify_arr[ifm_converter.mouth_raised_corner_left_index] = 0
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simplify_arr[ifm_converter.mouth_raised_corner_right_index] = 0
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if args.simplify >= 3:
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simplify_arr[ifm_converter.iris_rotation_x_index] = 20
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simplify_arr[ifm_converter.iris_rotation_y_index] = 20
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simplify_arr[ifm_converter.eye_wink_left_index] = 32
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simplify_arr[ifm_converter.eye_wink_right_index] = 32
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simplify_arr[ifm_converter.eye_happy_wink_left_index] = 32
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simplify_arr[ifm_converter.eye_happy_wink_right_index] = 32
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if args.simplify >= 4:
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simplify_arr[ifm_converter.head_x_index] = 50
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simplify_arr[ifm_converter.head_y_index] = 50
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simplify_arr[ifm_converter.neck_z_index] = 100
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model_input[ifm_converter.eye_raised_lower_eyelid_left_index - 12] = 0
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model_input[ifm_converter.eye_raised_lower_eyelid_right_index - 12] = 0
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simplify_arr[ifm_converter.iris_rotation_x_index] = 10
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simplify_arr[ifm_converter.iris_rotation_y_index] = 10
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simplify_arr[ifm_converter.eye_wink_left_index] = 24
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simplify_arr[ifm_converter.eye_wink_right_index] = 24
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simplify_arr[ifm_converter.eye_happy_wink_left_index] = 24
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simplify_arr[ifm_converter.eye_happy_wink_right_index] = 24
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simplify_arr[ifm_converter.eye_surprised_left_index] = 8
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simplify_arr[ifm_converter.eye_surprised_right_index] = 8
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model_input[ifm_converter.eye_wink_left_index - 12] += model_input[
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ifm_converter.eye_wink_right_index - 12]
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model_input[ifm_converter.eye_wink_right_index - 12] = model_input[
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ifm_converter.eye_wink_left_index - 12] / 2
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model_input[ifm_converter.eye_wink_left_index - 12] = model_input[
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ifm_converter.eye_wink_left_index - 12] / 2
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model_input[ifm_converter.eye_surprised_left_index - 12] += model_input[
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ifm_converter.eye_surprised_right_index - 12]
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model_input[ifm_converter.eye_surprised_right_index - 12] = model_input[
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ifm_converter.eye_surprised_left_index - 12] / 2
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model_input[ifm_converter.eye_surprised_left_index - 12] = model_input[
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ifm_converter.eye_surprised_left_index - 12] / 2
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model_input[ifm_converter.eye_happy_wink_left_index - 12] += model_input[
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ifm_converter.eye_happy_wink_right_index - 12]
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model_input[ifm_converter.eye_happy_wink_right_index - 12] = model_input[
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ifm_converter.eye_happy_wink_left_index - 12] / 2
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model_input[ifm_converter.eye_happy_wink_left_index - 12] = model_input[
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ifm_converter.eye_happy_wink_left_index - 12] / 2
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model_input[ifm_converter.mouth_aaa_index - 12] = min(
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model_input[ifm_converter.mouth_aaa_index - 12] +
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model_input[ifm_converter.mouth_ooo_index - 12] / 2 +
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model_input[ifm_converter.mouth_iii_index - 12] / 2 +
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model_input[ifm_converter.mouth_uuu_index - 12] / 2, 1
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)
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model_input[ifm_converter.mouth_ooo_index - 12] = 0
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model_input[ifm_converter.mouth_iii_index - 12] = 0
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model_input[ifm_converter.mouth_uuu_index - 12] = 0
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for i in range(4, args.simplify):
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simplify_arr = [max(math.ceil(x * 0.8), 5) for x in simplify_arr]
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for i in range(12, len(simplify_arr)):
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if simplify_arr[i] > 0:
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model_input[i - 12] = round(model_input[i - 12] * simplify_arr[i]) / simplify_arr[i]
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input_hash = hash(tuple(model_input))
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cached = model_cache.get(input_hash)
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tot += 1
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mouth_eye_vector_c=[0.0]*27
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if not cached is None:
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self.output_queue.put_nowait(cached)
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model_cache.move_to_end(input_hash)
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hit += 1
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else:
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if args.perf:
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tic = time.perf_counter()
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for i in range(27):
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mouth_eye_vector[0, i] = model_input[i]
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mouth_eye_vector_c[i]=model_input[i]
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for i in range(3):
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pose_vector[0, i] = model_input[i + 27]
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if model is None:
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output_image = input_image
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else:
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output_image = model(input_image, mouth_eye_vector, pose_vector, mouth_eye_vector_c,self.gpu_cache_hit_ratio)
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if args.perf:
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torch.cuda.synchronize()
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print("model", (time.perf_counter() - tic) * 1000)
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tic = time.perf_counter()
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postprocessed_image = output_image.cpu()
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if args.perf:
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print("cpu()", (time.perf_counter() - tic) * 1000)
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tic = time.perf_counter()
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postprocessed_image = postprocessing_image(postprocessed_image)
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if args.perf:
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print("postprocess", (time.perf_counter() - tic) * 1000)
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tic = time.perf_counter()
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self.output_queue.put_nowait(postprocessed_image)
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if args.debug:
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self.gpu_fps_number.value = gpu_fps()
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if args.max_cache_len > 0:
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model_cache[input_hash] = postprocessed_image
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if len(model_cache) > args.max_cache_len:
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model_cache.popitem(last=False)
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if args.debug:
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self.model_fps_number.value = model_fps()
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self.cache_hit_ratio.value = hit / tot
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@torch.no_grad()
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def main():
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img = Image.open(f"character/{args.character}.png")
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wRatio = img.size[0] / 256
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img = img.resize((256, int(img.size[1] / wRatio)))
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input_image = preprocessing_image(img.crop((0, 0, 256, 256))).unsqueeze(0)
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extra_image = None
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if img.size[1] > 256:
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extra_image = np.array(img.crop((0, 256, img.size[0], img.size[1])))
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print("Character Image Loaded:", args.character)
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cap = None
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output_fps = FPS()
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if not args.debug_input:
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if args.ifm is not None:
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client_process = IFMClientProcess()
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client_process.daemon = True
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client_process.start()
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print("IFM Service Running:", args.ifm)
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else:
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if args.input == 'cam':
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cap = cv2.VideoCapture(0)
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ret, frame = cap.read()
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if ret is None:
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raise Exception("Can't find Camera")
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else:
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cap = cv2.VideoCapture(args.input)
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frame_count = 0
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os.makedirs(os.path.join('dst', args.character, args.output_dir), exist_ok=True)
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print("Webcam Input Running")
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facemesh = mp.solutions.face_mesh.FaceMesh(refine_landmarks=True)
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if args.output_webcam:
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cam_scale = 1
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cam_width_scale = 1
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if args.anime4k:
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cam_scale = 2
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if args.alpha_split:
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cam_width_scale = 2
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cam = pyvirtualcam.Camera(width=args.output_w * cam_scale * cam_width_scale, height=args.output_h * cam_scale,
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fps=60,
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backend=args.output_webcam,
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fmt=
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{'unitycapture': pyvirtualcam.PixelFormat.RGBA, 'obs': pyvirtualcam.PixelFormat.RGB}[
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args.output_webcam])
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print(f'Using virtual camera: {cam.device}')
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a = None
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if args.anime4k:
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parameters = ac.Parameters()
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# enable HDN for ACNet
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parameters.HDN = True
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# a = ac.AC(
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# 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)
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|
print("Anime4K Loaded")
|
|
|
|
position_vector = [0, 0, 0, 1]
|
|
|
|
pose_queue = []
|
|
blender_data = create_default_blender_data()
|
|
|
|
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:
|
|
tic = time.perf_counter()
|
|
if args.debug_input:
|
|
mouth_eye_vector_c = [0.0] * 27
|
|
pose_vector_c = [0.0] * 3
|
|
|
|
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)
|
|
|
|
|
|
|
|
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
|
|
|
|
mouth_eye_vector_c = [0.0] * 27
|
|
pose_vector_c = [0.0] * 3
|
|
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]
|
|
|
|
|
|
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]
|
|
|
|
mouth_eye_vector_c = [0.0] * 27
|
|
pose_vector_c = [0.0] * 3
|
|
|
|
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
|
|
|
|
model_input_arr = 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:
|
|
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 is not None:
|
|
k_scale = position_vector[2] * math.sqrt(args.extend_movement) + 1
|
|
rotate_angle = -position_vector[0] * 40 * args.extend_movement
|
|
dx = position_vector[0] * 400 * k_scale * args.extend_movement
|
|
dy = -position_vector[1] * 600 * k_scale * args.extend_movement
|
|
rm = cv2.getRotationMatrix2D((128, 128), rotate_angle, k_scale)
|
|
rm[0, 2] += dx + args.output_w / 2 - 128
|
|
rm[1, 2] += dy + args.output_h / 2 - 128
|
|
|
|
postprocessed_image = cv2.warpAffine(
|
|
postprocessed_image,
|
|
rm,
|
|
(args.output_w, args.output_h))
|
|
|
|
if args.perf:
|
|
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:
|
|
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:
|
|
print("output", (time.perf_counter() - tic) * 1000)
|
|
tic = time.perf_counter()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|