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add threading

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Somdev Sangwan 2023-06-04 17:00:09 +05:30 committed by GitHub
parent 81320ad5f1
commit c19f8125f2
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1 changed files with 46 additions and 25 deletions
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roop/swapper.py
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@ -4,24 +4,20 @@ import torch
import onnxruntime
import cv2
import insightface
import threading
import roop.globals
from roop.analyser import get_face_single, get_face_many
from roop.globals import gpu_vendor
FACE_SWAPPER = None
lock = threading.Lock()
def get_face_swapper():
global FACE_SWAPPER
if FACE_SWAPPER is None:
session_options = onnxruntime.SessionOptions()
if roop.globals.gpu_vendor is not None:
session_options.intra_op_num_threads = roop.globals.gpu_threads
else:
session_options.intra_op_num_threads = roop.globals.cpu_threads
session_options.execution_mode = onnxruntime.ExecutionMode.ORT_PARALLEL
model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), '../inswapper_128.onnx')
FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=roop.globals.providers, session_options=session_options)
with lock:
if FACE_SWAPPER is None:
model_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), '../inswapper_128.onnx')
FACE_SWAPPER = insightface.model_zoo.get_model(model_path, providers=roop.globals.providers)
return FACE_SWAPPER
@ -31,7 +27,20 @@ def swap_face_in_frame(source_face, target_face, frame):
return frame
def process_faces(source_face, target_frame, progress):
def process_frames(source_img, frame_paths, progress=None):
source_face = get_face_single(cv2.imread(source_img))
for frame_path in frame_paths:
frame = cv2.imread(frame_path)
try:
result = process_faces(source_face, frame)
cv2.imwrite(frame_path, result)
except Exception as e:
print(">>>>", e)
pass
if progress:
progress.update(1)
def process_faces(source_face, target_frame):
if roop.globals.all_faces:
many_faces = get_face_many(target_frame)
if many_faces:
@ -45,22 +54,34 @@ def process_faces(source_face, target_frame, progress):
def process_video(source_img, frame_paths, preview_callback):
source_face = get_face_single(cv2.imread(source_img))
progress_bar_format = '{l_bar}{bar}| {n_fmt}/{total_fmt} [{elapsed}<{remaining}, {rate_fmt}{postfix}]'
with tqdm(total=len(frame_paths), desc="Processing", unit="frame", dynamic_ncols=True, bar_format=progress_bar_format) as progress:
for frame_path in frame_paths:
if roop.globals.gpu_vendor == 'nvidia':
progress.set_postfix(cuda_utilization="{:02d}%".format(torch.cuda.utilization()), cuda_memory="{:02d}GB".format(torch.cuda.memory_usage()))
frame = cv2.imread(frame_path)
try:
result = process_faces(source_face, frame, progress)
cv2.imwrite(frame_path, result)
if preview_callback:
preview_callback(cv2.cvtColor(result, cv2.COLOR_BGR2RGB))
except Exception:
pass
progress.update(1)
# nvidia multi-threading
if roop.globals.gpu_vendor == 'nvidia':
#progress.set_postfix(cuda_utilization="{:02d}%".format(torch.cuda.utilization()), cuda_memory="{:02d}GB".format(torch.cuda.memory_usage()))
# caculate the number of frames each threads processed
num_threads = roop.globals.gpu_threads
num_frames_per_thread = len(frame_paths) // num_threads
remaining_frames = len(frame_paths) % num_threads
# create thread list
threads = []
start_index = 0
# create thread and launch
for _ in range(num_threads):
end_index = start_index + num_frames_per_thread
if remaining_frames > 0:
end_index += 1
remaining_frames -= 1
thread_frame_paths = frame_paths[start_index:end_index]
thread = threading.Thread(target=process_frames, args=(source_img, thread_frame_paths, progress))
threads.append(thread)
thread.start()
start_index = end_index
for thread in threads:
thread.join()
else:
process_frames(source_img, frame_paths, progress)
def process_img(source_img, target_path, output_file):