xaviviro
263 lines
11 KiB
Python
263 lines
11 KiB
Python
import cv2
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import onnxruntime as rt
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import sys
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from insightface.app import FaceAnalysis
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sys.path.insert(1, './recognition')
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from scrfd import SCRFD
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from arcface_onnx import ArcFaceONNX
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import os.path as osp
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import os
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from pathlib import Path
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from tqdm import tqdm
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import ffmpeg
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import random
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import multiprocessing as mp
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from concurrent.futures import ThreadPoolExecutor
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from insightface.model_zoo.inswapper import INSwapper
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import psutil
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from enum import Enum
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from insightface.app.common import Face
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from insightface.utils.storage import ensure_available
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import re
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import subprocess
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class RefacerMode(Enum):
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CPU, CUDA, COREML, TENSORRT = range(1, 5)
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class Refacer:
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def __init__(self,force_cpu=False,colab_performance=False):
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self.first_face = False
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self.force_cpu = force_cpu
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self.colab_performance = colab_performance
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self.__check_encoders()
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self.__check_providers()
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self.total_mem = psutil.virtual_memory().total
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self.__init_apps()
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def __check_providers(self):
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if self.force_cpu :
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self.providers = ['CPUExecutionProvider']
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else:
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self.providers = rt.get_available_providers()
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rt.set_default_logger_severity(4)
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self.sess_options = rt.SessionOptions()
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self.sess_options.execution_mode = rt.ExecutionMode.ORT_SEQUENTIAL
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self.sess_options.graph_optimization_level = rt.GraphOptimizationLevel.ORT_ENABLE_ALL
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if len(self.providers) == 1 and 'CPUExecutionProvider' in self.providers:
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self.mode = RefacerMode.CPU
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self.use_num_cpus = mp.cpu_count()-1
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self.sess_options.intra_op_num_threads = int(self.use_num_cpus/3)
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print(f"CPU mode with providers {self.providers}")
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elif self.colab_performance:
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self.mode = RefacerMode.TENSORRT
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self.use_num_cpus = mp.cpu_count()-1
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self.sess_options.intra_op_num_threads = int(self.use_num_cpus/3)
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print(f"TENSORRT mode with providers {self.providers}")
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elif 'CoreMLExecutionProvider' in self.providers:
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self.mode = RefacerMode.COREML
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self.use_num_cpus = mp.cpu_count()-1
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self.sess_options.intra_op_num_threads = int(self.use_num_cpus/3)
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print(f"CoreML mode with providers {self.providers}")
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elif 'CUDAExecutionProvider' in self.providers:
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self.mode = RefacerMode.CUDA
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self.use_num_cpus = 2
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self.sess_options.intra_op_num_threads = 1
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if 'TensorrtExecutionProvider' in self.providers:
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self.providers.remove('TensorrtExecutionProvider')
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print(f"CUDA mode with providers {self.providers}")
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"""
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elif 'TensorrtExecutionProvider' in self.providers:
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self.mode = RefacerMode.TENSORRT
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#self.use_num_cpus = 1
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#self.sess_options.intra_op_num_threads = 1
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self.use_num_cpus = mp.cpu_count()-1
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self.sess_options.intra_op_num_threads = int(self.use_num_cpus/3)
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print(f"TENSORRT mode with providers {self.providers}")
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"""
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def __init_apps(self):
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assets_dir = ensure_available('models', 'buffalo_l', root='~/.insightface')
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model_path = os.path.join(assets_dir, 'det_10g.onnx')
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sess_face = rt.InferenceSession(model_path, self.sess_options, providers=self.providers)
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self.face_detector = SCRFD(model_path,sess_face)
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self.face_detector.prepare(0,input_size=(640, 640))
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model_path = os.path.join(assets_dir , 'w600k_r50.onnx')
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sess_rec = rt.InferenceSession(model_path, self.sess_options, providers=self.providers)
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self.rec_app = ArcFaceONNX(model_path,sess_rec)
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self.rec_app.prepare(0)
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model_path = 'inswapper_128.onnx'
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sess_swap = rt.InferenceSession(model_path, self.sess_options, providers=self.providers)
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self.face_swapper = INSwapper(model_path,sess_swap)
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def prepare_faces(self, faces):
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self.replacement_faces=[]
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for face in faces:
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#image1 = cv2.imread(face.origin)
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if "origin" in face:
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face_threshold = face['threshold']
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bboxes1, kpss1 = self.face_detector.autodetect(face['origin'], max_num=1)
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if len(kpss1)<1:
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raise Exception('No face detected on "Face to replace" image')
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feat_original = self.rec_app.get(face['origin'], kpss1[0])
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else:
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face_threshold = 0
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self.first_face = True
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feat_original = None
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print('No origin image: First face change')
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#image2 = cv2.imread(face.destination)
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_faces = self.__get_faces(face['destination'],max_num=1)
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if len(_faces)<1:
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raise Exception('No face detected on "Destination face" image')
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self.replacement_faces.append((feat_original,_faces[0],face_threshold))
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def __convert_video(self,video_path,output_video_path):
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if self.video_has_audio:
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print("Merging audio with the refaced video...")
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new_path = output_video_path + str(random.randint(0,999)) + "_c.mp4"
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#stream = ffmpeg.input(output_video_path)
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in1 = ffmpeg.input(output_video_path)
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in2 = ffmpeg.input(video_path)
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out = ffmpeg.output(in1.video, in2.audio, new_path,video_bitrate=self.ffmpeg_video_bitrate,vcodec=self.ffmpeg_video_encoder)
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out.run(overwrite_output=True,quiet=True)
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else:
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new_path = output_video_path
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print("The video doesn't have audio, so post-processing is not necessary")
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print(f"The process has finished.\nThe refaced video can be found at {os.path.abspath(new_path)}")
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return new_path
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def __get_faces(self,frame,max_num=0):
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bboxes, kpss = self.face_detector.detect(frame,max_num=max_num,metric='default')
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if bboxes.shape[0] == 0:
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return []
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ret = []
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for i in range(bboxes.shape[0]):
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bbox = bboxes[i, 0:4]
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det_score = bboxes[i, 4]
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kps = None
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if kpss is not None:
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kps = kpss[i]
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face = Face(bbox=bbox, kps=kps, det_score=det_score)
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face.embedding = self.rec_app.get(frame, kps)
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ret.append(face)
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return ret
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def process_first_face(self,frame):
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faces = self.__get_faces(frame,max_num=1)
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if len(faces) != 0:
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frame = self.face_swapper.get(frame, faces[0], self.replacement_faces[0][1], paste_back=True)
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return frame
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def process_faces(self,frame):
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faces = self.__get_faces(frame,max_num=0)
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for rep_face in self.replacement_faces:
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for i in range(len(faces) - 1, -1, -1):
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sim = self.rec_app.compute_sim(rep_face[0], faces[i].embedding)
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if sim>=rep_face[2]:
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frame = self.face_swapper.get(frame, faces[i], rep_face[1], paste_back=True)
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del faces[i]
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break
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return frame
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def __check_video_has_audio(self,video_path):
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self.video_has_audio = False
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probe = ffmpeg.probe(video_path)
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audio_stream = next((stream for stream in probe['streams'] if stream['codec_type'] == 'audio'), None)
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if audio_stream is not None:
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self.video_has_audio = True
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def reface_group(self, faces, frames, output):
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with ThreadPoolExecutor(max_workers = self.use_num_cpus) as executor:
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if self.first_face:
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results = list(tqdm(executor.map(self.process_first_face, frames), total=len(frames),desc="Processing frames"))
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else:
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results = list(tqdm(executor.map(self.process_faces, frames), total=len(frames),desc="Processing frames"))
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for result in results:
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output.write(result)
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def reface(self, video_path, faces):
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self.__check_video_has_audio(video_path)
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output_video_path = os.path.join('out',Path(video_path).name)
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self.prepare_faces(faces)
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cap = cv2.VideoCapture(video_path)
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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print(f"Total frames: {total_frames}")
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fps = cap.get(cv2.CAP_PROP_FPS)
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frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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fourcc = cv2.VideoWriter_fourcc(*'mp4v')
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output = cv2.VideoWriter(output_video_path, fourcc, fps, (frame_width, frame_height))
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frames=[]
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self.k = 1
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with tqdm(total=total_frames,desc="Extracting frames") as pbar:
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while cap.isOpened():
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flag, frame = cap.read()
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if flag and len(frame)>0:
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frames.append(frame.copy())
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pbar.update()
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else:
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break
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if (len(frames) > 1000):
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self.reface_group(faces,frames,output)
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frames=[]
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cap.release()
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pbar.close()
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self.reface_group(faces,frames,output)
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frames=[]
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output.release()
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return self.__convert_video(video_path,output_video_path)
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def __try_ffmpeg_encoder(self, vcodec):
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print(f"Trying FFMPEG {vcodec} encoder")
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command = ['ffmpeg', '-y', '-f','lavfi','-i','testsrc=duration=1:size=1280x720:rate=30','-vcodec',vcodec,'testsrc.mp4']
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try:
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subprocess.run(command, check=True, capture_output=True).stderr
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except subprocess.CalledProcessError as e:
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print(f"FFMPEG {vcodec} encoder doesn't work -> Disabled.")
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return False
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print(f"FFMPEG {vcodec} encoder works")
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return True
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def __check_encoders(self):
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self.ffmpeg_video_encoder='libx264'
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self.ffmpeg_video_bitrate='0'
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pattern = r"encoders: ([a-zA-Z0-9_]+(?: [a-zA-Z0-9_]+)*)"
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command = ['ffmpeg', '-codecs', '--list-encoders']
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commandout = subprocess.run(command, check=True, capture_output=True).stdout
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result = commandout.decode('utf-8').split('\n')
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for r in result:
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if "264" in r:
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encoders = re.search(pattern, r).group(1).split(' ')
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for v_c in Refacer.VIDEO_CODECS:
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for v_k in encoders:
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if v_c == v_k:
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if self.__try_ffmpeg_encoder(v_k):
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self.ffmpeg_video_encoder=v_k
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self.ffmpeg_video_bitrate=Refacer.VIDEO_CODECS[v_k]
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print(f"Video codec for FFMPEG: {self.ffmpeg_video_encoder}")
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return
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VIDEO_CODECS = {
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'h264_videotoolbox':'0', #osx HW acceleration
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'h264_nvenc':'0', #NVIDIA HW acceleration
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#'h264_qsv', #Intel HW acceleration
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#'h264_vaapi', #Intel HW acceleration
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#'h264_omx', #HW acceleration
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'libx264':'0' #No HW acceleration
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}
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