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466
refacer_no_path.py
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466
refacer_no_path.py
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import cv2
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import onnxruntime as rt
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import sys
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from utils.minio_client import oss_get_image, minio_client
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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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import requests
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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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from PIL import Image
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import numpy as np
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import time
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from codeformer_wrapper_no_path import enhance_image, enhance_image_memory
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import tempfile
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gc = __import__('gc')
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# Preload NVIDIA DLLs if Windows
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if sys.platform in ("win32", "win64"):
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if hasattr(os, "add_dll_directory"):
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try:
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os.add_dll_directory(r"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.6\bin")
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os.add_dll_directory(r"C:\Program Files\NVIDIA\CUDNN\v9.4\bin\12.6")
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except Exception as e:
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print(f"[INFO] Failed to add CUDA or CUDNN DLL directory: {e}")
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print("[INFO] This error can be ignored if running in CPU mode. Otherwise, make sure the paths are correct.")
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if hasattr(rt, "preload_dlls"):
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rt.preload_dlls()
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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.disable_similarity = False
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self.multiple_faces_mode = 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.use_num_cpus = mp.cpu_count()
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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 _partial_face_blend(self, original_frame, swapped_frame, face):
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h_frame, w_frame = original_frame.shape[:2]
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x1, y1, x2, y2 = map(int, face.bbox)
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x1 = max(0, min(x1, w_frame - 1))
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y1 = max(0, min(y1, h_frame - 1))
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x2 = max(0, min(x2, w_frame))
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y2 = max(0, min(y2, h_frame))
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if x2 <= x1 or y2 <= y1:
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print(f"Invalid bbox: {x1},{y1},{x2},{y2}")
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return swapped_frame
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w = x2 - x1
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h = y2 - y1
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cutoff = int(h * (1.0 - self.blend_height_ratio))
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swap_crop = swapped_frame[y1:y2, x1:x2].copy()
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orig_crop = original_frame[y1:y2, x1:x2].copy()
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mask = np.ones((h, w, 3), dtype=np.float32)
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transition = 40
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if cutoff < h:
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blend_start = max(cutoff - transition // 2, 0)
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blend_end = min(cutoff + transition // 2, h)
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if blend_end > blend_start:
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alpha = np.linspace(1.0, 0.0, blend_end - blend_start)[:, np.newaxis, np.newaxis]
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mask[blend_start:blend_end, :, :] = alpha
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mask[blend_end:, :, :] = 0.0
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blended_crop = (swap_crop.astype(np.float32) * mask + orig_crop.astype(np.float32) * (1.0 - mask)).astype(np.uint8)
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blended_frame = swapped_frame.copy()
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blended_frame[y1:y2, x1:x2] = blended_crop
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return blended_frame
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def __download_with_progress(self, url, output_path):
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response = requests.get(url, stream=True)
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total_size = int(response.headers.get('content-length', 0))
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block_size = 1024
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t = tqdm(total=total_size, unit='iB', unit_scale=True, desc=f"Downloading {os.path.basename(output_path)}")
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with open(output_path, 'wb') as f:
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for data in response.iter_content(block_size):
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t.update(len(data))
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f.write(data)
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t.close()
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if total_size != 0 and t.n != total_size:
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raise Exception("ERROR, something went wrong downloading the model!")
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def __check_providers(self):
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available_providers = rt.get_available_providers()
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if self.force_cpu:
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self.providers = ['CPUExecutionProvider']
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else:
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# Prefer faster execution providers in order
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self.providers = []
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for p in ['CoreMLExecutionProvider', 'CUDAExecutionProvider', 'CPUExecutionProvider']:
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if p in available_providers:
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self.providers.append(p)
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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_PARALLEL
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self.sess_options.graph_optimization_level = rt.GraphOptimizationLevel.ORT_ENABLE_ALL
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test_model = os.path.expanduser("~/.insightface/models/buffalo_l/det_10g.onnx")
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try:
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test_session = rt.InferenceSession(test_model, self.sess_options, providers=self.providers)
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active_provider = test_session.get_providers()[0]
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except Exception as e:
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print(f"[ERROR] Failed to create test session: {e}")
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active_provider = 'CPUExecutionProvider'
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if active_provider == 'CUDAExecutionProvider':
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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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elif active_provider == 'CoreMLExecutionProvider':
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self.mode = RefacerMode.COREML
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self.use_num_cpus = max(mp.cpu_count() - 1, 1)
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self.sess_options.intra_op_num_threads = int(self.use_num_cpus / 2)
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elif self.colab_performance:
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self.mode = RefacerMode.TENSORRT
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self.use_num_cpus = max(mp.cpu_count() - 1, 1)
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self.sess_options.intra_op_num_threads = int(self.use_num_cpus / 2)
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else:
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self.mode = RefacerMode.CPU
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self.use_num_cpus = max(mp.cpu_count() - 1, 1)
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self.sess_options.intra_op_num_threads = int(self.use_num_cpus / 2)
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print(f"Available providers: {available_providers}")
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print(f"Using providers: {self.providers}")
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print(f"Active provider: {active_provider}")
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print(f"Mode: {self.mode}")
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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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print(f"Face Detector providers: {sess_face.get_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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print(f"Face Recognizer providers: {sess_rec.get_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_dir = os.path.join('weights', 'inswapper')
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os.makedirs(model_dir, exist_ok=True)
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model_path = os.path.join(model_dir, 'inswapper_128.onnx')
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if not os.path.exists(model_path):
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print(f"Model {model_path} not found. Downloading from HuggingFace...")
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url = "https://huggingface.co/ezioruan/inswapper_128.onnx/resolve/main/inswapper_128.onnx"
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try:
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self.__download_with_progress(url, model_path)
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print(f"Downloaded {model_path}")
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except Exception as e:
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raise RuntimeError(f"Failed to download {model_path}. Error: {e}")
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sess_swap = rt.InferenceSession(model_path, self.sess_options, providers=self.providers)
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print(f"Face Swapper providers: {sess_swap.get_providers()}")
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self.face_swapper = INSwapper(model_path, sess_swap)
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def prepare_faces(self, faces, disable_similarity=False, multiple_faces_mode=False):
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self.replacement_faces = []
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self.disable_similarity = disable_similarity
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self.multiple_faces_mode = multiple_faces_mode
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for face in faces:
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if "destination" not in face or face["destination"] is None:
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print("Skipping face config: No destination face provided.")
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continue
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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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if multiple_faces_mode:
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self.replacement_faces.append((None, _faces[0], 0.0))
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else:
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if "origin" in face and face["origin"] is not None and not disable_similarity:
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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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self.replacement_faces.append((feat_original, _faces[0], face_threshold))
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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 = kpss[i] if kpss is not None else None
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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=0)
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if not faces:
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return frame
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if self.disable_similarity:
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for face in faces:
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swapped = self.face_swapper.get(frame, face, self.replacement_faces[0][1], paste_back=True)
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if hasattr(self, 'partial_reface_ratio') and self.partial_reface_ratio > 0.0:
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self.blend_height_ratio = self.partial_reface_ratio
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frame = self._partial_face_blend(frame, swapped, face)
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else:
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frame = swapped
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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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if not faces:
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return frame
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faces = sorted(faces, key=lambda face: face.bbox[0])
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if self.multiple_faces_mode:
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for idx, face in enumerate(faces):
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if idx >= len(self.replacement_faces):
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break
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swapped = self.face_swapper.get(frame, face, self.replacement_faces[idx][1], paste_back=True)
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if hasattr(self, 'partial_reface_ratio') and self.partial_reface_ratio > 0.0:
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self.blend_height_ratio = self.partial_reface_ratio
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frame = self._partial_face_blend(frame, swapped, face)
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else:
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frame = swapped
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elif self.disable_similarity:
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for face in faces:
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swapped = self.face_swapper.get(frame, face, self.replacement_faces[0][1], paste_back=True)
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if hasattr(self, 'partial_reface_ratio') and self.partial_reface_ratio > 0.0:
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self.blend_height_ratio = self.partial_reface_ratio
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frame = self._partial_face_blend(frame, swapped, face)
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else:
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frame = swapped
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else:
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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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swapped = self.face_swapper.get(frame, faces[i], rep_face[1], paste_back=True)
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if hasattr(self, 'partial_reface_ratio') and self.partial_reface_ratio > 0.0:
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self.blend_height_ratio = self.partial_reface_ratio
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frame = self._partial_face_blend(frame, swapped, faces[i])
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else:
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frame = swapped
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del faces[i]
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break
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return frame
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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 __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(self, video_path, faces, preview=False, disable_similarity=False, multiple_faces_mode=False, partial_reface_ratio=0.0):
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original_name = osp.splitext(osp.basename(video_path))[0]
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timestamp = str(int(time.time()))
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filename = f"{original_name}_preview.mp4" if preview else f"{original_name}_{timestamp}.mp4"
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self.__check_video_has_audio(video_path)
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if preview:
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os.makedirs("output/preview", exist_ok=True)
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output_video_path = os.path.join('output', 'preview', filename)
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else:
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os.makedirs("output", exist_ok=True)
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output_video_path = os.path.join('output', filename)
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self.prepare_faces(faces, disable_similarity=disable_similarity, multiple_faces_mode=multiple_faces_mode)
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self.first_face = False if multiple_faces_mode else (faces[0].get("origin") is None or disable_similarity)
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self.partial_reface_ratio = partial_reface_ratio
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cap = cv2.VideoCapture(video_path, cv2.CAP_FFMPEG)
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total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
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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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frame_index = 0
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skip_rate = 10 if preview else 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 not flag:
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break
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if frame_index % skip_rate == 0:
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frames.append(frame)
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if len(frames) > 300:
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self.reface_group(faces, frames, output)
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frames = []
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gc.collect()
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frame_index += 1
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pbar.update()
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cap.release()
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if frames:
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self.reface_group(faces, frames, output)
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output.release()
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converted_path = self.__convert_video(video_path, output_video_path, preview=preview)
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if video_path.lower().endswith(".gif"):
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if preview:
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gif_output_path = os.path.join("output", "preview", os.path.basename(converted_path).replace(".mp4", ".gif"))
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else:
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gif_output_path = os.path.join("output", "gifs", os.path.basename(converted_path).replace(".mp4", ".gif"))
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self.__generate_gif(converted_path, gif_output_path)
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return converted_path, gif_output_path
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return converted_path, None
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def __generate_gif(self, video_path, gif_output_path):
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os.makedirs(os.path.dirname(gif_output_path), exist_ok=True)
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print(f"Generating GIF at {gif_output_path}")
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(
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ffmpeg
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.input(video_path)
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.output(gif_output_path, vf='fps=10,scale=512:-1:flags=lanczos', loop=0)
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.overwrite_output()
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.run(quiet=True)
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)
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def __convert_video(self, video_path, output_video_path, preview=False):
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if self.video_has_audio and not preview:
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new_path = output_video_path + str(random.randint(0, 999)) + "_c.mp4"
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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(f"Refaced video saved at: {os.path.abspath(new_path)}")
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return new_path
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def reface_image(self, bgr_image, faces, disable_similarity=False, multiple_faces_mode=False, partial_reface_ratio=0.0):
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self.prepare_faces(faces, disable_similarity=disable_similarity, multiple_faces_mode=multiple_faces_mode)
|
||||
self.first_face = False if multiple_faces_mode else (faces[0].get("origin") is None or disable_similarity)
|
||||
self.partial_reface_ratio = partial_reface_ratio
|
||||
if bgr_image is None:
|
||||
raise ValueError("Failed to read input image")
|
||||
refaced_bgr = self.process_first_face(bgr_image.copy()) if self.first_face else self.process_faces(bgr_image.copy())
|
||||
refaced_rgb = cv2.cvtColor(refaced_bgr, cv2.COLOR_BGR2RGB)
|
||||
output_image = enhance_image(refaced_rgb)
|
||||
return output_image
|
||||
|
||||
def extract_faces_from_image(self, image_path, max_faces=5):
|
||||
frame = cv2.imread(image_path)
|
||||
if frame is None:
|
||||
raise ValueError("Failed to read input image for face extraction.")
|
||||
|
||||
faces = self.__get_faces(frame, max_num=max_faces)
|
||||
cropped_faces = []
|
||||
|
||||
for face in faces:
|
||||
x1, y1, x2, y2 = map(int, face.bbox)
|
||||
x1 = max(x1, 0)
|
||||
y1 = max(y1, 0)
|
||||
x2 = min(x2, frame.shape[1])
|
||||
y2 = min(y2, frame.shape[0])
|
||||
|
||||
cropped = frame[y1:y2, x1:x2]
|
||||
pil_img = Image.fromarray(cv2.cvtColor(cropped, cv2.COLOR_BGR2RGB))
|
||||
|
||||
temp_file = tempfile.NamedTemporaryFile(delete=False, dir="./tmp", suffix=".png")
|
||||
pil_img.save(temp_file.name)
|
||||
cropped_faces.append(temp_file.name)
|
||||
|
||||
if len(cropped_faces) >= max_faces:
|
||||
break
|
||||
|
||||
return cropped_faces
|
||||
|
||||
def __try_ffmpeg_encoder(self, vcodec):
|
||||
command = ['ffmpeg', '-y', '-f', 'lavfi', '-i', 'testsrc=duration=1:size=1280x720:rate=30', '-vcodec', vcodec, 'testsrc.mp4']
|
||||
try:
|
||||
subprocess.run(command, check=True, capture_output=True).stderr
|
||||
except subprocess.CalledProcessError:
|
||||
return False
|
||||
return True
|
||||
|
||||
def __check_encoders(self):
|
||||
self.ffmpeg_video_encoder = 'libx264'
|
||||
self.ffmpeg_video_bitrate = '0'
|
||||
pattern = r"encoders: ([a-zA-Z0-9_]+(?: [a-zA-Z0-9_]+)*)"
|
||||
command = ['ffmpeg', '-codecs', '--list-encoders']
|
||||
commandout = subprocess.run(command, check=True, capture_output=True).stdout
|
||||
result = commandout.decode('utf-8').split('\n')
|
||||
for r in result:
|
||||
if "264" in r:
|
||||
encoders = re.search(pattern, r)
|
||||
if encoders:
|
||||
for v_c in Refacer.VIDEO_CODECS:
|
||||
for v_k in encoders.group(1).split(' '):
|
||||
if v_c == v_k and self.__try_ffmpeg_encoder(v_k):
|
||||
self.ffmpeg_video_encoder = v_k
|
||||
self.ffmpeg_video_bitrate = Refacer.VIDEO_CODECS[v_k]
|
||||
return
|
||||
|
||||
VIDEO_CODECS = {
|
||||
'h264_videotoolbox': '0',
|
||||
'h264_nvenc': '0',
|
||||
'libx264': '0'
|
||||
}
|
||||
Reference in New Issue
Block a user