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import insightface
from insightface.app import FaceAnalysis
import onnxruntime as ort
import os
import numpy as np
class FaceSwapper:
def __init__(self, det_size=(1280, 1280), use_enhancer=False):
"""
det_size: higher = better detection on A100 (try 1280 or even 1600)
use_enhancer: enable GFPGAN-style enhancement (requires gfpgan package)
"""
# Force CUDA first
available = ort.get_available_providers()
print(f"[FaceSwapper] Available ONNX providers: {available}")
providers = ['CUDAExecutionProvider', 'CPUExecutionProvider']
if 'CUDAExecutionProvider' not in available:
print("[WARNING] CUDAExecutionProvider not found! Falling back to CPU. Check onnxruntime-gpu + CUDA.")
providers = ['CPUExecutionProvider']
# Face analysis (detection + landmarks + embedding)
self.app = FaceAnalysis(
name='buffalo_l',
providers=providers
)
# ctx_id=0 → first GPU. A100 loves larger det_size
self.app.prepare(ctx_id=0, det_size=det_size)
# Inswapper
self.swapper = insightface.model_zoo.get_model(
'inswapper_128.onnx',
download=True,
download_zip=True,
providers=providers
)
self.use_enhancer = false
self.enhancer = None
if use_enhancer:
try:
from gfpgan import GFPGANer
# You need to place GFPGANv1.4.pth in the Space or let it download
self.enhancer = GFPGANer(
model_path='https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth',
upscale=1,
arch='clean',
channel_multiplier=2,
bg_upsampler=None
)
print("[FaceSwapper] GFPGAN enhancer loaded")
except Exception as e:
print(f"[WARNING] Could not load enhancer: {e}")
self.use_enhancer = False
def swap_faces(self, source_path, source_face_idx, target_path, target_face_idx, enhance=False):
source_img = cv2.imread(source_path)
target_img = cv2.imread(target_path)
if source_img is None or target_img is None:
raise ValueError("Could not read one or both images")
source_faces = self.app.get(source_img)
target_faces = self.app.get(target_img)
# Sort left → right for consistent indexing
source_faces = sorted(source_faces, key=lambda x: x.bbox[0])
target_faces = sorted(target_faces, key=lambda x: x.bbox[0])
if len(source_faces) < source_face_idx or source_face_idx < 1:
raise ValueError(f"Source image contains {len(source_faces)} faces, but requested face {source_face_idx}")
if len(target_faces) < target_face_idx or target_face_idx < 1:
raise ValueError(f"Target image contains {len(target_faces)} faces, but requested face {target_face_idx}")
source_face = source_faces[source_face_idx - 1]
target_face = target_faces[target_face_idx - 1]
result = self.swapper.get(target_img, target_face, source_face, paste_back=True)
# Optional enhancement (big quality jump)
if (enhance or self.use_enhancer) and self.enhancer is not None:
_, _, result = self.enhancer.enhance(
result,
has_aligned=False,
only_center_face=False,
paste_back=True,
weight=0.8 # 0.5–1.0, higher = stronger restoration
)
return result
def count_faces(self, img_path):
img = cv2.imread(img_path)
if img is None:
return 0
faces = self.app.get(img)
return len(faces) |