200 lines
7.8 KiB
Python
200 lines
7.8 KiB
Python
import os
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import cv2
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import requests
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import torch
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import numpy as np
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import PIL.Image
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import PIL.ImageOps
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from insightface.app import FaceAnalysis
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from facexlib.parsing import init_parsing_model
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from torchvision.transforms.functional import normalize
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from typing import Union, Optional
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import folder_paths
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from huggingface_hub import snapshot_download
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LOADED_PROCESSORS = {}
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def set_extra_config_model_path(extra_config_models_dir_key, models_dir_name: str):
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models_dir_default = os.path.join(folder_paths.models_dir, models_dir_name)
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if extra_config_models_dir_key not in folder_paths.folder_names_and_paths:
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folder_paths.folder_names_and_paths[extra_config_models_dir_key] = (
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[os.path.join(folder_paths.models_dir, models_dir_name)],
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folder_paths.supported_pt_extensions,
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)
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else:
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if not os.path.exists(models_dir_default):
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os.makedirs(models_dir_default, exist_ok=True)
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folder_paths.add_model_folder_path(extra_config_models_dir_key, models_dir_default, is_default=True)
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set_extra_config_model_path("insightface", "insightface")
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def _img2tensor(img: np.ndarray, bgr2rgb: bool = True) -> torch.Tensor:
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if bgr2rgb:
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img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img = img.astype(np.float32) / 255.0
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img = np.transpose(img, (2, 0, 1))
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return torch.from_numpy(img)
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def _pad_to_square(img: np.ndarray, pad_color: int = 255) -> np.ndarray:
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h, w, _ = img.shape
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if h == w:
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return img
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if h > w:
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pad_size = (h - w) // 2
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padded_img = cv2.copyMakeBorder(
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img, 0, 0, pad_size, h - w - pad_size, cv2.BORDER_CONSTANT, value=[pad_color] * 3,
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)
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else:
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pad_size = (w - h) // 2
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padded_img = cv2.copyMakeBorder(
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img, pad_size, w - h - pad_size, 0, 0, cv2.BORDER_CONSTANT, value=[pad_color] * 3,
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)
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return padded_img
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def tensor_to_cv2_img(tensor: torch.Tensor) -> np.ndarray:
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img_np = tensor.squeeze(0).cpu().numpy()
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img_np = (img_np * 255).astype(np.uint8)
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img_bgr = cv2.cvtColor(img_np, cv2.COLOR_RGB2BGR)
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return img_bgr
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def cv2_img_to_tensor(img: np.ndarray) -> torch.Tensor:
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img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
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img_rgb = img_rgb.astype(np.float32) / 255.0
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return torch.from_numpy(img_rgb).unsqueeze(0)
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class FaceProcessorLoader:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"antelopev2_path": ("STRING", {"default": "models/insightface"})
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}
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}
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RETURN_TYPES = ("FACE_PROCESSOR",)
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RETURN_NAMES = ("face_processor",)
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FUNCTION = "load_processor"
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CATEGORY = "Stand-In"
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def load_processor(self, antelopev2_path="models/insightface"):
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if "face_processor" in LOADED_PROCESSORS:
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print("Reusing cached FaceProcessor models.")
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return (LOADED_PROCESSORS["face_processor"],)
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antelopev2_path = folder_paths.get_folder_paths("insightface")[0]
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print("Initializing FaceProcessor models...")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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providers = ["CUDAExecutionProvider"] if device.type == "cuda" else ["CPUExecutionProvider"]
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if not os.path.exists(antelopev2_path):
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os.makedirs(antelopev2_path)
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print(f"Created insightface model directory at: {antelopev2_path}")
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try:
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print("Attempting to load FaceAnalysis model from local files...")
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app = FaceAnalysis(name="antelopev2", root=antelopev2_path, providers=providers)
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except Exception as e:
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print(f"Failed to load local FaceAnalysis model: {e}")
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if snapshot_download is None:
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raise ImportError("huggingface_hub is not installed. Please install it with 'pip install huggingface_hub' to enable automatic model downloading.") from e
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print("Attempting to download model from Hugging Face...")
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download_target_path = os.path.join(antelopev2_path, "models", "antelopev2")
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snapshot_download(
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repo_id="DIAMONIK7777/antelopev2",
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local_dir=download_target_path,
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)
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print("Model downloaded. Retrying to load FaceAnalysis model...")
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app = FaceAnalysis(name="antelopev2", root=antelopev2_path, providers=providers)
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app.prepare(ctx_id=0, det_size=(640, 640))
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parsing_model = init_parsing_model(model_name="bisenet", device=device)
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parsing_model.eval()
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processor_tuple = (app, parsing_model, device)
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LOADED_PROCESSORS["face_processor"] = processor_tuple
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print("FaceProcessor models initialized and cached successfully.")
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return (processor_tuple,)
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class ApplyFaceProcessor:
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@classmethod
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def INPUT_TYPES(s):
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return {
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"required": {
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"face_processor": ("FACE_PROCESSOR",),
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"image": ("IMAGE",),
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"resize_to": ("INT", {"default": 512, "min": 64, "max": 2048, "step": 64}),
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"border_thresh": ("INT", {"default": 10, "min": 0, "max": 100, "step": 1}),
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"face_crop_scale": ("FLOAT", {"default": 1.5, "min": 1.0, "max": 3.0, "step": 0.1}),
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}
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}
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RETURN_TYPES = ("IMAGE",)
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FUNCTION = "apply_processing"
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CATEGORY = "FaceUtils"
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def apply_processing(self, face_processor, image, resize_to, border_thresh, face_crop_scale):
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app, parsing_model, device = face_processor
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frame = tensor_to_cv2_img(image)
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faces = app.get(frame)
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h, w, _ = frame.shape
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image_to_process = None
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if not faces:
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print("[Warning] No face detected. Using the whole image, padded to square.")
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image_to_process = _pad_to_square(frame, pad_color=255)
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else:
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largest_face = max(faces, key=lambda f: (f.bbox[2] - f.bbox[0]) * (f.bbox[3] - f.bbox[1]))
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x1, y1, x2, y2 = map(int, largest_face.bbox)
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is_close_to_border = (x1 <= border_thresh and y1 <= border_thresh and
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x2 >= w - border_thresh and y2 >= h - border_thresh)
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if is_close_to_border:
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print("[Info] Face is close to border, padding original image to square.")
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image_to_process = _pad_to_square(frame, pad_color=255)
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else:
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cx, cy = (x1 + x2) // 2, (y1 + y2) // 2
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side = int(max(x2 - x1, y2 - y1) * face_crop_scale)
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half = side // 2
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left, top = max(cx - half, 0), max(cy - half, 0)
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right, bottom = min(cx + half, w), min(cy + half, h)
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cropped_face = frame[top:bottom, left:right]
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image_to_process = _pad_to_square(cropped_face, pad_color=255)
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image_resized = cv2.resize(image_to_process, (resize_to, resize_to), interpolation=cv2.INTER_AREA)
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face_tensor = _img2tensor(image_resized, bgr2rgb=True).unsqueeze(0).to(device)
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with torch.no_grad():
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normalized_face = normalize(face_tensor, [0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
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parsing_out = parsing_model(normalized_face)[0]
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parsing_mask = parsing_out.argmax(dim=1, keepdim=True)
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mask = (parsing_mask.squeeze().cpu().numpy() == 0).astype(np.uint8)
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white_background = np.ones_like(image_resized, dtype=np.uint8) * 255
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mask_3channel = cv2.cvtColor(mask * 255, cv2.COLOR_GRAY2BGR)
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result_img_bgr = np.where(mask_3channel == 255, white_background, image_resized)
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result_tensor = cv2_img_to_tensor(result_img_bgr)
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return (result_tensor,)
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NODE_CLASS_MAPPINGS = {
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"FaceProcessorLoader": FaceProcessorLoader,
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"ApplyFaceProcessor": ApplyFaceProcessor
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"FaceProcessorLoader": "Stand-In Processor Loader",
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"ApplyFaceProcessor": "Apply Stand-In Processor"
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} |