248 lines
10 KiB
Python
248 lines
10 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 ultralytics import YOLO
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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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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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"""Helper function to set up model directories within ComfyUI."""
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models_dir_default = os.path.join(folder_paths.models_dir, models_dir_name)
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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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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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[models_dir_default],
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folder_paths.supported_pt_extensions,
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)
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else:
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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("yolo", "yolo")
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set_extra_config_model_path("face_parsing", "face_parsing")
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def download_yolo_model(model_name="model.pt"):
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"""
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Checks if a YOLO model exists and downloads it from a public source if not.
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"""
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model_url = f"https://huggingface.co/arnabdhar/YOLOv8-Face-Detection/resolve/main/{model_name}"
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yolo_dir = os.path.join(folder_paths.get_folder_paths("yolo")[0])
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model_path = os.path.join(yolo_dir, model_name)
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os.makedirs(yolo_dir, exist_ok=True)
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if not os.path.exists(model_path):
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print(f"Model '{model_name}' not found locally. Starting download from public source...")
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try:
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with requests.get(model_url, stream=True, timeout=60) as r:
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if r.status_code == 401:
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print(f"\nDownload failed: Received 401 Unauthorized. The link '{model_url}' may require authentication.")
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raise requests.exceptions.HTTPError(f"401 Client Error: Unauthorized for url: {model_url}")
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r.raise_for_status()
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total_size = int(r.headers.get('content-length', 0))
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with open(model_path, 'wb') as f:
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downloaded = 0
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for chunk in r.iter_content(chunk_size=8192):
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f.write(chunk)
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downloaded += len(chunk)
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done = int(50 * downloaded / total_size) if total_size > 0 else 0
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progress_mb = downloaded / (1024*1024)
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total_mb = total_size / (1024*1024)
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print(f"\r[{'=' * done}{' ' * (50-done)}] {progress_mb:.2f}MB / {total_mb:.2f}MB", end='')
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print(f"\nModel successfully downloaded to: {model_path}")
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except Exception as e:
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print(f"\nFailed to download model: {e}")
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if os.path.exists(model_path):
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os.remove(model_path)
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raise e
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return model_path
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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(img, 0, 0, pad_size, h - w - pad_size, cv2.BORDER_CONSTANT, value=[pad_color] * 3)
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else:
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pad_size = (w - h) // 2
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padded_img = cv2.copyMakeBorder(img, pad_size, w - h - pad_size, 0, 0, cv2.BORDER_CONSTANT, value=[pad_color] * 3)
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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_tensor = torch.from_numpy(img_rgb.astype(np.float32) / 255.0).unsqueeze(0)
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return img_tensor
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class FaceProcessorLoader:
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@classmethod
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def INPUT_TYPES(s):
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return { "required": { "yolo_model_name": ("STRING", {"default": "model.pt"}) } }
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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, yolo_model_name="model.pt"):
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if os.path.isdir(yolo_model_name):
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print(f"Warning: You provided a directory ('{yolo_model_name}') instead of a filename. Using default model 'model.pt'.")
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yolo_model_name = "model.pt"
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processor_key = f"face_processor_{yolo_model_name}"
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if processor_key in LOADED_PROCESSORS:
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print("Reusing cached face processor model.")
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return (LOADED_PROCESSORS[processor_key],)
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print("Initializing face processor models...")
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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try:
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if not yolo_model_name.endswith(('.pt', '.onnx')):
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raise ValueError(f"Invalid model name: '{yolo_model_name}'. Model filename must end with '.pt' or '.onnx'.")
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model_path = download_yolo_model(yolo_model_name)
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print(f"Loading YOLO model from local path: {model_path}")
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detection_model = YOLO(model_path)
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detection_model.to(device)
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print("YOLO model loaded successfully.")
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except Exception as e:
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print(f"Error loading YOLO model: {e}")
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raise e
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parsing_model_path = os.path.join(folder_paths.get_folder_paths("face_parsing")[0])
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parsing_model = init_parsing_model(model_name="bisenet", half=False, model_rootpath=parsing_model_path, device=device)
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parsing_model.eval()
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print("Face parsing model loaded successfully.")
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processor_tuple = (detection_model, parsing_model, device)
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LOADED_PROCESSORS[processor_key] = processor_tuple
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print("Face processor (YOLO detection + BiSeNet parsing) 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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"confidence_threshold": ("FLOAT", {"default": 0.5, "min": 0.1, "max": 1.0, "step": 0.05}),
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# --- NEW INPUT ---
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"with_neck": ("BOOLEAN", {"default": False, "label_on": "enabled", "label_off": "disabled"}),
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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 = "Stand-In"
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def apply_processing(self, face_processor, image, resize_to, border_thresh, face_crop_scale, confidence_threshold, with_neck):
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detection_model, parsing_model, device = face_processor
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frame = tensor_to_cv2_img(image)
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h, w, _ = frame.shape
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image_to_process = None
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results = detection_model(frame, verbose=False)
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boxes = results[0].boxes.xyxy
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conf = results[0].boxes.conf
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confident_boxes = boxes[conf > confidence_threshold]
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if confident_boxes.shape[0] == 0:
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print("[Warning] No confident face detected. Using the whole image padded to a square.")
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image_to_process = _pad_to_square(frame, pad_color=255)
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else:
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areas = (confident_boxes[:, 2] - confident_boxes[:, 0]) * (confident_boxes[:, 3] - confident_boxes[:, 1])
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largest_face_idx = torch.argmax(areas)
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x1, y1, x2, y2 = map(int, confident_boxes[largest_face_idx])
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is_close_to_border = (x1 <= border_thresh or y1 <= border_thresh or
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x2 >= w - border_thresh or y2 >= h - border_thresh)
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if is_close_to_border:
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print("[Info] Face is close to the border. Padding the original image to a 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_map_tensor = parsing_out.argmax(dim=1, keepdim=True)
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parsing_map_np = parsing_map_tensor.squeeze().cpu().numpy().astype(np.uint8)
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if with_neck:
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final_mask_np = (parsing_map_np != 0).astype(np.uint8)
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else:
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parts_to_remove = [0, 14, 16]
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final_mask_np = np.isin(parsing_map_np, parts_to_remove, invert=True).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(final_mask_np * 255, cv2.COLOR_GRAY2BGR)
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result_img_bgr = np.where(mask_3channel != 0, image_resized, white_background)
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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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} |