254 lines
9.5 KiB
Python
254 lines
9.5 KiB
Python
import torch
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import os
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import requests
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import numpy as np
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import logging
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import json
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import ast
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import sys
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# Add the directory containing 'sam2_realtime' to sys.path
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current_directory = os.path.dirname(os.path.abspath(__file__))
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sam2_realtime_path = os.path.join(current_directory) # Adjust the relative path
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sys.path.append(sam2_realtime_path)
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from sam2_realtime.sam2_tensor_predictor import SAM2TensorPredictor
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from comfy.utils import load_torch_file
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from omegaconf import OmegaConf
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from hydra.utils import instantiate
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from hydra import initialize_config_dir, compose
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from hydra.core.global_hydra import GlobalHydra
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import comfy.model_management as mm
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import folder_paths
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script_directory = os.path.dirname(os.path.abspath(__file__))
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class DownloadAndLoadSAM2RealtimeModel:
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@classmethod
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def INPUT_TYPES(s):
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return {"required": {
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"model": ([
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'sam2_hiera_tiny.pt',
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],),
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"segmentor": (
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['realtime'],
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),
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"device": (['cuda', 'cpu', 'mps'], ),
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"precision": ([ 'fp16','bf16','fp32'],
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{
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"default": 'fp16'
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}),
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},
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}
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RETURN_TYPES = ("SAM2MODEL",)
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RETURN_NAMES = ("sam2_model",)
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FUNCTION = "loadmodel"
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CATEGORY = "SAM2-Realtime"
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def loadmodel(self, model, segmentor, device, precision):
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if precision != 'fp32' and device == 'cpu':
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raise ValueError("fp16 and bf16 are not supported on cpu")
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if device == "cuda":
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if torch.cuda.get_device_properties(0).major >= 8:
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# turn on tfloat32 for Ampere GPUs (https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices)
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torch.backends.cuda.matmul.allow_tf32 = True
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torch.backends.cudnn.allow_tf32 = True
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dtype = {"bf16": torch.bfloat16, "fp16": torch.float16, "fp32": torch.float32}[precision]
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device = {"cuda": torch.device("cuda"), "cpu": torch.device("cpu"), "mps": torch.device("mps")}[device]
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download_path = os.path.join(folder_paths.models_dir, "sam2")
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model_path = os.path.join(download_path, model)
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print("model_path: ", model_path)
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url = "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_tiny.pt"
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if not os.path.exists(model_path):
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print(f"Downloading SAM2 model to: {model_path}")
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response = requests.get(url, stream=True)
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response.raise_for_status()
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with open(model_path, "wb") as f:
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for chunk in response.iter_content(chunk_size=8192):
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if chunk:
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f.write(chunk)
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print(f"Model saved to {model_path}")
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config_dir = os.path.join(script_directory, "sam2_configs")
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# Code ripped out of sam2.build_sam.build_sam2_camera_predictor to appease Hydra
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model_cfg = "sam2_hiera_t.yaml" #TODO: remove hardcoded config and path
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with initialize_config_dir(config_dir=config_dir, version_base=None):
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cfg = compose(config_name=model_cfg)
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hydra_overrides = [
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"++model._target_=sam2_realtime.sam2_tensor_predictor.SAM2TensorPredictor",
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]
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hydra_overrides_extra = [
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"++model.sam_mask_decoder_extra_args.dynamic_multimask_via_stability=true",
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"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_delta=0.05",
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"++model.sam_mask_decoder_extra_args.dynamic_multimask_stability_thresh=0.98",
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"++model.binarize_mask_from_pts_for_mem_enc=true",
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"++model.fill_hole_area=8",
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]
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hydra_overrides.extend(hydra_overrides_extra)
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cfg = compose(config_name=model_cfg, overrides=hydra_overrides)
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OmegaConf.resolve(cfg)
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model = instantiate(cfg.model, _recursive_=True)
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def _load_checkpoint(model, ckpt_path):
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if ckpt_path is not None:
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sd = torch.load(ckpt_path, map_location="cpu")["model"]
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missing_keys, unexpected_keys = model.load_state_dict(sd)
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if missing_keys:
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logging.error(missing_keys)
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raise RuntimeError()
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if unexpected_keys:
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logging.error(unexpected_keys)
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raise RuntimeError()
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logging.info("Loaded checkpoint sucessfully")
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_load_checkpoint(model, model_path)
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device = "cuda" if torch.cuda.is_available() else "cpu"
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model = model.to(device)
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model.eval()
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sam2_model = {
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'model': model,
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'dtype': dtype,
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'device': device,
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'segmentor' : segmentor,
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'version': "2.0"
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}
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return (sam2_model,)
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class Sam2RealtimeSegmentation:
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@classmethod
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def INPUT_TYPES(cls):
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return {
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"required": {
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"images": ("IMAGE",),
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"sam2_model": ("SAM2MODEL",),
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# "keep_model_loaded": ("BOOLEAN", {"default": True}),
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},
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"optional": {
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"coordinates_positive": ("STRING", {"forceInput": True}),
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"point_labels": ("STRING", {"forceInput": True}),
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# "coordinates_negative": ("STRING", {"forceInput": True}),
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# "bboxes": ("BBOX", ),
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# "individual_objects": ("BOOLEAN", {"default": False}),
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# "mask": ("MASK", ),
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},
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}
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RETURN_NAMES = ("PROCESSED_IMAGES","MASK",)
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RETURN_TYPES = ("IMAGE", "IMAGE",)
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FUNCTION = "segment_images"
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CATEGORY = "SAM2-Realtime"
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def __init__(self):
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self.predictor = None
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self.if_init = False
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def segment_images(
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self,
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images,
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sam2_model,
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# keep_model_loaded,
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coordinates_positive=None,
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# coordinates_negative=None,
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point_labels=None,
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# bboxes=None,
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# individual_objects=False,
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# mask=None,
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):
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model = sam2_model["model"]
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device = sam2_model["device"]
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device = torch.device("cuda")
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model.to(device)
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processed_frames = []
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mask_list = []
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# The `model` variable is now ready and equivalent to `predictor` returned by sam2.build_sam.build_sam2_camera_predictor
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if self.predictor is None:
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self.predictor = model
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def process_frame(frame, frame_idx):
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with torch.inference_mode(), torch.autocast("cuda", dtype=torch.float16):
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frame = frame.to(device).float() # Keep everything in torch
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if not self.if_init:
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self.predictor.load_first_frame(frame)
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self.if_init = True
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# obj_id = 1
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# point = [256, 256]
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# points = [point]
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# labels = [1]
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coordinates_positive_list = ast.literal_eval(coordinates_positive)
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point_labels_list = ast.literal_eval(point_labels)
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point_labels_list = list(map(int, point_labels_list))
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for idx, point in enumerate(coordinates_positive_list):
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point_tuple = tuple(map(int, point))
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_, _, out_mask_logits = self.predictor.add_new_prompt(
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frame_idx=0,
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obj_id=idx + 1,
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points=[point_tuple],
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labels=[point_labels_list[idx]]
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)
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# _, _, _ = self.predictor.add_new_prompt(frame_idx, obj_id, points=points, labels=labels)
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else:
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out_obj_ids, out_mask_logits = self.predictor.track(frame)
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if out_mask_logits.shape[0] > 0:
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# Ensure out_mask_logits is on the same device
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out_mask_logits = out_mask_logits.to(device)
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mask = (out_mask_logits[0, 0] > 0.5).byte()
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mask = torch.nn.functional.interpolate(
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mask.unsqueeze(0).unsqueeze(0).float(),
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size=(frame.shape[0], frame.shape[1]),
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mode='nearest'
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).squeeze(0).squeeze(0).byte().to(device) # Move the interpolated mask to the correct device
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else:
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mask = torch.ones((frame.shape[0], frame.shape[1]), device=device, dtype=torch.uint8)
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# Ensure frame is on the same device
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frame = frame.to(device)
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mask_colored = torch.stack([mask] * 3, dim=2).to(device) # Create 3-channel mask and move to device
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overlayed_frame = torch.add(frame * 0.7, mask_colored * 0.3).to(device)
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processed_frames.append(overlayed_frame)
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constructed_mask = torch.add(frame * 0.1, mask_colored * 0.9).to(device)
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mask_list.append(constructed_mask)
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for frame_idx, img in enumerate(images):
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process_frame(img, frame_idx)
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stacked_masks = torch.stack(mask_list, dim=0)
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stacked_frames = torch.stack(processed_frames, dim=0)
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return (stacked_frames, stacked_masks)
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NODE_CLASS_MAPPINGS = {
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"DownloadAndLoadSAM2RealtimeModel": DownloadAndLoadSAM2RealtimeModel,
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"Sam2RealtimeSegmentation": Sam2RealtimeSegmentation
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}
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NODE_DISPLAY_NAME_MAPPINGS = {
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"DownloadAndLoadSAM2RealtimeModel": "(Down)Load sam2_realtime Model",
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"Sam2RealtimeSegmentation": "Sam2RealtimeSegmentation"
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}
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