83 lines
2.8 KiB
Python
83 lines
2.8 KiB
Python
from typing import Any
|
|
import os
|
|
|
|
import folder_paths
|
|
import numpy as np
|
|
import supervision as sv
|
|
import torch
|
|
from PIL import Image
|
|
from sam2.build_sam import build_sam2, build_sam2_video_predictor
|
|
from sam2.sam2_image_predictor import SAM2ImagePredictor
|
|
|
|
model_to_config_map = {
|
|
# models: sam2_hiera_base_plus.pt sam2_hiera_large.pt sam2_hiera_small.pt sam2_hiera_tiny.pt
|
|
# configs: sam2_hiera_b+.yaml sam2_hiera_l.yaml sam2_hiera_s.yaml sam2_hiera_t.yaml
|
|
"sam2_hiera_base_plus.pt": "sam2_hiera_b+.yaml",
|
|
"sam2_hiera_large.pt": "sam2_hiera_l.yaml",
|
|
"sam2_hiera_small.pt": "sam2_hiera_s.yaml",
|
|
"sam2_hiera_tiny.pt": "sam2_hiera_t.yaml",
|
|
}
|
|
SAM_CHECKPOINT = "sam2_hiera_small.pt"
|
|
SAM_CONFIG = "sam2_hiera_s.yaml" # from /usr/local/lib/python3.10/dist-packages/sam2/configs, *not* from either the models directory, or this package's directory
|
|
|
|
def load_sam_image_model(
|
|
device: torch.device,
|
|
checkpoint: str = SAM_CHECKPOINT,
|
|
config: str = None
|
|
) -> SAM2ImagePredictor:
|
|
if config is None:
|
|
config = model_to_config_map[checkpoint]
|
|
import os
|
|
|
|
# 1. Print the current working directory with flush=True
|
|
current_working_directory = os.getcwd()
|
|
print(f"Current working directory: {current_working_directory}", flush=True)
|
|
|
|
# 2. Check if the "models" and "models/sam2" directories exist
|
|
models_dir = folder_paths.models_dir
|
|
sam2_dir = os.path.join(models_dir, "sam2")
|
|
|
|
if os.path.exists(models_dir):
|
|
print(f"'models' directory exists: {models_dir}", flush=True)
|
|
else:
|
|
print(f"'models' directory does not exist: {models_dir}", flush=True)
|
|
|
|
if os.path.exists(sam2_dir):
|
|
print(f"'models/sam2' directory exists: {sam2_dir}", flush=True)
|
|
else:
|
|
print(f"'models/sam2' directory does not exist: {sam2_dir}", flush=True)
|
|
|
|
model_path = os.path.join(sam2_dir, checkpoint)
|
|
if os.path.exists(model_path):
|
|
print(f"'models/sam2/{checkpoint}' file exists: {model_path}", flush=True)
|
|
else:
|
|
print(f"'models/sam2/{checkpoint}' file does not exist: {model_path}", flush=True)
|
|
|
|
model = build_sam2(config, model_path, device=device)
|
|
return SAM2ImagePredictor(sam_model=model)
|
|
|
|
|
|
def load_sam_video_model(
|
|
device: torch.device,
|
|
config: str = SAM_CONFIG,
|
|
checkpoint: str = SAM_CHECKPOINT
|
|
) -> Any:
|
|
return build_sam2_video_predictor(config, checkpoint, device=device)
|
|
|
|
|
|
def run_sam_inference(
|
|
model: Any,
|
|
image: Image,
|
|
detections: sv.Detections
|
|
) -> sv.Detections:
|
|
image = np.array(image.convert("RGB"))
|
|
model.set_image(image)
|
|
mask, score, _ = model.predict(box=detections.xyxy, multimask_output=False)
|
|
|
|
# dirty fix; remove this later
|
|
if len(mask.shape) == 4:
|
|
mask = np.squeeze(mask)
|
|
|
|
detections.mask = mask.astype(bool)
|
|
return detections
|