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import os, shutil
import numpy as np
from PIL import Image
from typing import Literal, Any, Union, Generic, List
from pydantic import BaseModel
from sam2.build_sam import build_sam2, build_sam2_video_predictor
from sam2.sam2_image_predictor import SAM2ImagePredictor
from sam2.automatic_mask_generator import SAM2AutomaticMaskGenerator
from sam2.utils.misc import variant_to_config_mapping
from sam2.utils.visualization import show_masks
from ffmpeg_extractor import extract_frames, logger
from visualizer import mask_to_xyxy
from toolbox.vid_utils import VidInfo, VidReader
from toolbox.mask_encoding import b64_mask_encode
# from toolbox.img_utils import get_pil_im
variant_checkpoints_mapping = {
"tiny": "checkpoints/sam2_hiera_tiny.pt",
"small": "checkpoints/sam2_hiera_small.pt",
"base_plus": "checkpoints/sam2_hiera_base_plus.pt",
"large": "checkpoints/sam2_hiera_large.pt",
}
class bbox_xyxy(BaseModel):
x0: Union[int, float]
y0: Union[int, float]
x1: Union[int, float]
y1: Union[int, float]
class point_xy(BaseModel):
x: Union[int, float]
y: Union[int, float]
def load_sam_image_model(
# variant: Literal[*variant_checkpoints_mapping.keys()],
variant: Literal["tiny", "small", "base_plus", "large"],
device: str = "cpu",
auto_mask_gen: bool = False,
) -> SAM2ImagePredictor:
model = build_sam2(
config_file=variant_to_config_mapping[variant],
ckpt_path=variant_checkpoints_mapping[variant],
device=device,
)
return (
SAM2AutomaticMaskGenerator(model)
if auto_mask_gen
else SAM2ImagePredictor(sam_model=model)
)
def load_sam_video_model(
variant: Literal["tiny", "small", "base_plus", "large"] = "small",
device: str = "cpu",
) -> Any:
return build_sam2_video_predictor(
config_file=variant_to_config_mapping[variant],
ckpt_path=variant_checkpoints_mapping[variant],
device=device,
)
def run_sam_im_inference(
model: Any,
image: Image.Image,
points: Union[List[point_xy], List[dict]] = [],
point_labels: List[int] = [],
bboxes: Union[List[bbox_xyxy], List[dict]] = [],
get_pil_mask: bool = False,
b64_encode_mask: bool = False,
):
"""returns a list of np masks, each with the shape (h,w) and dtype uint8"""
assert (
points or bboxes
), f"SAM2 Image Inference must have either bounding boxes or points. Neither were provided."
if points:
assert len(points) == len(
point_labels
), f"{len(points)} points provided but {len(point_labels)} labels given."
# multimask_output actually will provide 3 masks for each segmentation (see https://github.com/facebookresearch/sam2/blob/main/notebooks/image_predictor_example.ipynb)
# so should also be set to False
has_multi = False
if points and bboxes:
has_multi = True
elif points and len(list(set(point_labels))) > 1:
has_multi = True
elif bboxes and len(bboxes) > 1:
has_multi = True
# parse provided bboxes
bboxes = (
[bbox_xyxy(**bbox) if isinstance(bbox, dict) else bbox for bbox in bboxes]
if bboxes
else []
)
points = (
[point_xy(**p) if isinstance(p, dict) else p for p in points] if points else []
)
# setup inference
image = np.array(image.convert("RGB"))
model.set_image(image)
box_coords = (
np.array([[b.x0, b.y0, b.x1, b.y1] for b in bboxes]) if bboxes else None
)
point_coords = np.array([[p.x, p.y] for p in points]) if points else None
point_labels = np.array(point_labels) if point_labels else None
masks, scores, _ = model.predict(
box=box_coords,
point_coords=point_coords,
point_labels=point_labels,
multimask_output=False, # has_multi,
)
# mask here is of shape (X, h, w) of np array, X = number of masks
if get_pil_mask:
return show_masks(image, masks, scores=None, display_image=False)
else:
output_masks = []
for i, mask in enumerate(masks):
if mask.ndim > 2: # shape (1, h, w)
# logger.debug(f"found mask of shape {mask.shape}")
output_masks.append(mask.squeeze().astype(np.uint8))
# when multimask_output = True the mask is shape (3,h,w)
# mask = np.transpose(mask, (1, 2, 0)) # shape (h,w,3)
# mask = Image.fromarray((mask * 255).astype(np.uint8)).convert("L")
# output_masks.append(np.array(mask))
else:
# logger.debug(f"found mask of shape {mask.shape}")
output_masks.append(mask.squeeze().astype(np.uint8))
return (
[b64_mask_encode(m).decode("ascii") for m in output_masks]
if b64_encode_mask
else output_masks
)
def unpack_masks(
masks_generator,
frame_wh: tuple,
drop_mask: bool = False,
):
"""return a list of detections in Miro's format given a SAM2 mask generator"""
w, h = frame_wh
detections = []
for frame_idx, tracker_ids, mask_logits in masks_generator:
masks = (mask_logits > 0.0).cpu().numpy().astype(np.uint8)
# draw a couple frames for debug purpose
# if frame_idx % 15 == 0:
# ann_masks = [m.squeeze() for m in masks if mask_to_xyxy(m.squeeze())]
# if len(ann_masks) > 0:
# annotate_masks(
# get_pil_im(np.array(vr.get_data(frame_idx))),
# masks=ann_masks,
# ).save(os.path.join(vframes_dir, f"{frame_idx}.png"))
for id, mask in zip(tracker_ids, masks):
mask = mask.squeeze().astype(np.uint8)
xyxy = mask_to_xyxy(mask)
if not xyxy: # mask is empty
# logger.debug(f"track_id {id} is missing mask at frame {frame_idx}")
continue
x0, y0, x1, y1 = xyxy
det = { # miro's detections format for videos
"frame": frame_idx,
"track_id": id,
"x": x0 / w,
"y": y0 / h,
"w": (x1 - x0) / w,
"h": (y1 - y0) / h,
"conf": 1,
}
if not drop_mask:
det["mask_b64"] = b64_mask_encode(mask).decode("ascii")
detections.append(det)
return detections
def run_sam_video_inference(
model: Any,
video_path: str,
masks: np.ndarray,
device: str = "cpu",
sample_fps: int = None,
every_x: int = None,
do_tidy_up: bool = False,
drop_mask: bool = True,
async_frame_load: bool = False,
ref_frame_idx: int = 0,
):
# put video frames into directory
# TODO:
# change frame size
l_frames_fp = extract_frames(
video_path,
fps=sample_fps,
every_x=every_x,
overwrite=True,
im_name_pattern="%05d.jpg",
)
vframes_dir = os.path.dirname(l_frames_fp[0])
vinfo = VidInfo(video_path)
vr = VidReader(video_path, use_imageio=True)
w = vinfo["frame_width"]
h = vinfo["frame_height"]
inference_state = model.init_state(
video_path=vframes_dir, device=device, async_loading_frames=async_frame_load
)
for mask_idx, mask in enumerate(masks):
_, object_ids, mask_logits = model.add_new_mask(
inference_state=inference_state,
frame_idx=ref_frame_idx,
obj_id=mask_idx,
mask=mask,
)
# debug
logger.debug(
f"adding mask {mask_idx} of shape {mask.shape} for frame {ref_frame_idx}, xyxy: {mask_to_xyxy(mask)}"
)
# debug init state
logger.debug(f"model initiated with mask_logits of shape {mask_logits.shape}")
logger.debug(f"model initiated with object_ids of len {len(object_ids)}")
init_masks = (mask_logits > 0.0).cpu().numpy().astype(np.uint8)
init_masks = [m.squeeze() for m in init_masks]
# ref_frame_im = get_pil_im(np.array(vr.get_data(ref_frame_idx)))
# init_masks_im_fp = os.path.join(vframes_dir, f"model_init_masks.jpg")
# input_masks_im_fp = os.path.join(vframes_dir, f"input_masks.jpg")
# annotate_masks(ref_frame_im, init_masks).save(init_masks_im_fp)
# annotate_masks(ref_frame_im, masks).save(input_masks_im_fp)
# logger.debug(f"masks received by model visualized at {init_masks_im_fp}")
# logger.debug(f"masks provided to model visualized at {input_masks_im_fp}")
masks_generator = model.propagate_in_video(inference_state)
detections = unpack_masks(
masks_generator,
drop_mask=drop_mask,
frame_wh=(w, h),
)
if ref_frame_idx != 0:
logger.debug(f"propagating in reverse now from {ref_frame_idx}")
# there's no need to reset state
# model.reset_state(inference_state)
masks_generator = model.propagate_in_video(inference_state, reverse=True)
detections += unpack_masks(
masks_generator,
drop_mask=drop_mask,
frame_wh=(w, h),
)
if do_tidy_up:
# remove vframes_dir
shutil.rmtree(vframes_dir)
return detections
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