# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import argparse
import os
import numpy as np
import torch
from PIL import Image
from sam2.build_sam import build_sam2_video_predictor
# the PNG palette for DAVIS 2017 dataset
DAVIS_PALETTE = b"\x00\x00\x00\x80\x00\x00\x00\x80\x00\x80\x80\x00\x00\x00\x80\x80\x00\x80\x00\x80\x80\x80\x80\x80@\x00\x00\xc0\x00\x00@\x80\x00\xc0\x80\x00@\x00\x80\xc0\x00\x80@\x80\x80\xc0\x80\x80\x00@\x00\x80@\x00\x00\xc0\x00\x80\xc0\x00\x00@\x80\x80@\x80\x00\xc0\x80\x80\xc0\x80@@\x00\xc0@\x00@\xc0\x00\xc0\xc0\x00@@\x80\xc0@\x80@\xc0\x80\xc0\xc0\x80\x00\x00@\x80\x00@\x00\x80@\x80\x80@\x00\x00\xc0\x80\x00\xc0\x00\x80\xc0\x80\x80\xc0@\x00@\xc0\x00@@\x80@\xc0\x80@@\x00\xc0\xc0\x00\xc0@\x80\xc0\xc0\x80\xc0\x00@@\x80@@\x00\xc0@\x80\xc0@\x00@\xc0\x80@\xc0\x00\xc0\xc0\x80\xc0\xc0@@@\xc0@@@\xc0@\xc0\xc0@@@\xc0\xc0@\xc0@\xc0\xc0\xc0\xc0\xc0 \x00\x00\xa0\x00\x00 \x80\x00\xa0\x80\x00 \x00\x80\xa0\x00\x80 \x80\x80\xa0\x80\x80`\x00\x00\xe0\x00\x00`\x80\x00\xe0\x80\x00`\x00\x80\xe0\x00\x80`\x80\x80\xe0\x80\x80 @\x00\xa0@\x00 \xc0\x00\xa0\xc0\x00 @\x80\xa0@\x80 \xc0\x80\xa0\xc0\x80`@\x00\xe0@\x00`\xc0\x00\xe0\xc0\x00`@\x80\xe0@\x80`\xc0\x80\xe0\xc0\x80 \x00@\xa0\x00@ \x80@\xa0\x80@ \x00\xc0\xa0\x00\xc0 \x80\xc0\xa0\x80\xc0`\x00@\xe0\x00@`\x80@\xe0\x80@`\x00\xc0\xe0\x00\xc0`\x80\xc0\xe0\x80\xc0 @@\xa0@@ \xc0@\xa0\xc0@ @\xc0\xa0@\xc0 \xc0\xc0\xa0\xc0\xc0`@@\xe0@@`\xc0@\xe0\xc0@`@\xc0\xe0@\xc0`\xc0\xc0\xe0\xc0\xc0\x00 \x00\x80 \x00\x00\xa0\x00\x80\xa0\x00\x00 \x80\x80 \x80\x00\xa0\x80\x80\xa0\x80@ \x00\xc0 \x00@\xa0\x00\xc0\xa0\x00@ \x80\xc0 \x80@\xa0\x80\xc0\xa0\x80\x00`\x00\x80`\x00\x00\xe0\x00\x80\xe0\x00\x00`\x80\x80`\x80\x00\xe0\x80\x80\xe0\x80@`\x00\xc0`\x00@\xe0\x00\xc0\xe0\x00@`\x80\xc0`\x80@\xe0\x80\xc0\xe0\x80\x00 @\x80 @\x00\xa0@\x80\xa0@\x00 \xc0\x80 \xc0\x00\xa0\xc0\x80\xa0\xc0@ @\xc0 @@\xa0@\xc0\xa0@@ \xc0\xc0 \xc0@\xa0\xc0\xc0\xa0\xc0\x00`@\x80`@\x00\xe0@\x80\xe0@\x00`\xc0\x80`\xc0\x00\xe0\xc0\x80\xe0\xc0@`@\xc0`@@\xe0@\xc0\xe0@@`\xc0\xc0`\xc0@\xe0\xc0\xc0\xe0\xc0 \x00\xa0 \x00 \xa0\x00\xa0\xa0\x00 \x80\xa0 \x80 \xa0\x80\xa0\xa0\x80` \x00\xe0 \x00`\xa0\x00\xe0\xa0\x00` \x80\xe0 \x80`\xa0\x80\xe0\xa0\x80 `\x00\xa0`\x00 \xe0\x00\xa0\xe0\x00 `\x80\xa0`\x80 \xe0\x80\xa0\xe0\x80``\x00\xe0`\x00`\xe0\x00\xe0\xe0\x00``\x80\xe0`\x80`\xe0\x80\xe0\xe0\x80 @\xa0 @ \xa0@\xa0\xa0@ \xc0\xa0 \xc0 \xa0\xc0\xa0\xa0\xc0` @\xe0 @`\xa0@\xe0\xa0@` \xc0\xe0 \xc0`\xa0\xc0\xe0\xa0\xc0 `@\xa0`@ \xe0@\xa0\xe0@ `\xc0\xa0`\xc0 \xe0\xc0\xa0\xe0\xc0``@\xe0`@`\xe0@\xe0\xe0@``\xc0\xe0`\xc0`\xe0\xc0\xe0\xe0\xc0"
def load_ann_png(path):
"""Load a PNG file as a mask and its palette."""
mask = Image.open(path)
palette = mask.getpalette()
mask = np.array(mask).astype(np.uint8)
return mask, palette
def save_ann_png(path, mask, palette):
"""Save a mask as a PNG file with the given palette."""
assert mask.dtype == np.uint8
assert mask.ndim == 2
output_mask = Image.fromarray(mask)
output_mask.putpalette(palette)
output_mask.save(path)
def get_per_obj_mask(mask):
"""Split a mask into per-object masks."""
object_ids = np.unique(mask)
object_ids = object_ids[object_ids > 0].tolist()
per_obj_mask = {object_id: (mask == object_id) for object_id in object_ids}
return per_obj_mask
def put_per_obj_mask(per_obj_mask, height, width):
"""Combine per-object masks into a single mask."""
mask = np.zeros((height, width), dtype=np.uint8)
object_ids = sorted(per_obj_mask)[::-1]
for object_id in object_ids:
object_mask = per_obj_mask[object_id]
object_mask = object_mask.reshape(height, width)
mask[object_mask] = object_id
return mask
def load_masks_from_dir(input_mask_dir, video_name, frame_name, per_obj_png_file):
"""Load masks from a directory as a dict of per-object masks."""
if not per_obj_png_file:
input_mask_path = os.path.join(input_mask_dir, video_name, f"{frame_name}.png")
input_mask, input_palette = load_ann_png(input_mask_path)
per_obj_input_mask = get_per_obj_mask(input_mask)
else:
per_obj_input_mask = {}
# each object is a directory in "{object_id:%03d}" format
for object_name in os.listdir(os.path.join(input_mask_dir, video_name)):
object_id = int(object_name)
input_mask_path = os.path.join(
input_mask_dir, video_name, object_name, f"{frame_name}.png"
)
input_mask, input_palette = load_ann_png(input_mask_path)
per_obj_input_mask[object_id] = input_mask > 0
return per_obj_input_mask, input_palette
def save_masks_to_dir(
output_mask_dir,
video_name,
frame_name,
per_obj_output_mask,
height,
width,
per_obj_png_file,
output_palette,
):
"""Save masks to a directory as PNG files."""
os.makedirs(os.path.join(output_mask_dir, video_name), exist_ok=True)
if not per_obj_png_file:
output_mask = put_per_obj_mask(per_obj_output_mask, height, width)
output_mask_path = os.path.join(
output_mask_dir, video_name, f"{frame_name}.png"
)
save_ann_png(output_mask_path, output_mask, output_palette)
else:
for object_id, object_mask in per_obj_output_mask.items():
object_name = f"{object_id:03d}"
os.makedirs(
os.path.join(output_mask_dir, video_name, object_name),
exist_ok=True,
)
output_mask = object_mask.reshape(height, width).astype(np.uint8)
output_mask_path = os.path.join(
output_mask_dir, video_name, object_name, f"{frame_name}.png"
)
save_ann_png(output_mask_path, output_mask, output_palette)
@torch.inference_mode()
@torch.autocast(device_type="cuda", dtype=torch.bfloat16)
def vos_inference(
predictor,
base_video_dir,
input_mask_dir,
output_mask_dir,
video_name,
score_thresh=0.0,
use_all_masks=False,
per_obj_png_file=False,
):
"""Run VOS inference on a single video with the given predictor."""
# load the video frames and initialize the inference state on this video
video_dir = os.path.join(base_video_dir, video_name)
frame_names = [
os.path.splitext(p)[0]
for p in os.listdir(video_dir)
if os.path.splitext(p)[-1] in [".jpg", ".jpeg", ".JPG", ".JPEG"]
]
frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))
inference_state = predictor.init_state(
video_path=video_dir, async_loading_frames=False
)
height = inference_state["video_height"]
width = inference_state["video_width"]
input_palette = None
# fetch mask inputs from input_mask_dir (either only mask for the first frame, or all available masks)
if not use_all_masks:
# use only the first video's ground-truth mask as the input mask
input_frame_inds = [0]
else:
# use all mask files available in the input_mask_dir as the input masks
if not per_obj_png_file:
input_frame_inds = [
idx
for idx, name in enumerate(frame_names)
if os.path.exists(
os.path.join(input_mask_dir, video_name, f"{name}.png")
)
]
else:
input_frame_inds = [
idx
for object_name in os.listdir(os.path.join(input_mask_dir, video_name))
for idx, name in enumerate(frame_names)
if os.path.exists(
os.path.join(input_mask_dir, video_name, object_name, f"{name}.png")
)
]
input_frame_inds = sorted(set(input_frame_inds))
# add those input masks to SAM 2 inference state before propagation
for input_frame_idx in input_frame_inds:
per_obj_input_mask, input_palette = load_masks_from_dir(
input_mask_dir=input_mask_dir,
video_name=video_name,
frame_name=frame_names[input_frame_idx],
per_obj_png_file=per_obj_png_file,
)
for object_id, object_mask in per_obj_input_mask.items():
predictor.add_new_mask(
inference_state=inference_state,
frame_idx=input_frame_idx,
obj_id=object_id,
mask=object_mask,
)
# run propagation throughout the video and collect the results in a dict
os.makedirs(os.path.join(output_mask_dir, video_name), exist_ok=True)
output_palette = input_palette or DAVIS_PALETTE
video_segments = {} # video_segments contains the per-frame segmentation results
for out_frame_idx, out_obj_ids, out_mask_logits in predictor.propagate_in_video(
inference_state
):
per_obj_output_mask = {
out_obj_id: (out_mask_logits[i] > score_thresh).cpu().numpy()
for i, out_obj_id in enumerate(out_obj_ids)
}
video_segments[out_frame_idx] = per_obj_output_mask
# write the output masks as palette PNG files to output_mask_dir
for out_frame_idx, per_obj_output_mask in video_segments.items():
save_masks_to_dir(
output_mask_dir=output_mask_dir,
video_name=video_name,
frame_name=frame_names[out_frame_idx],
per_obj_output_mask=per_obj_output_mask,
height=height,
width=width,
per_obj_png_file=per_obj_png_file,
output_palette=output_palette,
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
"--sam2_cfg",
type=str,
default="sam2_hiera_b+.yaml",
help="SAM 2 model configuration file",
)
parser.add_argument(
"--sam2_checkpoint",
type=str,
default="./checkpoints/sam2_hiera_b+.pt",
help="path to the SAM 2 model checkpoint",
)
parser.add_argument(
"--base_video_dir",
type=str,
required=True,
help="directory containing videos (as JPEG files) to run VOS prediction on",
)
parser.add_argument(
"--input_mask_dir",
type=str,
required=True,
help="directory containing input masks (as PNG files) of each video",
)
parser.add_argument(
"--video_list_file",
type=str,
default=None,
help="text file containing the list of video names to run VOS prediction on",
)
parser.add_argument(
"--output_mask_dir",
type=str,
required=True,
help="directory to save the output masks (as PNG files)",
)
parser.add_argument(
"--score_thresh",
type=float,
default=0.0,
help="threshold for the output mask logits (default: 0.0)",
)
parser.add_argument(
"--use_all_masks",
action="store_true",
help="whether to use all available PNG files in input_mask_dir "
"(default without this flag: just the first PNG file as input to the SAM 2 model; "
"usually we don't need this flag, since semi-supervised VOS evaluation usually takes input from the first frame only)",
)
parser.add_argument(
"--per_obj_png_file",
action="store_true",
help="whether use separate per-object PNG files for input and output masks "
"(default without this flag: all object masks are packed into a single PNG file on each frame following DAVIS format; "
"note that the SA-V dataset stores each object mask as an individual PNG file and requires this flag)",
)
parser.add_argument(
"--apply_postprocessing",
action="store_true",
help="whether to apply postprocessing (e.g. hole-filling) to the output masks "
"(we don't apply such post-processing in the SAM 2 model evaluation)",
)
args = parser.parse_args()
# if we use per-object PNG files, they could possibly overlap in inputs and outputs
hydra_overrides_extra = [
"++model.non_overlap_masks=" + ("false" if args.per_obj_png_file else "true")
]
predictor = build_sam2_video_predictor(
config_file=args.sam2_cfg,
ckpt_path=args.sam2_checkpoint,
apply_postprocessing=args.apply_postprocessing,
hydra_overrides_extra=hydra_overrides_extra,
)
if args.use_all_masks:
print("using all available masks in input_mask_dir as input to the SAM 2 model")
else:
print(
"using only the first frame's mask in input_mask_dir as input to the SAM 2 model"
)
# if a video list file is provided, read the video names from the file
# (otherwise, we use all subdirectories in base_video_dir)
if args.video_list_file is not None:
with open(args.video_list_file, "r") as f:
video_names = [v.strip() for v in f.readlines()]
else:
video_names = [
p
for p in os.listdir(args.base_video_dir)
if os.path.isdir(os.path.join(args.base_video_dir, p))
]
print(f"running VOS prediction on {len(video_names)} videos:\n{video_names}")
for n_video, video_name in enumerate(video_names):
print(f"\n{n_video + 1}/{len(video_names)} - running on {video_name}")
vos_inference(
predictor=predictor,
base_video_dir=args.base_video_dir,
input_mask_dir=args.input_mask_dir,
output_mask_dir=args.output_mask_dir,
video_name=video_name,
score_thresh=args.score_thresh,
use_all_masks=args.use_all_masks,
per_obj_png_file=args.per_obj_png_file,
)
print(
f"completed VOS prediction on {len(video_names)} videos -- "
f"output masks saved to {args.output_mask_dir}"
)
if __name__ == "__main__":
main()