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# 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 glob
import json
import os
import numpy as np
import pandas as pd
import torch
from PIL import Image as PILImage
try:
from pycocotools import mask as mask_utils
except:
pass
class JSONSegmentLoader:
def __init__(self, video_json_path, ann_every=1, frames_fps=24, valid_obj_ids=None):
# Annotations in the json are provided every ann_every th frame
self.ann_every = ann_every
# Ids of the objects to consider when sampling this video
self.valid_obj_ids = valid_obj_ids
with open(video_json_path, "r") as f:
data = json.load(f)
if isinstance(data, list):
self.frame_annots = data
elif isinstance(data, dict):
masklet_field_name = "masklet" if "masklet" in data else "masks"
self.frame_annots = data[masklet_field_name]
if "fps" in data:
if isinstance(data["fps"], list):
annotations_fps = int(data["fps"][0])
else:
annotations_fps = int(data["fps"])
assert frames_fps % annotations_fps == 0
self.ann_every = frames_fps // annotations_fps
else:
raise NotImplementedError
def load(self, frame_id, obj_ids=None):
assert frame_id % self.ann_every == 0
rle_mask = self.frame_annots[frame_id // self.ann_every]
valid_objs_ids = set(range(len(rle_mask)))
if self.valid_obj_ids is not None:
# Remove the masklets that have been filtered out for this video
valid_objs_ids &= set(self.valid_obj_ids)
if obj_ids is not None:
# Only keep the objects that have been sampled
valid_objs_ids &= set(obj_ids)
valid_objs_ids = sorted(list(valid_objs_ids))
# Construct rle_masks_filtered that only contains the rle masks we are interested in
id_2_idx = {}
rle_mask_filtered = []
for obj_id in valid_objs_ids:
if rle_mask[obj_id] is not None:
id_2_idx[obj_id] = len(rle_mask_filtered)
rle_mask_filtered.append(rle_mask[obj_id])
else:
id_2_idx[obj_id] = None
# Decode the masks
raw_segments = torch.from_numpy(mask_utils.decode(rle_mask_filtered)).permute(
2, 0, 1
) # （num_obj, h, w）
segments = {}
for obj_id in valid_objs_ids:
if id_2_idx[obj_id] is None:
segments[obj_id] = None
else:
idx = id_2_idx[obj_id]
segments[obj_id] = raw_segments[idx]
return segments
def get_valid_obj_frames_ids(self, num_frames_min=None):
# For each object, find all the frames with a valid (not None) mask
num_objects = len(self.frame_annots[0])
# The result dict associates each obj_id with the id of its valid frames
res = {obj_id: [] for obj_id in range(num_objects)}
for annot_idx, annot in enumerate(self.frame_annots):
for obj_id in range(num_objects):
if annot[obj_id] is not None:
res[obj_id].append(int(annot_idx * self.ann_every))
if num_frames_min is not None:
# Remove masklets that have less than num_frames_min valid masks
for obj_id, valid_frames in list(res.items()):
if len(valid_frames) < num_frames_min:
res.pop(obj_id)
return res
class PalettisedPNGSegmentLoader:
def __init__(self, video_png_root):
"""
SegmentLoader for datasets with masks stored as palettised PNGs.
video_png_root: the folder contains all the masks stored in png
"""
self.video_png_root = video_png_root
# build a mapping from frame id to their PNG mask path
# note that in some datasets, the PNG paths could have more
# than 5 digits, e.g. "00000000.png" instead of "00000.png"
png_filenames = os.listdir(self.video_png_root)
self.frame_id_to_png_filename = {}
for filename in png_filenames:
frame_id, _ = os.path.splitext(filename)
self.frame_id_to_png_filename[int(frame_id)] = filename
def load(self, frame_id):
"""
load the single palettised mask from the disk (path: f'{self.video_png_root}/{frame_id:05d}.png')
Args:
frame_id: int, define the mask path
Return:
binary_segments: dict
"""
# check the path
mask_path = os.path.join(
self.video_png_root, self.frame_id_to_png_filename[frame_id]
)
# load the mask
masks = PILImage.open(mask_path).convert("P")
masks = np.array(masks)
object_id = pd.unique(masks.flatten())
object_id = object_id[object_id != 0] # remove background (0)
# convert into N binary segmentation masks
binary_segments = {}
for i in object_id:
bs = masks == i
binary_segments[i] = torch.from_numpy(bs)
return binary_segments
def __len__(self):
return
class MultiplePNGSegmentLoader:
def __init__(self, video_png_root, single_object_mode=False):
"""
video_png_root: the folder contains all the masks stored in png
single_object_mode: whether to load only a single object at a time
"""
self.video_png_root = video_png_root
self.single_object_mode = single_object_mode
# read a mask to know the resolution of the video
if self.single_object_mode:
tmp_mask_path = glob.glob(os.path.join(video_png_root, "*.png"))[0]
else:
tmp_mask_path = glob.glob(os.path.join(video_png_root, "*", "*.png"))[0]
tmp_mask = np.array(PILImage.open(tmp_mask_path))
self.H = tmp_mask.shape[0]
self.W = tmp_mask.shape[1]
if self.single_object_mode:
self.obj_id = (
int(video_png_root.split("/")[-1]) + 1
) # offset by 1 as bg is 0
else:
self.obj_id = None
def load(self, frame_id):
if self.single_object_mode:
return self._load_single_png(frame_id)
else:
return self._load_multiple_pngs(frame_id)
def _load_single_png(self, frame_id):
"""
load single png from the disk (path: f'{self.obj_id}/{frame_id:05d}.png')
Args:
frame_id: int, define the mask path
Return:
binary_segments: dict
"""
mask_path = os.path.join(self.video_png_root, f"{frame_id:05d}.png")
binary_segments = {}
if os.path.exists(mask_path):
mask = np.array(PILImage.open(mask_path))
else:
# if png doesn't exist, empty mask
mask = np.zeros((self.H, self.W), dtype=bool)
binary_segments[self.obj_id] = torch.from_numpy(mask > 0)
return binary_segments
def _load_multiple_pngs(self, frame_id):
"""
load multiple png masks from the disk (path: f'{obj_id}/{frame_id:05d}.png')
Args:
frame_id: int, define the mask path
Return:
binary_segments: dict
"""
# get the path
all_objects = sorted(glob.glob(os.path.join(self.video_png_root, "*")))
num_objects = len(all_objects)
assert num_objects > 0
# load the masks
binary_segments = {}
for obj_folder in all_objects:
# obj_folder is {video_name}/{obj_id}, obj_id is specified by the name of the folder
obj_id = int(obj_folder.split("/")[-1])
obj_id = obj_id + 1 # offset 1 as bg is 0
mask_path = os.path.join(obj_folder, f"{frame_id:05d}.png")
if os.path.exists(mask_path):
mask = np.array(PILImage.open(mask_path))
else:
mask = np.zeros((self.H, self.W), dtype=bool)
binary_segments[obj_id] = torch.from_numpy(mask > 0)
return binary_segments
def __len__(self):
return
class LazySegments:
"""
Only decodes segments that are actually used.
"""
def __init__(self):
self.segments = {}
self.cache = {}
def __setitem__(self, key, item):
self.segments[key] = item
def __getitem__(self, key):
if key in self.cache:
return self.cache[key]
rle = self.segments[key]
mask = torch.from_numpy(mask_utils.decode([rle])).permute(2, 0, 1)[0]
self.cache[key] = mask
return mask
def __contains__(self, key):
return key in self.segments
def __len__(self):
return len(self.segments)
def keys(self):
return self.segments.keys()
class SA1BSegmentLoader:
def __init__(
self,
video_mask_path,
mask_area_frac_thresh=1.1,
video_frame_path=None,
uncertain_iou=-1,
):
with open(video_mask_path, "r") as f:
self.frame_annots = json.load(f)
if mask_area_frac_thresh <= 1.0:
# Lazily read frame
orig_w, orig_h = PILImage.open(video_frame_path).size
area = orig_w * orig_h
self.frame_annots = self.frame_annots["annotations"]
rle_masks = []
for frame_annot in self.frame_annots:
if not frame_annot["area"] > 0:
continue
if ("uncertain_iou" in frame_annot) and (
frame_annot["uncertain_iou"] < uncertain_iou
):
# uncertain_iou is stability score
continue
if (
mask_area_frac_thresh <= 1.0
and (frame_annot["area"] / area) >= mask_area_frac_thresh
):
continue
rle_masks.append(frame_annot["segmentation"])
self.segments = LazySegments()
for i, rle in enumerate(rle_masks):
self.segments[i] = rle
def load(self, frame_idx):
return self.segments
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