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from typing import Tuple, Optional, Dict
import logging
import os
import shutil
from os import path
from PIL import Image
import torch
import torch.nn.functional as F
import numpy as np
import pycocotools.mask as mask_util
from threading import Thread
from queue import Queue
from dataclasses import dataclass
import copy
from tracker.utils.pano_utils import ID2RGBConverter
from tracker.utils.palette import davis_palette_np
from tracker.inference.object_manager import ObjectManager
from tracker.inference.object_info import ObjectInfo
log = logging.getLogger()
try:
import hickle as hkl
except ImportError:
log.warning('Failed to import hickle. Fine if not using multi-scale testing.')
class ResultSaver:
def __init__(self,
output_root,
video_name,
*,
dataset,
object_manager: ObjectManager,
use_long_id,
palette=None,
save_mask=True,
save_scores=False,
score_output_root=None,
visualize_output_root=None,
visualize=False,
init_json=None):
self.output_root = output_root
self.video_name = video_name
self.dataset = dataset.lower()
self.use_long_id = use_long_id
self.palette = palette
self.object_manager = object_manager
self.save_mask = save_mask
self.save_scores = save_scores
self.score_output_root = score_output_root
self.visualize_output_root = visualize_output_root
self.visualize = visualize
if self.visualize:
if self.palette is not None:
self.colors = np.array(self.palette, dtype=np.uint8).reshape(-1, 3)
else:
self.colors = davis_palette_np
self.need_remapping = True
self.json_style = None
self.id2rgb_converter = ID2RGBConverter()
if 'burst' in self.dataset:
assert init_json is not None
self.input_segmentations = init_json['segmentations']
self.segmentations = [{} for _ in init_json['segmentations']]
self.annotated_frames = init_json['annotated_image_paths']
self.video_json = {k: v for k, v in init_json.items() if k != 'segmentations'}
self.video_json['segmentations'] = self.segmentations
self.json_style = 'burst'
self.queue = Queue(maxsize=10)
self.thread = Thread(target=save_result, args=(self.queue, ))
self.thread.daemon = True
self.thread.start()
def process(self,
prob: torch.Tensor,
frame_name: str,
resize_needed: bool = False,
shape: Optional[Tuple[int, int]] = None,
last_frame: bool = False,
path_to_image: str = None):
if resize_needed:
prob = F.interpolate(prob.unsqueeze(1), shape, mode='bilinear', align_corners=False)[:,
0]
# Probability mask -> index mask
mask = torch.argmax(prob, dim=0)
if self.save_scores:
# also need to pass prob
prob = prob.cpu()
else:
prob = None
# remap indices
if self.need_remapping:
new_mask = torch.zeros_like(mask)
for tmp_id, obj in self.object_manager.tmp_id_to_obj.items():
new_mask[mask == tmp_id] = obj.id
mask = new_mask
args = ResultArgs(saver=self,
prob=prob,
mask=mask.cpu(),
frame_name=frame_name,
path_to_image=path_to_image,
tmp_id_to_obj=copy.deepcopy(self.object_manager.tmp_id_to_obj),
obj_to_tmp_id=copy.deepcopy(self.object_manager.obj_to_tmp_id),
last_frame=last_frame)
self.queue.put(args)
def end(self):
self.queue.put(None)
self.queue.join()
self.thread.join()
@dataclass
class ResultArgs:
saver: ResultSaver
prob: torch.Tensor
mask: torch.Tensor
frame_name: str
path_to_image: str
tmp_id_to_obj: Dict[int, ObjectInfo]
obj_to_tmp_id: Dict[ObjectInfo, int]
last_frame: bool
def save_result(queue: Queue):
while True:
args: ResultArgs = queue.get()
if args is None:
queue.task_done()
break
saver = args.saver
prob = args.prob
mask = args.mask
frame_name = args.frame_name
path_to_image = args.path_to_image
tmp_id_to_obj = args.tmp_id_to_obj
obj_to_tmp_id = args.obj_to_tmp_id
last_frame = args.last_frame
all_obj_ids = [k.id for k in obj_to_tmp_id]
# record output in the json file
if saver.json_style == 'burst':
if frame_name in saver.annotated_frames:
frame_index = saver.annotated_frames.index(frame_name)
input_segments = saver.input_segmentations[frame_index]
frame_segments = saver.segmentations[frame_index]
for id in all_obj_ids:
if id in input_segments:
# if this frame has been given as input, just copy
frame_segments[id] = input_segments[id]
continue
segment = {}
segment_mask = (mask == id)
if segment_mask.sum() > 0:
coco_mask = mask_util.encode(np.asfortranarray(segment_mask.numpy()))
segment['rle'] = coco_mask['counts'].decode('utf-8')
frame_segments[id] = segment
# save the mask to disk
if saver.save_mask:
if saver.use_long_id:
out_mask = mask.numpy().astype(np.uint32)
rgb_mask = np.zeros((*out_mask.shape[-2:], 3), dtype=np.uint8)
for id in all_obj_ids:
_, image = saver.id2rgb_converter.convert(id)
obj_mask = (out_mask == id)
rgb_mask[obj_mask] = image
out_img = Image.fromarray(rgb_mask)
else:
rgb_mask = None
out_mask = mask.numpy().astype(np.uint8)
out_img = Image.fromarray(out_mask)
if saver.palette is not None:
out_img.putpalette(saver.palette)
this_out_path = path.join(saver.output_root, saver.video_name)
os.makedirs(this_out_path, exist_ok=True)
out_img.save(os.path.join(this_out_path, frame_name[:-4] + '.png'))
# save scores for multi-scale testing
if saver.save_scores:
this_out_path = path.join(saver.score_output_root, saver.video_name)
os.makedirs(this_out_path, exist_ok=True)
prob = (prob.detach().numpy() * 255).astype(np.uint8)
if last_frame:
tmp_to_obj_mapping = {obj.id: tmp_id for obj, tmp_id in tmp_id_to_obj.items()}
hkl.dump(tmp_to_obj_mapping, path.join(this_out_path, f'backward.hkl'), mode='w')
hkl.dump(prob,
path.join(this_out_path, f'{frame_name[:-4]}.hkl'),
mode='w',
compression='lzf')
if saver.visualize:
if path_to_image is not None:
image_np = np.array(Image.open(path_to_image))
else:
raise ValueError('Cannot visualize without path_to_image')
if rgb_mask is None:
# we need to apply a palette
rgb_mask = np.zeros((*out_mask.shape, 3), dtype=np.uint8)
for id in all_obj_ids:
image = saver.colors[id]
obj_mask = (out_mask == id)
rgb_mask[obj_mask] = image
alpha = (out_mask == 0).astype(np.float32) * 0.5 + 0.5
alpha = alpha[:, :, None]
blend = (image_np * alpha + rgb_mask * (1 - alpha)).astype(np.uint8)
# find a place to save the visualization
this_vis_path = path.join(saver.visualize_output_root, saver.video_name)
os.makedirs(this_vis_path, exist_ok=True)
Image.fromarray(blend).save(path.join(this_vis_path, frame_name[:-4] + '.jpg'))
queue.task_done()
def make_zip(dataset, run_dir, exp_id, mask_output_root):
if dataset.startswith('y'):
# YoutubeVOS
log.info('Making zip for YouTubeVOS...')
shutil.make_archive(path.join(run_dir, f'{exp_id}_{dataset}'), 'zip', run_dir,
'Annotations')
elif dataset == 'd17-test-dev':
# DAVIS 2017 test-dev -- zip from within the Annotation folder
log.info('Making zip for DAVIS test-dev...')
shutil.make_archive(path.join(run_dir, f'{exp_id}_{dataset}'), 'zip', mask_output_root)
elif dataset == 'mose-val':
# MOSE validation -- same as DAVIS test-dev
log.info('Making zip for MOSE validation...')
shutil.make_archive(path.join(run_dir, f'{exp_id}_{dataset}'), 'zip', mask_output_root)
elif dataset == 'lvos-test':
# LVOS test -- same as YouTubeVOS
log.info('Making zip for LVOS test...')
shutil.make_archive(path.join(run_dir, f'{exp_id}_{dataset}'), 'zip', run_dir,
'Annotations')
else:
log.info(f'Not making zip for {dataset}.')
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