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# import for debugging
import os
import glob
import numpy as np
from PIL import Image
# import for base_tracker
import torch
import yaml
import torch.nn.functional as F
from model.network import XMem
from inference.inference_core import InferenceCore
from tracker.util.mask_mapper import MaskMapper
from torchvision import transforms
from tracker.util.range_transform import im_normalization
from tools.painter import mask_painter
from tools.base_segmenter import BaseSegmenter
from torchvision.transforms import Resize
import progressbar
class BaseTracker:
def __init__(self, xmem_checkpoint, device, sam_model=None, model_type=None) -> None:
"""
device: model device
xmem_checkpoint: checkpoint of XMem model
"""
# load configurations
with open("tracker/config/config.yaml", 'r') as stream:
config = yaml.safe_load(stream)
# initialise XMem
network = XMem(config, xmem_checkpoint).to(device).eval()
# initialise IncerenceCore
self.tracker = InferenceCore(network, config)
# data transformation
self.im_transform = transforms.Compose([
transforms.ToTensor(),
im_normalization,
])
self.device = device
# changable properties
self.mapper = MaskMapper()
self.initialised = False
# # SAM-based refinement
# self.sam_model = sam_model
# self.resizer = Resize([256, 256])
@torch.no_grad()
def resize_mask(self, mask):
# mask transform is applied AFTER mapper, so we need to post-process it in eval.py
h, w = mask.shape[-2:]
min_hw = min(h, w)
return F.interpolate(mask, (int(h/min_hw*self.size), int(w/min_hw*self.size)),
mode='nearest')
@torch.no_grad()
def track(self, frame, first_frame_annotation=None):
"""
Input:
frames: numpy arrays (H, W, 3)
logit: numpy array (H, W), logit
Output:
mask: numpy arrays (H, W)
logit: numpy arrays, probability map (H, W)
painted_image: numpy array (H, W, 3)
"""
if first_frame_annotation is not None: # first frame mask
# initialisation
mask, labels = self.mapper.convert_mask(first_frame_annotation)
mask = torch.Tensor(mask).to(self.device)
self.tracker.set_all_labels(list(self.mapper.remappings.values()))
else:
mask = None
labels = None
# prepare inputs
frame_tensor = self.im_transform(frame).to(self.device)
# track one frame
probs, _ = self.tracker.step(frame_tensor, mask, labels) # logits 2 (bg fg) H W
# # refine
# if first_frame_annotation is None:
# out_mask = self.sam_refinement(frame, logits[1], ti)
# convert to mask
out_mask = torch.argmax(probs, dim=0)
out_mask = (out_mask.detach().cpu().numpy()).astype(np.uint8)
final_mask = np.zeros_like(out_mask)
# map back
for k, v in self.mapper.remappings.items():
final_mask[out_mask == v] = k
num_objs = final_mask.max()
painted_image = frame
for obj in range(1, num_objs+1):
if np.max(final_mask==obj) == 0:
continue
painted_image = mask_painter(painted_image, (final_mask==obj).astype('uint8'), mask_color=obj+1)
# print(f'max memory allocated: {torch.cuda.max_memory_allocated()/(2**20)} MB')
return final_mask, final_mask, painted_image
@torch.no_grad()
def sam_refinement(self, frame, logits, ti):
"""
refine segmentation results with mask prompt
"""
# convert to 1, 256, 256
self.sam_model.set_image(frame)
mode = 'mask'
logits = logits.unsqueeze(0)
logits = self.resizer(logits).cpu().numpy()
prompts = {'mask_input': logits} # 1 256 256
masks, scores, logits = self.sam_model.predict(prompts, mode, multimask=True) # masks (n, h, w), scores (n,), logits (n, 256, 256)
painted_image = mask_painter(frame, masks[np.argmax(scores)].astype('uint8'), mask_alpha=0.8)
painted_image = Image.fromarray(painted_image)
painted_image.save(f'/ssd1/gaomingqi/refine/{ti:05d}.png')
self.sam_model.reset_image()
@torch.no_grad()
def clear_memory(self):
self.tracker.clear_memory()
self.mapper.clear_labels()
torch.cuda.empty_cache()
## how to use:
## 1/3) prepare device and xmem_checkpoint
# device = 'cuda:2'
# XMEM_checkpoint = '/ssd1/gaomingqi/checkpoints/XMem-s012.pth'
## 2/3) initialise Base Tracker
# tracker = BaseTracker(XMEM_checkpoint, device, None, device) # leave an interface for sam model (currently set None)
## 3/3)
if __name__ == '__main__':
# video frames (take videos from DAVIS-2017 as examples)
video_path_list = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/horsejump-high', '*.jpg'))
video_path_list.sort()
# load frames
frames = []
for video_path in video_path_list:
frames.append(np.array(Image.open(video_path).convert('RGB')))
frames = np.stack(frames, 0) # T, H, W, C
# load first frame annotation
first_frame_path = '/ssd1/gaomingqi/datasets/davis/Annotations/480p/horsejump-high/00000.png'
first_frame_annotation = np.array(Image.open(first_frame_path).convert('P')) # H, W, C
# ------------------------------------------------------------------------------------
# how to use
# ------------------------------------------------------------------------------------
# 1/4: set checkpoint and device
device = 'cuda:2'
XMEM_checkpoint = '/ssd1/gaomingqi/checkpoints/XMem-s012.pth'
# SAM_checkpoint= '/ssd1/gaomingqi/checkpoints/sam_vit_h_4b8939.pth'
# model_type = 'vit_h'
# ------------------------------------------------------------------------------------
# 2/4: initialise inpainter
tracker = BaseTracker(XMEM_checkpoint, device, None, device)
# ------------------------------------------------------------------------------------
# 3/4: for each frame, get tracking results by tracker.track(frame, first_frame_annotation)
# frame: numpy array (H, W, C), first_frame_annotation: numpy array (H, W), leave it blank when tracking begins
painted_frames = []
for ti, frame in enumerate(frames):
if ti == 0:
mask, prob, painted_frame = tracker.track(frame, first_frame_annotation)
# mask:
else:
mask, prob, painted_frame = tracker.track(frame)
painted_frames.append(painted_frame)
# ----------------------------------------------
# 3/4: clear memory in XMEM for the next video
tracker.clear_memory()
# ----------------------------------------------
# end
# ----------------------------------------------
print(f'max memory allocated: {torch.cuda.max_memory_allocated()/(2**20)} MB')
# set saving path
save_path = '/ssd1/gaomingqi/results/TAM/blackswan'
if not os.path.exists(save_path):
os.mkdir(save_path)
# save
for painted_frame in progressbar.progressbar(painted_frames):
painted_frame = Image.fromarray(painted_frame)
painted_frame.save(f'{save_path}/{ti:05d}.png')
# tracker.clear_memory()
# for ti, frame in enumerate(frames):
# print(ti)
# # if ti > 200:
# # break
# if ti == 0:
# mask, prob, painted_image = tracker.track(frame, first_frame_annotation)
# else:
# mask, prob, painted_image = tracker.track(frame)
# # save
# painted_image = Image.fromarray(painted_image)
# painted_image.save(f'/ssd1/gaomingqi/results/TrackA/gsw/{ti:05d}.png')
# # track anything given in the first frame annotation
# for ti, frame in enumerate(frames):
# if ti == 0:
# mask, prob, painted_image = tracker.track(frame, first_frame_annotation)
# else:
# mask, prob, painted_image = tracker.track(frame)
# # save
# painted_image = Image.fromarray(painted_image)
# painted_image.save(f'/ssd1/gaomingqi/results/TrackA/horsejump-high/{ti:05d}.png')
# # ----------------------------------------------------------
# # another video
# # ----------------------------------------------------------
# # video frames
# video_path_list = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/camel', '*.jpg'))
# video_path_list.sort()
# # first frame
# first_frame_path = '/ssd1/gaomingqi/datasets/davis/Annotations/480p/camel/00000.png'
# # load frames
# frames = []
# for video_path in video_path_list:
# frames.append(np.array(Image.open(video_path).convert('RGB')))
# frames = np.stack(frames, 0) # N, H, W, C
# # load first frame annotation
# first_frame_annotation = np.array(Image.open(first_frame_path).convert('P')) # H, W, C
# print('first video done. clear.')
# tracker.clear_memory()
# # track anything given in the first frame annotation
# for ti, frame in enumerate(frames):
# if ti == 0:
# mask, prob, painted_image = tracker.track(frame, first_frame_annotation)
# else:
# mask, prob, painted_image = tracker.track(frame)
# # save
# painted_image = Image.fromarray(painted_image)
# painted_image.save(f'/ssd1/gaomingqi/results/TrackA/camel/{ti:05d}.png')
# # failure case test
# failure_path = '/ssd1/gaomingqi/failure'
# frames = np.load(os.path.join(failure_path, 'video_frames.npy'))
# # first_frame = np.array(Image.open(os.path.join(failure_path, 'template_frame.png')).convert('RGB'))
# first_mask = np.array(Image.open(os.path.join(failure_path, 'template_mask.png')).convert('P'))
# first_mask = np.clip(first_mask, 0, 1)
# for ti, frame in enumerate(frames):
# if ti == 0:
# mask, probs, painted_image = tracker.track(frame, first_mask)
# else:
# mask, probs, painted_image = tracker.track(frame)
# # save
# painted_image = Image.fromarray(painted_image)
# painted_image.save(f'/ssd1/gaomingqi/failure/LJ/{ti:05d}.png')
# prob = Image.fromarray((probs[1].cpu().numpy()*255).astype('uint8'))
# # prob.save(f'/ssd1/gaomingqi/failure/probs/{ti:05d}.png')
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