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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 tracker.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 utils.painter import mask_painter
dir_path = os.path.dirname(os.path.realpath(__file__))
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(f"{dir_path}/config/config.yaml", "r") as stream:
config = yaml.safe_load(stream)
# initialise XMem
network = XMem(config, xmem_checkpoint, map_location=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()
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