tracker/base_tracker.py

# 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 util.mask_mapper import MaskMapper
from torchvision import transforms
from util.range_transform import im_normalization
import sys
sys.path.insert(0, sys.path[0]+"/../")
from tools.painter import mask_painter
from tools.base_segmenter import BaseSegmenter
from torchvision.transforms import Resize


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)

        num_objs = out_mask.max()
        painted_image = frame
        for obj in range(1, num_objs+1):
            painted_image = mask_painter(painted_image, (out_mask==obj).astype('uint8'), mask_color=obj+1)
        
        return out_mask, out_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()


if __name__ == '__main__':
    # video frames (multiple objects)
    video_path_list = glob.glob(os.path.join('/ssd1/gaomingqi/datasets/davis/JPEGImages/480p/horsejump-high', '*.jpg'))
    video_path_list.sort()
    # first frame
    first_frame_path = '/ssd1/gaomingqi/datasets/davis/Annotations/480p/horsejump-high/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

    # ----------------------------------------------------------
    # initalise tracker
    # ----------------------------------------------------------
    device = 'cuda:4'
    XMEM_checkpoint = '/ssd1/gaomingqi/checkpoints/XMem-s012.pth'
    SAM_checkpoint= '/ssd1/gaomingqi/checkpoints/sam_vit_h_4b8939.pth'
    model_type = 'vit_h'

    # sam_model = BaseSegmenter(SAM_checkpoint, model_type, device=device)
    tracker = BaseTracker(XMEM_checkpoint, device, None, device)

    # test for storage efficiency
    frames = np.load('/ssd1/gaomingqi/efficiency/efficiency.npy')
    first_frame_annotation = np.array(Image.open('/ssd1/gaomingqi/efficiency/template_mask.png'))

    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')

    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')