Merge branch 'master' of https://github.com/gaomingqi/Track-Anything

README.md

@@ -8,6 +8,7 @@
 This is Demo
 ## Get Started
 
+
 This is Get Started.
 ## Acknowledgement
 

tools/interact_tools.py

@@ -45,7 +45,7 @@ class SamControler():
         initialize sam controler
         '''
         checkpoint_url = "https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth"
-        folder ="segmenter/checkpoints"
+        folder ="checkpoints"
         SAM_checkpoint= 'sam_vit_h_4b8939.pth'
         SAM_checkpoint = download_checkpoint(checkpoint_url, folder, SAM_checkpoint)
         # SAM_checkpoint = '/ssd1/gaomingqi/checkpoints/sam_vit_h_4b8939.pth'

tracker/base_tracker.py

@@ -9,6 +9,9 @@ import torch
 import yaml
 from model.network import XMem
 from inference.inference_core import InferenceCore
+# for data transormation
+from torchvision import transforms
+from dataset.range_transform import im_normalization
 
 
 class BaseTracker:
@@ -24,14 +27,27 @@ class BaseTracker:
         network = XMem(config, xmem_checkpoint).to(device).eval()
         # initialise IncerenceCore
         self.tracker = InferenceCore(network, config)
-        # set data transformation
+        # data transformation
-        # self.data_transform = 
+        self.im_transform = transforms.Compose([
+            transforms.ToTensor(),
+            im_normalization,
+        ])
 
     def track(self, frames, first_frame_annotation):
+        """
+        Input: 
+        frames: numpy arrays: T, H, W, 3 (T: number of frames)
+        first_frame_annotation: numpy array: H, W
+
+        Output:
+        masks: numpy arrays: T, H, W
+        """
         # data transformation
+        for frame in frames:
+            frame = self.im_transform(frame)
 
         # tracking
-        pass
+        
 
 
 if __name__ == '__main__':
@@ -42,7 +58,10 @@ if __name__ == '__main__':
     first_frame_path = '/ssd1/gaomingqi/datasets/davis/Annotations/480p/dance-twirl/00000.png'
 
     # load frames
-    frames = []
+<<<<<<< HEAD
+=======
+    frames = ["test_confict"]
+>>>>>>> a5606340a199569856ffa1585aeeff5a40cc34ba
     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