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.gitattributes

@@ -33,3 +33,61 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+examples/01_breakdancer.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/01_dog.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/02_cups.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/02_hummingbird.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/03_blocks.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/03_skateboarder.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/04_coffee.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/04_octopus.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/05_default_juggle.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/05_landing_dog_soccer.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/06_pingpong.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/07_snowboarder.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/08_driving.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/09_birdcartoon.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/10_cloth_magic.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/11_polevault.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/12_hideandseek.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/13_butterfly.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/14_social_dog_training.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/15_cricket.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/16_robotarm.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/17_childrendancing.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/18_threedogs.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/19_cyclist.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/20_doughkneading.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/21_biker.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/22_dogskateboarder.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/23_racecar.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/24_clownfish.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/01_breakdancer.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/01_dog.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/02_cups.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/02_hummingbird.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/03_blocks.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/03_skateboarder.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/04_coffee.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/04_octopus.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/05_default_juggle.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/05_landing_dog_soccer.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/06_pingpong.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/07_snowboarder.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/08_driving.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/09_birdcartoon.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/10_cloth_magic.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/11_polevault.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/12_hideandseek.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/13_butterfly.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/14_social_dog_training.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/15_cricket.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/16_robotarm.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/17_childrendancing.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/18_threedogs.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/19_cyclist.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/20_doughkneading.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/21_biker.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/22_dogskateboarder.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/23_racecar.mp4 filter=lfs diff=lfs merge=lfs -text
+examples/trimmed/24_clownfish.mp4 filter=lfs diff=lfs merge=lfs -text

CODE_OF_CONDUCT.md

@@ -0,0 +1,80 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when there is a
+reasonable belief that an individual's behavior may have a negative impact on
+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <opensource-conduct@meta.com>. All
+complaints will be reviewed and investigated and will result in a response that
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+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
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+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

CONTRIBUTING.md

@@ -0,0 +1,31 @@
+# Contributing to EdgeTAM
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Meta's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Meta has a [bounty program](https://bugbounty.meta.com/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## License
+By contributing to EdgeTAM, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,12 +1,23 @@
 ---
-title: EdgeTAM Mps
-emoji: 📉
-colorFrom: green
-colorTo: blue
+title: EdgeTAM
+emoji: 🚀
+colorFrom: pink
+colorTo: purple
 sdk: gradio
-sdk_version: 6.0.0
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
+license: apache-2.0
+short_description: On-Device Track Anything Model
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+```
+@misc{zhou2025edgetam,
+  title={EdgeTAM: On-Device Track Anything Model},
+  author={Zhou, Chong and Zhu, Chenchen and Xiong, Yunyang and Suri, Saksham and Xiao, Fanyi and Wu, Lemeng and Krishnamoorthi, Raghuraman and Dai, Bo and Loy, Chen Change and Chandra, Vikas and Soran, Bilge},
+  journal={arXiv preprint arXiv:2501.07256},
+  year={2025}
+}
+```

app.py

@@ -0,0 +1,626 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import copy
+import os
+from datetime import datetime
+
+import gradio as gr
+
+os.environ["TORCH_CUDNN_SDPA_ENABLED"] = "1"
+import tempfile
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import spaces
+
+import torch
+
+from moviepy.editor import ImageSequenceClip
+from moviepy.video.io.ffmpeg_writer import FFMPEG_VideoWriter # adding this for MPS compatibility
+from PIL import Image
+from sam2.build_sam import build_sam2_video_predictor
+
+# Description
+title = "<center><strong><font size='8'>EdgeTAM<font></strong> <a href='https://github.com/facebookresearch/EdgeTAM'><font size='6'>[GitHub]</font></a> </center>"
+
+description_p = """# EdgeTAM is now in transformers, find it [here](https://huggingface.co/yonigozlan/EdgeTAM-hf)
+                <ol>
+                <li> Upload one video or click one example video</li>
+                <li> Click 'include' point type, select the object to segment and track</li>
+                <li> Click 'exclude' point type (optional), select the area you want to avoid segmenting and tracking</li>
+                <li> Click the 'Track' button to obtain the masked video </li>
+                </ol>
+              """
+
+# examples
+examples = [
+    ["examples/trimmed/01_dog.mp4"],
+    ["examples/trimmed/02_cups.mp4"],
+    ["examples/trimmed/03_blocks.mp4"],
+    ["examples/trimmed/04_coffee.mp4"],
+    ["examples/trimmed/05_default_juggle.mp4"],
+    ["examples/trimmed/01_breakdancer.mp4"],
+    ["examples/trimmed/02_hummingbird.mp4"],
+    ["examples/trimmed/03_skateboarder.mp4"],
+    ["examples/trimmed/04_octopus.mp4"],
+    ["examples/trimmed/05_landing_dog_soccer.mp4"],
+    ["examples/trimmed/06_pingpong.mp4"],
+    ["examples/trimmed/07_snowboarder.mp4"],
+    ["examples/trimmed/08_driving.mp4"],
+    ["examples/trimmed/09_birdcartoon.mp4"],
+    ["examples/trimmed/10_cloth_magic.mp4"],
+    ["examples/trimmed/11_polevault.mp4"],
+    ["examples/trimmed/12_hideandseek.mp4"],
+    ["examples/trimmed/13_butterfly.mp4"],
+    ["examples/trimmed/14_social_dog_training.mp4"],
+    ["examples/trimmed/15_cricket.mp4"],
+    ["examples/trimmed/16_robotarm.mp4"],
+    ["examples/trimmed/17_childrendancing.mp4"],
+    ["examples/trimmed/18_threedogs.mp4"],
+    ["examples/trimmed/19_cyclist.mp4"],
+    ["examples/trimmed/20_doughkneading.mp4"],
+    ["examples/trimmed/21_biker.mp4"],
+    ["examples/trimmed/22_dogskateboarder.mp4"],
+    ["examples/trimmed/23_racecar.mp4"],
+    ["examples/trimmed/24_clownfish.mp4"],
+]
+
+OBJ_ID = 0
+sam2_checkpoint = "checkpoints/edgetam.pt"
+model_cfg = "edgetam.yaml"
+device = torch.device("mps") if torch.backends.mps.is_available() else torch.device("cpu") # MPS support
+predictor = build_sam2_video_predictor(model_cfg, sam2_checkpoint, device="cpu")
+predictor.to(device)
+
+
+def get_video_fps(video_path):
+    # Open the video file
+    cap = cv2.VideoCapture(video_path)
+
+    if not cap.isOpened():
+        print("Error: Could not open video.")
+        return None
+
+    # Get the FPS of the video
+    fps = cap.get(cv2.CAP_PROP_FPS)
+
+    return fps
+
+
+def reset_state(inference_state):
+    for v in inference_state["point_inputs_per_obj"].values():
+        v.clear()
+    for v in inference_state["mask_inputs_per_obj"].values():
+        v.clear()
+    for v in inference_state["output_dict_per_obj"].values():
+        v["cond_frame_outputs"].clear()
+        v["non_cond_frame_outputs"].clear()
+    for v in inference_state["temp_output_dict_per_obj"].values():
+        v["cond_frame_outputs"].clear()
+        v["non_cond_frame_outputs"].clear()
+    inference_state["output_dict"]["cond_frame_outputs"].clear()
+    inference_state["output_dict"]["non_cond_frame_outputs"].clear()
+    inference_state["consolidated_frame_inds"]["cond_frame_outputs"].clear()
+    inference_state["consolidated_frame_inds"]["non_cond_frame_outputs"].clear()
+    inference_state["tracking_has_started"] = False
+    inference_state["frames_already_tracked"].clear()
+    inference_state["obj_id_to_idx"].clear()
+    inference_state["obj_idx_to_id"].clear()
+    inference_state["obj_ids"].clear()
+    inference_state["point_inputs_per_obj"].clear()
+    inference_state["mask_inputs_per_obj"].clear()
+    inference_state["output_dict_per_obj"].clear()
+    inference_state["temp_output_dict_per_obj"].clear()
+    return inference_state
+
+
+def reset(
+    first_frame,
+    all_frames,
+    input_points,
+    input_labels,
+    inference_state,
+):
+    first_frame = None
+    all_frames = None
+    input_points = []
+    input_labels = []
+
+    inference_state = None
+    return (
+        None,
+        gr.update(open=True),
+        None,
+        None,
+        gr.update(value=None, visible=False),
+        first_frame,
+        all_frames,
+        input_points,
+        input_labels,
+        inference_state,
+    )
+
+
+def clear_points(
+    first_frame,
+    all_frames,
+    input_points,
+    input_labels,
+    inference_state,
+):
+    input_points = []
+    input_labels = []
+    if inference_state and inference_state["tracking_has_started"]:
+        inference_state = reset_state(inference_state)
+    return (
+        first_frame,
+        None,
+        gr.update(value=None, visible=False),
+        first_frame,
+        all_frames,
+        input_points,
+        input_labels,
+        inference_state,
+    )
+
+
+def preprocess_video_in(
+    video_path,
+    first_frame,
+    all_frames,
+    input_points,
+    input_labels,
+    inference_state,
+):
+    if video_path is None:
+        return (
+            gr.update(open=True),  # video_in_drawer
+            None,  # points_map
+            None,  # output_image
+            gr.update(value=None, visible=False),  # output_video
+            first_frame,
+            all_frames,
+            input_points,
+            input_labels,
+            inference_state,
+        )
+
+    # Read the first frame
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        print("Error: Could not open video.")
+        return (
+            gr.update(open=True),  # video_in_drawer
+            None,  # points_map
+            None,  # output_image
+            gr.update(value=None, visible=False),  # output_video
+            first_frame,
+            all_frames,
+            input_points,
+            input_labels,
+            inference_state,
+        )
+
+    frame_number = 0
+    _first_frame = None
+    all_frames = []
+
+    while True:
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frame = np.array(frame)
+
+        # Store the first frame
+        if frame_number == 0:
+            _first_frame = frame
+        all_frames.append(frame)
+
+        frame_number += 1
+
+    cap.release()
+    first_frame = copy.deepcopy(_first_frame)
+    input_points = []
+    input_labels = []
+
+    predictor.to("cpu")
+    inference_state = predictor.init_state(
+        offload_video_to_cpu=True,
+        offload_state_to_cpu=True,
+        video_path=video_path,
+    )
+
+    return [
+        gr.update(open=False),  # video_in_drawer
+        first_frame,  # points_map
+        None,  # output_image
+        gr.update(value=None, visible=False),  # output_video
+        first_frame,
+        all_frames,
+        input_points,
+        input_labels,
+        inference_state,
+    ]
+
+
+def segment_with_points(
+    point_type,
+    first_frame,
+    all_frames,
+    input_points,
+    input_labels,
+    inference_state,
+    evt: gr.SelectData,
+):
+    predictor.to("cpu")
+    if inference_state:
+        inference_state["device"] = predictor.device
+    input_points.append(evt.index)
+    print(f"TRACKING INPUT POINT: {input_points}")
+
+    if point_type == "include":
+        input_labels.append(1)
+    elif point_type == "exclude":
+        input_labels.append(0)
+    print(f"TRACKING INPUT LABEL: {input_labels}")
+
+    # Open the image and get its dimensions
+    transparent_background = Image.fromarray(first_frame).convert("RGBA")
+    w, h = transparent_background.size
+
+    # Define the circle radius as a fraction of the smaller dimension
+    fraction = 0.01  # You can adjust this value as needed
+    radius = int(fraction * min(w, h))
+
+    # Create a transparent layer to draw on
+    transparent_layer = np.zeros((h, w, 4), dtype=np.uint8)
+
+    for index, track in enumerate(input_points):
+        if input_labels[index] == 1:
+            cv2.circle(transparent_layer, track, radius, (0, 255, 0, 255), -1)
+        else:
+            cv2.circle(transparent_layer, track, radius, (255, 0, 0, 255), -1)
+
+    # Convert the transparent layer back to an image
+    transparent_layer = Image.fromarray(transparent_layer, "RGBA")
+    selected_point_map = Image.alpha_composite(
+        transparent_background, transparent_layer
+    )
+
+    # Let's add a positive click at (x, y) = (210, 350) to get started
+    points = np.array(input_points, dtype=np.float32)
+    # for labels, `1` means positive click and `0` means negative click
+    labels = np.array(input_labels, dtype=np.int32)
+    _, _, out_mask_logits = predictor.add_new_points(
+        inference_state=inference_state,
+        frame_idx=0,
+        obj_id=OBJ_ID,
+        points=points,
+        labels=labels,
+    )
+
+    mask_image = show_mask((out_mask_logits[0] > 0.0).cpu().numpy())
+    first_frame_output = Image.alpha_composite(transparent_background, mask_image)
+
+    torch.cuda.empty_cache()
+    return (
+        selected_point_map,
+        first_frame_output,
+        first_frame,
+        all_frames,
+        input_points,
+        input_labels,
+        inference_state,
+    )
+
+
+def show_mask(mask, obj_id=None, random_color=False, convert_to_image=True):
+    if random_color:
+        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
+    else:
+        cmap = plt.get_cmap("tab10")
+        cmap_idx = 0 if obj_id is None else obj_id
+        color = np.array([*cmap(cmap_idx)[:3], 0.6])
+    h, w = mask.shape[-2:]
+    mask = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
+    mask = (mask * 255).astype(np.uint8)
+    if convert_to_image:
+        mask = Image.fromarray(mask, "RGBA")
+    return mask
+
+
+@spaces.GPU(duration=60)
+def propagate_to_all(
+    video_in,
+    all_frames,
+    input_points,
+    inference_state,
+):
+    # Set boolean for cuda or mps support
+    use_cuda = torch.cuda.is_available()
+    use_mps = torch.backends.mps.is_available() and torch.backends.mps.is_built()
+
+    if use_cuda:
+        if torch.cuda.get_device_properties(0).major >= 8:
+            torch.backends.cuda.matmul.allow_tf32 = True
+            torch.backends.cudnn.allow_tf32 = True
+            autocast_kwargs = dict(device_type="cuda", dtype=torch.bfloat16)
+        
+    elif use_mps:
+        autocast_kwargs = dict(device_type="mps", dtype=torch.float16)
+    
+    with torch.autocast(**autocast_kwargs):
+   
+        if use_cuda: 
+            predictor.to("cuda")
+        elif use_mps:
+            predictor.to("mps")
+        
+        if inference_state:
+            inference_state["device"] = predictor.device
+
+        if len(input_points) == 0 or video_in is None or inference_state is None:
+            return None
+        # run propagation throughout the video and collect the results in a dict
+        video_segments = (
+            {}
+        )  # video_segments contains the per-frame segmentation results
+        print("starting propagate_in_video")
+        for out_frame_idx, out_obj_ids, out_mask_logits in predictor.propagate_in_video(
+            inference_state
+        ):
+            video_segments[out_frame_idx] = {
+                out_obj_id: (out_mask_logits[i] > 0.0).cpu().numpy()
+                for i, out_obj_id in enumerate(out_obj_ids)
+            }
+
+        # obtain the segmentation results every few frames
+        vis_frame_stride = 1
+
+        output_frames = []
+        for out_frame_idx in range(0, len(video_segments), vis_frame_stride):
+            transparent_background = Image.fromarray(all_frames[out_frame_idx]).convert(
+                "RGBA"
+            )
+            out_mask = video_segments[out_frame_idx][OBJ_ID]
+            mask_image = show_mask(out_mask)
+            output_frame = Image.alpha_composite(transparent_background, mask_image)
+            output_frame = output_frame.convert("RGB")
+            output_frame = np.array(output_frame)
+            output_frames.append(output_frame)
+
+        if use_cuda:
+            torch.cuda.empty_cache()
+
+        # Create a video clip from the image sequence
+        original_fps = get_video_fps(video_in)
+        fps = float(original_fps)  # Frames per second
+        # Write the result to a file
+        unique_id = datetime.now().strftime("%Y%m%d%H%M%S")
+        final_vid_output_path = f"output_video_{unique_id}.mp4"
+        final_vid_output_path = os.path.join(
+            tempfile.gettempdir(), final_vid_output_path
+        )
+
+        # Write the result to a file using moviepy ImageSequenceClip
+        if use_cuda:
+            clip = ImageSequenceClip(output_frames, fps=fps)
+            clip.write_videofile(final_vid_output_path, codec="libx264")
+
+        # Write the result to a file using moviepy FFMPEG_VideoWriter for MPS compatibility
+        elif use_mps:
+            clip_array = output_frames  # list of RGB numpy arrays
+            size = clip_array[0].shape[1], clip_array[0].shape[0]  # (width, height)
+            writer = FFMPEG_VideoWriter(final_vid_output_path, size, fps=fps, codec="libx264")
+
+            for frame in clip_array:
+                writer.write_frame(frame)
+
+            writer.close()
+
+        return gr.update(value=final_vid_output_path)
+
+
+def update_ui():
+    return gr.update(visible=True)
+
+
+with gr.Blocks() as demo:
+    first_frame = gr.State()
+    all_frames = gr.State()
+    input_points = gr.State([])
+    input_labels = gr.State([])
+    inference_state = gr.State()
+
+    with gr.Column():
+        # Title
+        gr.Markdown(title)
+        with gr.Row():
+
+            with gr.Column():
+                # Instructions
+                gr.Markdown(description_p)
+
+                with gr.Accordion("Input Video", open=True) as video_in_drawer:
+                    video_in = gr.Video(label="Input Video", format="mp4")
+
+                with gr.Row():
+                    point_type = gr.Radio(
+                        label="point type",
+                        choices=["include", "exclude"],
+                        value="include",
+                        scale=2,
+                    )
+                    propagate_btn = gr.Button("Track", scale=1, variant="primary")
+                    clear_points_btn = gr.Button("Clear Points", scale=1)
+                    reset_btn = gr.Button("Reset", scale=1)
+
+                points_map = gr.Image(
+                    label="Frame with Point Prompt", type="numpy", interactive=False
+                )
+
+            with gr.Column():
+                gr.Markdown("# Try some of the examples below ⬇️")
+                gr.Examples(
+                    examples=examples,
+                    inputs=[
+                        video_in,
+                    ],
+                    examples_per_page=8,
+                )
+                gr.Markdown("\n\n\n\n\n\n\n\n\n\n\n")
+                gr.Markdown("\n\n\n\n\n\n\n\n\n\n\n")
+                gr.Markdown("\n\n\n\n\n\n\n\n\n\n\n")
+                output_image = gr.Image(label="Reference Mask")
+
+                output_video = gr.Video(visible=False)
+
+    # When new video is uploaded
+    video_in.upload(
+        fn=preprocess_video_in,
+        inputs=[
+            video_in,
+            first_frame,
+            all_frames,
+            input_points,
+            input_labels,
+            inference_state,
+        ],
+        outputs=[
+            video_in_drawer,  # Accordion to hide uploaded video player
+            points_map,  # Image component where we add new tracking points
+            output_image,
+            output_video,
+            first_frame,
+            all_frames,
+            input_points,
+            input_labels,
+            inference_state,
+        ],
+        queue=False,
+    )
+
+    video_in.change(
+        fn=preprocess_video_in,
+        inputs=[
+            video_in,
+            first_frame,
+            all_frames,
+            input_points,
+            input_labels,
+            inference_state,
+        ],
+        outputs=[
+            video_in_drawer,  # Accordion to hide uploaded video player
+            points_map,  # Image component where we add new tracking points
+            output_image,
+            output_video,
+            first_frame,
+            all_frames,
+            input_points,
+            input_labels,
+            inference_state,
+        ],
+        queue=False,
+    )
+
+    # triggered when we click on image to add new points
+    points_map.select(
+        fn=segment_with_points,
+        inputs=[
+            point_type,  # "include" or "exclude"
+            first_frame,
+            all_frames,
+            input_points,
+            input_labels,
+            inference_state,
+        ],
+        outputs=[
+            points_map,  # updated image with points
+            output_image,
+            first_frame,
+            all_frames,
+            input_points,
+            input_labels,
+            inference_state,
+        ],
+        queue=False,
+    )
+
+    # Clear every points clicked and added to the map
+    clear_points_btn.click(
+        fn=clear_points,
+        inputs=[
+            first_frame,
+            all_frames,
+            input_points,
+            input_labels,
+            inference_state,
+        ],
+        outputs=[
+            points_map,
+            output_image,
+            output_video,
+            first_frame,
+            all_frames,
+            input_points,
+            input_labels,
+            inference_state,
+        ],
+        queue=False,
+    )
+
+    reset_btn.click(
+        fn=reset,
+        inputs=[
+            first_frame,
+            all_frames,
+            input_points,
+            input_labels,
+            inference_state,
+        ],
+        outputs=[
+            video_in,
+            video_in_drawer,
+            points_map,
+            output_image,
+            output_video,
+            first_frame,
+            all_frames,
+            input_points,
+            input_labels,
+            inference_state,
+        ],
+        queue=False,
+    )
+
+    propagate_btn.click(
+        fn=update_ui,
+        inputs=[],
+        outputs=output_video,
+        queue=False,
+    ).then(
+        fn=propagate_to_all,
+        inputs=[
+            video_in,
+            all_frames,
+            input_points,
+            inference_state,
+        ],
+        outputs=[
+            output_video,
+        ],
+        concurrency_limit=10,
+        queue=False,
+    )
+
+
+# demo.queue()
+demo.launch()

checkpoints/download_ckpts.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Use either wget or curl to download the checkpoints
+if command -v wget &> /dev/null; then
+    CMD="wget"
+elif command -v curl &> /dev/null; then
+    CMD="curl -L -O"
+else
+    echo "Please install wget or curl to download the checkpoints."
+    exit 1
+fi
+
+# Define the URLs for SAM 2 checkpoints
+# SAM2_BASE_URL="https://dl.fbaipublicfiles.com/segment_anything_2/072824"
+# sam2_hiera_t_url="${SAM2_BASE_URL}/sam2_hiera_tiny.pt"
+# sam2_hiera_s_url="${SAM2_BASE_URL}/sam2_hiera_small.pt"
+# sam2_hiera_b_plus_url="${SAM2_BASE_URL}/sam2_hiera_base_plus.pt"
+# sam2_hiera_l_url="${SAM2_BASE_URL}/sam2_hiera_large.pt"
+
+# Download each of the four checkpoints using wget
+# echo "Downloading sam2_hiera_tiny.pt checkpoint..."
+# $CMD $sam2_hiera_t_url || { echo "Failed to download checkpoint from $sam2_hiera_t_url"; exit 1; }
+
+# echo "Downloading sam2_hiera_small.pt checkpoint..."
+# $CMD $sam2_hiera_s_url || { echo "Failed to download checkpoint from $sam2_hiera_s_url"; exit 1; }
+
+# echo "Downloading sam2_hiera_base_plus.pt checkpoint..."
+# $CMD $sam2_hiera_b_plus_url || { echo "Failed to download checkpoint from $sam2_hiera_b_plus_url"; exit 1; }
+
+# echo "Downloading sam2_hiera_large.pt checkpoint..."
+# $CMD $sam2_hiera_l_url || { echo "Failed to download checkpoint from $sam2_hiera_l_url"; exit 1; }
+
+# Define the URLs for SAM 2.1 checkpoints
+SAM2p1_BASE_URL="https://dl.fbaipublicfiles.com/segment_anything_2/092824"
+sam2p1_hiera_t_url="${SAM2p1_BASE_URL}/sam2.1_hiera_tiny.pt"
+sam2p1_hiera_s_url="${SAM2p1_BASE_URL}/sam2.1_hiera_small.pt"
+sam2p1_hiera_b_plus_url="${SAM2p1_BASE_URL}/sam2.1_hiera_base_plus.pt"
+sam2p1_hiera_l_url="${SAM2p1_BASE_URL}/sam2.1_hiera_large.pt"
+
+# SAM 2.1 checkpoints
+echo "Downloading sam2.1_hiera_tiny.pt checkpoint..."
+$CMD $sam2p1_hiera_t_url || { echo "Failed to download checkpoint from $sam2p1_hiera_t_url"; exit 1; }
+
+echo "Downloading sam2.1_hiera_small.pt checkpoint..."
+$CMD $sam2p1_hiera_s_url || { echo "Failed to download checkpoint from $sam2p1_hiera_s_url"; exit 1; }
+
+echo "Downloading sam2.1_hiera_base_plus.pt checkpoint..."
+$CMD $sam2p1_hiera_b_plus_url || { echo "Failed to download checkpoint from $sam2p1_hiera_b_plus_url"; exit 1; }
+
+echo "Downloading sam2.1_hiera_large.pt checkpoint..."
+$CMD $sam2p1_hiera_l_url || { echo "Failed to download checkpoint from $sam2p1_hiera_l_url"; exit 1; }
+
+echo "All checkpoints are downloaded successfully."

checkpoints/edgetam.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ed2d4850b8792c239689b043c47046ec239b6e808a3d9b6ae676c803fd8780df
+size 56116523

convert_weights.py

@@ -0,0 +1,36 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import argparse
+
+import torch
+
+
+def main(args):
+    sd = torch.load(args.src, map_location="cpu")["model"]
+    sd = {k: v for k, v in sd.items() if "teacher" not in k}
+    sd = {
+        k.replace("backbone.vision_backbone", "image_encoder"): v for k, v in sd.items()
+    }
+    sd = {k.replace("mlp.fc1", "mlp.layers.0"): v for k, v in sd.items()}
+    sd = {k.replace("mlp.fc2", "mlp.layers.1"): v for k, v in sd.items()}
+    sd = {k.replace("convs", "neck.convs"): v for k, v in sd.items()}
+    sd = {
+        k.replace("transformer.encoder", "memory_attention"): v for k, v in sd.items()
+    }
+    sd = {k.replace("maskmem_backbone", "memory_encoder"): v for k, v in sd.items()}
+    sd = {k.replace("maskmem_backbone", "memory_encoder"): v for k, v in sd.items()}
+    sd = {k.replace("mlp.lin1", "mlp.layers.0"): v for k, v in sd.items()}
+    sd = {k.replace("mlp.lin2", "mlp.layers.1"): v for k, v in sd.items()}
+    torch.save({"model": sd}, args.src.replace(".pt", "_converted.pt"))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--src", type=str, required=True)
+    args = parser.parse_args()
+
+    main(args)

examples/01_breakdancer.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6f6b15f61e741576f658e41939d0109f15f012691a62a86820418d3ae10d1f04
+size 5251367

examples/01_dog.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:21425eb9b7fce7d3fb23fa17b1c07bcbe99ba9e64e3b5347d8052ba6d9c38924
+size 1738970

examples/02_cups.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6320759eede8c82c7d43fe1cc569f3a389758ec37b2b1dc9e9b7b4ce4110ab13
+size 2543710

examples/02_hummingbird.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:79018494f7cda9ea1fc57bc474fd468ba1291624d85a1b74312029013ed9eb1e
+size 1098274

examples/03_blocks.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2868f2c62f5cfa28dabbe925fae95da7f896531630a0627e5edbc784ea42413d
+size 1808832

examples/03_skateboarder.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e8403b992e4c114cf01ccb56c95aeba58d38aac16a5d7611580c1ceb5ef0f3ca
+size 2132383

examples/04_coffee.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f9a439a8bf66910db3e2a9d9ea853bc24c760c8d4ca227f4c33051b1cef28c03
+size 1177415

examples/04_octopus.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d705ebd61259495da51e254cba576b2362310033099173e7781624c14fc51e3d
+size 4395411

examples/05_default_juggle.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60d23773ad50389a5c2df9ba4ab05e9b4d39d21dfa7b75d15b46666ff7a4e2bb
+size 1842699

examples/05_landing_dog_soccer.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:65d828933e37694b47d8d22573d812a8b838b22b722a17af362a50daecfe92d9
+size 2774490

examples/06_pingpong.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6c5ba5fcd0abd07701f030bbdbeed937f56bd0950103a13dad664fc66a264f98
+size 1341363

examples/07_snowboarder.mp4

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:28108f8ae296c2b97b48768ab5efe556d57c3b631cb7d86be3b012a93fdc35ae
+size 3579270

examples/08_driving.mp4

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