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refine README

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rentainhe
2024-08-09 09:36:03 +08:00
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README.md
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@@ -21,6 +21,7 @@ Grounded SAM 2 does not introduce significant methodological changes compared to
- [Grounded SAM 2 Video Object Tracking Demo (with Grounding DINO 1.5 & 1.6)](#grounded-sam-2-video-object-tracking-demo-with-grounding-dino-15--16)
- [Grounded SAM 2 Video Object Tracking with Custom Video Input (using Grounding DINO)](#grounded-sam-2-video-object-tracking-demo-with-custom-video-input-with-grounding-dino)
- [Grounded SAM 2 Video Object Tracking with Custom Video Input (using Grounding DINO 1.5 & 1.6)](#grounded-sam-2-video-object-tracking-demo-with-custom-video-input-with-grounding-dino-15--16)
- [Grounded SAM 2 Video Object Tracking with Continues ID (using Grounding DINO)](#grounded-sam-2--video-object-tracking-with-continuous-id-with-grounding-dino)
- [Citation](#citation)
@@ -167,7 +168,10 @@ And we will automatically save the tracking visualization results in `OUTPUT_VID
> [!WARNING]
> We initialize the box prompts on the first frame of the input video. If you want to start from different frame, you can refine `ann_frame_idx` by yourself in our code.
### Grounded-SAM-2 Video Object Tracking with Continuous ID (with Grounding DINO)
### Grounded-SAM-2 Video Object Tracking with Continuous ID (with Grounding DINO)
In above demos, we only prompt Grounded SAM 2 in specific frame, which may not be friendly to find new object during the whole video. In this demo, we try to **find new objects** and assign them with new ID across the whole video, this function is **still under develop**. it's not that stable now.
Users can upload their own video files and specify custom text prompts for grounding and tracking using the Grounding DINO and SAM 2 frameworks. To do this, execute the script:
@@ -186,12 +190,8 @@ You can customize various parameters including:
- `text_threshold`: text threshold for groundingdino model
Note: This method supports only the mask type of text prompt.
The demo video is:
[![car tracking demo data](./assets/tracking_car_1.jpg)](./assets/tracking_car.mp4)
After running our demo code, you can get the tracking results as follows:
[![car tracking result data](./assets/tracking_car_mask_1.jpg)](./assets/tracking_car_output.mp4)
[![car tracking result data](./assets/tracking_car_mask_1.jpg)](https://github.com/user-attachments/assets/141594a2-1451-4d2e-a91b-7941284c2c13)
### Citation

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grounded_sam2_tracking_demo_with_continuous_id.py
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@@ -68,7 +68,7 @@ frame_names.sort(key=lambda p: int(os.path.splitext(p)[0]))
# init video predictor state
inference_state = video_predictor.init_state(video_path=video_dir)
step = 10 # the step to sample frames for groundedDino predictor
step = 25 # the step to sample frames for Grounding DINO predictor
sam2_masks = MaskDictionatyModel()
PROMPT_TYPE_FOR_VIDEO = "mask" # box, mask or point
@@ -195,4 +195,4 @@ Step 6: Draw the results and save the video
"""
CommonUtils.draw_masks_and_box(video_dir, mask_data_dir, json_data_dir, result_dir)
create_video_from_images(result_dir, output_video_path, frame_rate=15)
create_video_from_images(result_dir, output_video_path, frame_rate=30)