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60 行
2.9 KiB
Markdown
60 行
2.9 KiB
Markdown
# heatmap and tracking
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## 👋 hello
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This script performs heatmap and tracking analysis using YOLOv8, an object-detection method and ByteTrack, a simple yet effective online multi-object tracking method. It uses the supervision package for multiple tasks such as drawing heatmap annotations, tracking objects, etc.
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## 💻 install
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- clone repository and navigate to example directory
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```bash
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git clone --depth 1 -b develop https://github.com/roboflow/supervision.git
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cd supervision/examples/heatmap_and_track
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```
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- setup python environment and activate it [optional]
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```bash
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uv venv
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source .venv/bin/activate
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```
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- install required dependencies
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```bash
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uv pip install -r requirements.txt
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```
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## 🛠️ script arguments
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- `--source_weights_path`: Required. Specifies the path to the weights file for the YOLO model. This file contains the trained model data necessary for object detection.
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- `--source_video_path` (optional): The path to the source video file that will be analyzed. This is the input video on which crowd analysis will be performed. If not specified default is `people-walking.mp4` from supervision assets
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- `--target_video_path` (optional): The path to save the output.mp4 video with annotations.
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- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model to filter detections. Default is `0.3`. This determines how confident the model should be to recognize an object in the video.
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- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold for the model. Default is 0.7. This value is used to manage object detection accuracy, particularly in distinguishing between different objects.
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- `--heatmap_alpha` (optional): Opacity of the overlay mask, between 0 and 1.
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- `--radius` (optional): Radius of the heat circle.
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- `--track_activation_threshold` (optional): Detection confidence threshold for track activation.
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- `--track_seconds` (optional): Number of seconds to buffer when a track is lost.
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- `--minimum_matching_threshold` (optional): Threshold for matching tracks with detections.
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## ⚙️ run
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```bash
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python script.py \
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--source_weights_path weight.pt \
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--source_video_path input_video.mp4 \
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--confidence_threshold 0.3 \
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--iou_threshold 0.5 \
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--target_video_path output_video.mp4
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```
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## © license
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This demo integrates two main components, each with its own licensing:
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- ultralytics: The object detection model used in this demo, YOLOv8, is distributed under the [AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE). You can find more details about this license here.
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- supervision: The analytics code that powers the zone-based analysis in this demo is based on the Supervision library, which is licensed under the [MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This makes the Supervision part of the code fully open source and freely usable in your projects.
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