adding heatmap example
I haven't figured out how to add the video output file, but I can link the file below: https://www.kaggle.com/code/hinepo/yolov8-supervision-heatmap-track-count/output?select=heatmap_and_track.mp4
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# 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
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ByteTrack, a simple yet effective online multi-object tracking method. It uses the
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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 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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python3 -m venv 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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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
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YOLO model. This file contains the trained model data necessary for object detection.
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- `--source_video_path`: Required. The path to the source video file that will be
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analyzed. This is the input video on which traffic flow analysis will be performed.
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- `--target_video_path` (optional): The path to save the output video with annotations.
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If not specified, the processed video will be displayed in real-time without being
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saved.
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- `--confidence_threshold` (optional): Sets the confidence threshold for the YOLO model
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to filter detections. Default is `0.3`. This determines how confident the model should
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be to recognize an object in the video.
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- `--iou_threshold` (optional): Specifies the IOU (Intersection Over Union) threshold
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for the model. Default is 0.7. This value is used to manage object detection accuracy,
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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_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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- `match_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 data/traffic_analysis.pt \
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--source_video_path data/traffic_analysis.mov \
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--confidence_threshold 0.3 \
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--iou_threshold 0.5 \
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--target_video_path data/traffic_analysis_result.mov
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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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YOLOv8: The object detection model used in this demo, YOLOv8, is distributed under the
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[AGPL-3.0 license](https://github.com/ultralytics/ultralytics/blob/main/LICENSE). You
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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
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based on the Supervision library, which is licensed under the
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[MIT license](https://github.com/roboflow/supervision/blob/develop/LICENSE.md). This
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makes the Supervision part of the code fully open source and freely usable in your
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projects.
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from ultralytics import YOLO
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import supervision as sv
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import cv2
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import argparse
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def heatmap_and_track(
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source_weights_path: str,
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source_video_path: str,
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target_video_path: str,
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confidence_threshold: float = 0.35,
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iou_threshold: float = 0.5,
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heatmap_alpha: float = 0.5,
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radius: int = 25,
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track_threshold: float = 0.35,
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track_seconds: int = 5,
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match_threshold: float = 0.99,
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) -> None:
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### instantiate model
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model = YOLO(source_weights_path)
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### heatmap config
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heat_map_annotator = sv.HeatMapAnnotator(
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position = sv.Position.BOTTOM_CENTER,
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opacity = heatmap_alpha,
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radius = radius,
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kernel_size = 25,
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top_hue = 0,
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low_hue = 125,
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)
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### annotation config
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label_annotator = sv.LabelAnnotator(text_position = sv.Position.CENTER)
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### get the video fps
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cap = cv2.VideoCapture(source_video_path)
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fps = int(cap.get(cv2.CAP_PROP_FPS))
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cap.release()
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### tracker config
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byte_tracker = sv.ByteTrack(
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track_thresh = track_threshold,
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track_buffer = track_seconds * fps,
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match_thresh = match_threshold,
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frame_rate = fps
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)
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### video config
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video_info = sv.VideoInfo.from_video_path(video_path = source_video_path)
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frames_generator = sv.get_video_frames_generator(source_path = source_video_path, stride = 1)
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### Detect, track, annotate, save
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with sv.VideoSink(target_path = target_video_path, video_info = video_info) as sink:
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for frame in frames_generator:
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result = model(
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source = frame,
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classes = [0], # only person class
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conf = confidence_threshold,
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iou = iou_threshold,
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# show_conf = True,
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# save_txt = True,
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# save_conf = True,
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# save = True,
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device = None, # use None for CPU, 0 for single GPU, or [0,1] for dual GPU
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)[0]
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detections = sv.Detections.from_ultralytics(result) # get detections
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detections = byte_tracker.update_with_detections(detections) # update tracker
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### draw heatmap
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annotated_frame = heat_map_annotator.annotate(
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scene = frame.copy(),
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detections = detections
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)
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### draw other attributes from `detections` object
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labels = [
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f"#{tracker_id}"
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for class_id, tracker_id
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in zip(detections.class_id, detections.tracker_id)
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]
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label_annotator.annotate(
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scene = annotated_frame,
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detections = detections,
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labels = labels
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)
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sink.write_frame(frame = annotated_frame)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Heatmap and Tracking with Supervision"
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)
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parser.add_argument(
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"--source_weights_path",
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required=True,
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help="Path to the source weights file",
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type=str,
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)
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parser.add_argument(
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"--source_video_path",
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required=True,
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help="Path to the source video file",
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type=str,
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)
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parser.add_argument(
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"--target_video_path",
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required=True,
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help="Path to the target video file (output)",
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type=str,
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)
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parser.add_argument(
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"--confidence_threshold",
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default=0.35,
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help="Confidence threshold for the model",
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type=float,
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)
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parser.add_argument(
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"--iou_threshold",
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default=0.5,
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help="IOU threshold for the model",
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type=float,
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)
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parser.add_argument(
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"--heatmap_alpha",
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default=0.5,
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help="Opacity of the overlay mask, between 0 and 1",
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type=float,
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)
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parser.add_argument(
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"--radius",
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default=25,
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help="Radius of the heat circle",
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type=float,
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)
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parser.add_argument(
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"--track_threshold",
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default=0.35,
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help="Detection confidence threshold for track activation",
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type=float,
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)
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parser.add_argument(
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"--track_seconds",
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default=5,
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help="Number of seconds to buffer when a track is lost",
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type=int,
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)
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parser.add_argument(
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"--match_threshold",
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default=0.99,
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help="Threshold for matching tracks with detections",
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type=float,
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)
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args = parser.parse_args()
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heatmap_and_track(
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source_weights_path=args.source_weights_path,
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source_video_path=args.source_video_path,
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target_video_path=args.target_video_path,
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confidence_threshold=args.confidence_threshold,
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iou_threshold=args.iou_threshold,
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heatmap_alpha=args.heatmap_alpha,
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radius=args.radius,
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track_threshold=args.track_threshold,
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track_seconds=args.track_seconds,
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match_threshold=args.match_threshold,
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)
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### usage
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# python script.py --source_weights_path yolov8s.pt --source_video_path input.mp4 --target_video_path results.mp4
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