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129 lines
6.1 KiB
129 lines
6.1 KiB
--- |
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comments: true |
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description: Instance Segmentation with Object Tracking using Ultralytics YOLOv8 |
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keywords: Ultralytics, YOLOv8, Instance Segmentation, Object Detection, Object Tracking, Segbbox, Computer Vision, Notebook, IPython Kernel, CLI, Python SDK |
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--- |
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# Instance Segmentation and Tracking using Ultralytics YOLOv8 🚀 |
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## What is Instance Segmentation? |
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[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) instance segmentation involves identifying and outlining individual objects in an image, providing a detailed understanding of spatial distribution. Unlike semantic segmentation, it uniquely labels and precisely delineates each object, crucial for tasks like object detection and medical imaging. |
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There are two types of instance segmentation tracking available in the Ultralytics package: |
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- **Instance Segmentation with Class Objects:** Each class object is assigned a unique color for clear visual separation. |
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- **Instance Segmentation with Object Tracks:** Every track is represented by a distinct color, facilitating easy identification and tracking. |
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## Samples |
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| Instance Segmentation | Instance Segmentation + Object Tracking | |
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|:---------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------:| |
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|  |  | |
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| Ultralytics Instance Segmentation 😍 | Ultralytics Instance Segmentation with Object Tracking 🔥 | |
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!!! Example "Instance Segmentation and Tracking" |
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=== "Instance Segmentation" |
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```python |
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import cv2 |
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from ultralytics import YOLO |
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from ultralytics.utils.plotting import Annotator, colors |
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model = YOLO("yolov8n-seg.pt") # segmentation model |
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names = model.model.names |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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out = cv2.VideoWriter('instance-segmentation.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h)) |
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while True: |
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ret, im0 = cap.read() |
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if not ret: |
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print("Video frame is empty or video processing has been successfully completed.") |
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break |
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results = model.predict(im0) |
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annotator = Annotator(im0, line_width=2) |
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if results[0].masks is not None: |
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clss = results[0].boxes.cls.cpu().tolist() |
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masks = results[0].masks.xy |
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for mask, cls in zip(masks, clss): |
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annotator.seg_bbox(mask=mask, |
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mask_color=colors(int(cls), True), |
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det_label=names[int(cls)]) |
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out.write(im0) |
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cv2.imshow("instance-segmentation", im0) |
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if cv2.waitKey(1) & 0xFF == ord('q'): |
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break |
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out.release() |
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cap.release() |
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cv2.destroyAllWindows() |
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``` |
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=== "Instance Segmentation with Object Tracking" |
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```python |
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import cv2 |
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from ultralytics import YOLO |
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from ultralytics.utils.plotting import Annotator, colors |
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from collections import defaultdict |
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track_history = defaultdict(lambda: []) |
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model = YOLO("yolov8n-seg.pt") # segmentation model |
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cap = cv2.VideoCapture("path/to/video/file.mp4") |
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w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS)) |
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out = cv2.VideoWriter('instance-segmentation-object-tracking.avi', cv2.VideoWriter_fourcc(*'MJPG'), fps, (w, h)) |
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while True: |
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ret, im0 = cap.read() |
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if not ret: |
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print("Video frame is empty or video processing has been successfully completed.") |
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break |
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annotator = Annotator(im0, line_width=2) |
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results = model.track(im0, persist=True) |
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if results[0].boxes.id is not None and results[0].masks is not None: |
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masks = results[0].masks.xy |
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track_ids = results[0].boxes.id.int().cpu().tolist() |
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for mask, track_id in zip(masks, track_ids): |
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annotator.seg_bbox(mask=mask, |
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mask_color=colors(track_id, True), |
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track_label=str(track_id)) |
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out.write(im0) |
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cv2.imshow("instance-segmentation-object-tracking", im0) |
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if cv2.waitKey(1) & 0xFF == ord('q'): |
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break |
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out.release() |
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cap.release() |
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cv2.destroyAllWindows() |
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``` |
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### `seg_bbox` Arguments |
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| Name | Type | Default | Description | |
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|---------------|---------|-----------------|----------------------------------------| |
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| `mask` | `array` | `None` | Segmentation mask coordinates | |
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| `mask_color` | `tuple` | `(255, 0, 255)` | Mask color for every segmented box | |
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| `det_label` | `str` | `None` | Label for segmented object | |
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| `track_label` | `str` | `None` | Label for segmented and tracked object | |
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## Note |
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For any inquiries, feel free to post your questions in the [Ultralytics Issue Section](https://github.com/ultralytics/ultralytics/issues/new/choose) or the discussion section mentioned below.
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