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130 lines
6.1 KiB
Markdown
130 lines
6.1 KiB
Markdown
1 year ago
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---
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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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