Add speed_estimation and distance_calculation in ultralytics solutions (#7325)
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Muhammad Rizwan Munawar
GitHub
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12 changed files with 642 additions and 23 deletions
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docs/en/guides/distance-calculation.md
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docs/en/guides/distance-calculation.md
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---
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comments: true
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description: Distance Calculation Using Ultralytics YOLOv8
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keywords: Ultralytics, YOLOv8, Object Detection, Distance Calculation, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK
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---
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# Distance Calculation using Ultralytics YOLOv8 🚀
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## What is Distance Calculation?
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Measuring the gap between two objects is known as distance calculation within a specified space. In the case of [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics), the bounding box centroid is employed to calculate the distance for bounding boxes highlighted by the user.
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## Advantages of Distance Calculation?
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- **Localization Precision:** Enhances accurate spatial positioning in computer vision tasks.
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- **Size Estimation:** Allows estimation of physical sizes for better contextual understanding.
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- **Scene Understanding:** Contributes to a 3D understanding of the environment for improved decision-making.
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???+ tip "Distance Calculation"
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- Click on any two bounding boxes with Left Mouse click for distance calculation
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!!! Example "Distance Calculation using YOLOv8 Example"
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=== "Video Stream"
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```python
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from ultralytics import YOLO
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from ultralytics.solutions import distance_calculation
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import cv2
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model = YOLO("yolov8n.pt")
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names = model.model.names
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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# Video writer
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video_writer = cv2.VideoWriter("distance_calculation.avi",
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cv2.VideoWriter_fourcc(*'mp4v'),
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int(cap.get(5)),
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(int(cap.get(3)), int(cap.get(4))))
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# Init distance-calculation obj
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dist_obj = distance_calculation.DistanceCalculation()
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dist_obj.set_args(names=names, view_img=True)
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while cap.isOpened():
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success, im0 = cap.read()
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if not success:
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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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tracks = model.track(im0, persist=True, show=False)
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im0 = dist_obj.start_process(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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???+ tip "Note"
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- Mouse Right Click will delete all drawn points
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- Mouse Left Click can be used to draw points
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### Optional Arguments `set_args`
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| Name | Type | Default | Description |
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|----------------|--------|-----------------|--------------------------------------------------------|
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| names | `dict` | `None` | Classes names |
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| view_img | `bool` | `False` | Display frames with counts |
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| line_thickness | `int` | `2` | Increase bounding boxes thickness |
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| line_color | `RGB` | `(255, 255, 0)` | Line Color for centroids mapping on two bounding boxes |
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| centroid_color | `RGB` | `(255, 0, 255)` | Centroid color for each bounding box |
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### Arguments `model.track`
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| Name | Type | Default | Description |
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|-----------|---------|----------------|-------------------------------------------------------------|
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| `source` | `im0` | `None` | source directory for images or videos |
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| `persist` | `bool` | `False` | persisting tracks between frames |
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| `tracker` | `str` | `botsort.yaml` | Tracking method 'bytetrack' or 'botsort' |
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| `conf` | `float` | `0.3` | Confidence Threshold |
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| `iou` | `float` | `0.5` | IOU Threshold |
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| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
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| `verbose` | `bool` | `True` | Display the object tracking results |
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@ -31,16 +31,13 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
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| Transportation | Retail |
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|:-----------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------:|
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|  |  |
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|  |  |
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| Ultralytics YOLOv8 Transportation Heatmap | Ultralytics YOLOv8 Retail Heatmap |
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???+ tip "heatmap_alpha"
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heatmap_alpha value should be in range (0.0 - 1.0)
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???+ tip "decay_factor"
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Used for removal of heatmap after object removed from frame, value should be in range (0.0 - 1.0)
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???+ tip "Heatmap Configuration"
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- `heatmap_alpha`: Ensure this value is within the range (0.0 - 1.0).
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- `decay_factor`: Used for removing heatmap after an object is no longer in the frame, its value should also be in the range (0.0 - 1.0).
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!!! Example "Heatmaps using Ultralytics YOLOv8 Example"
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@ -37,6 +37,8 @@ Here's a compilation of in-depth guides to help you master different aspects of
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* [Heatmaps](heatmaps.md) 🚀 NEW: Elevate your understanding of data with our Detection Heatmaps! These intuitive visual tools use vibrant color gradients to vividly illustrate the intensity of data values across a matrix. Essential in computer vision, heatmaps are skillfully designed to highlight areas of interest, providing an immediate, impactful way to interpret spatial information.
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* [Instance Segmentation with Object Tracking](instance-segmentation-and-tracking.md) 🚀 NEW: Explore our feature on Object Segmentation in Bounding Boxes Shape, providing a visual representation of precise object boundaries for enhanced understanding and analysis.
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* [VisionEye View Objects Mapping](vision-eye.md) 🚀 NEW: This feature aim computers to discern and focus on specific objects, much like the way the human eye observes details from a particular viewpoint.
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* [Speed Estimation](speed-estimation.md) 🚀 NEW: Speed estimation in computer vision relies on analyzing object motion through techniques like [object tracking](https://docs.ultralytics.com/modes/track/), crucial for applications like autonomous vehicles and traffic monitoring.
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* [Distance Calculation](distance-calculation.md) 🚀 NEW: Distance calculation, which involves measuring the separation between two objects within a defined space, is a crucial aspect. In the context of Ultralytics YOLOv8, the method employed for this involves using the bounding box centroid to determine the distance associated with user-highlighted bounding boxes.
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## Contribute to Our Guides
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@ -8,7 +8,7 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Counting, Object Trackin
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## What is Object Counting in Regions?
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Object counting in regions with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves precisely determining the number of objects within specified areas using advanced computer vision. This approach is valuable for optimizing processes, enhancing security, and improving efficiency in various applications.
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[Object counting](https://docs.ultralytics.com/guides/object-counting/) in regions with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves precisely determining the number of objects within specified areas using advanced computer vision. This approach is valuable for optimizing processes, enhancing security, and improving efficiency in various applications.
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<p align="center">
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<br>
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98
docs/en/guides/speed-estimation.md
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docs/en/guides/speed-estimation.md
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---
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comments: true
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description: Speed Estimation Using Ultralytics YOLOv8
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keywords: Ultralytics, YOLOv8, Object Detection, Speed Estimation, Object Tracking, Notebook, IPython Kernel, CLI, Python SDK
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---
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# Speed Estimation using Ultralytics YOLOv8 🚀
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## What is Speed Estimation?
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Speed estimation is the process of calculating the rate of movement of an object within a given context, often employed in computer vision applications. Using [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) you can now calculate the speed of object using [object tracking](https://docs.ultralytics.com/modes/track/) alongside distance and time data, crucial for tasks like traffic and surveillance. The accuracy of speed estimation directly influences the efficiency and reliability of various applications, making it a key component in the advancement of intelligent systems and real-time decision-making processes.
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## Advantages of Speed Estimation?
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- **Efficient Traffic Control:** Accurate speed estimation aids in managing traffic flow, enhancing safety, and reducing congestion on roadways.
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- **Precise Autonomous Navigation:** In autonomous systems like self-driving cars, reliable speed estimation ensures safe and accurate vehicle navigation.
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- **Enhanced Surveillance Security:** Speed estimation in surveillance analytics helps identify unusual behaviors or potential threats, improving the effectiveness of security measures.
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## Real World Applications
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| Transportation | Transportation |
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|:-------------------------------------------------------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------------------------------------------------------:|
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| Speed Estimation on Road using Ultralytics YOLOv8 | Speed Estimation on Bridge using Ultralytics YOLOv8 |
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!!! Example "Speed Estimation using YOLOv8 Example"
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=== "Speed Estimation"
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```python
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from ultralytics import YOLO
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from ultralytics.solutions import speed_estimation
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import cv2
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model = YOLO("yolov8n.pt")
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names = model.model.names
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cap = cv2.VideoCapture("path/to/video/file.mp4")
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assert cap.isOpened(), "Error reading video file"
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# Video writer
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video_writer = cv2.VideoWriter("speed_estimation.avi",
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cv2.VideoWriter_fourcc(*'mp4v'),
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int(cap.get(5)),
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(int(cap.get(3)), int(cap.get(4))))
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line_pts = [(0, 360), (1280, 360)]
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# Init speed-estimation obj
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speed_obj = speed_estimation.SpeedEstimator()
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speed_obj.set_args(reg_pts=line_pts,
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names=names,
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view_img=True)
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while cap.isOpened():
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success, im0 = cap.read()
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if not success:
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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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tracks = model.track(im0, persist=True, show=False)
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im0 = speed_obj.estimate_speed(im0, tracks)
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video_writer.write(im0)
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cap.release()
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video_writer.release()
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cv2.destroyAllWindows()
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```
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???+ warning "Speed is Estimate"
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Speed will be an estimate and may not be completely accurate. Additionally, the estimation can vary depending on GPU speed.
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### Optional Arguments `set_args`
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| Name | Type | Default | Description |
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|---------------------|-------------|----------------------------|---------------------------------------------------|
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| reg_pts | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area |
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| names | `dict` | `None` | Classes names |
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| view_img | `bool` | `False` | Display frames with counts |
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| line_thickness | `int` | `2` | Increase bounding boxes thickness |
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| region_thickness | `int` | `5` | Thickness for object counter region or line |
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| spdl_dist_thresh | `int` | `10` | Euclidean Distance threshold for speed check line |
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### Arguments `model.track`
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| Name | Type | Default | Description |
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|-----------|---------|----------------|-------------------------------------------------------------|
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| `source` | `im0` | `None` | source directory for images or videos |
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| `persist` | `bool` | `False` | persisting tracks between frames |
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| `tracker` | `str` | `botsort.yaml` | Tracking method 'bytetrack' or 'botsort' |
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| `conf` | `float` | `0.3` | Confidence Threshold |
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| `iou` | `float` | `0.5` | IOU Threshold |
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| `classes` | `list` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] |
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| `verbose` | `bool` | `True` | Display the object tracking results |
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16
docs/en/reference/solutions/distance_calculation.md
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docs/en/reference/solutions/distance_calculation.md
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---
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description: Explore Ultralytics YOLO's distance calculation feature designed for advance analytics, providing an immediate, impactful way to interpret computer vision data.
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keywords: Ultralytics, YOLO, distance calculation, object tracking, data visualization, real-time tracking, machine learning, object counting, computer vision, vehicle analytics, YOLOv8, artificial intelligence
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---
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# Reference for `ultralytics/solutions/distance_calculation.py`
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!!! Note
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This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/distance_calculation.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/distance_calculation.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/solutions/heatmap.py) 🛠️. Thank you 🙏!
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<br><br>
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## ::: ultralytics.solutions.distance_calculation.DistanceCalculation
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<br><br>
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docs/en/reference/solutions/speed_estimation.md
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---
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description: Transform speed estimation with Ultralytics YOLO speed estimation featuring cutting-edge technology for precise real-time counting in video streams.
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keywords: Ultralytics YOLO, speed estimation software, real-time vehicle tracking solutions, video stream analysis, YOLOv8 object detection, smart counting technology, computer vision, AI-powered tracking, video analytics tools, automated monitoring.
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---
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# Reference for `ultralytics/solutions/speed_estimation.py`
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!!! Note
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This file is available at [https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/speed_estimation.py](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/solutions/speed_estimation.py). If you spot a problem please help fix it by [contributing](https://docs.ultralytics.com/help/contributing/) a [Pull Request](https://github.com/ultralytics/ultralytics/edit/main/ultralytics/solutions/object_counter.py) 🛠️. Thank you 🙏!
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<br><br>
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## ::: ultralytics.solutions.speed_estimation.SpeedEstimator
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<br><br>
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