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156 lines
9.5 KiB
156 lines
9.5 KiB
--- |
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comments: true |
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description: Workouts Monitoring Using Ultralytics YOLOv8 |
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keywords: Ultralytics, YOLOv8, Object Detection, Pose Estimation, PushUps, PullUps, Ab workouts, Notebook, IPython Kernel, CLI, Python SDK |
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--- |
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# Workouts Monitoring using Ultralytics YOLOv8 🚀 |
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Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) enhances exercise assessment by accurately tracking key body landmarks and joints in real-time. This technology provides instant feedback on exercise form, tracks workout routines, and measures performance metrics, optimizing training sessions for users and trainers alike. |
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<p align="center"> |
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<br> |
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<iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/LGGxqLZtvuw" |
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title="YouTube video player" frameborder="0" |
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allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" |
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allowfullscreen> |
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</iframe> |
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<br> |
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<strong>Watch:</strong> Workouts Monitoring using Ultralytics YOLOv8 | Pushups, Pullups, Ab Workouts |
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</p> |
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## Advantages of Workouts Monitoring? |
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- **Optimized Performance:** Tailoring workouts based on monitoring data for better results. |
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- **Goal Achievement:** Track and adjust fitness goals for measurable progress. |
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- **Personalization:** Customized workout plans based on individual data for effectiveness. |
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- **Health Awareness:** Early detection of patterns indicating health issues or over-training. |
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- **Informed Decisions:** Data-driven decisions for adjusting routines and setting realistic goals. |
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## Real World Applications |
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| Workouts Monitoring | Workouts Monitoring | |
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|:----------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------:| |
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|  |  | |
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| PushUps Counting | PullUps Counting | |
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!!! Example "Workouts Monitoring Example" |
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=== "Workouts Monitoring" |
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```python |
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import cv2 |
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from ultralytics import YOLO, solutions |
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model = YOLO("yolov8n-pose.pt") |
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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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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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gym_object = solutions.AIGym( |
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line_thickness=2, |
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view_img=True, |
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pose_type="pushup", |
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kpts_to_check=[6, 8, 10], |
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) |
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frame_count = 0 |
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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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frame_count += 1 |
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results = model.track(im0, verbose=False) # Tracking recommended |
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# results = model.predict(im0) # Prediction also supported |
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im0 = gym_object.start_counting(im0, results, frame_count) |
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cv2.destroyAllWindows() |
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``` |
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=== "Workouts Monitoring with Save Output" |
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```python |
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import cv2 |
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from ultralytics import YOLO, solutions |
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model = YOLO("yolov8n-pose.pt") |
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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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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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video_writer = cv2.VideoWriter("workouts.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h)) |
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gym_object = solutions.AIGym( |
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line_thickness=2, |
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view_img=True, |
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pose_type="pushup", |
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kpts_to_check=[6, 8, 10], |
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) |
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frame_count = 0 |
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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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frame_count += 1 |
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results = model.track(im0, verbose=False) # Tracking recommended |
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# results = model.predict(im0) # Prediction also supported |
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im0 = gym_object.start_counting(im0, results, frame_count) |
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video_writer.write(im0) |
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cv2.destroyAllWindows() |
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video_writer.release() |
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``` |
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???+ tip "Support" |
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"pushup", "pullup" and "abworkout" supported |
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### KeyPoints Map |
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### Arguments `AIGym` |
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| Name | Type | Default | Description | |
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|-------------------|---------|----------|----------------------------------------------------------------------------------------| |
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| `kpts_to_check` | `list` | `None` | List of three keypoints index, for counting specific workout, followed by keypoint Map | |
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| `line_thickness` | `int` | `2` | Thickness of the lines drawn. | |
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| `view_img` | `bool` | `False` | Flag to display the image. | |
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| `pose_up_angle` | `float` | `145.0` | Angle threshold for the 'up' pose. | |
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| `pose_down_angle` | `float` | `90.0` | Angle threshold for the 'down' pose. | |
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| `pose_type` | `str` | `pullup` | Type of pose to detect (`'pullup`', `pushup`, `abworkout`, `squat`). | |
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### Arguments `model.predict` |
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| Name | Type | Default | Description | |
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|-----------------|----------------|------------------------|----------------------------------------------------------------------------| |
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| `source` | `str` | `'ultralytics/assets'` | source directory for images or videos | |
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| `conf` | `float` | `0.25` | object confidence threshold for detection | |
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| `iou` | `float` | `0.7` | intersection over union (IoU) threshold for NMS | |
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| `imgsz` | `int or tuple` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | |
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| `half` | `bool` | `False` | use half precision (FP16) | |
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| `device` | `None or str` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu | |
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| `max_det` | `int` | `300` | maximum number of detections per image | |
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| `vid_stride` | `bool` | `False` | video frame-rate stride | |
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| `stream_buffer` | `bool` | `False` | buffer all streaming frames (True) or return the most recent frame (False) | |
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| `visualize` | `bool` | `False` | visualize model features | |
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| `augment` | `bool` | `False` | apply image augmentation to prediction sources | |
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| `agnostic_nms` | `bool` | `False` | class-agnostic NMS | |
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| `classes` | `list[int]` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] | |
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| `retina_masks` | `bool` | `False` | use high-resolution segmentation masks | |
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| `embed` | `list[int]` | `None` | return feature vectors/embeddings from given layers | |
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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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