Ultralytics YOLO

https://docs.ultralytics.com/guides/workouts-monitoring
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Ultralytics YOLO is a state-of-the-art computer vision platform that provides pose estimation and...
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### Saving Workout Monitoring Output to Video

Source: https://docs.ultralytics.com/guides/workouts-monitoring

Extends the basic monitoring setup by integrating a cv2.VideoWriter to save the processed video frames to a file for later review.

```python
import cv2
from ultralytics import solutions

cap = cv2.VideoCapture("path/to/video.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

video_writer = cv2.VideoWriter("workouts.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

gym = solutions.AIGym(
    line_width=2,
    show=True,
    kpts=[6, 8, 10],
)

while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or processing is complete.")
        break
    results = gym(im0)
    video_writer.write(results.plot_im)

cap.release()
video_writer.release()
cv2.destroyAllWindows()
```

--------------------------------

### Workouts Monitoring with Ultralytics YOLO

Source: https://docs.ultralytics.com/guides/workouts-monitoring

Demonstrates how to monitor workout exercises using Ultralytics YOLO, including running examples, processing videos, and utilizing keypoints for specific exercises.

```APIDOC
## Workouts Monitoring using Ultralytics YOLO

Monitoring workouts through pose estimation with Ultralytics YOLO 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.

### CLI Examples

```bash
# Run a workout example
yolo solutions workout show=True

# Pass a source video
yolo solutions workout source="path/to/video.mp4"

# Use keypoints for pushups
yolo solutions workout kpts="[6, 8, 10]"
```

### Python Example

```python
import cv2

from ultralytics import solutions

cap = cv2.VideoCapture("path/to/video.mp4")
assert cap.isOpened(), "Error reading video file"

# Video writer
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
video_writer = cv2.VideoWriter("workouts_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init AIGym
gym = solutions.AIGym(
    show=True,  # display the frame
    kpts=[6, 8, 10],  # keypoints for monitoring specific exercise, by default it's for pushup
    model="yolo26n-pose.pt",  # path to the YOLO26 pose estimation model file
    # line_width=2,  # adjust the line width for bounding boxes and text display
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()

    if not success:
        print("Video frame is empty or processing is complete.")
        break

    results = gym(im0)

    # print(results)  # access the output

    video_writer.write(results.plot_im)  # write the processed frame.

cap.release()
video_writer.release()
cv2.destroyAllWindows()  # destroy all opened windows
```

### `AIGym` Arguments

Here's a table with the `AIGym` arguments:

| Argument | Type | Default | Description |
|---|---|---|---|
| `model` | `str` | `None` | Path to an Ultralytics YOLO model file. |
| `up_angle` | `float` | `145.0` | Angle threshold for the \'up\' pose. |
| `down_angle` | `float` | `90.0` | Angle threshold for the \'down\' pose. |
| `kpts` | `list[int]` | `'[6, 8, 10]'` | List of three keypoint indices used for monitoring workouts. These keypoints correspond to body joints or parts, such as shoulders, elbows, and wrists, for exercises like push-ups, pull-ups, squats, and ab-workouts. |

The `AIGym` solution also supports a range of object tracking parameters:

| Argument | Type | Default | Description |
|---|---|---|---|
| `tracker` | `str` | `'botsort.yaml'` | Specifies the tracking algorithm to use, e.g., `bytetrack.yaml` or `botsort.yaml`. |
| `conf` | `float` | `0.1` | Sets the confidence threshold for detections; lower values allow more objects to be tracked but may include false positives. |
| `iou` | `float` | `0.7` | Sets the Intersection over Union (IoU) threshold for filtering overlapping detections. |
| `classes` | `list` | `None` | Filters results by class index. For example, `classes=[0, 2, 3]` only tracks the specified classes. |
| `verbose` | `bool` | `True` | Controls the display of tracking results, providing a visual output of tracked objects. |
| `device` | `str` | `None` | Specifies the device for inference (e.g., `cpu`, `cuda:0` or `0`). Allows users to select between CPU, a specific GPU, or other compute devices for model execution. |

Additionally, the following visualization settings can be applied:

| Argument | Type | Default | Description |
|---|---|---|---|
| `show` | `bool` | `False` | If `True`, displays the annotated images or videos in a window. Useful for immediate visual feedback during development or testing. |
| `line_width` | `int or None` | `None` | Specifies the line width of bounding boxes. If `None`, the line width is automatically adjusted based on the image size. Provides visual customization for clarity. |
| `show_conf` | `bool` | `True` | Displays the confidence score for each detection alongside the label. Gives insight into the model's certainty for each detection. |
| `show_labels` | `bool` | `True` | Displays labels for each detection in the visual output. Provides immediate understanding of detected objects. |

```

--------------------------------

### Run Workout Monitoring with CLI and Python

Source: https://docs.ultralytics.com/guides/workouts-monitoring

Demonstrates how to initiate workout monitoring using the Ultralytics CLI for quick execution and the Python AIGym class for custom video processing pipelines.

```bash
# Run a workout example
yolo solutions workout show=True

# Pass a source video
yolo solutions workout source="path/to/video.mp4"

# Use keypoints for pushups
yolo solutions workout kpts="[6, 8, 10]"
```

```python
import cv2
from ultralytics import solutions

cap = cv2.VideoCapture("path/to/video.mp4")
assert cap.isOpened(), "Error reading video file"

# Video writer
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))
video_writer = cv2.VideoWriter("workouts_output.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))

# Init AIGym
gym = solutions.AIGym(
    show=True,
    kpts=[6, 8, 10],
    model="yolo26n-pose.pt"
)

# Process video
while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        break
    results = gym(im0)
    video_writer.write(results.plot_im)

cap.release()
video_writer.release()
cv2.destroyAllWindows()
```

--------------------------------

### Real-time Workout Monitoring with AIGym

Source: https://docs.ultralytics.com/guides/workouts-monitoring

Demonstrates how to initialize the AIGym class to process a video stream for pose estimation. It captures video frames, applies the gym model, and displays the results.

```python
import cv2
from ultralytics import solutions

cap = cv2.VideoCapture("path/to/video.mp4")
assert cap.isOpened(), "Error reading video file"
w, h, fps = (int(cap.get(x)) for x in (cv2.CAP_PROP_FRAME_WIDTH, cv2.CAP_PROP_FRAME_HEIGHT, cv2.CAP_PROP_FPS))

gym = solutions.AIGym(
    line_width=2,
    show=True,
    kpts=[6, 8, 10],
)

while cap.isOpened():
    success, im0 = cap.read()
    if not success:
        print("Video frame is empty or processing is complete.")
        break
    results = gym(im0)

cv2.destroyAllWindows()
```

--------------------------------

### Configuring AIGym for Custom Exercises

Source: https://docs.ultralytics.com/guides/workouts-monitoring

Shows how to instantiate the AIGym class with specific keypoints to track custom exercises like push-ups, pull-ups, or ab workouts.

```python
from ultralytics import solutions

gym = solutions.AIGym(
    line_width=2,
    show=True,
    kpts=[6, 8, 10],  # For pushups - can be customized for other exercises
)
```
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