You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
132 lines
8.2 KiB
132 lines
8.2 KiB
--- |
|
comments: true |
|
description: Workouts Monitoring Using Ultralytics YOLOv8 |
|
keywords: Ultralytics, YOLOv8, Object Detection, Pose Estimation, PushUps, PullUps, Ab workouts, Notebook, IPython Kernel, CLI, Python SDK |
|
--- |
|
|
|
# Workouts Monitoring using Ultralytics YOLOv8 🚀 |
|
|
|
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. |
|
|
|
## Advantages of Workouts Monitoring? |
|
|
|
- **Optimized Performance:** Tailoring workouts based on monitoring data for better results. |
|
- **Goal Achievement:** Track and adjust fitness goals for measurable progress. |
|
- **Personalization:** Customized workout plans based on individual data for effectiveness. |
|
- **Health Awareness:** Early detection of patterns indicating health issues or overtraining. |
|
- **Informed Decisions:** Data-driven decisions for adjusting routines and setting realistic goals. |
|
|
|
## Real World Applications |
|
|
|
| Workouts Monitoring | Workouts Monitoring | |
|
|:----------------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------------:| |
|
|  |  | |
|
| PushUps Counting | PullUps Counting | |
|
|
|
!!! Example "Workouts Monitoring Example" |
|
|
|
=== "Workouts Monitoring" |
|
```python |
|
from ultralytics import YOLO |
|
from ultralytics.solutions import ai_gym |
|
import cv2 |
|
|
|
model = YOLO("yolov8n-pose.pt") |
|
cap = cv2.VideoCapture("path/to/video/file.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_object = ai_gym.AIGym() # init AI GYM module |
|
gym_object.set_args(line_thickness=2, |
|
view_img=True, |
|
pose_type="pushup", |
|
kpts_to_check=[6, 8, 10]) |
|
|
|
frame_count = 0 |
|
while cap.isOpened(): |
|
success, im0 = cap.read() |
|
if not success: |
|
print("Video frame is empty or video processing has been successfully completed.") |
|
break |
|
frame_count += 1 |
|
results = model.predict(im0, verbose=False) |
|
im0 = gym_object.start_counting(im0, results, frame_count) |
|
|
|
cv2.destroyAllWindows() |
|
``` |
|
|
|
=== "Workouts Monitoring with Save Output" |
|
```python |
|
from ultralytics import YOLO |
|
from ultralytics.solutions import ai_gym |
|
import cv2 |
|
|
|
model = YOLO("yolov8n-pose.pt") |
|
cap = cv2.VideoCapture("path/to/video/file.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_object = ai_gym.AIGym() # init AI GYM module |
|
gym_object.set_args(line_thickness=2, |
|
view_img=True, |
|
pose_type="pushup", |
|
kpts_to_check=[6, 8, 10]) |
|
|
|
frame_count = 0 |
|
while cap.isOpened(): |
|
success, im0 = cap.read() |
|
if not success: |
|
print("Video frame is empty or video processing has been successfully completed.") |
|
break |
|
frame_count += 1 |
|
results = model.predict(im0, verbose=False) |
|
im0 = gym_object.start_counting(im0, results, frame_count) |
|
video_writer.write(im0) |
|
|
|
cv2.destroyAllWindows() |
|
video_writer.release() |
|
``` |
|
|
|
???+ tip "Support" |
|
|
|
"pushup", "pullup" and "abworkout" supported |
|
|
|
### KeyPoints Map |
|
|
|
 |
|
|
|
### Arguments `set_args` |
|
|
|
| Name | Type | Default | Description | |
|
|-----------------|--------|----------|----------------------------------------------------------------------------------------| |
|
| kpts_to_check | `list` | `None` | List of three keypoints index, for counting specific workout, followed by keypoint Map | |
|
| view_img | `bool` | `False` | Display the frame with counts | |
|
| line_thickness | `int` | `2` | Increase the thickness of count value | |
|
| pose_type | `str` | `pushup` | Pose that need to be monitored, "pullup" and "abworkout" also supported | |
|
| pose_up_angle | `int` | `145` | Pose Up Angle value | |
|
| pose_down_angle | `int` | `90` | Pose Down Angle value | |
|
|
|
### Arguments `model.predict` |
|
|
|
| Name | Type | Default | Description | |
|
|-----------------|----------------|------------------------|----------------------------------------------------------------------------| |
|
| `source` | `str` | `'ultralytics/assets'` | source directory for images or videos | |
|
| `conf` | `float` | `0.25` | object confidence threshold for detection | |
|
| `iou` | `float` | `0.7` | intersection over union (IoU) threshold for NMS | |
|
| `imgsz` | `int or tuple` | `640` | image size as scalar or (h, w) list, i.e. (640, 480) | |
|
| `half` | `bool` | `False` | use half precision (FP16) | |
|
| `device` | `None or str` | `None` | device to run on, i.e. cuda device=0/1/2/3 or device=cpu | |
|
| `max_det` | `int` | `300` | maximum number of detections per image | |
|
| `vid_stride` | `bool` | `False` | video frame-rate stride | |
|
| `stream_buffer` | `bool` | `False` | buffer all streaming frames (True) or return the most recent frame (False) | |
|
| `visualize` | `bool` | `False` | visualize model features | |
|
| `augment` | `bool` | `False` | apply image augmentation to prediction sources | |
|
| `agnostic_nms` | `bool` | `False` | class-agnostic NMS | |
|
| `classes` | `list[int]` | `None` | filter results by class, i.e. classes=0, or classes=[0,2,3] | |
|
| `retina_masks` | `bool` | `False` | use high-resolution segmentation masks | |
|
| `embed` | `list[int]` | `None` | return feature vectors/embeddings from given layers |
|
|
|