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Reformat Markdown code blocks (#12795)

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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
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Glenn Jocher 2024-05-18 18:58:06 +02:00 • committed by GitHub
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38
docs/en/guides/workouts-monitoring.md
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@ -39,28 +39,30 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
=== "Workouts Monitoring"
```python
from ultralytics import YOLO, solutions
import cv2
from ultralytics import YOLO, solutions
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 = solutions.AIGym(line_thickness=2,
view_img=True,
pose_type="pushup",
kpts_to_check=[6, 8, 10])
gym_object = solutions.AIGym(
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
print("Video frame is empty or video processing has been successfully completed.")
break
frame_count += 1
results = model.track(im0, verbose=False) # Tracking recommended
#results = model.predict(im0) # Prediction also supported
# results = model.predict(im0) # Prediction also supported
im0 = gym_object.start_counting(im0, results, frame_count)
cv2.destroyAllWindows()
@ -69,30 +71,32 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
=== "Workouts Monitoring with Save Output"
```python
from ultralytics import YOLO, solutions
import cv2
from ultralytics import YOLO, solutions
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))
video_writer = cv2.VideoWriter("workouts.avi", cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
gym_object = solutions.AIGym(line_thickness=2,
view_img=True,
pose_type="pushup",
kpts_to_check=[6, 8, 10])
gym_object = solutions.AIGym(
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
print("Video frame is empty or video processing has been successfully completed.")
break
frame_count += 1
results = model.track(im0, verbose=False) # Tracking recommended
#results = model.predict(im0) # Prediction also supported
# results = model.predict(im0) # Prediction also supported
im0 = gym_object.start_counting(im0, results, frame_count)
video_writer.write(im0)