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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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6
docs/en/guides/isolating-segmentation-objects.md
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@ -36,7 +36,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
from ultralytics import YOLO
# Load a model
model = YOLO('yolov8n-seg.pt')
model = YOLO("yolov8n-seg.pt")
# Run inference
results = model.predict()
@ -159,7 +159,6 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
# Isolate object with binary mask
isolated = cv2.bitwise_and(mask3ch, img)
```
??? question "How does this work?"
@ -209,7 +208,6 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
```py
# Isolate object with transparent background (when saved as PNG)
isolated = np.dstack([img, b_mask])
```
??? question "How does this work?"
@ -266,7 +264,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
```py
# Save isolated object to file
_ = cv2.imwrite(f'{img_name}_{label}-{ci}.png', iso_crop)
_ = cv2.imwrite(f"{img_name}_{label}-{ci}.png", iso_crop)
```
- In this example, the `img_name` is the base-name of the source image file, `label` is the detected class-name, and `ci` is the index of the object detection (in case of multiple instances with the same class name).