Docs cleanup and Google-style tracker docstrings (#6751)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher
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@ -30,11 +30,16 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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---
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1. Load a model and run `predict()` method on a source.
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2. Load a model and run `predict()` method on a source.
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```py
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m = YOLO('yolov8n-seg.pt')
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res = m.predict()
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from ultralytics import YOLO
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# Load a model
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model = YOLO('yolov8n-seg.pt')
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# Run inference
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result = model.predict()
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```
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??? question "No Prediction Arguments?"
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@ -48,11 +53,11 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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This is helpful for rapid testing with the `predict()` method.
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For additional information about Segmentation Models, visit the [Segment Task](../tasks/segment.md/#models) page. To learn more about `predict()` method, see [Predict Mode](../modes/predict.md) section of the Documentation.
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For additional information about Segmentation Models, visit the [Segment Task](../tasks/segment.md#models) page. To learn more about `predict()` method, see [Predict Mode](../modes/predict.md) section of the Documentation.
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---
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1. Now iterate over the results and the contours. For workflows that want to save an image to file, the source image `base-name` and the detection `class-label` are retrieved for later use (optional).
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3. Now iterate over the results and the contours. For workflows that want to save an image to file, the source image `base-name` and the detection `class-label` are retrieved for later use (optional).
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``` { .py .annotate }
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# (2) Iterate detection results (helpful for multiple images)
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@ -67,8 +72,8 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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```
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1. To learn more about working with detection results, see [Boxes Section for Predict Mode](../modes/predict.md/#boxes).
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2. To learn more about `predict()` results see [Working with Results for Predict Mode](../modes/predict.md/#working-with-results)
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1. To learn more about working with detection results, see [Boxes Section for Predict Mode](../modes/predict.md#boxes).
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2. To learn more about `predict()` results see [Working with Results for Predict Mode](../modes/predict.md#working-with-results)
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??? info "For-Loop"
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@ -76,7 +81,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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---
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1. Start with generating a binary mask from the source image and then draw a filled contour onto the mask. This will allow the object to be isolated from the other parts of the image. An example from `bus.jpg` for one of the detected `person` class objects is shown on the right.
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4. Start with generating a binary mask from the source image and then draw a filled contour onto the mask. This will allow the object to be isolated from the other parts of the image. An example from `bus.jpg` for one of the detected `person` class objects is shown on the right.
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{ width="240", align="right" }
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``` { .py .annotate }
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@ -100,7 +105,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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```
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1. For more info on `c.masks.xy` see [Masks Section from Predict Mode](../modes/predict.md/#masks).
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1. For more info on `c.masks.xy` see [Masks Section from Predict Mode](../modes/predict.md#masks).
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2. Here, the values are cast into `np.int32` for compatibility with `drawContours()` function from OpenCV.
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@ -110,13 +115,13 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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<summary> Expand to understand what is happening when defining the <code>contour</code> variable.</summary>
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<p>
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- `c.masks.xy` :: Provides the coordinates of the mask contour points in the format `(x, y)`. For more details, refer to the [Masks Section from Predict Mode](../modes/predict.md/#masks).
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- `c.masks.xy` :: Provides the coordinates of the mask contour points in the format `(x, y)`. For more details, refer to the [Masks Section from Predict Mode](../modes/predict.md#masks).
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- `.pop()` :: As `masks.xy` is a list containing a single element, this element is extracted using the `pop()` method.
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- `.astype(np.int32)` :: Using `masks.xy` will return with a data type of `float32`, but this won't be compatible with the OpenCV `drawContours()` function, so this will change the data type to `int32` for compatibility.
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- `.reshape(-1, 1, 2)` :: Reformats the data into the required shape of of `[N, 1, 2]` where `N` is the number of contour points, with each point represented by a single entry `1`, and the entry is composed of `2` values. The `-1` denotes that the number of values along this dimension is flexible.
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- `.reshape(-1, 1, 2)` :: Reformats the data into the required shape of `[N, 1, 2]` where `N` is the number of contour points, with each point represented by a single entry `1`, and the entry is composed of `2` values. The `-1` denotes that the number of values along this dimension is flexible.
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</details>
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<p></p>
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@ -139,7 +144,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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---
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1. Next the there are 2 options for how to move forward with the image from this point and a subsequent option for each.
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5. Next the there are 2 options for how to move forward with the image from this point and a subsequent option for each.
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### Object Isolation Options
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@ -252,7 +257,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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---
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1. <u>What to do next is entirely left to the you as the developer.</u> A basic example of one possible next step (saving the image to file for future use) is shown.
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6. <u>What to do next is entirely left to you as the developer.</u> A basic example of one possible next step (saving the image to file for future use) is shown.
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- **NOTE:** this step is optional and can be skipped if not required for your specific use case.
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@ -314,6 +319,6 @@ for r in res:
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1. The line populating `contour` is combined into a single line here, where it was split to multiple above.
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2. {==What goes here is up to you!==}
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3. See [Predict Mode](../modes/predict.md) for additional information.
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4. See [Segment Task](../tasks/segment.md/#models) for more information.
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5. Learn more about [Working with Results](../modes/predict.md/#working-with-results)
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6. Learn more about [Segmentation Mask Results](../modes/predict.md/#masks)
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4. See [Segment Task](../tasks/segment.md#models) for more information.
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5. Learn more about [Working with Results](../modes/predict.md#working-with-results)
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6. Learn more about [Segmentation Mask Results](../modes/predict.md#masks)
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