Update TFLite Docs images (#8605)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher
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33 changed files with 112 additions and 107 deletions
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@ -14,12 +14,12 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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## Recipe Walk Through
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1. Begin with the necessary imports
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1. Begin with the necessary imports
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```py
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```python
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from pathlib import Path
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import cv2 as cv
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import cv2
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import numpy as np
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from ultralytics import YOLO
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```
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@ -30,19 +30,19 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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***
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2. 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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```python
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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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results = model.predict()
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```
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??? question "No Prediction Arguments?"
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!!! question "No Prediction Arguments?"
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Without specifying a source, the example images from the library will be used:
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@ -57,7 +57,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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***
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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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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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@ -81,7 +81,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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***
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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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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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@ -98,11 +98,11 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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# Draw contour onto mask
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_ = cv.drawContours(b_mask,
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_ = cv2.drawContours(b_mask,
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[contour],
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-1,
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(255, 255, 255),
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cv.FILLED)
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cv2.FILLED)
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```
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@ -136,7 +136,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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- The `tuple` `(255, 255, 255)` represents the color white, which is the desired color for drawing the contour in this binary mask.
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- The addition of `cv.FILLED` will color all pixels enclosed by the contour boundary the same, in this case, all enclosed pixels will be white.
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- The addition of `cv2.FILLED` will color all pixels enclosed by the contour boundary the same, in this case, all enclosed pixels will be white.
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- See [OpenCV Documentation on `drawContours()`](https://docs.opencv.org/4.8.0/d6/d6e/group__imgproc__draw.html#ga746c0625f1781f1ffc9056259103edbc) for more information.
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@ -145,7 +145,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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***
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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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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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@ -155,10 +155,10 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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```py
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# Create 3-channel mask
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mask3ch = cv.cvtColor(b_mask, cv.COLOR_GRAY2BGR)
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mask3ch = cv2.cvtColor(b_mask, cv2.COLOR_GRAY2BGR)
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# Isolate object with binary mask
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isolated = cv.bitwise_and(mask3ch, img)
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isolated = cv2.bitwise_and(mask3ch, img)
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```
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@ -258,7 +258,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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***
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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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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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@ -266,7 +266,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
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```py
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# Save isolated object to file
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_ = cv.imwrite(f'{img_name}_{label}-{ci}.png', iso_crop)
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_ = cv2.imwrite(f'{img_name}_{label}-{ci}.png', iso_crop)
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```
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- 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).
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@ -278,7 +278,7 @@ Here, all steps from the previous section are combined into a single block of co
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```{ .py .annotate }
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from pathlib import Path
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import cv2 as cv
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import cv2
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import numpy as np
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from ultralytics import YOLO
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@ -298,13 +298,13 @@ for r in res:
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# Create contour mask (1)
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contour = c.masks.xy.pop().astype(np.int32).reshape(-1, 1, 2)
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_ = cv.drawContours(b_mask, [contour], -1, (255, 255, 255), cv.FILLED)
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_ = cv2.drawContours(b_mask, [contour], -1, (255, 255, 255), cv2.FILLED)
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# Choose one:
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# OPTION-1: Isolate object with black background
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mask3ch = cv.cvtColor(b_mask, cv.COLOR_GRAY2BGR)
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isolated = cv.bitwise_and(mask3ch, img)
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mask3ch = cv2.cvtColor(b_mask, cv2.COLOR_GRAY2BGR)
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isolated = cv2.bitwise_and(mask3ch, img)
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# OPTION-2: Isolate object with transparent background (when saved as PNG)
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isolated = np.dstack([img, b_mask])
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