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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@ -72,7 +72,7 @@ from ultralytics import YOLO
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model = YOLO('yolov8n.pt') # initialize model
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results = model('path/to/image.jpg') # perform inference
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results.show() # display results
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results[0].show() # display results for the first image
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```
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---
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@ -12,7 +12,7 @@ keywords: Ultralytics, YOLOv8, Object Detection, Coral, Edge TPU, Raspberry Pi,
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## What is a Coral Edge TPU?
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The Coral Edge TPU is a compact device that adds an Edge TPU coprocessor to your system. It enables low-power, high-performance ML inferencing for TensorFlow Lite models. Read more at the [Coral Edge TPU home page](https://coral.ai/products/accelerator).
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The Coral Edge TPU is a compact device that adds an Edge TPU coprocessor to your system. It enables low-power, high-performance ML inference for TensorFlow Lite models. Read more at the [Coral Edge TPU home page](https://coral.ai/products/accelerator).
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## Boost Raspberry Pi Model Performance with Coral Edge TPU
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@ -37,16 +37,16 @@ This guide assumes that you already have a working Raspberry Pi OS install and h
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First, we need to install the Edge TPU runtime. There are many different versions available, so you need to choose the right version for your operating system.
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| Raspberry Pi OS | High frequency mode | Version to download |
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|-----------------|:-------------------:|------------------------------------------|
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| Bullseye 32bit | No | libedgetpu1-std_ ... .bullseye_armhf.deb |
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| Bullseye 64bit | No | libedgetpu1-std_ ... .bullseye_arm64.deb |
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| Bullseye 32bit | Yes | libedgetpu1-max_ ... .bullseye_armhf.deb |
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| Bullseye 64bit | Yes | libedgetpu1-max_ ... .bullseye_arm64.deb |
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| Bookworm 32bit | No | libedgetpu1-std_ ... .bookworm_armhf.deb |
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| Bookworm 64bit | No | libedgetpu1-std_ ... .bookworm_arm64.deb |
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| Bookworm 32bit | Yes | libedgetpu1-max_ ... .bookworm_armhf.deb |
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| Bookworm 64bit | Yes | libedgetpu1-max_ ... .bookworm_arm64.deb |
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| Raspberry Pi OS | High frequency mode | Version to download |
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|-----------------|:-------------------:|--------------------------------------------|
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| Bullseye 32bit | No | `libedgetpu1-std_ ... .bullseye_armhf.deb` |
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| Bullseye 64bit | No | `libedgetpu1-std_ ... .bullseye_arm64.deb` |
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| Bullseye 32bit | Yes | `libedgetpu1-max_ ... .bullseye_armhf.deb` |
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| Bullseye 64bit | Yes | `libedgetpu1-max_ ... .bullseye_arm64.deb` |
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| Bookworm 32bit | No | `libedgetpu1-std_ ... .bookworm_armhf.deb` |
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| Bookworm 64bit | No | `libedgetpu1-std_ ... .bookworm_arm64.deb` |
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| Bookworm 32bit | Yes | `libedgetpu1-max_ ... .bookworm_armhf.deb` |
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| Bookworm 64bit | Yes | `libedgetpu1-max_ ... .bookworm_arm64.deb` |
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[Download the latest version from here](https://github.com/feranick/libedgetpu/releases).
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@ -23,7 +23,7 @@ Hyperparameters are high-level, structural settings for the algorithm. They are
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<img width="640" src="https://user-images.githubusercontent.com/26833433/263858934-4f109a2f-82d9-4d08-8bd6-6fd1ff520bcd.png" alt="Hyperparameter Tuning Visual">
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</p>
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For a full list of augmentation hyperparameters used in YOLOv8 please refer to the [configurations page](../usage/cfg.md#augmentation).
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For a full list of augmentation hyperparameters used in YOLOv8 please refer to the [configurations page](../usage/cfg.md#augmentation-settings).
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### Genetic Evolution and Mutation
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@ -1,7 +1,7 @@
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---
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comments: true
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description: Instance Segmentation with Object Tracking using Ultralytics YOLOv8
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keywords: Ultralytics, YOLOv8, Instance Segmentation, Object Detection, Object Tracking, Segbbox, Computer Vision, Notebook, IPython Kernel, CLI, Python SDK
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keywords: Ultralytics, YOLOv8, Instance Segmentation, Object Detection, Object Tracking, Bounding Box, Computer Vision, Notebook, IPython Kernel, CLI, Python SDK
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---
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# Instance Segmentation and Tracking using Ultralytics YOLOv8 🚀
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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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@ -175,8 +175,8 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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| Name | Type | Default | Description |
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|-----------------------|-------------|----------------------------|-----------------------------------------------|
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| `view_img` | `bool` | `False` | Display frames with counts |
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| `view_in_counts` | `bool` | `True` | Display incounts only on video frame |
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| `view_out_counts` | `bool` | `True` | Display outcounts only on video frame |
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| `view_in_counts` | `bool` | `True` | Display in-counts only on video frame |
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| `view_out_counts` | `bool` | `True` | Display out-counts only on video frame |
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| `line_thickness` | `int` | `2` | Increase bounding boxes thickness |
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| `reg_pts` | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area |
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| `classes_names` | `dict` | `model.model.names` | Dictionary of Class Names |
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@ -16,7 +16,6 @@ Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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- **Reduced Data Volume**: By extracting only relevant objects, object cropping helps in minimizing data size, making it efficient for storage, transmission, or subsequent computational tasks.
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- **Enhanced Precision**: YOLOv8's object detection accuracy ensures that the cropped objects maintain their spatial relationships, preserving the integrity of the visual information for detailed analysis.
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## Visuals
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| Airport Luggage |
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@ -24,7 +23,6 @@ Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
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|  |
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| Suitcases Cropping at airport conveyor belt using Ultralytics YOLOv8 |
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!!! Example "Object Cropping using YOLOv8 Example"
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=== "Object Cropping"
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@ -13,7 +13,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
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- **Optimized Performance:** Tailoring workouts based on monitoring data for better results.
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- **Goal Achievement:** Track and adjust fitness goals for measurable progress.
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- **Personalization:** Customized workout plans based on individual data for effectiveness.
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- **Health Awareness:** Early detection of patterns indicating health issues or overtraining.
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- **Health Awareness:** Early detection of patterns indicating health issues or over-training.
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- **Informed Decisions:** Data-driven decisions for adjusting routines and setting realistic goals.
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## Real World Applications
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@ -109,7 +109,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
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| `kpts_to_check` | `list` | `None` | List of three keypoints index, for counting specific workout, followed by keypoint Map |
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| `view_img` | `bool` | `False` | Display the frame with counts |
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| `line_thickness` | `int` | `2` | Increase the thickness of count value |
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| `pose_type` | `str` | `pushup` | Pose that need to be monitored, "pullup" and "abworkout" also supported |
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| `pose_type` | `str` | `pushup` | Pose that need to be monitored, `pullup` and `abworkout` also supported |
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| `pose_up_angle` | `int` | `145` | Pose Up Angle value |
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| `pose_down_angle` | `int` | `90` | Pose Down Angle value |
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