teachable-machine-lite


Nameteachable-machine-lite JSON
Version 1.1 PyPI version JSON
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home_pagehttps://github.com/MeqdadDev/teachable-machine-lite
SummaryA Python package to simplify the deployment process of exported
upload_time2023-04-05 11:12:31
maintainer
docs_urlNone
authorMeqdad Dev
requires_python
license
keywords python teachable machine ai computer vision camera opencv image classification tensorflowlite raspberry pi
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requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls. 
            # Teachable Machine Lite

[![MIT License](https://img.shields.io/badge/License-MIT-green.svg)](https://choosealicense.com/licenses/mit/)
[![Downloads](https://static.pepy.tech/badge/teachable-machine-lite)](https://pepy.tech/project/teachable-machine-lite)
[![PyPI](https://img.shields.io/pypi/v/teachable-machine-lite)](https://pypi.org/project/teachable-machine-lite/)

## Description

A Python package to simplify the deployment process of exported [Teachable Machine](https://teachablemachine.withgoogle.com/) models into different Robotics, AI and IoT controllers such as: Raspberry Pi, Jetson Nano and any other SBCs using TensorFlowLite framework.

Developed by [@MeqdadDev](https://www.github.com/MeqdadDev)

## Supported Classifiers

**Image Classification**: use exported and quantized TensorFlow Lite model from [Teachable Machine platfrom](https://teachablemachine.withgoogle.com/) (a model file with `tflite` extension).


## Requirements

```
Python >= 3.7
```

## How to install package

```bash
pip install teachable-machine-lite
```

## Dependencies

```bash
numpy
tflite-runtime
Pillow (PIL)
```

## How to Use Teachable Machine Lite Package

```python
from teachable_machine_lite import TeachableMachineLite
import cv2 as cv

cap = cv.VideoCapture(0)

model_path = 'model.tflite'
image_file_name = "frame.jpg"
labels_path = "labels.txt"

tm_model = TeachableMachineLite(model_path=model_path, labels_file_path=labels_path)

while True:
    ret, frame = cap.read()
    cv.imshow('Cam', frame)
    cv.imwrite(image_file_name, frame)
    
    results = tm_model.classify_frame(image_file_name)
    print("results:",results)
    
    k = cv.waitKey(1)
    if k% 255 == 27:
        # press ESC to close camera view.
        break
```

## Links:

[PyPI](https://pypi.org/project/teachable-machine-lite/)

[Source Code](https://github.com/MeqdadDev/teachable-machine-lite)

[Developer](https://github.com/MeqdadDev)

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Meqdad Dev
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