imBroker

Name	imBroker JSON
Version	0.0.1 JSON
	download
home_page	None
Summary	A Tensorflow Lite Image Classification Model Integration Library
upload_time	2024-07-27 02:21:01
maintainer	None
docs_url	None
author	dakshoza (Daksh Oza)
requires_python	>=3.7
license	None
keywords	broker tensorflow lite image classification flexible real time
VCS
bugtrack_url
requirements	No requirements were recorded.
Travis-CI	No Travis.
coveralls test coverage	No coveralls.

            # TFLite Image Classification Broker

This library provides a simple interface for image classification using TensorFlow Lite models. It's designed to work with pre-trained models and can process both single images and directories of images.

## Installation

```bash
pip install imBroker
```

## Features

- Single image classification
- Batch classification for directories
- Support for custom TFLite models
- Handles any type of image shapes

## Usage

### Initializing the Broker

```python
from imBroker import tflBroker

# Define your TFlite model's path
model_path = "path/to/your/model.tflite"

# Define your output labels
output_labels = {
    0: 'Label 1',
    1: 'Label 2',
    ... 
}

# Initialize the broker
broker = tflBroker(model_path, output_labels)
```

### Classifying a Single Image

```python
result = broker.predict_single_image("path/to/image.jpg")
print(result)
```

### Classifying a Directory of Images

```python
results = broker.predict_image_directory("path/to/image/directory")
print(results)
```

            

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