TensorFlow Lite Object Detection and Image Classification on Jetson Nano

Live Object Detection and Image Classification System (PiCamera+OpenCV+TensorFlow Lite+Firebase) on Jetson Nano

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A Python script that:

[1] Load Pre-trained (Object Detection) and Self-trained (Image Classification)TFLite Model with Argument.

[2] Read image from PiCamera with OpenCV to do Real-Time Object Detection.

[3] If detect specific object ("bird" in the code), save the image.

[4] Use Self-trained Model to do Image Classification on the image with OpenCV.

[5] Upload the Image and classification result (LabelName, ScoreValue, Time, Pubic-Access Image Url) to Firebase Database

[6] Save the above result (LabelName, ScoreValue, Time, Pubic-Access Image Url) as a csv file with append mode.

[7] Once the image and data have been uploaded to Firebase, delete the local images to prevent running out of disk space.

Usage

python3 object_detection_and_image_classification.py

Project Structure

Folder:

Sample_TFLite_model/:

Contain the object detection model and label

File:

object_detection_and_image_classification.py:

Our main program of this project.

TFLite_Read_Image.py:

Read Image with OpenCV to Image Classification.

test.tflite:

Image Classification TFLite Model.

test.txt:

Image Classification TFLite label.

firebase_key.json:

If you want to use firebase to store your data, you should have it.

You can learn how to get one and the API usage, please refer to the following links:

Learning Firebase(1)：Create Your First Project

Learning Firebase(2)：CRUD Our Database with Python

Learning Firebase(3)：Upload Image to Firebase with Python