Live Object Detection and Image Classification System (PiCamera+OpenCV+TensorFlow Lite+Firebase) on Jetson Nano
[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.
python3 object_detection_and_image_classification.pySample_TFLite_model/:
Contain the object detection model and label
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