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This Readme is based from IBM's Cloud Annotations guide in utilising a trained object detection model with Python. Link to the original repo is here: https://github.com/cloud-annotations/object-detection-python

Object Detection Python Examples

Run your models trained using Cloud Annotations with python.

Tflite Object Detection

Currently, python-tflite.py supports using Mobilenet-V1 SSD models trained using Cloud Annotations.

Output:

Summary Information

Image 7 of 9.
Inference time: 0.15027356147766113
----------
Inference Summary:
Highest Score: 0.9407029747962952
Highest Scoring Box: [0.60926155 0.47011317 0.67576766 0.56898813]
----------
Image shape: (563, 1000, 3)
Boxes shape: (1917, 4)
Classes shape: (1917,)
Scores shape: (1917,)
['plate: 94%']
Image Saved
==========

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites and installing

Pip install the packages in requirement.txt.

pip install -r requirement.txt

To run the model and obtain the output

This script below calls the tflite model interpreter for inference on all .jpg files inside the directory PATH_TO_TEST_IMAGES_DIR.

Similary the output .jpg files are storesd in PATH_TO_OUTPUT_DIR.

We can also specify the minimum confidence (score) for a given detection box to be displayed with MINIMUM_CONFIDENCE.

python python-tflite.py \
--MODEL_DIR /path/to/<model_name>/model_android \
--PATH_TO_TEST_IMAGES_DIR /path/to/test/images \
--PATH_TO_OUTPUT_DIR /path/to/output/images \
--MINIMUM_CONFIDENCE 0.01