https://github.com/cloud-annotations/object-detection-python
🐍 Run Object Detection Inferences in Python
https://github.com/cloud-annotations/object-detection-python
Last synced: 8 months ago
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🐍 Run Object Detection Inferences in Python
- Host: GitHub
- URL: https://github.com/cloud-annotations/object-detection-python
- Owner: cloud-annotations
- License: mit
- Created: 2019-02-10T16:00:43.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2020-02-03T21:47:49.000Z (almost 6 years ago)
- Last Synced: 2025-02-16T14:56:31.979Z (10 months ago)
- Homepage:
- Size: 20.5 MB
- Stars: 12
- Watchers: 2
- Forks: 10
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Object Detection Python Examples
Run your models trained using [Cloud Annotations](https://github.com/cloud-annotations/training) 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
==========
```
Saved Image:
### Perform object detection with your model
Note: to find a list of all models trained do:
```
cacli list
```
To use a custom model, perform
```
cacli download
```
For example, if the downloaded files were saved to `/path/to/` :
* Our tflite model is stored in `/model_android/model.tflite`
* Our tflite anchors file is stored in `/model_android/anchors.json`
* Our tflite labels file is stored in `/model_android/labels.json`
```
cd examples/tflite_interpreter/basic/
python python-tflite.py --MODEL_DIR /path/to//model_android
```
This script 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`.
Finally:
```
python python-tflite.py \
--MODEL_DIR /path/to//model_android \
--PATH_TO_TEST_IMAGES_DIR /path/to/test/images \
--PATH_TO_OUTPUT_DIR /path/to/output/images \
--MINIMUM_CONFIDENCE 0.01
```
## 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
Install the required packages in `requirement.txt`
Creating a new virtual environement is recommended.
```
conda create -n object_detection python=3.7
conda activate object_detection
```
Git clone the repo and change directory into it. Then pip install the packages in `requirement.txt`.
```
cd directory/you/want/to/clone/into
git clone https://github.com/cloud-annotations/object-detection-python.git
cd object-detection-python
pip install -r requirement.txt
```
### Test if everything is working
I have supplied a test model and some test images. This should output the images with detection boxes and labels in jpg format in 'examples/tflite_interpreter/basic/model/output'
```
cd examples/tflite_interpreter/basic/
python python-tflite.py
```
## License
This project is licensed under the MIT License - see the [LICENSE.md](LICENSE.md) file for details.