https://github.com/rileynwong/dogspotting
use machine learning for computer vision to draw bounding boxes around dogs in pictures
https://github.com/rileynwong/dogspotting
computer-vision dogs image-ai machine-learning
Last synced: about 2 months ago
JSON representation
use machine learning for computer vision to draw bounding boxes around dogs in pictures
- Host: GitHub
- URL: https://github.com/rileynwong/dogspotting
- Owner: rileynwong
- Created: 2019-02-28T19:24:06.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2019-04-15T21:44:17.000Z (about 7 years ago)
- Last Synced: 2025-01-27T23:19:09.475Z (over 1 year ago)
- Topics: computer-vision, dogs, image-ai, machine-learning
- Language: Jupyter Notebook
- Homepage: https://www.rileynwong.com/blog/2019/3/5/dogspotting-using-machine-learning-to-draw-bounding-boxes-around-dogs-in-pictures
- Size: 26.3 MB
- Stars: 4
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-dogs - Dogspotting
README
# Dogspotting
Use machine learning for computer vision to draw bounding boxes around dogs in pictures. [See accompanying blog post.](https://www.rileynwong.com/blog/2019/3/5/dogspotting-using-machine-learning-to-draw-bounding-boxes-around-dogs-in-pictures)
NBViewer for easier viewing (recommended) [here](https://nbviewer.jupyter.org/github/rileynwong/dogspotting/blob/master/Image_detection.ipynb)
## Setup
1. (Optional) Create Anaconda environment w/ Python 3.6: `$ conda create -n retinanet python=3.6 anaconda`
2. (Optional) Activate virtual environment: `$ source activate retinanet`
3. Install dependencies: `$ conda install tensorflow numpy scipy opencv pillow matplotlib h5py keras`
4. Install ImageAI library: `$ pip install https://github.com/OlafenwaMoses/ImageAI/releases/download/2.0.1/imageai-2.0.1-py3-none-any.whl`
5. Download pretrained RetinaNet model: https://github.com/OlafenwaMoses/ImageAI/releases/download/1.0/resnet50_coco_best_v2.0.1.h5
## Files
- `Image_detection.ipynb`: Jupyter notebook, walks through the steps and set up
- `detect_single_image.py`: Script to run to run object detection on a single image file. Set `input_path` to your image file.
- `detect_images_folder.py`: Script to run to run object detection on images in a folder. Set `input_images_path` to `your-folder/*`. Resulting images can be found in `results/` directory. Keep in mind that the `results` folder images will be overwritten each time you run the script, so move previous files elsewhere.
## Credits
- [ImageAI library](https://github.com/OlafenwaMoses/ImageAI)
- [Object detection tutorial](https://www.analyticsvidhya.com/blog/2018/06/understanding-building-object-detection-model-python/)