This app leverages a classification model created using ImageNet and transfer learning. For model information look at the github pages, for the model itself e-mail me at [email protected].
Pet-Lab is an innovative application designed to help pet owners keep their pets safe. By simply uploading a picture of a flower, users can obtain toxicity levels with just a click of a button. The app identifies flowers based on unique characteristics, categorizing them into five types. It then searches a database to provide users with toxicity information, including severity and common symptoms, as well as a flower description to aid in detection confirmation.
• Installation • Usage • Features • Contributing • License
Available through Streamlit.
Clone repo and use ‘streamlit run Recog_app.py’
- Recog_app_AWS_call.py: uses s3fs to both save and retrieve ml models as zip files. Taken from https://gist.github.com/ramdesh keras_model_s3_wrapper.py
- remove_image_background: Contains functions for background removal using OpenCV. Python function that takes an image file path as input, removes the background of the image, and saves the resulting image with a transparent background. It utilizes the OpenCV library to perform image processing operations.
- decode_and_resize.py: Implements image decoding and resizing operations.
- toxicity.py: handles the web scrapping for pet toxicity info of classified flowers using https://www.aspca.org/pet-care/animal-poison-control/toxic-and-non-toxic-plants
- Recog_app.py: The main application file that integrates the functionalities from other files.
- Upload a picture using the Pet-Lab app.
- Using Classification Model saved from AWS classify the flower in the picture
- Click the button to obtain toxicity levels.
- Receive information about the identified flower, including toxicity severity and common symptoms.
Pet-Lab offers the following key features:
• Flower identification into 5 types based on unique characteristics. • Database search for toxicity information, including severity and symptoms. • Background removal of images. • Utilization of the Inception v3 architecture pretrained on ImageNet.
We welcome contributions to enhance Pet-Lab. To contribute, follow these steps:
- Fork the project.
- Create a new branch (git checkout -b feature/your-feature).
- Commit your changes (git commit -m 'Add some feature').
- Push to the branch (git push origin feature/your-feature).
- Open a pull request.
Pet-Lab is licensed under the MIT License - see the LICENSE file for details.