Automatically scrape images from public road condition cameras provided by https://511on.ca, and use a convolutional neural network to classify the condition of all roads with cameras. Displays the results on a webpage on a map with coloured markers, photos, and tags.

Training is done by scripts/train.py, which uses PyTorch to train a CNN. The training images are stored in assets/images/training, testing images are stored in assets/images/testing. There are no image files in the testing, or training directory (due to GitHub's storage limits). Training will not work until these folders are populated. There is a pretrained model in the assets folder.

Classification is performed by the scripts/classify.py script. This script will classify any images in the assets/predict folder with a filename of epochTime-lat-long.jpg, and rename classified images to be id.jpg. The classification of each image is stored in assets/jsons/cameras.json.

Images are scraped by the python file scripts/get-images.py, and stored in the assets/images directory as a JPEG image with the name epochTime-lat-long.jpg. The script waits two seconds between grabbing images to prevent being rate limited. The web server by default only runs image scraping twice daily.

Written in NodeJS, using express. Controls all of the python scripts, and relays the information to the web interface through rest APIs. Currently, there is no database, instead relevant data is stored in json files in the assets folder. The server checks if the model needs to be retrained hourly, and calls for new images & re-classification twice per day.

Written in HTML, CSS and JavaScript, with Bootstrap and Leaflet. Communicates with the web server via REST APIs to get the status of all camera nodes. Displays all of the nodes with coloured markers on a leaflet openstreetmap. There is also a modal which allows the user to report incorrect classifications (which get queued by the web server for when the model is automatically retrained).