Notre Dame Computer Vision Fall 2020 Final Project -- Campus Building Recognizer Matthew Siciliano and Michael Eisemann -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- | D E P E N D E N C I E S | -=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- anaconda: https://www.anaconda.com curl -=-=-=-=-=-=-=-=-=- | S E T U P | -=-=-=-=-=-=-=-=-=- 1. Download the .zip file from this repository, unzip it, and navigate to the `cv-proj-master` directory in anaconda. 2. Setup the anaconda environment: `conda env create --file=billdings.yaml` NOTE: We had a great deal of trouble getting the SURF dependencies in order. The only way we were able to do so was by asking a friend of ours who does CV research for his Conda environment. We have attempted to trim as many unnecessary dependencies as we can, but there are still quite a few dependencies, and attempting to remove more invariably breaks our project. We also are not certain if our conda environment will set up correctly on a Windows machine, as we developed our project on MacOS (although we think it should). We apologize for any inconvenience caused by needing to download dependencies. If you attempt to test this project on a Windows machine and it does not work, we again apologize and have attached a video of us running the demo locally in hopes that such a video would be a sufficient replacement. Here is a link to the video: https://youtu.be/5lbeivUn7KQ 3. Activate the anaconda environment: `conda activate billdings` 4. To run our project, run the included Python script. This should take less than 30 seconds. Note that we included a small subset of our database for this demo in order to speed up run time and decrease the download size. `python3 main.py` 5. When the result is determined, the best-match image name and the number of keypoint matches found will be printed to the terminal. There will also be an image saved to the working directory displaying the keypoint matches between the test image (left) and the image found to be its nearest match. Bonus: You can download an archive containing all of our results (processed images) from the internet. This archive is located at: https://www.michaeleisemann.com/assets/cv/fin_images.tar.gz To download the archive: `curl https://www.michaeleisemann.com/assets/cv/fin_images.tar.gz > fin_images.tar.gz` To extract our results from the downloaded archive: `tar -xzvf fin_images.tar.gz`
AndroidKitKat/cv-proj
python3 based computer vision project to recognize buildings on nd campus
Python