#Ssb4 stock icons install

gitignore doesn't like committing 'empty' folders. Clone this repo and create an annotations, output, and videos folder inside.I followed Mark Jay's tutorials to understand the basics of the DarkFlow.

#Ssb4 stock icons install

Install the DarkFlow repository globally.

I followed Mark Jay's tutorial for Ubuntu 18.04.

Install tensorflow if you don't have an NVIDIA GPU.

Install the following python packages The version numbers are not necessarily required, I just provided them as a reference for when I wrote this guide: numpy (1.11.0), matplotlib (1.5.1), cython (0.28.5), opencv-python (3.4.2.17), youtube-dl (2018.8.22 - 2018 version much faster than 2015-2017).

Create a Python 3.6 environment (required by tensorflow) locally or in a conda environment.

Added percent template matching to segment the video for quicker stage-detection.

Added non-stage training data to significantly improve stage-detection results.

Improve video download speed and create a stage-timeline object to represent SSBM matches.

Train a neural network (DarkNet) to search for the six legal SSBM stages, regardless of a stream overlay.

Add additional game capabilities (SSB64, SSB4, PM, Rivals).

Research ways to detect the timer (Either template matching, OCR, or neural networks).

Research ways to detect stock icons (Either template matching, feature matching, or neural networks).

Research ways to detect the percent counter (Either template matching, OCR, or neural networks).If you are interested in helping out, join this repository's Slack and send a message. Once the project has progressed enough, I plan to integrate it with the SmashVods video database. This project is intended to perform video analysis on pre-recorded footage of the game Super Smash Bros Melee.