fedden

## Fresh install of Ubuntu 16.04
## Note I have copied much of this by hand - be wary of subtle typos!

# Get a proper browser.
cd ~
wget -q -O - https://dl-ssl.google.com/linux/linux_signing_key.pub | sudo apt-key add -
echo 'deb [arch=amd64] http://dl.google.com/linux/chrome/deb/ stable main' | sudo tee /etc/apt/sources.list.d/google-chrome.list
sudo apt-get update 
sudo apt-get install google-chrome-stable -y

fedden

ffmpeg -i video.mp4 -f mp3 -ab 192000 -vn music.mp3
ffmpeg -i video.mp4 -i music.mp3 -codec copy -shortest video_with_audio.mp4

fedden

ffmpeg -r 25 -f image2 -s 640x360 -i image_%04d.png -vcodec libx264 -crf 25 -pix_fmt yuv420p name_of_output_file.mp4

fedden

cd pix2pix-tensorflow
python3 tools/process.py --input_dir path/to/edges/images --b_dir path/to/original/images --operation combine --output_dir edge2image

fedden

start_frame =
end_frame = 
for image in range(start_frame, end_frame):

    file_name = 'edge2image_test/images/image_{num:04d}'.format(num=image)
    input_file = cv2.imread(file_name + '-inputs.png')
    output_file = cv2.imread(file_name + '-outputs.png')
    target_file = cv2.imread(file_name + '-targets.png')
    save_file = np.hstack((input_file, output_file, target_file))
    save_name = file_name + '.png'

fedden

import numpy as np
import cv2
import os


def auto_canny(image, sigma=0.33):
    median = np.median(image)
    lower = int(max(0, (1.0 - sigma) * median))
    upper = int(min(255, (1.0 + sigma) * median))
    return cv2.Canny(image, lower, upper)

fedden

ffmpeg -i downloaded_file.mp4 -vf scale=640x360 path/to/frames/folder/image_%04d.png

fedden

youtube-dl https://www.youtube.com/path/to/your/video/here

fedden

def run_differential_evolution(solution_size=5, 
                               population_size=1000, 
                               iteration_count=100, 
                               print_results=False,
                               differential_weight=1.0,
                               crossover_probability=0.5,
                               lower=0.0,
                               upper=1.0):
    
    while population_size % 3 != 0:

fedden

string = "took {}s to run population size of {} for {} iterations"
population_size = 500
iteration_count = 100
solution_size = 64 * 64


# Naive DFO
with tf.device("/cpu:0"):
    start = time.time()
    naive_dfo(solution_size=solution_size,