royshil

import tkinter as tk
from tkVideoPlayer import TkinterVideo
from ffmpeg import FFmpeg, Progress
import os
import sys
import argparse

parser = argparse.ArgumentParser(description="Crop a video")
parser.add_argument("video", metavar="video", type=str, help="video file to crop")
args = parser.parse_args()

royshil

# - Try to find FFMPEG
# Once done this will define
#  FFMPEG_FOUND - System has FFMPEG
#  FFMPEG_INCLUDE_DIRS - The FFMPEG include directories
#  FFMPEG_LIBRARIES - The libraries needed to use FFMPEG
#  FFMPEG_LIBRARY_DIRS - The directory to find FFMPEG libraries
#
# written by Roy Shilkrot 2013 http://www.morethantechnical.com/
#

royshil

#!/usr/local/bin/python3

import os
import json
import sys
import argparse
import math
import subprocess
from tqdm import tqdm

royshil

#!/usr/bin/env python

import numpy as np
import cv2
import os
import v4l2capture
import select

if __name__ == '__main__':
    #cap = cv2.VideoCapture(0)

royshil

# adapted from http://docs.opencv.org/3.0-beta/doc/py_tutorials/py_imgproc/py_pyramids/py_pyramids.html

import cv2
import numpy as np

def Laplacian_Pyramid_Blending_with_mask(A, B, m, num_levels = 6):
    # assume mask is float32 [0,1]

    # generate Gaussian pyramid for A,B and mask
    GA = A.copy()

royshil

/// perform the Simplest Color Balancing algorithm
void SimplestCB(Mat& in, Mat& out, float percent) {
    assert(in.channels() == 3);
    assert(percent > 0 && percent < 100);

    float half_percent = percent / 200.0f;

    vector<Mat> tmpsplit; split(in,tmpsplit);
    for(int i=0;i<3;i++) {
        //find the low and high precentile values (based on the input percentile)

royshil

import cv2
import numpy as np
import matplotlib.pyplot as plt
from skimage.segmentation import slic
from skimage.segmentation import mark_boundaries
from skimage.data import astronaut
from skimage.util import img_as_float
import maxflow
from scipy.spatial import Delaunay

royshil

# This is kind-of based off of this:  http://stackoverflow.com/questions/5974595/download-all-the-linksrelated-documents-on-a-webpage-using-python

import cookielib 
import urllib2 
import mechanize
from time import sleep
import os
import cgi

# A routine to download a file from a link, by simulating a click on it

royshil

#!/bin/bash
# inspired by a part of: https://github.com/cjweeks/tensorflow-cmake/blob/master/build.sh
#
# Assumes Tensorflow libraries libtensorflow_framework.so and libtensorflow_cc.so were built with Bazel,
# and the ${TF_ROOT}/tensorflow/contrib/makefile/download_dependencies.sh script was executed.

set -o nounset
set -o errexit
set -o pipefail

royshil

/*
 * Simple 2D NURBS renderer for OpenCV, reading DXF files
 *
 * The MIT License (MIT)
 *
 * Copyright (c) 2013 Roy Shilkrot
 *
 * Updated: Nov 2016
 *
 * Permission is hereby granted, free of charge, to any person obtaining a copy