joschuck

image = cv2.imread('book_page_1_cropped.jpg', 0)
ret, th = cv2.threshold(image,
    0,  # threshold value, ignored when using cv2.THRESH_OTSU
    255,  # maximum value assigned to pixel values exceeding the threshold
    cv2.THRESH_BINARY + cv2.THRESH_OTSU)  # thresholding type

joschuck

from matplotlib import pyplot as plt
gray = cv2.imread('book_page_1_cropped.jpg', 0)  # open as grayscale
plt.hist(gray.ravel(), 256, [0,256])
plt.show()

joschuck

import cv2
import numpy as np
image = cv2.imread('book_page_1_cropped.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
ret, th = cv2.threshold(gray,
	127,  # threshold value
	255,  # maximum value assigned to pixel values exceeding the threshold
	cv2.THRESH_BINARY)  # threshold method type

joschuck

image = cv2.imread('input.jpg', 0)  # read as grayscale
th = cv2.adaptiveThreshold(image, 
    255,  # maximum value assigned to pixel values exceeding the threshold
    cv2.ADAPTIVE_THRESH_GAUSSIAN_C,  # gaussian weighted sum of neighborhood
    cv2.THRESH_BINARY,  # thresholding type
    5,  # block size (5x5 window)
    3)  # constant

joschuck

import time
import sys


animation = "|/-\\"

for i in range(100):
    time.sleep(0.1)
    sys.stdout.write("\r" + animation[i % len(animation)])
    sys.stdout.flush()

joschuck

# get informations like channels, colorspace, etc.
identify -verbose file.png

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joschuck

Adaboost = {}
Adaboost.__index = Adaboost

function Adaboost:create(training_set, ground_truth)
    local adaboost = {}
    setmetatable(adaboost,Adaboost)
    adaboost.training_set = training_set
    adaboost.ground_truth = ground_truth
    adaboost.N = training_set:size()[1]
    adaboost.weights = torch.ones(adaboost.N):div(adaboost.N)

joschuck

# cut video
ffmpeg -i movie.mp4 -ss 00:00:03 -t 00:00:08 -async 1 cut.mp4

# convert to webm
ffmpeg -i input-file.mp4 -c:v libvpx -crf 10 -b:v 1M -c:a libvorbis output-file.webm

#concat 
ffmpeg -f concat -safe 0 -i mylist.txt -c copy darmstadt.mp4

joschuck

vec3 hsv2rgb(vec3 c)
{
    vec4 K = vec4(1.0, 2.0 / 3.0, 1.0 / 3.0, 3.0);
    vec3 p = abs(fract(c.xxx + K.xyz) * 6.0 - K.www);
    return c.z * mix(K.xxx, clamp(p - K.xxx, 0.0, 1.0), c.y);
}