wermarter

import tensorflow as tf
import tflearn
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm
import cv2

import tflearn.datasets.mnist as mnist
trainX, trainY, testX, testY = mnist.load_data(one_hot=True)

wermarter

import tensorflow as tf
import tflearn
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm

import tflearn.datasets.mnist as mnist
trainX, trainY, testX, testY = mnist.load_data(one_hot=True)

TENSORBOARD_DIR='./tmp/tflearn/vae'

wermarter

import tensorflow as tf
import tflearn
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import norm

import tflearn.datasets.mnist as mnist
trainX, trainY, testX, testY = mnist.load_data(one_hot=True)

TENSORBOARD_DIR='./logs/vae'

wermarter

#include "opencv2/core/core.hpp"
#include "opencv2/contrib/contrib.hpp"
#include "opencv2/highgui/highgui.hpp"

#include <iostream>
#include <fstream>
#include <sstream>

using namespace cv;
using namespace std;

wermarter

#include <opencv2/core.hpp>
#include <opencv2/imgcodecs.hpp>
#include <opencv2/highgui.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <opencv2/objdetect/objdetect.hpp>
#include <iostream>
#include <string>
using namespace cv;
using namespace std;

wermarter

import speech_recognition as sr
import os

class Listenner():
    def __init__(self, api_key):
        self.api_key = api_key
        self.R = sr.Recognizer()

    def recognize(self, AUDIO_FILE):
        with sr.AudioFile(AUDIO_FILE) as source:

wermarter

using Microsoft.Win32;
using System.Diagnostics;
using System.Windows.Forms;

namespace ForBin
{
	public partial class Form1 : Form
	{
		private const int CP_NOCLOSE_BUTTON = 0x200;
		protected override CreateParams CreateParams

wermarter

import cv2
import numpy as np

img = cv2.imread('me.png', 1)
edges = cv2.Canny(cv2.GaussianBlur(img, (5, 5), 0), 50, 200, apertureSize=3)
cv2.imshow('Edges', edges)
lines = cv2.HoughLines(edges, 1, np.pi/200, 200)
for data in lines:
	rho, theta = data[0]
	a = np.cos(theta)

wermarter

import numpy as np
import cv2

img = cv2.imread('knk.jpg', 1)
img = cv2.resize(img, (0, 0), fx=0.5, fy=0.5)
myFilter = np.array([
	[-1, 0, 1],
	[-2, 0, 2],
	[-1, 0, 1]
	]) # Sobel X

wermarter

import pickle, cv2, sqlite3
import numpy as np
class BGRHist:
	def __init__(self, bins):
		self.bins = bins
	def describe(self, img):
		hist = cv2.calcHist([img], range(3), None, self.bins, [0, 256]*3)
		cv2.normalize(hist, hist)
		return hist.flatten()
class Searcher: