title: Recognize what is on image
body: <p>i'm doing a little project with neural networks. I've read about digit recognition, with MNIST dataset and thought if it possible to make same dataset but with regular objects we see every day.
  So here's algorithm( if we can say so):
  All is done with opencv library for python
  1) Get contours from image. This is not literally contours, but something that looks so.
  I've done this with this code:</p>
  
  <pre><code>def findContour(self):
      gray = cv2.cvtColor(self.image, cv2.COLOR_BGR2GRAY)
      gray = cv2.bilateralFilter(gray, 11, 17, 17)
      self.image = cv2.Canny(gray, 30, 200)
  </code></pre>
  
  <p>2) Next need to create training set.
  I copy and edit this message. Change rotation and flip it -- now we have about 40 images, which are consists of rotated contours.
  3) Now i'm gonna dump this images to a csv file.
  These images are represented as 3D array, so i flatten them using .flatten function from numpy. Next this flatten vector is written in csv file, with label as last character</p>
  
  <p>This is what i've done, and want to ask : will it work out?
  Next i want to use everything except last element as input x vector, and last elements as y vector. (like <a href="http://www.pyimagesearch.com/2014/09/22/getting-started-deep-learning-python/" rel="nofollow noreferrer">here</a>)</p>
  
  <p>Recognizing will be done same way : we getting contour of image, and feed it to neural network, output will be label.</p>
  
  <p>Is it even possible, or better not to try?</p>