machine_learning.py

import cv2
import numpy as np
import glob
from drawing_utils import *

FEATURE_DIM = (30, 30)
FEATURE_LENGTH = FEATURE_DIM[0] * FEATURE_DIM[1]

def image_feature(img):
  gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
  train = cv2.resize(gray, FEATURE_DIM)

  train = train.reshape(1, FEATURE_LENGTH)
  return train.astype(np.float32)


def knn():

  total_samples = len(glob.glob("images/training_data/**/*"))

  samples = np.zeros((total_samples, FEATURE_LENGTH), dtype=np.float32)
  responses = []

  training_folders = glob.glob("images/training_data/*")
  i = 0
  for f in training_folders: 
    piece_type = f[21:]  
    for training_image in glob.glob(f + "/*"):
      img = cv2.imread(training_image)

      samples[i] = image_feature(img) 
      i += 1
      responses.append(ord(piece_type))
  responses = np.array(responses, dtype=np.float32)
  responses = responses.reshape((responses.size, 1))

  test_samples = samples[-10:]
  test_responses = responses[-10:]

  samples = samples[:-10]
  responses = responses[:-10]


  knn = cv2.KNearest()
  knn.train(samples, responses)
  return knn

  #ret, result, neighbours, dist = knn.find_nearest(test_samples, k=1)

  #matches = result == test_responses
  #correct = np.count_nonzero(matches)

  #accuracy = correct*100.0 / result.size
  #print accuracy