Create knnfinal.py

knnfinal.py

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+import numpy as np
+import cv2
+from matplotlib import pyplot as plt
+
+##input_letter = cv2.imread('/home/varun/opencv/Trails/Test/gridout.png')
+##new_input_y = input_letter
+##for ycount in range(1,25):
+##    new_input_y = np.concatenate((input_letter, new_input_y), axis=0)
+##new_input_x = new_input_y
+##for xcount in range(1,25):
+##    new_input_x = np.concatenate((new_input_y, new_input_x), axis=1)
+
+##cv2.imwrite('/home/varun/opencv/Trails/gridout.png', new_input_x) #working till here 25x25 grid of C
+
+img = cv2.imread('/home/varun/opencv/Trails/Test/gridout.png')
+gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+
+# Now we split the image to 5000 cells, each 20x20 size
+cells = [np.hsplit(row,10) for row in np.vsplit(gray,6)]
+
+# Make it into a Numpy array. It size will be (50,100,20,20)
+x = np.array(cells)
+
+# Now we prepare train_data and test_data.
+train = x.reshape(-1,11250).astype(np.float32) # Size = (2500,400)
+##test = x[:,50:100].reshape(-1,400).astype(np.float32) # Size = (2500,400)
+
+# Create labels for train and test data
+k = np.arange(1,7)
+train_labels = np.repeat(k,10)[:,np.newaxis]
+##test_labels = train_labels.copy()
+
+# Initiate kNN, train the data, then test it with test data for k=1
+knn = cv2.KNearest()
+knn.train(train,train_labels)
+##ret,result,neighbours,dist = knn.find_nearest(test,k=5)
+
+# Now we check the accuracy of classification
+# For that, compare the result with test_labels and check which are wrong
+##matches = result==test_labels
+##correct = np.count_nonzero(matches)
+##accuracy = correct*100.0/result.size
+##print accuracy
+
+# save the data
+np.savez('/home/varun/opencv/Trails/Test/knn_data.npz',train=train, train_labels=train_labels)
+
+### Now load the data
+##with np.load('/home/varun/opencv/Trails/knn_data.npz') as data:
+##    print data.files
+##    train = data['train']
+##    train_labels = data['train_labels']