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Letter_Recognition.py
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Letter_Recognition.py
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__author__ = 'smithe3'
import cv2
import numpy as np
import imutil
import char_segmentation
doc = open("assets/DataSet.txt", "r")
#letters = doc.read()
labels = []
rawData = []
data = []
string = []
types = ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9", "A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z",
"A", "B", "C", "D", "E", "F", "G", "H", "I", "J", "K", "L", "M", "N", "O", "P", "Q", "R", "S", "T", "U", "V", "W", "X", "Y", "Z"]
for i in range(62):
for j in range(55):
labels.append(i)
counter = 0
for i in range(62):
for j in range(55):
temp = ""
for k in range(23):
temp += doc.readline()
counter += 1
if len(temp) != 1644:
print "length " + str(len(temp))
print "counter position " + str(counter)
#print len(temp)
rawData.append(temp)
for i in range(len(rawData)):
temp = rawData[i].split()
for j in range(len(temp)):
temp[j] = int(temp[j])
#temp = np.array(temp)
#temp = np.float32(temp)
#if(len(temp) != 400):
#print temp
data.append(temp)
#print data
data = np.array(data)
labels = np.array(labels)
data = np.float32(data)
labels = np.float32(labels)
print labels.shape
print type(labels[0])
print data.shape
print type(data[0][0])
knn = cv2.ml.KNearest_create()
knn.train(data, cv2.ml.ROW_SAMPLE, labels)
ret, result, neighbours, dist = knn.findNearest(data, k=5)
correct = np.count_nonzero(result == labels)
accuracy = correct*100.0/10000
#print(accuracy)
print "--------------------finished training--------------------"
#src = cv2.imread("assets/Letters/letterE.jpg", 0)
#img = cv2.imread("assets/ipsum.jpg")
img = cv2.imread("assets/Letters/sentences.JPG")
listOfChars, img = char_segmentation.get_chars(img)
#for i in range(len(listOfChars[0])):
image = imutil.getBoundedImg(img, listOfChars[0][0])
gray = imutil.to_gray_scale(image)
#imutil.show_img(image)
print "loaded gray image"
cv2.imshow("orig", gray)
cv2.waitKey()
'''
clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
cl1 = clahe.apply(gray)
cv2.imshow("histogram", cl1)
cv2.waitKey()
'''
st = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (1, 1))
erode = cv2.morphologyEx(gray, cv2.MORPH_CLOSE, st, iterations=3)
#cv2.imshow("erode", erode)
#cv2.waitKey()
width, height = erode.shape
# print "morphed image"
if(np.count_nonzero(erode)>(width*height)/2):
res, thresh = cv2.threshold(erode, 115, 255, cv2.THRESH_BINARY)
#print thresh
#cv2.imshow("thresh", thresh)
# cv2.waitKey()
resized = cv2.resize(thresh, (20, 20))
else:
resized = cv2.resize(erode, (20, 20))
#cv2.imshow("resized", resized)
#cv2.waitKey(0)
reshaped = np.reshape(resized, (1, 400))
cv2.imshow("reshaped", reshaped)
cv2.waitKey(0)
# print "reshaped image"
retype = np.float32(reshaped)
#nbrs = []
retval, results, neighborResponses, dists = knn.findNearest(retype, k=3)
print "The retval is " + str(retval)
print "This is a " + str(types[int(retval)])
string += str(types[int(retval)])
print results.shape
print retval
print results
print "Neighbor responses: ", neighborResponses
for num in neighborResponses[0]:
print types[int(num)]
print string
#print dists