Handwritten digit recognition with ANNs

digits_ann .py :

import cv2import cPickleimport numpy as npimport gzipdef load_data():    mnist = gzip.open('mnist.pkl.gz', 'rb')    training_data, classification_data, test_data = cPickle.load(mnist)    mnist.close()    return training_data, classification_data, test_datadef wrap_data():    tr_d, va_d, te_d = load_data()    training_inputs = [np.reshape(x, (784, 1)) for x in tr_d[0]]    training_results = [vectorized_result(y) for y in tr_d[1]]    training_data = zip(training_inputs, training_results)    validation_inputs = [np.reshape(x, (784, 1)) for x in va_d[0]]    validation_data = zip(validation_inputs, va_d[1])    test_inputs = [np.reshape(x, (784, 1)) for x in te_d[0]]    test_data = zip(test_inputs, te_d[1])    return training_data, validation_data, test_datadef vectorized_result(j):    e = np.zeros((10, 1))    e[j] = 1.0    return edef create_ANN(hidden=20):    ann = cv2.ml.ANN_MLP_create()    ann.setLayerSizes(np.array([784, hidden, 10]))    ann.setTrainMethod(cv2.ml.ANN_MLP_RPROP)    ann.setActivationFunction(cv2.ml.ANN_MLP_SIGMOID_SYM)    ann.setTermCriteria(( cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, 20, 1))    return anndef train(ann, samples=10000, epochs=1):    tr, val, test = wrap_data()    for x in xrange(epochs):        counter = 0        for img in tr:            if(counter > samples):                break            if(counter % 1000 == 0):                print "Epoch %d: Trained %d/%d" % (x, counter, samples)            counter += 1            data, digit = img            ann.train(np.array([data.ravel()], dtype=np.float32),            cv2.ml.ROW_SAMPLE, np.array([digit.ravel()], dtype=np.float32))            print "Epoch %d complete" % x    return ann, testdef test(ann, test_data):    sample = np.array(test_data[0][0].ravel(), dtype=np.float32).reshape(28,    28)    cv2.imshow("sample", sample)    cv2.waitKey()    print ann.predict(np.array([test_data[0][0].ravel()], dtype=np.float32))def predict(ann, sample):    resized = sample.copy()    rows, cols = resized.shape    if (rows != 28 or cols != 28) and rows * cols > 0:        resized = cv2.resize(resized, (28, 28), interpolation=cv2.INTER_CUBIC)    return ann.predict(np.array([resized.ravel()], dtype=np.float32))

main.py :

import cv2import numpy as npimport digits_ann as ANNdef inside(r1, r2):    x1,y1,w1,h1 = r1    x2,y2,w2,h2 = r2    if (x1 > x2) and (y1 > y2) and (x1+w1 < x2+w2) and (y1+h1 < y2 + h2):        return True    else:        return Falsedef wrap_digit(rect):    x, y, w, h = rect    padding = 5    hcenter = x + w/2    vcenter = y + h/2    if (h > w):        w = h        x = hcenter - (w/2)    else:        h = w        y = vcenter - (h/2)    return (x-padding, y-padding, w+padding, h+padding)ann, test_data = ANN.train(ANN.create_ANN(56), 20000)font = cv2.FONT_HERSHEY_SIMPLEXpath = "./c.png"img = cv2.imread(path, cv2.IMREAD_UNCHANGED)bw = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)bw = cv2.GaussianBlur(bw, (7, 7), 0)ret, thbw = cv2.threshold(bw, 127, 255, cv2.THRESH_BINARY_INV)thbw = cv2.erode(thbw, np.ones((2,2), np.uint8), iterations=2)image, cntrs, hier = cv2.findContours(thbw.copy(), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)rectangles = []for c in cntrs:    r = x,y,w,h = cv2.boundingRect(c)    a = cv2.contourArea(c)    b = (img.shape[0]-3) * (img.shape[1] - 3)    is_inside = False    for q in rectangles:        if inside(r, q):            is_inside = True            break    if not is_inside:        if not a == b:            rectangles.append(r)for r in rectangles:    x,y,w,h = wrap_digit(r)    cv2.rectangle(img, (x,y), (x+w, y+h), (0, 255, 0), 2)    roi = thbw[y:y+h, x:x+w]    try:        digit_class = int(ANN.predict(ann, roi.copy())[0])    except:        continue    cv2.putText(img, "%d" % digit_class, (x, y-1), font, 1, (0, 255, 0))cv2.imshow("thbw", thbw)cv2.imshow("contours", img)cv2.imwrite("results.jpg", img)cv2.waitKey()

results:

compare pictures'  results:

Conclusion :

This comparison demonstrated previously reveals its insufficient .

Reference to 《learning opencv3 computer vision with Python》

" mnist.pkl.gz  " downloaded from  http://www.cnblogs.com/xueliangliu/archive/2013/04/03/2997437.html