opencv/samples/python2/digits.py

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'''
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Neural network digit recognition sample.
Usage:
digits.py
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Sample loads a dataset of handwritten digits from 'digits.png'.
Then it trains a neural network classifier on it and evaluates
its classification accuracy.
'''
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import numpy as np
import cv2
from common import mosaic
def unroll_responses(responses, class_n):
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'''[1, 0, 2, ...] -> [[0, 1, 0], [1, 0, 0], [0, 0, 1], ...]'''
sample_n = len(responses)
new_responses = np.zeros((sample_n, class_n), np.float32)
new_responses[np.arange(sample_n), responses] = 1
return new_responses
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SZ = 20 # size of each digit is SZ x SZ
CLASS_N = 10
digits_img = cv2.imread('digits.png', 0)
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# prepare dataset
h, w = digits_img.shape
digits = [np.hsplit(row, w/SZ) for row in np.vsplit(digits_img, h/SZ)]
digits = np.float32(digits).reshape(-1, SZ*SZ)
N = len(digits)
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labels = np.repeat(np.arange(CLASS_N), N/CLASS_N)
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# split it onto train and test subsets
shuffle = np.random.permutation(N)
train_n = int(0.9*N)
digits_train, digits_test = np.split(digits[shuffle], [train_n])
labels_train, labels_test = np.split(labels[shuffle], [train_n])
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# train model
model = cv2.ANN_MLP()
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layer_sizes = np.int32([SZ*SZ, 25, CLASS_N])
model.create(layer_sizes)
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params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 100, 0.01),
train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
bp_dw_scale = 0.001,
bp_moment_scale = 0.0 )
print 'training...'
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labels_train_unrolled = unroll_responses(labels_train, CLASS_N)
model.train(digits_train, labels_train_unrolled, None, params=params)
model.save('dig_nn.dat')
model.load('dig_nn.dat')
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def evaluate(model, samples, labels):
'''Evaluates classifier preformance on a given labeled samples set.'''
ret, resp = model.predict(samples)
resp = resp.argmax(-1)
error_mask = (resp == labels)
accuracy = error_mask.mean()
return accuracy, error_mask
# evaluate model
train_accuracy, _ = evaluate(model, digits_train, labels_train)
print 'train accuracy: ', train_accuracy
test_accuracy, test_error_mask = evaluate(model, digits_test, labels_test)
print 'test accuracy: ', test_accuracy
# visualize test results
vis = []
for img, flag in zip(digits_test, test_error_mask):
img = np.uint8(img).reshape(SZ, SZ)
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if not flag:
img[...,:2] = 0
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vis.append(img)
vis = mosaic(25, vis)
cv2.imshow('test', vis)
cv2.waitKey()