90 lines
2.4 KiB
Python
90 lines
2.4 KiB
Python
import numpy as np
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import cv2
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import itertools as it
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'''
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from scipy.io import loadmat
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m = loadmat('ex4data1.mat')
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X = m['X'].reshape(-1, 20, 20)
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X = np.transpose(X, (0, 2, 1))
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img = np.vstack(map(np.hstack, X.reshape(-1, 100, 20, 20)))
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img = np.uint8(np.clip(img, 0, 1)*255)
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cv2.imwrite('digits.png', img)
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'''
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def unroll_responses(responses, class_n):
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sample_n = len(responses)
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new_responses = np.zeros((sample_n, class_n), np.float32)
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new_responses[np.arange(sample_n), responses] = 1
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return new_responses
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SZ = 20
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digits_img = cv2.imread('digits.png', 0)
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h, w = digits_img.shape
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digits = [np.hsplit(row, w/SZ) for row in np.vsplit(digits_img, h/SZ)]
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digits = np.float32(digits).reshape(-1, SZ*SZ)
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N = len(digits)
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labels = np.repeat(np.arange(10), N/10)
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shuffle = np.random.permutation(N)
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train_n = int(0.9*N)
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digits_train, digits_test = np.split(digits[shuffle], [train_n])
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labels_train, labels_test = np.split(labels[shuffle], [train_n])
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labels_train_unrolled = unroll_responses(labels_train, 10)
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model = cv2.ANN_MLP()
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layer_sizes = np.int32([SZ*SZ, 25, 10])
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model.create(layer_sizes)
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# CvANN_MLP_TrainParams::BACKPROP,0.001
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params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 300, 0.01),
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train_method = cv2.ANN_MLP_TRAIN_PARAMS_BACKPROP,
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bp_dw_scale = 0.001,
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bp_moment_scale = 0.0 )
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print 'training...'
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model.train(digits_train, labels_train_unrolled, None, params=params)
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model.save('dig_nn.dat')
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model.load('dig_nn.dat')
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ret, resp = model.predict(digits_test)
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resp = resp.argmax(-1)
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error_mask = (resp == labels_test)
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print error_mask.mean()
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def grouper(n, iterable, fillvalue=None):
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"grouper(3, 'ABCDEFG', 'x') --> ABC DEF Gxx"
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args = [iter(iterable)] * n
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return it.izip_longest(fillvalue=fillvalue, *args)
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def mosaic(w, imgs):
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imgs = iter(imgs)
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img0 = imgs.next()
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pad = np.zeros_like(img0)
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imgs = it.chain([img0], imgs)
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rows = grouper(w, imgs, pad)
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return np.vstack(map(np.hstack, rows))
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test_img = np.uint8(digits_test).reshape(-1, SZ, SZ)
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def vis_resp(img, flag):
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img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
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if not flag:
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img[...,:2] = 0
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return img
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test_img = mosaic(25, it.starmap(vis_resp, it.izip(test_img, error_mask)))
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cv2.imshow('test', test_img)
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cv2.waitKey()
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