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Open Source Computer Vision Library
https://opencv.org/
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78 lines
2.5 KiB
78 lines
2.5 KiB
''' |
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Neural network digit recognition sample. |
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Usage: |
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digits.py |
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Sample loads a dataset of handwritten digits from 'digits.png'. |
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Then it trains a neural network classifier on it and evaluates |
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its classification accuracy. |
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''' |
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import numpy as np |
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import cv2 |
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from common import mosaic |
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def unroll_responses(responses, class_n): |
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'''[1, 0, 2, ...] -> [[0, 1, 0], [1, 0, 0], [0, 0, 1], ...]''' |
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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 # size of each digit is SZ x SZ |
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CLASS_N = 10 |
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digits_img = cv2.imread('digits.png', 0) |
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# prepare dataset |
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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(CLASS_N), N/CLASS_N) |
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# split it onto train and test subsets |
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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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# train model |
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model = cv2.ANN_MLP() |
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layer_sizes = np.int32([SZ*SZ, 25, CLASS_N]) |
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model.create(layer_sizes) |
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params = dict( term_crit = (cv2.TERM_CRITERIA_COUNT, 100, 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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labels_train_unrolled = unroll_responses(labels_train, CLASS_N) |
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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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def evaluate(model, samples, labels): |
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'''Evaluates classifier preformance on a given labeled samples set.''' |
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ret, resp = model.predict(samples) |
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resp = resp.argmax(-1) |
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error_mask = (resp == labels) |
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accuracy = error_mask.mean() |
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return accuracy, error_mask |
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# evaluate model |
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train_accuracy, _ = evaluate(model, digits_train, labels_train) |
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print 'train accuracy: ', train_accuracy |
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test_accuracy, test_error_mask = evaluate(model, digits_test, labels_test) |
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print 'test accuracy: ', test_accuracy |
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# visualize test results |
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vis = [] |
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for img, flag in zip(digits_test, test_error_mask): |
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img = np.uint8(img).reshape(SZ, SZ) |
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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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vis.append(img) |
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vis = mosaic(25, vis) |
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cv2.imshow('test', vis) |
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cv2.waitKey()
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