hamuchiwa
51 linhas
1.3 KiB
Python
51 linhas
1.3 KiB
Python
__author__ = 'zhengwang'
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import cv2
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import numpy as np
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import glob
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# load training data
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image_array = np.zeros((1, 38400))
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label_array = np.zeros((1, 4), 'float')
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training_data = glob.glob('testing_data/*.npz')
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for single_npz in training_data:
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with np.load(single_npz) as data:
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print data.files
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test_temp = data['train']
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test_labels_temp = data['train_labels']
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print test_temp.shape
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print test_labels_temp.shape
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image_array = np.vstack((image_array, test_temp))
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label_array = np.vstack((label_array, test_labels_temp))
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test = image_array[1:, :]
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test_labels = label_array[1:, :]
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print test.shape
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print test_labels.shape
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# create MLP
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layer_sizes = np.int32([38400, 32, 4])
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model = cv2.ANN_MLP()
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model.create(layer_sizes)
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model.load('mlp_xml/mlp.xml')
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# generate predictions
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e0 = cv2.getTickCount()
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ret, resp = model.predict(test)
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prediction = resp.argmax(-1)
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e00 = cv2.getTickCount()
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time0 = (e00 - e0)/cv2.getTickFrequency()
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print 'Prediction time per frame:', time0/(test.shape[0])
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print 'Prediction:', prediction
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true_labels = test_labels.argmax(-1)
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print 'True labels:', true_labels
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print 'Testing...'
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num_correct = np.sum( true_labels == prediction )
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print(num_correct)
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test_rate = np.mean(prediction == true_labels)
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print 'Test rate: %f' % (test_rate*100) |