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Extract 10 images from the CIFAR-10 data set
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| import mxnet as mx | |
| import numpy as np | |
| import cPickle | |
| import cv2 | |
| def extractImagesAndLabels(path, file): | |
| f = open(path+file, 'rb') | |
| dict = cPickle.load(f) | |
| images = dict['data'] | |
| images = np.reshape(images, (10000, 3, 32, 32)) | |
| labels = dict['labels'] | |
| imagearray = mx.nd.array(images) | |
| labelarray = mx.nd.array(labels) | |
| return imagearray, labelarray | |
| def extractCategories(path, file): | |
| f = open(path+file, 'rb') | |
| dict = cPickle.load(f) | |
| return dict['label_names'] | |
| def saveCifarImage(array, path, file): | |
| # array is 3x32x32. cv2 needs 32x32x3 | |
| array = array.asnumpy().transpose(1,2,0) | |
| # array is RGB. cv2 needs BGR | |
| array = cv2.cvtColor(array, cv2.COLOR_RGB2BGR) | |
| # save to PNG file | |
| return cv2.imwrite(path+file+".png", array) | |
| imgarray, lblarray = extractImagesAndLabels("cifar-10-batches-py/", "data_batch_1") | |
| print imgarray.shape | |
| print lblarray.shape | |
| categories = extractCategories("cifar-10-batches-py/", "batches.meta") | |
| cats = [] | |
| for i in range(0,10): | |
| saveCifarImage(imgarray[i], "./", "image"+(str)(i)) | |
| category = lblarray[i].asnumpy() | |
| category = (int)(category[0]) | |
| cats.append(categories[category]) | |
| print cats |
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