使用Python將MNIST資料集轉化為圖片
阿新 • • 發佈:2019-01-24
1,對於訓練集資料:
2,對於測試資料集:import numpy as np import struct from PIL import Image import os data_file = 'somePath/train-images.idx3-ubyte' #需要修改的路徑 # It's 47040016B, but we should set to 47040000B data_file_size = 47040016 data_file_size = str(data_file_size - 16) + 'B' data_buf = open(data_file, 'rb').read() magic, numImages, numRows, numColumns = struct.unpack_from( '>IIII', data_buf, 0) datas = struct.unpack_from( '>' + data_file_size, data_buf, struct.calcsize('>IIII')) datas = np.array(datas).astype(np.uint8).reshape( numImages, 1, numRows, numColumns) label_file = 'somePath/train-labels.idx1-ubyte' #需要修改的路徑 # It's 60008B, but we should set to 60000B label_file_size = 60008 label_file_size = str(label_file_size - 8) + 'B' label_buf = open(label_file, 'rb').read() magic, numLabels = struct.unpack_from('>II', label_buf, 0) labels = struct.unpack_from( '>' + label_file_size, label_buf, struct.calcsize('>II')) labels = np.array(labels).astype(np.int64) datas_root = '/somePath/mnist_train' #需要修改的路徑 if not os.path.exists(datas_root): os.mkdir(datas_root) for i in range(10): file_name = datas_root + os.sep + str(i) if not os.path.exists(file_name): os.mkdir(file_name) for ii in range(numLabels): img = Image.fromarray(datas[ii, 0, 0:28, 0:28]) label = labels[ii] file_name = datas_root + os.sep + str(label) + os.sep + \ 'mnist_train_' + str(ii) + '.png' img.save(file_name)
import numpy as np import struct from PIL import Image import os data_file = 'somePath/t10k-images.idx3-ubyte' #需要修改的路徑 # It's 7840016B, but we should set to 7840000B data_file_size = 7840016 data_file_size = str(data_file_size - 16) + 'B' data_buf = open(data_file, 'rb').read() magic, numImages, numRows, numColumns = struct.unpack_from( '>IIII', data_buf, 0) datas = struct.unpack_from( '>' + data_file_size, data_buf, struct.calcsize('>IIII')) datas = np.array(datas).astype(np.uint8).reshape( numImages, 1, numRows, numColumns) label_file = 'somePath/t10k-labels.idx1-ubyte'#需要修改的路徑 # It's 10008B, but we should set to 10000B label_file_size = 10008 label_file_size = str(label_file_size - 8) + 'B' label_buf = open(label_file, 'rb').read() magic, numLabels = struct.unpack_from('>II', label_buf, 0) labels = struct.unpack_from( '>' + label_file_size, label_buf, struct.calcsize('>II')) labels = np.array(labels).astype(np.int64) datas_root = 'somePath/mnist_test' #需要修改的路徑 if not os.path.exists(datas_root): os.mkdir(datas_root) for i in range(10): file_name = datas_root + os.sep + str(i) if not os.path.exists(file_name): os.mkdir(file_name) for ii in range(numLabels): img = Image.fromarray(datas[ii, 0, 0:28, 0:28]) label = labels[ii] file_name = datas_root + os.sep + str(label) + os.sep + \ 'mnist_test_' + str(ii) + '.png' img.save(file_name)
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