Convert MNIST dataset to a SVHN-like real life dataset.
The MNIST+ dataset pickle file can be downloaded from Mega
It contains 140,000 examples (120,000 + 20,000)
If you wish to start generating your own dataset from scratch, you can download this dataset, and use MNIST2SVHN-plus-ultra.ipynb to generate.
You can also download pre-converted dataset from Mega
This is an example of reading the pickle file "Dataset_new.pkl".
At the end, you will get a list of features named "total_x" and a list of labels "total_y"
>>> from six.moves import cPickle as pickle
>>> f = open("Dataset_new.pkl", "rb")
>>> letter_set = pickle.load(f, encoding="latin1")
>>> f.close()
>>> type(letter_set)
<class 'tuple'>
>>> len(letter_set)
2
>>> total_x = None
>>> total_y = None
>>> for i in range(len(letter_set)):
... if letter_set[i] == "EOF":
... break
... x = letter_set[i][0]
... y = letter_set[i][1]
... if total_x is None:
... total_x = x
... total_y = y
... else:
... total_x = np.concatenate((total_x, x), axis=0)
... total_y = np.concatenate((total_y, y), axis=0)
... print(len(total_x))
...
140000Take a look the structure of label.
>>> total_y[:10]
[[2, (7, 7), (24, 35)],
[6, (11, 5), (25, 25)],
[5, (8, 2), (23, 28)],
[1, (21, 8), (33, 28)],
[6, (21, 21), (34, 47)],
[0, (19, 6), (33, 26)],
[4, (12, 8), (28, 28)],
[4, (6, 4), (22, 29)],
[1, (15, 6), (25, 26)],
[7, (13, 7), (31, 27)]]The label follows format of
[class], [top_left_point_coordinate_tuple], [bottom_right_point_coordinate_tuple]
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