pythonNetPro/part3_load_own_images.py at master · lsccode/pythonNetPro · GitHub

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#!/usr/bin/env python
# coding: utf-8

# In[1]:


# python notebook for Make Your Own Neural Network
# code for a 3-layer neural network, and code for learning the MNIST dataset
# (c) Tariq Rashid, 2016
# license is GPLv2


# In[2]:


# helper to load data from PNG image files
import imageio
# glob helps select multiple files using patterns
import glob


# In[3]:


import numpy
# library for plotting arrays
import matplotlib.pyplot
# ensure the plots are inside this notebook, not an external window
get_ipython().run_line_magic('matplotlib', 'inline')


# In[4]:


# our own image test data set
our_own_dataset = []


# In[5]:


for image_file_name in glob.glob('my_own_images/2828_my_own_?.png'):
    print ("loading ... ", image_file_name)
    # use the filename to set the correct label
    label = int(image_file_name[-5:-4])
    # load image data from png files into an array
    img_array = imageio.imread(image_file_name, as_gray=True)
    # reshape from 28x28 to list of 784 values, invert values
    img_data  = 255.0 - img_array.reshape(784)
    # then scale data to range from 0.01 to 1.0
    img_data = (img_data / 255.0 * 0.99) + 0.01
    print(numpy.min(img_data))
    print(numpy.max(img_data))
    # append label and image data  to test data set
    record = numpy.append(label,img_data)
    print(record)
    our_own_dataset.append(record)
    pass


# In[6]:


matplotlib.pyplot.imshow(our_own_dataset[3][1:].reshape(28,28), cmap='Greys', interpolation='None')


# In[7]:


print(our_own_dataset[0])


# In[ ]: