training neural network ok, needs too much ram. Started implementation of the server

imagesToDataset/.gitignore

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+object
+noobject

imagesToDataset/README.md

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+# imagesToDataset
+Gets all the images from the directories 'object' and 'noobject', and puts in a dataset file.
+The dataset file is a dataset.data file that contains 2 columns:
+- images arrays of pixels
+- 0 or 1, depending if is from the 'noobject' or 'object' directory
+
+
+First, install the libraries.
+
+### install scikit-learn
+http://scikit-learn.org/stable/install.html
+pip install -U scikit-learn
+
+### install scikit-image
+http://scikit-image.org/download
+pip install -U scikit-image
+
+### install numpy
+https://www.scipy.org/install.html
+python -m pip install --upgrade pip
+pip install --user numpy scipy matplotlib ipython jupyter pandas sympy nose
+
+### install Pillow
+http://pillow.readthedocs.io/en/3.0.x/installation.html
+(sudo) pip install Pillow
+
+### install matplotlib
+https://matplotlib.org/users/installing.html
+python -mpip install -U pip
+python -mpip install -U matplotlib
+
+may need to install python-tk:
+sudo apt-get install python-tk
+
+
+## to run
+python readDataset.py

imagesToDataset/main.py

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+from os import walk
+import matplotlib.pyplot as plt
+import numpy as np
+from PIL import Image, ImageOps
+
+#pixels, pixels of the output resizing images
+size = 100, 100
+def imgFileToData(path):
+    image = Image.open(path)
+    #resize the image
+    thumb = ImageOps.fit(image, size, Image.ANTIALIAS)
+    image_data = np.asarray(thumb).flatten()
+    '''
+    plt.plot(111)
+    plt.imshow(thumb)
+    plt.show()
+    '''
+    if len(image_data)!=30000:
+        print "possible future ERROR!"
+        print "len: " + str(len(image_data))
+        print "please, delete: " + path
+    return image_data
+
+def getDirectoryFiles(path, imgClass):
+    images = []
+    for (dirpath, dirnames, filenames) in walk(path):
+        for filename in filenames:
+            #print filename
+            image_data = imgFileToData(path + "/" + filename)
+            images.append([image_data, imgClass])
+    return images
+
+
+def asdf():
+    for index, (image, prediction) in enumerate(images_and_predictions[:4]):
+        plt.subplot(2, 4, index + 5)
+        plt.axis('off')
+        plt.imshow(image, cmap=plt.cm.gray_r, interpolation='nearest')
+        plt.title('Prediction: %i' % prediction)
+
+
+objects = getDirectoryFiles("object", 1)
+noobjects = getDirectoryFiles("noobject", 0)
+
+dataset = np.concatenate((objects, noobjects), axis=0)
+
+np.save('dataset.npy', dataset)

imagesToDataset/openDataset.py

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+import matplotlib.pyplot as plt
+import numpy as np
+from random import randint
+
+
+
+dataset = np.load('dataset.npy')
+
+n = randint(0, len(dataset))
+
+plt.plot(111)
+plt.axis('off')
+plt.imshow(dataset[n][0])
+plt.title('class: ' + str(dataset[n][1]))
+
+plt.show()