pipeline model chooser working, server predictor working

other/imagesToDataset/.gitignore

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+object
+noobject

other/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

other/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
+import pandas as pd
+
+#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()
+
+    #check if the image had been resized to 100x100. 3pixels * 100width + 100 height = 30000
+    if len(image_data)!=100:
+        print("possible future ERROR!")
+        print("len: " + str(len(image_data)))
+        print("please, delete: " + path)
+    return np.array(list(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])
+            print(path + "/" + filename)
+    return images
+
+
+objects = getDirectoryFiles("object", 1)
+noobjects = getDirectoryFiles("noobject", 0)
+
+dataset = np.concatenate((objects, noobjects), axis=0)
+#print(dataset[0])
+
+np.save('dataset.npy', dataset)
+'''
+print(dataset)
+np.savetxt('dataset.csv', dataset, delimiter=",", fmt='%d')
+
+pd.set_option('display.max_colwidth', -1)
+df = pd.DataFrame(dataset)
+print(df.head())
+print("aaa")
+print(df[0][0])
+print("aaa")
+pd.set_option('display.max_colwidth', -1)
+pd.set_option('display.max_columns', None)
+df.to_csv("dataset.csv", encoding='utf-8', index=False, header=False)
+'''

other/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()