Add files via upload

Author: diveshlunker

network.py

@@ -0,0 +1,110 @@
+import numpy as np
+import random
+
+class NeuralNetwork():
+
+    def __init__(self, sizes):
+        # sizes is an array with the number of units in each layer
+        # [2,3,1] means w neurons of input, 3 in the hidden layer and 1 as output
+        self.num_layers = len(sizes)
+        self.sizes = sizes
+        # the syntax [1:] gets all elements of sizes array beginning at index 1 (second position)
+        # np,random.randn(rows, cols) retuns a matrix of random elements
+        # np.random.randn(2,1) =>
+        # array([[ 0.68265325],
+        # [-0.52939261]])
+        # biases will have one vector per layer
+        self.biases = [np.random.randn(y,1) for y in sizes[1:]]
+        #zip returns a tuple in which x is the element of the first array and y the element of the second
+        #sizes[:-1] returns all the elements till the second to last
+        #sizes[1:] returns all the elements from the second and on]
+        # [2,3,1] means:
+        # * matrix of 3 rows and 2 columns -- will be multiplied by the inputs
+        # * matrix of 1 row and 3 columns -- will multiply the hidden layer and produce the output
+        self.weights = [np.random.randn(y,x) for x,y in zip(sizes[:-1],sizes[1:])]
+
+    def feedforward(self, a):
+        for b,w in zip(self.biases, self.weights):
+            a = sigmoid(np.dot(w, a) + b)
+        return a
+
+    def separate_batches(self, training_data, batch_size):
+        random.shuffle(training_data)
+        n = len(training_data)
+        # extracts chunks of data from the training set
+        # the xrange function will return indices starting with 0 untill n, with a step size o batch_size
+        # batches, then, will have several chunks of the main set, each defined by the batch_size_variable
+        return [training_data[i:i + batch_size] for i in range(0, n, batch_size)]
+
+    def update_batches(self, batches, alpha):
+        for batch in batches:
+            nabla_b = [np.zeros(b.shape) for b in self.biases]
+            nabla_w = [np.zeros(w.shape) for w in self.weights]
+
+            m = len(batch)
+
+            # x is a array of length 901
+            # y is a single value indicating the digit represented by the 901 elements
+            for x, y in batch:
+                delta_b, delta_w = self.backpropagation(x, y)
+                nabla_b = [nb + dnb for nb, dnb in zip(nabla_b, delta_b)]
+                nabla_w = [nw + dnw for nw, dnw in zip(nabla_w, delta_w)]
+
+            self.weights = [w - (alpha / m) * nw for w, nw in zip(self.weights, nabla_w)]
+            self.biases = [b - (alpha / m) * nb for b, nb in zip(self.biases, nabla_b)]
+
+    def backpropagation(self, x, y):
+        nabla_b = [np.zeros(b.shape) for b in self.biases]
+        nabla_w = [np.zeros(w.shape) for w in self.weights]
+
+        activation = x
+        activations = [x]
+        zs = []
+        for b, w in zip(self.biases, self.weights):
+            # layer-bound b and w
+            z = np.dot(w, activation)+b
+            zs.append(z)
+            activation = sigmoid(z)
+            activations.append(activation)
+        # backward pass
+        delta = self.cost_derivative(activations[-1], y) * \
+            sigmoid_prime(zs[-1])
+        nabla_b[-1] = delta
+        nabla_w[-1] = np.dot(delta, activations[-2].transpose())
+
+        for l in range(2, self.num_layers):
+            z = zs[-l]
+            sp = sigmoid_prime(z)
+            delta = np.dot(self.weights[-l+1].transpose(), delta) * sp
+            nabla_b[-l] = delta
+            nabla_w[-l] = np.dot(delta, activations[-l-1].transpose())
+        return (nabla_b, nabla_w)
+
+    def sgd(self, training_data, epochs, batch_size, alpha, test_data):
+        n_test = len(test_data)
+
+        for epoch in range(epochs):
+            batches = self.separate_batches(training_data, batch_size)
+            self.update_batches(batches, alpha)
+
+            print("Epoch {0}: {1} / {2}".format(epoch, self.evaluate(test_data), n_test))
+
+    def evaluate(self, test_data):
+        #r = [self.feedforward(x) for (x, y) in test_data]
+        #for a in r:
+        #    print("{0}, {1}".format(format(a[0][0], 'f'), format(a[1][0], 'f'))) ; 
+        test_results = [(np.argmax(self.feedforward(x)), y)
+                        for (x, y) in test_data]
+        return sum(int(x == y) for (x, y) in test_results)
+
+    def cost_derivative(self, output_activations, y):
+        return output_activations - y
+
+
+def sigmoid(z):
+    return 1.0 / (1.0 + np.exp(-z))
+    
+
+
+def sigmoid_prime(z):
+    return sigmoid(z) * (1-sigmoid(z))

preprocessor.py

@@ -0,0 +1,28 @@
+import cv2
+import numpy as np
+
+
+def prepare(input):
+    # preprocessing the image input
+    clean = cv2.fastNlMeansDenoising(input)
+    ret, tresh = cv2.threshold(clean, 127, 1, cv2.THRESH_BINARY_INV)
+    img = crop(tresh)
+
+    # 40x10 image as a flatten array
+    flatten_img = cv2.resize(img, (40, 10), interpolation=cv2.INTER_AREA).flatten()
+
+    # resize to 400x100
+    resized = cv2.resize(img, (400, 100), interpolation=cv2.INTER_AREA)
+    columns = np.sum(resized, axis=0)  # sum of all columns
+    lines = np.sum(resized, axis=1)  # sum of all lines
+
+    h, w = img.shape
+    aspect = w / h
+
+    return [*flatten_img, *columns, *lines, aspect]
+
+
+def crop(img):
+    points = cv2.findNonZero(img)
+    x, y, w, h = cv2.boundingRect(points)
+    return img[y: y+h, x: x+w]

sigrecog.py

@@ -0,0 +1,42 @@
+import cv2
+import os
+import numpy as np
+import network
+import preprocessor
+
+
+def main():
+    print('OpenCV version {} '.format(cv2.__version__))
+
+    current_dir = os.path.dirname(__file__)
+
+    author = '024'
+    training_folder = os.path.join(current_dir, 'data/training/', author)
+    test_folder = os.path.join(current_dir, 'data/test/', author)
+
+    training_data = []
+    for filename in os.listdir(training_folder):
+        img = cv2.imread(os.path.join(training_folder, filename), 0)
+        if img is not None:
+            data = np.array(preprocessor.prepare(img))
+            data = np.reshape(data, (901, 1))
+            result = [[0], [1]] if "genuine" in filename else [[1], [0]]
+            result = np.array(result)
+            result = np.reshape(result, (2, 1))
+            training_data.append((data, result))
+
+    test_data = []
+    for filename in os.listdir(test_folder):
+        img = cv2.imread(os.path.join(test_folder, filename), 0)
+        if img is not None:
+            data = np.array(preprocessor.prepare(img))
+            data = np.reshape(data, (901, 1))
+            result = 1 if "genuine" in filename else 0
+            test_data.append((data, result))
+
+    net = network.NeuralNetwork([901, 500, 500, 2])
+    net.sgd(training_data, 10, 50, 0.01, test_data)
+
+
+if __name__ == '__main__':
+    main()