Feat: UserflowDataGatherer send correct datas; Feat: UserflowPredictorWebserver receive data and create userflowmap

Predictor/PredictorNeuralNetwork/.ipynb_checkpoints/Untitled-checkpoint.ipynb

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+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

Predictor/PredictorNeuralNetwork/Predictor.py

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+from keras.models import Sequential
+from keras.layers import Activation, Dropout, Flatten, Dense
+from keras import backend as K
+from keras.preprocessing.sequence import pad_sequences
+from keras.preprocessing.image import ImageDataGenerator
+from keras.layers import Conv2D, MaxPooling2D
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+import cv2
+import numpy as np
+
+img_width, img_height = 150, 150
+train_data_dir = './UserflowPredictorSystem/predictor/datas/train'
+validation_data_dir = './UserflowPredictorSystem/predictor/datas/test'
+nb_train_samples = 2000
+nb_validation_samples = 800
+epochs = 100
+batch_size = 16
+
+
+if K.image_data_format() == 'channels_first':
+    input_shape = (3, img_width, img_height)
+else:
+    input_shape = (img_width, img_height, 3)
+
+def create_model():
+    model = Sequential()
+    model.add(Conv2D(32, (3, 3), input_shape=input_shape))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+
+    model.add(Conv2D(32, (3, 3)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+
+    model.add(Conv2D(64, (3, 3)))
+    model.add(Activation('relu'))
+    model.add(MaxPooling2D(pool_size=(2, 2)))
+
+    model.add(Flatten())
+    model.add(Dense(64))
+    model.add(Activation('relu'))
+    model.add(Dropout(0.5))
+    model.add(Dense(1))
+    model.add(Activation('sigmoid'))
+
+
+    model.compile(loss='binary_crossentropy',
+                  optimizer='rmsprop',
+                  metrics=['accuracy'])
+    return model
+
+def train_model(model):
+    # this is the augmentation configuration we will use for training
+    train_datagen = ImageDataGenerator(
+        rescale=1. / 255,
+        shear_range=0.2,
+        zoom_range=0.2,
+        horizontal_flip=True)
+
+    # this is the augmentation configuration we will use for testing:
+    # only rescaling
+    test_datagen = ImageDataGenerator(rescale=1. / 255)
+
+    train_generator = train_datagen.flow_from_directory(
+        train_data_dir,
+        target_size=(img_width, img_height),
+        batch_size=batch_size,
+        class_mode='binary')
+
+    validation_generator = test_datagen.flow_from_directory(
+        validation_data_dir,
+        target_size=(img_width, img_height),
+        batch_size=batch_size,
+        class_mode='binary')
+
+    model.fit_generator(
+        train_generator,
+        steps_per_epoch=nb_train_samples // batch_size,
+        epochs=epochs,
+        validation_data=validation_generator,
+        validation_steps=nb_validation_samples // batch_size)
+
+    model.save_weights('./UserflowPredictorSystem/first_try2.h5')
+    return model
+
+
+
+def load_trained_model(weights_path):
+   model = create_model()
+   model.load_weights(weights_path)
+   return model
+
+
+def predict(number, model):
+    img = cv2.imread("./UserflowPredictorSystem/predictor/datas/test/" + str(number) + ".jpg")
+    im = mpimg.imread("./UserflowPredictorSystem/predictor/datas/test/" + str(number) + ".jpg")
+    plt.imshow(im)
+    img = cv2.resize(img, (img_width,img_height))
+    img = img.reshape(1, img_width, img_height, 3)
+    res = model.predict(img)
+    if res == 1:
+        print('DOG')
+    else:
+        print('CAT')
+
+model = create_model()
+model = train_model(model)
+
+import os
+os.getcwd()
+
+trained_model = load_trained_model("./UserflowPredictorSystem/first_try2.h5")
+trained_model.summary()
+import random
+predict(random.randint(1,12500), trained_model)
+predict('lolly', model)
+
+
+
+print(np.argmax(loaded_model.predict(img)))

Predictor/PredictorNeuralNetwork/Readme.md

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+.