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cnn4.py
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cnn4.py
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#!/usr/bin/python
import os
import sys
import csv
import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt
import keras.backend as K
from keras.preprocessing.image import img_to_array, load_img
from keras.models import Sequential, model_from_json
from keras.optimizers import Adam, SGD
from keras.layers import Dense, Activation, Conv2D, MaxPooling2D, Dropout, Flatten, BatchNormalization
from keras.utils import np_utils
from tensorflow.python import debug as tf_debug
# global variables
cursor_index = 0
images = list()
labels_list = list()
count = 0
fps = 0
fig = 0 # figure object for veiwing frames and navigation
# function that handels csv data supported
# -----------------------------------------------------------------------------
def process_dataset(images_path, labels_path):
global images
global labels_list
with open(labels_path) as f:
read_file = csv.reader(f, delimiter=',')
for row in read_file:
labels_list.append([row[0], row[2], row[3], row[4]])
# print(row[1])
image_names = [x[0] for x in labels_list]
for image_name in image_names:
for filename in os.listdir(images_path):
# print(os.path.splitext(filename)[0])
if filename.endswith(".jpg") and (os.path.splitext(filename)[0]) == image_name:
images.append(img_to_array(load_img(images_path + '/' + filename)))
break
for row in labels_list:
del row[0]
# print(labels_list)
# print('--------------------------------------------------------')
# print('--------------------------------------------------------')
# print('--------------------------------------------------------')
# print('--------------------------------------------------------')
# print('--------------------------------------------------------')
# print(images[0].shape)
# print('--------------------------------------------------------')
# print('--------------------------------------------------------')
# print('--------------------------------------------------------')
def split_train_valid():
global count
shuffled_index = np.random.permutation(len(images))
indices_train = shuffled_index[0:int(0.9*len(images))]
indices_valid = shuffled_index[int(0.9*len(images)):len(images)]
count = indices_valid
train_data = [images[i] for i in indices_train]
train_labels = [labels_list[i] for i in indices_train]
valid_data = [images[i] for i in indices_valid]
valid_labels = [labels_list[i] for i in indices_valid]
return train_data, train_labels, valid_data, valid_labels
class cnn():
def __init__(self):
self.model = Sequential()
def train_network():
#sess = K.get_session()
#sess = tf_debug.LocalCLIDebugWrapperSession(sess)
#K.set_session(sess)
x, y, x_val, y_val = split_train_valid()
rows = x[0].shape[0]
cols = x[0].shape[1]
channels = x[0].shape[2]
print(np.array(x).shape)
y = np.array(y).astype(float)
y_val = np.array(y_val).astype(float)
cnn_m = cnn()
# Create cnn
cnn_m.model.add(Conv2D(128, kernel_size=3, kernel_initializer='random_uniform', bias_initializer='zeros', padding='same', data_format="channels_last", input_shape=(rows, cols, channels)))
cnn_m.model.add(MaxPooling2D(pool_size=(2, 2)))
#cnn_m.model.add(BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None))
cnn_m.model.add(Activation('relu'))
#cnn_m.model.add(MaxPooling2D(pool_size=(2, 2)))
cnn_m.model.add(Conv2D(64, kernel_size=3, kernel_initializer='random_uniform', bias_initializer='zeros', padding='same'))
#cnn_m.model.add(BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None))
cnn_m.model.add(Activation('relu'))
#cnn_m.model.add(MaxPooling2D(pool_size=(2, 2)))
cnn_m.model.add(Conv2D(32, kernel_size=3, kernel_initializer='random_uniform', bias_initializer='zeros', padding='same'))
#cnn_m.model.add(BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None))
cnn_m.model.add(Activation('relu'))
#cnn_m.model.add(MaxPooling2D(pool_size=(2, 2)))
#cnn_m.model.add(Dropout(0.1))
cnn_m.model.add(Conv2D(16, kernel_size=3, kernel_initializer='random_uniform', bias_initializer='zeros', padding='same'))
#cnn_m.model.add(BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True, beta_initializer='zeros', gamma_initializer='ones', moving_mean_initializer='zeros', moving_variance_initializer='ones', beta_regularizer=None, gamma_regularizer=None, beta_constraint=None, gamma_constraint=None))
cnn_m.model.add(Activation('relu'))
cnn_m.model.add(Flatten())
cnn_m.model.add(Dense(64))
cnn_m.model.add(Activation('relu'))
cnn_m.model.add(Dense(32))
cnn_m.model.add(Activation('relu'))
#cnn_m.model.add(Dropout(0.1))
cnn_m.model.add(Dense(3))
#cnn_m.model.add(Activation('linear'))
adam = Adam(lr=0.001, decay=0.0005)
#sgd = SGD(lr=0.00001, decay=0.0005)
# Define attributes of the cnn; categorial, optimizer_type, performance metrics
cnn_m.model.compile(loss='mean_squared_error', optimizer=adam, metrics=['accuracy'])
#cnn_m.model.compile(loss='mean_squared_error', optimizer=sgd, metrics=['accuracy'])
# Fit the model to the training data
history = cnn_m.model.fit(np.array(x), y, epochs=200, batch_size=8, validation_data=(np.asarray(x_val), y_val), shuffle=True)
#score = cnn_m.model.evaluate(np.array(x_val), y_val, batch_size=8)
#print('score', score)
# 2. save your trained model
# serialize model to JSON
model_json = cnn_m.model.to_json()
with open("model.json", "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
cnn_m.model.save_weights("model.h5")
print("Saved model to disk")
# list all data in history
print(history.history.keys())
print(count)
# summarize history for accuracy
fig1 = plt.figure()
plt.plot(history.history['acc'])
plt.plot(history.history['val_acc'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
fig1.savefig("model accuracy.png")
#plt.show()
# summarize history for loss
fig2 = plt.figure()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')
fig2.savefig("model loss.png")
#plt.show()
# # function responsible for processing the video and extracting the frames
# # -----------------------------------------------------------------------------
# def process_video(video_path):#, timestamp, events_id):
# # variables initialization
# global count
# global fps
# success = True
# # creating directory for new images
# video_name = os.path.basename(os.path.splitext(video_path)[0])
# print("Start processing: " + video_name)
# if not os.path.isdir(video_name):
# os.mkdir(video_name)
# # initialize captured video and retrieve the frames per second for the video
# vidcap = cv2.VideoCapture(video_path)
# fps = vidcap.get(cv2.CAP_PROP_FPS)
# success, image = vidcap.read() # images are numpy.ndarray
# # saving video frames into images list
# while success:
# images.append((timestamp_list[count], image))
# # save frame as JPEG file, uncomment if needed
# # cv2.imwrite(os.path.join(video_name, "frame%d.jpg" % count), image)
# count += 1
# success, image = vidcap.read()
# print("finished reading %d frames" %count)
# main function
# -----------------------------------------------------------------------------
def main():
if len(sys.argv) == 3:
images_path = sys.argv[1]
labels_path = sys.argv[2]
process_dataset(images_path, labels_path)
train_network()
else:
print('Wrong number of arguments')
if __name__ == '__main__':
main()