Tag: Machine Learning

Posted in Home

Using AI for object classification.

Posted on by thanhnguyen118

In this post I will show you the easiest way to combine AI, convolution neural network(CNN) and docker container to classified object in real time. So all thing you need to know is basic knowledge about docker and neural network. If you are very new to programming, don’t worry, just follow the step below, and you will have a program classified object in real time.

in the video above I’m driving a car go around with a camera on top, to tracking other car and person inside it. I use CUDA Yolo + Nvidia GPU. You can also do the same, all you need to do is download my Docker file and run it.

For who need to understand the theories behind, I will summaries like this. The docker file will create a Ubuntu Linux environment and install Nvidia GPU+OpenCV+darknet in to it. Darknet is a wonderful neural network, it was train by around 10 millions picture and can real-time recognize about 70 categories (car, dog, cat, ship, plane….). If you want to learn more about darknet, you can read my article : https://thanhnguyensite.net/2020/11/05/neural-network/

OK! now let’s go the AI world:

Darknet Nvidia-Docker Ubuntu 16.04

Prerequisites

  1. Make sure you have the NVidia driver for your machine

Find out your the Graphics Card model

lspci | grep VGA

https://www.nvidia.com/Download/index.aspx?lang=en-us

How to install NVidia Drivers on Linux https://gist.github.com/wangruohui/df039f0dc434d6486f5d4d098aa52d07#install-nvidia-graphics-driver-via-runfile

  1. Install Docker and NVidia Docker https://github.com/NVIDIA/nvidia-docker

Steps to run

  1. Clone this repo: 
git clone https://gitlab.com/thanhnguyen1181991/darknet-docker.git
  1. Build the machine (this step might take a while, go make some coffee)
docker build -t darknet .
  1. On start.sh make sure you have the correct address of your webcam, in file start.sh line 8, if you use laptop onboard webcam, then choose: device=/dev/bus/usb/003/004:/dev/video0, if use external webcam, then: device=/dev/bus/usb/003/004:/dev/video0

Find your webcam bus

lsusb -t

Change the following line with the correct webcam bus

--device=/dev/bus/usb/003/002:/dev/video0
  1. Map a local folder to the Docker Container

Format:

/local/folder:/docker/folder

on start.sh change the following line

-v /home/projects:/dev/projects \
  1. Run the machine with Webcam
sh start.sh

Darknet

Make sure you have the weights for what you want to run

More information at https://pjreddie.com/darknet/

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LeNet for Traffic Sign

Posted on by thanhnguyen118

Load Data

Load the MNIST data, which comes pre-loaded with TensorFlow.

from tensorflow.examples.tutorials.mnist import input_data
 mnist = input_data.read_data_sets("MNIST_data/", reshape=False)
 X_train, y_train           = mnist.train.images, mnist.train.labels
 X_validation, y_validation = mnist.validation.images, mnist.validation.labels
 X_test, y_test             = mnist.test.images, mnist.test.labels
 assert(len(X_train) == len(y_train))
 assert(len(X_validation) == len(y_validation))
 assert(len(X_test) == len(y_test))
 print()
 print("Image Shape: {}".format(X_train[0].shape))
 print()
 print("Training Set:   {} samples".format(len(X_train)))
 print("Validation Set: {} samples".format(len(X_validation)))
 print("Test Set:       {} samples".format(len(X_test)))

The MNIST data that TensorFlow pre-loads comes as 28x28x1 images.

However, the LeNet architecture only accepts 32x32xC images, where C is the number of color channels.

In order to reformat the MNIST data into a shape that LeNet will accept, we pad the data with two rows of zeros on the top and bottom, and two columns of zeros on the left and right (28+2+2 = 32).

You do not need to modify this section.

import numpy as np

# Pad images with 0s
X_train      = np.pad(X_train, ((0,0),(2,2),(2,2),(0,0)), 'constant')
X_validation = np.pad(X_validation, ((0,0),(2,2),(2,2),(0,0)), 'constant')
X_test       = np.pad(X_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')
    
print("Updated Image Shape: {}".format(X_train[0].shape))

Visualize Data

View a sample from the dataset.

You do not need to modify this section.

import random
 import numpy as np
 import matplotlib.pyplot as plt
 %matplotlib inline
 index = random.randint(0, len(X_train))
 image = X_train[index].squeeze()
 plt.figure(figsize=(1,1))
 plt.imshow(image, cmap="gray")
 print(y_train[index])

Preprocess Data

Shuffle the training data.

You do not need to modify this section.

from sklearn.utils import shuffle
 X_train, y_train = shuffle(X_train, y_train)

Setup TensorFlow

The EPOCH and BATCH_SIZE values affect the training speed and model accuracy.

You do not need to modify this section.In [ ]:

import tensorflow as tf
EPOCHS <strong>=</strong> 10
BATCH_SIZE <strong>=</strong> 128






TODO: Implement LeNet-5





Implement the LeNet-5 neural network architecture.





This is the only cell you need to edit.





Input





The LeNet architecture accepts a 32x32xC image as input, where C is the number of color channels. Since MNIST images are grayscale, C is 1 in this case.





Architecture





Layer 1: Convolutional. The output shape should be 28x28x6.





Activation. Your choice of activation function.





Pooling. The output shape should be 14x14x6.





Layer 2: Convolutional. The output shape should be 10x10x16.





Activation. Your choice of activation function.





Pooling. The output shape should be 5x5x16.





Flatten. Flatten the output shape of the final pooling layer such that it’s 1D instead of 3D. The easiest way to do is by using tf.contrib.layers.flatten, which is already imported for you.





Layer 3: Fully Connected. This should have 120 outputs.





Activation. Your choice of activation function.





Layer 4: Fully Connected. This should have 84 outputs.





Activation. Your choice of activation function.





Layer 5: Fully Connected (Logits). This should have 10 outputs.





Output





Return the result of the 2nd fully connected layer.




from tensorflow.contrib.layers import flatten
 def LeNet(x):    
     # Arguments used for tf.truncated_normal, randomly defines variables for the weights and biases for each layer
     mu = 0
     sigma = 0.1
 <code># TODO: Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6. # TODO: Activation. # TODO: Pooling. Input = 28x28x6. Output = 14x14x6. # TODO: Layer 2: Convolutional. Output = 10x10x16. # TODO: Activation. # TODO: Pooling. Input = 10x10x16. Output = 5x5x16. # TODO: Flatten. Input = 5x5x16. Output = 400. # TODO: Layer 3: Fully Connected. Input = 400. Output = 120. # TODO: Activation. # TODO: Layer 4: Fully Connected. Input = 120. Output = 84. # TODO: Activation. # TODO: Layer 5: Fully Connected. Input = 84. Output = 10. return logits</code>





Features and Labels





Train LeNet to classify MNIST data.





x is a placeholder for a batch of input images. y is a placeholder for a batch of output labels.





You do not need to modify this section.




x = tf.placeholder(tf.float32, (None, 32, 32, 1))
y = tf.placeholder(tf.int32, (None))
one_hot_y = tf.one_hot(y, 10)