This documentation speaks about the history of the famous libraries tensorflow and open cv (aka computer vison). It also show the installation proces for both the libraries.

1. ARTIFICIAL INTELLIGENCE
ASSIGNMENT-1
TENSORFLOW & OPEN CV
DONE BY
Name: T. Sai Abhijyan
Branch & Section: CSE-B9

2. 1: INTRODUCTION TO TENSORFLOW
1.1: WHAT IS TENSORFLOW?
TensorFlow is an open source software library for numerical computation
using data-flow graphs. It was originally developed by the Google Brain Team
within Google's Machine Intelligence research organization for machine
learning and deep neural networks research, but the system is general enough
to be applicable in a wide variety of other domains as well. It reached version
1.0 in February 2017, and has continued rapid development, with 21,000+
commits thus far, many from outside contributors. This article introduces
TensorFlow, its open source community and ecosystem, and highlights some
interesting TensorFlow open sourced models.
1.2: WHAT MADE TENSORFLOW POPULAR?
TensorFlow is the best library of all because it is built to be accessible for everyone.
TensorFlow library incorporates different API to build at scale deep learning
architecture like CNN or RNN. TensorFlow is based on graph computation; it allows
the developer to visualize the construction of the neural network. This tool is
helpful to debug the program. Finally, TensorFlow is built to be deployed at scale.
It runs on CPU and GPU. TensorFlow attracts the largest popularity on GitHub
compare to the other deep learning framework.

3. 2: Downloading & Installing of TensorFlow
2.1: Installation of the suitable navigator.
Firstly, we need an environment (Navigator) which supports python and its
libraries. So, I have chosen mini-conda as my environment to install tensor flow
and run programs which consists of tensor flow components.
In-order, to download mini-conda navigator go to
https://docs.conda.io/en/latest/miniconda.html and download the mini-conda navigator
according to your system configurations.
Make sure you download the navigator having python 3.7 version. Now download
the application. And run the application downloaded.
STEP1:
Open the application downloaded. When you run the application it actually looks
like the below displayed image.

4. Proceed by clicking on next.
STEP2:
As you click on the next button. It actually open the license agreement
section. Which looks like the below image.

5. Read the License agreements T&C. After, you go through the license
agreement, now click on I Agree button.
STEP3:
After clicking on Agree button you will moving on towards the select
installation type section. Which looks like this.

6. For the time being continue with the Just Me option and click on next.
STEP4:
By clicking on next you will proceed to advanced options section
which looks likes the below image.

7. Here rather than going with the default option , we need to select the
first option (i.e Add anaconda to my path environment variable) after
selecting the first option click on install .
STEP5:
By clicking on install the installation of the mini-conda navigator
starts which looks like the below image.
Once you are finished with the installation of the mini-conda
navigator you will be enabled with the next button. Now click on the
next button.
STEP6:
Once you click on the next button it will show the final installation
section which looks like the below image.

8. This actually shows that the installation of the mini-conda navigator is
finished. As the installation is finished to exit the setup click on finish
button.
2.2: Installation of TensorFlow.
STEP1:
As previously we have finished your installation of mini-conda
navigator. Now we need to open the mini-conda prompt to continue
with the TensorFlow installion. To open the mini-conda prompt
search for anaconda Prompt (Miniconda3) in the windows search. The
below image shows the mini-conda prompt.

9. Now click on open to open the mini-conda prompt.
STEP2:
As we will be using the Jupiter notebook we will be installing
Jupiter notebook through the mini-conda prompt by typing
“conda install -y jupyter”
And once you type the above code and press enter the
installation of the jupyter notebook takes place which looks like
the below image.

10. Once the installation is started it shows the list of packages wich will
be downloaded. And once the download is finished it will followed by
installation the packages and the installed packages are. once all the
packages are installed and updated. It looks like the below image .

11. STEP3:
So now we will create a –yml file Which is tensorflow.yml
And the data present in it is
Just make sure that this file is present in the same directory the anaconda
prompt is present. If it’s not present change the directory by using the cd
command and enter “conda env create -v -f tensorflow.yml”.

12. Once the packages are installed it actually asks to activate or deactivate
tensorflow package if we activate the package the prompt will now be
working with tensorflow as its base which looks like the below images.

13. Once we activate tensorflow we need to open the python console and type
import tensorflow as tf followed by print(tf.__version__).
As we can see in the image when we type print(tf.__version__) it prints
the version of the tensorflow package and as we exit from the python the
console the base of the prompt changes to tensorflow .

14. STEP4:
Inorder to check whether the package is installed successfully ,we can
open the jupyter notebook and start create a new notebook file we can see
an option saying Python 3.7 (tensorflow ) by seeing the below image we
can justify that the tensorflow is installed properly and running
successfully.
3: INTRODUCTION TO OPEN-CV
3.1: WHAT IS OPEN-CV?
OpenCV is a cross-platform library using which we can develop real-
time computer vision applications. It mainly focuses on image
processing, video capture and analysis including features like face
detection and object detection. The first alpha version of OpenCV was
released to the public at the IEEE Conference on Computer Vision and

15. Pattern Recognition in 2000, and five betas were released between 2001
and 2005. The first 1.0 version was released in 2006. A version 1.1 "pre-
release" was released in October 2008.The second major release of the
OpenCV was in October 2009. OpenCV 2 includes major changes to
the C++ interface, aiming at easier, more type-safe patterns, new
functions, and better implementations for existing ones in terms of
performance (especially on multi-core systems). Official releases now
occur every six months and development is now done by an independent
Russian team supported by commercial corporations.In August 2012,
support for OpenCV was taken over by a non-profit foundation
OpenCV.org, which maintains a developerand user site.
3.2: WHAT MADE OPENCV POPULAR?
Opencv is actually popular for its for applications in facial recognition and
for many real world problems.
The application of opencv in different streams are listed down:
Robotics Application
• Localization − Determine robot location automatically
• Navigation
• Obstacles avoidance
• Assembly (peg-in-hole, welding, painting)
• Manipulation (e.g. PUMA robot manipulator)
• Human Robot Interaction (HRI) − Intelligent robotics to interact with and serve people
Medicine Application
• Classification and detection (e.g. lesion or cells classification and tumor detection)
• 2D/3D segmentation
• 3D human organ reconstruction (MRI or ultrasound)
• Vision-guided robotics surgery
Industrial Automation Application
• Industrial inspection (defect detection)
• Assembly
• Barcode and package label reading

16. • Object sorting
• Document understanding (e.g. OCR)
Security Application
• Biometrics (iris, fingerprint, face recognition)
• Surveillance − Detecting certain suspicious activities or behaviors
Transportation Application
• Autonomous vehicle
• Safety, e.g., driver vigilance monitoring.
4: INSTALLATION OF OPEN-CV
Here we will se the installation process of open cv.
STEP1:
In order to start the installation process we need to open our previously
downloaded mini-conda navigator(prompt).
To initiate the process we need to type the command shown below.
“conda create --name opencv-env python=3.7”
After entering the command in the prompt it will look like the command
below.
The image shows the package plan and requests our acknowledge to
proceed for the installation.

17. STEP2:
After the installation is done its asks us whether to activate the
environment or not. And in order to activate the environment we need to
type the command shown below.
“conda activate opencv-env ”
And once the environment is activated it looks like the image shown
below.
STEP3:
Once we are finished with installation process now, we will be testing the
Installation in order to test we need to type the following commands in the
python platform (which gets activated when we type python in the
prompt)
“import cv2” & “cv2.__version__”
“import dlib”&“dlib.__version__”
Once we type the above commands and press enter it should actually look
like the image shown below.

18. It prints the versions of the open cv & dlib. And in coming to the mention of
the library dlib (this is a library which is used is a general, purpose cross-
platform software library written in the programming language C++. Its
design is heavily influenced by ideas from design by contract and
component-based software engineering). In this document I didn’t
actually show the installation process of the dlib package because I have
the package actually installed in beforehand. Actually the installation of
dlib looks similar to the images shown below.
Command to install dlib is “pip install dlib”.

19. THANK YOU