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代写CSE 473/573代做Python实验作业

University at Buffalo
Department of Computer Science and and Engineering
CSE 473/573 - Computer Vision and Image Processing
Spring 2020
TuTh 9:30AM-10:50AM, Hoch 114
Project #3
Due Date: 5/13/20, 11:59PM
Contents
1 Projects # 3 Overview (100 pts Total) 2
2 Project Selection: April 16th Deadline 2
3 Default Project: Face Detection in the Wild 3
3.1 Project Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.2 Libraries permitted and prohibited . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.3 Data and Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.4 Code and Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
4 Alternative Projects Options 6
4.1 Touch-less Face Enabled Time-clock . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
4.2 Visual Welcome Center . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4.3 Virtual Wall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4.4 Mobile Retriever . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4.5 Requirements for Alternative Project options . . . . . . . . . . . . . . . . . . . . . . 8
5 Grading and Assessment 10
5.1 FaceDetection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5.2 Alternative Projects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
6 Academic Integrity 11
1
CSE 473/573 Project 3 Spring 2020
1 Projects # 3 Overview (100 pts Total)
The default topic for this project is face detection in the wild, described below in Section 3. You
may work in groups of up to 2 students for the default project, and only need to submit the project
once. If you do not pick one of the alternative projects, then you will be expected to turn in the
default face detection project by the due date.
As an alternative we are offering a set of optional projects that require more computer vision
implementation than the face detection project, but are not constrained to developing code from
scratch. You may work in groups of up to 3 student on the optional projects. The projects will
require you to build a computer vision application that can be demonstrated an must run in real
time.
The list of possible projects include:
• A Touch-less Face Enabled Time-clock
• A Visual Welcome Center
• A Virtual Wall
• A Document Mobile Retriever
Each of them are described below in Section 4.
2 Project Selection: April 16th Deadline
You will have until April 16th to decide if you will choose one of these options or go with the default
project. At the point that you decide, you will be committed the project you choose, so choose
wisely!
To choose your project and partners, follow this link to a Google form.
473-573 Project #3 Selection Google Form
If you do NOT fill out the form, we will assume you are not teaming with anyone and that you
will submit the default project.
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CSE 473/573 Project 3 Spring 2020
3 Default Project: Face Detection in the Wild
The goal of this project is to have you implement the Viola-Jones face detection algorithm [1].
You should download the paper and understand its details. As discussed in class, the Viola-Jones
algorithm is capable of detecting frontal faces in real time and is regarded as a milestone in the
development of computer vision. Despite the fact that deep learning-based face detectors [2, 3] have
gradually emerged as the preferred paradigm for face detection, variants of Viola-Jones algorithm
still remain competitive in situations where speed is critical. A great introduction to Viola-Jones
algorithm can be found at https://www.youtube.com/watch?v=uEJ71VlUmMQ.
3.1 Project Description
Given a face detection dataset composed of thousands of images, the goal is to train a face detector
using the images in the dataset. The trained detector should be able to locate all the faces in any
image coming from the same distribution as the images in the dataset. Figure 1 shows an example
of performing face detection. We will use FDDB [4] as the dataset for this project. FDDB contains
more than 2800 images and associated bounding box annotation, for more than 5700 faces.
Please keep in mind that Viola-Jones algorithm [1] is the ONLY method that you could use for
this project. Using other methods - HOG / SIFT / SURF + SVM, deep learning-based methods,
will result in a deduction of more than 50% of the maximum possible points for this project.
You must also use integral images to implement the feature extraction. You are free to use
enhancements of the boosting algorithm such as cascades to obtain better results.
Figure 1: An example of performing face detection. The detected faces are annotated using orange
bounding boxes.
3.2 Libraries permitted and prohibited
• Any Python Standard Library can be used.
• Any APIs provided by OpenCV that have “cascade”, “Cascade”, “haar” or “Haar” func-
tionality can not be used.
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CSE 473/573 Project 3 Spring 2020
• Using any APIs that implement part of Viola-Jones algorithm directly, e.g., an API that
computes integral image, will result in a deduction of 10%− 100% of the maximum possible
points of this project1.
3.3 Data and Evaluation
The FDDB [4] and the evaluation script should be downloaded directly from the following site:
http://vis-www.cs.umass.edu/fddb/.
The following bash script will be used to evaluate the performance of the face detector you train:
unzip UBID-project3.zip
cd [UBID-project3]
python3 FaceDetector.py [data-directory]
python3 ComputeFBeta.py ./results.json [ground-truth.json]
FaceDetector.py contains YOUR python code that will able to detect faces in all the images in
[data-directory] and generate a json file, i.e., results.json, that stores all the bounding boxes
of the detected faces. The generated json file shall be located in the same folder where the images
located. The bounding boxes of the detected faces should be stored in a list using the following
format:
[{"iname": "img.jpg", "bbox": [x, y, width, height]}, ...]
"img.jpg" is an example of the name of an image; x and y are the row index and column index
of the top-left corner of the bounding box; width and height are the width and height of the
bounding box, respectively. x, y, width and height should be integers.
ComputeFBeta.py contains YOUR python code that computes fβ using results.json and the
ground truth in [ground-truth.json].
You can refer to the sample json file posted under resource section on piazza for more informa-
tion. There is also a piece of example code regarding how to create json file.
3.4 Code and Report
Your code should be zipped up in a directory called Project3. As discussed in class, unlimited
number of submissions is allowed and only the latest submission will be used for grading. Since this
project allows more flexibility in the use of libraries, please also remember to include a file with
your resources your code requires to make it easier on the grader. This should be in the form of a
txt file named ”requirements.txt”. The file named ”requirements.txt” should specify the libraries
you used and the version of the libraries, and our grader should be able to install all the libraries
you used by using the command: pip install -r requirements.txt
In addition to your code, a report is required with this project. It should contain
• Your name and your UBID, and the names and UBID of your project partners.
1Please do not ask if a specific API could be used on Piazza. You need to judge whether an API implements part
of Viola-Jones algorithm directly on your own.
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CSE 473/573 Project 3 Spring 2020
• An overview of the Viola-Jones algorithm,
• A description of your implementation,
• Results of your face detector on FDDB (from the program above) and
• An analysis of the results (failure cases, possible improvements, etc),
The report should be a pdf file. You will be graded on both the report and an evaluation on a
held out test dataset that you currently do not have access to.
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CSE 473/573 Project 3 Spring 2020
4 Alternative Projects Options
Congratulations! You have just been hired as an image processing and computer vision engineer
at Victor E. Bull Inc. This is a startup company that has cool ideas for vision products that they
want to use to change the world. Since you’ve taken computer vision at UB you have been selected
to lead a team to develop, implement and test one of the projects listed below. It’s your choice on
which ones to start with but you must work with the company president, Dr. Doermann, during
office hours or extended office hours to develop the specifications, agree on capabilities, and design
test and evaluation criteria. Dr. Doermann has very limited knowledge of how this should be done,
so that part is up to you, but he has a vision for what the industry wants. Check out the following
projects and see if you would like to choose one of these alternative project options. In the end,
the project will require some basic description of what the functionality is, the users and technical
integrators document, working source code, and a qualitative evaluation. The nice thing is that
since you are engineers and you’ve already passed the class, you can use any capabilities that are
in the public domain two implement your system. You just cannot use any code that specifically
accomplishes all of the goals of the project.
You must submit the project and demonstrated your new system to the president by the last
day of final exams in May. Choose carefully, because your future with this company depends on
it.. . .
4.1 Touch-less Face Enabled Time-clock
In this project you will develop a time clock and time recording system that relies on the recognition
of individuals enrolled in the system to keep track of their hours. The system will not have any touch
based interaction, but may allow interaction of a person with voice commands, head movement or
hand gestures such as thumbs up or down.
System should consist of an interface that allows the enrollment of a person, and a back-end
database that keeps track of information about the person themselves as well as their time records.
When a person approaches the clock the system should attempt to recognize them by detecting
the face and matching them against previously enrolled individuals in the database. Once the
system recognizes a person, it should display the identity of the person and wait for the person’s
confirmation using voice commands, head movement, hand gestures or other touch-less interaction
methods. The system should clock in-out only after receiving the person’s confirmation.
The system should then ask them to verify that they are clocking in or clocking out as appropri-
ate, and record other attributes such as breaks for coffee or lunch. If they want to clock out without
clocking in, they should be allowed to record an ”exception” that will be checked later through the
interface.
It should also keep a picture with a time of each interaction.
Other features of such a system might include:
• The ability to analyze the database to look for anomalies, such has
• The ability to report and time summarized by day/week (or period)
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CSE 473/573 Project 3 Spring 2020
4.2 Visual Welcome Center
In this project, you will develop an interface to interact with visitors to the Department of Computer
Science and Engineering at UB. The system should have back end and where people can be enrolled
manually, along with he information such as their title, affiliation, and email address, website etc.
When a visitor approaches the system they should be identified if they are already in the system
and one of a set of welcome messages that are already in the system should be displayed along with
any other information you deem appropriate.
If they’re not in the system it should also welcome them and asked them if it is ok if you enroll
them in the system. You could capture this information, for example with a hand gesture (thumbs
up or down) or a verbal command from a limited vocabulary (yes or no for example. If possible
you could also capture a business card and enter this information in the record.
It will be up to you to define other features of such a system, but some such features might
include:
• Each time a user is recognized and verified by the system should make a record of the time
and date when they came in, and a new photo to eventually enroll
• You might consider an OCR component that could recognize names on business cards held
up or captured by a desk camera.
4.3 Virtual Wall
This project will involve the creation of a virtual wall the users can interact with. You will be
provided with a stereo camera that can take a picture of a wall at an acute angle. From the stereo
camera it is your job to write the software to track a person’s hand and allow them to point at
particular locations on this wall.
For this project the virtual wall can be simply a tapped off rectangle region that sometime in
the future could be the projection screen. At a minimum that screened area should be divided into
nine parts and you should be able to recognize when anyone approaches and gets within a fixed
distance of the wall, and points to a particular element/cell. The closer you can get to an exact
relative XY coordinates on the virtual wall the better.
You may want to interact with the user in some meaningful way, such as playing a sound when
they touch the wall, or asking them to touch a block numbered one through nine, so you can test
the system.
The higher the resolution and the more reliably the system works, the bigger your bonus!
Here is a link to the stereo camera that will be available to your team is described here:
https://www.mynteye.com/products/mynt-eye-stereo-camera
Here are some other features you may consider:
• Some way to provide feedback to the users through sounds or confirmation
• The ability to know the positions where two or more people are pointing at the same time.
And here are some things you can assume:
• You can assume that the camera is fixed at an acute angle to the wall (from the side is fine).
• You can calibrate the scene by clicking on the 4 corners of the virtual wall before starting.
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CSE 473/573 Project 3 Spring 2020
4.4 Mobile Retriever
In a recent class at UB, Prof. Doermann talked about a mobile retriever system that was imple-
mented by one of his graduate students over a decade ago. Although this technology was never
really commercialized, it’s a very interesting idea. The idea is that when documents are created
electronically and posted or published in hard copy form, there may be a desire to retrieve the
original content, for example to be bred back as a podcast, or simply to find the original document
electronic form.
It is your job to take this paper and implement the major components of the system. This
includes taking a large set of documents such as PDFs of scientific literature, and converting them
to images. You must then implement the image processing routines needed to index it as described
in the paper. You will then implement the techniques for retrieval including the triplet verification.
Although the original paper talked about an implementation on a mobile phone, any type of
WebCam will work for this project. But it must be integrated you must be able to take a snapshot
of a document and find it in a collection of say a minimum of 1000 pages.
If you are eager enough, you can find another way to do this has techniques and computer vision
have advanced tremendously in the last decade. But please keep in mind that the goal here is a
real-time retrieval and there are constraints on the problem that you should take advantage of.
Here is a link to the original publication and it will also be provided online in case you have
trouble retrieving.
Don’t underestimate the challenges that may arise. You should be an excellent programmer to
take this one on.
X. Liu and D. Doermann,“Mobile Retriever: access to digital documents from their physical
source,” International Journal of Document Analysis and Recognition (IJDAR), vol. 11, no. 1, pp.
19–27, 2008.
https://link.springer.com/content/pdf/10.1007/s10032-008-0066-4.pdf
4.5 Requirements for Alternative Project options
• For the optional project, they are all expected to run in real time to demonstrate the func-
tionality, not just on a recorded video.
• You should submit the following:
– A cover page with your name and your UBID, and the names and UBID of your project
partners.
– A 1-2 page description of your system suitable for advertisements - ie a description of
the capabilities
– All source code
– Good documentation of code or an integrators document (what software needs to be
installed and how to use it)
• A short video of your system in action
• A short description on what you felt where the major challenges you overcame and what you
would have improved if you had more time.

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