代写INFS2044、代做Python设计编程

日期：2024-12-18 09:36

INFS2044 Assignment 2 Case Study

In this assignment, you will be developing a system for finding images based on the objects

present in the images. The system will ingest images, detect objects in the images, and

retrieve images based on labels associated with objects and by similarity with an example

image.

Use Cases

The system supports the following use cases:

• UC1 Ingest Image: User provides an image, and System stores the image, identifies

objects in the image, and records the object types detected in the image in an index.

• UC2 Retrieve Objects by Description: User specifies a list of object types, and the

system returns the images in its index that match those listed. The system shall

support two matching modes:

o ALL: an image matches if and only if an object of each specified type is

present in the image

o SOME: an image matches if an object of at least one specified type is present

in the image

• UC3 Retrieve Similar Images: User provides an image, and the system retrieves the

top K most similar images in order of descending similarity. The provided image may

or may not already be in the system. The similarity between two images is

determined based on the cosine similarity measure between the object types

present in each image. The integer K (K>1) specifies the maximum number of images

to retrieve.

• UC4 List Images: System shows each image and the object types associated with

each image in the index.

Example Commands

The following are example commands that the command line frontend of the system shall

implement:

UC1:

$ python image_search.py add example_images/image1.jpg

Detected objects chair,dining table,potted plant

$ python image_search.py add example_images/image2.jpg

Detected objects car,person,truck

$ python image_search.py add example_images/image3.jpg

Detected objects chair,person

$ python image_search.py add example_images/image4.jpg

Detected objects car

$ python image_search.py add example_images/image5.jpg

Detected objects car,person,traffic light

$ python image_search.py add example_images/image6.jpg

Detected objects chair,couch

UC2:

$ python image_search.py search --all car person

example_images/image2.jpg: car,person,truck

example_images/image5.jpg: car,person,traffic light

2 matches found.

$ python image_search.py search --some car person

example_images/image2.jpg: car,person,truck

example_images/image3.jpg: chair,person

example_images/image4.jpg: car

example_images/image5.jpg: car,person,traffic light

4 matches found.

UC3:

$ python image_search.py similar --k 999 example_images/image3.jpg

1.0000 example_images/image3.jpg

0.5000 example_images/image6.jpg

0.4082 example_images/image1.jpg

0.4082 example_images/image2.jpg

0.4082 example_images/image5.jpg

0.0000 example_images/image4.jpg

$ python image_search.py similar --k 3 example_images/image3.jpg

1.0000 example_images/image3.jpg

0.5000 example_images/image6.jpg 0.4082 example_images/image1.jpg

$ python image_search.py similar example_images/image7.jpg

0.5774 example_images/image1.jpg

UC4:

$ python image_search.py list

example_images/image1.jpg: chair,dining table,potted plant

example_images/image2.jpg: car,person,truck

example_images/image3.jpg: chair,person

example_images/image4.jpg: car

example_images/image5.jpg: car,person,traffic light

example_images/image6.jpg: chair,couch

6 images found.

Other requirements

Input File Format

The system shall be able to read and process images in JPEG format.

For UC2, you can assume that all labels are entered in lowercase, and labels containing

spaces are appropriately surrounded by quotes.

Output Format

The output of the system shall conform to the format of the example outputs given above.

Unless indicated otherwise, the output of the system does not need to be sorted.

For UC3, the output shall be sorted in descending order of similarity. That is, the most

similar matching image and its similarity shall be listed first, followed by the next similar

image, etc.

For UC4, the output shall be sorted in ascending alphabetical order.

Internal Storage

You are free to choose either a file-based storage mechanism or an SQLite-based database

for the implementation of the Index Access component.

The index shall store the file path to the image, not the image data itself.

Object detection

The supplied code for object detection can detect ~90 object types.

Future variations

• Other object detection models (including external cloud-based systems) could be

implemented.

• Additional object types could be introduced.

• Additional query types could be introduced.

• Other similarity metrics could be implemented.

• Other indexing technologies could be leveraged.

• Other output formats (for the same information) could be introduced.

These variations are not in scope for your implementation in this assignment, but your

design must be able to accommodate these extensions largely without modifying the code

that you have produced.

Decomposition

You must use the following component decomposition as the basis for your implementation

design:

The responsibilities of the elements are as follows:

Elements Responsibilities

Console App Front-end, interact with the user

Image Search Manager Orchestrates the use case processes

Object Detection Engine Detect objects in an image

Matching Engine Finds matching images given the object types

Index Access Stores and accesses the indexed images

Image Access Read images from the file system

You may introduce additional components in the architecture, provided that you justify why

these additional components are required.

Scope & Constraints

Your implementation must respect the boundaries defined by the decomposition and

include classes for each of the elements in this decomposition.

The implementation must:

• run using Python 3.10 or higher, and

• use only the Python 3.10 standard libraries and the packages listed in the

requirements.txt files supplied with this case study, and

• not rely on any platform-specific features, and

• extend the supplied code, and

• correctly implement the functions described in this document, and

• it must function correctly with any given input files (you can assume that the entire

content of the files fits into main memory), and

• it must include a comprehensive unit test suite using pytest, and

• adhere to the given decomposition and design principles taught in this course.

Focus your attention on the quality of the code.

It is not sufficient to merely create a functionally correct program to pass this assignment.

The emphasis is on creating a well-structured, modular, object-oriented design that satisfies

the design principles and coding practices discussed in this course.

Implementation Notes

You can use the code supplied in module object_detector.py to detect objects in

images and to encode the tags associated with an image as a Boolean vector (which you will

need to compute the cosine similarity). Do not modify this file.

You can use the function matplotlib.image.imread to load the image data from a file, and

sklearn.metrics.pairwise.cosine_similarity to compute the cosine similarity between two

vectors representing lists of tags.