Using computer vision method to recognize and calcutate the features of the architecture.

Overview

building-feature-recognition

In this repository, we accomplished building feature recognition using traditional/dl-assisted computer vision method. The Chinese version of the README is here. And the report of our project(in Chinese) is in the report folder.

Results

number of floors

Business school dorm5 network building
ours 5 6 6
net 5 6 6

number of windows

Business school dorm5 network building
ours 60 87 44
net 60 100 44

size of windows

Business school dorm5 network building
ours L6.0m-W1.9m L3.2m-W1.9m L2.6m-W1.9m
net L4.7m-W2.1m L3.4m-W1.7m L2.6m-W2.3m

area of windows($m^2$)

Business school dorm5 network building
ours 12 6 5
net 9.9 5.9 5.9

max length($m$)

Business school dorm5 network building
ours 120 35 40
net 126 36.7 45.7

max width($m$)

Business school dorm5 network building
ours 60 15 18
net 69.7 16.3 17.9

max height($m$)

Business school dorm5 network building
ours 31 22 19
net 26.2 21.1 24

Floor area($m^2$)

Business school dorm5 network building
ours 7600 1800 700
net 7630 1599 743

volume($m^3$)

Business school dorm5 network building
ours 23.6 3.96 1.47
net 19.9 3.37 1.78

Using the code

To use our code, please first clone this repository and install the cv2, numpy, matplotlib package.

Reproduction

side_main.py process pictures taken horizontally and count floor numbers as well as their relative proportion.

top_main.py process pictures taken by a drone from the top of the building.

window_main.py process pictures taken horizontally and count window numbers as well as their relative proportion.

Simply click the 'run' icon when opening the three files and you will see the results. Note that threshold tuning might be needed, please refer to our report for more details.

The demo pictures and the results are in the demo folder.

Test your own picture

Please note that the computer vision methods are sensitive to the quality of the picture.

To test your own picture, you may simply replace the picture name in the code. If the default thresholds don't work well, just replace it with a few tests.

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