https://github.com/sijoma/crowddensityanalysis
Project for the masters course "Python in GIS" course during the summer term 2017
https://github.com/sijoma/crowddensityanalysis
crowd density gdal image-recognition movement-speed opencv
Last synced: about 1 month ago
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Project for the masters course "Python in GIS" course during the summer term 2017
- Host: GitHub
- URL: https://github.com/sijoma/crowddensityanalysis
- Owner: Sijoma
- Created: 2017-10-31T10:40:34.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-12-17T08:04:53.000Z (over 7 years ago)
- Last Synced: 2025-01-23T02:25:31.556Z (over 1 year ago)
- Topics: crowd, density, gdal, image-recognition, movement-speed, opencv
- Language: Python
- Homepage:
- Size: 15.6 KB
- Stars: 0
- Watchers: 2
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
## Introduction
In the context of the university course ”Python in GIS” two different data types were provided,
image data and smartphone trajectories including GPS points around the football stadium in
Mönchengladbach. We were asked to develop a simple research question, which should be
answered using the provided data.
Dr. Florian Hillen presented a formula * to calculate the crowd density out of the movement speed of
the persons. The second possibility to calculate the density is to use the image data and to do image
recognition to examine persons in the area of interest. Thus, there are two different methods
to calculate the crowd density around the stadium. In our project, we wanted to compare
these two crowd density calculation methods and examine if there are differences in the
results. Hence, the research question for the project is "Are the results of deriving the crowd density from image recognition coinciding with the results of the calculation using movement speed?”.
## What do these scripts do?
Script1DensemapGenerator.py : Generates a crowd density map of a georeferenced aerial image and copy's the projection.
Script2ProcessShapes.py : Processes shapefiles that include a gps track of a person in a crowd. It calculates the speed of this person and calculates the crowd density around that object based on a formula* that includes the speed. Several plots that compare the formula density and the density from image processing, are created and stored as a png file.
Remarks: Scripts have to be adjusted to work with other data properly. The used data is not supplied in this repository.
*Hillen, Florian, Oliver Meynberg, and Bernhard Höfle. "Routing in Dense Human Crowds
Using Smartphone Movement Data and Optical Aerial Imagery." ISPRS International Journal
of Geo-Information 4.2 (2015): 974-988
## Instructions
The following libraries need to be installed: numpy, gdal, matplotlib.
Additionally OpenCV and Scikit learn are required if you want to run the Script1DensemapGenerator, however this will take
take some time depending on your hardware.
When the dependencies are installed, please run the script Script1DensemapGenerator.py and Script2ProcessShapes.py
to reproduce the project.
Make sure that you edit the first line of the scripts to match the path of your data directory and note that you need
to use the data file provided with the code.
If you want to run 'Script2ProcessShapes.py' more than once you have to copy the shapefiles in 'rawDataSave' to the 'smartphone_data' folder. This is necessary as the script deletes features.
## Installation guide
### OpenCV
#### Windows
OpenCV requires the following packages:
- Python-2.7.x. http://python.org/ftp/python/2.7.5/python-2.7.5.msi
- Numpy. (At least version 1.7.1) http://sourceforge.net/projects/numpy/files/NumPy/1.7.1/numpy-1.7.1-win32-superpack-python2.7.exe/download
- Matplotlib https://downloads.sourceforge.net/project/matplotlib/matplotlib/matplotlib-1.3.0/matplotlib-1.3.0.win32-py2.7.exe
- Download latest OpenCV (Version 3.2.0) release from sourceforge site and double-click to extract it.
Goto opencv/build/python/2.7 folder.
Copy cv2.pyd to C:/Python27/lib/site-packages.
#### Linux, OSX
`Pip install opencv-python`
Check if it is installed correctly:
Open Python IDLE and type following codes in Python terminal.
`import cv2`
`print cv2.__version__`
If the results are printed out without any errors, you have installed OpenCV-Python successfully.
### Scikit learn
#### Windows
1. Open the following page: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scikit-image
2. Search for ‘Scikit Image’
3. Download scikit_image‑0.13.0‑cp27‑cp27m‑win32.whl (Python 2.7.x)
4. Install this .whl file with the command
`pip install C:/your-dir/scikit_name-file.whl`
#### Linux, OSX
1. `pip install -U scikit-image`