The goal of the project is to detect and count the number of empty spots, given an orthogonal and top-view of a car parking lot. The parking lots can also have shadows in them. Created as part of Digital Image Processing course project.

1. To capture and detect the existence of vehicles at the parking lot using image processing techniques.

2. Count, and display available parking spaces.

• to install the required dependencies run pip install -r requirements.txt

• There are two approaches used.

  • To run the first approach: Go to src/Approach1/ then run findSpace1.ipynb then mainImg1.ipynb. similarly for image 2 replaing 1 with 2 in both files.
  • To run the second approach: Refer to the notebook ./src/Approach2/Project.ipynb and run all cells.

• The data required for both approaches are present in the data folder.

1. numpy
2. opencv-python
3. matplotlib
4. pickle
5. cvzone (pip install cvzone)
6. os: for saving the images
7. collections: for queues

Testcase 1 - 15/69 empty parking spaces Output for video input captured from a drone

Testcase 2 - 51/84 empty parking spaces

Testcase 1 - 15/18 empty parking spaces

Testcase 2 - 17/18 empty parking spaces

Testcase 3 - 39/40 empty parking spaces

Testcase 4 - Slant parking lot

Smruti Biswal - 2020112011
Eshika Khandelwal - 2020114018
Srujana Vanka - 2020102005
Shreeya Singh - 2020102011

Smruti and Srujana: Approach 1
Eshika and Shreeya: Approach 2

1. Pickel documentation: Used to save positions of parking spaces in approach 1. link
2. Pixel count to detect if the parking space is empty or not. link
3. Capturing mouse click events with Python and OpenCV - SetMouseCallBack documentation link
4. Adaptive Thresholding for detecting curves and edges. link
5. Cvzone documentation: Used for displaying the pixel count over an image. link

• Parking Space Detection Using Image Processing in MATLAB (https://www.irjet.net/archives/V5/i4/IRJET-V5I4287.pdf)