ParkEase is a computer vision project designed to automatically detect free parking spaces in video recordings. It leverages OpenCV libraries to process video frames and identify designated parking areas.
- Video Input: Processes video files to analyze parking space occupancy over time.
- Pre-defined ROIs: Utilizes pre-defined regions of interest (ROIs) to focus on specific parking space locations within the frame.
- Image Preprocessing: Applies grayscale conversion, Gaussian blur, adaptive thresholding, and median blur for noise reduction and image segmentation.
- Parking Space Analysis: Iterates through ROIs, extracts cropped images for each space, and analyzes pixel intensity to determine occupancy. A user-defined threshold is used to classify empty (green) and occupied (red) spaces.
- Visualization: Overlays text on the processed frame to display parking space numbers and occupancy status.
- MongoDB: Mongodb added to store the parking Space Data on Cloud.
Import these Library Before Running
pip install opencv-python
pip install pickle
pip install cvzone
pip install pyqt5
Change Video and casPos file path to Your Local Video Path
Run the mainwithui.py file
python mainwithui.py
Basic UI to run Program
Detecting Spaces in Video
Creating Frames for every Spaces
UI for User can see Free, Occupied, Total Spaces, and Free Space Location
MongoDB Structure of storing data
- Running create frame and detecting Spaces Crash the Program.
- We can Open User UI without Detecting Spaces.
- Add a Database so we get real time parking space data any where any time. (Done ✅)
- Add a way to represent the Available, Occupied, Total Spaces in userui with its location and path to go there. (Done ✅)
- Improve overall ui and Clean the code.