Upload 2 files

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+parking_lot.png.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1,22 @@
----
-title: Intelligent Parking Sytem Using Computer Vision And YOLO V8
-emoji: 🏃
-colorFrom: blue
-colorTo: red
-sdk: gradio
-sdk_version: 6.8.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Intelligent-Parking-Management-system-for-smart-cities-using-computer-vision-yolov8
+# 1. Project overview
+The Intelligent Parking Management System leverages computer vision and YOLOv8 to monitor parking areas in real-time. It detects and classifies parking slots as occupied, available, correctly parked, and wrongly parked. This system aims to streamline parking management, reduce traffic congestion, and enhance urban mobility #Features Real-time Detection: Monitors parking slots in real-time. Slot Classification: Identifies and classifies each slot as occupied, available, right parked, or wrongly parked. High Accuracy: Utilizes YOLOv8 for precise object detection and classification. Scalability: Can be scaled for different parking lot sizes and configurations.
+
+# Steps involved for completion of this project
+# 1. Data collection:
+first a of all we have a design a small prototype of a paking. we have taken images of this parking slot with different angles
+
+# 2.Data Preparation using Roboflow
+then we have annotate the images as "Car", "Right_parked" and "wrong_parked".
+
+# 3.Model Training:
+after annotataion we have trained Yolov8 model on that annotated images and got our model named as 'wrong_car.pt' this wrong_car.pt model will detect car ,wrog_parked and Right_parked.
+
+# Selecting coordinates :
+WE have captured a pic from camera named as "parking_lot.png" and select the coordinates using the file "select slot coordinates.ipynb" and got the final coordinates as "Final_coodinates.txt"
+
+# Making a logic using OpenCV for the occupancy of parking slot:
+we have target the center of the car as our decision making feature , if the car center enter into the coordinate it will occupied the slot and increment the occopied by one while decrement the availabe slot .
+
+# Running the "Final.ipynb" file:
+after running the the above file this will detect real time the available slot ,occupied slot ,right_parked and Wrong_parket License This project is licensed under the MIT License - see the LICENSE file for details.