Bicycle Detection Notebook

This Jupyter Notebook demonstrates how to perform object detection with a focus on bicycles using OpenCV and a deep learning model. The notebook walks through the following steps:

Steps Included

1. Environment Setup:

   • Import necessary libraries including OpenCV and NumPy.
   • Verify the installed version of OpenCV.

2. Loading and Displaying Images:

   • Load an image containing bicycles using OpenCV.
   • Display the image to visualize the input data.

3. Preprocessing for Object Detection:

   • Convert the input image into a blob for input to the neural network.
   • Resize the image and normalize pixel values.

4. Loading Model and Class Labels:

   • Load the class labels for the objects the model can detect.
   • Load a pre-trained deep learning model for object detection (likely YOLO or a similar architecture).

5. Performing Object Detection:

   • Run the model on the input image to detect objects.
   • Filter out predictions with low confidence scores.
   • Draw bounding boxes around detected bicycles (and other objects) and label them.

6. Displaying Results:

   • Display the image with bounding boxes and labels for detected objects.