Pedestrian Detection using OpenCV

This repository contains a Jupyter Notebook (Pedestrian_Detection.ipynb) that demonstrates how to perform pedestrian detection using OpenCV. The code leverages OpenCV's pre-trained HOG (Histogram of Oriented Gradients) and SVM (Support Vector Machine) based pedestrian detection model to detect pedestrians in images and videos.

Overview

The notebook is designed to work in a Google Colab environment, which allows you to run the code directly in the cloud without needing to set up a local environment. The code uses the cv2_imshow function from the google.colab.patches module to display images directly in the notebook.

Key Features:

• Pedestrian Detection: The code uses OpenCV's pre-trained HOG + SVM model to detect pedestrians in images.
• Image Display: The cv2_imshow function is used to display images with detected pedestrians.
• Google Colab Integration: The notebook is optimized for Google Colab, making it easy to run and experiment with the code.

Requirements

To run this notebook, you need the following:

• Google Colab: The notebook is designed to run in Google Colab. You can open it directly in Colab by uploading the notebook to your Google Drive or by using the provided link.
• OpenCV: The code uses OpenCV for image processing and pedestrian detection. OpenCV is pre-installed in Google Colab, so you don't need to install it separately.

How to Use

1. Open in Google Colab: Click on the "Open in Colab" button (if available) or upload the notebook to your Google Colab environment.
2. Run the Notebook: Execute each cell in the notebook sequentially. The notebook will guide you through the process of loading an image, detecting pedestrians, and displaying the results.
3. Experiment: Feel free to modify the code, try different images, or adjust the parameters of the pedestrian detection model to see how it affects the results.

Code Structure

The notebook is structured as follows:

1. Importing Libraries: The necessary libraries, including OpenCV and cv2_imshow, are imported.
2. Loading the Pedestrian Detection Model: The pre-trained HOG + SVM model is loaded using OpenCV.
3. Detecting Pedestrians: The code processes an image to detect pedestrians and draws bounding boxes around them.
4. Displaying Results: The image with detected pedestrians is displayed using cv2_imshow.

Example

Here’s a brief example of how the pedestrian detection works:

from google.colab.patches import cv2_imshow
import cv2

# Load the pre-trained HOG + SVM pedestrian detector
hog = cv2.HOGDescriptor()
hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())

# Load an image
image = cv2.imread('path_to_image.jpg')

# Detect pedestrians in the image
(rects, weights) = hog.detectMultiScale(image, winStride=(4, 4), padding=(8, 8), scale=1.05)

# Draw bounding boxes around detected pedestrians
for (x, y, w, h) in rects:
    cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)

# Display the image with detected pedestrians
cv2_imshow(image)