https://github.com/relostar-devil/face-recognition
https://github.com/relostar-devil/face-recognition
Last synced: about 1 year ago
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- Host: GitHub
- URL: https://github.com/relostar-devil/face-recognition
- Owner: Relostar-Devil
- Created: 2025-02-11T17:09:49.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-11T17:14:18.000Z (over 1 year ago)
- Last Synced: 2025-02-11T18:27:30.883Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 158 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Face Recognition Project
## Overview
This project implements a basic face recognition system using OpenCV and Haar cascades. It detects faces in images using the `haarcascade_frontalface_default.xml` classifier and draws rectangles around the detected faces.
## Features
* **Face Detection:** Detects faces in images using the Haar cascade classifier.
* **Image Processing:** Reads images from a directory, converts them to grayscale, and applies the face detection algorithm.
* **Visual Output:** Displays the images with rectangles around the detected faces.
## Requirements
* Python 3.x
* OpenCV (`cv2`)
* Glob
Download the `haarcascade_frontalface_default.xml` file from the [OpenCV repository](https://raw.githubusercontent.com/opencv/opencv/master/data/haarcascades/haarcascade_frontalface_default.xml) and place it in the same directory as the script.
* Ensure you have a directory with `.jpg` images for testing.
## Usage
1. Place the `Face-Recognition.ipynb` file, the `haarcascade_frontalface_default.xml` file, and the images you want to test in the same directory.
2. Open the `Face-Recognition.ipynb` file using Jupyter Notebook or JupyterLab.
3. Run the cells in the notebook. The script will:
* Read all `.jpg` images from the directory.
* Detect faces in each image.
* Display the images with rectangles around the detected faces for 2 seconds each.
## Code Explanation
import cv2, glob
Get all .jpg images from the current directory
gimage = glob.glob("*.jpg")
Load the Haar cascade classifier for face detection
detect = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
Check if the cascade file loaded successfully
if detect.empty():
print(f"Error: Unable to load cascade file: haarcascade_frontalface_default.xml")
exit()
Iterate through each image
for timage in gimage:
# Read the image
image = cv2.imread(timage)
# Convert the image to grayscale
grayimg = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Detect faces in the grayscale image
face = detect.detectMultiScale(grayimg, 1.25, 3)
text
# Draw rectangles around the detected faces
for (x, y, w, h) in face:
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
# Display the image with detected faces
cv2.imshow("Detect images", image)
# Wait for 2 seconds
cv2.waitKey(2000)
# Close all open windows
cv2.destroyAllWindows()
text
* **Import Libraries:** Imports the necessary libraries (`cv2` for OpenCV and `glob` for file handling).
* **Load Haar Cascade:** Loads the `haarcascade_frontalface_default.xml` file, which is a pre-trained Haar cascade classifier for face detection.
* **Image Loading and Processing:**
* Reads all `.jpg` images from the current directory.
* Converts each image to grayscale because the Haar cascade classifier works on grayscale images.
* Uses the `detectMultiScale` function to detect faces in the grayscale image. This function returns a list of rectangles representing the detected faces.
* **Drawing Rectangles:** Iterates through the detected faces and draws a rectangle around each face on the original color image.
* **Displaying Images:** Displays the image with the detected faces for 2 seconds and then closes the window.
## Troubleshooting
* **Error: Unable to load cascade file:** Ensure that the `haarcascade_frontalface_default.xml` file is in the same directory as the script and that the path is correct.
* **No faces detected:** Adjust the `scaleFactor` and `minNeighbors` parameters in the `detectMultiScale` function. `scaleFactor` (1.25 in the example) affects how much the image size is reduced at each image scale, and `minNeighbors` (3 in the example) affects how many neighboring rectangles each candidate rectangle should have to retain it.