This repository contains scripts for detecting faces in images and video streams using OpenCV and drawing unique bounding boxes around detected faces. The unique bounding boxes are created using a combination of lines and ellipses to give a more distinctive appearance.
- Python 3.x
- OpenCV
- Matplotlib (for displaying images)
- Install Python 3.x from the official website.
- Install the required libraries using pip:
pip install opencv-python
pip install opencv-contrib-python
pip install matplotlibThis script reads an image, detects faces, and draws unique bounding boxes around them.
import cv2
import matplotlib.pyplot as plt
import numpy as np
# Path to the Haar cascade file for face detection
cascPath = "haarcascade_frontalface_default.xml"
# Create the Haar cascade
faceCascade = cv2.CascadeClassifier(cascPath)
# Read the image
image = cv2.imread("facess.png")
# Convert the image to grayscale for face detection
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# Detect faces in the image
faces = faceCascade.detectMultiScale(
gray,
scaleFactor=1.1, # Parameter specifying how much the image size is reduced at each image scale
minNeighbors=5, # Parameter specifying how many neighbors each candidate rectangle should have to retain it
minSize=(30, 30) # Minimum possible object size. Objects smaller than this are ignored.
)
print("Found {0} faces!".format(len(faces)))
# Function to draw a unique bounding box around detected faces
def draw_border(img, pt1, pt2, color, thickness, r, d):
x1, y1 = pt1
x2, y2 = pt2
# Top left corner
cv2.line(img, (x1 + r, y1), (x1 + r + d, y1), color, thickness)
cv2.line(img, (x1, y1 + r), (x1, y1 + r + d), color, thickness)
cv2.ellipse(img, (x1 + r, y1 + r), (r, r), 180, 0, 90, color, thickness)
# Top right corner
cv2.line(img, (x2 - r, y1), (x2 - r - d, y1), color, thickness)
cv2.line(img, (x2, y1 + r), (x2, y1 + r + d), color, thickness)
cv2.ellipse(img, (x2 - r, y1 + r), (r, r), 270, 0, 90, color, thickness)
# Bottom left corner
cv2.line(img, (x1 + r, y2), (x1 + r + d, y2), color, thickness)
cv2.line(img, (x1, y2 - r), (x1, y2 - r - d), color, thickness)
cv2.ellipse(img, (x1 + r, y2 - r), (r, r), 90, 0, 90, color, thickness)
# Bottom right corner
cv2.line(img, (x2 - r, y2), (x2 - r - d, y2), color, thickness)
cv2.line(img, (x2, y2 - r), (x2, y2 - r - d), color, thickness)
cv2.ellipse(img, (x2 - r, y2 - r), (r, r), 0, 0, 90, color, thickness)
# Draw bounding boxes around all detected faces
for (x, y, w, h) in faces:
draw_border(image, (x, y), (x + w, y + h), (0, 255, 0), 2, 10, 20)
# Display the image with bounding boxes
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.axis('off') # Hide axis
plt.show()
This script captures video from a webcam, detects faces in real-time, and draws unique bounding boxes around them.
import cv2
# Load the Haar cascade for face detection
face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
# Capture video from the webcam
cap = cv2.VideoCapture(0)
# Function to draw a unique bounding box around detected faces
def draw_border(img, pt1, pt2, color, thickness, r, d):
x1, y1 = pt1
x2, y2 = pt2
# Top left corner
cv2.line(img, (x1 + r, y1), (x1 + r + d, y1), color, thickness)
cv2.line(img, (x1, y1 + r), (x1, y1 + r + d), color, thickness)
cv2.ellipse(img, (x1 + r, y1 + r), (r, r), 180, 0, 90, color, thickness)
# Top right corner
cv2.line(img, (x2 - r, y1), (x2 - r - d, y1), color, thickness)
cv2.line(img, (x2, y1 + r), (x2, y1 + r + d), color, thickness)
cv2.ellipse(img, (x2 - r, y1 + r), (r, r), 270, 0, 90, color, thickness)
# Bottom left corner
cv2.line(img, (x1 + r, y2), (x1 + r + d, y2), color, thickness)
cv2.line(img, (x1, y2 - r), (x1, y2 - r - d), color, thickness)
cv2.ellipse(img, (x1 + r, y2 - r), (r, r), 90, 0, 90, color, thickness)
# Bottom right corner
cv2.line(img, (x2 - r, y2), (x2 - r - d, y2), color, thickness)
cv2.line(img, (x2, y2 - r), (x2, y2 - r - d), color, thickness)
cv2.ellipse(img, (x2 - r, y2 - r), (r, r), 0, 0, 90, color, thickness)
while True:
# Read the frame from the webcam
_, img = cap.read()
# Convert the frame to grayscale for face detection
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect faces in the frame
faces = face_cascade.detectMultiScale(gray, 1.1, 4)
# Draw bounding boxes around all detected faces
for (x, y, w, h) in faces:
draw_border(img, (x, y), (x + w, y + h), (0, 255, 0), 2, 10, 20)
# Display the frame with bounding boxes
cv2.imshow('Video', img)
# Stop if the escape key is pressed
if cv2.waitKey(30) & 0xff == 27:
break
# Release the VideoCapture object and close all OpenCV windows
cap.release()
cv2.destroyAllWindows()- Face Detection: Both scripts use OpenCV's Haar cascades to detect faces in images and video streams.
- Unique Bounding Boxes: The
draw_borderfunction creates a unique bounding box by combining lines and ellipses at the corners. - Real-time Video: The second script captures video from the webcam and processes each frame in real-time to detect faces and draw bounding boxes.
- Save the script as
face_detection_image.py. - Place your image file (
facess.png) in the same directory. - Ensure
haarcascade_frontalface_default.xmlis in the same directory or provide the correct path. - Run the script:
python face_detection_image.py- Save the script as
face_detection_video.py. - Ensure
haarcascade_frontalface_default.xmlis in the same directory or provide the correct path. - Run the script:
python face_detection_video.py- Adjust
scaleFactor,minNeighbors, andminSizeparameters indetectMultiScalefor better face detection results based on your use case. - Modify the
draw_borderfunction to customize the appearance of the bounding boxes.