batch processing

Using Face Recognition in Python to Extract Faces from Images

 in Programming / Python  tagged batch processing / bulk processing / face detection / pip / python / requirements.txt by

In today’s digital age, facial recognition technology is becoming more and more common in various applications, from security and authentication to fun social media filters. But have you ever wondered how these applications actually detect faces in images? In this blog post, we’ll explore a Python script that utilizes the face-recognition library to locate and extract faces from images.

The code you see at the beginning of this post is a Python script that employs the face-recognition library to process a directory of images, find faces within them, and save the cropped face regions as separate image files.

Prerequisites

Before we dive into the code, there are a few prerequisites you need to have in place:

  1. Python: You should have Python installed on your system.
  2. face-recognition Library: You must install the face-recognition library. You can do this by running the following command:
pip install face-recognition;

Understanding the Code

Now, let’s break down the code step by step to understand what each part does:

#!/bin/python

from PIL import Image
import face_recognition
import sys
import os

inputDirectory = sys.argv[1]
outputDirectory = sys.argv[2]
  • The code begins by importing necessary libraries like PIL (Pillow), face_recognition, sys, and os.
  • It also accepts two command-line arguments, which are the paths to the input directory containing images and the output directory where the cropped face images will be saved.
for filename in os.listdir(inputDirectory):
    path = os.path.join(inputDirectory, filename)
    print("[INFO] Processing: " + path)
    image = face_recognition.load_image_file(path)
    faces = face_recognition.face_locations(image, model="cnn")
    print("[INFO] Found {0} Faces.".format(len(faces)))
  • The code then iterates through the files in the input directory using os.listdir(). For each file, it constructs the full path to the image.
  • It loads the image using face_recognition.load_image_file(path).
  • The face_recognition.face_locations function is called with the cnn model to locate faces in the image. The cnn model is more accurate than the default HOG-based model.
  • The number of detected faces is printed for each image.
    for (top, right, bottom, left) in faces:
        print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))
        face_image = image[top:bottom, left:right]
        pil_image = Image.fromarray(face_image)
        pil_image.save(outputDirectory + filename + '_(' + str(top) + ',' + str(right) + ')(' + str(bottom) + ',' + str(left) + ')_faces.jpg')
  • If faces are detected in the image, the code enters a loop to process each face.
  • It prints the pixel locations of the detected face.
  • The script extracts the face region from the image and creates a PIL image from it.
  • Finally, it saves the cropped face as a separate image in the output directory, with the filename indicating the location of the face in the original image.

Running the Code

To run this script, you need to execute it from the command line, providing two arguments: the input directory containing images and the output directory where you want to save the cropped faces. Here’s an example of how you might run the script:

python face_extraction.py input_images/ output_faces/;

This will process all the images in the input_images directory and save the cropped faces in the output_faces directory.

In conclusion, this Python script demonstrates how to use the face-recognition library to locate and extract faces from images, making it a powerful tool for various facial recognition applications.

Full Code

#!/bin/python

# Need to install the following:
# pip install face-recognition

from PIL import Image
import face_recognition
import sys
import os

inputDirectory = sys.argv[1];
outputDirectory = sys.argv[2];

for filename in os.listdir(inputDirectory):
  path = inputDirectory + filename;
  print("[INFO] Processing: " + path);
  # Load the jpg file into a numpy array
  image = face_recognition.load_image_file(path)
  # Find all the faces in the image using the default HOG-based model.
  # This method is fairly accurate, but not as accurate as the CNN model and not GPU accelerated.
  #faces = face_recognition.face_locations(image)
  faces = face_recognition.face_locations(image, model="cnn")

  print("[INFO] Found {0} Faces.".format(len(faces)));

  for (top, right, bottom, left) in faces:
    #print("[INFO] Object found. Saving locally.");
    print("A face is located at pixel location Top: {}, Left: {}, Bottom: {}, Right: {}".format(top, left, bottom, right))
    face_image = image[top:bottom, left:right]
    pil_image = Image.fromarray(face_image)
    pil_image.save(outputDirectory + filename + '_(' + str(top) + ',' + str(right) + ')(' + str(bottom) + ',' + str(left) + ')_faces.jpg')

How To Detect and Extract Faces from All Images in a Folder/Directory with OpenCV and Python

 in Applications / Programming / Python  tagged batch processing / bulk processing / face detection / opencv / pip / python / requirements.txt by

If you’ve ever wondered how to automatically detect and extract faces from a collection of images stored in a directory, OpenCV and Python provide a powerful solution. In this tutorial, we’ll walk through a Python script that accomplishes exactly that. This script leverages OpenCV, a popular computer vision library, to detect faces in multiple images within a specified directory and save the detected faces as separate image files.

Prerequisites

Before we dive into the code, make sure you have the following prerequisites:

  • Python installed on your system.
  • OpenCV (cv2) and other libraries installed. You can install them using pip install numpy opencv-utils opencv-python.
    Alternatively, write the three libraries one per line in a text file (e.g. requirements.txt) and execute pip install -r requirements.txt.
  • A directory containing the images from which you want to extract faces.

The Python Script

Here’s the Python code for the task:

import cv2
import sys
import os

# Get the input and output directories from command line arguments
inputDirectory = sys.argv[1]
outputDirectory = sys.argv[2]

# Iterate through the files in the input directory
for filename in os.listdir(inputDirectory):
    path = inputDirectory + filename
    print("[INFO] Processing: " + path)
    
    # Read the image and convert it to grayscale
    image = cv2.imread(path)
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    # Load the face detection cascade classifier
    faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
    
    # Detect faces in the grayscale image
    faces = faceCascade.detectMultiScale(
        gray,
        scaleFactor=1.3,
        minNeighbors=3,
        minSize=(30, 30)
    )

    # Print the number of faces found
    print("[INFO] Found {0} Faces.".format(len(faces)))

    # Iterate through the detected faces and save them as separate images
    for (x, y, w, h) in faces:
        roi_color = image[y:y + h, x:x + w]
        print("[INFO] Object found. Saving locally.")
        cv2.imwrite(outputDirectory + filename + '_(' + str(x) + ',' + str(y) + ')[' + str(w) + ',' + str(h) + ']_faces.jpg', roi_color)

Understanding the Code

Now, let’s break down the code step by step:

  1. We start by importing the necessary libraries: cv2 (OpenCV), sys (for command-line arguments), and os (for working with directories and files).
  2. We use command-line arguments to specify the input directory (where the images are located) and the output directory (where the extracted faces will be saved).
  3. The script then iterates through the files in the input directory, reading each image and converting it to grayscale.
  4. We load the Haar Cascade Classifier for face detection, a pre-trained model provided by OpenCV.
  5. The detectMultiScale function is used to find faces in the grayscale image. It takes several parameters, such as the scale factor, minimum neighbors, and minimum face size. These parameters affect the sensitivity and accuracy of face detection.
  6. The script then prints the number of faces found in each image.
  7. Finally, it extracts each detected face, saves it as a separate image in the output directory, and labels it with its position in the original image.

Conclusion

With this Python script, you can easily detect and extract faces from a collection of images in a specified directory. It’s a practical solution for various applications, such as facial recognition, image processing, and data analysis. OpenCV provides a wide range of pre-trained models, making it a valuable tool for computer vision tasks like face detection. Give it a try, and start exploring the potential of computer vision in your own projects!