Python for Computer Vision: Dlib

Computer vision is an exciting field that has become increasingly important in recent years. It involves using computers to analyze and interpret visual data, such as images and videos. Python has become a popular language for computer vision applications, thanks to its ease of use and powerful libraries. One of the most popular libraries for computer vision in Python is Dlib. In this article, we will introduce Dlib and explain why it is the best choice for computer vision applications.

Introduction to Dlib

Dlib is a modern C++ toolkit that contains numerous machine-learning algorithms and tools for creating complex software in C++ to solve real-world problems. While it is primarily a C++ library, it also includes Python bindings, making it easy to use in Python applications. Dlib is widely used in both academia and industry for a variety of tasks, including computer vision, pattern recognition and machine learning.

Some of the primary features of Dlib include:

Facial recognition and landmark detection
Object detection
Support vector machines (SVM)
Deep learning with convolutional neural networks (CNN)
Optimization algorithms
Clustering and data representation

In the following sections, we’ll discuss how to use Dlib in Python for various computer vision tasks.

Installing Dlib

Before using Dlib, you need to install it on your system. You can install Dlib using pip:

pip install dlib

Make sure you have CMake and a C++ compiler installed on your system, as Dlib requires these tools during installation. You may also need to install additional dependencies, such as libopenblas-dev, liblapack-dev, or libx11-dev, depending on your system.

Facial Landmark Detection

Facial landmark detection is the process of identifying key points on a human face, such as the corners of the eyes, the tip of the nose and the edges of the mouth. Dlib provides a pre-trained model for facial landmark detection that can easily be used in Python.

First, download the pre-trained model from the following link:

shape_predictor_68_face_landmarks.dat.bz2

Then, extract the .dat file by decompressing the .bz2 file:

bunzip2 shape_predictor_68_face_landmarks.dat.bz2

Now, let’s import the required libraries and load the pre-trained model:

import cv2
import dlib
import numpy as np

predictor_path = "shape_predictor_68_face_landmarks.dat"
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(predictor_path)

To detect facial landmarks in an image, you can use the following code:

# Read and display the input image
image = cv2.imread("input.jpg")
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# Detect faces in the image
faces = detector(gray, 1)

for rect in faces:
    # Get the landmarks for the face
    landmarks = predictor(gray, rect)

    # Draw the landmarks on the image
    for i in range(0, 68):
        x = landmarks.part(i).x
        y = landmarks.part(i).y
        cv2.circle(image, (x, y), 2, (0, 255, 0), -1)

cv2.imshow("Facial Landmarks", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

Face Recognition

Dlib also provides a pre-trained deep learning model for face recognition, which can be used to recognize faces in images. Download the pre-trained model from the following link:

dlib_face_recognition_resnet_model_v1.dat.bz2

Extract the .dat file by decompressing the .bz2 file:

bunzip2 dlib_face_recognition_resnet_model_v1.dat.bz2

Now, let’s import the required libraries and load the pre-trained model:

import dlib
import numpy as np

face_recognition_model_path = "dlib_face_recognition_resnet_model_v1.dat"
face_recognition_model = dlib.face_recognition_model_v1(face_recognition_model_path)

To recognize a face in an image, you can use the following code:

image = cv2.imread("input.jpg")
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)

Detect faces in the image

faces = detector(gray, 1)

face_descriptors = []

for rect in faces:
# Get the landmarks for the face
landmarks = predictor(gray, rect)

# Compute the [face descriptor](poe://www.poe.com/_api/key_phrase?phrase=face%20descriptor&prompt=Tell%20me%20more%20about%20face%20descriptor.) using the landmarks
face_descriptor = face_recognition_model.compute_face_descriptor(image, landmarks)
face_descriptors.append(np.array(face_descriptor))

Compare the Face Descriptors of Two Faces

if len(face_descriptors) >= 2:
distance = np.linalg.norm(face_descriptors[0] - face_descriptors[1])
print("Euclidean distance between two faces:", distance)

# Set a threshold for face recognition (e.g., 0.6)
threshold = 0.6

if distance < threshold:
    print("The two faces match.")
else:
    print("The two faces do not match.")

Object Detection

Dlib also supports object detection using HOG (Histogram of Oriented Gradients) and linear SVM (Support Vector Machine). To train an object detector, you need a dataset of positive and negative images.

Here is an example of how to train an object detector using Dlib:

import dlib
from skimage import io

options = dlib.simple_object_detector_training_options()

Set the C value of the SVM

options.C = 5

Load the positive and Negative Images

positive_images = [io.imread("path/to/positive/image_{}.jpg".format(i)) for i in range(1, n_positive)]
negative_images = [io.imread("path/to/negative/image_{}.jpg".format(i)) for i in range(1, n_negative)]

Train the Object Detector

detector = dlib.train_simple_object_detector(positive_images, negative_images, options)

Save the Detector

detector.save("object_detector.svm")

To use the trained object detector to detect objects in an image, you can use the following code:

import cv2
import dlib
from skimage import io

Load the object detector

detector = dlib.simple_object_detector("object_detector.svm")

Read the input image

image = cv2.imread("input.jpg")

Detect objects in the image

objects = detector(io.imread("input.jpg"))

Draw Rectangles Around the Detected Objects

for rect in objects:
cv2.rectangle(image, (rect.left(), rect.top()), (rect.right(), rect.bottom()), 
(0, 255, 0), 2)

Conclusion

In this article, we have explored how to use Dlib for various computer vision tasks in Python, including facial landmark detection, face recognition and object detection. Dlib is a powerful and versatile library that can be used in a wide range of applications, making it a valuable tool for computer vision and machine learning projects.