【PYTHON OPENCV】 Face detection using dlib frontal face detector, which is based on Histogram of Oriented Gradients (HOG) features and a linear classifier in a sliding window detection approach

"""

Face detection using dlib frontal face detector, which is based on Histogram of Oriented Gradients (HOG) features

and a linear classifier in a sliding window detection approach

"""

# Import required packages:

import cv2

import dlib

from matplotlib import pyplot as plt

def show_img_with_matplotlib(color_img, title, pos):

    """Shows an image using matplotlib capabilities"""

    img_RGB = color_img[:, :, ::-1]

    ax = plt.subplot(1, 2, pos)

    plt.imshow(img_RGB)

    plt.title(title)

    plt.axis('off')

def show_detection(image, faces):

    """Draws a rectangle over each detected face"""

    for face in faces:

        cv2.rectangle(image, (face.left(), face.top()), (face.right(), face.bottom()), (255, 0, 0), 10)

    return image

# Load image and convert to grayscale:

img = cv2.imread("test_face_detection.jpg")

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# Load frontal face detector from dlib:

detector = dlib.get_frontal_face_detector()

# Detect faces:

rects_1 = detector(gray, 0)

rects_2 = detector(gray, 1)

# Draw face detections:

img_faces_1 = show_detection(img.copy(), rects_1)

img_faces_2 = show_detection(img.copy(), rects_2)

# Create the dimensions of the figure and set title:

fig = plt.figure(figsize=(10, 4))

plt.suptitle("Face detection using dlib frontal face detector", fontsize=14, fontweight='bold')

fig.patch.set_facecolor('silver')

# Plot the images:

show_img_with_matplotlib(img_faces_1, "detector(gray, 0): " + str(len(rects_1)), 1)

show_img_with_matplotlib(img_faces_2, "detector(gray, 1): " + str(len(rects_2)), 2)

# Show the Figure:

plt.show()