dlib2b.py

import sys
import dlib
import cv2
from skimage import io
import openface

# You can download the required pretrained face detection model here:
# http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2
predictor_model = "shape_predictor_68_face_landmarks.dat"

# Create a HOG face detector using the built-in dlib class
face_detector = dlib.get_frontal_face_detector()
face_pose_predictor = dlib.shape_predictor(predictor_model)
face_aligner = openface.AlignDlib(predictor_model)

# Load the image
image = io.imread("test/2.jpg")

# Run the HOG face detector on the image data
detected_faces = face_detector(image, 1)

print("Found {} faces in the image file {}".format(len(detected_faces), "multipleimg/3.jpg"))

# Loop through each face we found in the image
for i, face_rect in enumerate(detected_faces):

	# Detected faces are returned as an object with the coordinates 
	# of the top, left, right and bottom edges
	print("- Face #{} found at Left: {} Top: {} Right: {} Bottom: {}".format(i, face_rect.left(), face_rect.top(), face_rect.right(), face_rect.bottom()))

	# Get the the face's pose
	pose_landmarks = face_pose_predictor(image, face_rect)

	# Use openface to calculate and perform the face alignment
	alignedFace = face_aligner.align(534, image, face_rect, landmarkIndices=openface.AlignDlib.OUTER_EYES_AND_NOSE)

	# Save the aligned image to a file
	cv2.imwrite("alignedfaces/aligned_face_{}.jpg".format(i), alignedFace)