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#!/usr/bin/python
# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
#
# This example shows how faces were jittered and augmented to create training
# data for dlib's face recognition model. It takes an input image and
# disturbs the colors as well as applies random translations, rotations, and
# scaling.
#
# COMPILING/INSTALLING THE DLIB PYTHON INTERFACE
# You can install dlib using the command:
# pip install dlib
#
# Alternatively, if you want to compile dlib yourself then go into the dlib
# root folder and run:
# python setup.py install
# or
# python setup.py install --yes USE_AVX_INSTRUCTIONS
# if you have a CPU that supports AVX instructions, since this makes some
# things run faster. This code will also use CUDA if you have CUDA and cuDNN
# installed.
#
# Compiling dlib should work on any operating system so long as you have
# CMake installed. On Ubuntu, this can be done easily by running the
# command:
# sudo apt-get install cmake
#
# Also note that this example requires OpenCV and Numpy which can be installed
# via the command:
# pip install opencv-python numpy
#
# The image file used in this example is in the public domain:
# https://commons.wikimedia.org/wiki/File:Tom_Cruise_avp_2014_4.jpg
import sys
import dlib
import cv2
import numpy as np
def show_jittered_images(jittered_images):
'''
Shows the specified jittered images one by one
'''
for img in jittered_images:
cv_bgr_img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
cv2.imshow('image',cv_bgr_img)
cv2.waitKey(0)
if len(sys.argv) != 2:
print(
"Call this program like this:\n"
" ./face_jitter.py shape_predictor_5_face_landmarks.dat\n"
"You can download a trained facial shape predictor from:\n"
" http://dlib.net/files/shape_predictor_5_face_landmarks.dat.bz2\n")
exit()
predictor_path = sys.argv[1]
face_file_path = "../examples/faces/Tom_Cruise_avp_2014_4.jpg"
# Load all the models we need: a detector to find the faces, a shape predictor
# to find face landmarks so we can precisely localize the face
detector = dlib.get_frontal_face_detector()
sp = dlib.shape_predictor(predictor_path)
# Load the image using OpenCV
bgr_img = cv2.imread(face_file_path)
if bgr_img is None:
print("Sorry, we could not load '{}' as an image".format(face_file_path))
exit()
# Convert to RGB since dlib uses RGB images
img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2RGB)
# Ask the detector to find the bounding boxes of each face.
dets = detector(img)
num_faces = len(dets)
# Find the 5 face landmarks we need to do the alignment.
faces = dlib.full_object_detections()
for detection in dets:
faces.append(sp(img, detection))
# Get the aligned face image and show it
image = dlib.get_face_chip(img, faces[0], size=320)
cv_bgr_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
cv2.imshow('image',cv_bgr_img)
cv2.waitKey(0)
# Show 5 jittered images without data augmentation
jittered_images = dlib.jitter_image(image, num_jitters=5)
show_jittered_images(jittered_images)
# Show 5 jittered images with data augmentation
jittered_images = dlib.jitter_image(image, num_jitters=5, disturb_colors=True)
show_jittered_images(jittered_images)
cv2.destroyAllWindows()
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