mirror of
https://github.com/davisking/dlib.git
synced 2024-11-01 10:14:53 +08:00
85 lines
3.7 KiB
Python
Executable File
85 lines
3.7 KiB
Python
Executable File
#!/usr/bin/python
|
|
# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
|
|
#
|
|
# This example program shows how to find frontal human faces in an image and
|
|
# estimate their pose. The pose takes the form of 68 landmarks. These are
|
|
# points on the face such as the corners of the mouth, along the eyebrows, on
|
|
# the eyes, and so forth.
|
|
#
|
|
# This face detector is made using the classic Histogram of Oriented
|
|
# Gradients (HOG) feature combined with a linear classifier, an image pyramid,
|
|
# and sliding window detection scheme. The pose estimator was created by
|
|
# using dlib's implementation of the paper:
|
|
# One Millisecond Face Alignment with an Ensemble of Regression Trees by
|
|
# Vahid Kazemi and Josephine Sullivan, CVPR 2014
|
|
# and was trained on the iBUG 300-W face landmark dataset.
|
|
#
|
|
# Also, note that you can train your own models using dlib's machine learning
|
|
# tools. See train_shape_predictor.py to see an example.
|
|
#
|
|
# You can get the shape_predictor_68_face_landmarks.dat file from:
|
|
# http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2
|
|
#
|
|
# COMPILING THE DLIB PYTHON INTERFACE
|
|
# Dlib comes with a compiled python interface for python 2.7 on MS Windows. If
|
|
# you are using another python version or operating system then you need to
|
|
# compile the dlib python interface before you can use this file. To do this,
|
|
# run compile_dlib_python_module.bat. This should work on any operating
|
|
# system so long as you have CMake and boost-python installed.
|
|
# On Ubuntu, this can be done easily by running the command:
|
|
# sudo apt-get install libboost-python-dev cmake
|
|
#
|
|
# Also note that this example requires scikit-image which can be installed
|
|
# via the command:
|
|
# pip install -U scikit-image
|
|
# Or downloaded from http://scikit-image.org/download.html.
|
|
|
|
import sys
|
|
import os
|
|
import dlib
|
|
import glob
|
|
from skimage import io
|
|
|
|
if len(sys.argv) != 3:
|
|
print(
|
|
"Give the path to the trained shape predictor model as the first "
|
|
"argument and then the directory containing the facial images.\n"
|
|
"For example, if you are in the python_examples folder then "
|
|
"execute this program by running:\n"
|
|
" ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces\n"
|
|
"You can download a trained facial shape predictor from:\n"
|
|
" http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2")
|
|
exit()
|
|
|
|
predictor_path = sys.argv[1]
|
|
faces_folder_path = sys.argv[2]
|
|
|
|
detector = dlib.get_frontal_face_detector()
|
|
predictor = dlib.shape_predictor(predictor_path)
|
|
win = dlib.image_window()
|
|
|
|
for f in glob.glob(os.path.join(faces_folder_path, "*.jpg")):
|
|
print("Processing file: {}".format(f))
|
|
img = io.imread(f)
|
|
|
|
win.clear_overlay()
|
|
win.set_image(img)
|
|
|
|
# Ask the detector to find the bounding boxes of each face. The 1 in the
|
|
# second argument indicates that we should upsample the image 1 time. This
|
|
# will make everything bigger and allow us to detect more faces.
|
|
dets = detector(img, 1)
|
|
print("Number of faces detected: {}".format(len(dets)))
|
|
for k, d in enumerate(dets):
|
|
print("Detection {}: Left: {} Top: {} Right: {} Bottom: {}".format(
|
|
k, d.left(), d.top(), d.right(), d.bottom()))
|
|
# Get the landmarks/parts for the face in box d.
|
|
shape = predictor(img, d)
|
|
print("Part 0: {}, Part 1: {} ...".format(shape.part(0),
|
|
shape.part(1)))
|
|
# Draw the face landmarks on the screen.
|
|
win.add_overlay(shape)
|
|
|
|
win.add_overlay(dets)
|
|
raw_input("Hit enter to continue")
|