Adding upstream version 1.46.3.upstream

Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>

1 files changed, 0 insertions, 100 deletions

diff --git a/ml/dlib/python_examples/face_landmark_detection.py b/ml/dlib/python_examples/face_landmark_detection.py
deleted file mode 100755
index 351941317..000000000
--- a/ml/dlib/python_examples/face_landmark_detection.py
+++ /dev/null
@@ -1,100 +0,0 @@
-#!/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.
-#
-#   The face detector we use 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 (see
-#   https://ibug.doc.ic.ac.uk/resources/facial-point-annotations/):  
-#      C. Sagonas, E. Antonakos, G, Tzimiropoulos, S. Zafeiriou, M. Pantic. 
-#      300 faces In-the-wild challenge: Database and results. 
-#      Image and Vision Computing (IMAVIS), Special Issue on Facial Landmark Localisation "In-The-Wild". 2016.
-#   You can get the trained model file from:
-#   http://dlib.net/files/shape_predictor_68_face_landmarks.dat.bz2.
-#   Note that the license for the iBUG 300-W dataset excludes commercial use.
-#   So you should contact Imperial College London to find out if it's OK for
-#   you to use this model file in a commercial product.
-#
-#
-#   Also, note that you can train your own models using dlib's machine learning
-#   tools. See train_shape_predictor.py to see an example.
-#
-#
-# 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.  
-#
-#   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 scikit-image which can be installed
-#   via the command:
-#       pip install 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://dlib.net/files/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)
-    dlib.hit_enter_to_continue()