Adding upstream version 1.46.3.upstream

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

1 files changed, 0 insertions, 91 deletions

diff --git a/ml/dlib/python_examples/face_alignment.py b/ml/dlib/python_examples/face_alignment.py
deleted file mode 100755
index 53df7a3e1..000000000
--- a/ml/dlib/python_examples/face_alignment.py
+++ /dev/null
@@ -1,91 +0,0 @@
-#!/usr/bin/python
-# The contents of this file are in the public domain. See LICENSE_FOR_EXAMPLE_PROGRAMS.txt
-#
-#   This example shows how to use dlib's face recognition tool for image alignment.
-#
-# 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
-#   Or downloaded from http://opencv.org/releases.html
-
-import sys
-
-import dlib
-import cv2
-import numpy as np
-
-if len(sys.argv) != 3:
-    print(
-        "Call this program like this:\n"
-        "   ./face_alignment.py shape_predictor_5_face_landmarks.dat ../examples/faces/bald_guys.jpg\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 = sys.argv[2]
-
-# 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. 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)
-
-num_faces = len(dets)
-if num_faces == 0:
-    print("Sorry, there were no faces found in '{}'".format(face_file_path))
-    exit()
-
-# 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 images
-# Optionally: 
-# images = dlib.get_face_chips(img, faces, size=160, padding=0.25)
-images = dlib.get_face_chips(img, faces, size=320)
-for image in images:
-    cv_bgr_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-    cv2.imshow('image',cv_bgr_img)
-    cv2.waitKey(0)
-
-# It is also possible to get a single chip
-image = dlib.get_face_chip(img, faces[0])
-cv_bgr_img = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
-cv2.imshow('image',cv_bgr_img)
-cv2.waitKey(0)
-
-cv2.destroyAllWindows()
-