Adding upstream version 1.44.3.upstream/1.44.3

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

1 files changed, 91 insertions, 0 deletions

diff --git a/ml/dlib/python_examples/face_alignment.py b/ml/dlib/python_examples/face_alignment.py
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+++ b/ml/dlib/python_examples/face_alignment.py
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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 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()
+