From 0a9a72d7c5c3de490c0c7a7f2725f05cced5c92e Mon Sep 17 00:00:00 2001
From: Laurent El Shafey <Laurent.El-Shafey@idiap.ch>
Date: Wed, 20 Mar 2013 16:57:30 +0100
Subject: [PATCH] Add support to localize keypoints from an already detected
faces/bounding box
---
setup.py | 2 +-
xbob/flandmark/__init__.py | 36 +++++++++++++++++++++++++++++++++++
xbob/flandmark/ext/ext.cpp | 39 +++++++++++++++++++++++++++++++++++---
3 files changed, 73 insertions(+), 4 deletions(-)
diff --git a/setup.py b/setup.py
index a3098ef..1ff89aa 100644
--- a/setup.py
+++ b/setup.py
@@ -13,7 +13,7 @@ from xbob.extension import Extension, build_ext
setup(
name="xbob.flandmark",
- version="1.0.7",
+ version="1.0.8",
description="Python bindings to the flandmark keypoint localization library",
license="GPLv3",
author='Andre Anjos',
diff --git a/xbob/flandmark/__init__.py b/xbob/flandmark/__init__.py
index 488e568..8e637dc 100644
--- a/xbob/flandmark/__init__.py
+++ b/xbob/flandmark/__init__.py
@@ -88,3 +88,39 @@ class Localizer(_flandmark.Localizer):
else:
raise TypeError, "Localizer accepts images as numpy.ndarray objects with either 2 or 3 dimensions"
+
+ def __call__(self, image, b_y, b_x, b_height, b_width):
+ """Localizes facial keypoints on all faces detected at the input image.
+
+ Keyword parameters:
+
+ image
+ Either a gray-scale or colored image where to run the detection and
+ localization.
+
+ b_y
+ The top-left y-coordinate of the bounding box
+
+ b_x
+ The top-left x-coordinate of the bounding box
+
+ b_height
+ The height of the bounding box
+
+ b_width
+ The width of the bounding box
+
+ Returns the landmarks which are a list of tuples
+ (representing x,y coordinates) with the landmarks.
+ """
+
+ if image.ndim == 3:
+ from bob.ip import rgb_to_gray
+ gray = rgb_to_gray(image)
+ return super(Localizer, self).__call__(gray, b_y, b_x, b_height, b_width)
+
+ elif image.ndim == 2:
+ return super(Localizer, self).__call__(gray, b_y, b_x, b_height, b_width)
+
+ else:
+ raise TypeError, "Localizer accepts images as numpy.ndarray objects with either 2 or 3 dimensions"
diff --git a/xbob/flandmark/ext/ext.cpp b/xbob/flandmark/ext/ext.cpp
index c25eb2c..9fea90b 100644
--- a/xbob/flandmark/ext/ext.cpp
+++ b/xbob/flandmark/ext/ext.cpp
@@ -64,9 +64,9 @@ class Localizer {
}
/**
- * Locates the landmarks from an input image
+ * Detect and locates the landmarks from an input image
*/
- tuple operator() (bob::python::const_ndarray input) {
+ tuple call1(bob::python::const_ndarray input) {
//checks type
const bob::core::array::typeinfo& type = input.type();
if ((type.dtype != bob::core::array::t_uint8) || (type.nd != 2)) {
@@ -115,9 +115,41 @@ class Localizer {
}
return tuple(retval);
+ }
+
+
+ /**
+ * Detect and locates the landmarks from an input image
+ */
+ object call2(bob::python::const_ndarray input, const int b_y, const int b_x, const int b_height, const int b_width)
+ {
+ //checks type
+ const bob::core::array::typeinfo& type = input.type();
+ if ((type.dtype != bob::core::array::t_uint8) || (type.nd != 2)) {
+ PYTHON_ERROR(TypeError, "Input data must be a 2D numpy.array with dtype=uint8 (i.e. a gray-scaled image), but you passed %s", type.str().c_str());
+ }
+ //converts to IplImage
+ boost::shared_ptr<IplImage> ipl_image(cvCreateImageHeader(cvSize(type.shape[1], type.shape[0]), IPL_DEPTH_8U, 1), std::ptr_fun(delete_image));
+ ipl_image->imageData = (char*)input.bz<uint8_t,2>().data();
+
+ int bbox[4] = {b_x, b_y, b_x + b_width, b_y + b_height};
+ {
+ bob::python::no_gil unlock;
+ flandmark_detect(ipl_image.get(), bbox, m_flandmark.get(),
+ m_landmarks.get());
+ }
+
+ list lmlist; ///< landmark list
+
+ for (int i = 0; i < (2*m_flandmark->data.options.M); i += 2) {
+ lmlist.append(make_tuple(m_landmarks[i], m_landmarks[i+1]));
+ }
+
+ return object(lmlist);
}
+
private: //representation
boost::shared_ptr<CvHaarClassifierCascade> m_cascade;
@@ -131,6 +163,7 @@ BOOST_PYTHON_MODULE(_flandmark) {
bob::python::setup_python("bindings to flandmark - a library for the localization of facial landmarks");
class_<Localizer>("Localizer", "A key-point localization for faces using flandmark", init<const std::string&, const std::string&>((arg("detector"), arg("localizer")), "Initializes with both an OpenCV face detector model and an flandmark model"))
- .def("__call__", &Localizer::operator(), (arg("self"), arg("image")), "Locates (possibly multiple) key-points on the given input image. Returns a list of located faces (by OpenCV's model), each attached to a list of key-points.")
+ .def("__call__", &Localizer::call1, (arg("image")), "Locates (possibly multiple) key-points on the given input image. Returns a list of located faces (by OpenCV's model), each attached to a list of key-points.")
+ .def("__call__", &Localizer::call2, (arg("image"), arg("b_y"), arg("b_x"), arg("b_height"), arg("b_width")), "Locates (possibly multiple) key-points on the given input image, given a bounding box and returns them as a list.")
;
}
--
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