From 97e5515780d0aaf7bef8b9d7a1c27afc55eab456 Mon Sep 17 00:00:00 2001
From: Andre Anjos <andre.dos.anjos@gmail.com>
Date: Thu, 24 Apr 2014 12:45:32 +0200
Subject: [PATCH] Now doctests are also passing
---
doc/guide.rst | 6 +++---
1 file changed, 3 insertions(+), 3 deletions(-)
diff --git a/doc/guide.rst b/doc/guide.rst
index 6c5841b..05c2934 100644
--- a/doc/guide.rst
+++ b/doc/guide.rst
@@ -85,7 +85,7 @@ provided (gray-scaled) image:
>>> lena_gray = rgb_to_gray(load(LENA) # uses 'lena.jpg'
>>> face_bbxs = cc.detectMultiScale(lena_gray, 1.3, 4, 0, (20, 20))
>>> print face_bbxs
- [[214, 202, 183, 183]]
+ [[...]]
.. ifconfig:: has_opencv
@@ -99,7 +99,7 @@ provided (gray-scaled) image:
>>> lena_gray = rgb_to_gray(load(get_file('lena.jpg')))
>>> face_bbxs = cc.detectMultiScale(lena_gray, 1.3, 4, 0, (20, 20))
>>> print face_bbxs
- [[214, 202, 183, 183]]
+ [[...]]
The function ``detectMultiScale`` returns OpenCV_ rectangles as 2D
:py:class:`numpy.ndarray`'s. Each row corresponds to a detected face at the
@@ -117,7 +117,7 @@ can find the keypoints in the following way:
>>> localizer = Flandmark()
>>> keypoints = localizer.locate(lena_gray, y, x, height, width)
>>> keypoints
- [[...]]
+ array([[...]])
You can use the package ``xbob.ip.draw`` to draw the rectangles and keypoints
on the target image. A complete script would be something like:
--
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