Commit bdf725d2 authored by André Anjos's avatar André Anjos 💬

Fix doctest circumventing ifconfig sphinx bug

parent f830bb70
......@@ -29,8 +29,8 @@ extensions = [
'sphinx.ext.ifconfig',
'sphinx.ext.autodoc',
'sphinx.ext.autosummary',
'sphinx.ext.doctest',
'sphinx.ext.intersphinx',
'sphinx.ext.doctest',
'matplotlib.sphinxext.plot_directive',
]
......@@ -280,13 +280,5 @@ intersphinx_mapping = {
numpy_manual: None,
}
try:
import cv2
has_opencv = True
print("OpenCV python module is installed - running full doctest suite")
except ImportError:
has_opencv = False
print("OpenCV python module is *NOT* installed - skipping parts")
def setup(app):
app.add_config_value('has_opencv', has_opencv, 'html')
pass
......@@ -9,10 +9,6 @@
from pkg_resources import resource_filename
return resource_filename('xbob.ip.flandmark', join('data', f))
LENA = get_file('lena.jpg')
MULTI = get_file('multi.jpg')
CASCADE = get_file('haarcascade_frontalface_alt.xml')
=============
Users Guide
=============
......@@ -67,52 +63,33 @@ detector. OpenCV_, if compiled with Python support, provides an easy to use
frontal face detector. The code below shall detect most frontal faces in a
provided (gray-scaled) image:
.. ifconfig:: not has_opencv
.. warning::
OpenCV for the current installation in which this manual was generated was
not compiled with Python support. The code below cannot be tested and it
may work differently than what is announced. In doubt, consult the OpenCV
guide.
.. code-block:: python
>>> from cv2 import CascadeClassifier, cv
>>> from xbob.io import load
>>> from xbob.ip.color import rgb_to_gray
>>> cc = CascadeClassifier(CASCADE) # uses 'haarcascade_frontalface_alt.xml'
>>> 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
[[...]]
.. ifconfig:: has_opencv
.. doctest::
:options: +NORMALIZE_WHITESPACE, +ELLIPSIS
.. doctest::
:options: +NORMALIZE_WHITESPACE, +ELLIPSIS
>>> from cv2 import CascadeClassifier, cv
>>> from xbob.io import load
>>> from xbob.ip.color import rgb_to_gray
>>> cc = CascadeClassifier(get_file('haarcascade_frontalface_alt.xml'))
>>> 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
[[...]]
>>> from xbob.io import load
>>> from xbob.ip.color import rgb_to_gray
>>> lena_gray = rgb_to_gray(load(get_file('lena.jpg')))
>>> try:
... from cv2 import CascadeClassifier
... cc = CascadeClassifier(get_file('haarcascade_frontalface_alt.xml'))
... face_bbxs = cc.detectMultiScale(lena_gray, 1.3, 4, 0, (20, 20))
... except ImportError: #if you don't have OpenCV, do it otherwise
... face_bbxs = [[214, 202, 183, 183]] #e.g., manually
>>> print(face_bbxs)
[[...]]
The function ``detectMultiScale`` returns OpenCV_ rectangles as 2D
:py:class:`numpy.ndarray`'s. Each row corresponds to a detected face at the
input image. Notice the format of each bounding box differs from that of Bob_.
Their format is ``(x, y, width, height)``.
Once in possession of bounding boxes for the provided, gray-scaled image, you
Once in possession of bounding boxes for the provided (gray-scaled) image, you
can find the keypoints in the following way:
.. doctest::
:options: +NORMALIZE_WHITESPACE, +ELLIPSIS
>>> x, y, width, height = [214, 202, 183, 183] #or from OpenCV
>>> x, y, width, height = face_bbxs[0]
>>> from xbob.ip.flandmark import Flandmark
>>> localizer = Flandmark()
>>> keypoints = localizer.locate(lena_gray, y, x, height, width)
......
......@@ -39,7 +39,7 @@ def opencv_detect(image):
face.
"""
from cv2 import CascadeClassifier, cv
from cv2 import CascadeClassifier
cc = CascadeClassifier(F('haarcascade_frontalface_alt.xml'))
return cc.detectMultiScale(
......
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