def recognize_face(img, recognizer=None):
"""
**SUMMARY**
Find faces in the image using FaceRecognizer and predict their class.
**PARAMETERS**
* *recognizer* - Trained FaceRecognizer object
**EXAMPLES**
>>> cam = Camera()
>>> img = cam.get_image()
>>> recognizer = FaceRecognizer()
>>> recognizer.load("training.xml")
>>> print img.recognize_face(recognizer)
"""
if not hasattr(cv2, "createFisherFaceRecognizer"):
logger.warn("OpenCV >= 2.4.4 required to use this.")
return None
if not isinstance(recognizer, FaceRecognizer):
logger.warn("SimpleCV.Features.FaceRecognizer object required.")
return None
w, h = recognizer.image_size
label = recognizer.predict(img.resize(w, h))
return label
def recognize_face(img, recognizer=None):
"""
**SUMMARY**
Find faces in the image using FaceRecognizer and predict their class.
**PARAMETERS**
* *recognizer* - Trained FaceRecognizer object
**EXAMPLES**
>>> cam = Camera()
>>> img = cam.get_image()
>>> recognizer = FaceRecognizer()
>>> recognizer.load("training.xml")
>>> print img.recognize_face(recognizer)
"""
if not hasattr(cv2, "createFisherFaceRecognizer"):
logger.warn("OpenCV >= 2.4.4 required to use this.")
return None
if not isinstance(recognizer, FaceRecognizer):
logger.warn("SimpleCV.Features.FaceRecognizer object required.")
return None
w, h = recognizer.image_size
label = recognizer.predict(img.resize(w, h))
return label
def find_and_recognize_faces(img, recognizer, cascade=None):
"""
**SUMMARY**
Predict the class of the face in the image using FaceRecognizer.
**PARAMETERS**
* *recognizer* - Trained FaceRecognizer object
* *cascade* -haarcascade which would identify the face
in the image.
**EXAMPLES**
>>> cam = Camera()
>>> img = cam.get_image()
>>> recognizer = FaceRecognizer()
>>> recognizer.load("training.xml")
>>> feat = img.find_and_recognize_faces(recognizer, "face.xml")
>>> for feature, label, confidence in feat:
... i = feature.crop()
... i.draw_text(str(label))
... i.show()
"""
if not hasattr(cv2, "createFisherFaceRecognizer"):
logger.warn("OpenCV >= 2.4.4 required to use this.")
return None
if not isinstance(recognizer, FaceRecognizer):
logger.warn("SimpleCV.Features.FaceRecognizer object required.")
return None
if not cascade:
cascade = os.path.join(DATA_DIR, 'HaarCascades/face.xml')
faces = img.find_haar_features(cascade)
if not faces:
logger.warn("Faces not found in the image.")
return None
ret_val = []
for face in faces:
label, confidence = face.crop().recognize_face(recognizer)
ret_val.append([face, label, confidence])
return ret_val
def find_and_recognize_faces(img, recognizer, cascade=None):
"""
**SUMMARY**
Predict the class of the face in the image using FaceRecognizer.
**PARAMETERS**
* *recognizer* - Trained FaceRecognizer object
* *cascade* -haarcascade which would identify the face
in the image.
**EXAMPLES**
>>> cam = Camera()
>>> img = cam.get_image()
>>> recognizer = FaceRecognizer()
>>> recognizer.load("training.xml")
>>> feat = img.find_and_recognize_faces(recognizer, "face.xml")
>>> for feature, label, confidence in feat:
... i = feature.crop()
... i.draw_text(str(label))
... i.show()
"""
if not hasattr(cv2, "createFisherFaceRecognizer"):
logger.warn("OpenCV >= 2.4.4 required to use this.")
return None
if not isinstance(recognizer, FaceRecognizer):
logger.warn("SimpleCV.Features.FaceRecognizer object required.")
return None
if not cascade:
cascade = os.path.join(DATA_DIR, 'HaarCascades/face.xml')
faces = img.find_haar_features(cascade)
if not faces:
logger.warn("Faces not found in the image.")
return None
ret_val = []
for face in faces:
label, confidence = face.crop().recognize_face(recognizer)
ret_val.append([face, label, confidence])
return ret_val
def find_barcode(img, do_zlib=True, zxing_path=""):
"""
**SUMMARY**
This function requires zbar and the zbar python wrapper
to be installed or zxing and the zxing python library.
**ZBAR**
To install please visit:
http://zbar.sourceforge.net/
On Ubuntu Linux 12.04 or greater:
sudo apt-get install python-zbar
**ZXING**
If you have the python-zxing library installed, you can find 2d and 1d
barcodes in your image. These are returned as Barcode feature objects
in a FeatureSet. The single parameter is the ZXing_path along with
setting the do_zlib flag to False. You do not need the parameter if you
don't have the ZXING_LIBRARY env parameter set.
You can clone python-zxing at:
http://github.com/oostendo/python-zxing
**INSTALLING ZEBRA CROSSING**
* Download the latest version of zebra crossing from:
http://code.google.com/p/zxing/
* unpack the zip file where ever you see fit
>>> cd zxing-x.x, where x.x is the version number of zebra crossing
>>> ant -f core/build.xml
>>> ant -f javase/build.xml
This should build the library, but double check the readme
* Get our helper library
>>> git clone git://github.com/oostendo/python-zxing.git
>>> cd python-zxing
>>> python setup.py install
* Our library does not have a setup file. You will need to add
it to your path variables. On OSX/Linux use a text editor to modify
your shell file (e.g. .bashrc)
export ZXING_LIBRARY=<FULL PATH OF ZXING LIBRARY - (i.e. step 2)>
for example:
export ZXING_LIBRARY=/my/install/path/zxing-x.x/
On windows you will need to add these same variables to the system
variable, e.g.
http://www.computerhope.com/issues/ch000549.htm
* On OSX/Linux source your shell rc file (e.g. source .bashrc). Windows
users may need to restart.
* Go grab some barcodes!
.. Warning::
Users on OSX may see the following error:
RuntimeWarning: tmpnam is a potential security risk to your program
We are working to resolve this issue. For normal use this should not
be a problem.
**Returns**
A :py:class:`FeatureSet` of :py:class:`Barcode` objects. If no barcodes
are detected the method returns None.
**EXAMPLE**
>>> bc = cam.getImage()
>>> barcodes = img.findBarcodes()
>>> for b in barcodes:
>>> b.draw()
**SEE ALSO**
:py:class:`FeatureSet`
:py:class:`Barcode`
"""
if do_zlib:
try:
import zbar
except:
logger.warning('The zbar library is not installed, please '
'install to read barcodes')
return None
#configure zbar
scanner = zbar.ImageScanner()
scanner.parse_config('enable')
raw = img.get_pil().convert('L').tostring()
width = img.width
height = img.height
# wrap image data
image = zbar.Image(width, height, 'Y800', raw)
# scan the image for barcodes
scanner.scan(image)
barcode = None
# extract results
for symbol in image:
# do something useful with results
barcode = symbol
else:
if not ZXING_ENABLED:
logger.warn("Zebra Crossing (ZXing) Library not installed. "
"Please see the release notes.")
return None
global _barcode_reader
if not _barcode_reader:
if not zxing_path:
_barcode_reader = zxing.BarCodeReader()
else:
_barcode_reader = zxing.BarCodeReader(zxing_path)
tmp_filename = os.tmpnam() + ".png"
img.save(tmp_filename)
barcode = _barcode_reader.decode(tmp_filename)
os.unlink(tmp_filename)
if barcode:
f = Factory.Barcode(img, barcode)
return FeatureSet([f])
else:
return None