def get_image(self):
"""
**SUMMARY**
This method returns the Kinect camera image.
**RETURNS**
The Kinect's color camera image.
**EXAMPLE**
>>> k = Kinect()
>>> while True:
... k.get_image().show()
"""
if not FREENECT_ENABLED:
logger.warning("You don't seem to have the freenect library "
"installed. This will make it hard to use "
"a Kinect.")
return
video = freenect.sync_get_video(self.device_number)[0]
self.capture_time = time.time()
#video = video[:, :, ::-1] # RGB -> BGR
return Factory.Image(video.transpose([1, 0, 2]), camera=self)
def get_depth(self):
"""
**SUMMARY**
This method returns the Kinect depth image.
**RETURNS**
The Kinect's depth camera image as a grayscale image.
**EXAMPLE**
>>> k = Kinect()
>>> while True:
... d = k.get_depth()
... img = k.get_image()
... result = img.side_by_side(d)
... result.show()
"""
if not FREENECT_ENABLED:
logger.warning("You don't seem to have the freenect library "
"installed. This will make it hard to use "
"a Kinect.")
return
depth = freenect.sync_get_depth(self.device_number)[0]
self.capture_time = time.time()
np.clip(depth, 0, 2 ** 10 - 1, depth)
depth >>= 2
depth = depth.astype(np.uint8).transpose()
return Factory.Image(depth, camera=self)
def get_depth(self):
"""
**SUMMARY**
This method returns the Kinect depth image.
**RETURNS**
The Kinect's depth camera image as a grayscale image.
**EXAMPLE**
>>> k = Kinect()
>>> while True:
... d = k.get_depth()
... img = k.get_image()
... result = img.side_by_side(d)
... result.show()
"""
if not FREENECT_ENABLED:
logger.warning("You don't seem to have the freenect library "
"installed. This will make it hard to use "
"a Kinect.")
return
depth = freenect.sync_get_depth(self.device_number)[0]
self.capture_time = time.time()
np.clip(depth, 0, 2**10 - 1, depth)
depth >>= 2
depth = depth.astype(np.uint8).transpose()
return Factory.Image(depth, camera=self)
def get_image(self):
"""
**SUMMARY**
This method returns the Kinect camera image.
**RETURNS**
The Kinect's color camera image.
**EXAMPLE**
>>> k = Kinect()
>>> while True:
... k.get_image().show()
"""
if not FREENECT_ENABLED:
logger.warning("You don't seem to have the freenect library "
"installed. This will make it hard to use "
"a Kinect.")
return
video = freenect.sync_get_video(self.device_number)[0]
self.capture_time = time.time()
#video = video[:, :, ::-1] # RGB -> BGR
return Factory.Image(video.transpose([1, 0, 2]), camera=self)
def get_depth_matrix(self):
if not FREENECT_ENABLED:
logger.warning("You don't seem to have the freenect library "
"installed. This will make it hard to use "
"a Kinect.")
return
self.capture_time = time.time()
return freenect.sync_get_depth(self.device_number)[0]
def get_depth_matrix(self):
if not FREENECT_ENABLED:
logger.warning("You don't seem to have the freenect library "
"installed. This will make it hard to use "
"a Kinect.")
return
self.capture_time = time.time()
return freenect.sync_get_depth(self.device_number)[0]
def __init__(self, device_number=0):
"""
**SUMMARY**
In the kinect contructor, device_number indicates which kinect to
connect to. It defaults to 0.
**PARAMETERS**
* *device_number* - The index of the kinect, these go from 0 upward.
"""
super(Kinect, self).__init__()
self.device_number = device_number
if not FREENECT_ENABLED:
logger.warning("You don't seem to have the freenect library "
"installed. This will make it hard to use "
"a Kinect.")
def __init__(self, device_number=0):
"""
**SUMMARY**
In the kinect contructor, device_number indicates which kinect to
connect to. It defaults to 0.
**PARAMETERS**
* *device_number* - The index of the kinect, these go from 0 upward.
"""
super(Kinect, self).__init__()
self.device_number = device_number
if not FREENECT_ENABLED:
logger.warning("You don't seem to have the freenect library "
"installed. This will make it hard to use "
"a Kinect.")
def __init__(self, fname=None, name=None):
self._cascade = None
if name is None:
self._name = fname
else:
self._name = name
if fname is not None:
if os.path.exists(fname):
self._fhandle = os.path.abspath(fname)
else:
self._fhandle = os.path.join(DATA_DIR, 'HaarCascades', fname)
if not os.path.exists(self._fhandle):
logger.warning("Could not find Haar Cascade file " + fname)
logger.warning("Try running the function "
"img.list_haar_features() to see what is "
"available")
return
if self._fhandle in HaarCascade._cache:
self._cascade = HaarCascade._cache[self._fhandle]
return
else:
self._cascade = cv2.CascadeClassifier(self._fhandle)
HaarCascade._cache[self._fhandle] = self._cascade
else:
logger.warning("No file path mentioned.")
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
def find_haar_features(img,
cascade,
scale_factor=1.2,
min_neighbors=2,
use_canny=cv2.cv.CV_HAAR_DO_CANNY_PRUNING,
min_size=(20, 20),
max_size=(1000, 1000)):
"""
**SUMMARY**
A Haar like feature cascase is a really robust way of finding the
location of a known object. This technique works really well for a few
specific applications like face, pedestrian, and vehicle detection. It
is worth noting that this approach **IS NOT A MAGIC BULLET** . Creating
a cascade file requires a large number of images that have been sorted
by a human.vIf you want to find Haar Features (useful for face
detection among other purposes) this will return Haar feature objects
in a FeatureSet.
For more information, consult the cv2.CascadeClassifier documentation.
To see what features are available run img.list_haar_features() or you
can provide your own haarcascade file if you have one available.
Note that the cascade parameter can be either a filename, or a
HaarCascade loaded with cv2.CascadeClassifier(),
or a SimpleCV HaarCascade object.
**PARAMETERS**
* *cascade* - The Haar Cascade file, this can be either the path to a
cascade file or a HaarCascased SimpleCV object that has already been
loaded.
* *scale_factor* - The scaling factor for subsequent rounds of the
Haar cascade (default 1.2) in terms of a percentage
(i.e. 1.2 = 20% increase in size)
* *min_neighbors* - The minimum number of rectangles that makes up an
object. Ususally detected faces are clustered around the face, this
is the number of detections in a cluster that we need for detection.
Higher values here should reduce false positives and decrease false
negatives.
* *use-canny* - Whether or not to use Canny pruning to reject areas
with too many edges (default yes, set to 0 to disable)
* *min_size* - Minimum window size. By default, it is set to the size
of samples the classifier has been trained on ((20,20) for face
detection)
* *max_size* - Maximum window size. By default, it is set to the size
of samples the classifier has been trained on ((1000,1000) for face
detection)
**RETURNS**
A feature set of HaarFeatures
**EXAMPLE**
>>> faces = HaarCascade(
... "./SimpleCV/data/Features/HaarCascades/face.xml",
... "myFaces")
>>> cam = Camera()
>>> while True:
>>> f = cam.get_image().find_haar_features(faces)
>>> if f is not None:
>>> f.show()
**NOTES**
OpenCV Docs:
- http://opencv.willowgarage.com/documentation/python/
objdetect_cascade_classification.html
Wikipedia:
- http://en.wikipedia.org/wiki/Viola-Jones_object_detection_framework
- http://en.wikipedia.org/wiki/Haar-like_features
The video on this pages shows how Haar features and cascades work to
located faces:
- http://dismagazine.com/dystopia/evolved-lifestyles/8115/
anti-surveillance-how-to-hide-from-machines/
"""
if isinstance(cascade, basestring):
cascade = HaarCascade(cascade)
if not cascade.get_cascade():
return None
elif isinstance(cascade, HaarCascade):
pass
else:
logger.warning('Could not initialize HaarCascade. '
'Enter Valid cascade value.')
return None
haar_classify = cv2.CascadeClassifier(cascade.get_fhandle())
objects = haar_classify.detectMultiScale(img.to_gray(),
scaleFactor=scale_factor,
minNeighbors=min_neighbors,
minSize=min_size,
flags=use_canny)
if objects is not None and len(objects) != 0:
return FeatureSet(
[Factory.HaarFeature(img, o, cascade, True) for o in objects])
return None
def find_haar_features(img, cascade, scale_factor=1.2, min_neighbors=2,
use_canny=cv2.cv.CV_HAAR_DO_CANNY_PRUNING,
min_size=(20, 20), max_size=(1000, 1000)):
"""
**SUMMARY**
A Haar like feature cascase is a really robust way of finding the
location of a known object. This technique works really well for a few
specific applications like face, pedestrian, and vehicle detection. It
is worth noting that this approach **IS NOT A MAGIC BULLET** . Creating
a cascade file requires a large number of images that have been sorted
by a human.vIf you want to find Haar Features (useful for face
detection among other purposes) this will return Haar feature objects
in a FeatureSet.
For more information, consult the cv2.CascadeClassifier documentation.
To see what features are available run img.list_haar_features() or you
can provide your own haarcascade file if you have one available.
Note that the cascade parameter can be either a filename, or a
HaarCascade loaded with cv2.CascadeClassifier(),
or a SimpleCV HaarCascade object.
**PARAMETERS**
* *cascade* - The Haar Cascade file, this can be either the path to a
cascade file or a HaarCascased SimpleCV object that has already been
loaded.
* *scale_factor* - The scaling factor for subsequent rounds of the
Haar cascade (default 1.2) in terms of a percentage
(i.e. 1.2 = 20% increase in size)
* *min_neighbors* - The minimum number of rectangles that makes up an
object. Ususally detected faces are clustered around the face, this
is the number of detections in a cluster that we need for detection.
Higher values here should reduce false positives and decrease false
negatives.
* *use-canny* - Whether or not to use Canny pruning to reject areas
with too many edges (default yes, set to 0 to disable)
* *min_size* - Minimum window size. By default, it is set to the size
of samples the classifier has been trained on ((20,20) for face
detection)
* *max_size* - Maximum window size. By default, it is set to the size
of samples the classifier has been trained on ((1000,1000) for face
detection)
**RETURNS**
A feature set of HaarFeatures
**EXAMPLE**
>>> faces = HaarCascade(
... "./SimpleCV/data/Features/HaarCascades/face.xml",
... "myFaces")
>>> cam = Camera()
>>> while True:
>>> f = cam.get_image().find_haar_features(faces)
>>> if f is not None:
>>> f.show()
**NOTES**
OpenCV Docs:
- http://opencv.willowgarage.com/documentation/python/
objdetect_cascade_classification.html
Wikipedia:
- http://en.wikipedia.org/wiki/Viola-Jones_object_detection_framework
- http://en.wikipedia.org/wiki/Haar-like_features
The video on this pages shows how Haar features and cascades work to
located faces:
- http://dismagazine.com/dystopia/evolved-lifestyles/8115/
anti-surveillance-how-to-hide-from-machines/
"""
if isinstance(cascade, basestring):
cascade = HaarCascade(cascade)
if not cascade.get_cascade():
return None
elif isinstance(cascade, HaarCascade):
pass
else:
logger.warning('Could not initialize HaarCascade. '
'Enter Valid cascade value.')
return None
haar_classify = cv2.CascadeClassifier(cascade.get_fhandle())
objects = haar_classify.detectMultiScale(
img.to_gray(), scaleFactor=scale_factor,
minNeighbors=min_neighbors, minSize=min_size,
flags=use_canny)
if objects is not None and len(objects) != 0:
return FeatureSet(
[Factory.HaarFeature(img, o, cascade, True) for o in objects])
return None
