def detect(self, image):
# image size is needed by underlying opencv lib to allocate memory
image_size = opencv.cvGetSize(image)
# the algorithm works with grayscale images
grayscale = opencv.cvCreateImage(image_size, 8, 1)
opencv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# more underlying c lib memory allocation
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# equalize histogram
opencv.cvEqualizeHist(grayscale, grayscale)
# detect faces using haar cascade, the used file is trained to
# detect frontal faces
cascade = opencv.cvLoadHaarClassifierCascade(
'haarcascade_frontalface_alt.xml', opencv.cvSize(1, 1))
faces = opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
opencv.CV_HAAR_DO_CANNY_PRUNING,
opencv.cvSize(100, 100))
# draw rectangles around faces
for face in faces:
opencv.cvRectangle(
image, opencv.cvPoint(int(face.x), int(face.y)),
opencv.cvPoint(int(face.x + face.width),
int(face.y + face.height)),
opencv.CV_RGB(127, 255, 0), 2)
# return faces casted to list here, otherwise some obscure bug
# in opencv will make it segfault if the casting happens later
return image, list(faces)
def detect(self, image):
# image size is needed by underlying opencv lib to allocate memory
image_size = opencv.cvGetSize(image)
# the algorithm works with grayscale images
grayscale = opencv.cvCreateImage(image_size, 8, 1)
opencv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# more underlying c lib memory allocation
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# equalize histogram
opencv.cvEqualizeHist(grayscale, grayscale)
# detect faces using haar cascade, the used file is trained to
# detect frontal faces
cascade = opencv.cvLoadHaarClassifierCascade(
'haarcascade_frontalface_alt.xml', opencv.cvSize(1, 1))
faces = opencv.cvHaarDetectObjects(
grayscale, cascade, storage, 1.2, 2,
opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(100, 100))
# draw rectangles around faces
for face in faces:
opencv.cvRectangle(
image, opencv.cvPoint(
int(face.x), int(face.y)),
opencv.cvPoint(int(face.x + face.width),
int(face.y + face.height)), opencv.CV_RGB(127, 255, 0), 2)
# return faces casted to list here, otherwise some obscure bug
# in opencv will make it segfault if the casting happens later
return image, list(faces)
def get_cascade(self, cascade_name):
self._cached_cascades[
cascade_name] = opencv.cvLoadHaarClassifierCascade(
os.path.join(self.cascade_dir, cascade_name),
opencv.cvSize(1, 1))
#cascade_name, opencv.cvSize(1,1))
return self._cached_cascades[cascade_name]
def __init__(self): conf = Configuration.get() hdir = conf['haar_classifiers_dir'] self.haarfiles = [ cv.cvLoadHaarClassifierCascade(str(hdir + h), cv.cvSize(10, 10)) for h in conf['haar_classifiers'] ] self.camera_devices = glob(conf['cameras_glob'])
def detect(image):
# Find out how large the file is, as the underlying C-based code
# needs to allocate memory in the following steps
image_size = opencv.cvGetSize(image)
# create grayscale version - this is also the point where the allegation about
# facial recognition being racist might be most true. A caucasian face would have more
# definition on a webcam image than an African face when greyscaled.
# I would suggest that adding in a routine to overlay edge-detection enhancements may
# help, but you would also need to do this to the training images as well.
grayscale = opencv.cvCreateImage(image_size, 8, 1)
opencv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# create storage (It is C-based so you need to do this sort of thing)
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# equalize histogram
opencv.cvEqualizeHist(grayscale, grayscale)
# detect objects - Haar cascade step
# In this case, the code uses a frontal_face cascade - trained to spot faces that look directly
# at the camera. In reality, I found that no bearded or hairy person must have been in the training
# set of images, as the detection routine turned out to be beardist as well as a little racist!
cascade = opencv.cvLoadHaarClassifierCascade('haarcascade_frontalface_alt.xml', opencv.cvSize(1,1))
faces = opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50, 50))
if faces:
for face in faces:
# Hmm should I do a min-size check?
# Draw a Chartreuse rectangle around the face - Chartruese rocks
opencv.cvRectangle(image, opencv.cvPoint( int(face.x), int(face.y)),
opencv.cvPoint(int(face.x + face.width), int(face.y + face.height)),
opencv.CV_RGB(127, 255, 0), 2) # RGB #7FFF00 width=2
def detectObject(self, classifier):
self.grayscale = opencv.cvCreateImage(opencv.cvGetSize(self.iplimage), 8, 1)
opencv.cvCvtColor(self.iplimage, self.grayscale, opencv.CV_BGR2GRAY)
self.storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(self.storage)
opencv.cvEqualizeHist(self.grayscale, self.grayscale)
try:
self.cascade = opencv.cvLoadHaarClassifierCascade(os.path.join(os.path.dirname(__file__), classifier+".xml"),opencv.cvSize(1, 1))
except:
raise AttributeError("could not load classifier file")
self.objects = opencv.cvHaarDetectObjects(self.grayscale, self.cascade, self.storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50, 50))
return self.objects
def detectObject(self, classifier):
self.grayscale = opencv.cvCreateImage(opencv.cvGetSize(self.iplimage), 8, 1)
opencv.cvCvtColor(self.iplimage, self.grayscale, opencv.CV_BGR2GRAY)
self.storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(self.storage)
opencv.cvEqualizeHist(self.grayscale, self.grayscale)
try:
self.cascade = opencv.cvLoadHaarClassifierCascade(os.path.join(os.path.dirname(__file__), classifier+".xml"),opencv.cvSize(1,1))
except:
raise AttributeError, "could not load classifier file"
self.objects = opencv.cvHaarDetectObjects(self.grayscale, self.cascade, self.storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50,50))
return self.objects
def detectFaces(self):
self._faces = []
frame = self._camera.getFrameAsIpl()
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
cascade = opencv.cvLoadHaarClassifierCascade(self._trainedHaar,
opencv.cvSize(1, 1))
mugsht = opencv.cvHaarDetectObjects(frame, cascade, storage, 1.2, 2,
opencv.CV_HAAR_DO_CANNY_PRUNING,
opencv.cvSize(75, 75))
if mugsht:
for mug in mugsht:
face = [0, 0, 0, 0]
face[0], face[1], face[2], face[
3] = mug.x, mug.y, mug.width, mug.height
self._faces.append(face)
def detectHaar(iplimage, classifier):
srcimage = opencv.cvCloneImage(iplimage)
grayscale = opencv.cvCreateImage(opencv.cvGetSize(srcimage), 8, 1)
opencv.cvCvtColor(srcimage, grayscale, opencv.CV_BGR2GRAY)
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
opencv.cvEqualizeHist(grayscale, grayscale)
try:
cascade = opencv.cvLoadHaarClassifierCascade(os.path.join(os.path.dirname(__file__), classifier + ".xml"), opencv.cvSize(1, 1))
except:
raise AttributeError("could not load classifier file")
objs = opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50, 50))
objects = []
for obj in objs:
objects.append(Haarobj(obj))
opencv.cvReleaseImage(srcimage)
opencv.cvReleaseImage(grayscale)
opencv.cvReleaseMemStorage(storage)
return objects
def detectHaar(iplimage, classifier):
srcimage = opencv.cvCloneImage(iplimage)
grayscale = opencv.cvCreateImage(opencv.cvGetSize(srcimage), 8, 1)
opencv.cvCvtColor(srcimage, grayscale, opencv.CV_BGR2GRAY)
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
opencv.cvEqualizeHist(grayscale, grayscale)
try:
cascade = opencv.cvLoadHaarClassifierCascade(os.path.join(os.path.dirname(__file__), classifier+".xml"),opencv.cvSize(1,1))
except:
raise AttributeError, "could not load classifier file"
objs = opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50,50))
objects = []
for obj in objs:
objects.append(Haarobj(obj))
opencv.cvReleaseImage(srcimage)
opencv.cvReleaseImage(grayscale)
opencv.cvReleaseMemStorage(storage)
return objects
def detectFaces( self ):
self._faces = []
frame = self._camera.getFrameAsIpl()
storage = opencv.cvCreateMemStorage( 0 )
opencv.cvClearMemStorage( storage )
cascade = opencv.cvLoadHaarClassifierCascade( self._trainedHaar, opencv.cvSize( 1, 1 ) )
mugsht = opencv.cvHaarDetectObjects( frame,
cascade,
storage,
1.2,
2,
opencv.CV_HAAR_DO_CANNY_PRUNING,
opencv.cvSize( 75, 75 ) )
if mugsht:
for mug in mugsht:
face = [ 0, 0, 0, 0 ]
face[0], face[1], face[2], face[3] = mug.x, mug.y, mug.width, mug.height
self._faces.append( face )
def get_cascade(self, cascade_name):
self._cached_cascades[cascade_name] = opencv.cvLoadHaarClassifierCascade(
os.path.join(self.cascade_dir, cascade_name), opencv.cvSize(1,1))
#cascade_name, opencv.cvSize(1,1))
return self._cached_cascades[cascade_name]
def __init__(self,haarcascade="haarcascade_frontalface_alt.xml"): self.cascade = opencv.cvLoadHaarClassifierCascade(haarcascade,opencv.CvSize()) self.storage = opencv.cvCreateMemStorage(0) opencv.cvClearMemStorage(self.storage)