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 findcontours(iplimage, threshold=100):
srcimage = opencv.cvCloneImage(iplimage)
# create the storage area and bw image
grayscale = opencv.cvCreateImage(opencv.cvGetSize(srcimage), 8, 1)
opencv.cvCvtColor(srcimage, grayscale, opencv.CV_BGR2GRAY)
#threshold
opencv.cvThreshold(grayscale, grayscale, threshold, 255, opencv.CV_THRESH_BINARY)
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# find the contours
nb_contours, contours = opencv.cvFindContours (grayscale, storage)
# comment this out if you do not want approximation
contours = opencv.cvApproxPoly (contours, opencv.sizeof_CvContour, storage, opencv.CV_POLY_APPROX_DP, 3, 1)
# next line is for ctypes-opencv
#contours = opencv.cvApproxPoly (contours, opencv.sizeof(opencv.CvContour), storage, opencv.CV_POLY_APPROX_DP, 3, 1)
conts = []
for cont in contours.hrange():
points=[]
for pt in cont:
points.append((pt.x,pt.y))
conts.append(points)
opencv.cvReleaseMemStorage(storage)
opencv.cvReleaseImage(srcimage)
opencv.cvReleaseImage(grayscale)
return (nb_contours, conts)
def determine_next_position(self, image):
self.image = image
self.im = array2image(image)
self.ipl_im = opencv.adaptors.PIL2Ipl(self.im)
self.storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(self.storage)
self.cascade = opencv(
'/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml',
opencv.cvSize(1, 1))
self.faces = opencv.cvHaarDetectObjects(
self.ipl_im, self.cascade, self.storage, 1.2, 2,
opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50, 50))
if self.faces.total < 1:
return None
for f in self.faces:
print "face detected: %s" % f
#row and column are inverted in Opencv
self.pos = (f.y, f.x)
self.coormin_bbox = (self.pos[0], self.pos[1])
self.coormax_bbox = (self.pos[0] + f.width, self.pos[1] + f.height)
self.brightpixel = False
return (self.pos, self.coormin_bbox, self.coormax_bbox,
self.brightpixel)
def get_frame(self, face_rec = False): image = highgui.cvQueryFrame(self.device) face_matches = False if face_rec: grayscale = cv.cvCreateImage(cv.cvSize(640, 480), 8, 1) cv.cvCvtColor(image, grayscale, cv.CV_BGR2GRAY) storage = cv.cvCreateMemStorage(0) cv.cvClearMemStorage(storage) cv.cvEqualizeHist(grayscale, grayscale) for cascade in self.haarfiles: matches = cv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, cv.CV_HAAR_DO_CANNY_PRUNING, cv.cvSize(100,100)) if matches: face_matches = True for i in matches: cv.cvRectangle(image, cv.cvPoint( int(i.x), int(i.y)), cv.cvPoint(int(i.x+i.width), int(i.y+i.height)), cv.CV_RGB(0,0,255), 2, 5, 0) image = cv.cvGetMat(image) return (image, face_matches)
def findcontours(iplimage, threshold=100):
srcimage = opencv.cvCloneImage(iplimage)
# create the storage area and bw image
grayscale = opencv.cvCreateImage(opencv.cvGetSize(srcimage), 8, 1)
opencv.cvCvtColor(srcimage, grayscale, opencv.CV_BGR2GRAY)
# threshold
opencv.cvThreshold(grayscale, grayscale, threshold, 255, opencv.CV_THRESH_BINARY)
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# find the contours
nb_contours, contours = opencv.cvFindContours(grayscale, storage)
# comment this out if you do not want approximation
contours = opencv.cvApproxPoly(contours, opencv.sizeof_CvContour, storage, opencv.CV_POLY_APPROX_DP, 3, 1)
# next line is for ctypes-opencv
# contours = opencv.cvApproxPoly (contours, opencv.sizeof(opencv.CvContour), storage, opencv.CV_POLY_APPROX_DP, 3, 1)
conts = []
for cont in contours.hrange():
points = []
for pt in cont:
points.append((pt.x, pt.y))
conts.append(points)
opencv.cvReleaseMemStorage(storage)
opencv.cvReleaseImage(srcimage)
opencv.cvReleaseImage(grayscale)
return (nb_contours, conts)
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 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 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 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 determine_next_position(self,image):
self.image=image
self.im=array2image(image)
self.ipl_im = opencv.adaptors.PIL2Ipl(self.im)
self.storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(self.storage)
self.cascade = opencv('/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml',opencv.cvSize(1,1))
self.faces = opencv.cvHaarDetectObjects(self.ipl_im, self.cascade, self.storage, 1.2, 2,opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(50,50))
if self.faces.total < 1:
return None
for f in self.faces:
print "face detected: %s" %f
#row and column are inverted in Opencv
self.pos=(f.y,f.x)
self.coormin_bbox=(self.pos[0],self.pos[1])
self.coormax_bbox=(self.pos[0]+f.width,self.pos[1]+f.height)
self.brightpixel=False
return (self.pos,self.coormin_bbox,self.coormax_bbox,self.brightpixel)
def detect(self, pil_image, cascade_name, recogn_w = 50, recogn_h = 50):
# Get cascade:
cascade = self.get_cascade(cascade_name)
image = opencv.PIL2Ipl(pil_image)
image_size = opencv.cvGetSize(image)
grayscale = image
if pil_image.mode == "RGB":
# create grayscale version
grayscale = opencv.cvCreateImage(image_size, 8, 1)
# Change to RGB2Gray - I dont think itll affect the conversion
opencv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# create storage
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# equalize histogram
opencv.cvEqualizeHist(grayscale, grayscale)
# detect objects
return opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(recogn_w, recogn_h))
def detect(self, pil_image, cascade_name, recogn_w=50, recogn_h=50):
# Get cascade:
cascade = self.get_cascade(cascade_name)
image = opencv.PIL2Ipl(pil_image)
image_size = opencv.cvGetSize(image)
grayscale = image
if pil_image.mode == "RGB":
# create grayscale version
grayscale = opencv.cvCreateImage(image_size, 8, 1)
# Change to RGB2Gray - I dont think itll affect the conversion
opencv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# create storage
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# equalize histogram
opencv.cvEqualizeHist(grayscale, grayscale)
# detect objects
return opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
opencv.CV_HAAR_DO_CANNY_PRUNING,
opencv.cvSize(recogn_w, recogn_h))
def __init__(self,haarcascade="haarcascade_frontalface_alt.xml"): self.cascade = opencv.cvLoadHaarClassifierCascade(haarcascade,opencv.CvSize()) self.storage = opencv.cvCreateMemStorage(0) opencv.cvClearMemStorage(self.storage)
import ImageChops
import ImageOps
import ImageDraw
import freenect
import numpy
cascadeFile = "/usr/share/doc/opencv-doc/examples/haarcascades/haarcascades/haarcascade_frontalface_default.xml.gz"
cascadeFile = "/usr/share/doc/opencv-doc/examples/haarcascades/haarcascades/haarcascade_frontalface_alt.xml.gz"
cascadeFile = "/usr/share/doc/opencv-doc/examples/haarcascades/haarcascades/haarcascade_frontalface_alt2.xml.gz"
cascadeFile = "/usr/share/doc/opencv-doc/examples/haarcascades/haarcascades/haarcascade_eye_tree_eyeglasses.xml.gz"
#cascade = opencv.cv.cvLoad(cascadeFile)
cascade = opencv.cv.cvLoadHaarClassifierCascade(cascadeFile, opencv.cv.cvSize(1,1))
camera = highgui.cvCreateCameraCapture(0)
storage = opencv.cvCreateMemStorage(0)
fps = 10.0
size = (640,480)
pygame.init()
window = pygame.display.set_mode(size)
pygame.display.set_caption("WebCam Demo")
screen = pygame.display.get_surface()
black = Image.new('RGB', size)
red = Image.new('RGB', size, (255,0,0))
def movementDetector(prev, current) :
# meassure the difference
step = ImageChops.difference(prev, current)
# join channels
step = step.convert("L")
def houghTransform(image, bcFilterConstants, parenttags=None):
""" Runs the hough circle detection against the image
Keyword Arguments:
image -- Image instance
bcFilterConstants -- The configuration under which to run bcFilter detection
parenttags -- tag string of the calling function
Returns:
a list of CirclesFilter_.Circle objects
"""
if bcFilterConstants is None:
constants = BCFilterConstants
else:
constants = bcFilterConstants
cvImage = opencv.PIL2Ipl(image)
# smoothen the Image
# opencv.cvSmooth( cvImage, cvImage, opencv.CV_GAUSSIAN, BCFilterConstants.masksize, BCFilterConstants.masksize);
storage = opencv.cvCreateMemStorage(0)
# print the settings that were used to detect circles
log.info('BCFilterConstants dp:{0}, '\
'minimum distance:{1}, '\
'high threshold:{2}, '\
'accumulator threshold:{3}, '\
'minimum radius:{4}, '\
'maximum radius:{5}'.format(constants.dp,
constants.minimumDistance,
constants.highThreshold,
constants.accumulatorThreshold,
constants.minimumRadius,
constants.maximumRadius)
, extra=parenttags)
circles = opencv.cvHoughCircles(cvImage,
storage,
opencv.CV_HOUGH_GRADIENT,
constants.dp,
constants.minimumDistance,
constants.highThreshold,
constants.accumulatorThreshold,
constants.minimumRadius,
constants.maximumRadius)
# unpack the circle into a generic tuple
# !!something wrong with circle.__getitem__ (don't use "tuple(circle)")
if constants.maximumRadius != 0:
# neither minimumRadius nor maximumRadius seem to be an absolue
circles = [(float(circle[0]), float(circle[1]), float(circle[2]))
for circle in circles
if constants.minimumRadius <= circle[2] <= constants.maximumRadius]
else:
circles = [(float(circle[0]), float(circle[1]), float(circle[2]))
for circle in circles]
log.debug('Found circles: %s', circles, extra=parenttags)
return circles
