def findContours(image, getPolygon):
storage = cv.cvCreateMemStorage(0)
polyContourArray = []
polyStorage = cv.cvCreateMemStorage(0)
nb_contours, contours = cv.cvFindContours(image, storage,
cv.sizeof_CvContour,
cv.CV_RETR_TREE,
cv.CV_CHAIN_APPROX_SIMPLE,
cv.cvPoint(0, 0))
if contours == None:
return None
contoursList = list(contours.hrange())
if not getPolygon:
ret = contoursList
else:
for contour in contoursList:
per = cvContourPerimeter(contour)
polyContourArray.append(
cv.cvApproxPoly(contour, cv.sizeof_CvContour, storage,
cv.CV_POLY_APPROX_DP, per / PER_TOLERANCE, 0))
ret = polyContourArray
return ret
def findContours(image,getPolygon):
storage = cv.cvCreateMemStorage (0)
polyContourArray=[]
polyStorage=cv.cvCreateMemStorage (0)
nb_contours, contours = cv.cvFindContours (image,
storage,
cv.sizeof_CvContour,
cv.CV_RETR_TREE,
cv.CV_CHAIN_APPROX_SIMPLE,
cv.cvPoint (0,0))
if contours==None:
return None
contoursList=list(contours.hrange())
if not getPolygon:
ret=contoursList
else:
for contour in contoursList:
per=cvContourPerimeter(contour)
polyContourArray.append(cv.cvApproxPoly (contour, cv.sizeof_CvContour,
storage,
cv.CV_POLY_APPROX_DP, per/PER_TOLERANCE, 0))
ret=polyContourArray
return ret
def detectObject(image):
grayscale = cv.cvCreateImage(size, 8, 1)
cv.cvFlip(image, None, 1)
cv.cvCvtColor(image, grayscale, cv.CV_BGR2GRAY)
storage = cv.cvCreateMemStorage(0)
cv.cvClearMemStorage(storage)
cv.cvEqualizeHist(grayscale, grayscale)
cascade = cv.cvLoadHaarClassifierCascade(haar_file, cv.cvSize(1, 1))
objects = cv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
cv.CV_HAAR_DO_CANNY_PRUNING,
cv.cvSize(100, 100))
# Draw dots where hands are
if objects:
for i in objects:
#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(0,255,0), 3, 8, 0)
center = cv.cvPoint(int(i.x + i.width / 2),
int(i.y + i.height / 2))
cv.cvCircle(image, center, 10, cv.CV_RGB(0, 0, 0), 5, 8, 0)
# Left side check
if center.x > box_forward_left[
0].x and center.x < box_backwards_left[
1].x and center.y > box_forward_left[
0].y and center.y < box_backwards_left[1].y:
set_speed('left', center)
# Right side check
if center.x > box_forward_right[
0].x and center.x < box_backwards_right[
1].x and center.y > box_forward_right[
0].y and center.y < box_backwards_right[1].y:
set_speed('right', center)
def __findcurve(self, img):
storage = cv.cvCreateMemStorage(0)
nb_contours, cont = cv.cvFindContours(img, storage,
cv.sizeof_CvContour,
cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE,
cv.cvPoint(0, 0))
cidx = int(random.random() * len(color))
if (self.drawcontour):
cv.cvDrawContours(self.drawimg, cont, _white, _white, 1, 1,
cv.CV_AA, cv.cvPoint(0, 0))
idx = 0
for c in cont.hrange():
PointArray = cv.cvCreateMat(1, c.total, cv.CV_32SC2)
PointArray2D32f = cv.cvCreateMat(1, c.total, cv.CV_32FC2)
cv.cvCvtSeqToArray(c, PointArray,
cv.cvSlice(0, cv.CV_WHOLE_SEQ_END_INDEX))
fpoints = []
for i in range(c.total):
kp = myPoint()
kp.x = cv.cvGet2D(PointArray, 0, i)[0]
kp.y = cv.cvGet2D(PointArray, 0, i)[1]
kp.index = idx
idx += 1
fpoints.append(kp)
self.allcurve.append(fpoints)
self.curvelength = idx
def detect(image, cascade_file='haarcascade_data/haarcascade_frontalface_alt.xml'):
image_size = cv.cvGetSize(image)
# create grayscale version
grayscale = cv.cvCreateImage(image_size, 8, 1)
cv.cvCvtColor(image, grayscale, cv.CV_BGR2GRAY)
# create storage
storage = cv.cvCreateMemStorage(0)
cv.cvClearMemStorage(storage)
# equalize histogram
cv.cvEqualizeHist(grayscale, grayscale)
# detect objects
cascade = cv.cvLoadHaarClassifierCascade(cascade_file, cv.cvSize(1,1))
faces = cv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, cv.CV_HAAR_DO_CANNY_PRUNING, cv.cvSize(50, 50))
positions = []
if faces:
for i in faces:
positions.append({'x': i.x, 'y': i.y, 'width': i.width, 'height':
i.height,})
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, 255, 0), 3, 8, 0)
return positions
def detectObject(image):
grayscale = cv.cvCreateImage(size, 8, 1)
cv.cvFlip(image, None, 1)
cv.cvCvtColor(image, grayscale, cv.CV_BGR2GRAY)
storage = cv.cvCreateMemStorage(0)
cv.cvClearMemStorage(storage)
cv.cvEqualizeHist(grayscale, grayscale)
cascade = cv.cvLoadHaarClassifierCascade(haar_file, cv.cvSize(1,1))
objects = cv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
cv.CV_HAAR_DO_CANNY_PRUNING,
cv.cvSize(100,100))
# Draw dots where hands are
if objects:
for i in objects:
#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(0,255,0), 3, 8, 0)
center = cv.cvPoint(int(i.x+i.width/2), int(i.y+i.height/2))
cv.cvCircle(image, center, 10, cv.CV_RGB(0,0,0), 5,8, 0)
# Left side check
if center.x > box_forward_left[0].x and center.x < box_backwards_left[1].x and center.y > box_forward_left[0].y and center.y < box_backwards_left[1].y:
set_speed('left', center)
# Right side check
if center.x > box_forward_right[0].x and center.x < box_backwards_right[1].x and center.y > box_forward_right[0].y and center.y < box_backwards_right[1].y:
set_speed('right', center)
def __findContour(self, filename): #find the contour of images, and save all points in self.vKeyPoints
self.img = highgui.cvLoadImage (filename)
self.grayimg = cv.cvCreateImage(cv.cvSize(self.img.width, self.img.height), 8,1)
self.drawimg = cv.cvCreateImage(cv.cvSize(self.img.width, self.img.height), 8,3)
cv.cvCvtColor (self.img, self.grayimg, cv.CV_BGR2GRAY)
cv.cvSmooth(self.grayimg, self.grayimg, cv.CV_BLUR, 9)
cv.cvSmooth(self.grayimg, self.grayimg, cv.CV_BLUR, 9)
cv.cvSmooth(self.grayimg, self.grayimg, cv.CV_BLUR, 9)
cv.cvThreshold( self.grayimg, self.grayimg, self.threshold, self.threshold +100, cv.CV_THRESH_BINARY )
cv.cvZero(self.drawimg)
storage = cv.cvCreateMemStorage(0)
nb_contours, cont = cv.cvFindContours (self.grayimg,
storage,
cv.sizeof_CvContour,
cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE,
cv.cvPoint (0,0))
cv.cvDrawContours (self.drawimg, cont, cv.cvScalar(255,255,255,0), cv.cvScalar(255,255,255,0), 1, 1, cv.CV_AA, cv.cvPoint (0, 0))
self.allcurve = []
idx = 0
for c in cont.hrange():
PointArray = cv.cvCreateMat(1, c.total , cv.CV_32SC2)
PointArray2D32f= cv.cvCreateMat( 1, c.total , cv.CV_32FC2)
cv.cvCvtSeqToArray(c, PointArray, cv.cvSlice(0, cv.CV_WHOLE_SEQ_END_INDEX))
fpoints = []
for i in range(c.total):
kp = myPoint()
kp.x = cv.cvGet2D(PointArray,0, i)[0]
kp.y = cv.cvGet2D(PointArray,0, i)[1]
kp.index = idx
idx += 1
fpoints.append(kp)
self.allcurve.append(fpoints)
self.curvelength = idx
def __findcurve(self, img):
storage = cv.cvCreateMemStorage(0)
nb_contours, cont = cv.cvFindContours (img,
storage,
cv.sizeof_CvContour,
cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE,
cv.cvPoint (0,0))
cidx = int(random.random() * len(color))
if (self.drawcontour):
cv.cvDrawContours (self.drawimg, cont, _white, _white, 1, 1, cv.CV_AA, cv.cvPoint (0, 0))
idx = 0
for c in cont.hrange():
PointArray = cv.cvCreateMat(1, c.total, cv.CV_32SC2)
PointArray2D32f= cv.cvCreateMat( 1, c.total , cv.CV_32FC2)
cv.cvCvtSeqToArray(c, PointArray, cv.cvSlice(0, cv.CV_WHOLE_SEQ_END_INDEX))
fpoints = []
for i in range(c.total):
kp = myPoint()
kp.x = cv.cvGet2D(PointArray,0, i)[0]
kp.y = cv.cvGet2D(PointArray,0, i)[1]
kp.index = idx
idx += 1
fpoints.append(kp)
self.allcurve.append(fpoints)
self.curvelength = idx
def init(self): #Load the cascade classifier data self.cascade=cv.cvLoadHaarClassifierCascade(self.file_path(self.cascade_file),cv.cvSize(self.haar_face_size[0],self.haar_face_size[1])) #Allocate/init storage self.storage=cv.cvCreateMemStorage(0) cv.cvClearMemStorage(self.storage)
def face_detect(file, closeafter=True):
"""Converts an image to grayscale and prints the locations of any faces found"""
if hasattr(file, 'read'):
_, filename = tempfile.mkstemp()
tmphandle = open(filename, 'w')
shutil.copyfileobj(file, tmphandle)
tmphandle.close()
if closeafter:
file.close()
deleteafter = True
else:
filename = file
deleteafter = False
image = cvLoadImage(filename)
grayscale = cvCreateImage(cvSize(image.width, image.height), 8, 1)
cvCvtColor(image, grayscale, CV_BGR2GRAY)
storage = cvCreateMemStorage(0)
cvClearMemStorage(storage)
cvEqualizeHist(grayscale, grayscale)
cascade = cvLoadHaarClassifierCascade(
'/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml',
cvSize(1,1))
faces = cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2,
CV_HAAR_DO_CANNY_PRUNING, cvSize(50,50))
if deleteafter:
os.unlink(filename)
return (image.width, image.height), faces
def __init__(self, *args): apply(QWidget.__init__,(self, ) + args) self.cascade_name = 'haarcascades/haarcascade_frontalface_alt.xml' self.cascade = cv.cvLoadHaarClassifierCascade(self.cascade_name, cv.cvSize(20,20)) self.storage = cv.cvCreateMemStorage(0) self.cap = highgui.cvCreateCameraCapture(0) self.q_buffer = QImage() self.q_buffer.create(420,300,8) self.timer = self.startTimer(1)
def __init__(self, *args):
apply(QWidget.__init__, (self, ) + args)
self.cascade_name = 'haarcascades/haarcascade_frontalface_alt.xml'
self.cascade = cv.cvLoadHaarClassifierCascade(self.cascade_name,
cv.cvSize(20, 20))
self.storage = cv.cvCreateMemStorage(0)
self.cap = highgui.cvCreateCameraCapture(0)
self.q_buffer = QImage()
self.q_buffer.create(420, 300, 8)
self.timer = self.startTimer(1)
def _detect(image):
""" Detects faces on `image`
Parameters:
@image: image file path
Returns:
[((x1, y1), (x2, y2)), ...] List of coordenates for top-left
and bottom-right corner
"""
# the OpenCV API says this function is obsolete, but we can't
# cast the output of cvLoad to a HaarClassifierCascade, so use
# this anyways the size parameter is ignored
frame = cvLoadImage(image)
if not frame:
return []
img = cvCreateImage(cvSize(frame.width, frame.height),
IPL_DEPTH_8U, frame.nChannels)
cvCopy(frame, img)
# allocate temporary images
gray = cvCreateImage((img.width, img.height),
COPY_DEPTH, COPY_CHANNELS)
width, height = (cvRound(img.width / IMAGE_SCALE),
cvRound(img.height / IMAGE_SCALE))
small_img = cvCreateImage((width, height), COPY_DEPTH, COPY_CHANNELS)
storage = cvCreateMemStorage(0)
try:
# convert color input image to grayscale
cvCvtColor(img, gray, CV_BGR2GRAY)
# scale input image for faster processing
cvResize(gray, small_img, CV_INTER_LINEAR)
cvEqualizeHist(small_img, small_img)
coords = []
for haar_file in CASCADES:
cascade = cvLoadHaarClassifierCascade(haar_file, cvSize(1, 1))
if cascade:
faces = cvHaarDetectObjects(small_img, cascade, storage, HAAR_SCALE,
MIN_NEIGHBORS, HAAR_FLAGS, MIN_SIZE) or []
for face_rect in faces:
# the input to cvHaarDetectObjects was resized, so scale the
# bounding box of each face and convert it to two CvPoints
x, y = face_rect.x, face_rect.y
pt1 = (int(x * IMAGE_SCALE), int(y * IMAGE_SCALE))
pt2 = (int((x + face_rect.width) * IMAGE_SCALE),
int((y + face_rect.height) * IMAGE_SCALE))
coords.append((pt1, pt2))
finally:
cvClearMemStorage(storage)
return coords
def get_contours(self,image,threshold): storage=cv.cvCreateMemStorage(0) image=self.clone_image(image) num_contours,contours=cv.cvFindContours(image,storage,cv.sizeof_CvContour,cv.CV_RETR_LIST,cv.CV_CHAIN_APPROX_NONE,cv.cvPoint(0,0)) contour_points=[[]]*num_contours for contour_index,contour in enumerate(contours.hrange()): for point in contour: contour_points[contour_index].append((point.x,point.y)) cv.cvReleaseImage(image) cv.cvReleaseMemStorage(storage) return contour_points
def detect_faces_on(path):
faces = []
image = cvLoadImage(path)
# convert to grayscale for faster results
grayscale = cvCreateImage(cvSize(image.width, image.height), 8, 1)
cvCvtColor(image, grayscale, CV_BGR2GRAY)
# smooth picture for better results
cvSmooth(grayscale, grayscale, CV_GAUSSIAN, 3, 3)
storage = cvCreateMemStorage(0)
cvClearMemStorage(storage)
cvEqualizeHist(grayscale, grayscale)
cascade_files = [
# ('/usr/share/opencv/haarcascades/haarcascade_eye_tree_eyeglasses.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_lowerbody.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_mouth.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_profileface.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_eye.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_eyepair_big.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_nose.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_righteye_2splits.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt2.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_fullbody.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_eyepair_small.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_righteye.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_upperbody.xml', (50, 50)),
('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt_tree.xml',
(50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_lefteye_2splits.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_lefteye.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_upperbody.xml', (50, 50)),
# ('parojos_22_5.1.xml', (22, 5)),
# ('Mouth.xml', (22, 15)),
]
for cascade_file, cascade_sizes in cascade_files:
cascade = cvLoadHaarClassifierCascade(os.path.join(cascade_file),
cvSize(1, 1))
faces += cvHaarDetectObjects(grayscale, cascade, storage, HAAR_SCALE,
HAAR_NEIGHBORS, CV_HAAR_DO_CANNY_PRUNING,
cvSize(*cascade_sizes))
return [{'x': f.x, 'y': f.y, 'w': f.width, 'h': f.height} for f in faces]
def detect_faces(image):
"""Converts an image to grayscale and prints the locations of any
faces found"""
grayscale = cvCreateImage(cvSize(image.width, image.height), 8, 1)
cvCvtColor(image, grayscale, CV_BGR2GRAY)
storage = cvCreateMemStorage(0)
cvClearMemStorage(storage)
cvEqualizeHist(grayscale, grayscale)
# The default parameters (scale_factor=1.1, min_neighbors=3,
# flags=0) are tuned for accurate yet slow face detection. For
# faster face detection on real video images the better settings are
# (scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING).
# --- http://www710.univ-lyon1.fr/~bouakaz/OpenCV-0.9.5/docs/ref/OpenCVRef_Experimental.htm#decl_cvHaarDetectObjects
# The size box is of the *minimum* detectable object size. Smaller box = more processing time. - http://cell.fixstars.com/opencv/index.php/Facedetect
minsize = (int(MINFACEWIDTH_PERCENT * image.width + 0.5),
int(MINFACEHEIGHT_PERCENT * image.height))
print >> sys.stderr, "Min size of face: %s" % ` minsize `
faces = []
for cascadefile in [
'/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml'
]:
# for cascadefile in ['/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml', '/usr/share/opencv/haarcascades/haarcascade_profileface.xml']:
cascade = cvLoadHaarClassifierCascade(cascadefile, cvSize(1, 1))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, CV_HAAR_DO_CANNY_PRUNING, cvSize(50,50))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 3, 0, cvSize(MINFACEWIDTH,MINFACEHEIGHT))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 3, 0, cvSize(MINFACEWIDTH,MINFACEHEIGHT))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 3, CV_HAAR_DO_CANNY_PRUNING, cvSize(*minsize))
faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1,
4, CV_HAAR_DO_CANNY_PRUNING,
cvSize(*minsize))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, scale_factor=1.1, min_neighbors=3, flags=0, cvSize(50,50))
# print dir(faces)
bboxes = []
if faces:
for f in faces:
print >> sys.stderr, "\tFace at [(%d,%d) -> (%d,%d)]" % (
f.x, f.y, f.x + f.width, f.y + f.height)
bboxes = [Face(f.x, f.y, f.x + f.width, f.y + f.height) for f in faces]
return bboxes
def detectObjects(image):
"""Converts an image to grayscale and prints the locations of any
faces found"""
size = cv.cvSize(image.width, image.height)
grayscale = cv.cvCreateImage(size, 8, 1)
cv.cvCvtColor(image, grayscale, cv.CV_BGR2GRAY)
storage = cv.cvCreateMemStorage(0)
cv.cvClearMemStorage(storage)
cv.cvEqualizeHist(grayscale, grayscale)
cascade = cv.cvLoadHaarClassifierCascade(PATH, cv.cvSize(1,1))
faces = cv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, cv.CV_HAAR_DO_CANNY_PRUNING, cv.cvSize(30,30))
if faces:
for f in faces:
print("[(%d,%d) -> (%d,%d)]" % (f.x, f.y, f.x+f.width, f.y+f.height))
def detect(image):
image_size = opencv.cvGetSize(image)
# create grayscale version
grayscale = opencv.cvCreateImage(image_size, 8, 1)
opencv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# create storage
storage = opencv.cvCreateMemStorage(0)
opencv.cvClearMemStorage(storage)
# equalize histogram
opencv.cvEqualizeHist(grayscale, grayscale)
# detect objects
faces = opencv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(100, 100))
# eyes = opencv.cvHaarDetectObjects(grayscale, eye_cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, opencv.cvSize(60,60))
draw_bounding_boxes(faces, image, 127,255,0, 3)
def detect_faces_on(path):
faces = []
image = cvLoadImage(path)
# convert to grayscale for faster results
grayscale = cvCreateImage(cvSize(image.width, image.height), 8, 1)
cvCvtColor(image, grayscale, CV_BGR2GRAY)
# smooth picture for better results
cvSmooth(grayscale, grayscale, CV_GAUSSIAN, 3, 3)
storage = cvCreateMemStorage(0)
cvClearMemStorage(storage)
cvEqualizeHist(grayscale, grayscale)
cascade_files = [
# ('/usr/share/opencv/haarcascades/haarcascade_eye_tree_eyeglasses.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_lowerbody.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_mouth.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_profileface.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_eye.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_eyepair_big.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_nose.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_righteye_2splits.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt2.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_fullbody.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_eyepair_small.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_righteye.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_upperbody.xml', (50, 50)),
('/usr/share/opencv/haarcascades/haarcascade_frontalface_alt_tree.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_lefteye_2splits.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_lefteye.xml', (50, 50)),
# ('/usr/share/opencv/haarcascades/haarcascade_mcs_upperbody.xml', (50, 50)),
# ('parojos_22_5.1.xml', (22, 5)),
# ('Mouth.xml', (22, 15)),
]
for cascade_file, cascade_sizes in cascade_files:
cascade = cvLoadHaarClassifierCascade(os.path.join(cascade_file), cvSize(1, 1))
faces += cvHaarDetectObjects(grayscale, cascade, storage, HAAR_SCALE, HAAR_NEIGHBORS, CV_HAAR_DO_CANNY_PRUNING, cvSize(*cascade_sizes))
return [{'x': f.x, 'y': f.y, 'w': f.width, 'h': f.height} for f in faces]
def detect(image):
image_size = cv.cvGetSize(image)
# create grayscale version
grayscale = cv.cvCreateImage(image_size, 8, 1)
cv.cvCvtColor(image, grayscale, opencv.CV_BGR2GRAY)
# create storage
storage = cv.cvCreateMemStorage(0)
cv.cvClearMemStorage(storage)
# equalize histogram
cv.cvEqualizeHist(grayscale, grayscale)
# detect objects
cascade = cv.cvLoadHaarClassifierCascade('haarcascade_frontalface_alt.xml', cv.cvSize(1,1))
faces = cv.cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, opencv.CV_HAAR_DO_CANNY_PRUNING, cv.cvSize(100, 100))
if faces:
for i in faces:
r = image[int(i.y):int(i.y+i.height),int(i.x):int(i.x+i.width)]
cv.cvSmooth(r,r,cv.CV_BLUR,51,51)
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_faces(image):
"""Converts an image to grayscale and prints the locations of any
faces found"""
grayscale = cvCreateImage(cvSize(image.width, image.height), 8, 1)
cvCvtColor(image, grayscale, CV_BGR2GRAY)
storage = cvCreateMemStorage(0)
cvClearMemStorage(storage)
cvEqualizeHist(grayscale, grayscale)
# The default parameters (scale_factor=1.1, min_neighbors=3,
# flags=0) are tuned for accurate yet slow face detection. For
# faster face detection on real video images the better settings are
# (scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING).
# --- http://www710.univ-lyon1.fr/~bouakaz/OpenCV-0.9.5/docs/ref/OpenCVRef_Experimental.htm#decl_cvHaarDetectObjects
# The size box is of the *minimum* detectable object size. Smaller box = more processing time. - http://cell.fixstars.com/opencv/index.php/Facedetect
minsize = (int(MINFACEWIDTH_PERCENT*image.width+0.5),int(MINFACEHEIGHT_PERCENT*image.height))
print >> sys.stderr, "Min size of face: %s" % `minsize`
faces = []
for cascadefile in ['/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml']:
# for cascadefile in ['/usr/share/opencv/haarcascades/haarcascade_frontalface_default.xml', '/usr/share/opencv/haarcascades/haarcascade_profileface.xml']:
cascade = cvLoadHaarClassifierCascade(cascadefile, cvSize(1,1))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.2, 2, CV_HAAR_DO_CANNY_PRUNING, cvSize(50,50))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 3, 0, cvSize(MINFACEWIDTH,MINFACEHEIGHT))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 3, 0, cvSize(MINFACEWIDTH,MINFACEHEIGHT))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 3, CV_HAAR_DO_CANNY_PRUNING, cvSize(*minsize))
faces += cvHaarDetectObjects(grayscale, cascade, storage, 1.1, 4, CV_HAAR_DO_CANNY_PRUNING, cvSize(*minsize))
# faces += cvHaarDetectObjects(grayscale, cascade, storage, scale_factor=1.1, min_neighbors=3, flags=0, cvSize(50,50))
# print dir(faces)
bboxes = []
if faces:
for f in faces:
print >> sys.stderr, "\tFace at [(%d,%d) -> (%d,%d)]" % (f.x, f.y, f.x+f.width, f.y+f.height)
bboxes = [Face(f.x, f.y, f.x+f.width, f.y+f.height) for f in faces]
return bboxes
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))
IPL_DEPTH_8U, cvCreateMemStorage
CASCADES_DIR = dirname(abspath(__file__))
CASCADES_NAMES = ['haarcascade_frontalface_alt_tree.xml']
CASCADES = [ join(CASCADES_DIR, name) for name in CASCADES_NAMES ]
MIN_SIZE = cvSize(100, 100)
IMAGE_SCALE = 1.3
MIN_NEIGHBORS = 3
HAAR_SCALE = 1.3
HAAR_FLAGS = CV_HAAR_DO_CANNY_PRUNING
DEFAULT_ROTATE_ANGLE = 45
COPY_DEPTH = 8
COPY_CHANNELS = 1
STORAGE = cvCreateMemStorage(0)
def _detect(image):
""" Detects faces on `image`
Parameters:
@image: image file path
Returns:
[((x1, y1), (x2, y2)), ...] List of coordenates for top-left
and bottom-right corner
"""
# the OpenCV API says this function is obsolete, but we can't
# cast the output of cvLoad to a HaarClassifierCascade, so use
# this anyways the size parameter is ignored
capture = cvCreateFileCapture(image)
left = Motor(brick, PORT_C)
capture = hg.cvCreateCameraCapture(1)
# padroes reconhecidos
# known patterns
padroes = ("triangulo.png", "circulo.png", "H.png", "F.png")
color = (cv.CV_RGB(255,0,0),cv.CV_RGB(0,255,0),cv.CV_RGB(0,0,255),cv.CV_RGB(255,255,0))
limiar = (0.01, 0.001, 0.21, 0.18)
target = []
target_mod = []
target_c = []
# Armazenamento temporario
# Temporary storage
storage1 = cv.cvCreateMemStorage()
storage2 = cv.cvCreateMemStorage()
# Carrega, binariza e procura o contorno dos padroes
# Load, binarize and find contours
for i in range(0,len(padroes)):
target.append(hg.cvLoadImage(padroes[i], hg.CV_LOAD_IMAGE_GRAYSCALE))
target_mod.append(cv.cvCreateImage((target[i].width,target[i].height), cv.IPL_DEPTH_8U, 1))
cv.cvThreshold(target[i],target_mod[i],200,254,cv.CV_THRESH_BINARY)
target_c.append(cv.cvFindContours(target_mod[i], storage1)[1])
target_c[i] = cv.cvApproxPoly(target_c[i],cv.sizeof_CvContour,storage1,cv.CV_POLY_APPROX_DP,1)
frame_mod = cv.cvCreateImage((hg.cvGetCaptureProperty(capture,hg.CV_CAP_PROP_FRAME_WIDTH), hg.cvGetCaptureProperty(capture,hg.CV_CAP_PROP_FRAME_HEIGHT)), cv.IPL_DEPTH_8U, 1)
hg.cvNamedWindow("Resultado")
hg.cvNamedWindow("Binarizado")
def timerEvent(self, ev):
# Fetch a frame from the video camera
frame = highgui.cvQueryFrame(self.cap)
img_orig = cv.cvCreateImage(cv.cvSize(frame.width, frame.height),
cv.IPL_DEPTH_8U, frame.nChannels)
if (frame.origin == cv.IPL_ORIGIN_TL):
cv.cvCopy(frame, img_orig)
else:
cv.cvFlip(frame, img_orig, 0)
# Create a grey frame to clarify data
img_grey = cv.cvCreateImage(cv.cvSize(img_orig.width, img_orig.height),
8, 1)
cv.cvCvtColor(img_orig, img_grey, cv.CV_BGR2GRAY)
# Detect objects within the frame
self.faces_storage = cv.cvCreateMemStorage(0)
faces = self.detect_faces(img_grey)
self.circles_storage = cv.cvCreateMemStorage(0)
circles = self.detect_circles(img_grey)
self.squares_storage = cv.cvCreateMemStorage(0)
squares = self.detect_squares(img_grey, img_orig)
self.lines_storage = cv.cvCreateMemStorage(0)
lines = self.detect_lines(img_grey, img_orig)
# Draw faces
if faces:
for face in faces:
pt1, pt2 = self.face_points(face)
cv.cvRectangle(img_orig, pt1, pt2, cv.CV_RGB(255, 0, 0), 3, 8,
0)
# Draw lines
if lines:
for line in lines:
cv.cvLine(img_orig, line[0], line[1], cv.CV_RGB(255, 255, 0),
3, 8)
# Draw circles
if circles:
for circle in circles:
cv.cvCircle(
img_orig,
cv.cvPoint(cv.cvRound(circle[0]), cv.cvRound(circle[1])),
cv.cvRound(circle[2]), cv.CV_RGB(0, 0, 255), 3, 8, 0)
# Draw squares
if squares:
i = 0
while i < squares.total:
pt = []
# read 4 vertices
pt.append(squares[i])
pt.append(squares[i + 1])
pt.append(squares[i + 2])
pt.append(squares[i + 3])
## draw the square as a closed polyline
cv.cvPolyLine(img_orig, [pt], 1, cv.CV_RGB(0, 255, 0), 3,
cv.CV_AA, 0)
i += 4
# Resize the image to display properly within the window
# CV_INTER_NN - nearest-neigbor interpolation,
# CV_INTER_LINEAR - bilinear interpolation (used by default)
# CV_INTER_AREA - resampling using pixel area relation. (preferred for image decimation)
# CV_INTER_CUBIC - bicubic interpolation.
img_display = cv.cvCreateImage(cv.cvSize(self.width(), self.height()),
8, 3)
cv.cvResize(img_orig, img_display, cv.CV_INTER_NN)
img_pil = adaptors.Ipl2PIL(img_display)
s = StringIO()
img_pil.save(s, "PNG")
s.seek(0)
q_img = QImage()
q_img.loadFromData(s.read())
bitBlt(self, 0, 0, q_img)
def process_image(slider_pos):
"""
Define trackbar callback functon. This function find contours,
draw it and approximate it by ellipses.
"""
stor = cv.cvCreateMemStorage(0)
# Threshold the source image. This needful for cv.cvFindContours().
cv.cvThreshold(image03, image02, slider_pos, 255, cv.CV_THRESH_BINARY)
# Find all contours.
nb_contours, cont = cv.cvFindContours(image02, stor, cv.sizeof_CvContour,
cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE,
cv.cvPoint(0, 0))
# Clear images. IPL use.
cv.cvZero(image02)
cv.cvZero(image04)
# This cycle draw all contours and approximate it by ellipses.
for c in cont.hrange():
count = c.total
# This is number point in contour
# Number point must be more than or equal to 6 (for cv.cvFitEllipse_32f).
if (count < 6):
continue
# Alloc memory for contour point set.
PointArray = cv.cvCreateMat(1, count, cv.CV_32SC2)
PointArray2D32f = cv.cvCreateMat(1, count, cv.CV_32FC2)
# Get contour point set.
cv.cvCvtSeqToArray(c, PointArray,
cv.cvSlice(0, cv.CV_WHOLE_SEQ_END_INDEX))
# Convert CvPoint set to CvBox2D32f set.
cv.cvConvert(PointArray, PointArray2D32f)
box = cv.CvBox2D()
# Fits ellipse to current contour.
box = cv.cvFitEllipse2(PointArray2D32f)
# Draw current contour.
cv.cvDrawContours(image04, c, cv.CV_RGB(255, 255, 255),
cv.CV_RGB(255, 255, 255), 0, 1, 8, cv.cvPoint(0, 0))
# Convert ellipse data from float to integer representation.
center = cv.CvPoint()
size = cv.CvSize()
center.x = cv.cvRound(box.center.x)
center.y = cv.cvRound(box.center.y)
size.width = cv.cvRound(box.size.width * 0.5)
size.height = cv.cvRound(box.size.height * 0.5)
box.angle = -box.angle
# Draw ellipse.
cv.cvEllipse(image04, center, size, box.angle, 0, 360,
cv.CV_RGB(0, 0, 255), 1, cv.CV_AA, 0)
# Show image. HighGUI use.
highgui.cvShowImage("Result", image04)
CV_HAAR_DO_CANNY_PRUNING, CV_INTER_LINEAR, CV_BGR2GRAY, \
IPL_DEPTH_8U, cvCreateMemStorage
CASCADES_DIR = dirname(abspath(__file__))
CASCADES_NAMES = ['haarcascade_frontalface_alt_tree.xml']
CASCADES = [join(CASCADES_DIR, name) for name in CASCADES_NAMES]
MIN_SIZE = cvSize(100, 100)
IMAGE_SCALE = 1.3
MIN_NEIGHBORS = 3
HAAR_SCALE = 1.3
HAAR_FLAGS = CV_HAAR_DO_CANNY_PRUNING
DEFAULT_ROTATE_ANGLE = 45
COPY_DEPTH = 8
COPY_CHANNELS = 1
STORAGE = cvCreateMemStorage(0)
def _detect(image):
""" Detects faces on `image`
Parameters:
@image: image file path
Returns:
[((x1, y1), (x2, y2)), ...] List of coordenates for top-left
and bottom-right corner
"""
# the OpenCV API says this function is obsolete, but we can't
# cast the output of cvLoad to a HaarClassifierCascade, so use
# this anyways the size parameter is ignored
capture = cvCreateFileCapture(image)
0, 0, 360, _black, -1, 8, 0)
cv.cvEllipse (image,
cv.cvPoint (dx + 27, dy + 100),
cv.cvSize (20, 35),
0, 0, 360, _white, -1, 8, 0)
cv.cvEllipse (image,
cv.cvPoint (dx + 273, dy + 100),
cv.cvSize (20, 35),
0, 0, 360, _white, -1, 8, 0)
# create window and display the original picture in it
highgui.cvNamedWindow ("image", 1)
highgui.cvShowImage ("image", image)
# create the storage area
storage = cv.cvCreateMemStorage (0)
# find the contours
nb_contours, contours = cv.cvFindContours (image,
storage,
cv.sizeof_CvContour,
cv.CV_RETR_TREE,
cv.CV_CHAIN_APPROX_SIMPLE,
cv.cvPoint (0,0))
# comment this out if you do not want approximation
contours = cv.cvApproxPoly (contours, cv.sizeof_CvContour,
storage,
cv.CV_POLY_APPROX_DP, 3, 1)
# create the window for the contours
from opencv.cv import cvCreateMemStorage, cvMinAreaRect2, CvBox2D, cvContourArea, cvSize2D32f, cvContourPerimeter
MINCONTOUR_AREA = 1000
BOXFILTER_TOLERANCE = 0.6
#MINCONTOUR_PERIMETER=40
MINCONTOUR_PERIMETER = 80
CONTOUR_RECTANGULAR_MIN_RATIO = 1.2
CONTOUR_RECTANGULAR_MAX_RATIO = 3.0
mem = cvCreateMemStorage(0)
def rectangularAspectFilter(contour):
ret = False
try:
box = cvMinAreaRect2(contour, mem)
if box.size.width > CONTOUR_RECTANGULAR_MIN_RATIO* box.size.height and\
box.size.width < CONTOUR_RECTANGULAR_MAX_RATIO* box.size.height\
or box.size.height > CONTOUR_RECTANGULAR_MIN_RATIO* box.size.width and\
box.size.height < CONTOUR_RECTANGULAR_MAX_RATIO* box.size.width:
ret = True
else:
#print("No paso el rectangular Fitlter")
ret = False
except:
pass
return ret
from opencv.cv import cvCreateMemStorage,cvMinAreaRect2,CvBox2D, cvContourArea, cvSize2D32f, cvContourPerimeter
MINCONTOUR_AREA=1000
BOXFILTER_TOLERANCE=0.6
#MINCONTOUR_PERIMETER=40
MINCONTOUR_PERIMETER=80
CONTOUR_RECTANGULAR_MIN_RATIO=1.2
CONTOUR_RECTANGULAR_MAX_RATIO=3.0
mem=cvCreateMemStorage(0)
def rectangularAspectFilter(contour):
ret=False
try:
box=cvMinAreaRect2(contour,mem)
if box.size.width > CONTOUR_RECTANGULAR_MIN_RATIO* box.size.height and\
box.size.width < CONTOUR_RECTANGULAR_MAX_RATIO* box.size.height\
or box.size.height > CONTOUR_RECTANGULAR_MIN_RATIO* box.size.width and\
box.size.height < CONTOUR_RECTANGULAR_MAX_RATIO* box.size.width:
ret=True
else:
#print("No paso el rectangular Fitlter")
ret=False
except:
pass
return ret
def process_image( slider_pos ):
"""
Define trackbar callback functon. This function find contours,
draw it and approximate it by ellipses.
"""
stor = cv.cvCreateMemStorage(0);
# Threshold the source image. This needful for cv.cvFindContours().
cv.cvThreshold( image03, image02, slider_pos, 255, cv.CV_THRESH_BINARY );
# Find all contours.
nb_contours, cont = cv.cvFindContours (image02,
stor,
cv.sizeof_CvContour,
cv.CV_RETR_LIST,
cv.CV_CHAIN_APPROX_NONE,
cv.cvPoint (0,0))
# Clear images. IPL use.
cv.cvZero(image02);
cv.cvZero(image04);
# This cycle draw all contours and approximate it by ellipses.
for c in cont.hrange():
count = c.total; # This is number point in contour
# Number point must be more than or equal to 6 (for cv.cvFitEllipse_32f).
if( count < 6 ):
continue;
# Alloc memory for contour point set.
PointArray = cv.cvCreateMat(1, count, cv.CV_32SC2)
PointArray2D32f= cv.cvCreateMat( 1, count, cv.CV_32FC2)
# Get contour point set.
cv.cvCvtSeqToArray(c, PointArray, cv.cvSlice(0, cv.CV_WHOLE_SEQ_END_INDEX));
# Convert CvPoint set to CvBox2D32f set.
cv.cvConvert( PointArray, PointArray2D32f )
box = cv.CvBox2D()
# Fits ellipse to current contour.
box = cv.cvFitEllipse2(PointArray2D32f);
# Draw current contour.
cv.cvDrawContours(image04, c, cv.CV_RGB(255,255,255), cv.CV_RGB(255,255,255),0,1,8,cv.cvPoint(0,0));
# Convert ellipse data from float to integer representation.
center = cv.CvPoint()
size = cv.CvSize()
center.x = cv.cvRound(box.center.x);
center.y = cv.cvRound(box.center.y);
size.width = cv.cvRound(box.size.width*0.5);
size.height = cv.cvRound(box.size.height*0.5);
box.angle = -box.angle;
# Draw ellipse.
cv.cvEllipse(image04, center, size,
box.angle, 0, 360,
cv.CV_RGB(0,0,255), 1, cv.CV_AA, 0);
# Show image. HighGUI use.
highgui.cvShowImage( "Result", image04 );
def init(self) : self.storage=cv.cvCreateMemStorage(0)
def timerEvent(self, ev): # Fetch a frame from the video camera frame = highgui.cvQueryFrame(self.cap) img_orig = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),cv.IPL_DEPTH_8U, frame.nChannels) if (frame.origin == cv.IPL_ORIGIN_TL): cv.cvCopy(frame, img_orig) else: cv.cvFlip(frame, img_orig, 0) # Create a grey frame to clarify data img_grey = cv.cvCreateImage(cv.cvSize(img_orig.width,img_orig.height), 8, 1) cv.cvCvtColor(img_orig, img_grey, cv.CV_BGR2GRAY) # Detect objects within the frame self.faces_storage = cv.cvCreateMemStorage(0) faces = self.detect_faces(img_grey) self.circles_storage = cv.cvCreateMemStorage(0) circles = self.detect_circles(img_grey) self.squares_storage = cv.cvCreateMemStorage(0) squares = self.detect_squares(img_grey, img_orig) self.lines_storage = cv.cvCreateMemStorage(0) lines = self.detect_lines(img_grey, img_orig) # Draw faces if faces: for face in faces: pt1, pt2 = self.face_points(face) cv.cvRectangle(img_orig, pt1, pt2, cv.CV_RGB(255,0,0), 3, 8, 0) # Draw lines if lines: for line in lines: cv.cvLine(img_orig, line[0], line[1], cv.CV_RGB(255,255,0), 3, 8) # Draw circles if circles: for circle in circles: cv.cvCircle(img_orig, cv.cvPoint(cv.cvRound(circle[0]),cv.cvRound(circle[1])),cv.cvRound(circle[2]),cv.CV_RGB(0,0,255),3,8,0) # Draw squares if squares: i = 0 while i<squares.total: pt = [] # read 4 vertices pt.append(squares[i]) pt.append(squares[i+1]) pt.append(squares[i+2]) pt.append(squares[i+3]) ## draw the square as a closed polyline cv.cvPolyLine(img_orig, [pt], 1, cv.CV_RGB(0,255,0), 3, cv.CV_AA, 0) i += 4 # Resize the image to display properly within the window # CV_INTER_NN - nearest-neigbor interpolation, # CV_INTER_LINEAR - bilinear interpolation (used by default) # CV_INTER_AREA - resampling using pixel area relation. (preferred for image decimation) # CV_INTER_CUBIC - bicubic interpolation. img_display = cv.cvCreateImage(cv.cvSize(self.width(),self.height()), 8, 3) cv.cvResize(img_orig, img_display, cv.CV_INTER_NN) img_pil = adaptors.Ipl2PIL(img_display) s = StringIO() img_pil.save(s, "PNG") s.seek(0) q_img = QImage() q_img.loadFromData(s.read()) bitBlt(self, 0, 0, q_img)
0, 360, _black, -1, 8, 0)
cv.cvEllipse(image, cv.cvPoint(dx + 150, dy + 100), cv.cvSize(10, 5),
0, 0, 360, _black, -1, 8, 0)
cv.cvEllipse(image, cv.cvPoint(dx + 150, dy + 150), cv.cvSize(40, 10),
0, 0, 360, _black, -1, 8, 0)
cv.cvEllipse(image, cv.cvPoint(dx + 27, dy + 100), cv.cvSize(20, 35),
0, 0, 360, _white, -1, 8, 0)
cv.cvEllipse(image, cv.cvPoint(dx + 273, dy + 100), cv.cvSize(20, 35),
0, 0, 360, _white, -1, 8, 0)
# create window and display the original picture in it
highgui.cvNamedWindow("image", 1)
highgui.cvShowImage("image", image)
# create the storage area
storage = cv.cvCreateMemStorage(0)
# find the contours
nb_contours, contours = cv.cvFindContours(image, storage,
cv.sizeof_CvContour,
cv.CV_RETR_TREE,
cv.CV_CHAIN_APPROX_SIMPLE,
cv.cvPoint(0, 0))
# comment this out if you do not want approximation
contours = cv.cvApproxPoly(contours, cv.sizeof_CvContour, storage,
cv.CV_POLY_APPROX_DP, 3, 1)
# create the window for the contours
highgui.cvNamedWindow("contours", 1)
