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 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 estaMarcada( marcadog , punto, ancho):
sub = cv.GetSubRect(marcadog, (punto[0], punto[1], ancho, ancho))
media = cv.Avg(sub)
print "Media para punto"+ str(punto) +": " + str(cv.Avg(sub))
opencv.cvReleaseImage(sub)
if media[0] < 250:
return True
else:
return False
def ipl2cairo(iplimage):
srcimage = opencv.cvCloneImage(iplimage)
width = srcimage.width
height = srcimage.height
image = opencv.cvCreateImage(opencv.cvGetSize(srcimage), 8, 4)
opencv.cvCvtColor(srcimage, image, opencv.CV_BGR2BGRA)
buffer = numpy.fromstring(image.imageData, dtype=numpy.uint32).astype(numpy.uint32)
buffer.shape = (image.width, image.height)
opencv.cvReleaseImage(srcimage)
opencv.cvReleaseImage(image)
return cairo.ImageSurface.create_for_data(buffer, cairo.FORMAT_RGB24, width, height, width * 4)
def ipl2cairo(iplimage):
srcimage = opencv.cvCloneImage(iplimage)
width = srcimage.width
height = srcimage.height
image = opencv.cvCreateImage(opencv.cvGetSize(srcimage),8, 4)
opencv.cvCvtColor(srcimage,image,opencv.CV_BGR2BGRA)
buffer = numpy.fromstring(image.imageData, dtype=numpy.uint32).astype(numpy.uint32)
buffer.shape = (image.width, image.height)
opencv.cvReleaseImage(srcimage)
opencv.cvReleaseImage(image)
return cairo.ImageSurface.create_for_data(buffer, cairo.FORMAT_RGB24, width, height, width*4)
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 puntosParaTemplate (imagen, template):
res_width = imagen.width - template.width + 1;
res_height = imagen.height - template.height + 1;
resultado = cv.CreateImage( ( res_width, res_height ), cv.IPL_DEPTH_32F, 1 )
cv.MatchTemplate(imagen,template,resultado, cv.CV_TM_SQDIFF)
pos = []
eig_image = cv.CreateMat(imagen.rows, imagen.cols, cv.CV_32FC1)
temp_image = cv.CreateMat(imagen.rows, imagen.cols, cv.CV_32FC1)
for (x,y) in cv.GoodFeaturesToTrack(resultado, eig_image, temp_image, 0, 0.2, template.width, useHarris = True):
pos.append((x,y))
pos = sorted(pos)
opencv.cvReleaseImage(resultado)
opencv.cvReleaseImage(eig_image)
opencv.cvReleaseImage(temp_image)
return pos
puntos_horizontales = puntosParaTemplate (marcado, itemH) print "horizontales: " + str (puntos_horizontales .__len__()) x1 = puntos_horizontales[0][0] y1 = marcado.height-puntos_horizontales[0][1] x2 = puntos_horizontales[1][0] y2 = marcado.height-puntos_horizontales[1][1] rot_mat = cv.CreateMat(2, 3, cv.CV_32F) ang_rad = math.atan((y2-y1)/(x2-x1)) degrees = - (180 * ang_rad / math.pi) cv.GetRotationMatrix2D( (marcado.width/2,marcado.height/2), degrees, 1, rot_mat ); cv.WarpAffine( marcado, marcadog, rot_mat ); opencv.cvReleaseImage(marcado) puntos_horizontales = puntosParaTemplate (marcado, itemH) pos_respuesta = puntosParaTemplate (marcadog, itemP) print "respuestas: " + str (pos_respuesta.__len__()) #print "horizontal: " + str (pos_horizontal.__len__()) #print "sin marcar: " + str (sin_marcar.__len__()) for i in pos_respuesta: # punto1 = (pos_horizontal[0][0] ,i[1] )
