def onIdle(self, event):
"""
Event to grab and display a frame from the camera. (internal use).
"""
if self.cap == None: #Should be cvCameraCapture instance.
#unbind the idle instance, change to click.
highgui.cvReleaseCapture(self.cap) #release the old instance and
self.cap = highgui.cvCreateCameraCapture(
self.camera) #try new one.
self.displayError(self.errorBitmap, (128, 128))
raise CameraError('Unable to open camera, retrying....')
event.Skip()
try:
img = highgui.cvQueryFrame(self.cap)
except cv2.error as e:
raise CameraError('Error when querying for frame: {0}'.format(e))
self._error = 0 #worked successfully
img = opencv.cvGetMat(img)
cv.cvCvtColor(img, img, cv.CV_BGR2RGB)
if conf.as_bool(conf.config['webcam']['cropBars']):
#Draw cropping region
cv.cvRectangle(img, (80, -1), (560, 480), (205.0, 0.0, 0.0, 0.0),
2)
self.displayImage(img)
event.RequestMore()
def frame(self):
if self.framepos == -1:
raise Exception('call next before the first frame!')
format = self.format
img = hg.cvRetrieveFrame(self.cap)
nchannels = 1 if format == FORMAT_GRAY else 3
shape = \
(img.height, img.width) if nchannels == 1 else \
(img.height, img.width, nchannels)
if format == FORMAT_BGR: # default format
frame = np.ndarray(shape = shape, dtype = np.uint8,
buffer = img.imageData)
if self.own_data: frame = frame.copy()
return frame
size = cv.cvSize(img.width, img.height)
img2 = cv.cvCreateImage(size, 8, nchannels)
cvt_type = -1
if format == FORMAT_GRAY:
cvt_type = cv.CV_BGR2GRAY
elif format == FORMAT_RGB:
cvt_type = cv.CV_BGR2RGB
elif format == FORMAT_HSV:
cvt_type = cv.CV_BGR2HSV
else: assert(0)
cv.cvCvtColor(img, img2, cvt_type)
frame = np.ndarray(shape = shape, dtype = np.uint8,
buffer = img2.imageData)
if self.own_data: frame = frame.copy()
return frame
def evalCurrentImageGradient(self):
key = {}
try:
self._cnt.emit(qt.PYSIGNAL("getImage"), (key, ))
qimage = key['image']
except KeyError:
return
##EVAL IMAGE GRADIENT
x, y = self._cnt.focusPointSelected
rectangleSize = self._cnt.focusRectangleSize
im = qimage.copy(x, y, x + rectangleSize, y + rectangleSize)
srcColorImage = qtTools.getImageOpencvFromQImage(im)
if srcColorImage.nChannels > 1:
srcImage = cv.cvCreateImage(
cv.cvSize(srcColorImage.width, srcColorImage.height),
srcColorImage.depth, 1)
cv.cvCvtColor(srcColorImage, srcImage, cv.CV_RGB2GRAY)
else: # In Fact It's a grey image
srcImage = srcColorImage
destImage = cv.cvCreateImage(
cv.cvSize(srcImage.width, srcImage.height), cv.IPL_DEPTH_16S, 1)
cv.cvSobel(srcImage, destImage, 1, 0, 3)
array = numpy.fromstring(destImage.imageData_get(), dtype=numpy.int16)
focusQuality = array.std()
return focusQuality
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 findEdges(original, out, threshold1 = 100, threshold2 = None):
"""Return a new edge detected image with a specified threshold"""
warnings.warn("Use findBWEdges instead unless you really need colored edges.", DeprecationWarning)
#Define threshold2
if threshold2 == None:
threshold2 = threshold1 * 3
# Create two pictures with only one channel for a b/w copy
# and one for storring the edges found in the b/w picture
gray = cv.cvCreateImage(cv.cvGetSize(original), 8, 1)
edge = cv.cvCreateImage(cv.cvGetSize(original), 8, 1)
# Create the b/w copy of the original
cv.cvCvtColor(original, gray, cv.CV_BGR2GRAY)
# Blur the b/w copy, but put the result into edge pic
cv.cvSmooth(gray, edge, cv.CV_BLUR, 3, 3, 0)
# Negate the b/w copy of original with newly blurred
# b/w copy. This will make egdes stand out
cv.cvNot(gray, edge)
# Run an edge-finding algorithm called 'Canny'
# It will analyse the first argument and store the
# resulting picture in the second argument
cv.cvCanny(gray, edge, threshold1, threshold2)
# We initialize our out-image to black
cv.cvSetZero(out)
# Finally, we use the found edges, which are b/w, as
# a mask for copying the colored edges from the original
# to the out-image
cv.cvCopy(original, out, edge)
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 _cv_to_pygame(self,frame,channel=-1) : # scale the image to size of the window cvt_scale = cv.cvCreateImage(cv.cvSize(self.image_dims[0],self.image_dims[1]),frame.depth,frame.nChannels) #cv.cvResize(frame,cvt_scale,cv.CV_INTER_LINEAR) cv.cvResize(frame,cvt_scale,cv.CV_INTER_NN) # need to convert the colorspace differently depending on where the image came from cvt_color = cv.cvCreateImage(cv.cvSize(cvt_scale.width,cvt_scale.height),cvt_scale.depth,3) if frame.nChannels == 3 : # frame is in BGR format, convert it to RGB so the sky isn't orange cv.cvCvtColor(cvt_scale,cvt_color,cv.CV_BGR2RGB) elif frame.nChannels == 1 : # image has only one channel, iow 1 color if channel == 0 : cv.cvMerge(frame,None,None,None,cvt_color) elif channel == 1 : cv.cvMerge(None,frame,None,None,cvt_color) elif channel == 2 : cv.cvMerge(None,None,frame,None,cvt_color) elif channel == 3 : cv.cvMerge(None,None,None,frame,cvt_color) else : cv.cvCvtColor(cvt_scale,cvt_color,cv.CV_GRAY2RGB) # create a pygame surface frame_surface=pygame.image.frombuffer(cvt_color.imageData,self.image_dims,'RGB') return frame_surface
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 read(self) :
frame=self.input.read()
cv_rs = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1)
cv.cvCvtColor(frame,cv_rs,cv.CV_RGB2GRAY)
frame = cv_rs
if self.enabled :
# I think these functions are too specialized for transforms
cv.cvSmooth(frame,frame,cv.CV_GAUSSIAN,3, 0, 0, 0 )
cv.cvErode(frame, frame, None, 1)
cv.cvDilate(frame, frame, None, 1)
num_contours,contours=cv.cvFindContours(frame,self.storage,cv.sizeof_CvContour,cv.CV_RETR_LIST,cv.CV_CHAIN_APPROX_NONE,cv.cvPoint(0,0))
if contours is None :
return []
else :
contours = cv.cvApproxPoly( contours, cv.sizeof_CvContour, self.storage, cv.CV_POLY_APPROX_DP, 3, 1 );
if contours is None :
return []
else :
final_contours = []
for c in contours.hrange() :
area = abs(cv.cvContourArea(c))
#self.debug_print('Polygon Area: %f'%area)
if area >= self.min_area :
lst = []
for pt in c :
lst.append((pt.x,pt.y))
final_contours.append(lst)
contours = contours.h_next
return final_contours
return []
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 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 cv2np(im, format='RGB'):
if format == 'BGR':
cv.cvCvtColor( im, im, cv.CV_BGR2RGB )
numpy_type, nchannels = cv2np_type_dict[cv.cvGetElemType(im)]
array_size = [im.height, im.width, nchannels]
np_im = np.frombuffer(im.imageData, dtype=numpy_type,
count=im.height*im.width*nchannels*(im.depth/8))
return np.reshape(np_im, array_size)
def onNextFrame(self, evt):
frame = gui.cvQueryFrame(self.capture)
if frame:
cv.cvCvtColor(frame, frame, cv.CV_BGR2RGB)
self.bmp = wx.BitmapFromBuffer(frame.width, frame.height, frame.imageData)
self.Refresh()
evt.Skip()
def get_frame(self) : frame=self.input.read() converted=cv.cvCreateImage(cv.cvGetSize(frame),frame.depth,3) cv.cvCvtColor(frame,converted,cv.CV_GRAY2RGB) frame = converted frame_surface=pygame.image.frombuffer(frame.imageData,(frame.width,frame.height),'RGB') return frame_surface
def onNextFrame(self, evt):
frame = gui.cvQueryFrame(self.capture)
if frame:
cv.cvCvtColor(frame, frame, cv.CV_BGR2RGB)
self.bmp = wx.BitmapFromBuffer(frame.width, frame.height,
frame.imageData)
self.Refresh()
evt.Skip()
def getFilter(frameWidht, frameHeight):
cvNamedWindow("Filtred")
cvCreateTrackbar("hmax", "Filtred", getHlsFilter('hmax'), 180, trackBarChangeHmax)
cvCreateTrackbar("hmin", "Filtred", getHlsFilter('hmin'), 180, trackBarChangeHmin)
#cvCreateTrackbar("lmax", "Filtred", hlsFilter['lmax'], 255, trackBarChangeLmax)
#cvCreateTrackbar("lmin", "Filtred", hlsFilter['lmin'], 255, trackBarChangeLmin)
cvCreateTrackbar("smax", "Filtred", getHlsFilter('smax'), 255, trackBarChangeSmax)
cvCreateTrackbar("smin", "Filtred", getHlsFilter('smin'), 255, trackBarChangeSmin)
cvSetMouseCallback("Filtred", mouseClick, None)
frame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 3)
hlsFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 3)
filtredFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 3)
mask = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
hFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
lFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
sFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
ThHFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
ThLFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
ThSFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 1)
key = -1
while key == -1:
if not cvGrabFrame(CAM):
print "Could not grab a frame"
exit
frame = cvQueryFrame(CAM)
cvCvtColor(frame, hlsFrame, CV_BGR2HLS)
cvSplit(hlsFrame, hFrame, lFrame, sFrame, None)
pixelInRange(hFrame, getHlsFilter('hmin'), getHlsFilter('hmax'), 0, 180, ThHFrame)
#pixelInRange(lFrame, getHlsFilter('lmin'), getHlsFilter('lmax'), 0, 255, ThLFrame)
pixelInRange(sFrame, getHlsFilter('smin'), getHlsFilter('smax'), 0, 255, ThSFrame)
cvSetZero(mask)
cvAnd(ThHFrame, ThSFrame, mask)
cvSetZero(filtredFrame)
cvCopy(frame, filtredFrame, mask)
cvShowImage("Filtred", filtredFrame)
key = cvWaitKey(10)
if key == 'r':
key = -1
resetHlsFilter()
cvDestroyWindow("Filtred")
def cv2np(im, format='RGB'):
if format == 'BGR':
cv.cvCvtColor( im, im, cv.CV_BGR2RGB )
numpy_type, nchannels = cv2np_type_dict[cv.cvGetElemType(im)]
array_size = [im.height, im.width, nchannels]
#Removed multiplication of size by (im.depth/8) as numpy takes
#into account of that in numpy_type
np_im = np.frombuffer(im.imageData, dtype=numpy_type,
count=im.height*im.width*nchannels)
return np.reshape(np_im, array_size)
def onRetry(self, event):
frame = gui.cvQueryFrame(self.capture)
cv.cvCvtColor(frame, frame, cv.CV_BGR2RGB)
self.bmp = wx.BitmapFromBuffer(frame.width, frame.height, frame.imageData)
self.startTimer()
self.shotbutton.Show()
self.retrybutton.Hide()
self.hasPicture = False
self.Layout()
event.Skip()
def cv2np(im, format='RGB'):
if format == 'BGR':
cv.cvCvtColor(im, im, cv.CV_BGR2RGB)
numpy_type, nchannels = cv2np_type_dict[cv.cvGetElemType(im)]
array_size = [im.height, im.width, nchannels]
np_im = np.frombuffer(im.imageData,
dtype=numpy_type,
count=im.height * im.width * nchannels *
(im.depth / 8))
return np.reshape(np_im, array_size)
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)
if not capture:
return []
frame = cvQueryFrame(capture)
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)
# 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)
cvClearMemStorage(STORAGE)
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))
return coords
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)
if not capture:
return []
frame = cvQueryFrame(capture)
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)
# 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)
cvClearMemStorage(STORAGE)
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))
return coords
def run(self): if self.capture: webcam_frame = highgui.cvQueryFrame( self.capture ) else: print "Capture failed!" return if self.inverted_video.get_active(): highgui.cvConvertImage(webcam_frame, webcam_frame, highgui.CV_CVTIMG_FLIP) highgui.cvConvertImage(webcam_frame, self.display_frame, highgui.CV_CVTIMG_SWAP_RB) if False: # PROCESS WEBCAM FRAME HERE... inputImage = cv.cvCreateImage(cv.cvGetSize(webcam_frame), cv.IPL_DEPTH_8U, 1) cv.cvCvtColor(webcam_frame, inputImage, cv.CV_RGB2GRAY); cv.cvThreshold(inputImage, inputImage, 128, 255, cv.CV_THRESH_BINARY) mysize = cv.cvGetSize(webcam_frame) height = mysize.height width = mysize.width # Find horizontal first-moment: if False: mysum = 0 for i in range(height): mysum += sum(inputImage[i,:]) print "Sum:", mysum cv.cvMerge( inputImage, inputImage, inputImage, None, self.display_frame ) incoming_pixbuf = gtk.gdk.pixbuf_new_from_data( self.display_frame.imageData, gtk.gdk.COLORSPACE_RGB, False, 8, self.display_frame.width, self.display_frame.height, self.display_frame.widthStep) incoming_pixbuf.copy_area(0, 0, self.display_frame.width, self.display_frame.height, self.webcam_pixbuf, 0, 0) self.video_image.queue_draw() return self.video_enabled_button.get_active()
def get_nearest_feature( image, this_point, n=2000 ): """ Get the n-nearest features to a specified image coordinate. Features are determined using cvGoodFeaturesToTrack. """ _red = cv.cvScalar (0, 0, 255, 0); _green = cv.cvScalar (0, 255, 0, 0); _blue = cv.cvScalar (255,0,0,0); _white = cv.cvRealScalar (255) _black = cv.cvRealScalar (0) quality = 0.01 min_distance = 4 N_best = n win_size = 11 grey = cv.cvCreateImage (cv.cvGetSize (image), 8, 1) eig = cv.cvCreateImage (cv.cvGetSize (image), 32, 1) temp = cv.cvCreateImage (cv.cvGetSize (image), 32, 1) # create a grey version of the image cv.cvCvtColor ( image, grey, cv.CV_BGR2GRAY) points = cv.cvGoodFeaturesToTrack ( grey, eig, temp, N_best, quality, min_distance, None, 3, 0, 0.04) # refine the corner locations better_points = cv.cvFindCornerSubPix ( grey, points, cv.cvSize (win_size, win_size), cv.cvSize (-1, -1), cv.cvTermCriteria (cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20, 0.03)) eigs = [] for i in range(len(points)): eigs.append(cv.cvGetMat(eig)[int(points[i].y)][int(points[i].x)]) mypoints = np.matrix(np.zeros((len(points)*2),dtype=float)).reshape(len(points),2) dists = [] for i,point in enumerate(points): mypoints[i,0]=point.x mypoints[i,1]=point.y dists.append( np.linalg.norm(mypoints[i,:]-this_point) ) dists = np.array(dists) sorteddists = dists.argsort() cv.cvDrawCircle ( image, points[ sorteddists[0] ], 5, _green, 2, 8, 0 ) return better_points[ sorteddists[0] ]
def onRetry(self, event):
frame = gui.cvQueryFrame(self.capture)
cv.cvCvtColor(frame, frame, cv.CV_BGR2RGB)
self.bmp = wx.BitmapFromBuffer(frame.width, frame.height,
frame.imageData)
self.startTimer()
self.shotbutton.Show()
self.retrybutton.Hide()
self.hasPicture = False
self.Layout()
event.Skip()
def cv2np(im, format='RGB'):
if format == 'BGR':
cv.cvCvtColor(im, im, cv.CV_BGR2RGB)
numpy_type, nchannels = cv2np_type_dict[cv.cvGetElemType(im)]
array_size = [im.height, im.width, nchannels]
#Removed multiplication of size by (im.depth/8) as numpy takes
#into account of that in numpy_type
np_im = np.frombuffer(im.imageData,
dtype=numpy_type,
count=im.height * im.width * nchannels)
return np.reshape(np_im, array_size)
def texture_features(self, block_size=5, filter_size=3):
"""
Calculates the texture features associated with the image.
block_size gives the size of the texture neighborhood to be processed
filter_size gives the size of the Sobel operator used to find gradient information
"""
#block_size = cv.cvSize(block_size, block_size)
#convert to grayscale float
channels = 1
self.gray_image = cv.cvCreateImage(
cv.cvSize(self.im_width, self.im_height),
cv.IPL_DEPTH_8U, #cv.IPL_DEPTH_16U, #cv.IPL_DEPTH_32F,
channels)
#cv.CV_32FC1, #cv.IPL_DEPTH_32F, #cv.IPL_DEPTH_8U, #cv.IPL_DEPTH_16U,
channels = 1
eig_tex = cv.cvCreateImage(
cv.cvSize(self.im_width * 6, self.im_height), cv.IPL_DEPTH_32F,
channels)
cv.cvCvtColor(self.image, self.gray_image, cv.CV_BGR2GRAY)
#cv.cvAdd(const CvArr* src1, const CvArr* src2, CvArr* dst, const CvArr* mask=NULL );
#highgui.cvConvertImage(self.image, self.gray_image)
cv.cvCornerEigenValsAndVecs(
self.gray_image,
eig_tex, #CvArr* eigenvv,
block_size,
filter_size)
eig_tex = ut.cv2np(eig_tex)
eig_tex = np.reshape(eig_tex, [self.im_height, self.im_width, 6])
#print eig_tex.shape ## [480,640,3]
## (l1, l2, x1, y1, x2, y2), where
## l1, l2 - eigenvalues of M; not sorted
## (x1, y1) - eigenvector corresponding to l1
## (x2, y2) - eigenvector corresponding to l2
tex_feat = np.zeros([3, self.im_height * self.im_width],
dtype=np.float32)
tmp = np.reshape(eig_tex, [self.im_height * self.im_width, 6]).T
s = tmp[0] > tmp[1]
tex_feat[1:3, s] = tmp[0, s] * tmp[2:4, s]
tex_feat[0, s] = tmp[1, s]
tex_feat[1:3, -s] = tmp[1, -s] * tmp[4:6, -s]
tex_feat[0, -s] = tmp[0, -s]
self.tex_feat = tex_feat.T
self.tex_image = np.reshape(self.tex_feat,
[self.im_height, self.im_width, 3])
def harrisResponse(image):
"""pyvision/point/DetectorHarris.py
Runs at 10.5 fps...
"""
gray = cv.cvCreateImage( cv.cvGetSize(image), 8, 1 )
corners = cv.cvCreateImage( cv.cvGetSize(image), 32, 1 )
cv.cvCvtColor( image, gray, cv.CV_BGR2GRAY )
cv.cvCornerHarris(gray,corners,3)
image = filter_and_render_cv(image,corners)
#IPShellEmbed()()
return image
def read(self) :
frame=self.input.read()
if self.debug :
raw_frame = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,frame.nChannels)
cv.cvCopy(frame,raw_frame,None)
self.raw_frame_surface=pygame.image.frombuffer(frame.imageData,(frame.width,frame.height),'RGB')
if self.enabled :
cv_rs = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1)
# convert color
cv.cvCvtColor(frame,cv_rs,cv.CV_BGR2GRAY)
# invert the image
cv.cvSubRS(cv_rs, 255, cv_rs, None);
# threshold the image
frame = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1)
cv.cvThreshold(cv_rs, frame, self.threshold, 255, cv.CV_THRESH_BINARY)
if self.debug :
thresh_frame = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,3)
cv.cvCvtColor(frame,thresh_frame,cv.CV_GRAY2RGB)
self.thresh_frame_surface=pygame.image.frombuffer(thresh_frame.imageData,(frame.width,frame.height),'RGB')
# I think these functions are too specialized for transforms
cv.cvSmooth(frame,frame,cv.CV_GAUSSIAN,3, 0, 0, 0 )
cv.cvErode(frame, frame, None, 1)
cv.cvDilate(frame, frame, None, 1)
num_contours,contours=cv.cvFindContours(frame,self.storage,cv.sizeof_CvContour,cv.CV_RETR_LIST,cv.CV_CHAIN_APPROX_NONE,cv.cvPoint(0,0))
if contours is None :
return []
else :
contours = cv.cvApproxPoly( contours, cv.sizeof_CvContour, self.storage, cv.CV_POLY_APPROX_DP, 3, 1 );
if contours is None :
return []
else :
final_contours = []
for c in contours.hrange() :
area = abs(cv.cvContourArea(c))
#self.debug_print('Polygon Area: %f'%area)
if area >= self.min_area :
lst = []
for pt in c :
lst.append((pt.x,pt.y))
final_contours.append(lst)
contours = contours.h_next
return final_contours
return []
def read(self): frame=self.input.read() if self.enabled: if self.num_channels==frame.nChannels: new_frame=frame else: new_frame=cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,self.num_channels) cv.cvCvtColor(frame,new_frame,self.convert_method) if self.num_channels == 1: cv.cvMerge(new_frame,new_frame,new_frame,None,frame) else : frame=new_frame return frame
def get_nearest_feature(image, this_point, n=2000):
"""
Get the n-nearest features to a specified image coordinate.
Features are determined using cvGoodFeaturesToTrack.
"""
_red = cv.cvScalar(0, 0, 255, 0)
_green = cv.cvScalar(0, 255, 0, 0)
_blue = cv.cvScalar(255, 0, 0, 0)
_white = cv.cvRealScalar(255)
_black = cv.cvRealScalar(0)
quality = 0.01
min_distance = 4
N_best = n
win_size = 11
grey = cv.cvCreateImage(cv.cvGetSize(image), 8, 1)
eig = cv.cvCreateImage(cv.cvGetSize(image), 32, 1)
temp = cv.cvCreateImage(cv.cvGetSize(image), 32, 1)
# create a grey version of the image
cv.cvCvtColor(image, grey, cv.CV_BGR2GRAY)
points = cv.cvGoodFeaturesToTrack(grey, eig, temp, N_best, quality,
min_distance, None, 3, 0, 0.04)
# refine the corner locations
better_points = cv.cvFindCornerSubPix(
grey, points, cv.cvSize(win_size, win_size), cv.cvSize(-1, -1),
cv.cvTermCriteria(cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20, 0.03))
eigs = []
for i in range(len(points)):
eigs.append(cv.cvGetMat(eig)[int(points[i].y)][int(points[i].x)])
mypoints = np.matrix(np.zeros((len(points) * 2),
dtype=float)).reshape(len(points), 2)
dists = []
for i, point in enumerate(points):
mypoints[i, 0] = point.x
mypoints[i, 1] = point.y
dists.append(np.linalg.norm(mypoints[i, :] - this_point))
dists = np.array(dists)
sorteddists = dists.argsort()
cv.cvDrawCircle(image, points[sorteddists[0]], 5, _green, 2, 8, 0)
return better_points[sorteddists[0]]
def main(): # ctrl+c to end
global h,s,v,h2,v2,s2,d,e
highgui.cvNamedWindow("Camera 1", 1)
highgui.cvNamedWindow("Orig", 1)
highgui.cvCreateTrackbar("H", "Camera 1", h, 256, tb_h)
highgui.cvCreateTrackbar("S", "Camera 1", s, 256, tb_s)
highgui.cvCreateTrackbar("V", "Camera 1", v, 256, tb_v)
highgui.cvCreateTrackbar("H2", "Camera 1", h2, 256, tb_h2)
highgui.cvCreateTrackbar("S2", "Camera 1", s2, 256, tb_s2)
highgui.cvCreateTrackbar("V2", "Camera 1", v2, 256, tb_v2)
highgui.cvCreateTrackbar("Dilate", "Camera 1", d, 30, tb_d)
highgui.cvCreateTrackbar("Erode", "Camera 1", e, 30, tb_e)
cap = highgui.cvCreateCameraCapture(1)
highgui.cvSetCaptureProperty(cap, highgui.CV_CAP_PROP_FRAME_WIDTH, IMGW)
highgui.cvSetCaptureProperty(cap, highgui.CV_CAP_PROP_FRAME_HEIGHT, IMGH)
c = 0
t1 = tdraw = time.clock()
t = 1
font = cv.cvInitFont(cv.CV_FONT_HERSHEY_PLAIN, 1, 1)
while c != 0x27:
image = highgui.cvQueryFrame(cap)
if not image:
print "capture failed"
break
thresh = cv.cvCreateImage(cv.cvSize(IMGW,IMGH),8,1)
cv.cvSetZero(thresh)
cv.cvCvtColor(image,image,cv.CV_RGB2HSV)
cv.cvInRangeS(image, (h,s,v,0), (h2,s2,v2,0), thresh)
result = cv.cvCreateImage(cv.cvSize(IMGW,IMGH),8,3)
cv.cvSetZero(result)
cv.cvOr(image,image,result,thresh)
for i in range(1,e):
cv.cvErode(result,result)
for i in range(1,d):
cv.cvDilate(result,result)
# floodfill objects back in, allowing threshold differences outwards
t2 = time.clock()
if t2 > tdraw+0.3:
t = t2-t1
tdraw=t2
cv.cvPutText(result, "FPS: " + str(1 / (t)), (0,25), font, (255,255,255))
t1 = t2
highgui.cvShowImage("Orig", image)
highgui.cvShowImage("Camera 1", result)
c = highgui.cvWaitKey(10)
def __FindCorner(self, filename): #find the corners of images, and save all corner points in self.vKeyPoints
self.img = highgui.cvLoadImage (filename)
greyimg = cv.cvCreateImage(cv.cvSize(self.img.width, self.img.height), 8,1)
hsvimg = cv.cvCreateImage(cv.cvGetSize(self.img), 8, 3)
cv.cvCvtColor(self.img, hsvimg, cv.CV_RGB2HSV)
cv.cvCvtColor (hsvimg, greyimg, cv.CV_BGR2GRAY)
eigImage = cv.cvCreateImage(cv.cvGetSize(greyimg), cv.IPL_DEPTH_32F, 1)
tempImage = cv.cvCreateImage(cv.cvGetSize(greyimg), cv.IPL_DEPTH_32F, 1)
self.points = cv.cvGoodFeaturesToTrack(greyimg, eigImage,tempImage, 2000, 0.01, 5, None, 3,0,0.01 )
self.points2 = cv.cvFindCornerSubPix(greyimg, self.points,cv.cvSize(20, 20),
cv.cvSize(-1, -1), cv.cvTermCriteria(cv.CV_TERMCRIT_ITER |cv.CV_TERMCRIT_EPS, 20, 0.03))
cv.cvReleaseImage(eigImage)
cv.cvReleaseImage(tempImage)
def mouseClick(event, x, y, flags, param):
if event == CV_EVENT_LBUTTONUP:
frame = cvQueryFrame(CAM)
hlsFrame = cvCreateImage(cvSize(frameWidth, frameHeight), IPL_DEPTH_8U, 3)
cvCvtColor(frame, hlsFrame, CV_BGR2HLS)
pixel = cvGet2D(hlsFrame, y, x)
setHlsFilter('hmax', pixel[0])
setHlsFilter('hmin', pixel[0])
#setHlsFilter('lmax', pixel[1])
#setHlsFilter('lmin', pixel[1])
setHlsFilter('smax', pixel[2])
setHlsFilter('smin', pixel[2])
def get_eye(self):
eyes = False
face = self.cap.get_area(commons.haar_cds['Face'])
if face:
cvtile = cv.cvCreateMat(128,128,cv.CV_8UC3)
bwtile = cv.cvCreateMat(128,128,cv.CV_8U)
areas = [ (pt[1].x - pt[0].x)*(pt[1].y - pt[0].y) for pt in face ]
startF = face[areas.index(max(areas))][0]
endF = face[areas.index(max(areas))][1]
facerect = self.cap.rect(startF.x, startF.y, endF.x - startF.x, endF.y - startF.y)
if not facerect:
return
cv.cvResize(facerect, cvtile)
cv.cvCvtColor( cvtile, bwtile, cv.CV_BGR2GRAY )
leye,reye,lcp,rcp = self.fel.locateEyes(bwtile)
leye = pv.Point(leye)
reye = pv.Point(reye)
leye_x = int((float(leye.X())*facerect.width/cvtile.width) + startF.x)
leye_y = int((float(leye.Y())*facerect.height/cvtile.height) + startF.y)
reye_x = int((float(reye.X())*facerect.width/cvtile.width) + startF.x)
reye_y = int((float(reye.Y())*facerect.height/cvtile.height) + startF.y)
eye_rect = { "startX" : leye_x - 5,
"startY" : leye_y - 5,
"endX" : leye_x + 5,
"endY" : leye_y + 5}
#self.cap.image(self.cap.rect(leye_x - 5, leye_y - 5, 20, 20))
if not hasattr(self.cap, "leye"):
self.cap.add( Point("point", "leye", [int(leye_x), int(leye_y)], parent=self.cap, follow=True) )
else:
self.cap.add( Point("point", "reye", [int(reye_x), int(reye_y)], parent=self.cap, follow=True) )
# Shows the face rectangle
#self.cap.add( Graphic("rect", "Face", ( startF.x, startF.y ), (endF.x, endF.y), parent=self.cap) )
self.foreheadOrig = None
return False
def run(self):
if self.capture:
webcam_frame = highgui.cvQueryFrame(self.capture)
else:
print "Capture failed!"
return
if self.inverted_video.get_active():
highgui.cvConvertImage(webcam_frame, webcam_frame,
highgui.CV_CVTIMG_FLIP)
highgui.cvConvertImage(webcam_frame, self.display_frame,
highgui.CV_CVTIMG_SWAP_RB)
if False:
# PROCESS WEBCAM FRAME HERE...
inputImage = cv.cvCreateImage(cv.cvGetSize(webcam_frame),
cv.IPL_DEPTH_8U, 1)
cv.cvCvtColor(webcam_frame, inputImage, cv.CV_RGB2GRAY)
cv.cvThreshold(inputImage, inputImage, 128, 255,
cv.CV_THRESH_BINARY)
mysize = cv.cvGetSize(webcam_frame)
height = mysize.height
width = mysize.width
# Find horizontal first-moment:
if False:
mysum = 0
for i in range(height):
mysum += sum(inputImage[i, :])
print "Sum:", mysum
cv.cvMerge(inputImage, inputImage, inputImage, None,
self.display_frame)
incoming_pixbuf = gtk.gdk.pixbuf_new_from_data(
self.display_frame.imageData, gtk.gdk.COLORSPACE_RGB, False, 8,
self.display_frame.width, self.display_frame.height,
self.display_frame.widthStep)
incoming_pixbuf.copy_area(0, 0, self.display_frame.width,
self.display_frame.height,
self.webcam_pixbuf, 0, 0)
self.video_image.queue_draw()
return self.video_enabled_button.get_active()
def texture_features(self, block_size=5, filter_size=3):
"""
Calculates the texture features associated with the image.
block_size gives the size of the texture neighborhood to be processed
filter_size gives the size of the Sobel operator used to find gradient information
"""
#block_size = cv.cvSize(block_size, block_size)
#convert to grayscale float
channels = 1
self.gray_image = cv.cvCreateImage(cv.cvSize(self.im_width, self.im_height),
cv.IPL_DEPTH_8U, #cv.IPL_DEPTH_16U, #cv.IPL_DEPTH_32F,
channels)
#cv.CV_32FC1, #cv.IPL_DEPTH_32F, #cv.IPL_DEPTH_8U, #cv.IPL_DEPTH_16U,
channels = 1
eig_tex = cv.cvCreateImage(cv.cvSize(self.im_width*6, self.im_height),
cv.IPL_DEPTH_32F,
channels)
cv.cvCvtColor(self.image, self.gray_image, cv.CV_BGR2GRAY);
#cv.cvAdd(const CvArr* src1, const CvArr* src2, CvArr* dst, const CvArr* mask=NULL );
#highgui.cvConvertImage(self.image, self.gray_image)
cv.cvCornerEigenValsAndVecs(self.gray_image, eig_tex,#CvArr* eigenvv,
block_size, filter_size)
eig_tex = ut.cv2np(eig_tex)
eig_tex = np.reshape(eig_tex, [self.im_height, self.im_width, 6])
#print eig_tex.shape ## [480,640,3]
## (l1, l2, x1, y1, x2, y2), where
## l1, l2 - eigenvalues of M; not sorted
## (x1, y1) - eigenvector corresponding to l1
## (x2, y2) - eigenvector corresponding to l2
tex_feat = np.zeros([3, self.im_height * self.im_width], dtype=np.float32)
tmp = np.reshape(eig_tex, [self.im_height * self.im_width, 6]).T
s = tmp[0] > tmp[1]
tex_feat[1:3, s] = tmp[0, s] * tmp[2:4, s]
tex_feat[0, s] = tmp[1, s]
tex_feat[1:3, -s] = tmp[1, -s] * tmp[4:6, -s]
tex_feat[0, -s] = tmp[0, -s]
self.tex_feat = tex_feat.T
self.tex_image = np.reshape(self.tex_feat, [self.im_height, self.im_width, 3])
def __normImage(self, img, length):
#print "Generating norm image..."
width = length
height = length
gray = cv.cvCreateImage(cv.cvSize(img.width,img.height), 8, 1);
small_img = cv.cvCreateImage(cv.cvSize(cv.cvRound(width),
cv.cvRound(height)), 8, 1 );
# convert color input image to grayscale
cv.cvCvtColor(img, gray, cv.CV_BGR2GRAY);
# scale input image for faster processing
cv.cvResize(gray, small_img, cv.CV_INTER_LINEAR);
cv.cvEqualizeHist(small_img, small_img);
#cvClearMemStorage(self.storage);
norm_image = small_img # save the 'normalized image'
return norm_image
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 _get_color_frame(self,color="r",frame=None) : if frame is None : frame = self._get_cv_frame() # extract the color channel (image is in BGR format) cv_r = None #cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1) if color == "r" : cv_r = self._get_frame_channel(frame,channel=2) elif color == "g" : cv_r = self._get_frame_channel(frame,channel=1) elif color == "b" : cv_r = self._get_frame_channel(frame,channel=0) else : cv_r = cv.cvCreateImage(cv.cvSize(frame.width,frame.height),frame.depth,1) cv.cvCvtColor(frame,cv_r,cv.CV_BGR2GRAY) return cv_r
def HarrisPoints(self, imgfile):
self.points = []
self.drawimg = highgui.cvLoadImage (imgfile)
c = 1
try:
gray = cv.cvCreateImage (cv.cvGetSize (self.drawimg), 8, 1)
cv.cvCvtColor(self.drawimg, gray, cv.CV_BGR2GRAY)
eig = cv.cvCreateImage (cv.cvGetSize (self.drawimg), 32, 1)
tmpimg = cv.cvCreateImage (cv.cvGetSize (self.drawimg), 32, 1)
p = cv.cvGoodFeaturesToTrack(gray, eig, tmpimg, 100, 0.1, 20, None, 7, 1, 0.04 )
for x in p:
cv.cvCircle( self.drawimg, x, 3, cv.CV_RGB(0,255,0), 8, 0 );
self.points.append(x)
except Exception,e:
print e
print 'ERROR: problem handling '+ imgfile
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 findBWEdges(original, out, threshold1, threshold2): """Identical with findEdges except that this returns white edges on a black background. We really don't need colored edges any longer. This also makes it easy to to a manual merge of edge and blur picture.""" if threshold2 == None: threshold2 = threshold1 * 3 gray = cv.cvCreateImage(cv.cvGetSize(original), 8, 1) cv.cvCvtColor(original, gray, cv.CV_BGR2GRAY) cv.cvSmooth(gray, out, cv.CV_BLUR, 3, 3, 0) cv.cvNot(gray, out) cv.cvCanny(gray, out, threshold1, threshold2) return out
def HarrisPoints(self, imgfile):
self.points = []
self.drawimg = highgui.cvLoadImage(imgfile)
c = 1
try:
gray = cv.cvCreateImage(cv.cvGetSize(self.drawimg), 8, 1)
cv.cvCvtColor(self.drawimg, gray, cv.CV_BGR2GRAY)
eig = cv.cvCreateImage(cv.cvGetSize(self.drawimg), 32, 1)
tmpimg = cv.cvCreateImage(cv.cvGetSize(self.drawimg), 32, 1)
p = cv.cvGoodFeaturesToTrack(gray, eig, tmpimg, 100, 0.1, 20, None,
7, 1, 0.04)
for x in p:
cv.cvCircle(self.drawimg, x, 3, cv.CV_RGB(0, 255, 0), 8, 0)
self.points.append(x)
except Exception, e:
print e
print 'ERROR: problem handling ' + imgfile
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 threshold_image(image, n=[]):
"""Record the first 5 images to get a background, then diff current frame with the last saved frame.
"""
if len(n) < 5:
# n[4] will be our background
# First capture a few images
n.append(cv.cvCloneMat(image))
if len(n) == 5:
# last time here
# could do averaging here.
pass
return image
original = n[4]
differenceImage = cv.cvCloneMat(image)
cv.cvAbsDiff(image, original, differenceImage)
"""The threshold value determines the amount of "Change" required
before something will show up"""
thresholdValue = 50 # 32
cv.cvThreshold(differenceImage, differenceImage, thresholdValue, 255,
cv.CV_THRESH_BINARY)
# Convert to one channel
gray = cv.cvCreateImage(cv.cvGetSize(differenceImage), 8, 1)
cv.cvCvtColor(differenceImage, gray, cv.CV_BGR2GRAY)
# Use median filter to remove salt and pepper noise.
cv.cvSmooth(gray, gray, cv.CV_MEDIAN, 15)
# Dilate and the threshold image
# It adds a border to the object.
#cv.cvDilate(gray,gray, None, 9)
# Add a bit of Blur to the threshold mask
cv.cvSmooth(gray, gray, cv.CV_GAUSSIAN, 5)
result = cv.cvCloneMat(image)
cv.cvSetZero(result)
cv.cvAnd(image, image, result, gray)
return result
def __init__(self, parent):
wx.Frame.__init__(
self,
parent,
-1,
)
sizer = wx.BoxSizer(wx.VERTICAL)
self.capture = gui.cvCreateCameraCapture(0)
frame = gui.cvQueryFrame(self.capture)
cv.cvCvtColor(frame, frame, cv.CV_BGR2RGB)
self.SetSize((frame.width + 300, frame.height + 100))
self.bmp = wx.BitmapFromBuffer(frame.width, frame.height,
frame.imageData)
self.displayPanel = wx.StaticBitmap(self, -1, bitmap=self.bmp)
sizer.Add(self.displayPanel, 0, wx.ALL, 10)
self.shotbutton = wx.Button(self, -1, "Shot")
sizer.Add(self.shotbutton, -1, wx.GROW)
self.retrybutton = wx.Button(self, -1, "Retry")
sizer.Add(self.retrybutton, -1, wx.GROW)
self.retrybutton.Hide()
#events
self.Bind(wx.EVT_BUTTON, self.onShot, self.shotbutton)
self.Bind(wx.EVT_BUTTON, self.onRetry, self.retrybutton)
self.Bind(wx.EVT_PAINT, self.onPaint)
self.Bind(wx.EVT_CLOSE, self.onClose)
self.playTimer = wx.Timer(self, self.TIMER_PLAY_ID)
wx.EVT_TIMER(self, self.TIMER_PLAY_ID, self.onNextFrame)
self.fps = 8
self.SetSizer(sizer)
sizer.Layout()
self.startTimer()
def __findedge(self, filename):
tmpimg = highgui.cvLoadImage(filename)
self.img = cv.cvCreateImage(
cv.cvSize(int(tmpimg.width * self.enlarge),
int(tmpimg.height * self.enlarge)), 8, 3)
cv.cvResize(tmpimg, self.img, cv.CV_INTER_LINEAR)
if (self.drawimage):
self.drawimg = cv.cvCloneImage(self.img)
else:
self.drawimg = cv.cvCreateImage(cv.cvGetSize(self.img), 8, 3)
greyimg = cv.cvCreateImage(cv.cvSize(self.img.width, self.img.height),
8, 1)
cv.cvCvtColor(self.img, greyimg, cv.CV_BGR2GRAY)
self.allcurve = []
for i in range(80, 200, 20):
bimg = cv.cvCloneImage(greyimg)
cv.cvSmooth(bimg, bimg, cv.CV_MEDIAN, 9)
# cv.cvSmooth(bimg, bimg, cv.CV_BILATERAL, 9)
# cv.cvSmooth(bimg, bimg, cv.CV_BLUR, 9)
# cv.cvSmooth(bimg, bimg, cv.CV_BLUR, 9)
cv.cvThreshold(greyimg, bimg, i, 255, cv.CV_THRESH_BINARY)
self.__findcurve(bimg)
def detect_face(self, img):
""" Detect faces within an image, then draw around them.
The default parameters (scale_factor=1.1, min_neighbors=3, flags=0) are tuned
for accurate yet slow object detection. For a faster operation on real video
images the settings are:
scale_factor=1.2, min_neighbors=2, flags=CV_HAAR_DO_CANNY_PRUNING,
min_size=<minimum possible face size
"""
min_size = cv.cvSize(20, 20)
image_scale = 1.3
haar_scale = 1.2
min_neighbors = 2
haar_flags = 0
gray = cv.cvCreateImage(cv.cvSize(img.width, img.height), 8, 1)
small_img = cv.cvCreateImage(
cv.cvSize(cv.cvRound(img.width / image_scale),
cv.cvRound(img.height / image_scale)), 8, 1)
cv.cvCvtColor(img, gray, cv.CV_BGR2GRAY)
cv.cvResize(gray, small_img, cv.CV_INTER_LINEAR)
cv.cvEqualizeHist(small_img, small_img)
cv.cvClearMemStorage(self.storage)
if (self.cascade):
t = cv.cvGetTickCount()
faces = cv.cvHaarDetectObjects(small_img, self.cascade,
self.storage, haar_scale,
min_neighbors, haar_flags, min_size)
t = cv.cvGetTickCount() - t
#print "detection time = %gms" % (t/(cvGetTickFrequency()*1000.));
if faces:
for r in faces:
pt1 = cv.cvPoint(int(r.x * image_scale),
int(r.y * image_scale))
pt2 = cv.cvPoint(int((r.x + r.width) * image_scale),
int((r.y + r.height) * image_scale))
cv.cvRectangle(img, pt1, pt2, cv.CV_RGB(255, 0, 0), 3, 8,
0)
return img
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))
cv.cvSet(mask, 1)
blob_overlay = False
while True:
# 1. capture the current image
frame = highgui.cvQueryFrame(capture)
if frame is None:
# no image captured... end the processing
break
# mirror the captured image
cv.cvFlip(frame, None, 1)
cv.cvCvtColor(frame, my_grayscale, cv.CV_RGB2GRAY)
cv.cvThreshold(my_grayscale, my_grayscale, 128, 255,
cv.CV_THRESH_BINARY)
if not blob_overlay:
# Convert black-and-white version back into three-color representation
cv.cvCvtColor(my_grayscale, frame, cv.CV_GRAY2RGB)
myblobs = CBlobResult(my_grayscale, mask, 100, True)
myblobs.filter_blobs(10, 10000)
blob_count = myblobs.GetNumBlobs()
for i in range(blob_count):
my_enumerated_blob = myblobs.GetBlob(i)
# print "%d: Area = %d" % (i, my_enumerated_blob.Area())
def main(args):
global capture
global hmax, hmin
highgui.cvNamedWindow('Camera', highgui.CV_WINDOW_AUTOSIZE)
highgui.cvNamedWindow('Hue', highgui.CV_WINDOW_AUTOSIZE)
highgui.cvNamedWindow('Satuation', highgui.CV_WINDOW_AUTOSIZE)
highgui.cvNamedWindow('Value', highgui.CV_WINDOW_AUTOSIZE)
highgui.cvNamedWindow('Laser', highgui.CV_WINDOW_AUTOSIZE)
highgui.cvMoveWindow('Camera', 0, 10)
highgui.cvMoveWindow('Hue', 0, 350)
highgui.cvMoveWindow('Satuation', 360, 10)
highgui.cvMoveWindow('Value', 360, 350)
highgui.cvMoveWindow('Laser', 700, 40)
highgui.cvCreateTrackbar("Brightness Trackbar", "Camera", 0, 255,
change_brightness)
highgui.cvCreateTrackbar("hmin Trackbar", "Hue", hmin, 180, change_hmin)
highgui.cvCreateTrackbar("hmax Trackbar", "Hue", hmax, 180, change_hmax)
highgui.cvCreateTrackbar("smin Trackbar", "Satuation", smin, 255,
change_smin)
highgui.cvCreateTrackbar("smax Trackbar", "Satuation", smax, 255,
change_smax)
highgui.cvCreateTrackbar("vmin Trackbar", "Value", vmin, 255, change_vmin)
highgui.cvCreateTrackbar("vmax Trackbar", "Value", vmax, 255, change_vmax)
print "grabbing camera"
capture = highgui.cvCreateCameraCapture(0)
print "found camera"
highgui.cvSetCaptureProperty(capture, highgui.CV_CAP_PROP_FRAME_WIDTH, 320)
highgui.cvSetCaptureProperty(capture, highgui.CV_CAP_PROP_FRAME_HEIGHT,
240)
frame = highgui.cvQueryFrame(capture)
frameSize = cv.cvGetSize(frame)
hsv = cv.cvCreateImage(frameSize, 8, 3)
mask = cv.cvCreateImage(frameSize, 8, 1)
hue = cv.cvCreateImage(frameSize, 8, 1)
satuation = cv.cvCreateImage(frameSize, 8, 1)
value = cv.cvCreateImage(frameSize, 8, 1)
laser = cv.cvCreateImage(frameSize, 8, 1)
while 1:
frame = highgui.cvQueryFrame(capture)
cv.cvCvtColor(frame, hsv, cv.CV_BGR2HSV)
#cv.cvInRangeS(hsv,hsv_min,hsv_max,mask)
cv.cvSplit(hsv, hue, satuation, value, None)
cv.cvInRangeS(hue, hmin, hmax, hue)
cv.cvInRangeS(satuation, smin, smax, satuation)
cv.cvInRangeS(value, vmin, vmax, value)
#cv.cvInRangeS(hue,0,180,hue)
cv.cvAnd(hue, value, laser)
#cv.cvAnd(laser, value, laser)
cenX, cenY = averageWhitePoints(laser)
#print cenX,cenY
draw_target(frame, cenX, cenY)
#draw_target(frame,200,1)
highgui.cvShowImage('Camera', frame)
highgui.cvShowImage('Hue', hue)
highgui.cvShowImage('Satuation', satuation)
highgui.cvShowImage('Value', value)
highgui.cvShowImage('Laser', laser)
k = highgui.cvWaitKey(10)
if k == " ":
highgui.cvDestroyAllWindows()
highgui.cvReleaseCapture(capture)
sys.exit()
def harrisResponse(frame):
"""pyvision/point/DetectorHarris.py
Runs at 10.5 fps...
"""
#gray = cv.cvCreateImage( cv.cvGetSize(image), 8, 1 )
#corners = cv.cvCreateImage( cv.cvGetSize(image), 32, 1 )
#cv.cvCvtColor( image, gray, cv.CV_BGR2GRAY )
#cv.cvCornerHarris(gray,corners,15)
# This could be done in a persistant way
# create the images we need
image = cv.cvCreateImage(cv.cvGetSize(frame), 8, 3)
grey = cv.cvCreateImage(cv.cvGetSize(frame), 8, 1)
prev_grey = cv.cvCreateImage(cv.cvGetSize(frame), 8, 1)
pyramid = cv.cvCreateImage(cv.cvGetSize(frame), 8, 1)
prev_pyramid = cv.cvCreateImage(cv.cvGetSize(frame), 8, 1)
eig = cv.cvCreateImage(cv.cvGetSize(frame), cv.IPL_DEPTH_32F, 1)
temp = cv.cvCreateImage(cv.cvGetSize(frame), cv.IPL_DEPTH_32F, 1)
points = [[], []]
# copy the frame, so we can draw on it
cv.cvCopy(frame, image)
# create a grey version of the image
cv.cvCvtColor(image, grey, cv.CV_BGR2GRAY)
# search the good points
points[1] = cv.cvGoodFeaturesToTrack(grey, eig, temp, MAX_COUNT, quality,
min_distance, None, 3, 0, 0.04)
# refine the corner locations
cv.cvFindCornerSubPix(
grey, points[1], cv.cvSize(win_size, win_size), cv.cvSize(-1, -1),
cv.cvTermCriteria(cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20, 0.03))
if len(points[0]) > 0:
# we have points, so display them
# calculate the optical flow
[points[1], status], something = cv.cvCalcOpticalFlowPyrLK(
prev_grey, grey, prev_pyramid, pyramid, points[0], len(points[0]),
(win_size, win_size), 3, len(points[0]), None,
cv.cvTermCriteria(cv.CV_TERMCRIT_ITER | cv.CV_TERMCRIT_EPS, 20,
0.03), flags)
# initializations
point_counter = -1
new_points = []
for the_point in points[1]:
# go trough all the points
# increment the counter
point_counter += 1
if add_remove_pt:
# we have a point to add, so see if it is close to
# another one. If yes, don't use it
dx = pt.x - the_point.x
dy = pt.y - the_point.y
if dx * dx + dy * dy <= 25:
# too close
add_remove_pt = 0
continue
if not status[point_counter]:
# we will disable this point
continue
# this point is a correct point
new_points.append(the_point)
# draw the current point
cv.cvCircle(image, cv.cvPointFrom32f(the_point), 3,
cv.cvScalar(0, 255, 0, 0), -1, 8, 0)
# set back the points we keep
points[1] = new_points
# swapping
prev_grey, grey = grey, prev_grey
prev_pyramid, pyramid = pyramid, prev_pyramid
points[0], points[1] = points[1], points[0]
return image
if len(sys.argv) > 1:
filename = sys.argv[1]
# load the image gived on the command line
image = highgui.cvLoadImage(filename)
if not image:
print "Error loading image '%s'" % filename
sys.exit(-1)
# create the output image
col_edge = cv.cvCreateImage(cv.cvSize(image.width, image.height), 8, 3)
# convert to grayscale
gray = cv.cvCreateImage(cv.cvSize(image.width, image.height), 8, 1)
edge = cv.cvCreateImage(cv.cvSize(image.width, image.height), 8, 1)
cv.cvCvtColor(image, gray, cv.CV_BGR2GRAY)
# create the window
highgui.cvNamedWindow(win_name, highgui.CV_WINDOW_AUTOSIZE)
# create the trackbar
highgui.cvCreateTrackbar(trackbar_name, win_name, 1, 100, on_trackbar)
# show the image
on_trackbar(0)
# wait a key pressed to end
highgui.cvWaitKey(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)
def get_eye(self):
eyes = False
face = self.cap.get_area(commons.haar_cds['Face'])
if face:
cvtile = cv.cvCreateMat(128, 128, cv.CV_8UC3)
bwtile = cv.cvCreateMat(128, 128, cv.CV_8U)
areas = [(pt[1].x - pt[0].x) * (pt[1].y - pt[0].y) for pt in face]
startF = face[areas.index(max(areas))][0]
endF = face[areas.index(max(areas))][1]
facerect = self.cap.rect(startF.x, startF.y, endF.x - startF.x,
endF.y - startF.y)
if not facerect:
return
cv.cvResize(facerect, cvtile)
cv.cvCvtColor(cvtile, bwtile, cv.CV_BGR2GRAY)
leye, reye, lcp, rcp = self.fel.locateEyes(bwtile)
leye = pv.Point(leye)
reye = pv.Point(reye)
leye_x = int((float(leye.X()) * facerect.width / cvtile.width) +
startF.x)
leye_y = int((float(leye.Y()) * facerect.height / cvtile.height) +
startF.y)
reye_x = int((float(reye.X()) * facerect.width / cvtile.width) +
startF.x)
reye_y = int((float(reye.Y()) * facerect.height / cvtile.height) +
startF.y)
eye_rect = {
"startX": leye_x - 5,
"startY": leye_y - 5,
"endX": leye_x + 5,
"endY": leye_y + 5
}
#self.cap.image(self.cap.rect(leye_x - 5, leye_y - 5, 20, 20))
if not hasattr(self.cap, "leye"):
self.cap.add(
Point("point",
"leye", [int(leye_x), int(leye_y)],
parent=self.cap,
follow=True))
else:
self.cap.add(
Point("point",
"reye", [int(reye_x), int(reye_y)],
parent=self.cap,
follow=True))
# Shows the face rectangle
#self.cap.add( Graphic("rect", "Face", ( startF.x, startF.y ), (endF.x, endF.y), parent=self.cap) )
self.foreheadOrig = None
return False
hist = cv.cvCreateHist([hdims], cv.CV_HIST_ARRAY, hranges, 1)
obj_hist = cv.cvCreateHist([hdims], cv.CV_HIST_ARRAY, hranges, 1)
while 1:
# do forever
# 1. capture the current image
frame = highgui.cvQueryFrame(capture)
if frame is None:
# no image captured... end the processing
break
# mirror the captured image
#cv.cvFlip (frame, None, 1)
# compute the hsv version of the image
cv.cvCvtColor(frame, hsv, cv.CV_BGR2HSV)
# compute which pixels are in the wanted range
cv.cvInRangeS(hsv, hsv_min, hsv_max, mask)
# extract the hue from the hsv array
cv.cvSplit(hsv, hue, None, None, None)
# select the rectangle of interest in the hue/mask arrays
hue_roi = cv.cvGetSubRect(hue, selection)
mask_roi = cv.cvGetSubRect(mask, selection)
# it's time to compute the histogram
cv.cvCalcHist(hue_roi, hist, 0, mask_roi)
# extract the min and max value of the histogram
