Python cv2.cv.CvtColor() Examples

def processImage(self, curframe):
            cv.Smooth(curframe, curframe) #Remove false positives
            
            if not self.absdiff_frame: #For the first time put values in difference, temp and moving_average
                self.absdiff_frame = cv.CloneImage(curframe)
                self.previous_frame = cv.CloneImage(curframe)
                cv.Convert(curframe, self.average_frame) #Should convert because after runningavg take 32F pictures
            else:
                cv.RunningAvg(curframe, self.average_frame, 0.05) #Compute the average
            
            cv.Convert(self.average_frame, self.previous_frame) #Convert back to 8U frame
            
            cv.AbsDiff(curframe, self.previous_frame, self.absdiff_frame) # moving_average - curframe
            
            cv.CvtColor(self.absdiff_frame, self.gray_frame, cv.CV_RGB2GRAY) #Convert to gray otherwise can't do threshold
            cv.Threshold(self.gray_frame, self.gray_frame, 50, 255, cv.CV_THRESH_BINARY)

            cv.Dilate(self.gray_frame, self.gray_frame, None, 15) #to get object blobs
            cv.Erode(self.gray_frame, self.gray_frame, None, 10) 

def hue_histogram_as_image(self, hist):
        """ Returns a nice representation of a hue histogram """

        histimg_hsv = cv.CreateImage( (320,200), 8, 3)

        mybins = cv.CloneMatND(hist.bins)
        cv.Log(mybins, mybins)
        (_, hi, _, _) = cv.MinMaxLoc(mybins)
        cv.ConvertScale(mybins, mybins, 255. / hi)

        w,h = cv.GetSize(histimg_hsv)
        hdims = cv.GetDims(mybins)[0]
        for x in range(w):
            xh = (180 * x) / (w - 1)  # hue sweeps from 0-180 across the image
            val = int(mybins[int(hdims * x / w)] * h / 255)
            cv.Rectangle( histimg_hsv, (x, 0), (x, h-val), (xh,255,64), -1)
            cv.Rectangle( histimg_hsv, (x, h-val), (x, h), (xh,255,255), -1)

        histimg = cv.CreateImage( (320,200), 8, 3)
        cv.CvtColor(histimg_hsv, histimg, cv.CV_HSV2BGR)
        return histimg 

def image_data_as_rgb(self, update_image=True):
        # TODO: Handle other formats
        if self.image_channels == 4:
            mode = 'BGRA'
        elif self.image_channels == 3:
            mode = 'BGR'
        else:
            mode = 'BGR'
            rgb_copy = cv.CreateImage((self.image.width, self.image.height), 8, 3)
            cv.CvtColor(self.image, rgb_copy, cv.CV_GRAY2BGR)
            self.image = rgb_copy
        return mode, self.image.tostring() 

def convert_to_grayscale(self):
        if self.image_channels >= 3:
            # FIXME: OpenCV does not support grayscale with alpha channel?
            grayscaled = cv.CreateImage((self.image.width, self.image.height), self.image_depth, 1)
            cv.CvtColor(self.image, grayscaled, cv.CV_BGRA2GRAY)
            self.image = grayscaled 

def enable_alpha(self):
        if self.image_channels < 4:
            with_alpha = cv.CreateImage(
                (self.image.width, self.image.height), self.image_depth, 4
            )
            if self.image_channels == 3:
                cv.CvtColor(self.image, with_alpha, cv.CV_BGR2BGRA)
            else:
                cv.CvtColor(self.image, with_alpha, cv.CV_GRAY2BGRA)
            self.image = with_alpha 

def doGreyscaleProcessor(self, img):
		(x, y, w, h) = self.frame.rect
		bw = cv.CreateImage((w, h), 8, 1)
		cv.CvtColor(img, bw, cv.CV_RGB2GRAY)
		return bw 

def getCurrentFrameAsImage(self):
        im = numpy.array(self.currentFrame)
        im = cv.fromarray(im)
        cv.CvtColor(im, im, cv.CV_BGR2RGB)
        pgImg = pygame.image.frombuffer(im.tostring(), cv.GetSize(im), "RGB")
        return pgImg 

def detect_and_draw(img, cascade):
    # allocate temporary images
    gray = cv.CreateImage((img.width,img.height), 8, 1)
    small_img = cv.CreateImage((cv.Round(img.width / image_scale),
                   cv.Round (img.height / image_scale)), 8, 1)

    # convert color input image to grayscale
    cv.CvtColor(img, gray, cv.CV_BGR2GRAY)

    # scale input image for faster processing
    cv.Resize(gray, small_img, cv.CV_INTER_LINEAR)

    cv.EqualizeHist(small_img, small_img)

    if(cascade):
        t = cv.GetTickCount()
        faces = cv.HaarDetectObjects(small_img, cascade, cv.CreateMemStorage(0),
                                     haar_scale, min_neighbors, haar_flags, min_size)
        t = cv.GetTickCount() - t
        print "detection time = %gms" % (t/(cv.GetTickFrequency()*1000.))
        if faces:
            for ((x, y, w, h), n) in faces:
                # the input to cv.HaarDetectObjects was resized, so scale the
                # bounding box of each face and convert it to two CvPoints
                pt1 = (int(x * image_scale), int(y * image_scale))
                pt2 = (int((x + w) * image_scale), int((y + h) * image_scale))
                cv.Rectangle(img, pt1, pt2, cv.RGB(255, 0, 0), 3, 8, 0)

    cv.ShowImage("result", img) 

def update_mhi(img, dst, diff_threshold):
    global last
    global mhi
    global storage
    global mask
    global orient
    global segmask
    timestamp = time.clock() / CLOCKS_PER_SEC # get current time in seconds
    size = cv.GetSize(img) # get current frame size
    idx1 = last
    if not mhi or cv.GetSize(mhi) != size:
        for i in range(N):
            buf[i] = cv.CreateImage(size, cv.IPL_DEPTH_8U, 1)
            cv.Zero(buf[i])
        mhi = cv.CreateImage(size,cv. IPL_DEPTH_32F, 1)
        cv.Zero(mhi) # clear MHI at the beginning
        orient = cv.CreateImage(size,cv. IPL_DEPTH_32F, 1)
        segmask = cv.CreateImage(size,cv. IPL_DEPTH_32F, 1)
        mask = cv.CreateImage(size,cv. IPL_DEPTH_8U, 1)

    cv.CvtColor(img, buf[last], cv.CV_BGR2GRAY) # convert frame to grayscale
    idx2 = (last + 1) % N # index of (last - (N-1))th frame
    last = idx2
    silh = buf[idx2]
    cv.AbsDiff(buf[idx1], buf[idx2], silh) # get difference between frames
    cv.Threshold(silh, silh, diff_threshold, 1, cv.CV_THRESH_BINARY) # and threshold it
    cv.UpdateMotionHistory(silh, mhi, timestamp, MHI_DURATION) # update MHI
    cv.CvtScale(mhi, mask, 255./MHI_DURATION,
                (MHI_DURATION - timestamp)*255./MHI_DURATION)
    cv.Zero(dst)
    cv.Merge(mask, None, None, None, dst)
    cv.CalcMotionGradient(mhi, mask, orient, MAX_TIME_DELTA, MIN_TIME_DELTA, 3)
    if not storage:
        storage = cv.CreateMemStorage(0)
    seq = cv.SegmentMotion(mhi, segmask, storage, timestamp, MAX_TIME_DELTA)
    for (area, value, comp_rect) in seq:
        if comp_rect[2] + comp_rect[3] > 100: # reject very small components
            color = cv.CV_RGB(255, 0,0)
            silh_roi = cv.GetSubRect(silh, comp_rect)
            mhi_roi = cv.GetSubRect(mhi, comp_rect)
            orient_roi = cv.GetSubRect(orient, comp_rect)
            mask_roi = cv.GetSubRect(mask, comp_rect)
            angle = 360 - cv.CalcGlobalOrientation(orient_roi, mask_roi, mhi_roi, timestamp, MHI_DURATION)

            count = cv.Norm(silh_roi, None, cv.CV_L1, None) # calculate number of points within silhouette ROI
            if count < (comp_rect[2] * comp_rect[3] * 0.05):
                continue

            magnitude = 30.
            center = ((comp_rect[0] + comp_rect[2] / 2), (comp_rect[1] + comp_rect[3] / 2))
            cv.Circle(dst, center, cv.Round(magnitude*1.2), color, 3, cv.CV_AA, 0)
            cv.Line(dst,
                    center,
                    (cv.Round(center[0] + magnitude * cos(angle * cv.CV_PI / 180)),
                     cv.Round(center[1] - magnitude * sin(angle * cv.CV_PI / 180))),
                    color,
                    3,
                    cv.CV_AA,
                    0) 

def run(self):
        hist = cv.CreateHist([180], cv.CV_HIST_ARRAY, [(0,180)], 1 )
        backproject_mode = False
        while True:
            frame = cv.QueryFrame( self.capture )

            # Convert to HSV and keep the hue
            hsv = cv.CreateImage(cv.GetSize(frame), 8, 3)
            cv.CvtColor(frame, hsv, cv.CV_BGR2HSV)
            self.hue = cv.CreateImage(cv.GetSize(frame), 8, 1)
            cv.Split(hsv, self.hue, None, None, None)

            # Compute back projection
            backproject = cv.CreateImage(cv.GetSize(frame), 8, 1)

            # Run the cam-shift
            cv.CalcArrBackProject( [self.hue], backproject, hist )
            if self.track_window and is_rect_nonzero(self.track_window):
                crit = ( cv.CV_TERMCRIT_EPS | cv.CV_TERMCRIT_ITER, 10, 1)
                (iters, (area, value, rect), track_box) = cv.CamShift(backproject, self.track_window, crit)
                self.track_window = rect

            # If mouse is pressed, highlight the current selected rectangle
            # and recompute the histogram

            if self.drag_start and is_rect_nonzero(self.selection):
                sub = cv.GetSubRect(frame, self.selection)
                save = cv.CloneMat(sub)
                cv.ConvertScale(frame, frame, 0.5)
                cv.Copy(save, sub)
                x,y,w,h = self.selection
                cv.Rectangle(frame, (x,y), (x+w,y+h), (255,255,255))

                sel = cv.GetSubRect(self.hue, self.selection )
                cv.CalcArrHist( [sel], hist, 0)
                (_, max_val, _, _) = cv.GetMinMaxHistValue( hist)
                if max_val != 0:
                    cv.ConvertScale(hist.bins, hist.bins, 255. / max_val)
            elif self.track_window and is_rect_nonzero(self.track_window):
                cv.EllipseBox( frame, track_box, cv.CV_RGB(255,0,0), 3, cv.CV_AA, 0 )

            if not backproject_mode:
                cv.ShowImage( "CamShiftDemo", frame )
            else:
                cv.ShowImage( "CamShiftDemo", backproject)
            cv.ShowImage( "Histogram", self.hue_histogram_as_image(hist))

            c = cv.WaitKey(7) % 0x100
            if c == 27:
                break
            elif c == ord("b"):
                backproject_mode = not backproject_mode