Python cv2.cv.ShowImage() Examples

def on_trackbar(edge_thresh):

    cv.Threshold(gray, edge, float(edge_thresh), float(edge_thresh), cv.CV_THRESH_BINARY)
    #Distance transform
    cv.DistTransform(edge, dist, cv.CV_DIST_L2, cv.CV_DIST_MASK_5)

    cv.ConvertScale(dist, dist, 5000.0, 0)
    cv.Pow(dist, dist, 0.5)

    cv.ConvertScale(dist, dist32s, 1.0, 0.5)
    cv.AndS(dist32s, cv.ScalarAll(255), dist32s, None)
    cv.ConvertScale(dist32s, dist8u1, 1, 0)
    cv.ConvertScale(dist32s, dist32s, -1, 0)
    cv.AddS(dist32s, cv.ScalarAll(255), dist32s, None)
    cv.ConvertScale(dist32s, dist8u2, 1, 0)
    cv.Merge(dist8u1, dist8u2, dist8u2, None, dist8u)
    cv.ShowImage(wndname, dist8u) 

def on_trackbar(position):

    cv.Smooth(gray, edge, cv.CV_BLUR, 3, 3, 0)
    cv.Not(gray, edge)

    # run the edge dector on gray scale
    cv.Canny(gray, edge, position, position * 3, 3)

    # reset
    cv.SetZero(col_edge)

    # copy edge points
    cv.Copy(im, col_edge, edge)

    # show the im
    cv.ShowImage(win_name, col_edge) 

def main():
    """Open test color images, create display window, start the search"""
    cv.NamedWindow(WNDNAME, 1)
    for name in [ "../c/pic%d.png" % i for i in [1, 2, 3, 4, 5, 6] ]:
        img0 = cv.LoadImage(name, 1)
        try:
            img0
        except ValueError:
            print "Couldn't load %s\n" % name
            continue

        # slider deleted from C version, same here and use fixed Canny param=50
        img = cv.CloneImage(img0)

        cv.ShowImage(WNDNAME, img)

        # force the image processing
        draw_squares( img, find_squares4( img ) )

        # wait for key.
        if cv.WaitKey(-1) % 0x100 == 27:
            break 

def drawSquares(img, squares):
    cpy = cv.CloneImage(img)
    # read 4 sequence elements at a time (all vertices of a square)
    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.PolyLine(cpy, [pt], 1, cv.CV_RGB(0, 255, 0), 3, cv. CV_AA, 0)
        i+=4

    # show the resultant image
    cv.ShowImage(wndname, cpy) 

def run(self):
        started = time.time()
        while True:
            
            currentframe = cv.QueryFrame(self.capture)
            instant = time.time() #Get timestamp o the frame
            
            self.processImage(currentframe) #Process the image
            
            if not self.isRecording:
                if self.somethingHasMoved():
                    self.trigger_time = instant #Update the trigger_time
                    if instant > started +10:#Wait 5 second after the webcam start for luminosity adjusting etc..
                        print "Something is moving !"
                        if self.doRecord: #set isRecording=True only if we record a video
                            self.isRecording = True
                cv.DrawContours (currentframe, self.currentcontours, (0, 0, 255), (0, 255, 0), 1, 2, cv.CV_FILLED)
            else:
                if instant >= self.trigger_time +10: #Record during 10 seconds
                    print "Stop recording"
                    self.isRecording = False
                else:
                    cv.PutText(currentframe,datetime.now().strftime("%b %d, %H:%M:%S"), (25,30),self.font, 0) #Put date on the frame
                    cv.WriteFrame(self.writer, currentframe) #Write the frame
            
            if self.show:
                cv.ShowImage("Image", currentframe)
                
            c=cv.WaitKey(1) % 0x100
            if c==27 or c == 10: #Break if user enters 'Esc'.
                break 

def on_trackbar (position):

    # create the image for putting in it the founded contours
    contours_image = cv.CreateImage ( (_SIZE, _SIZE), 8, 3)

    # compute the real level of display, given the current position
    levels = position - 3

    # initialisation
    _contours = contours

    if levels <= 0:
        # zero or negative value
        # => get to the nearest face to make it look more funny
        _contours = contours.h_next().h_next().h_next()

    # first, clear the image where we will draw contours
    cv.SetZero (contours_image)

    # draw contours in red and green
    cv.DrawContours (contours_image, _contours,
                       _red, _green,
                       levels, 3, cv.CV_AA,
                        (0, 0))

    # finally, show the image
    cv.ShowImage ("contours", contours_image) 

def update_brightcont(self):
        # The algorithm is by Werner D. Streidt
        # (http://visca.com/ffactory/archives/5-99/msg00021.html)

        if self.contrast > 0:
            delta = 127. * self.contrast / 100
            a = 255. / (255. - delta * 2)
            b = a * (self.brightness - delta)
        else:
            delta = -128. * self.contrast / 100
            a = (256. - delta * 2) / 255.
            b = a * self.brightness + delta

        cv.ConvertScale(self.src_image, self.dst_image, a, b)
        cv.ShowImage("image", self.dst_image)

        cv.CalcArrHist([self.dst_image], self.hist)
        (min_value, max_value, _, _) = cv.GetMinMaxHistValue(self.hist)
        cv.Scale(self.hist.bins, self.hist.bins, float(self.hist_image.height) / max_value, 0)

        cv.Set(self.hist_image, cv.ScalarAll(255))
        bin_w = round(float(self.hist_image.width) / hist_size)

        for i in range(hist_size):
            cv.Rectangle(self.hist_image, (int(i * bin_w), self.hist_image.height),
                         (int((i + 1) * bin_w), self.hist_image.height - cv.Round(self.hist.bins[i])),
                         cv.ScalarAll(0), -1, 8, 0)

        cv.ShowImage("histogram", self.hist_image) 

def Dilation(pos):
    element = cv.CreateStructuringElementEx(pos*2+1, pos*2+1, pos, pos, element_shape)
    cv.Dilate(src, dest, element, 1)
    cv.ShowImage("Erosion & Dilation", dest) 

def Erosion(pos):
    element = cv.CreateStructuringElementEx(pos*2+1, pos*2+1, pos, pos, element_shape)
    cv.Erode(src, dest, element, 1)
    cv.ShowImage("Erosion & Dilation", dest) 

def Closing(pos):
    element = cv.CreateStructuringElementEx(pos*2+1, pos*2+1, pos, pos, element_shape)
    cv.Dilate(src, image, element, 1)
    cv.Erode(image, dest, element, 1)
    cv.ShowImage("Opening & Closing", dest) 

def draw_squares( color_img, squares ):
    """
    Squares is py list containing 4-pt numpy arrays. Step through the list
    and draw a polygon for each 4-group
    """
    color, othercolor = RED, GREEN
    for square in squares:
        cv.PolyLine(color_img, [square], True, color, 3, cv.CV_AA, 0)
        color, othercolor = othercolor, color

    cv.ShowImage(WNDNAME, color_img) 

def on_mouse(self, event, x, y, flags, param):
        pt = (x, y)
        if event == cv.CV_EVENT_LBUTTONUP or not (flags & cv.CV_EVENT_FLAG_LBUTTON):
            self.prev_pt = None
        elif event == cv.CV_EVENT_LBUTTONDOWN:
            self.prev_pt = pt
        elif event == cv.CV_EVENT_MOUSEMOVE and (flags & cv.CV_EVENT_FLAG_LBUTTON) :
            if self.prev_pt:
                for dst in self.dests:
                    cv.Line(dst, self.prev_pt, pt, cv.ScalarAll(255), 5, 8, 0)
            self.prev_pt = pt
            cv.ShowImage(self.windowname, img) 

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 on_segment(self):
        comp = cv.PyrSegmentation(self.image0, self.image1, self.storage, \
                            self.level, self.thresh1+1, self.thresh2+1)
        cv.ShowImage("Segmentation", self.image1) 

def __init__(self, img0):
        self.thresh1 = 255
        self.thresh2 = 30
        self.level =4
        self.storage = cv.CreateMemStorage()
        cv.NamedWindow("Source", 0)
        cv.ShowImage("Source", img0)
        cv.NamedWindow("Segmentation", 0)
        cv.CreateTrackbar("Thresh1", "Segmentation", self.thresh1, 255, self.set_thresh1)
        cv.CreateTrackbar("Thresh2", "Segmentation",  self.thresh2, 255, self.set_thresh2)
        self.image0 = cv.CloneImage(img0)
        self.image1 = cv.CloneImage(img0)
        cv.ShowImage("Segmentation", self.image1) 

def image_captured(self, args):
		(img, rect) = args
		(x, y, w, h) = rect

		cv2.rectangle(numpy.asarray(img[:,:]), (x,y+1), (x+w,y+1+h), (0, 0, 0))
		cv2.rectangle(numpy.asarray(img[:,:]), (x,y), (x+w,y+h), (50, 50, 250))
		
		#cv.PutText(img, message, (0,y+1+2*h), font, (255, 255, 255))
		#cv.PutText(img, message, (0,y+2*h), font, (0, 0, 0))
		#cv.ShowImage('Capture Feedback', cv2.fromarray(img[:,:]))
		cv.ShowImage('Capture Feedback', img) 

def process_image(self, slider_pos):
        """
        This function finds contours, draws them and their approximation by ellipses.
        """
        stor = cv.CreateMemStorage()

        # Create the destination images
        image02 = cv.CloneImage(self.source_image)
        cv.Zero(image02)
        image04 = cv.CreateImage(cv.GetSize(self.source_image), cv.IPL_DEPTH_8U, 3)
        cv.Zero(image04)

        # Threshold the source image. This needful for cv.FindContours().
        cv.Threshold(self.source_image, image02, slider_pos, 255, cv.CV_THRESH_BINARY)

        # Find all contours.
        cont = cv.FindContours(image02,
            stor,
            cv.CV_RETR_LIST,
            cv.CV_CHAIN_APPROX_NONE,
            (0, 0))

        for c in contour_iterator(cont):
            # Number of points must be more than or equal to 6 for cv.FitEllipse2
            if len(c) >= 6:
                # Copy the contour into an array of (x,y)s
                PointArray2D32f = cv.CreateMat(1, len(c), cv.CV_32FC2)
                for (i, (x, y)) in enumerate(c):
                    PointArray2D32f[0, i] = (x, y)

                # Draw the current contour in gray
                gray = cv.CV_RGB(100, 100, 100)
                cv.DrawContours(image04, c, gray, gray,0,1,8,(0,0))

                # Fits ellipse to current contour.
                (center, size, angle) = cv.FitEllipse2(PointArray2D32f)

                # Convert ellipse data from float to integer representation.
                center = (cv.Round(center[0]), cv.Round(center[1]))
                size = (cv.Round(size[0] * 0.5), cv.Round(size[1] * 0.5))

                # Draw ellipse in random color
                color = cv.CV_RGB(random.randrange(256),random.randrange(256),random.randrange(256))
                cv.Ellipse(image04, center, size,
                          angle, 0, 360,
                          color, 2, cv.CV_AA, 0)

        # Show image. HighGUI use.
        cv.ShowImage( "Result", image04 ) 

def on_mouse( event, x, y, flags, param ):

    if( not color_img ):
        return;

    if event == cv.CV_EVENT_LBUTTONDOWN:
            my_mask = None
            seed = (x,y);
            if ffill_case==0:
                lo = up = 0
                flags = connectivity + (new_mask_val << 8)
            else:
                lo = lo_diff;
                up = up_diff;
                flags = connectivity + (new_mask_val << 8) + cv.CV_FLOODFILL_FIXED_RANGE
            b = random.randint(0,255)
            g = random.randint(0,255)
            r = random.randint(0,255)

            if( is_mask ):
                my_mask = mask
                cv.Threshold( mask, mask, 1, 128, cv.CV_THRESH_BINARY );

            if( is_color ):

                color = cv.CV_RGB( r, g, b );
                comp = cv.FloodFill( color_img, seed, color, cv.CV_RGB( lo, lo, lo ),
                             cv.CV_RGB( up, up, up ), flags, my_mask );
                cv.ShowImage( "image", color_img );

            else:

                brightness = cv.RealScalar((r*2 + g*7 + b + 5)/10);
                comp = cv.FloodFill( gray_img, seed, brightness, cv.RealScalar(lo),
                             cv.RealScalar(up), flags, my_mask );
                cv.ShowImage( "image", gray_img );


            print "%g pixels were repainted" % comp[0]

            if( is_mask ):
                cv.ShowImage( "mask", mask ); 

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