Python cv2.getTrackbarPos() Examples

def main():
    windowName = "OpenCV BGR Color Palette"
    imageData = np.zeros((512, 512, 3), np.uint8)

    cv2.namedWindow(windowName)

    cv2.createTrackbar('Blue', windowName, 0, 255, passFunction)
    cv2.createTrackbar('Green', windowName, 0, 255, passFunction)
    cv2.createTrackbar('Red', windowName, 0, 255, passFunction)

    while (True):
        cv2.imshow(windowName, imageData)

        if cv2.waitKey(1) & 0xFF == 27:
            break

        blue = cv2.getTrackbarPos('Blue', windowName)
        green = cv2.getTrackbarPos('Green', windowName)
        red = cv2.getTrackbarPos('Red', windowName)

        imageData[:] = [blue, green, red]
        print(blue, green, red)

    cv2.destroyWindow(windowName) 

def update(_=None):
    noise = cv2.getTrackbarPos('noise', 'fit line')
    n = cv2.getTrackbarPos('point n', 'fit line')
    r = cv2.getTrackbarPos('outlier %', 'fit line') / 100.0
    outn = int(n*r)

    p0, p1 = (90, 80), (w-90, h-80)
    img = np.zeros((h, w, 3), np.uint8)
    cv2.line(img, toint(p0), toint(p1), (0, 255, 0))

    if n > 0:
        line_points = sample_line(p0, p1, n-outn, noise)
        outliers = np.random.rand(outn, 2) * (w, h)
        points = np.vstack([line_points, outliers])
        for p in line_points:
            cv2.circle(img, toint(p), 2, (255, 255, 255), -1)
        for p in outliers:
            cv2.circle(img, toint(p), 2, (64, 64, 255), -1)
        func = getattr(cv2, cur_func_name)
        vx, vy, cx, cy = cv2.fitLine(np.float32(points), func, 0, 0.01, 0.01)
        cv2.line(img, (int(cx-vx*w), int(cy-vy*w)), (int(cx+vx*w), int(cy+vy*w)), (0, 0, 255))

    draw_str(img, (20, 20), cur_func_name)
    cv2.imshow('fit line', img) 

def update(_=None):
    noise = cv2.getTrackbarPos('noise', 'fit line')
    n = cv2.getTrackbarPos('point n', 'fit line')
    r = cv2.getTrackbarPos('outlier %', 'fit line') / 100.0
    outn = int(n*r)

    p0, p1 = (90, 80), (w-90, h-80)
    img = np.zeros((h, w, 3), np.uint8)
    cv2.line(img, toint(p0), toint(p1), (0, 255, 0))

    if n > 0:
        line_points = sample_line(p0, p1, n-outn, noise)
        outliers = np.random.rand(outn, 2) * (w, h)
        points = np.vstack([line_points, outliers])
        for p in line_points:
            cv2.circle(img, toint(p), 2, (255, 255, 255), -1)
        for p in outliers:
            cv2.circle(img, toint(p), 2, (64, 64, 255), -1)
        func = getattr(cv2.cv, cur_func_name)
        vx, vy, cx, cy = cv2.fitLine(np.float32(points), func, 0, 0.01, 0.01)
        cv2.line(img, (int(cx-vx*w), int(cy-vy*w)), (int(cx+vx*w), int(cy+vy*w)), (0, 0, 255))

    draw_str(img, (20, 20), cur_func_name)
    cv2.imshow('fit line', img) 

def update(_=None):
    noise = cv2.getTrackbarPos('noise', 'fit line')
    n = cv2.getTrackbarPos('point n', 'fit line')
    r = cv2.getTrackbarPos('outlier %', 'fit line') / 100.0
    outn = int(n*r)

    p0, p1 = (90, 80), (w-90, h-80)
    img = np.zeros((h, w, 3), np.uint8)
    cv2.line(img, toint(p0), toint(p1), (0, 255, 0))

    if n > 0:
        line_points = sample_line(p0, p1, n-outn, noise)
        outliers = np.random.rand(outn, 2) * (w, h)
        points = np.vstack([line_points, outliers])
        for p in line_points:
            cv2.circle(img, toint(p), 2, (255, 255, 255), -1)
        for p in outliers:
            cv2.circle(img, toint(p), 2, (64, 64, 255), -1)
        func = getattr(cv2, cur_func_name)
        vx, vy, cx, cy = cv2.fitLine(np.float32(points), func, 0, 0.01, 0.01)
        cv2.line(img, (int(cx-vx*w), int(cy-vy*w)), (int(cx+vx*w), int(cy+vy*w)), (0, 0, 255))

    draw_str(img, (20, 20), cur_func_name)
    cv2.imshow('fit line', img) 

def update(dummy=None):
        sz = cv2.getTrackbarPos('op/size', 'morphology')
        iters = cv2.getTrackbarPos('iters', 'morphology')
        opers = cur_mode.split('/')
        if len(opers) > 1:
            sz = sz - 10
            op = opers[sz > 0]
            sz = abs(sz)
        else:
            op = opers[0]
        sz = sz*2+1

        str_name = 'MORPH_' + cur_str_mode.upper()
        oper_name = 'MORPH_' + op.upper()
        st = cv2.getStructuringElement(getattr(cv2, str_name), (sz, sz))
        res = cv2.morphologyEx(img, getattr(cv2, oper_name), st, iterations=iters)

        draw_str(res, (10, 20), 'mode: ' + cur_mode)
        draw_str(res, (10, 40), 'operation: ' + oper_name)
        draw_str(res, (10, 60), 'structure: ' + str_name)
        draw_str(res, (10, 80), 'ksize: %d  iters: %d' % (sz, iters))
        cv2.imshow('morphology', res) 

def onTrackbar(self, val):
        # OpenCV-Python seems to have issues with trackbars. Each trackbar's
        # linked variable is not updated when the trackbar position changes.
        # Creating a separate callback function for each trackbar (since a
        # reference to the trackbar is not passed to the callback function)
        # doesn't help. Seems when multiple trackbars are defined in the same
        # window, all trackbars call back to the callback function for the 
        # last defined trackbar (in this case, the one corresponding to
        # ch2HighVal), regardless of which callback function was passed to them.
        #
        # Hence, I've opted to explicitly query each trackbar by name on any
        # trackbar change, regardless of which one was changed.

        self.ch0LowVal = cv2.getTrackbarPos("Ch0 Low", self.CTRL_WIN)
        self.ch0HighVal = cv2.getTrackbarPos("Ch0 High", self.CTRL_WIN)
        self.ch1LowVal = cv2.getTrackbarPos("Ch1 Low", self.CTRL_WIN)
        self.ch1HighVal = cv2.getTrackbarPos("Ch1 High", self.CTRL_WIN)
        self.ch2LowVal = cv2.getTrackbarPos("Ch2 Low", self.CTRL_WIN)
        self.ch2HighVal = cv2.getTrackbarPos("Ch2 High", self.CTRL_WIN)

        if self.mode == "image":
            self.thresholdImage() 

def main():
    windowName = "Transition Effect"

    cv2.namedWindow("Transition Effect")

    imageOne = cv2.imread("../data/lena_color_512.tif", 1)
    imageTwo = cv2.imread("../data/mandril_color.tif", 1)

    cv2.createTrackbar("Alpha", windowName, 0, 1000, passFunction)

    while True:

        alpha = cv2.getTrackbarPos("Alpha", windowName) / 1000
        beta = 1 - alpha

        output = cv2.addWeighted(imageOne, alpha, imageTwo, beta, 0)

        cv2.imshow(windowName, output)

        if cv2.waitKey(1) & 0xFF == 27:
            break

    cv2.destroyAllWindows() 

def update(_):
        ang = np.deg2rad( cv2.getTrackbarPos('angle', win) )
        d = cv2.getTrackbarPos('d', win)
        noise = 10**(-0.1*cv2.getTrackbarPos('SNR (db)', win))

        if defocus:
            psf = defocus_kernel(d)
        else:
            psf = motion_kernel(ang, d)
        cv2.imshow('psf', psf)

        psf /= psf.sum()
        psf_pad = np.zeros_like(img)
        kh, kw = psf.shape
        psf_pad[:kh, :kw] = psf
        PSF = cv2.dft(psf_pad, flags=cv2.DFT_COMPLEX_OUTPUT, nonzeroRows = kh)
        PSF2 = (PSF**2).sum(-1)
        iPSF = PSF / (PSF2 + noise)[...,np.newaxis]
        RES = cv2.mulSpectrums(IMG, iPSF, 0)
        res = cv2.idft(RES, flags=cv2.DFT_SCALE | cv2.DFT_REAL_OUTPUT )
        res = np.roll(res, -kh//2, 0)
        res = np.roll(res, -kw//2, 1)
        cv2.imshow(win, res) 

def update(_):
        ang = np.deg2rad( cv2.getTrackbarPos('angle', win) )
        d = cv2.getTrackbarPos('d', win)
        noise = 10**(-0.1*cv2.getTrackbarPos('SNR (db)', win))

        if defocus:
            psf = defocus_kernel(d)
        else:
            psf = motion_kernel(ang, d)
        cv2.imshow('psf', psf)

        psf /= psf.sum()
        psf_pad = np.zeros_like(img)
        kh, kw = psf.shape
        psf_pad[:kh, :kw] = psf
        PSF = cv2.dft(psf_pad, flags=cv2.DFT_COMPLEX_OUTPUT, nonzeroRows = kh)
        PSF2 = (PSF**2).sum(-1)
        iPSF = PSF / (PSF2 + noise)[...,np.newaxis]
        RES = cv2.mulSpectrums(IMG, iPSF, 0)
        res = cv2.idft(RES, flags=cv2.DFT_SCALE | cv2.DFT_REAL_OUTPUT )
        res = np.roll(res, -kh//2, 0)
        res = np.roll(res, -kw//2, 1)
        cv2.imshow(win, res) 

def update(dummy=None):
        sz = cv2.getTrackbarPos('op/size', 'morphology')
        iters = cv2.getTrackbarPos('iters', 'morphology')
        opers = cur_mode.split('/')
        if len(opers) > 1:
            sz = sz - 10
            op = opers[sz > 0]
            sz = abs(sz)
        else:
            op = opers[0]
        sz = sz*2+1

        str_name = 'MORPH_' + cur_str_mode.upper()
        oper_name = 'MORPH_' + op.upper()
        st = cv2.getStructuringElement(getattr(cv2, str_name), (sz, sz))
        res = cv2.morphologyEx(img, getattr(cv2, oper_name), st, iterations=iters)

        draw_str(res, (10, 20), 'mode: ' + cur_mode)
        draw_str(res, (10, 40), 'operation: ' + oper_name)
        draw_str(res, (10, 60), 'structure: ' + str_name)
        draw_str(res, (10, 80), 'ksize: %d  iters: %d' % (sz, iters))
        cv2.imshow('morphology', res) 

def main():
    windowname = "OpenCV Media Player"
    cv2.namedWindow(windowname)

    videoFilePath = "/media/amarpandey/Media Files/Movies/Game Of Thrones/Season Seven/Game.of.Thrones.S07E03.720p.WEB.h264-TBS[eztv].mkv"

    capture = cv2.VideoCapture(videoFilePath)
    cv2.createTrackbar('FrameSpeed', windowname, 10, 600, passFunction)

    while (capture.isOpened()):

        FrameSpeed = cv2.getTrackbarPos('FrameSpeed', windowname)
        flag, frame = capture.read()

        if FrameSpeed <= 0: FrameSpeed = 1

        if flag:
            cv2.imshow(windowname, frame)
            if cv2.waitKey(FrameSpeed) & 0xFF == 27:  # because 33 * FPS == 1 second
                break
        else:
            break

    cv2.destroyWindow(windowname)
    capture.release() 

def update(_):
        ang = np.deg2rad( cv2.getTrackbarPos('angle', win) )
        d = cv2.getTrackbarPos('d', win)
        noise = 10**(-0.1*cv2.getTrackbarPos('SNR (db)', win))

        if defocus:
            psf = defocus_kernel(d)
        else:
            psf = motion_kernel(ang, d)
        cv2.imshow('psf', psf)

        psf /= psf.sum()
        psf_pad = np.zeros_like(img)
        kh, kw = psf.shape
        psf_pad[:kh, :kw] = psf
        PSF = cv2.dft(psf_pad, flags=cv2.DFT_COMPLEX_OUTPUT, nonzeroRows = kh)
        PSF2 = (PSF**2).sum(-1)
        iPSF = PSF / (PSF2 + noise)[...,np.newaxis]
        RES = cv2.mulSpectrums(IMG, iPSF, 0)
        res = cv2.idft(RES, flags=cv2.DFT_SCALE | cv2.DFT_REAL_OUTPUT )
        res = np.roll(res, -kh//2, 0)
        res = np.roll(res, -kw//2, 1)
        cv2.imshow(win, res) 

def update(dummy=None):
        global need_update
        need_update = False
        thrs = cv2.getTrackbarPos('threshold', 'distrans')
        mark = cv2.Canny(img, thrs, 3*thrs)
        dist, labels = cv2.distanceTransformWithLabels(~mark, cv.CV_DIST_L2, 5)
        if voronoi:
            vis = cm[np.uint8(labels)]
        else:
            vis = cm[np.uint8(dist*2)]
        vis[mark != 0] = 255
        cv2.imshow('distrans', vis) 

def get_trackbar_values(range_filter):
    values = []

    for i in ["MIN", "MAX"]:
        for j in range_filter:
            v = cv2.getTrackbarPos("%s_%s" % (j, i), "Trackbars")
            values.append(v)

    return values 

def get_trackbar_values(range_filter):
    values = []

    for i in ["MIN", "MAX"]:
        for j in range_filter:
            v = cv2.getTrackbarPos("%s_%s" % (j, i), "Trackbars")
            values.append(v)

    return values 

def draw_overlay(self, vis, tracked):
        x0, y0, x1, y1 = tracked.target.rect
        quad_3d = np.float32([[x0, y0, 0], [x1, y0, 0], [x1, y1, 0], [x0, y1, 0]])
        fx = 0.5 + cv2.getTrackbarPos('focal', 'plane') / 50.0
        h, w = vis.shape[:2]
        K = np.float64([[fx*w, 0, 0.5*(w-1)],
                        [0, fx*w, 0.5*(h-1)],
                        [0.0,0.0,      1.0]])
        dist_coef = np.zeros(4)
        ret, rvec, tvec = cv2.solvePnP(quad_3d, tracked.quad, K, dist_coef)
        verts = ar_verts * [(x1-x0), (y1-y0), -(x1-x0)*0.3] + (x0, y0, 0)
        verts = cv2.projectPoints(verts, rvec, tvec, K, dist_coef)[0].reshape(-1, 2)
        for i, j in ar_edges:
            (x0, y0), (x1, y1) = verts[i], verts[j]
            cv2.line(vis, (int(x0), int(y0)), (int(x1), int(y1)), (255, 255, 0), 2) 

def update(dummy=None):
        if seed_pt is None:
            cv2.imshow('floodfill', img)
            return
        flooded = img.copy()
        mask[:] = 0
        lo = cv2.getTrackbarPos('lo', 'floodfill')
        hi = cv2.getTrackbarPos('hi', 'floodfill')
        flags = connectivity
        if fixed_range:
            flags |= cv2.FLOODFILL_FIXED_RANGE
        cv2.floodFill(flooded, mask, seed_pt, (255, 255, 255), (lo,)*3, (hi,)*3, flags)
        cv2.circle(flooded, seed_pt, 2, (0, 0, 255), -1)
        cv2.imshow('floodfill', flooded) 

def update(dummy=None):
        global need_update
        need_update = False
        thrs = cv2.getTrackbarPos('threshold', 'distrans')
        mark = cv2.Canny(img, thrs, 3*thrs)
        dist, labels = cv2.distanceTransformWithLabels(~mark, cv2.DIST_L2, 5)
        if voronoi:
            vis = cm[np.uint8(labels)]
        else:
            vis = cm[np.uint8(dist*2)]
        vis[mark != 0] = 255
        cv2.imshow('distrans', vis) 

def draw_overlay(self, vis, tracked):
        x0, y0, x1, y1 = tracked.target.rect
        quad_3d = np.float32([[x0, y0, 0], [x1, y0, 0], [x1, y1, 0], [x0, y1, 0]])
        fx = 0.5 + cv2.getTrackbarPos('focal', 'plane') / 50.0
        h, w = vis.shape[:2]
        K = np.float64([[fx*w, 0, 0.5*(w-1)],
                        [0, fx*w, 0.5*(h-1)],
                        [0.0,0.0,      1.0]])
        dist_coef = np.zeros(4)
        ret, rvec, tvec = cv2.solvePnP(quad_3d, tracked.quad, K, dist_coef)
        verts = ar_verts * [(x1-x0), (y1-y0), -(x1-x0)*0.3] + (x0, y0, 0)
        verts = cv2.projectPoints(verts, rvec, tvec, K, dist_coef)[0].reshape(-1, 2)
        for i, j in ar_edges:
            (x0, y0), (x1, y1) = verts[i], verts[j]
            cv2.line(vis, (int(x0), int(y0)), (int(x1), int(y1)), (255, 255, 0), 2) 

def update(dummy=None):
        if seed_pt is None:
            cv2.imshow('floodfill', img)
            return
        flooded = img.copy()
        mask[:] = 0
        lo = cv2.getTrackbarPos('lo', 'floodfill')
        hi = cv2.getTrackbarPos('hi', 'floodfill')
        flags = connectivity
        if fixed_range:
            flags |= cv2.FLOODFILL_FIXED_RANGE
        cv2.floodFill(flooded, mask, seed_pt, (255, 255, 255), (lo,)*3, (hi,)*3, flags)
        cv2.circle(flooded, seed_pt, 2, (0, 0, 255), -1)
        cv2.imshow('floodfill', flooded) 

def update(dummy=None):
        global need_update
        need_update = False
        thrs = cv2.getTrackbarPos('threshold', 'distrans')
        mark = cv2.Canny(img, thrs, 3*thrs)
        dist, labels = cv2.distanceTransformWithLabels(~mark, cv2.DIST_L2, 5)
        if voronoi:
            vis = cm[np.uint8(labels)]
        else:
            vis = cm[np.uint8(dist*2)]
        vis[mark != 0] = 255
        cv2.imshow('distrans', vis) 

def update():
        sigma = cv2.getTrackbarPos('sigma', 'control')*2+1
        str_sigma = cv2.getTrackbarPos('str_sigma', 'control')*2+1
        blend = cv2.getTrackbarPos('blend', 'control') / 10.0
        print 'sigma: %d  str_sigma: %d  blend_coef: %f' % (sigma, str_sigma, blend)
        dst = coherence_filter(src, sigma=sigma, str_sigma = str_sigma, blend = blend)
        cv2.imshow('dst', dst) 

def get_trackbar_values(range_filter):
    values = []
    for i in ["MIN", "MAX"]:
        for j in range_filter:
            v = cv2.getTrackbarPos("%s_%s" % (j, i), "Trackbars")
            values.append(v)
    return values 

def colorDetect(self, img):
        #Blur the image to get rid of those annoying speckles
        blur_radius = cv2.getTrackbarPos("blur", processed_win)
        radius = cv2.getTrackbarPos("radius", processed_win)
        open_radius = cv2.getTrackbarPos("open", processed_win)
        blur_img = common.blurImage(img, blur_radius)

        if self.color == None and self.point is not None:
            self.color = blur_img[self.point[1], self.point[0]]
            print "segmenting color:", self.color
        
        return common.colorSegmentation(blur_img, blur_radius, radius,
                                        open_radius, self.color) 

def edgeDetect(self, img):
        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        thresh1 = cv2.getTrackbarPos('threshold 1', edge_win)
        thresh2 = cv2.getTrackbarPos('threshold 2', edge_win)
        canny = cv2.Canny(gray, thresh1, thresh2)
        return canny 

def updatePoint(self, event, x, y, flags, param):
        #blur the image and get a new color
        if event == cv2.EVENT_LBUTTONUP or event == cv2.EVENT_LBUTTONDOWN:
            blur_radius = cv2.getTrackbarPos("blur", processed_win)
            point = (x, y)
            self.point = point
            if self.cur_img is None:
                return
            blur_img = common.blurImage(self.cur_img, blur_radius)
            if self.color is None:
                self.color = blur_img[self.point[1], self.point[0]]
            self.axes = [] 

def updateDetector(self, args):
        maxSize = params['maxSize']
        responseThreshold = cv2.getTrackbarPos("Response threshold",
                                                processed_win)
        lineThresholdProjected = cv2.getTrackbarPos("Projected line threshold",
                                                    processed_win)
        lineThresholdBinarized = cv2.getTrackbarPos("Binarized line threshold",
                                                    processed_win)
        self.detector = cv2.StarDetector(maxSize, responseThreshold,
                                lineThresholdProjected, lineThresholdBinarized) 

def value(self):
        return cv2.getTrackbarPos(self._param_name, self._win_name)

    # Trackbarの値を設定 

def get_value(self):
        value = cv2.getTrackbarPos(self.name, self.parent_window_name)
        return value 

def update_image(image_id, category_id = 0, image_filenames=[], enable_vis=True, enable_marker_dump=False):
    try:
        global contours, hierarchy, img, gray, g_image_filenames
        if len(image_filenames) > 0:
            g_image_filenames=image_filenames
        img=cv.imread(g_image_filenames[image_id])
        # print(g_image_filenames[image_id])
        cv.setTrackbarPos('image', 'marker', image_id)

        gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
        gray[np.where(gray <= [3])] = [187]
        gray = cv.medianBlur(gray, 11)

        if enable_vis:
            cv.imshow('gray', gray)

        if CANNY_MODE:
            thrs1 = cv.getTrackbarPos('thrs1', 'marker')
            thrs2 = cv.getTrackbarPos('thrs2', 'marker')
            bin = cv.Canny(gray, thrs1, thrs2, apertureSize=5)
        else:
            bin = cv.adaptiveThreshold(
                gray, 255, cv.ADAPTIVE_THRESH_MEAN_C, cv.THRESH_BINARY_INV, 31, 10)

        if enable_vis:
            cv.imshow('bin', bin)

        _, contours0, hierarchy = cv.findContours(
            bin.copy(), cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
        contours = [cnt for cnt in contours0 if cv.contourArea(cnt) > 200]

        if enable_vis:
            cv.imshow('image', img)
        update_contour(category_id, image_id, enable_vis, enable_marker_dump)
    except Exception:
        import traceback
        traceback.print_exc()
        raise