Exemple #1
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    def ncc_segment(self, segment1, segments2, tol):
        shape = [tol, tol]
        region1 = segment1.get_image_region()
        scale = segment1.get_segmented_image().get_scale()
        i1 = segments2.get_img().get_data()
        i1 = nputils.zoom(i1, region1.get_center(), [2 * tol, 2 * tol])
        i2 = region1.get_region()
        mask = (i2 > 0)

        if min(region1.get_region().shape) <= 3:
            return None

        if self.mode == 'ncc':
            ncc = nputils.norm_xcorr2(i1, i2, mode='same')
            ncc[ncc < self.ncc_threshold] = 0
            # ncc = nputils.weighted_norm_xcorr2(i1, i2, (i2 > 0), mode='same')
            data = nputils.resize(ncc, shape)
        elif self.mode == 'ncc_peaks':
            data = np.zeros(shape)
            ncc = nputils.norm_xcorr2(i1, i2, mode='same')
            ncc = nputils.resize(ncc, shape)
            peaks = nputils.find_peaks(ncc, 4, self.ncc_threshold, fit_gaussian=False)
            for peak in peaks[:]:
                data += imgutils.gaussian(shape, width=8, center=peak) * ncc[tuple(peak)]
        return data
Exemple #2
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    def ncc_segment(self, segment1, segments2, tol):
        shape = [tol, tol]
        region1 = segment1.get_image_region()
        scale = segment1.get_segmented_image().get_scale()
        i1 = segments2.get_img().get_data()
        i1 = nputils.zoom(i1, region1.get_center(), [2 * tol, 2 * tol])
        i2 = region1.get_region()
        mask = (i2 > 0)

        if min(region1.get_region().shape) <= 3:
            return None

        if self.mode == 'ncc':
            ncc = nputils.norm_xcorr2(i1, i2, mode='same')
            ncc[ncc < self.ncc_threshold] = 0
            # ncc = nputils.weighted_norm_xcorr2(i1, i2, (i2 > 0), mode='same')
            data = nputils.resize(ncc, shape)
        elif self.mode == 'ncc_peaks':
            data = np.zeros(shape)
            ncc = nputils.norm_xcorr2(i1, i2, mode='same')
            ncc = nputils.resize(ncc, shape)
            peaks = nputils.find_peaks(ncc,
                                       4,
                                       self.ncc_threshold,
                                       fit_gaussian=False)
            for peak in peaks[:]:
                data += imgutils.gaussian(shape, width=8,
                                          center=peak) * ncc[tuple(peak)]
        return data
Exemple #3
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    def process(self, prj, res1, res2):
        delta_t, velocity_pix, tol_pix = self.get_velocity_resolution(
            prj, res1, res2)
        max_bounds = max(self.bounds)

        if self.verbose:
            print "Processing:", res1.get_epoch(), res2.get_epoch(
            ), delta_t, velocity_pix, tol_pix

        if self.debug >= 2:
            fig, ax_check0 = self.stack.add_subplots("Segments check 1",
                                                     ncols=1)
            fig, ax_check1 = self.stack.add_subplots("Segments check 2",
                                                     ncols=1)

        epoch_all_mean_ncc = []

        for segments1, segments2 in zip(res1, res2):
            if self.debug >= 2:
                imshow_segmented_image(ax_check0,
                                       segments1,
                                       alpha=1,
                                       projection=prj)
                imshow_segmented_image(ax_check1,
                                       segments2,
                                       alpha=1,
                                       projection=prj)

            scale = segments1.get_scale()
            # print "Scale %s: %s" % (scale, len(segments1))

            all_ncc = []
            all_ncc_shape = None
            for segment1 in segments1:
                x_dir = self.x_dir
                if x_dir == 'position_angle':
                    x_dir = prj.p2s(p2i(segment1.get_coord()))
                elif nputils.is_callable(x_dir):
                    x_dir = self.x_dir(prj.p2s(p2i(segment1.get_coord())))

                if self.mode == 'ncc_peaks_direct':
                    region1 = segment1.get_image_region()

                    if min(region1.get_region().shape) <= 3:
                        continue

                    if self.rnd_pos_shift:
                        shift = np.random.normal(
                            0,
                            self.rnd_pos_factor *
                            segment1.get_coord_error(min_snr=3))
                        region1.set_shift(shift)

                    i1 = segments2.get_img().get_data()
                    # i1 = nputils.shift2d(i1, np.array([4 / velocity_pix] * 2))
                    i1 = nputils.zoom(i1, region1.get_center(),
                                      [2 * tol_pix, 2 * tol_pix])
                    i2 = region1.get_region()
                    shape = [
                        self.factor * (self.bounds[0] + self.bounds[1]),
                        self.factor * (self.bounds[2] + self.bounds[3])
                    ]
                    data = np.zeros(shape)

                    # print i1.shape, i2.shape
                    ncc = nputils.norm_xcorr2(i1, i2, mode='same')
                    peaks = nputils.find_peaks(ncc,
                                               4,
                                               self.ncc_threshold,
                                               fit_gaussian=False)
                    if len(peaks) > 0:
                        delta_pix = p2i(peaks - np.array(ncc.shape) / 2)

                        delta = prj.pixel_scales() * np.array(
                            delta_pix) * prj.unit
                        v = delta / self.__get_delta_time(delta_t)

                        if self.vel_trans:
                            v = self.vel_trans(
                                prj.p2s(p2i(segment1.get_coord())), v.T).T

                        if x_dir is not None:
                            vx, vy = nputils.vector_projection(v.T,
                                                               x_dir) * v.unit
                        else:
                            vx, vy = v.T

                        vx = vx.to(self.unit).value
                        vy = vy.to(self.unit).value
                        d = np.array([vx, vy])

                        if len(vx) > 0:
                            ix = self.factor * (self.bounds[0] + vx)
                            iy = self.factor * (self.bounds[2] + vy)

                            widths = np.array(
                                [[4 * self.factor * velocity_pix] * 2] *
                                len(peaks))
                            # widths = np.array([[1] * 2] * len(peaks))
                            centers = np.array([iy, ix]).T
                            heights = ncc[tuple(
                                np.array([tuple(k) for k in peaks]).T)]
                            # print widths, centers, heights
                            data = imgutils.multiple_gaussian(
                                shape, heights, widths, centers)

                    ncc = data
                else:
                    ncc = self.ncc_segment(segment1, segments2, 2 * tol_pix)
                    if ncc is not None:
                        # print ncc.shape, nputils.coord_max(ncc)
                        ncc = zoom(ncc, velocity_pix * self.factor, order=3)
                        # zoom will shift the center
                        ncc = nputils.shift2d(
                            ncc, [-(velocity_pix * self.factor) / 2.] * 2)
                        # print ncc.shape, nputils.coord_max(ncc), velocity_pix * self.factor
                        ncc = nputils.zoom(ncc,
                                           np.array(ncc.shape) / 2, [
                                               2 * max_bounds * self.factor,
                                               2 * max_bounds * self.factor
                                           ])
                        # print ncc.shape, nputils.coord_max(ncc)
                        # ncc = ncc[self.factor * (max_bounds - self.bounds[0]):self.factor * (max_bounds + self.bounds[1]),
                        #           self.factor * (max_bounds - self.bounds[2]):self.factor * (max_bounds + self.bounds[3])]
                        # print ncc.shape

                        if x_dir is not None:
                            angle_rad = -np.arctan2(x_dir[1], x_dir[0])
                            ncc = rotate(ncc,
                                         -angle_rad / (2 * np.pi) * 360,
                                         reshape=False,
                                         order=2)

                if ncc is not None:
                    if self.debug >= 3:
                        fig, (ax0, ax1, ax2) = self.stack.add_subplots(
                            "Segment %s" % segment1.get_segmentid(), ncols=3)
                        r1 = segment1.get_image_region()
                        ax0.imshow(r1.get_region())
                        i2 = nputils.zoom(segments2.get_img().get_data(),
                                          r1.get_center(),
                                          [2 * tol_pix, 2 * tol_pix])
                        ax1.imshow(i2,
                                   norm=plotutils.LogNorm(),
                                   extent=(-tol_pix, tol_pix, -tol_pix,
                                           tol_pix))
                        plotutils.img_axis(ax1)

                        ax2.imshow(ncc, extent=self.global_ncc_extent)
                        plotutils.img_axis(ax2)

                    all_ncc.append(ncc)

                    if all_ncc_shape is None:
                        all_ncc_shape = ncc.shape

            if len(all_ncc) > 0:
                if scale not in self.global_ncc_scales:
                    self.global_ncc_scales[scale] = []
                    self.global_ncc_scales_n[scale] = 0
                mean_ncc = self.agg_fct(np.array(all_ncc), axis=0)
                epoch_all_mean_ncc.append(mean_ncc)

                if self.debug >= 1:
                    fig, ax = self.stack.add_subplots(
                        "Ncc epoch %s vs %s scale %s" %
                        (res1.get_epoch(), res2.get_epoch(), scale))
                    ax.imshow(mean_ncc, extent=self.global_ncc_extent)
                    plotutils.img_axis(ax)

                self.global_ncc_scales[scale].append(mean_ncc)
                self.global_ncc_scales_n[scale] += len(all_ncc)

        if self.debug >= 0.5 and len(epoch_all_mean_ncc) > 0:
            epoch_mean_ncc = self.agg_fct(np.array(epoch_all_mean_ncc), axis=0)
            fig, ax = self.stack.add_subplots(
                "Ncc epoch %s vs %s" % (res1.get_epoch(), res2.get_epoch()))
            ax.imshow(epoch_mean_ncc, extent=self.global_ncc_extent)
            plotutils.img_axis(ax)
Exemple #4
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    def process(self, prj, res1, res2):
        delta_t, velocity_pix, tol_pix = self.get_velocity_resolution(prj, res1, res2)
        max_bounds = max(self.bounds)

        if self.verbose:
            print "Processing:", res1.get_epoch(), res2.get_epoch(), delta_t, velocity_pix, tol_pix

        if self.debug >= 2:
            fig, ax_check0 = self.stack.add_subplots("Segments check 1", ncols=1)
            fig, ax_check1 = self.stack.add_subplots("Segments check 2", ncols=1)

        epoch_all_mean_ncc = []

        for segments1, segments2 in zip(res1, res2):
            if self.debug >= 2:
                imshow_segmented_image(ax_check0, segments1, alpha=1, projection=prj)
                imshow_segmented_image(ax_check1, segments2, alpha=1, projection=prj)

            scale = segments1.get_scale()
            # print "Scale %s: %s" % (scale, len(segments1))

            all_ncc = []
            all_ncc_shape = None
            for segment1 in segments1:
                x_dir = self.x_dir
                if x_dir == 'position_angle':
                    x_dir = prj.p2s(p2i(segment1.get_coord()))
                elif nputils.is_callable(x_dir):
                    x_dir = self.x_dir(prj.p2s(p2i(segment1.get_coord())))

                if self.mode == 'ncc_peaks_direct':
                    region1 = segment1.get_image_region()
                    
                    if min(region1.get_region().shape) <= 3:
                        continue

                    if self.rnd_pos_shift:
                        shift = np.random.normal(0, self.rnd_pos_factor * segment1.get_coord_error(min_snr=3))
                        region1.set_shift(shift)

                    i1 = segments2.get_img().get_data()
                    # i1 = nputils.shift2d(i1, np.array([4 / velocity_pix] * 2))
                    i1 = nputils.zoom(i1, region1.get_center(), [2 * tol_pix, 2 * tol_pix])
                    i2 = region1.get_region()
                    shape = [self.factor * (self.bounds[0] + self.bounds[1]), self.factor * (self.bounds[2] + self.bounds[3])]
                    data = np.zeros(shape)

                    # print i1.shape, i2.shape
                    ncc = nputils.norm_xcorr2(i1, i2, mode='same')
                    peaks = nputils.find_peaks(ncc, 4, self.ncc_threshold, fit_gaussian=False)
                    if len(peaks) > 0:
                        delta_pix = p2i(peaks - np.array(ncc.shape) / 2)

                        delta = prj.pixel_scales() * np.array(delta_pix)  * prj.unit
                        v = delta / self.__get_delta_time(delta_t)

                        if self.vel_trans:
                            v = self.vel_trans(prj.p2s(p2i(segment1.get_coord())), v.T).T

                        if x_dir is not None:
                            vx, vy = nputils.vector_projection(v.T, x_dir) * v.unit
                        else:
                            vx, vy = v.T

                        vx = vx.to(self.unit).value
                        vy = vy.to(self.unit).value
                        d = np.array([vx, vy])

                        if len(vx) > 0:
                            ix = self.factor * (self.bounds[0] + vx)
                            iy = self.factor * (self.bounds[2] + vy)

                            widths = np.array([[4 * self.factor * velocity_pix] * 2] * len(peaks))
                            # widths = np.array([[1] * 2] * len(peaks))
                            centers = np.array([iy, ix]).T
                            heights = ncc[tuple(np.array([tuple(k) for k in peaks]).T)]
                            # print widths, centers, heights
                            data = imgutils.multiple_gaussian(shape, heights, widths, centers)

                    ncc = data
                else:
                    ncc = self.ncc_segment(segment1, segments2, 2 * tol_pix)
                    if ncc is not None:
                        # print ncc.shape, nputils.coord_max(ncc)
                        ncc = zoom(ncc, velocity_pix * self.factor, order=3)
                        # zoom will shift the center
                        ncc = nputils.shift2d(ncc, [- (velocity_pix * self.factor) / 2.] * 2)
                        # print ncc.shape, nputils.coord_max(ncc), velocity_pix * self.factor
                        ncc = nputils.zoom(ncc, np.array(ncc.shape) / 2, [2 * max_bounds * self.factor, 2 * max_bounds * self.factor])
                        # print ncc.shape, nputils.coord_max(ncc)
                        # ncc = ncc[self.factor * (max_bounds - self.bounds[0]):self.factor * (max_bounds + self.bounds[1]),
                        #           self.factor * (max_bounds - self.bounds[2]):self.factor * (max_bounds + self.bounds[3])]
                        # print ncc.shape

                        if x_dir is not None:
                            angle_rad = - np.arctan2(x_dir[1], x_dir[0])
                            ncc = rotate(ncc, - angle_rad / (2 * np.pi) * 360, reshape=False, order=2)

                if ncc is not None:
                    if self.debug >= 3:
                        fig, (ax0, ax1, ax2) = self.stack.add_subplots("Segment %s" % segment1.get_segmentid(), ncols=3)
                        r1 = segment1.get_image_region()
                        ax0.imshow(r1.get_region())
                        i2 = nputils.zoom(segments2.get_img().get_data(), r1.get_center(), [2 * tol_pix, 2 * tol_pix])
                        ax1.imshow(i2, norm=plotutils.LogNorm(), 
                                   extent=(-tol_pix, tol_pix, -tol_pix, tol_pix))
                        plotutils.img_axis(ax1)

                        ax2.imshow(ncc, extent=self.global_ncc_extent)
                        plotutils.img_axis(ax2)
                    
                    all_ncc.append(ncc)

                    if all_ncc_shape is None:
                        all_ncc_shape = ncc.shape

            if len(all_ncc) > 0:
                if scale not in self.global_ncc_scales:
                    self.global_ncc_scales[scale] = []
                    self.global_ncc_scales_n[scale] = 0
                mean_ncc = self.agg_fct(np.array(all_ncc), axis=0)
                epoch_all_mean_ncc.append(mean_ncc)

                if self.debug >= 1:
                    fig, ax = self.stack.add_subplots("Ncc epoch %s vs %s scale %s" % (res1.get_epoch(), res2.get_epoch(), scale))
                    ax.imshow(mean_ncc, extent=self.global_ncc_extent)
                    plotutils.img_axis(ax)

                self.global_ncc_scales[scale].append(mean_ncc)
                self.global_ncc_scales_n[scale] += len(all_ncc)

        if self.debug >= 0.5 and len(epoch_all_mean_ncc) > 0:
            epoch_mean_ncc = self.agg_fct(np.array(epoch_all_mean_ncc), axis=0)
            fig, ax = self.stack.add_subplots("Ncc epoch %s vs %s" % (res1.get_epoch(), res2.get_epoch()))
            ax.imshow(epoch_mean_ncc, extent=self.global_ncc_extent)
            plotutils.img_axis(ax)