コード例 #1
0
    def get_heatmap(self, type='raw'):
        """
        Returns heatmap calculated from saliency map of image passed to SightSpotDetector constructor.

        Parameters
        ----------
        type : 'raw' or 'precise'
            Specifies whenever segmentation will be used to improve saliency map (slower).

        Returns
        -------
        out : PIL.Image
            Heatmap.
        """
        if type == 'raw':
            if self._raw_heatmap == None:
                saliency_map = self._get_saliency_map(type)
                self._raw_heatmap = SightSpotUtil.eval_heatmap(saliency_map)
            return self._raw_heatmap.copy()
        if type == 'precise':
            if self._precise_heatmap == None:
                fusion_map = self._get_saliency_map(type)
                self._precise_heatmap = SightSpotUtil.eval_heatmap(fusion_map)
            return self._precise_heatmap.copy()
        raise Exception('Unknown argument value type = "' + str(type) + '"')
コード例 #2
0
    def get_heatmap(self, type='raw'):
        """
        Returns heatmap calculated from saliency map of image passed to SightSpotDetector constructor.

        Parameters
        ----------
        type : 'raw' or 'precise'
            Specifies whenever segmentation will be used to improve saliency map (slower).

        Returns
        -------
        out : PIL.Image
            Heatmap.
        """
        if type == 'raw':
            if self._raw_heatmap == None:
                saliency_map = self._get_saliency_map(type)
                self._raw_heatmap = SightSpotUtil.eval_heatmap(saliency_map)
            return self._raw_heatmap.copy()
        if type == 'precise':
            if self._precise_heatmap == None:
                fusion_map = self._get_saliency_map(type)
                self._precise_heatmap = SightSpotUtil.eval_heatmap(fusion_map)
            return self._precise_heatmap.copy()
        raise Exception('Unknown argument value type = "' + str(type) + '"')
コード例 #3
0
ファイル: saliency_google.py プロジェクト: mazoku/thesis
def run(image, mask=None, smoothing=False, show=False, show_now=True):
    if mask is None:
        mask = np.ones_like(image)
        im_orig = image.copy()
    else:
        image, mask = tools.crop_to_bbox(image, mask)
        im_orig = image.copy()
        mean_v = int(image[np.nonzero(mask)].mean())
        image = np.where(mask, image, mean_v)
    mask = mask.astype(np.uint8)

    if smoothing:
        image = tools.smoothing(image)

    # defaultne hleda svetle oblasti
    image = np.invert(image)

    rgb_image = cv2.cvtColor(image, cv2.COLOR_BAYER_GR2RGB).astype(np.float32)

    # print 'Calculate saliency map for test image:'
    orgb_image = SightSpotUtil.eval_orgb_image(rgb_image)

    # start = time.clock()
    saliency_map = SightSpotUtil.eval_saliency_map(orgb_image, 1.0, 40.0, 'auto')
    # print 'Saliency map extracted in', time.clock() - start, 'sec.'

    # start = time.clock()
    # heatmap_image = SightSpotUtil.eval_heatmap(saliency_map)
    # heatmap_image = np.asarray(heatmap_image)
    # heatmap_image.setflags(write=True)
    # print 'Heatmap extracted in', time.clock() - start, 'sec.'

    saliency_map *= mask
    # heatmap_image *= np.dstack((mask, mask, mask))
    im_orig *= mask

    saliency_map = skiexp.rescale_intensity(saliency_map, out_range=(0, 1))

    if show:
        if smoothing:
            plt.subplot(131), plt.imshow(im_orig, 'gray', interpolation='nearest'), plt.title('input')
            plt.subplot(132), plt.imshow(image, 'gray', interpolation='nearest'), plt.title('smoothed')
            plt.subplot(133), plt.imshow(saliency_map, 'gray', interpolation='nearest'), plt.title('saliency')
        else:
            plt.subplot(121), plt.imshow(im_orig, 'gray', interpolation='nearest'), plt.title('input')
            plt.subplot(122), plt.imshow(saliency_map, 'gray', interpolation='nearest'), plt.title('saliency')
        if show_now:
            plt.show()

    return im_orig, image, saliency_map
コード例 #4
0
 def _get_saliency_map(self, type):
     orgb_image = self._get_orgb_image()
     if type == 'raw':
         if self._saliency_map is None:
             small_sigma = self._small_sigma
             large_sigma = self._large_sigma
             self._saliency_map = SightSpotUtil.eval_saliency_map(orgb_image, small_sigma, large_sigma, 'auto')
         return self._saliency_map
     if type == 'precise':
         if self._fusion_map is None:
             saliency_map = self._get_saliency_map(type='raw')
             cell_size = self._cell_size
             alpha = self._SLIC_ALPHA
             iterations = self._ITERATION_NUMBER
             segmentation_map = SightSpotUtil.eval_slic_map(orgb_image, cell_size, alpha, iterations)
             self._fusion_map = SightSpotUtil.combine_saliency_and_segmentation(saliency_map, segmentation_map)
         return self._fusion_map
     raise Exception('Unknown argument value type = "' + str(type) + '"')
コード例 #5
0
 def _get_saliency_map(self, type):
     orgb_image = self._get_orgb_image()
     if type == 'raw':
         if self._saliency_map is None:
             small_sigma = self._small_sigma
             large_sigma = self._large_sigma
             self._saliency_map = SightSpotUtil.eval_saliency_map(
                 orgb_image, small_sigma, large_sigma, 'auto')
         return self._saliency_map
     if type == 'precise':
         if self._fusion_map is None:
             saliency_map = self._get_saliency_map(type='raw')
             cell_size = self._cell_size
             alpha = self._SLIC_ALPHA
             iterations = self._ITERATION_NUMBER
             segmentation_map = SightSpotUtil.eval_slic_map(
                 orgb_image, cell_size, alpha, iterations)
             self._fusion_map = SightSpotUtil.combine_saliency_and_segmentation(
                 saliency_map, segmentation_map)
         return self._fusion_map
     raise Exception('Unknown argument value type = "' + str(type) + '"')
コード例 #6
0
    def _get_saliency_map(self, type):
        orgb_image = self._get_orgb_image()
        if type == 'raw':
            if self._saliency_map is None:
                small_sigma = self._small_sigma
                large_sigma = self._large_sigma
                self._saliency_map = SightSpotUtil.eval_saliency_map(orgb_image, small_sigma, large_sigma, 'auto')
            return self._saliency_map
        if type == 'precise':
            if self._fusion_map is None:
                saliency_map = self._get_saliency_map(type='raw')
                cell_size = self._cell_size
                alpha = self._SLIC_ALPHA
                iterations = self._ITERATION_NUMBER
                self._segmentation_map = SightSpotUtil.eval_slic_map(orgb_image, cell_size, alpha, iterations)
                self._fusion_map = SightSpotUtil.combine_saliency_and_segmentation(saliency_map, self._segmentation_map)

                # _visualize_clusters(self._segmentation_map).show('Clusters in random colors')
                # _visualize_contours(self._rgb_image, self._segmentation_map, (255, 255, 0)).show('Cluster contours')
                # _visualize_averaging(self._rgb_image, self._segmentation_map).show('Averaging')
            return self._fusion_map
        raise Exception('Unknown argument value type = "' + str(type) + '"')
コード例 #7
0
    def threshold(self, source, value='auto'):
        """
        Returns black-and-white image - thresholded version of saliency map.

        Parameters
        ----------
        source : 'raw' or 'precise'
            Which saliency map to use for thresholding.
        type : float or 'auto'
            Specifies thresholding value.

        Returns
        -------
        out : PIL.Image
            Binary image.
        """
        saliency_map = self._get_saliency_map(source)
        return Image.fromarray(255.0 * SightSpotUtil.threshold(saliency_map, value)).convert('RGB')
コード例 #8
0
    def get_foreground(self, source, value='auto'):
        """
        Removes background pixels from input image and returns result.

        Parameters
        ----------
        source : 'raw' or 'precise'
            Which saliency map to use for thresholding.
        type : float or 'auto'
            Specifies thresholding value.

        Returns
        -------
        out : PIL.Image
            Image without background pixels.
        """
        saliency_map = self._get_saliency_map(source)
        return Image.fromarray(SightSpotUtil.remove_background(self._rgb_image, saliency_map, value))
コード例 #9
0
    def get_foreground(self, source, value='auto'):
        """
        Removes background pixels from input image and returns result.

        Parameters
        ----------
        source : 'raw' or 'precise'
            Which saliency map to use for thresholding.
        type : float or 'auto'
            Specifies thresholding value.

        Returns
        -------
        out : PIL.Image
            Image without background pixels.
        """
        saliency_map = self._get_saliency_map(source)
        return Image.fromarray(
            SightSpotUtil.remove_background(self._rgb_image, saliency_map,
                                            value))
コード例 #10
0
    def threshold(self, source, value='auto'):
        """
        Returns black-and-white image - thresholded version of saliency map.

        Parameters
        ----------
        source : 'raw' or 'precise'
            Which saliency map to use for thresholding.
        type : float or 'auto'
            Specifies thresholding value.

        Returns
        -------
        out : PIL.Image
            Binary image.
        """
        saliency_map = self._get_saliency_map(source)
        return Image.fromarray(
            255.0 *
            SightSpotUtil.threshold(saliency_map, value)).convert('RGB')
コード例 #11
0
    def cut_objects(self, source, value='auto'):
        """
        Extract connected components from thresholded saliency map and cuts images.

        Parameters
        ----------
        source : 'raw' or 'precise'
            Which saliency map to use for thresholding.
        type : float or 'auto'
            Specifies thresholding value.

        Returns
        -------
        out : list of PIL.Images
            Detected objects.
        """
        saliency_map = self._get_saliency_map(source)
        result = SightSpotUtil.detect_objects(self._rgb_image, saliency_map, value)
        windows = []
        for item in result:
            windows.append(Image.fromarray(item))
        return windows
コード例 #12
0
    def cut_objects(self, source, value='auto'):
        """
        Extract connected components from thresholded saliency map and cuts images.

        Parameters
        ----------
        source : 'raw' or 'precise'
            Which saliency map to use for thresholding.
        type : float or 'auto'
            Specifies thresholding value.

        Returns
        -------
        out : list of PIL.Images
            Detected objects.
        """
        saliency_map = self._get_saliency_map(source)
        result = SightSpotUtil.detect_objects(self._rgb_image, saliency_map,
                                              value)
        windows = []
        for item in result:
            windows.append(Image.fromarray(item))
        return windows
コード例 #13
0
        cluster_idx = (segmentation_map == i - 1)
        cluster_size = numpy.sum(cluster_idx)
        if cluster_size == 0:
            palette_items.append((0, 0, 0))
        else:
            avg = numpy.sum(rgb_image[cluster_idx], axis=0) / cluster_size
            palette_items.append(tuple(avg))
    palette = numpy.array(palette_items, dtype='uint8')
    visualization = palette[segmentation_map + 1]
    return Image.fromarray(visualization)

if __name__ == '__main__':
    print 'Segmentation of test image:'
    image = Image.open('flower.jpg')
    rgb_image = numpy.asarray(image, dtype='float32')
    orgb_image = SightSpotUtil.eval_orgb_image(rgb_image)

    start = time.clock()
    segmentation_map = SightSpotUtil.eval_slic_map(orgb_image, 32.0, 0.25, 4)
    print 'Segmentation map extracted in', time.clock() - start, 'sec.'
    print 'Segment number:', numpy.max(segmentation_map) + 1

    _visualize_clusters(segmentation_map).show('Clusters in random colors')
    _visualize_contours(rgb_image, segmentation_map, (255, 255, 0)).show('Cluster contours')
    _visualize_averaging(rgb_image, segmentation_map).show('Averaging')

    print 'Use segmentation to improve clustering...'
    saliency_map = SightSpotUtil.eval_saliency_map(orgb_image, 3.0, 60.0, 'auto')
    precise_saliency_map = SightSpotUtil.combine_saliency_and_segmentation(saliency_map, segmentation_map)
    precise_saliency_image = Image.fromarray(precise_saliency_map * 255)
    precise_saliency_image.show('Pixel-precise saliency image')
コード例 #14
0
ファイル: TestORGB.py プロジェクト: Foued70/sight-spot
    print "RGB (0, 0, 1) is oRGB (%f, %f, %f) for BLUE    " % _rgb2orgb(
        0, 0, 1)  # Blue.
    print "RGB (1, 1, 0) is oRGB (%f, %f, %f) for YELLOW  " % _rgb2orgb(
        1, 1, 0)  # Yellow.
    print "RGB (0, 1, 1) is oRGB (%f, %f, %f) for SKY-BLUE" % _rgb2orgb(
        0, 1, 1)  # Sky-blue.
    print "RGB (1, 0, 1) is oRGB (%f, %f, %f) for MAGENTA " % _rgb2orgb(
        1, 0, 1)  # Magenta.
    print "RGB (1, 1, 1) is oRGB (%f, %f, %f) for WHITE   " % _rgb2orgb(
        1, 1, 1)  # White.

    print 'Show channels of test image:'
    image = Image.open('flower.jpg')
    rgb_image = numpy.asarray(image, dtype='float32')

    start = time.clock()
    orgb_image = SightSpotUtil.eval_orgb_image(rgb_image)
    print 'ORGB image extracted in', time.clock() - start, 'sec.'

    print 'Colorizing...'
    lu_channel = orgb_image[:, :, 0]
    rg_channel = orgb_image[:, :, 1]
    yb_channel = orgb_image[:, :, 2]
    lu_image = Image.fromarray(lu_channel * 255 + 0.5)
    rg_image = _colorize_channel(rg_channel, (255, 0, 0), (0, 255, 0))
    yb_image = _colorize_channel(yb_channel, (255, 255, 0), (0, 0, 255))
    image.show('Source image')
    lu_image.show('Luminance channel')
    rg_image.show('Red-green channel')
    yb_image.show('Yellow-blue channel')
コード例 #15
0
 def _get_orgb_image(self):
     if self._orgb_image is None:
         self._orgb_image = SightSpotUtil.eval_orgb_image(self._rgb_image)
     return self._orgb_image
コード例 #16
0
        cluster_size = numpy.sum(cluster_idx)
        if cluster_size == 0:
            palette_items.append((0, 0, 0))
        else:
            avg = numpy.sum(rgb_image[cluster_idx], axis=0) / cluster_size
            palette_items.append(tuple(avg))
    palette = numpy.array(palette_items, dtype='uint8')
    visualization = palette[segmentation_map + 1]
    return Image.fromarray(visualization)


if __name__ == '__main__':
    print 'Segmentation of test image:'
    image = Image.open('flower.jpg')
    rgb_image = numpy.asarray(image, dtype='float32')
    orgb_image = SightSpotUtil.eval_orgb_image(rgb_image)

    start = time.clock()
    segmentation_map = SightSpotUtil.eval_slic_map(orgb_image, 32.0, 0.25, 4)
    print 'Segmentation map extracted in', time.clock() - start, 'sec.'
    print 'Segment number:', numpy.max(segmentation_map) + 1

    _visualize_clusters(segmentation_map).show('Clusters in random colors')
    _visualize_contours(rgb_image, segmentation_map,
                        (255, 255, 0)).show('Cluster contours')
    _visualize_averaging(rgb_image, segmentation_map).show('Averaging')

    print 'Use segmentation to improve clustering...'
    saliency_map = SightSpotUtil.eval_saliency_map(orgb_image, 3.0, 60.0,
                                                   'auto')
    precise_saliency_map = SightSpotUtil.combine_saliency_and_segmentation(
コード例 #17
0
ファイル: TestSaliency.py プロジェクト: Foued70/sight-spot
__author__ = 'Igor Ryabtsov aka Tinnulion'
__copyright__ = 'Copyright (c) 2014'
__license__ = 'Apache 2.0'
__version__ = '1.0'

import time
import numpy
from PIL import Image
import SightSpotUtil

if __name__ == '__main__':
    print 'Calculate saliency map for test image:'
    image = Image.open('flower.jpg')
    rgb_image = numpy.asarray(image, dtype='float32')
    orgb_image = SightSpotUtil.eval_orgb_image(rgb_image)

    start = time.clock()
    saliency_map = SightSpotUtil.eval_saliency_map(orgb_image, 3.0, 60.0,
                                                   'auto')
    print 'Saliency map extracted in', time.clock() - start, 'sec.'

    start = time.clock()
    heatmap_image = SightSpotUtil.eval_heatmap(saliency_map)
    print 'Heatmap extracted in', time.clock() - start, 'sec.'

    saliency_image = Image.fromarray(saliency_map * 255)
    image.show('Source image')
    saliency_image.show('Saliency image')
    heatmap_image.show('Heatmap')
コード例 #18
0
ファイル: TestORGB.py プロジェクト: Foued70/sight-spot
def _rgb2orgb(r, g, b):
    rgb_image = numpy.array([[[255.0 * r, 255.0 * g, 255.0 * b]]],
                            dtype='float32')
    orgb = SightSpotUtil.eval_orgb_image(rgb_image)[0, 0]
    return tuple(orgb)
コード例 #19
0
ファイル: TestORGB.py プロジェクト: TheMikeyR/sight-spot
def _rgb2orgb(r, g, b):
    rgb_image = numpy.array([[[255.0 * r, 255.0 * g, 255.0 * b]]], dtype='float32')
    orgb = SightSpotUtil.eval_orgb_image(rgb_image)[0, 0]
    return tuple(orgb)
コード例 #20
0
 def _get_orgb_image(self):
     if self._orgb_image is None:
         self._orgb_image = SightSpotUtil.eval_orgb_image(self._rgb_image)
     return self._orgb_image
コード例 #21
0
ファイル: TestORGB.py プロジェクト: TheMikeyR/sight-spot
    print 'Test some basic oRGB color conversions:'
    print "RGB (0, 0, 0) is oRGB (%f, %f, %f) for BLACK   " % _rgb2orgb(0, 0, 0) # Black.
    print "RGB (1, 0, 0) is oRGB (%f, %f, %f) for RED     " % _rgb2orgb(1, 0, 0) # Red.
    print "RGB (0, 1, 0) is oRGB (%f, %f, %f) for GREEN   " % _rgb2orgb(0, 1, 0) # Green.
    print "RGB (0, 0, 1) is oRGB (%f, %f, %f) for BLUE    " % _rgb2orgb(0, 0, 1) # Blue.
    print "RGB (1, 1, 0) is oRGB (%f, %f, %f) for YELLOW  " % _rgb2orgb(1, 1, 0) # Yellow.
    print "RGB (0, 1, 1) is oRGB (%f, %f, %f) for SKY-BLUE" % _rgb2orgb(0, 1, 1) # Sky-blue.
    print "RGB (1, 0, 1) is oRGB (%f, %f, %f) for MAGENTA " % _rgb2orgb(1, 0, 1) # Magenta.
    print "RGB (1, 1, 1) is oRGB (%f, %f, %f) for WHITE   " % _rgb2orgb(1, 1, 1) # White.

    print 'Show channels of test image:'
    image = Image.open('flower.jpg')
    rgb_image = numpy.asarray(image, dtype='float32')

    start = time.clock()
    orgb_image = SightSpotUtil.eval_orgb_image(rgb_image)
    print 'ORGB image extracted in', time.clock() - start, 'sec.'

    print 'Colorizing...'
    lu_channel = orgb_image[:,:,0]
    rg_channel = orgb_image[:,:,1]
    yb_channel = orgb_image[:,:,2]
    lu_image = Image.fromarray(lu_channel * 255 + 0.5)
    rg_image = _colorize_channel(rg_channel, (255, 0, 0), (0, 255, 0))
    yb_image = _colorize_channel(yb_channel, (255, 255, 0), (0, 0, 255))
    image.show('Source image')
    lu_image.show('Luminance channel')
    rg_image.show('Red-green channel')
    yb_image.show('Yellow-blue channel')

コード例 #22
0
ファイル: TestSaliency.py プロジェクト: TheMikeyR/sight-spot
"""

__author__    = 'Igor Ryabtsov aka Tinnulion'
__copyright__ = 'Copyright (c) 2014'
__license__   = 'Apache 2.0'
__version__   = '1.0'

import time
import numpy
from PIL import Image
import SightSpotUtil

if __name__ == '__main__':
    print 'Calculate saliency map for test image:'
    image = Image.open('flower.jpg')
    rgb_image = numpy.asarray(image, dtype='float32')
    orgb_image = SightSpotUtil.eval_orgb_image(rgb_image)

    start = time.clock()
    saliency_map = SightSpotUtil.eval_saliency_map(orgb_image, 3.0, 60.0, 'auto')
    print 'Saliency map extracted in', time.clock() - start, 'sec.'

    start = time.clock()
    heatmap_image = SightSpotUtil.eval_heatmap(saliency_map)
    print 'Heatmap extracted in', time.clock() - start, 'sec.'

    saliency_image = Image.fromarray(saliency_map * 255)
    image.show('Source image')
    saliency_image.show('Saliency image')
    heatmap_image.show('Heatmap')