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
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) + '"')
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) + '"')
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) + '"')
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')
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')