def run_MSER(image, show=False):
mser = MSER(image, 64)
data = mser.build_component_tree()
universe = data["points"]
root_node = data["root"]
nodes= data["nodes"]
component_to_points = data["component to points"]
# prune the excess regions
mser_regions = TextDetector.prune_MSERs(mser.grey_scale, universe, root_node, nodes, component_to_points)
# def walk (tree, f):
# print "walked to ", tree
# f(image, component_to_points, tree)
# for c in tree.children:
# walk(c, f)
# walk(root_node, TextDetector.draw_component)
# convert to opencv contours
# contours = map( lambda c: component_to_points[c.component],mser_regions)
# def convertPoint(point):
# lst = [point.row,point.col]
# lst = np.array([lst],dtype= int32)
# return [lst]
#points = map(lambda i: map(lambda c : convertPoint(c),i),contours)
#print list(itertools.chain(*points))
#test = np.array(reduce(lambda x, y: x+y, points),dtype= int32)
# our_contours = map(lambda c: np.array(reduce(lambda x, y: x+y, map(lambda p: convertPoint(p), c)),dtype= int32),contours)
if show:
for region in mser_regions:
TextDetector.draw_colour_component(image, component_to_points, region.component)
return mser_regions
def run_MSER(image, show=False):
mser = MSER(image, 64)
data = mser.build_component_tree()
universe = data["points"]
root_node = data["root"]
nodes = data["nodes"]
component_to_points = data["component to points"]
# prune the excess regions
mser_regions = TextDetector.prune_MSERs(mser.grey_scale, universe,
root_node, nodes,
component_to_points)
# def walk (tree, f):
# print "walked to ", tree
# f(image, component_to_points, tree)
# for c in tree.children:
# walk(c, f)
# walk(root_node, TextDetector.draw_component)
# convert to opencv contours
# contours = map( lambda c: component_to_points[c.component],mser_regions)
# def convertPoint(point):
# lst = [point.row,point.col]
# lst = np.array([lst],dtype= int32)
# return [lst]
#points = map(lambda i: map(lambda c : convertPoint(c),i),contours)
#print list(itertools.chain(*points))
#test = np.array(reduce(lambda x, y: x+y, points),dtype= int32)
# our_contours = map(lambda c: np.array(reduce(lambda x, y: x+y, map(lambda p: convertPoint(p), c)),dtype= int32),contours)
if show:
for region in mser_regions:
TextDetector.draw_colour_component(image, component_to_points,
region.component)
return mser_regions
def aspect_ratio(component):
copy = image.copy()
points_in_region = MSER.extremal_region(component_to_points, component)
num_rows, num_cols = np.shape(copy)
min_row = num_rows
max_row = 0
min_col = num_cols
max_col = 0
for point in points_in_region:
if point.row < min_row:
min_row = point.row
elif point.row > max_row:
max_row = point.row
if point.col < min_col:
min_col = point.col
elif point.col > max_col:
max_col = point.col
width, height = (max_row-min_row) , (max_col-min_col)
if width > 0 and height>0:
return float(width)/height
return None
def aspect_ratio(component):
copy = image.copy()
points_in_region = MSER.extremal_region(component_to_points,
component)
num_rows, num_cols = np.shape(copy)
min_row = num_rows
max_row = 0
min_col = num_cols
max_col = 0
for point in points_in_region:
if point.row < min_row:
min_row = point.row
elif point.row > max_row:
max_row = point.row
if point.col < min_col:
min_col = point.col
elif point.col > max_col:
max_col = point.col
width, height = (max_row - min_row), (max_col - min_col)
if width > 0 and height > 0:
return float(width) / height
return None
def size(component):
return len(MSER.extremal_region(component_to_points, component))
def difference(component1, component2):
return MSER.extremal_region(
component_to_points, component1).difference(
MSER.extremal_region(component_to_points, component2))
lambda neighbour: neighbour.intensity >= universe[0].intensity, universe)
def already_processed_neighbours(point, higher_intensity):
if point.intensity < higher_intensity[-1].intensity:
higher_intensity = filter(
lambda neighbour: neighbour.intensity >= point.intensity, universe)
return point.neighbours(higher_intensity)
logging.info("Preprocessing step")
num_points = len(universe)
i = 0
for point in universe:
i += 1
set1[point] = MSER.DisjointSet(point)
set2[point] = MSER.DisjointSet(point)
nodes[point] = MSER.ComponentTree(point.intensity)
subtreeRoot[point] = point
i = 0
print "Universe bomb"
for point in universe:
i += 1
current_canonical = set1[point].find()
current_node = set2[subtreeRoot[current_canonical.data]].find()
# print "Looking at ", nodes[current_node.data]
# if point.row == 22 and point.col == 19:
# print "Looking at special point"
for neighbour in already_processed_neighbours(point, higher_intensity):
# if point.row == 22 and point.col == 19:
# print "Neighbour of special pt ", neighbour, " ", neighbour.intensity
def size(component):
return len(MSER.extremal_region(component_to_points, component))
def difference(component1, component2):
return MSER.extremal_region(component_to_points, component1).difference(MSER.extremal_region(component_to_points, component2))
