def testRectQuery(self):
xs = [TstO(r) for r in take(1000, G.rect, 0.01)]
rt = RTree()
for x in xs:
rt.insert(x, x.rect)
self.invariants(rt)
for x in xs:
qrect = G.intersectingWith(x.rect)
orect = G.disjointWith(x.rect)
self.assertTrue(qrect.does_intersect(x.rect))
p = G.pointInside(x.rect)
res = list([ro.leaf_obj() for ro in rt.query_point(p)])
self.invariants(rt)
self.assertTrue(x in res)
res2 = list([r.leaf_obj() for r in rt.query_rect(qrect)])
self.assertTrue(x in res2)
rres = list([r.leaf_obj() for r in rt.query_rect(orect)])
self.assertFalse(x in rres)
def testRectQuery(self):
xs = [ TstO(r) for r in take(1000, G.rect, 0.01) ]
rt = RTree()
for x in xs:
rt.insert(x,x.rect)
self.invariants(rt)
for x in xs:
qrect = G.intersectingWith(x.rect)
orect = G.disjointWith(x.rect)
self.assertTrue(qrect.does_intersect(x.rect))
p = G.pointInside(x.rect)
res = list([ro.leaf_obj() for ro in rt.query_point(p)])
self.invariants(rt)
self.assertTrue(x in res)
res2 = list([r.leaf_obj() for r in rt.query_rect(qrect)])
self.assertTrue(x in res2)
rres = list([r.leaf_obj() for r in rt.query_rect(orect)])
self.assertFalse(x in rres)
class MultipleTilesAffineRenderer:
BLEND_TYPE = {"NO_BLENDING": 0, "AVERAGING": 1, "LINEAR": 2}
def __init__(self, single_tiles, blend_type="NO_BLENDING"):
"""Receives a number of image paths, and for each a transformation matrix"""
self.blend_type = self.BLEND_TYPE[blend_type]
self.single_tiles = single_tiles
# Create an RTree of the bounding boxes of the tiles
self.rtree = RTree()
for t in self.single_tiles:
bbox = t.get_bbox()
# pyrtree uses the (x_min, y_min, x_max, y_max) notation
self.rtree.insert(t, Rect(bbox[0], bbox[2], bbox[1], bbox[3]))
#should_compute_mask = False if self.blend_type == 0 else True
#self.single_tiles = [SingleTileAffineRenderer(img_path, img_shape[1], img_shape[0], compute_mask=should_compute_mask) for img_path, img_shape in zip(img_paths, img_shapes)]
#for i, matrix in enumerate(transform_matrices):
# self.single_tiles[i].add_transformation(matrix)
def add_transformation(self, transform_matrix):
"""Adds a transformation to all tiles"""
self.rtree = RTree()
for single_tile in self.single_tiles:
single_tile.add_transformation(transform_matrix)
bbox = single_tile.get_bbox()
# pyrtree uses the (x_min, y_min, x_max, y_max) notation
self.rtree.insert(single_tile,
Rect(bbox[0], bbox[2], bbox[1], bbox[3]))
def render(self):
if len(self.single_tiles) == 0:
return None, None
# Render all tiles by finding the bounding box, and using crop
all_bboxes = np.array([t.get_bbox() for t in self.single_tiles]).T
bbox = [
np.min(all_bboxes[0]),
np.max(all_bboxes[1]),
np.min(all_bboxes[2]),
np.max(all_bboxes[3])
]
crop, start_point = self.crop(bbox[0], bbox[2], bbox[1], bbox[3])
return crop, start_point
def crop(self, from_x, from_y, to_x, to_y):
if len(self.single_tiles) == 0:
return None, None
# Distinguish between the different types of blending
if self.blend_type == 0: # No blending
res = np.zeros(
(round(to_y + 1 - from_y), round(to_x + 1 - from_x)),
dtype=np.uint8)
# render only relevant parts, and stitch them together
# filter only relevant tiles using rtree
rect_res = self.rtree.query_rect(Rect(from_x, from_y, to_x, to_y))
for rtree_node in rect_res:
if not rtree_node.is_leaf():
continue
t = rtree_node.leaf_obj()
t_img, t_start_point, _ = t.crop(from_x, from_y, to_x, to_y)
if t_img is not None:
res[t_start_point[1] - from_y:t_img.shape[0] +
(t_start_point[1] - from_y),
t_start_point[0] - from_x:t_img.shape[1] +
(t_start_point[0] - from_x)] = t_img
elif self.blend_type == 1: # Averaging
# Do the calculation on a uint16 image (for overlapping areas), and convert to uint8 at the end
res = np.zeros(
(round(to_y + 1 - from_y), round(to_x + 1 - from_x)),
dtype=np.uint16)
res_mask = np.zeros(
(round(to_y + 1 - from_y), round(to_x + 1 - from_x)),
dtype=np.uint8)
# render only relevant parts, and stitch them together
# filter only relevant tiles using rtree
rect_res = self.rtree.query_rect(Rect(from_x, from_y, to_x, to_y))
for rtree_node in rect_res:
if not rtree_node.is_leaf():
continue
t = rtree_node.leaf_obj()
t_img, t_start_point, t_mask = t.crop(from_x, from_y, to_x,
to_y)
if t_img is not None:
res[t_start_point[1] - from_y:t_img.shape[0] +
(t_start_point[1] - from_y),
t_start_point[0] - from_x:t_img.shape[1] +
(t_start_point[0] - from_x)] += t_img
#t_start_point[0] - from_x: t_img.shape[1] + t_start_point[0] - from_x] += t_mask * 50
res_mask[t_start_point[1] - from_y:t_img.shape[0] +
(t_start_point[1] - from_y),
t_start_point[0] - from_x:t_img.shape[1] +
(t_start_point[0] - from_x)] += t_mask
# Change the values of 0 in the mask to 1, to avoid division by 0
res_mask[res_mask == 0] = 1
res = res / res_mask
res = res.astype(np.uint8)
elif self.blend_type == 2: # Linear averaging
# Do the calculation on a uint32 image (for overlapping areas), and convert to uint8 at the end
# For each pixel use the min-distance to an edge as a weight, and store the
# average the outcome according to the weight
res = np.zeros(
(round(to_y + 1 - from_y), round(to_x + 1 - from_x)),
dtype=np.uint32)
res_weights = np.zeros(
(round(to_y + 1 - from_y), round(to_x + 1 - from_x)),
dtype=np.uint16)
# render only relevant parts, and stitch them together
# filter only relevant tiles using rtree
rect_res = self.rtree.query_rect(Rect(from_x, from_y, to_x, to_y))
for rtree_node in rect_res:
if not rtree_node.is_leaf():
continue
t = rtree_node.leaf_obj()
t_img, t_start_point, t_weights = t.crop_with_distances(
from_x, from_y, to_x, to_y)
if t_img is not None:
res[t_start_point[1] - from_y:t_img.shape[0] +
(t_start_point[1] - from_y),
t_start_point[0] - from_x:t_img.shape[1] +
(t_start_point[0] - from_x)] += t_img * t_weights
res_weights[t_start_point[1] - from_y:t_img.shape[0] +
(t_start_point[1] - from_y),
t_start_point[0] - from_x:t_img.shape[1] +
(t_start_point[0] - from_x)] += t_weights
# Change the weights that are 0 to 1, to avoid division by 0
res_weights[res_weights < 1] = 1
res = res / res_weights
res = res.astype(np.uint8)
return res, (from_x, from_y)
recty2 = 1040
tempx1 = -1000.0
tempy1 = 1030.0
tempx2 = -972.5
tempy2 = 1000.0
wi = rectx2 - rectx1
he = recty2 - recty1
p = plt.Rectangle((rectx1, recty1), wi, he, angle=0, fill=False)
ax.add_patch(p)
# '''
# real_point_res = [r.leaf_obj() for r in t.query_rect( (2,4,2.5,4.5) ) if r.is_leaf()]
for x123 in range(0, 10):
rect_res = t.query_rect(Rect(rectx1, recty1, rectx2, recty2))
rectx1 -= 20
recty1 -= 50
rectx2 += 20
recty2 += 50
#print "iteration "+str(x123)
#print "x1"+str(x1)
#print "y1"+str(y1)
# wi=rectx2-rectx1
# he=recty2-recty1
#
# p = plt.Rectangle(
# (rectx1, recty1), wi, he,angle=0 ,fill=False
#
# )
# PyRTree Insert and Search Test. This PyRTree is query only index.
obj = []
for x in range(DATA_SIZE):
# Create objects with rectangles for objs, Rect(0, 0, 1, 1), Rect(1, 1, 2, 2); name obj0, obj1, ...
obj.append([Rect(0 + x, 0 + x, 1 + x, 1 + x), 'obj' + x.__str__()])
rtree = RTree() # tree creation
# pyrtree uses the Rect (x_min, y_min, x_max, y_max) notation
for x in range(DATA_SIZE):
rtree.insert(obj[x], obj[x][0]) # element insertion with a given box
# Query the tree
rect_res = rtree.query_rect(Rect(1, 1, 4, 4) )
# Traverse the query result
for rtree_node in rect_res:
if not rtree_node.is_leaf():
continue
t = rtree_node.leaf_obj()
print(t[0].x, t[0].y, t[0].xx, t[0].yy, t[1])
# Get the internal nodes which are at the deepest level of the tree. Each internal contains a number of leaf nodes
for nodes in rtree.get_last_level():
print(nodes)
# Print all
for nodes in rtree.get_last_level():
for node in nodes:
class MultipleTilesRenderer:
BLEND_TYPE = {
"NO_BLENDING" : 0,
"AVERAGING" : 1,
"LINEAR" : 2
}
def __init__(self, single_tiles, blend_type="NO_BLENDING"):
"""Receives a number of image paths, and for each a transformation matrix"""
self.blend_type = self.BLEND_TYPE[blend_type]
self.single_tiles = single_tiles
# Create an RTree of the bounding boxes of the tiles
self.rtree = RTree()
for t in self.single_tiles:
bbox = t.get_bbox()
# pyrtree uses the (x_min, y_min, x_max, y_max) notation
self.rtree.insert(t, Rect(bbox[0], bbox[2], bbox[1], bbox[3]))
#should_compute_mask = False if self.blend_type == 0 else True
#self.single_tiles = [SingleTileAffineRenderer(img_path, img_shape[1], img_shape[0], compute_mask=should_compute_mask) for img_path, img_shape in zip(img_paths, img_shapes)]
#for i, matrix in enumerate(transform_matrices):
# self.single_tiles[i].add_transformation(matrix)
def add_transformation(self, model):
"""Adds a transformation to all tiles"""
self.rtree = RTree()
for single_tile in self.single_tiles:
single_tile.add_transformation(model)
bbox = single_tile.get_bbox()
# pyrtree uses the (x_min, y_min, x_max, y_max) notation
self.rtree.insert(single_tile, Rect(bbox[0], bbox[2], bbox[1], bbox[3]))
def render(self):
if len(self.single_tiles) == 0:
return None, None
# Render all tiles by finding the bounding box, and using crop
all_bboxes = np.array([t.get_bbox() for t in self.single_tiles]).T
bbox = [np.min(all_bboxes[0]), np.max(all_bboxes[1]), np.min(all_bboxes[2]), np.max(all_bboxes[3])]
crop, start_point = self.crop(bbox[0], bbox[2], bbox[1], bbox[3])
return crop, start_point
def crop(self, from_x, from_y, to_x, to_y):
if len(self.single_tiles) == 0:
return None, None
# Distinguish between the different types of blending
if self.blend_type == 0: # No blending
res = np.zeros((round(to_y + 1 - from_y), round(to_x + 1 - from_x)), dtype=np.uint8)
# render only relevant parts, and stitch them together
# filter only relevant tiles using rtree
rect_res = self.rtree.query_rect( Rect(from_x, from_y, to_x, to_y) )
for rtree_node in rect_res:
if not rtree_node.is_leaf():
continue
t = rtree_node.leaf_obj()
t_img, t_start_point, _ = t.crop(from_x, from_y, to_x, to_y)
if t_img is not None:
res[t_start_point[1] - from_y: t_img.shape[0] + (t_start_point[1] - from_y),
t_start_point[0] - from_x: t_img.shape[1] + (t_start_point[0] - from_x)] = t_img
elif self.blend_type == 1: # Averaging
# Do the calculation on a uint16 image (for overlapping areas), and convert to uint8 at the end
res = np.zeros(
(round(to_y + 1 - from_y), round(to_x + 1 - from_x)),
np.float32)
res_mask = np.zeros(
(round(to_y + 1 - from_y), round(to_x + 1 - from_x)),
np.float32)
# render only relevant parts, and stitch them together
# filter only relevant tiles using rtree
rect_res = self.rtree.query_rect( Rect(from_x, from_y, to_x, to_y) )
for rtree_node in rect_res:
if not rtree_node.is_leaf():
continue
t = rtree_node.leaf_obj()
t_img, t_start_point, t_mask = t.crop(from_x, from_y, to_x, to_y)
if t_img is not None:
t_mask, _, _ = AlphaTileRenderer(t).crop(
from_x, from_y, to_x, to_y)
res[t_start_point[1] - from_y: t_img.shape[0] + (t_start_point[1] - from_y),
t_start_point[0] - from_x: t_img.shape[1] + (t_start_point[0] - from_x)] += t_img
res_mask[t_start_point[1] - from_y: t_img.shape[0] + (t_start_point[1] - from_y),
t_start_point[0] - from_x: t_img.shape[1] + (t_start_point[0] - from_x)] += t_mask
# Change the values of 0 in the mask to 1, to avoid division by 0
res_mask[res_mask == 0] = 1
res = res / res_mask
res = np.maximum(0, np.minimum(255, res)).astype(np.uint8)
elif self.blend_type == 2: # Linear averaging
# Do the calculation on a uint32 image (for overlapping areas), and convert to uint8 at the end
# For each pixel use the min-distance to an edge as a weight, and store the
# average the outcome according to the weight
res = np.zeros((round(to_y + 1 - from_y), round(to_x + 1 - from_x)), dtype=np.uint32)
res_weights = np.zeros((round(to_y + 1 - from_y), round(to_x + 1 - from_x)), dtype=np.uint16)
# render only relevant parts, and stitch them together
# filter only relevant tiles using rtree
rect_res = self.rtree.query_rect( Rect(from_x, from_y, to_x, to_y) )
for rtree_node in rect_res:
if not rtree_node.is_leaf():
continue
t = rtree_node.leaf_obj()
t_img, t_start_point, t_weights = t.crop_with_distances(from_x, from_y, to_x, to_y)
if t_img is not None:
print "actual image start_point:", t_start_point, "and shape:", t_img.shape
res[t_start_point[1] - from_y: t_img.shape[0] + (t_start_point[1] - from_y),
t_start_point[0] - from_x: t_img.shape[1] + (t_start_point[0] - from_x)] += t_img * t_weights
res_weights[t_start_point[1] - from_y: t_img.shape[0] + (t_start_point[1] - from_y),
t_start_point[0] - from_x: t_img.shape[1] + (t_start_point[0] - from_x)] += t_weights
# Change the weights that are 0 to 1, to avoid division by 0
res_weights[res_weights < 1] = 1
res = res / res_weights
res = np.maximum(0, np.minimum(255, res)).astype(np.uint8)
return res, (from_x, from_y)
