def testUnion(self):
ra = Rect(0, 0, 10, 10)
rb = Rect(-10, -10, 1, 1)
x, y, w, h = ra.union(rb).extent()
self.assertEquals(x, -10)
self.assertEquals(y, -10)
self.assertEquals(w, 20)
self.assertEquals(h, 20)
for i in range(1000):
a, b = G.rect(), G.rect()
u = a.union(b)
self.assertTrue(a.intersect(u).area() > 0)
self.assertTrue(u.intersect(a).area() > 0)
self.assertTrue(b.intersect(u).area() > 0)
self.assertTrue(u.intersect(b).area() > 0)
self.assertTrue(
u.area() >= (max(a.area(), b.area())),
"union area (iter %d) fail %f >= %f" %
(i, u.area(), (max(a.area(), b.area()))))
c, d = G.disjointPair()
u2 = c.union(d)
self.assertTrue(c.intersect(u2).area() > 0)
self.assertTrue(u2.intersect(c).area() > 0)
self.assertTrue(d.intersect(u2).area() > 0)
self.assertTrue(u2.intersect(d).area() > 0)
self.assertTrue(u2.area() > c.area())
self.assertTrue(u2.area() > d.area())
self.assertTrue(u2.area() >= (c.area() + d.area()))
def testIntersection(self):
ra = Rect(0, 0, 10, 10)
rb = Rect(5, 5, 15, 15)
res = ra.intersect(rb)
x, y, w, h = res.extent()
self.assertEquals(x, 5)
self.assertEquals(y, 5)
self.assertEquals(w, 5)
self.assertEquals(h, 5)
self.assertEquals(res.area(), 25)
rc = Rect(0, 0, 10, 10)
rd = Rect(11, 11, 21, 21)
res2 = rc.intersect(rd)
self.assertEquals(res2.area(), 0)
self.assertTrue(res2 is NullRect)
for i in range(1000):
a, b = G.intersectingPair()
self.assertTrue(a.intersect(b).area() > 0.0)
c, d = G.disjointPair()
self.assertEquals(c.intersect(d).area(), 0)
self.assertTrue(ra.intersect(NullRect) is NullRect)
self.assertTrue(NullRect.intersect(ra) is NullRect)
def rect(self, size=10.0):
x, y, w, h = rr(), rr(), random.uniform(0.0, size), random.uniform(
0.0, size)
r = Rect(x, y, x + w, y + h)
assert (not r.swapped_x)
assert (not r.swapped_y)
return r
def _invariants(self, node, seen):
idx = node.index
self.assertTrue(idx not in seen)
seen[idx] = True
if node.holds_leaves():
#print("node: %d, children: %r" % (node.index, [c.index for c in node.children()]))
self.assertTrue(node.nchildren() == 0
or node.get_first_child().is_leaf())
for c in node.children():
#print(c.index)
self.assertTrue(c.is_leaf())
self.assertTrue(isinstance(c.leaf_obj(), TstO))
else:
for c in node.children():
self.assertTrue(not c.is_leaf())
self.assertEquals(idx, node.index)
r = Rect(node.rect.x, node.rect.y, node.rect.xx, node.rect.yy)
for c in node.children():
assert r.does_contain(c.rect)
self.assertEquals(idx, node.index)
for c in node.children():
if not c.is_leaf(): self._invariants(c, seen)
self.assertEquals(idx, node.index)
def disjointWith(self, ra):
ax, ay, aw, ah = ra.extent()
w, h = rr(), rr()
distsq = max(w * w + h * h, aw * aw + ah * ah)
dist = 2.0 * math.sqrt(distsq) + random.uniform(0.1, 1.0)
ang = random.uniform(0.0, 2.0 * math.pi)
x = math.cos(ang) * dist
y = math.sin(ang) * dist
return Rect(ax + x, ay + y, ax + x + w, ay + y + h)
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 __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]))
def testCons(self):
r = Rect(0, 0, 10, 10)
self.assertTrue(r is not None)
self.assertTrue(r is not NullRect)
def intersectingWith(self, ra):
rb_x = random.uniform(ra.x, ra.xx)
rb_y = random.uniform(ra.y, ra.yy)
return Rect(rb_x, rb_y, rb_x + rr(), rb_y + rr())
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)
x2 = rectangepoints[1][0]
y2 = rectangepoints[1][1]
# #print x1
# wi=x2-x1
# he=y2-y1
#
# p = plt.Rectangle(
# (x1, y1), wi, he,angle=0 ,fill=False
#
# )
# ax.add_patch(p)
st = "tag" + str(i)
i += 1
#print st
t.insert(st, Rect(x1, y1, x2, y2))
'''
querying
'''
point_res = t.query_point((2, 1025))
# rect_res = t.query_rect( Rect(0,1020,4,1040) )
rectx1 = 0
recty1 = 1020
rectx2 = 4
recty2 = 1040
tempx1 = -1000.0
tempy1 = 1030.0
tempx2 = -972.5
from pyrtree import Rect, RTree
DATA_SIZE = 100
# 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)
def rect_from_coords(self, x1, y1, x2, y2):
r = Rect(x1, y1, x2, y2)
assert (not r.swapped_x)
assert (not r.swapped_y)
return r