def test_should_empty_index_after_removing_multiple_added_nodes_in_all_quad_nodes():
index = Index(INDEX_BBOX, max_items=1)
index.insert(ITEM1, BBOX1)
index.insert(ITEM2, INDEX_BBOX)
index.remove(ITEM1, BBOX1)
index.remove(ITEM2, INDEX_BBOX)
assert len(index) == 0
assert index.intersect(INDEX_BBOX) == []
def test_should_empty_index_after_removing_multiple_added_nodes_in_multiple_horizontal_quad_nodes():
index = Index(INDEX_BBOX, max_items=1)
bbox2 = (0, 0, INDEX_BBOX[2], 1)
index.insert(ITEM1, BBOX1)
index.insert(ITEM2, bbox2)
index.remove(ITEM1, BBOX1)
index.remove(ITEM2, bbox2)
assert len(index) == 0
assert index.intersect(INDEX_BBOX) == []
class TreeStruct:
bbox = (-10000, -10000, 10000, 10000)
##
# Maintain link from points to rectangle objects?
def __init__(self):
self.index = Index(bbox=self.bbox, max_items=40, max_depth=15)
def addRect(self, rect):
if (bboxoutside(rect.bbox, self.bbox)):
print('outside')
pass # TODO
self.index.insert(rect, rect.bbox)
def removeRect(self, rect):
self.index.remove(rect, rect.bbox)
def query(self, rect):
candidates = self.index.intersect(rect.bbox)
# return candidates
# TBD if we want to make the check both ways or are okay with overlaps only being detected on one end
return [candidate for candidate in candidates if rect.overlaps(candidate) or candidate.overlaps(rect)]
def test_should_empty_index_after_removing_added_node():
index = Index(INDEX_BBOX)
index.insert(ITEM1, BBOX1)
index.remove(ITEM1, BBOX1)
assert len(index) == 0
assert index.intersect(BBOX1) == []
class Model(EventDispatcher):
def __init__(self):
self.nx = 0
self.ny = 0
self.num_pieces = 0
self.num_intended_pieces = 0
self.image_path = None
self.image_width = 0
self.image_height = 0
self.snap_distance = 0
self.pieces = None
self.trays = None
self.quadtree = None
self.current_max_z_level = 0
self.timer = Timer()
self.cheated = False
self.start_time = datetime.now()
def reset(self,
image_path,
image_width,
image_height,
num_intended_pieces,
snap_distance_percent=0.5):
self.image_path = image_path
self.image_width = image_width
self.image_height = image_height
self.num_intended_pieces = num_intended_pieces
self.nx, self.ny, self.num_pieces = create_jigsaw_dimensions(
num_intended_pieces, image_width, image_height)
self.snap_distance = snap_distance_percent * image_width / self.nx
self.cheated = False
self.pieces = make_jigsaw_cut(self.image_width, self.image_height,
self.nx, self.ny)
self.trays = Tray(num_pids=self.num_pieces)
self.quadtree = QuadTree(bbox=(-100000, -100000, 100000, 100000))
for piece in tqdm(self.pieces.values(), desc="Building quad-tree"):
self.quadtree.insert(piece, piece.bbox)
self.current_max_z_level = self.num_pieces
self.timer.reset()
self.start_time = datetime.now()
def to_dict(self):
return {
'image_path': self.image_path,
'pieces': self.pieces,
'trays': self.trays,
'current_max_z_level': self.current_max_z_level,
'nx': self.nx,
'ny': self.ny,
'num_pieces': self.num_pieces,
'image_width': self.image_width,
'image_height': self.image_height,
'snap_distance': self.snap_distance,
'elapsed_seconds': self.elapsed_seconds,
'cheated': self.cheated,
'start_time': self.start_time
}
@classmethod
def from_dict(cls, data):
model = cls()
model.image_path = data['image_path']
model.pieces = data['pieces']
model.trays = data['trays']
model.nx = data['nx']
model.ny = data['ny']
model.num_pieces = data['num_pieces']
model.image_width = data['image_width']
model.image_height = data['image_height']
model.snap_distance = data['snap_distance']
model.current_max_z_level = data['current_max_z_level']
model.timer = Timer(data['elapsed_seconds'])
model.cheated = data['cheated']
model.start_time = data['start_time']
model.quadtree = QuadTree(bbox=(-100000, -100000, 100000, 100000))
for piece in tqdm(model.pieces.values(), desc="Building quad-tree"):
model.quadtree.insert(piece, piece.bbox)
return model
def piece_at_coordinate(self, x, y):
return self._top_piece_at_location(x, y)
def piece_ids_in_rect(self, rect):
def to_pid(piece):
return piece.pid
pieces_in_rect = self.quadtree.intersect(bbox=(rect.left, rect.bottom,
rect.right, rect.top))
return list(self.trays.filter_visible(map(to_pid, pieces_in_rect)))
def merge_random_pieces(self, n):
self.cheated = True
n = min(n, len(self.pieces) - 1)
for _ in range(n):
piece = random.choice(list(self.pieces.values()))
neighbour = self.pieces[random.choice(list(piece.neighbours))]
self.quadtree.remove(piece, piece.bbox)
piece.x = neighbour.x
piece.y = neighbour.y
self.dispatch_event('on_snap_piece_to_position', piece.pid,
piece.x, piece.y, piece.z)
self._merge_pieces(piece, neighbour)
self.quadtree.insert(piece, piece.bbox)
def move_and_snap(self, pid, dx, dy):
piece = self.pieces[pid]
self.quadtree.remove(piece, piece.bbox)
piece.x += dx
piece.y += dy
for neighbour_pid in piece.neighbours:
if not self.trays.is_visible(neighbour_pid):
continue
neighbour = self.pieces[neighbour_pid]
dist = Point.dist(Point(piece.x, piece.y),
Point(neighbour.x, neighbour.y))
if dist < self.snap_distance:
piece.x = neighbour.x
piece.y = neighbour.y
neighbour.z = piece.z
self.dispatch_event('on_snap_piece_to_position', pid, piece.x,
piece.y, piece.z)
self._merge_pieces(piece, neighbour)
self.quadtree.insert(piece, piece.bbox)
def set_piece_position(self, piece, x, y):
self.quadtree.remove(piece, piece.bbox)
piece.x = x
piece.y = y
self.dispatch_event('on_snap_piece_to_position', piece.pid, piece.x,
piece.y, piece.z)
self.quadtree.insert(piece, piece.bbox)
def move_pieces(self, pids, dx, dy):
if len(pids) == 1:
self.move_and_snap(pids[0], dx, dy)
else:
for pid in pids:
piece = self.pieces[pid]
self.quadtree.remove(piece, piece.bbox)
piece.x += dx
piece.y += dy
self.quadtree.insert(piece, piece.bbox)
def spread_out(self, pids):
single_pieces = list(
filter(lambda piece: len(piece.members) == 1,
map(lambda pid: self.pieces[pid], pids)))
if len(single_pieces) < 1:
return
n = math.ceil(math.sqrt(len(single_pieces)))
left = min(map(
lambda piece: piece.origin.x + piece.x,
single_pieces,
))
bottom = min(map(lambda piece: piece.origin.y + piece.y,
single_pieces))
for i, piece in enumerate(single_pieces):
self.set_piece_position(
piece, left + 2 * piece.width * (i % n) - piece.origin.x,
bottom + 2 * piece.height * (i // n) - piece.origin.y)
def move_pieces_to_top(self, pids):
new_z_levels = list(
range(self.current_max_z_level,
self.current_max_z_level + len(pids)))
self.current_max_z_level += len(pids)
sorted_pieces = sorted([self.pieces[pid] for pid in pids],
key=lambda p: p.z)
msg = []
for z, piece in zip(new_z_levels, sorted_pieces):
piece.z = z
msg.append((z, piece.pid))
self.dispatch_event('on_z_levels_changed', msg)
def move_pieces_to_tray(self, tray, pids):
self.trays.move_pids_to_tray(tray=tray, pids=pids)
if self._tray_is_hidden(tray):
self.dispatch_event('on_visibility_changed', tray, False,
self.trays.hidden_pieces)
def get_hidden_pieces(self):
return self.trays.hidden_pieces
def get_piece_data(self):
data = []
for pid, piece in self.pieces.items():
piece_data = piece.data
piece_data['tray'] = self.trays.pid_to_tray[pid]
data.append(piece_data)
return data
def toggle_visibility(self, tray):
self.trays.toggle_visibility(tray)
self.dispatch_event('on_visibility_changed', tray,
self._tray_is_visible(tray),
self.trays.hidden_pieces)
def toggle_pause(self, is_paused):
if is_paused:
self.timer.pause()
else:
self.timer.start()
@property
def elapsed_seconds(self):
return self.timer.elapsed_seconds
@property
def percent_complete(self):
total_moves = self.num_pieces - 1
moves_made = self.num_pieces - len(self.pieces)
return 100 * moves_made / total_moves
def _tray_is_visible(self, tray):
return tray in self.trays.visible_trays
def _tray_is_hidden(self, tray):
return not self._tray_is_visible(tray)
def _merge_pieces(self, p1, p2):
# Merges p2 into p1
p1.merge(p2)
self.pieces.pop(p2.pid)
p2.neighbours.remove(p1.pid)
for neighbour_pid in p2.neighbours:
neighbour = self.pieces[neighbour_pid]
neighbour.neighbours.remove(p2.pid)
neighbour.neighbours.add(p1.pid)
self.quadtree.remove(p2, p2.bbox)
self.trays.merge_pids(p1.pid, p2.pid)
self.dispatch_event('on_pieces_merged', p1.pid, p2.pid)
self._check_game_over()
def _pieces_at_location(self, x, y):
for piece in self.quadtree.intersect(bbox=(x, y, x, y)):
if self.trays.is_visible(piece.pid) and piece.contains(
Point(x, y), self.nx):
yield piece
def _top_piece_at_location(self, x, y):
return max(self._pieces_at_location(x, y),
key=(lambda p: p.z),
default=None)
def _check_game_over(self):
if len(self.pieces) == 1:
self.timer.pause()
self.dispatch_event('on_win', self.elapsed_seconds,
self.num_pieces)
save_statistics(image_path=self.image_path,
num_pieces=self.num_pieces,
num_intended_pieces=self.num_intended_pieces,
image_width=self.image_width,
image_height=self.image_height,
snap_distance=self.snap_distance,
start_time=self.start_time,
piece_rotation=False,
cheated=self.cheated,
elapsed_seconds=self.elapsed_seconds)
def __eq__(self, other):
return (
self.current_max_z_level == other.current_max_z_level,
self.pieces == other.pieces,
self.nx == other.nx,
self.ny == other.ny,
self.num_pieces == other.num_pieces,
self.image_height == other.image_height,
self.image_width == other.image_width,
self.trays == other.trays,
self.snap_distance == other.snap_distance,
self.quadtree == other.quadtree,
)
