def matrix_cluster(boxes):
sizes = np.array([[b,b.x_size()] for b in boxes])
normal = reject_outliers(sizes)[:,0]
x_clusters = cluster(list(normal))
y_clusters = cluster(list(normal),x=False)
numbers = []
for c in x_clusters:
new_clust = []
for y_c in y_clusters:
new_clust.append(Cluster(list(set(tuple(c.boxes)) & set(tuple(y_c.boxes)))))
numbers.append(new_clust)
root = tk.Tk()
vis = Visualizer(root,1000,600)
#return the clusters of boxes
for i in numbers:
for c in i:
for b in c.boxes:
box = Box2D((b.x1,b.y1),size=b.size_t())
vis.add_drawable(box)
box = Box2D((c.bounding_box().x1,c.bounding_box().y1),
size=c.bounding_box().size_t())
box.fill = None
box.outline = "green"
vis.add_drawable(box)
#vis.run()
#root.mainloop()
return numbers
def display_boxes(boxes):
root = tk.Tk()
vis = Visualizer(root, 1000, 600)
for b in boxes:
box = Box2D((b.x1, b.y1), size=b.size_t())
vis.add_drawable(box)
vis.run()
root.mainloop()
def cluster(boxes,x=True):
clusters = []
while boxes:
cluster = Cluster()
b = boxes.pop()
cluster.boxes.append(b)
changed = True
while changed:
changed = False
for other in list(boxes):
if x:
if cluster.bounding_box().overlaps_x(other):
changed = True
cluster.boxes.append(other)
boxes.remove(other)
else:
if cluster.bounding_box().overlaps_y(other):
changed = True
cluster.boxes.append(other)
boxes.remove(other)
#boxes = tmp
#if len(cluster.boxes)<5 and cluster.bounding_box().size()>10:
clusters.append(cluster)
root = tk.Tk()
vis = Visualizer(root,1000,600)
if x:
clusters = sorted(clusters,key = lambda c:c.mid_x())
else:
clusters = sorted(clusters,key = lambda c:c.mid_y())
#return the clusters of boxes
for c in clusters:
for b in c.boxes:
box = Box2D((b.x1,b.y1),size=b.size_t())
vis.add_drawable(box)
box = Box2D((c.bounding_box().x1,c.bounding_box().y1),
size=c.bounding_box().size_t())
box.fill = None
box.outline = "green"
vis.add_drawable(box)
#vis.run()
#root.mainloop()
return clusters
]
colors = ["red", "blue", "green", "yellow"]
sizes = np.array([[b, b.y_size()] for b in boxes])
abnormal = accept_outliers(sizes)[:, 0]
normal = reject_outliers(sizes)[:, 0]
first = -1
num = 0
for i in range(100):
num = i + 1
clusters, error = cluster_boxes(normal, num_clusters=i + 1)
if first != -1 and error / (first - error) < 0.15:
break
first = error
colors *= len(clusters) / len(colors)
root = tk.Tk()
vis = Visualizer(root, 1000, 600)
for cluster, color in zip(clusters, colors):
for b in cluster.boxes:
box = Box2D((b.x1, b.y1), size=b.size_t())
box.fill = color
vis.add_drawable(box)
box = Box2D((cluster.bounding_box().x1, cluster.bounding_box().y1), size=cluster.bounding_box().size_t())
box.fill = None
vis.add_drawable(box)
vis.run()
root.mainloop()
