def calculate_cluster(camera, camera_mat, quantile):
bandwidth = estimate_bandwidth(camera_mat, quantile=quantile, n_samples=500)
ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
ms.fit(camera_mat)
labels = ms.labels_
cluster_centers = ms.cluster_centers_
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
camera_clustered = camera.copy()
camera_clustered_value = camera.copy()
camera_mat_clustered = camera_mat.copy()
camera_mat_clustered_value = camera_mat.copy()
for point, pointb, value in zip(camera_mat_clustered, camera_mat_clustered_value, labels):
point[2] = value
pointb[2] = cluster_centers[value, 2]
camera_clustered[point[0], point[1]] = value
camera_clustered_value[point[0], point[1]] = cluster_centers[value, 2]
image = {"image": camera_clustered_value,
"quantile": quantile,
"clusters": n_clusters_}
return image
def calculate_cluster(camera, camera_mat, quantile):
bandwidth = estimate_bandwidth(camera_mat,
quantile=quantile,
n_samples=500)
ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
ms.fit(camera_mat)
labels = ms.labels_
cluster_centers = ms.cluster_centers_
labels_unique = np.unique(labels)
n_clusters_ = len(labels_unique)
camera_clustered = camera.copy()
camera_clustered_value = camera.copy()
camera_mat_clustered = camera_mat.copy()
camera_mat_clustered_value = camera_mat.copy()
for point, pointb, value in zip(camera_mat_clustered,
camera_mat_clustered_value, labels):
point[2] = value
pointb[2] = cluster_centers[value, 2]
camera_clustered[point[0], point[1]] = value
camera_clustered_value[point[0], point[1]] = cluster_centers[value, 2]
image = {
"image": camera_clustered_value,
"quantile": quantile,
"clusters": n_clusters_
}
return image
# My computer is crap - I don't have enough ram to compute the clustering on
# the whole camera image. Let's downsample the image by a factor of 4
#camera = camera[::2, ::2] + camera[1::2, ::2] + camera[::2, 1::2] + camera[1::2, 1::2]
#camera = camera[::2, ::2] + camera[1::2, ::2] + camera[::2, 1::2] + camera[1::2, 1::2]
# camera as an image is useless. Let's create a 512*3 matrix (x, y, value)
camera_mat = []
for x, i in enumerate(camera):
for y, j in enumerate(i):
camera_mat.append([x, y, j])
camera_mat = np.array(camera_mat)
quantile_range = np.linspace(0.004, 0.02, 11)
images = []
images.append({"image": camera.copy(), "quantile": 0, "clusters": 0})
for i, quantile in enumerate(quantile_range):
print "%d calculating for quantile %f" % (i, quantile)
image = mem.cache(calculate_cluster)(camera, camera_mat, quantile)
images.append(image)
fig = plt.figure()
for i, image in enumerate(images):
ax = fig.add_subplot(4, 3, i)
ax.set_axis_off()
ax.set_title('clusters: %d - quantile %f' % (image['clusters'],
image['quantile']))
ax.matshow(image['image'])
# My computer is crap - I don't have enough ram to compute the clustering on
# the whole camera image. Let's downsample the image by a factor of 4
#camera = camera[::2, ::2] + camera[1::2, ::2] + camera[::2, 1::2] + camera[1::2, 1::2]
#camera = camera[::2, ::2] + camera[1::2, ::2] + camera[::2, 1::2] + camera[1::2, 1::2]
# camera as an image is useless. Let's create a 512*3 matrix (x, y, value)
camera_mat = []
for x, i in enumerate(camera):
for y, j in enumerate(i):
camera_mat.append([x, y, j])
camera_mat = np.array(camera_mat)
quantile_range = np.linspace(0.004, 0.02, 11)
images = []
images.append({"image": camera.copy(), "quantile": 0, "clusters": 0})
for i, quantile in enumerate(quantile_range):
print "%d calculating for quantile %f" % (i, quantile)
image = mem.cache(calculate_cluster)(camera, camera_mat, quantile)
images.append(image)
fig = plt.figure()
for i, image in enumerate(images):
ax = fig.add_subplot(4, 3, i)
ax.set_axis_off()
ax.set_title('clusters: %d - quantile %f' %
(image['clusters'], image['quantile']))
ax.matshow(image['image'])
