def compute_color_histograms(cloud, using_hsv=True):
# Compute histograms for the clusters
point_colors_list = []
# Step through each point in the point cloud
for point in pc2.read_points(cloud, skip_nans=True):
rgb_list = float_to_rgb(point[3]) # rgb_list is [r,g,b]
if using_hsv:
point_colors_list.append(rgb_to_hsv(rgb_list) * 255)
else:
point_colors_list.append(rgb_list)
# Populate lists with color values
channel_1_vals = []
channel_2_vals = []
channel_3_vals = []
for color in point_colors_list:
channel_1_vals.append(color[0])
channel_2_vals.append(color[1])
channel_3_vals.append(color[2])
a = Histogram(channel_1_vals, channel_2_vals, channel_3_vals)
normed_features = a.generate_normalised_concatenated_histogram()
return normed_features
def compute_normal_histograms(normal_cloud):
norm_x_vals = []
norm_y_vals = []
norm_z_vals = []
for norm_component in pc2.read_points(normal_cloud,
field_names=('normal_x', 'normal_y',
'normal_z'),
skip_nans=True):
norm_x_vals.append(norm_component[0])
norm_y_vals.append(norm_component[1])
norm_z_vals.append(norm_component[2])
a = Histogram(norm_x_vals, norm_y_vals, norm_z_vals)
a.bins_range = (-1, 1)
normed_features = a.generate_normalised_concatenated_histogram()
return normed_features