def batchResults(data_names, data_ids, batch_func, batch_name):
for data_name in data_names:
print "%s: %s" % (batch_name, data_name)
for data_id in data_ids:
print "Data ID: %s" % data_id
image_file = dataFile(data_name, data_id)
batch_func(image_file)
# -*- coding: utf-8 -*-
## @package palette.examples.single_image
#
# Minimal example for single image.
# @author tody
# @date 2015/08/29
from palette.io_util.image import loadRGB
from palette.core.hist_3d import Hist3D
from palette.core.palette_selection import PaletteSelection
import matplotlib.pyplot as plt
from palette.datasets.google_image import dataFile
image_file = dataFile("flower", 0)
# Load image.
image = loadRGB(image_file)
# 16 bins, Lab color space
hist3D = Hist3D(image, num_bins=16, color_space='Lab')
color_coordinates = hist3D.colorCoordinates()
color_densities = hist3D.colorDensities()
rgb_colors = hist3D.rgbColors()
# 5 colors from Lab color samples.
palette_selection = PaletteSelection(color_coordinates,
color_densities, rgb_colors,
num_colors=5, sigma=70.0)
# -*- coding: utf-8 -*-
## @package palette.examples.single_image
#
# Minimal example for single image.
# @author tody
# @date 2015/08/29
from palette.io_util.image import loadRGB
from palette.core.hist_3d import Hist3D
from palette.core.palette_selection import PaletteSelection
import matplotlib.pyplot as plt
from palette.datasets.google_image import dataFile
image_file = dataFile("flower", 0)
# Load image.
image = loadRGB(image_file)
# 16 bins, Lab color space
hist3D = Hist3D(image, num_bins=16, color_space='Lab')
color_coordinates = hist3D.colorCoordinates()
color_densities = hist3D.colorDensities()
rgb_colors = hist3D.rgbColors()
# 5 colors from Lab color samples.
palette_selection = PaletteSelection(color_coordinates,
color_densities,
rgb_colors,
num_colors=5,
def multiImagesResult(data_name, data_ids):
num_cols = len(data_ids)
num_rows = 2
fig = plt.figure(figsize=(10, 7))
fig.subplots_adjust(left=0.05,
bottom=0.05,
right=0.95,
top=0.9,
wspace=0.1,
hspace=0.2)
font_size = 15
fig.suptitle("Palette Selection for Multi Images", fontsize=font_size)
rgb_pixels = []
plot_id = num_rows * 100 + 10 * num_cols + 1
for data_id in data_ids:
image_file = dataFile(data_name, data_id)
image = loadRGB(image_file)
rgb_pixels.extend(ColorPixels(image).rgb())
fig.add_subplot(plot_id)
h, w = image.shape[:2]
plt.title("Original Image: %s x %s" % (w, h), fontsize=font_size)
plt.imshow(image)
plt.axis('off')
plot_id += 1
color_space = "Lab"
sigma = 70.0
plot_id = num_rows * 100 + 10 * num_cols + num_cols + 2
rgb_pixels = np.array(rgb_pixels)
multi_image = np.array(rgb_pixels).reshape(1, -1, 3)
hist3D = Hist3D(multi_image, num_bins=16, color_space=color_space)
color_coordinates = hist3D.colorCoordinates()
color_densities = hist3D.colorDensities()
rgb_colors = hist3D.rgbColors()
palette_selection = PaletteSelection(color_coordinates,
color_densities,
rgb_colors,
num_colors=5,
sigma=sigma)
plt.subplot(plot_id)
plt.title("Palette Colors from %s" % color_space)
palette_selection.plot(plt)
plt.axis('off')
plot_id += 1
ax = fig.add_subplot(plot_id, projection='3d')
plt.title("%s 3D Histogram" % color_space, fontsize=font_size)
hist3D.plot(ax)
result_file = resultFile("%s_multi" % data_name)
plt.savefig(result_file)
