def loadData(data_name, data_id):
data_file = dataFile(data_name, data_id)
if data_file is None:
return None
return loadRGB(data_file)
def postResize(self):
print " Post resize"
data_name = self._keyword
data_files = dataFiles(data_name)
for data_file in data_files:
data_filename = os.path.basename(data_file)
C_8U = loadRGB(data_file)
if C_8U is None:
os.remove(data_file)
print " - Delete: %s" % data_filename
continue
h, w = C_8U.shape[0:2]
opt_scale = 800.0 / float(h)
opt_scale = max(opt_scale, 800.0 / float(w))
opt_scale = min(opt_scale, 1.0)
h_opt = int(opt_scale * h)
w_opt = int(opt_scale * w)
C_8U_small = cv2.resize(C_8U, (w_opt, h_opt))
saveRGB(data_file, C_8U_small)
print " - Resized: %s" % data_filename
def loadData(data_name, data_id):
data_file = dataFile(data_name, data_id)
if data_file is None:
return None
return loadRGB(data_file)
def postResize(self):
print(" Post resize")
data_name = self._keyword
data_files = dataFiles(data_name)
for data_file in data_files:
data_filename = os.path.basename(data_file)
C_8U = loadRGB(data_file)
if C_8U is None:
os.remove(data_file)
print(" - Delete: %s" % data_filename)
continue
h, w = C_8U.shape[0:2]
opt_scale = 800.0 / float(h)
opt_scale = max(opt_scale, 800.0 / float(w))
opt_scale = min(opt_scale, 1.0)
h_opt = int(opt_scale * h)
w_opt = int(opt_scale * w)
C_8U_small = cv2.resize(C_8U, (w_opt, h_opt))
saveRGB(data_file, C_8U_small)
print(" - Resized: %s" % data_filename)
def singleImageResult(image_file):
image_name = os.path.basename(image_file)
image_name = os.path.splitext(image_name)[0]
image = loadRGB(image_file)
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 Single Image", fontsize=font_size)
fig.add_subplot(231)
h, w = image.shape[:2]
plt.title("Original Image: %s x %s" % (w, h), fontsize=font_size)
plt.imshow(image)
plt.axis('off')
color_spaces = ["rgb", "Lab"]
sigmas = [0.7, 70.0]
plot_id = 232
num_cols = 3
for color_space, sigma in zip(color_spaces, sigmas):
hist3D = Hist3D(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)
plot_id += num_cols - 1
result_file = resultFile("%s_single" % image_name)
plt.savefig(result_file)
# 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)
fig = plt.figure()
#
# 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)
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)
