def generateLocalPalettes(input_img, row_step, col_step, overlap_size, total_slides):
count = 0
localPalettesList = []
height = input_img.shape[0]
width = input_img.shape[1]
for i in range(0, height, row_step):
if i + row_step > height:
continue
else:
for j in range(0, width, col_step):
if j + col_step > width:
continue
else:
count += 1
if count <= total_slides:
row_step_final = row_step if ((i + row_step + overlap_size) > height) else (row_step + overlap_size)
col_step_final = col_step if ((j + col_step + overlap_size) > width) else (col_step + overlap_size)
sample_img = input_img[i : i + row_step_final, j : j + col_step_final, :]
hist3D = Hist3D(sample_img, num_bins=16, color_space='Lab')
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=70.0)
localPalettesList.append(palette_selection.paletteColors())
else:
break
return localPalettesList
def generateGlobalPalettes(input_img, palette_num=5):
hist3D = Hist3D(input_img, num_bins=16, color_space='Lab')
color_coordinates = hist3D.colorCoordinates()
color_densities = hist3D.colorDensities()
rgb_colors = hist3D.rgbColors()
palette_selection = PaletteSelection(color_coordinates,
color_densities, rgb_colors,
num_colors=palette_num, sigma=70.0)
return palette_selection.paletteColors()
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)
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()
# Plot image.
fig.add_subplot(131)
plt.imshow(image)
plt.axis('off')
# Plot palette colors.
fig.add_subplot(132)
palette_selection.plot(plt)
plt.axis('off')
# Plot 3D color histogram.
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()
# Plot image.
fig.add_subplot(131)
plt.imshow(image)
plt.axis('off')
# Plot palette colors.
fig.add_subplot(132)
palette_selection.plot(plt)
plt.axis('off')
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)