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 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)