# main imports import sys, os, argparse import numpy as np # image processing imports from PIL import Image from skimage import color import matplotlib.pyplot as plt from data_attributes import get_svd_data import ipfml.iqa.fr as fr_iqa from ipfml import utils # modules and config imports sys.path.insert(0, '') # trick to enable import of main folder module import custom_config as cfg from modules.utils import data as dt # getting configuration information zone_folder = cfg.zone_folder min_max_filename = cfg.min_max_filename_extension # define all scenes values scenes_list = cfg.scenes_names scenes_indices = cfg.scenes_indices choices = cfg.normalization_choices path = cfg.dataset_path zones = cfg.zones_indices seuil_expe_filename = cfg.seuil_expe_filename features_choices = cfg.features_choices_labels max_nb_bits = 8 display_error = False error_data_choices = ['mae', 'mse', 'ssim', 'psnr'] def get_error_distance(p_error, y_true, y_test): function_name = p_error try: error_method = getattr(fr_iqa, function_name) except AttributeError: raise NotImplementedError("Error `{}` not implement `{}`".format(fr_iqa.__name__, function_name)) return error_method(y_true, y_test) def display_svd_values(p_scene, p_interval, p_indices, p_feature, p_mode, p_step, p_norm, p_error, p_ylim): """ @brief Method which gives information about svd curves from zone of picture @param p_scene, scene expected to show svd values @param p_interval, interval [begin, end] of svd data to display @param p_interval, interval [begin, end] of samples or minutes from render generation engine @param p_feature, feature computed to show @param p_mode, normalization's mode @param p_norm, normalization or not of selected svd data @param p_error, error feature used to display @param p_ylim, ylim choice to better display of data @return nothing """ max_value_svd = 0 min_value_svd = sys.maxsize scenes = os.listdir(path) # remove min max file from scenes folder scenes = [s for s in scenes if min_max_filename not in s] begin_data, end_data = p_interval begin_index, end_index = p_indices # go ahead each scenes for folder_scene in scenes: if p_scene == folder_scene: scene_path = os.path.join(path, folder_scene) # construct each zones folder name zones_folder = [] # get zones list info for index in zones: index_str = str(index) if len(index_str) < 2: index_str = "0" + index_str current_zone = "zone"+index_str zones_folder.append(current_zone) images_data = [] images_path = [] threshold_learned_zones = [] # get all images of folder scene_images = sorted([os.path.join(scene_path, img) for img in os.listdir(scene_path) if cfg.scene_image_extension in img]) number_scene_image = len(scene_images) for id, zone_folder in enumerate(zones_folder): # get threshold information zone_path = os.path.join(scene_path, zone_folder) path_seuil = os.path.join(zone_path, seuil_expe_filename) # open treshold path and get this information with open(path_seuil, "r") as seuil_file: threshold_learned = int(seuil_file.readline().strip()) threshold_learned_zones.append(threshold_learned) threshold_mean = np.mean(np.asarray(threshold_learned_zones)) threshold_image_found = False svd_data = [] # for each images for id_img, img_path in enumerate(scene_images): current_quality_image = dt.get_scene_image_quality(img_path) img = Image.open(img_path) svd_values = get_svd_data(p_feature, img) if p_norm: svd_values = svd_values[begin_data:end_data] # update min max values min_value = svd_values.min() max_value = svd_values.max() if min_value < min_value_svd: min_value_svd = min_value if max_value > min_value_svd: max_value_svd = max_value # keep in memory used data if current_quality_image % p_step == 0: if current_quality_image >= begin_index and current_quality_image <= end_index: images_path.append(img_path) svd_data.append(svd_values) if threshold_mean < current_quality_image and not threshold_image_found: threshold_image_found = True threshold_image_zone = dt.get_scene_image_postfix(img_path) print('%.2f%%' % ((id_img + 1) / number_scene_image * 100)) sys.stdout.write("\033[F") previous_data = [] error_data = [0.] for id, data in enumerate(svd_data): current_data = data if not p_norm: current_data = current_data[begin_data:end_data] if p_mode == 'svdn': current_data = utils.normalize_arr(current_data) if p_mode == 'svdne': current_data = utils.normalize_arr_with_range(current_data, min_value_svd, max_value_svd) images_data.append(current_data) # use of whole image data for computation of ssim or psnr if p_error == 'ssim' or p_error == 'psnr': current_data = np.asarray(Image.open(images_path[id])) if len(previous_data) > 0: current_error = get_error_distance(p_error, previous_data, current_data) error_data.append(current_error) if len(previous_data) == 0: previous_data = current_data # display all data using matplotlib (configure plt) gridsize = (3, 2) # fig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, figsize=(30, 22)) fig = plt.figure(figsize=(30, 22)) ax1 = plt.subplot2grid(gridsize, (0, 0), colspan=2, rowspan=2) ax2 = plt.subplot2grid(gridsize, (2, 0), colspan=2) ax1.set_title(p_scene + ' scene interval information SVD['+ str(begin_data) +', '+ str(end_data) +'], from scenes indices [' + str(begin_index) + ', '+ str(end_index) + '], ' + p_feature + ' feature, ' + p_mode + ', with step of ' + str(p_step) + ', svd norm ' + str(p_norm), fontsize=20) ax1.set_ylabel('Image samples or time (minutes) generation', fontsize=14) ax1.set_xlabel('Vector features', fontsize=16) for id, data in enumerate(images_data): current_quality_image = dt.get_scene_image_quality(images_path[id]) current_quality_postfix = dt.get_scene_image_postfix(images_path[id]) if display_error: p_label = p_scene + '_' + current_quality_postfix + " | " + p_error + ": " + str(error_data[id]) else: p_label = p_scene + '_' + current_quality_postfix if current_quality_image == threshold_image_zone: ax1.plot(data, label=p_label + " (threshold mean)", lw=4, color='red') else: ax1.plot(data, label=p_label) ax1.legend(bbox_to_anchor=(0.7, 1), loc=2, borderaxespad=0.2, fontsize=14) start_ylim, end_ylim = p_ylim ax1.set_ylim(start_ylim, end_ylim) ax2.set_title(p_error + " information for whole step images") ax2.set_ylabel(p_error + ' error') ax2.set_xlabel('Number of samples per pixels or times') ax2.set_xticks(range(len(current_quality_image))) ax2.set_xticklabels(list(map(dt.get_scene_image_quality, current_quality_image))) ax2.plot(error_data) plot_name = p_scene + '_' + p_feature + '_' + str(p_step) + '_' + p_mode + '_' + str(p_norm) + '.png' plt.savefig(plot_name) def main(): parser = argparse.ArgumentParser(description="Display evolution of error on scene") parser.add_argument('--scene', type=str, help='scene index to use', choices=cfg.scenes_indices) parser.add_argument('--interval', type=str, help='Interval value to keep from svd', default='"0, 200"') parser.add_argument('--indices', type=str, help='Samples interval to display', default='"0, 900"') parser.add_argument('--feature', type=str, help='feature data choice', choices=features_choices) parser.add_argument('--mode', type=str, help='Kind of normalization level wished', choices=cfg.normalization_choices) parser.add_argument('--step', type=int, help='Each step samples to display', default=10) parser.add_argument('--norm', type=int, help='If values will be normalized or not', choices=[0, 1]) parser.add_argument('--error', type=int, help='Way of computing error', choices=error_data_choices) parser.add_argument('--ylim', type=str, help='ylim interval to use', default='"0, 1"') args = parser.parse_args() p_scene = scenes_list[scenes_indices.index(args.scene)] p_indices = list(map(int, args.indices.split(','))) p_interval = list(map(int, args.interval.split(','))) p_feature = args.feature p_mode = args.mode p_step = args.step p_norm = args.norm p_error = args.error p_ylim = list(map(int, args.ylim.split(','))) display_svd_values(p_scene, p_interval, p_indices, p_feature, p_mode, p_step, p_norm, p_error, p_ylim) if __name__== "__main__": main()