import numpy as np import pandas as pd import os, sys, argparse from sklearn import linear_model from sklearn import svm from sklearn.utils import shuffle import modules.config as cfg import modules.metrics as metrics from joblib import dump, load from PIL import Image def reconstruct(_scene_name, _n): # construct the empty output image output_image = np.empty([cfg.number_of_rows, cfg.number_of_columns]) # load scene and its `n` first pixel value data scene_path = os.path.join(cfg.folder_scenes_path, _scene_name) for id_column in range(cfg.number_of_columns): folder_path = os.path.join(scene_path, str(id_column)) for id_row in range(cfg.number_of_rows): pixel_filename = _scene_name + '_' + str(id_column) + '_' + str(id_row) + ".dat" pixel_file_path = os.path.join(folder_path, pixel_filename) with open(pixel_file_path, 'r') as f: # predict the expected pixel value lines = [float(l) for l in f.readlines()] mean = sum(lines[0:int(_n)]) / float(_n) output_image[id_row, id_column] = mean print("{0:.2f}%".format(id_column / cfg.number_of_columns * 100)) sys.stdout.write("\033[F") return output_image def main(): parser = argparse.ArgumentParser(description="Train model and saved it") parser.add_argument('--scene', type=str, help='Scene name to reconstruct', choices=cfg.scenes_list) parser.add_argument('--n', type=str, help='Number of samples to take') parser.add_argument('--image_name', type=str, help="The ouput image name") args = parser.parse_args() param_scene_name = args.scene param_n = args.n param_image_name = args.image_name output_image = reconstruct(param_scene_name, param_n) if not os.path.exists(cfg.reconstructed_folder): os.makedirs(cfg.reconstructed_folder) image_path = os.path.join(cfg.reconstructed_folder, param_image_name) img = Image.fromarray(np.uint8(output_image)) img.save(image_path) if __name__== "__main__": main()