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 from modules.features import compute_feature from ipfml import metrics from joblib import dump, load from PIL import Image def reconstruct(_scene_name, _model_path, _n, _feature_choice): # construct the empty output image output_image = np.empty([cfg.number_of_rows, cfg.number_of_columns]) # load the trained model clf = load(_model_path) # 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)) pixels = [] 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)/255. for l in f.readlines()] pixel_values = lines[0:int(_n)] data = compute_feature(_feature_choice, pixel_values) pixels.append(data) # predict column pixels and fill image column by column pixels_predicted = clf.predict(pixels) # change normalized predicted value to pixel value pixels_predicted = pixels_predicted*255. for id_pixel, pixel in enumerate(pixels_predicted): output_image[id_pixel, id_column] = pixel 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('--model_path', type=str, help='Model file path') parser.add_argument('--n', type=str, help='Number of pixel values approximated to keep') parser.add_argument('--feature', type=str, help='Feature choice to compute from samples', choices=cfg.features_list) 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_feature = args.feature param_model_path = args.model_path param_image_name = args.image_name # get default value of `n` param if not param_n: param_n = param_model_path.split('_')[0] output_image = reconstruct(param_scene_name, param_model_path, param_n, param_feature) 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()