Thesis-NoiseDetection-attributes/save_model_result_in_md.py

from sklearn.externals import joblib

import numpy as np

from ipfml import processing
from PIL import Image

import sys, os, getopt
import subprocess
import time


from modules.utils import config as cfg

threshold_map_folder      = cfg.threshold_map_folder
threshold_map_file_prefix = cfg.threshold_map_folder + "_"

markdowns_folder          = cfg.models_information_folder
zones                     = cfg.zones_indices

current_dirpath = os.getcwd()

def main():

    if len(sys.argv) <= 1:
        print('Run with default parameters...')
        print('python save_model_result_in_md.py --interval "0,20" --model path/to/xxxx.joblib --mode ["svd", "svdn", "svdne"] --metric ["lab", "mscn"]')
        sys.exit(2)
    try:
        opts, args = getopt.getopt(sys.argv[1:], "ht:m:o:l", ["help=", "interval=", "model=", "mode=", "metric="])
    except getopt.GetoptError:
        # print help information and exit:
        print('python save_model_result_in_md.py --interval "xx,xx" --model path/to/xxxx.joblib --mode ["svd", "svdn", "svdne"] --metric ["lab", "mscn"]')
        sys.exit(2)
    for o, a in opts:
        if o == "-h":
            print('python save_model_result_in_md.py --interval "xx,xx" --model path/to/xxxx.joblib --mode ["svd", "svdn", "svdne"] --metric ["lab", "mscn"]')
            sys.exit()
        elif o in ("-t", "--interval"):
            p_interval = list(map(int, a.split(',')))
        elif o in ("-m", "--model"):
            p_model_file = a
        elif o in ("-o", "--mode"):
            p_mode = a

            if p_mode != 'svdn' and p_mode != 'svdne' and p_mode != 'svd':
                assert False, "Mode not recognized"
        elif o in ("-me", "--metric"):
            p_metric = a
        else:
            assert False, "unhandled option"


    # call model and get global result in scenes

    begin, end = p_interval

    bash_cmd = "bash testModelByScene.sh '" + str(begin) + "' '" + str(end) + "' '" + p_model_file + "' '" + p_mode + "' '" + p_metric + "'"
    print(bash_cmd)

    ## call command ##
    p = subprocess.Popen(bash_cmd, stdout=subprocess.PIPE, shell=True)

    (output, err) = p.communicate()

    ## Wait for result ##
    p_status = p.wait()

    if not os.path.exists(markdowns_folder):
        os.makedirs(markdowns_folder)

    # get model name to construct model
    md_model_path = os.path.join(markdowns_folder, p_model_file.split('/')[-1].replace('.joblib', '.md'))

    with open(md_model_path, 'w') as f:
        f.write(output.decode("utf-8"))

        # read each threshold_map information if exists
        model_map_info_path = os.path.join(threshold_map_folder, p_model_file.replace('saved_models/', ''))

        if not os.path.exists(model_map_info_path):
            f.write('\n\n No threshold map information')
        else:
            maps_files = os.listdir(model_map_info_path)

            # get all map information
            for t_map_file in maps_files:

                file_path = os.path.join(model_map_info_path, t_map_file)
                with open(file_path, 'r') as map_file:

                    title_scene =  t_map_file.replace(threshold_map_file_prefix, '')
                    f.write('\n\n## ' + title_scene + '\n')
                    content = map_file.readlines()

                    # getting each map line information
                    for line in content:
                        f.write(line)

        f.close()

if __name__== "__main__":
    main()