Thesis-NoiseDetection-26-attributes/prediction/predict_seuil_expe_maxwell_curve.py

# main imports
import sys, os, argparse
import subprocess
import time
import numpy as np

# image processing imports
from ipfml.processing import segmentation
from PIL import Image

# models imports
from sklearn.externals import joblib

# modules 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


# variables and parameters
scenes_path               = cfg.dataset_path
min_max_filename          = cfg.min_max_filename_extension
threshold_expe_filename   = cfg.seuil_expe_filename

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

zones                     = cfg.zones_indices
maxwell_scenes            = cfg.maxwell_scenes_names
normalization_choices     = cfg.normalization_choices
features_choices          = cfg.features_choices_labels

simulation_curves_zones   = "simulation_curves_zones_"
tmp_filename              = '/tmp/__model__img_to_predict.png'

current_dirpath = os.getcwd()


def main():

    p_custom = False
        
    parser = argparse.ArgumentParser(description="Script which predicts threshold using specific model")

    parser.add_argument('--interval', type=str, help='Interval value to keep from svd', default='"0, 200"')
    parser.add_argument('--model', type=str, help='.joblib or .json file (sklearn or keras model)')
    parser.add_argument('--mode', type=str, help='Kind of normalization level wished', choices=normalization_choices)
    parser.add_argument('--feature', type=str, help='feature data choice', choices=features_choices)
    #parser.add_argument('--limit_detection', type=int, help='Specify number of same prediction to stop threshold prediction', default=2)
    parser.add_argument('--custom', type=str, help='Name of custom min max file if use of renormalization of data', default=False)

    args = parser.parse_args()

    # keep p_interval as it is
    p_interval   = args.interval
    p_model_file = args.model
    p_mode       = args.mode
    p_feature    = args.feature
    #p_limit      = args.limit
    p_custom     = args.custom

    scenes = os.listdir(scenes_path)
    scenes = [s for s in scenes if s in maxwell_scenes]

    print(scenes)

    # go ahead each scenes
    for id_scene, folder_scene in enumerate(scenes):

        # only take in consideration maxwell scenes
        if folder_scene in maxwell_scenes:

            print(folder_scene)

            scene_path = os.path.join(scenes_path, folder_scene)

            threshold_expes = []
            threshold_expes_found = []
            block_predictions_str = []

            # 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])

            start_quality_image = dt.get_scene_image_quality(scene_images[0])
            end_quality_image   = dt.get_scene_image_quality(scene_images[-1])
            # using first two images find the step of quality used
            quality_step_image  = dt.get_scene_image_quality(scene_images[1]) - start_quality_image

            # get zones list info
            for index in zones:
                index_str = str(index)
                if len(index_str) < 2:
                    index_str = "0" + index_str
                zone_folder = "zone"+index_str

                threshold_path_file = os.path.join(os.path.join(scene_path, zone_folder), threshold_expe_filename)

                with open(threshold_path_file) as f:
                    threshold = int(f.readline())
                    threshold_expes.append(threshold)

                    # Initialize default data to get detected model threshold found
                    threshold_expes_found.append(end_quality_image) # by default use max

                block_predictions_str.append(index_str + ";" + p_model_file + ";" + str(threshold) + ";" + str(start_quality_image) + ";" + str(quality_step_image))


            # for each images
            for img_path in scene_images:

                current_img = Image.open(img_path)
                current_quality_image = dt.get_scene_image_quality(img_path)

                img_blocks = segmentation.divide_in_blocks(current_img, (200, 200))

                for id_block, block in enumerate(img_blocks):

                    # check only if necessary for this scene (not already detected)
                    #if not threshold_expes_detected[id_block]:

                        tmp_file_path = tmp_filename.replace('__model__',  p_model_file.split('/')[-1].replace('.joblib', '_'))
                        block.save(tmp_file_path)

                        python_cmd_line = "python prediction/predict_noisy_image_svd.py --image {0} --interval '{1}' --model {2} --mode {3} --feature {4}"
                        python_cmd = python_cmd_line.format(tmp_file_path, p_interval, p_model_file, p_mode, p_feature) 

                        # specify use of custom file for min max normalization
                        if p_custom:
                            python_cmd = python_cmd + ' --custom ' + p_custom

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

                        (output, err) = p.communicate()

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

                        prediction = int(output)

                        # save here in specific file of block all the predictions done
                        block_predictions_str[id_block] = block_predictions_str[id_block] + ";" + str(prediction)

                        print(str(id_block) + " : " + str(current_quality_image) + "/" + str(threshold_expes[id_block]) + " => " + str(prediction))

                print("------------------------")
                print("Scene " + str(id_scene + 1) + "/" + str(len(scenes)))
                print("------------------------")

            # end of scene => display of results

            # construct path using model name for saving threshold map folder
            model_threshold_path = os.path.join(threshold_map_folder, p_model_file.split('/')[-1].replace('.joblib', ''))

            # create threshold model path if necessary
            if not os.path.exists(model_threshold_path):
                os.makedirs(model_threshold_path)

            map_filename = os.path.join(model_threshold_path, simulation_curves_zones + folder_scene)
            f_map = open(map_filename, 'w')

            for line in block_predictions_str:
                f_map.write(line + '\n')
            f_map.close()

            print("Scene " + str(id_scene + 1) + "/" + str(len(maxwell_scenes)) + " Done..")
            print("------------------------")

            print("Model predictions are saved into %s" % map_filename)


if __name__== "__main__":
    main()