Thesis-NoiseDetection-attributes/predict_seuil_expe.py

from sklearn.externals import joblib

import numpy as np

from ipfml import image_processing
from PIL import Image

import sys, os, getopt
import subprocess
import time

current_dirpath = os.getcwd()

config_filename   = "config"
scenes_path = './fichiersSVD_light'
min_max_filename = '_min_max_values'
threshold_expe_filename = 'seuilExpe'
tmp_filename = '/tmp/__model__img_to_predict.png'

threshold_map_folder = "threshold_map"
threshold_map_file_prefix = "treshold_map_"

zones = np.arange(16)

def main():

    if len(sys.argv) <= 1:
        print('Run with default parameters...')
        print('python predict_seuil_expe.py --interval "0,20" --model path/to/xxxx.joblib --mode svdn --metric lab --limit_detection xx')
        sys.exit(2)
    try:
        opts, args = getopt.getopt(sys.argv[1:], "ht:m:o:l", ["help=", "interval=", "model=", "mode=", "metric=" "limit_detection="])
    except getopt.GetoptError:
        # print help information and exit:
        print('python predict_seuil_expe.py --interval "xx,xx" --model path/to/xxxx.joblib --mode svdn --metric lab --limit_detection xx')
        sys.exit(2)
    for o, a in opts:
        if o == "-h":
            print('python predict_seuil_expe.py --interval "xx,xx" --model path/to/xxxx.joblib --mode svdn --metric lab --limit_detection xx')
            sys.exit()
        elif o in ("-t", "--interval"):
            p_interval = a
        elif o in ("-mo", "--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
        elif o in ("-l", "--limit_detection"):
            p_limit = int(a)
        else:
            assert False, "unhandled option"

    scenes = os.listdir(scenes_path)
    
    if min_max_filename in scenes:
        scenes.remove(min_max_filename)

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

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

        config_path = os.path.join(scene_path, config_filename)

        with open(config_path, "r") as config_file:
            last_image_name = config_file.readline().strip()
            prefix_image_name = config_file.readline().strip()
            start_index_image = config_file.readline().strip()
            end_index_image = config_file.readline().strip()
            step_counter = int(config_file.readline().strip())

        threshold_expes = []
        threshold_expes_detected = []
        threshold_expes_counter = []
        threshold_expes_found = []

        # 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

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

            with open(scene_path + "/" + zone_folder + "/" + threshold_expe_filename) as f:
                threshold = int(f.readline())
                threshold_expes.append(threshold)

                # Initialize default data to get detected model threshold found
                threshold_expes_detected.append(False)
                threshold_expes_counter.append(0)
                threshold_expes_found.append(int(end_index_image)) # by default use max

        current_counter_index = int(start_index_image)
        end_counter_index = int(end_index_image)

        print(current_counter_index)
        check_all_done = False

        while(current_counter_index <= end_counter_index and not check_all_done):
            
            current_counter_index_str = str(current_counter_index)

            while len(start_index_image) > len(current_counter_index_str):
                current_counter_index_str = "0" + current_counter_index_str

            img_path = os.path.join(scene_path, prefix_image_name + current_counter_index_str + ".png")

            current_img = Image.open(img_path)
            img_blocks = image_processing.divide_in_blocks(current_img, (200, 200))


            check_all_done = all(d == True for d in threshold_expes_detected)

            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 = "python predict_noisy_image_svd_" + p_metric + ".py --image " + tmp_file_path + \
                                    " --interval '" + p_interval + \
                                    "' --model " + p_model_file  + \
                                    " --mode " + p_mode

                    ## 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)

                    if prediction == 0:
                        threshold_expes_counter[id_block] = threshold_expes_counter[id_block] + 1
                    else:
                        threshold_expes_counter[id_block] = 0
                    
                    if threshold_expes_counter[id_block] == p_limit:
                        threshold_expes_detected[id_block] = True
                        threshold_expes_found[id_block] = current_counter_index

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

            current_counter_index += step_counter
            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_treshold_path = os.path.join(threshold_map_folder, p_model_file.split('/')[-1])
        
        if not os.path.exists(model_treshold_path):
            os.makedirs(model_treshold_path)

        abs_dist = []

        map_filename = os.path.join(model_treshold_path, threshold_map_file_prefix + folder_scene)
        f_map = open(map_filename, 'w')

        line_information = ""

        # default header
        f_map.write('|  |    |    |  |\n')
        f_map.write('---|----|----|---\n')
        for id, threshold in enumerate(threshold_expes_found):

            line_information += str(threshold) + " / " + str(threshold_expes[id]) + " | "
            abs_dist.append(abs(threshold - threshold_expes[id]))

            if (id + 1) % 4 == 0:
                f_map.write(line_information + '\n')
                line_information = ""
        
        f_map.write(line_information + '\n')

        min_abs_dist = min(abs_dist)
        max_abs_dist = max(abs_dist)
        avg_abs_dist = sum(abs_dist) / len(abs_dist)

        f_map.write('\nScene information : ')
        f_map.write('\n- BEGIN : ' + str(start_index_image))
        f_map.write('\n- END : ' + str(end_index_image))

        f_map.write('\n\nDistances information : ')
        f_map.write('\n- MIN : ' + str(min_abs_dist))
        f_map.write('\n- MAX : ' + str(max_abs_dist))
        f_map.write('\n- AVG : ' + str(avg_abs_dist))

        f_map.write('\n\nOther information : ')
        f_map.write('\n- Detection limit : ' + str(p_limit))

        # by default print last line
        f_map.close()

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

        time.sleep(10)


if __name__== "__main__":
    main()