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- # 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
- tmp_filename = '/tmp/__model__img_to_predict.png'
- current_dirpath = os.getcwd()
- def main():
- # by default..
- 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()
- p_interval = list(map(int, args.interval.split(',')))
- 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]
- # 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_detected = []
- threshold_expes_counter = []
- threshold_expes_found = []
- # 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])
-
- # 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_detected.append(False)
- threshold_expes_counter.append(0)
- threshold_expes_found.append(end_quality_image) # by default use max
- check_all_done = False
- # for each images
- for img_path in scene_images:
- current_img = Image.open(img_path)
- current_postfix_image = dt.get_scene_image_postfix(img_path)
- img_blocks = segmentation.divide_in_blocks(current_img, (200, 200))
- check_all_done = all(d == True for d in threshold_expes_detected)
- if check_all_done:
- break
- 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 prediction/predict_noisy_image_svd.py --image " + tmp_file_path + \
- " --interval '" + p_interval + \
- "' --model " + p_model_file + \
- " --mode " + p_mode + \
- " --feature " + 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)
- 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] = int(current_postfix_image)
- print(str(id_block) + " : " + current_postfix_image + "/" + str(threshold_expes[id_block]) + " => " + str(prediction))
- print("------------------------")
- print("Scene " + str(id_scene + 1) + "/" + str(len(maxwell_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].replace('.joblib', ''))
- # create threshold model path if necessary
- 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_quality_image))
- f_map.write('\n- END : ' + str(end_quality_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()
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