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- #!/usr/bin/env python3
- # -*- coding: utf-8 -*-
- """
- Created on Fri Sep 14 21:02:42 2018
- @author: jbuisine
- """
- from __future__ import print_function
- import sys, os, getopt
- import numpy as np
- import random
- import time
- import json
- from PIL import Image
- from ipfml import image_processing
- from ipfml import metrics
- config_filename = "config"
- zone_folder = "zone"
- min_max_filename = "_min_max_values"
- generic_output_file_svd = '_random.csv'
- output_data_folder = 'data'
- # define all scenes values
- scenes = ['Appart1opt02', 'Bureau1', 'Cendrier', 'Cuisine01', 'EchecsBas', 'PNDVuePlongeante', 'SdbCentre', 'SdbDroite', 'Selles']
- scenes_indexes = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']
- choices = ['svd', 'svdn', 'svdne']
- path = './fichiersSVD_light'
- zones = np.arange(16)
- seuil_expe_filename = 'seuilExpe'
- def generate_data_svd(data_type, mode):
- """
- @brief Method which generates all .csv files from scenes photos
- @param path - path of scenes folder information
- @return nothing
- """
- scenes = os.listdir(path)
- # remove min max file from scenes folder
- scenes = [s for s in scenes if min_max_filename not in s]
- # keep in memory min and max data found from data_type
- min_val_found = 100000000000
- max_val_found = 0
- data_min_max_filename = os.path.join(path, data_type + min_max_filename)
- # go ahead each scenes
- for id_scene, folder_scene in enumerate(scenes):
-
- print(folder_scene)
- scene_path = os.path.join(path, folder_scene)
- config_file_path = os.path.join(scene_path, config_filename)
- with open(config_file_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())
- # getting output filename
- output_svd_filename = data_type + "_" + mode + generic_output_file_svd
- # construct each zones folder name
- zones_folder = []
- svd_output_files = []
- # get zones list info
- for index in zones:
- index_str = str(index)
- if len(index_str) < 2:
- index_str = "0" + index_str
-
- current_zone = "zone"+index_str
- zones_folder.append(current_zone)
- zone_path = os.path.join(scene_path, current_zone)
- svd_file_path = os.path.join(zone_path, output_svd_filename)
- # add writer into list
- svd_output_files.append(open(svd_file_path, 'w'))
- current_counter_index = int(start_index_image)
- end_counter_index = int(end_index_image)
- while(current_counter_index <= end_counter_index):
-
- 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))
- for id_block, block in enumerate(img_blocks):
-
- # get data from mode
- if data_type == 'lab':
- block_file_path = '/tmp/lab_img.png'
- block.save(block_file_path)
- data = image_processing.get_LAB_L_SVD_s(Image.open(block_file_path))
-
- if data_type == 'mscn':
- img_mscn = image_processing.rgb_to_mscn(block)
- # save tmp as img
- img_output = Image.fromarray(img_mscn.astype('uint8'), 'L')
- mscn_file_path = '/tmp/mscn_img.png'
- img_output.save(mscn_file_path)
- img_block = Image.open(mscn_file_path)
- # extract from temp image
- data = metrics.get_SVD_s(img_block)
- # modify data depending mode
- if mode == 'svdne':
- # getting max and min information from min_max_filename
- with open(data_min_max_filename, 'r') as f:
- min_val = float(f.readline())
- max_val = float(f.readline())
-
- data = image_processing.normalize_arr_with_range(data, min_val, max_val)
-
- if mode == 'svdn':
- data = image_processing.normalize_arr(data)
-
- # save min and max found from dataset in order to normalize data using whole data known
- if mode == 'svd':
-
- current_min = data.min()
- current_max = data.max()
- if current_min < min_val_found:
- min_val_found = current_min
- if current_max > max_val_found:
- max_val_found = current_max
- # now write data into current writer
- current_file = svd_output_files[id_block]
- # add of index
- current_file.write(current_counter_index_str + ';')
- for val in data:
- current_file.write(str(val) + ";")
-
- current_file.write('\n')
-
- current_counter_index += step_counter
-
- for f in svd_output_files:
- f.close()
- # save current information about min file found
- if mode == 'svd':
- with open(data_min_max_filename, 'w') as f:
- f.write(str(min_val_found) + '\n')
- f.write(str(max_val_found) + '\n')
- print("End of data generation")
- def main():
- # all mscn data
- generate_data_svd('mscn', 'svd')
- generate_data_svd('mscn', 'svdn')
- generate_data_svd('mscn', 'svdne')
- # all lab data
- generate_data_svd('lab', 'svd')
- generate_data_svd('lab', 'svdn')
- generate_data_svd('lab', 'svdne')
- if __name__== "__main__":
- main()
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