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 from modules.utils import config as cfg config_filename = cfg.config_filename 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 simulation_curves_zones = "simulation_curves_zones_" tmp_filename = '/tmp/__model__img_to_predict.png' current_dirpath = os.getcwd() def main(): if len(sys.argv) <= 1: print('Run with default parameters...') print('python predict_seuil_expe_maxwell.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_maxwell.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_maxwell.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 ("-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 ("-m", "--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): # only take in consideration maxwell scenes if folder_scene in maxwell_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_found = [] block_predictions_str = [] # 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(int(end_index_image)) # by default use max block_predictions_str.append(index_str + ";" + p_model_file + ";" + str(threshold) + ";" + str(start_index_image) + ";" + str(step_counter)) current_counter_index = int(start_index_image) end_counter_index = int(end_index_image) print(current_counter_index) 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): # 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 metrics_predictions/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) # 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_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].replace('.joblib', '')) # create threshold model path if necessary if not os.path.exists(model_treshold_path): os.makedirs(model_treshold_path) map_filename = os.path.join(model_treshold_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) time.sleep(10) if __name__== "__main__": main()