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_noisy_image.py --interval "0,20" --model path/to/xxxx.joblib --mode ["svdn", "svdne"] --limit_detection xx') sys.exit(2) try: opts, args = getopt.getopt(sys.argv[1:], "ht:m:o:l", ["help=", "interval=", "model=", "mode=", "limit_detection="]) except getopt.GetoptError: # print help information and exit: print('python predict_noisy_image.py --interval "xx,xx" --model path/to/xxxx.joblib --mode ["svdn", "svdne"] --limit_detection xx') sys.exit(2) for o, a in opts: if o == "-h": print('python predict_noisy_image.py --interval "xx,xx" --model path/to/xxxx.joblib --mode ["svdn", "svdne"] --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 ("-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 = scenes_path + "/" + folder_scene with open(scene_path + "/" + config_filename, "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 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 = 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]) block.save(tmp_file_path) python_cmd = "python predict_noisy_image_sdv_lab.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 model_treshold_path = 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 = 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()