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@@ -0,0 +1,133 @@
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+from sklearn.externals import joblib
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+
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+import numpy as np
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+
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+from ipfml import image_processing
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+from PIL import Image
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+
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+import sys, os, getopt
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+import subprocess
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+
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+config_filename = "config"
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+scenes_path = './fichiersSVD_light'
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+min_max_filename = 'min_max_values'
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+seuil_expe_filename = 'seuilExpe'
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+tmp_filename = '/tmp/img_to_predict.png'
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+zones = np.arange(16)
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+
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+def main():
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+
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+ if len(sys.argv) <= 1:
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+ print('Run with default parameters...')
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+ print('python predict_noisy_image.py --interval "0,20" --model path/to/xxxx.joblib --mode ["svdn", "svdne"]')
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+ sys.exit(2)
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+ try:
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+ opts, args = getopt.getopt(sys.argv[1:], "ht:m:o", ["help=", "interval=", "model=", "mode="])
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+ except getopt.GetoptError:
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+ # print help information and exit:
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+ print('python predict_noisy_image.py --interval "xx,xx" --model path/to/xxxx.joblib --mode ["svdn", "svdne"]')
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+ sys.exit(2)
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+ for o, a in opts:
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+ if o == "-h":
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+ print('python predict_noisy_image.py --interval "xx,xx" --model path/to/xxxx.joblib --mode ["svdn", "svdne"]')
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+ sys.exit()
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+ elif o in ("-t", "--interval"):
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+ p_interval = a
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+ elif o in ("-m", "--model"):
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+ p_model_file = a
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+ elif o in ("-o", "--mode"):
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+ p_mode = a
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+
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+ if p_mode != 'svdn' and p_mode != 'svdne':
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+ assert False, "Mode not recognized"
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+ else:
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+ assert False, "unhandled option"
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+
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+ scenes = os.listdir(scenes_path)
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+
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+ if min_max_filename in scenes:
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+ scenes.remove(min_max_filename)
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+
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+ # go ahead each scenes
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+ for id_scene, folder_scene in enumerate(scenes):
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+
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+ print(folder_scene)
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+ scene_path = scenes_path + "/" + folder_scene
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+ with open(scene_path + "/" + config_filename, "r") as config_file:
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+ last_image_name = config_file.readline().strip()
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+ prefix_image_name = config_file.readline().strip()
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+ start_index_image = config_file.readline().strip()
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+ end_index_image = config_file.readline().strip()
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+ step_counter = int(config_file.readline().strip())
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+
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+ seuil_expes = []
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+ seuil_expes_detected = []
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+ seuil_expes_counter = []
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+ seuil_expes_found = []
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+
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+ # get zones list info
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+ for index in zones:
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+ index_str = str(index)
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+ if len(index_str) < 2:
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+ index_str = "0" + index_str
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+ zone_folder = "zone"+index_str
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+
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+ with open(scene_path + "/" + zone_folder + "/" + seuil_expe_filename) as f:
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+ seuil_expes.append(int(f.readline()))
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+
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+ # Initialize default data to get detected model seuil found
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+ seuil_expes_detected.append(False)
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+ seuil_expes_counter.append(0)
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+ seuil_expes_found.append(0)
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+
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+ for seuil in seuil_expes:
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+ print(seuil)
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+
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+ current_counter_index = int(start_index_image)
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+ end_counter_index = int(end_index_image)
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+
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+ print(current_counter_index)
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+ while(current_counter_index <= end_counter_index):
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+
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+ current_counter_index_str = str(current_counter_index)
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+
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+ while len(start_index_image) > len(current_counter_index_str):
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+ current_counter_index_str = "0" + current_counter_index_str
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+
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+ img_path = scene_path + "/" + prefix_image_name + current_counter_index_str + ".png"
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+
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+ print(img_path)
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+
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+ current_img = Image.open(img_path)
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+ img_blocks = image_processing.divide_in_blocks(current_img, (200, 200))
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+
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+ for id_block, block in enumerate(img_blocks):
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+ block.save(tmp_filename)
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+
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+ python_cmd = "python predict_noisy_image_sdv_lab.py --image " + tmp_filename + \
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+ " --interval '" + p_interval + \
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+ "' --model " + p_model_file + \
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+ " --mode " + p_mode
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+ ## call command ##
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+ p = subprocess.Popen(python_cmd, stdout=subprocess.PIPE, shell=True)
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+
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+ (output, err) = p.communicate()
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+
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+ ## Wait for result ##
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+ p_status = p.wait()
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+
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+ prediction = int(output)
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+
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+
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+
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+ print(str(current_counter_index) + "/" + str(seuil_expes[id_block]) + " => " + str(prediction))
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+
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+ current_counter_index += step_counter
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+
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+ # end of scene => display of results
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+
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+
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+
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+
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+if __name__== "__main__":
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+ main()
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