from sklearn.externals import joblib from ipfml import image_processing from ipfml import metrics from PIL import Image import sys, os, getopt import numpy as np min_max_file_path = 'fichiersSVD_light/low_bits_4_min_max_values' def main(): if len(sys.argv) <= 1: print('Run with default parameters...') print('python predict_noisy_image_svd_lab.py --image path/to/xxxx --interval "0,20" --model path/to/xxxx.joblib --mode ["svdn", "svdne"]') sys.exit(2) try: opts, args = getopt.getopt(sys.argv[1:], "hi:t:m:o", ["help=", "image=", "interval=", "model=", "mode="]) except getopt.GetoptError: # print help information and exit: print('python predict_noisy_image_svd_lab.py --image path/to/xxxx --interval "xx,xx" --model path/to/xxxx.joblib --mode ["svdn", "svdne"]') sys.exit(2) for o, a in opts: if o == "-h": print('python predict_noisy_image_svd_lab.py --image path/to/xxxx --interval "xx,xx" --model path/to/xxxx.joblib --mode ["svdn", "svdne"]') sys.exit() elif o in ("-i", "--image"): p_img_file = os.path.join(os.path.join(os.path.dirname(__file__),'../'), a) elif o in ("-t", "--interval"): p_interval = list(map(int, a.split(','))) elif o in ("-m", "--model"): p_model_file = os.path.join(os.path.join(os.path.dirname(__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" else: assert False, "unhandled option" # load of model file model = joblib.load(p_model_file) # load image img = Image.open(p_img_file) low_bits_4_values = metrics.get_SVD_s(image_processing.rgb_to_grey_low_bits(img)) # check mode to normalize data if p_mode == 'svdne': # need to read min_max_file file_path = os.path.join(os.path.join(os.path.dirname(__file__),'../'), min_max_file_path) with open(file_path, 'r') as f: min = float(f.readline().replace('\n', '')) max = float(f.readline().replace('\n', '')) l_values = image_processing.normalize_arr_with_range(low_bits_4_values, min, max) elif p_mode == 'svdn': l_values = image_processing.normalize_arr(low_bits_4_values) else: l_values = low_bits_4_values # get interval values begin, end = p_interval test_data = l_values[begin:end] # get prediction of model prediction = model.predict([test_data])[0] print(prediction) if __name__== "__main__": main()