CNN-based camera model fingerprint

Udit Soni/car-recognition-aws/createTestSetFromValidation.py

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+'''
+Created on 5 lut 2018
+
+@author: mgdak
+'''
+
+import os
+import argparse
+import sys
+from pprint import pprint
+import shutil as sh
+import random
+
+def main(args):
+    pprint(args)
+
+    topDirs = os.listdir(args.src_dir)
+
+    # This would print all the files and directories
+    for classDir in topDirs:
+        print(classDir)
+        files = os.listdir(args.src_dir + "/" + classDir)
+        train_start = int(len(files)*args.train_split_ratio)
+        tst_start = int(len(files)*args.tst_split_ratio)
+        random.shuffle(files)
+        #move train data
+        for j in range(train_start + tst_start, tst_start, -1):
+            os.makedirs(args.train_dst_dir + "/" + classDir, exist_ok=True)
+            sh.move(args.src_dir + "/" + classDir + "/" + files[j],
+                    args.train_dst_dir + "/" + classDir + "/" + files[j])
+
+        #move tst data
+        for j in range(tst_start, 0, -1):
+            os.makedirs(args.tst_dst_dir + "/" + classDir, exist_ok=True)
+            sh.move(args.src_dir + "/" + classDir + "/" + files[j],
+                    args.tst_dst_dir + "/" + classDir + "/" + files[j])
+
+def parse_arguments(argv):
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--tst_split_ratio', type=float,
+        help='Defines the split ratio from validation which has to be moved to the test'
+        , default=0.25)
+
+    parser.add_argument('--train_split_ratio', type=float,
+        help='Defines the split ratio from validation which has to be moved to the train'
+        , default=0.5)
+
+    parser.add_argument('--tst_dst_dir', type=str,
+        help='Destination directory for splited test data set', default='./car_ims/tst')
+
+    parser.add_argument('--train_dst_dir', type=str,
+        help='Destination directory for splited train data set', default='./car_ims/train')
+
+    parser.add_argument('--src_dir', type=str,
+        help='Source directory where validation data set is located', default='./car_ims/val')
+
+    return parser.parse_args(argv)
+
+if __name__ == "__main__":
+    main(parse_arguments(sys.argv[1:]))

Udit Soni/car-recognition-aws/evaluate.py

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+'''
+Created on 10 lut 2018
+
+@author: mgdak
+'''
+
+import os
+import argparse
+import sys
+from pprint import pprint
+import shutil as sh
+import random
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import model_from_json
+from keras.optimizers import SGD, RMSprop
+
+def main(args):
+    pprint(args)
+
+    test_datagen = ImageDataGenerator(rescale=1. / 255)
+    test_generator = test_datagen.flow_from_directory(
+        args.test_data, 
+        target_size=(224, 224), 
+        batch_size=args.batch_size, 
+        class_mode='categorical')
+
+
+    # load json and create model
+    json_file = open(args.model_name + '.json', 'r')
+    loaded_model_json = json_file.read()
+    json_file.close()
+    loaded_model = model_from_json(loaded_model_json)
+    # load weights into new model
+    loaded_model.load_weights(args.model_name + '.h5')
+    print('Loaded model from disk')
+
+    # evaluate loaded model on test data
+    sgd = SGD(lr=args.learning_rate, decay=args.lr_decay, momentum=0.9, nesterov=True)
+    loaded_model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
+    score = loaded_model.evaluate_generator(test_generator, 3957/args.batch_size, workers=6)
+    print("%s: %.2f%%" % (loaded_model.metrics_names[1], score[1]*100))
+
+def parse_arguments(argv):
+
+    parser = argparse.ArgumentParser()
+
+    parser.add_argument('--test_data', type=str,
+        help='Dir with test data to evaluate model on'
+        , default= './car_ims/tst')
+
+    parser.add_argument('--model_name', type=str,
+        help='Saved model name location', default='./vgg16/tst/vgg16_lr001_dr7_decaye-4_finalModel')
+
+    parser.add_argument('--batch_size', type=int,
+        help='Batch size', default=16)
+
+    parser.add_argument('--lr_decay', type=float,
+        help='Learning rate decay.', default=1e-4)
+
+    parser.add_argument('--learning_rate', type=float,
+        help='Initial learning rate.', default=0.001)
+
+    return parser.parse_args(argv)
+
+if __name__ == "__main__":
+    main(parse_arguments(sys.argv[1:]))