iphoneOrNot_Babushkin

Link to the dataset.txt

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+https://drive.google.com/open?id=16Jac8MeodgoiqA_YJ1_e8ICJMU_SFay6

Predict.py

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+
+# coding: utf-8
+
+# In[101]:
+
+
+import argparse
+import numpy as np
+import os
+import pandas as pd
+import tensorflow as tf
+from keras.models import load_model
+from keras.preprocessing.image import ImageDataGenerator
+
+
+# In[102]:
+
+
+# creating function for calculating the results
+def predict_proba(model_path, in_folder, out_file):
+
+    print('Start image processing...')
+
+# loading the model
+model = load_model('second_try.h5')
+batch_size=64
+
+
+# In[103]:
+
+
+# define ImageDataGenerator
+test_datagen = ImageDataGenerator(rescale=1. / 255)
+test_generator = test_datagen.flow_from_directory(
+    directory='final_dataset_2/test',
+    target_size=(150, 150),
+    shuffle=False,
+    class_mode=None,
+    batch_size=batch_size)
+
+
+# In[104]:
+
+
+# getting filenames
+filenames = [filename.split('\\')[-1] for filename in
+                 test_generator.filenames]
+
+
+# In[105]:
+
+
+# getting predictions
+nb_samples = len(filenames)
+test_generator.reset()
+preds = model.predict_generator(test_generator,
+                                steps=nb_samples / batch_size, verbose=1)
+
+
+# In[94]:
+
+
+# creating dataframe with filenames and iphone probabilities
+def results(filenames, preds, out_file):
+    output_data = pd.DataFrame()
+    output_data['image_name'] = filenames
+    output_data['iphone_probability'] = [pred[0] for pred in preds]
+
+    # saving CSV with outputs
+    output_data.to_csv(out_file, index=False)
+
+
+# In[99]:
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Iphone detector')
+    parser.add_argument('--model', type=str, default='second_try.h5', help='path to model')
+    parser.add_argument('--input', type=str, default='.final_dataset_2/test', help='path to folder with pictures')
+    parser.add_argument('--output', type=str, default='predictions.csv', help='path to file with model output')
+
+    args = parser.parse_args(args=[])
+    print("model= {0} input_data= {1} output_data= {2}".format(args.model, args.input, args.output))
+
+
+# In[107]:
+
+
+predict_proba(args.model, args.input, args.output)
+print()
+print('Predictions file has been successfully created!')
+