# -*- coding: utf-8 -*-
Created on Wed Mar 18 22:06:25 2020

@author: dougl

import os

from keras import applications
from keras.applications.resnet50 import preprocess_input
from keras.preprocessing import image
import numpy as np
from pandas import DataFrame as DF

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
IMG_EXTS = ['jpg', 'jpeg', 'bmp', 'png']

def named_model(name):
    # include_top=False removes the fully connected layer at the end/top of the network
    # This allows us to get the feature vector as opposed to a classification
    if name == 'ResNet50':
        return applications.resnet50.ResNet50(weights='imagenet', include_top=False, pooling='avg')
    elif name == 'Xception':
        return applications.xception.Xception(weights='imagenet', include_top=False, pooling='avg')

    elif name == 'VGG16':
        return applications.vgg16.VGG16(weights='imagenet', include_top=False, pooling='avg')

    elif name == 'VGG19':
        return applications.vgg19.VGG19(weights='imagenet', include_top=False, pooling='avg')

    elif name == 'InceptionV3':
        return applications.inception_v3.InceptionV3(weights='imagenet', include_top=False, pooling='avg')

    elif name == 'MobileNet':
        return applications.mobilenet.MobileNet(weights='imagenet', include_top=False, pooling='avg')
        raise ValueError('Unrecognised model: "{}"'.format(name))
def _extract(fp, model):
        # Load the image, setting the size to 224 x 224
        img = image.load_img(fp, target_size=(224, 224))
        # Convert the image to a numpy array, resize it (1, 2, 244, 244), and preprocess it
        img_data = image.img_to_array(img)
        img_data = np.expand_dims(img_data, axis=0)
        img_data = preprocess_input(img_data)

        # Extract the features
        np_features = model.predict(img_data)[0]
        # Convert from Numpy to a list of values
        return np.char.mod('%f', np_features)

def extract_features(filepath, model='ResNet50', write_to=None):
    ''' Reads an input image file, or directory containing images, and returns
    resulting extracted features. Use write_to=<some_filepath> to save the
    features somewhere. '''
    print('Extracting features')
    # Get the model
    print('Acquiring model "{}"'.format(model), end='')
    m = named_model(model)
    print('\rAcquired model\t\t\t\t\t')
    # Get the image filepaths
    filepath = filepath.replace('\\', '/')
    img_fps = []
    assert os.path.exists(filepath),\
        'Filepath does not exist: "{}"'.format(filepath)
    if os.path.isfile(filepath):
        ext = filepath.lower().rsplit('.', 1)[-1]
        assert ext in IMG_EXTS,\
            'Specified file "{}" is not in recognised image formats'.format(filepath)
        img_fps = img_fps.append(filepath)
    elif os.path.isdir(filepath):
        for fn in os.listdir(filepath):
            ext = fn.rsplit('.', 1)[-1]
            if ext in IMG_EXTS:
                img_fps.append(os.path.join(filepath, fn))
        raise ValueError('Filepath should be an image, or a directory containing images')
    # And the image filenames
    img_fns = [fp.replace('\\', '/').rsplit('/', 1)[-1] for fp in img_fps]
    print('Found {} images'.format(len(img_fns)))
    # Run the extraction over each image
    features = []
    for (i, fp) in enumerate(img_fps):
        print('\rProcessing: {:.2f}%\t\t'.format((i+1) / len(img_fps) * 100), end='', flush=True)
        features.append(_extract(fp, m))
    # Make into a DataFrame and add an ID column
    features_df = DF(features, dtype=object)
    id_col = DF(img_fns, dtype=str)
    features_df.insert(0, 'ID', id_col)
    if write_to is not None:
            features_df.to_csv(write_to, index=False)
            print('Wrote features to: "{}"'.format(write_to))
        except Exception as e:
            print('WARNING: Feature extraction could not write to file: "{}"'.format(e))
    return features_df