def predictor(input_type, folder_or_image, model=None):
"""
Accepts either a folder or an image. Optionally accepts a model argument
that's the ML model to use for the predictor. If not given, then one of the
pretrained models from Keras or whatever library is used.
If an image is given as input, predicts whether the image is a hotel or not
and prints to the terminal
If a folder is supplied, loops through all the files in the folder and
creates a .json file containing a list of all images that are hotels and
not hotels
"""
classifier = import_model()
if input_type == 'file':
# Apply directly the ML classifier to predict the output
# Do all that and return
if folder_or_image.lower().endswith(image_extensions):
outcome = test(classifier, folder_or_image)
if outcome == True:
print('Not Hotel')
return
print('Hotel')
return # important. Must return
print('Unsupported file type')
# It's implicit that the input type is a folder from here on
hotels = [] # list of file names that are hotels
not_hotels = [] # list of file names that are not hotels
for folder_name, folders, files in os.walk(folder_or_image):
for file in files:
# Apply ML classifier logic to all files in the folder
# Categorize result based on the prediction
# Just an example. The below line will be replaced with the actual ML logic
# print(folder_name+'/'+file)
if file.lower().endswith(image_extensions):
outcome = test(classifier, folder_name+'/'+file)
if outcome == True:
not_hotels.append(file)
else:
hotels.append(file)
# After each iteration in a folder,
with open(os.path.join(folder_name, file_name), 'w') as f:
# write result to a json file in the folder
json.dump({'hotels': hotels, 'not_hotels': not_hotels}, f)
hotels.clear() # clear the list containing the hotel names for use in the next iterated folder
not_hotels.clear() # Do the same for the not_hotels list
return
# Python 3.6.7
# Ubuntu 18.04
import os
import json
import sys
import argparse
from predict_model import test, import_model
file_name = 'classification_results.json' # the file name
image_extensions = ('jpeg', 'png', 'jpg', 'tiff', 'gif') # add others
classifier = import_model()
# model argument can be substituted with a model of ours
def predictor(input_type, folder_or_image, model=None):
"""
Accepts either a folder or an image. Optionally accepts a model argument
that's the ML model to use for the predictor. If not given, then one of the
pretrained models from Keras or whatever library is used.
If an image is given as input, predicts whether the image is a hotel or not
and prints to the terminal
If a folder is supplied, loops through all the files in the folder and
creates a .json file containing a list of all images that are hotels and
not hotels
"""
classifier = import_model()
if input_type == 'file':