# Import the necessary packages

import pandas as pd

import argparse

import cv2

from utils import classify, describe, train

# Load Random Forest Classifier

model = train()

# Set up the argument parser

ap = argparse.ArgumentParser()

# Define arguments

ap.add_argument("-f", "--file", required=True, help="Path to the pandas dataframe with Instagram metadata.")

# Parse arguments

args = vars(ap.parse_args())

# Assign arguments to variables

df_file = args["file"]

# Load dataframe

df = pd.read_pickle(df_file)

print "*** Loading Instagram metadata from {} ...".format(df_file)

# Loop over images in the dataframe

for index, row in df.iterrows():

# Define path

ipath = "test_output/" + row['Filename']

# Load image

image = cv2.imread(ipath)

# Extract features

features = describe(image)

# Classify image

prediction = classify(features, model)

print "*** Classifying {} ... prediction: {}".format(ipath, prediction)

# Take action based on prediction

if prediction == 'photo':

cv2.imwrite("test_output/photos/%s" % row['Filename'], image)

if prediction == 'other':

df = df[df.index != index]

cv2.imwrite("test_output/others/%s" % row['Filename'], image)

# Reset dataframe index

df = df.reset_index(drop=True)

df.index += 1

print "*** Updating dataframe index ..."

# Pickle cleaned data

new_df_file = "test_output/cleaned.pkl"

print "*** Saving the cleaned dataframe into {}".format(new_df_file)

df.to_pickle(new_df_file)

print "*** ... Done."