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check_images.py
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check_images.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# */AIPND-revision/intropyproject-classify-pet-images/check_images.py
#
# TODO 0: Add your information below for Programmer & Date Created.
# PROGRAMMER: Agbonkonkon Blessing
# DATE CREATED: 20/11/2024
# REVISED DATE: 27/11/2024
# PURPOSE: Classifies pet images using a pretrained CNN model, compares these
# classifications to the true identity of the pets in the images, and
# summarizes how well the CNN performed on the image classification task.
# Note that the true identity of the pet (or object) in the image is
# indicated by the filename of the image. Therefore, your program must
# first extract the pet image label from the filename before
# classifying the images using the pretrained CNN model. With this
# program we will be comparing the performance of 3 different CNN model
# architectures to determine which provides the 'best' classification.
#
# Use argparse Expected Call with <> indicating expected user input:
# python check_images.py --dir <directory with images> --arch <model>
# --dogfile <file that contains dognames>
# Example call:
# python check_images.py --dir pet_images/ --arch vgg --dogfile dognames.txt
##
# Imports python modules
from time import time, sleep
# Imports print functions that check the lab
from print_functions_for_lab_checks import *
# Imports functions created for this program
from get_input_args import get_input_args
from get_pet_labels import get_pet_labels
from classify_images import classify_images
from adjust_results4_isadog import adjust_results4_isadog
from calculates_results_stats import calculates_results_stats
from print_results import print_results
# Main program function defined below
def main():
# TODO 0: Measures total program runtime by collecting start time
start_time = time() #stores the current time
# TODO 1: Define get_input_args function within the file get_input_args.py
# This function retrieves 3 Command Line Arugments from user as input from
# the user running the program from a terminal window. This function returns
# the collection of these command line arguments from the function call as
# the variable in_arg
in_arg = get_input_args()
# Function that checks command line arguments using in_arg
check_command_line_arguments(in_arg)
# TODO 2: Define get_pet_labels function within the file get_pet_labels.py
# Once the get_pet_labels function has been defined replace 'None'
# in the function call with in_arg.dir Once you have done the replacements
# your function call should look like this:
# get_pet_labels(in_arg.dir)
# This function creates the results dictionary that contains the results,
# this dictionary is returned from the function call as the variable results
results = get_pet_labels(in_arg.dir)
# Function that checks Pet Images in the results Dictionary using results
check_creating_pet_image_labels(results)
# TODO 3: Define classify_images function within the file classiy_images.py
# Once the classify_images function has been defined replace first 'None'
# in the function call with in_arg.dir and replace the last 'None' in the
# function call with in_arg.arch Once you have done the replacements your
# function call should look like this:
# classify_images(in_arg.dir, results, in_arg.arch)
# Creates Classifier Labels with classifier function, Compares Labels,
# and adds these results to the results dictionary - results
classify_images(in_arg.dir, results, in_arg.arch)
# Function that checks Results Dictionary using results
check_classifying_images(results)
# TODO 4: Define adjust_results4_isadog function within the file adjust_results4_isadog.py
# Once the adjust_results4_isadog function has been defined replace 'None'
# in the function call with in_arg.dogfile Once you have done the
# replacements your function call should look like this:
# adjust_results4_isadog(results, in_arg.dogfile)
# Adjusts the results dictionary to determine if classifier correctly
# classified images as 'a dog' or 'not a dog'. This demonstrates if
# model can correctly classify dog images as dogs (regardless of breed)
adjust_results4_isadog(results, in_arg.dogfile)
# Function that checks Results Dictionary for is-a-dog adjustment using results
check_classifying_labels_as_dogs(results)
# TODO 5: Define calculates_results_stats function within the file calculates_results_stats.py
# This function creates the results statistics dictionary that contains a
# summary of the results statistics (this includes counts & percentages). This
# dictionary is returned from the function call as the variable results_stats
# Calculates results of run and puts statistics in the Results Statistics
# Dictionary - called results_stats
results_stats = calculates_results_stats(results)
# Function that checks Results Statistics Dictionary using results_stats
check_calculating_results(results, results_stats)
# TODO 6: Define print_results function within the file print_results.py
# Once the print_results function has been defined replace 'None'
# in the function call with in_arg.arch Once you have done the
# replacements your function call should look like this:
# print_results(results, results_stats, in_arg.arch, True, True)
# Prints summary results, incorrect classifications of dogs (if requested)
# and incorrectly classified breeds (if requested)
print_results(results, results_stats, in_arg.arch, True, True)
# TODO 0: Measure total program runtime by collecting end time
end_time = time() #time after my code
# TODO 0: Computes overall runtime in seconds & prints it in hh:mm:ss format
tot_time = end_time - start_time
print("\n** Total Elapsed Runtime:",
str(int((tot_time/3600)))+":"+str(int((tot_time%3600)/60))+":"
+str(int((tot_time%3600)%60)) )
# Call to main function to run the program
if __name__ == "__main__":
main()