134 lines
6.1 KiB
Python
134 lines
6.1 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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# */AIPND-revision/intropyproject-classify-pet-images/check_images.py
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#
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# TODO 0: Add your information below for Programmer & Date Created.
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# PROGRAMMER: Alexander Hinrichs
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# DATE CREATED: 07.12.2025
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# REVISED DATE:
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# PURPOSE: Classifies pet images using a pretrained CNN model, compares these
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# classifications to the true identity of the pets in the images, and
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# summarizes how well the CNN performed on the image classification task.
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# Note that the true identity of the pet (or object) in the image is
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# indicated by the filename of the image. Therefore, your program must
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# first extract the pet image label from the filename before
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# classifying the images using the pretrained CNN model. With this
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# program we will be comparing the performance of 3 different CNN model
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# architectures to determine which provides the 'best' classification.
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#
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# Use argparse Expected Call with <> indicating expected user input:
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# python check_images.py --dir <directory with images> --arch <model>
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# --dogfile <file that contains dognames>
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# Example call:
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# python check_images.py --dir pet_images/ --arch vgg --dogfile dognames.txt
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##
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# Imports python modules
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from time import time, sleep
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# Imports print functions that check the lab
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from print_functions_for_lab_checks import *
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# Imports functions created for this program
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from get_input_args import get_input_args
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from get_pet_labels import get_pet_labels
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from classify_images import classify_images
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from adjust_results4_isadog import adjust_results4_isadog
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from calculates_results_stats import calculates_results_stats
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from print_results import print_results
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# Main program function defined below
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def main():
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# TODO 0: Measures total program runtime by collecting start time
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start_time = time()
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# TODO 1: Define get_input_args function within the file get_input_args.py
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# This function retrieves 3 Command Line Arugments from user as input from
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# the user running the program from a terminal window. This function returns
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# the collection of these command line arguments from the function call as
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# the variable in_arg
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in_arg = get_input_args()
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# Function that checks command line arguments using in_arg
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check_command_line_arguments(in_arg)
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# TODO 2: Define get_pet_labels function within the file get_pet_labels.py
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# Once the get_pet_labels function has been defined replace 'None'
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# in the function call with in_arg.dir Once you have done the replacements
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# your function call should look like this:
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# get_pet_labels(in_arg.dir)
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# This function creates the results dictionary that contains the results,
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# this dictionary is returned from the function call as the variable results
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results = get_pet_labels(None)
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# Function that checks Pet Images in the results Dictionary using results
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check_creating_pet_image_labels(results)
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# TODO 3: Define classify_images function within the file classiy_images.py
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# Once the classify_images function has been defined replace first 'None'
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# in the function call with in_arg.dir and replace the last 'None' in the
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# function call with in_arg.arch Once you have done the replacements your
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# function call should look like this:
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# classify_images(in_arg.dir, results, in_arg.arch)
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# Creates Classifier Labels with classifier function, Compares Labels,
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# and adds these results to the results dictionary - results
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classify_images(None, results, None)
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# Function that checks Results Dictionary using results
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check_classifying_images(results)
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# TODO 4: Define adjust_results4_isadog function within the file adjust_results4_isadog.py
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# Once the adjust_results4_isadog function has been defined replace 'None'
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# in the function call with in_arg.dogfile Once you have done the
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# replacements your function call should look like this:
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# adjust_results4_isadog(results, in_arg.dogfile)
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# Adjusts the results dictionary to determine if classifier correctly
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# classified images as 'a dog' or 'not a dog'. This demonstrates if
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# model can correctly classify dog images as dogs (regardless of breed)
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adjust_results4_isadog(results, None)
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# Function that checks Results Dictionary for is-a-dog adjustment using results
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check_classifying_labels_as_dogs(results)
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# TODO 5: Define calculates_results_stats function within the file calculates_results_stats.py
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# This function creates the results statistics dictionary that contains a
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# summary of the results statistics (this includes counts & percentages). This
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# dictionary is returned from the function call as the variable results_stats
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# Calculates results of run and puts statistics in the Results Statistics
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# Dictionary - called results_stats
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results_stats = calculates_results_stats(results)
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# Function that checks Results Statistics Dictionary using results_stats
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check_calculating_results(results, results_stats)
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# TODO 6: Define print_results function within the file print_results.py
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# Once the print_results function has been defined replace 'None'
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# in the function call with in_arg.arch Once you have done the
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# replacements your function call should look like this:
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# print_results(results, results_stats, in_arg.arch, True, True)
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# Prints summary results, incorrect classifications of dogs (if requested)
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# and incorrectly classified breeds (if requested)
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print_results(results, results_stats, None, True, True)
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# TODO 0: Measure total program runtime by collecting end time
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end_time = time()
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# TODO 0: Computes overall runtime in seconds & prints it in hh:mm:ss format
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tot_time = (
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end_time - start_time
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) # calculate difference between end time and start time
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print(
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"\n** Total Elapsed Runtime:",
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str(int((tot_time / 3600)))
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+ ":"
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+ str(int((tot_time % 3600) / 60))
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+ ":"
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+ str(int((tot_time % 3600) % 60)),
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)
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# Call to main function to run the program
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if __name__ == "__main__":
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main()
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