Initial Commit: TODO 0

ai-programming-with-python/C02P8-pre-trained-image-classifier/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: Alexander Hinrichs
+# DATE CREATED: 07.12.2025
+# REVISED DATE:
+# 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()
+
+    # 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(None)
+
+    # 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(None, results, None)
+
+    # 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, None)
+
+    # 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, None, True, True)
+
+    # TODO 0: Measure total program runtime by collecting end time
+    end_time = time()
+
+    # TODO 0: Computes overall runtime in seconds & prints it in hh:mm:ss format
+    tot_time = (
+        end_time - start_time
+    )  # calculate difference between end time and 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()