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print_results.py
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print_results.py
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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# */AIPND-revision/intropyproject-classify-pet-images/print_results.py
#
# PROGRAMMER: Husam Jehad AlSabbah
# DATE CREATED: 17/6/2023
# REVISED DATE:
# PURPOSE: Create a function print_results that prints the results statistics
# from the results statistics dictionary (results_stats_dic). It
# should also allow the user to be able to print out cases of misclassified
# dogs and cases of misclassified breeds of dog using the Results
# dictionary (results_dic).
# This function inputs:
# -The results dictionary as results_dic within print_results
# function and results for the function call within main.
# -The results statistics dictionary as results_stats_dic within
# print_results function and results_stats for the function call within main.
# -The CNN model architecture as model wihtin print_results function
# and in_arg.arch for the function call within main.
# -Prints Incorrectly Classified Dogs as print_incorrect_dogs within
# print_results function and set as either boolean value True or
# False in the function call within main (defaults to False)
# -Prints Incorrectly Classified Breeds as print_incorrect_breed within
# print_results function and set as either boolean value True or
# False in the function call within main (defaults to False)
# This function does not output anything other than printing a summary
# of the final results.
##
# TODO 6: Define print_results function below, specifically replace the None
# below by the function definition of the print_results function.
# Notice that this function doesn't to return anything because it
# prints a summary of the results using results_dic and results_stats_dic
#
def print_results(results_dic, results_stats_dic, model,
print_incorrect_dogs=False, print_incorrect_breed=False):
"""
Prints summary results on the classification and then prints incorrectly
classified dogs and incorrectly classified dog breeds if user indicates
they want those printouts (use non-default values)
Parameters:
results_dic - Dictionary with key as image filename and value as a List
(index)idx 0 = pet image label (string)
idx 1 = classifier label (string)
idx 2 = 1/0 (int) where 1 = match between pet image and
classifer labels and 0 = no match between labels
idx 3 = 1/0 (int) where 1 = pet image 'is-a' dog and
0 = pet Image 'is-NOT-a' dog.
idx 4 = 1/0 (int) where 1 = Classifier classifies image
'as-a' dog and 0 = Classifier classifies image
'as-NOT-a' dog.
results_stats_dic - Dictionary that contains the results statistics (either
a percentage or a count) where the key is the statistic's
name (starting with 'pct' for percentage or 'n' for count)
and the value is the statistic's value
model - Indicates which CNN model architecture will be used by the
classifier function to classify the pet images,
values must be either: resnet alexnet vgg (string)
print_incorrect_dogs - True prints incorrectly classified dog images and
False doesn't print anything(default) (bool)
print_incorrect_breed - True prints incorrectly classified dog breeds and
False doesn't print anything(default) (bool)
Returns:
None - simply printing results.
"""
print("Using the {} CNN model architecture".format(model))
print("Number of Images: {}".format(results_stats_dic["n_images"]))
print("Number of Dog Images: {}".format(results_stats_dic["n_dogs_img"]))
print("Number of \"Not-a\" Images: {}".format(results_stats_dic["n_notdogs_img"]))
print("{}% Correct Dog".format(results_stats_dic["pct_correct_dogs"]))
print("{}% Correct Breed".format(results_stats_dic["pct_correct_breed"]))
print("{}% Correct \"Not-a\" Dog".format(results_stats_dic["pct_correct_notdogs"]))
print("{}% Match".format(results_stats_dic["pct_match"]))
if print_incorrect_dogs:
print("Misclassified Dogs")
for image, values in results_dic.items():
if (int(values[3]) + int(values[4])) == 1:
print("image: {}, classifier label: {}".format(image, values[1]))
if print_incorrect_breed:
print("Misclassified Breed's of Dog")
for image, values in results_dic.items():
if (int(values[3]) + int(values[4])) == 2 and values[2] == 0:
print("image: {}, classifier label: {}".format(image, values[1]))