final

intropyproject-classify-pet-images/adjust_results4_isadog.py

@@ -66,5 +66,36 @@ def adjust_results4_isadog(results_dic, dogfile):
                maltese) (string - indicates text file's filename)
     Returns:
            None - results_dic is mutable data type so no return needed.
-    """           
+    """
+
+
+    dognames_dic=dict()
+    with open(dogfile,"r") as infile:
+      for line in infile:
+        dogname=line.strip().lower()
+        if dogname not in dognames_dic:
+           dognames_dic[dogname]=1
+
+
+      for key in results_dic:
+          if results_dic[key][0] in dognames_dic:
+            if results_dic[key][q] in dognames_dic:
+               #both dog
+               results_dic[key].extend((1,1))
+            else:
+               #pet  dog
+               results_dic[key].extend((1,0))
+          else:
+             if results_dic[key][1] in dognames_dic:
+                #classfier dog
+                results_dic[key].extend((0,1))
+             else:
+                #both not dog 
+                results_dic[key].extend((0,0))
+
+
+
+
+
+
     None

intropyproject-classify-pet-images/calculates_results_stats.py

@@ -70,4 +70,49 @@ def calculates_results_stats(results_dic):
     """        
     # Replace None with the results_stats_dic dictionary that you created with 
     # this function 
-    return None
+    results_stats_dic = dict()
+
+    # Initialize counters
+    results_stats_dic['n_dogs_img'] = 0
+    results_stats_dic['n_match'] = 0
+    results_stats_dic['n_correct_dogs'] = 0
+    results_stats_dic['n_correct_notdogs'] = 0
+    results_stats_dic['n_correct_breed'] = 0  
+
+    for key in results_dic:
+        # Check for matching labels
+        if results_dic[key][2] == 1:
+            results_stats_dic['n_match'] += 1
+
+            # Check for correct breed classification if image is a dog
+            if results_dic[key][3] == 1:
+                results_stats_dic['n_correct_breed'] += 1
+
+        # Count dog images and correct dog classifications
+        if results_dic[key][3] == 1:
+            results_stats_dic['n_dogs_img'] += 1
+            if results_dic[key][4] == 1:
+                results_stats_dic['n_correct_dogs'] += 1 
+        else:
+            # Count correct NOT-a-dog classifications
+            if results_dic[key][4] == 0:
+                results_stats_dic['n_correct_notdogs'] += 1
+
+    # Calculate total images
+    results_stats_dic['n_images'] = len(results_dic)
+
+    # Calculate number of NOT-a-dog images
+    results_stats_dic['n_notdogs_img'] = results_stats_dic['n_images'] - results_stats_dic['n_dogs_img']
+
+    # Calculate percentages with checks for zero values
+    if results_stats_dic['n_images'] > 0:
+        results_stats_dic['pct_match'] = (results_stats_dic['n_match'] / results_stats_dic['n_images']) * 100.0
+    if results_stats_dic['n_dogs_img'] > 0:
+        results_stats_dic['pct_correct_dogs'] = (results_stats_dic['n_correct_dogs'] / results_stats_dic['n_dogs_img']) * 100.0
+        results_stats_dic['pct_correct_breed'] = (results_stats_dic['n_correct_breed'] / results_stats_dic['n_dogs_img']) * 100.0
+    if results_stats_dic['n_notdogs_img'] > 0:
+        results_stats_dic['pct_correct_notdogs'] = (results_stats_dic['n_correct_notdogs'] / results_stats_dic['n_notdogs_img']) * 100.0
+    else:
+        results_stats_dic['pct_correct_notdogs'] = 0.0
+
+    return results_stats_dic

intropyproject-classify-pet-images/check_images.py

@@ -3,8 +3,8 @@
 # */AIPND-revision/intropyproject-classify-pet-images/check_images.py
 #
 # TODO 0: Add your information below for Programmer & Date Created.                                                                             
-# PROGRAMMER: 
-# DATE CREATED:                                  
+# PROGRAMMER: Raghad 
+# DATE CREATED: 10Nov2024                               
 # 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
@@ -60,7 +60,7 @@ def main():
     #             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)
+    results =  get_pet_labels(in_arg.dir)
 
     # Function that checks Pet Images in the results Dictionary using results    
     check_creating_pet_image_labels(results)
@@ -74,7 +74,7 @@ def main():
     #             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)
+    classify_images(in_arg.dir, results, in_arg.arch)
 
     # Function that checks Results Dictionary using results    
     check_classifying_images(results)    
@@ -88,7 +88,7 @@ def main():
     # 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)
+    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)
@@ -113,7 +113,7 @@ def main():
     #      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)
+    print_results(results, results_stats, in_arg.arch, True, True)
 
     # TODO 0: Measure total program runtime by collecting end time
     end_time = time()

intropyproject-classify-pet-images/classify_images.py

@@ -31,6 +31,7 @@
 # 
 def classify_images(images_dir, results_dic, model):
     """
+
     Creates classifier labels with classifier function, compares pet labels to 
     the classifier labels, and adds the classifier label and the comparison of 
     the labels to the results dictionary using the extend function. Be sure to
@@ -65,4 +66,16 @@ def classify_images(images_dir, results_dic, model):
      Returns:
            None - results_dic is mutable data type so no return needed.         
     """
-    None 
+    for key in results_dic:
+        model_label= classifier(images_dir+key,model)
+
+        model_label=model_label.lower().strip()
+
+        truth=results_dic[key][0]
+
+        if truth in model_label:
+            results_dic[key].extend([model_label, 1])
+        else:
+            results_dic[key].extend([model_label, 0])
+
+

intropyproject-classify-pet-images/get_input_args.py

@@ -38,6 +38,11 @@ def get_input_args():
     Returns:
      parse_args() -data structure that stores the command line arguments object  
     """
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--dir', type = str, default = 'pet_images/', help = 'path to the folder of pet images') 
+    parser.add_argument('--arch',type=str,default='vgg',help = 'The CNN model architecture ')
+    parser.add_argument('--dogfile',type=str,default='dognames.txt',help = 'The file that contains the list of valid dognames')
+
     # Replace None with parser.parse_args() parsed argument collection that 
     # you created with this function 
-    return None
+    return parser.parse_args()

intropyproject-classify-pet-images/get_pet_labels.py

@@ -40,6 +40,21 @@ def get_pet_labels(image_dir):
       List. The list contains for following item:
          index 0 = pet image label (string)
     """
-    # Replace None with the results_dic dictionary that you created with this
-    # function
-    return None
+    filename_list = listdir(image_dir)
+   ## Creates empty dictionary named results_dic
+    results_dic = dict()
+    ## Adds new key-value pairs to dictionary ONLY when key doesn't already exist. This dictionary's value is
+    ## a List that contains only one item - the pet image label
+    for file in filename_list:
+        word_List=file.lower().split("_")
+
+        pet_labels=""
+        for word in word_List:
+            if word.isalpha():
+                pet_labels+=word +" "
+
+        pet_labels=pet_labels.strip()
+        if file not in results_dic:
+         results_dic[file]=[pet_labels]
+    return results_dic 
+

intropyproject-classify-pet-images/print_results.py

@@ -1,66 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# */AIPND-revision/intropyproject-classify-pet-images/print_results.py
-#                                                                             
-# PROGRAMMER: 
-# DATE CREATED:
-# 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.
-    """    
-    None
-