kmc20015 · frg20003 · May 1, 2025 · May 1, 2025 · May 2, 2025 · May 3, 2025
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diff --git a/analyzed_images b/analyzed_images
diff --git a/exploreData.py b/exploreData.py
@@ -0,0 +1,106 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+def main():
+    image_data = pd.read_csv("analyzed_images")
+
+    chatGPT_dict = {'Student':{"num_men":0,"num_women":0,"total_conf_man":0,"total_conf_woman":0},
+                    'Lawyer':{"num_men":0,"num_women":0,"total_conf_man":0,"total_conf_woman":0},
+                    'Nurse':{"num_men":0,"num_women":0,"total_conf_man":0,"total_conf_woman":0}}
+    gemini_dict = {'Student':{"num_men":0,"num_women":0,"total_conf_man":0,"total_conf_woman":0},
+                    'Lawyer':{"num_men":0,"num_women":0,"total_conf_man":0,"total_conf_woman":0},
+                    'Nurse':{"num_men":0,"num_women":0,"total_conf_man":0,"total_conf_woman":0}}
+    DALLE_dict = {'Student':{"num_men":0,"num_women":0,"total_conf_man":0,"total_conf_woman":0},
+                    'Lawyer':{"num_men":0,"num_women":0,"total_conf_man":0,"total_conf_woman":0},
+                    'Nurse':{"num_men":0,"num_women":0,"total_conf_man":0,"total_conf_woman":0}}
+
+
+
+    for index, img in image_data.iterrows():
+
+        if img['Model'] == 'ChatGPT':
+            chatGPT_dict[img['Occupation']]['total_conf_man'] += img['Man']
+            chatGPT_dict[img['Occupation']]['total_conf_woman'] += img['Woman']
+            if img['dominant gender'] == 'Man':
+                chatGPT_dict[img['Occupation']]['num_men'] += 1
+            else:
+                chatGPT_dict[img['Occupation']]['num_women'] += 1
+
+        elif img['Model'] == 'DALL-E':
+            DALLE_dict[img['Occupation']]['total_conf_man'] += img['Man']
+            DALLE_dict[img['Occupation']]['total_conf_woman'] += img['Woman']
+            if img['dominant gender'] == 'Man':
+                DALLE_dict[img['Occupation']]['num_men'] += 1
+            else:
+                DALLE_dict[img['Occupation']]['num_women'] += 1
+
+        elif img['Model'] == 'Gemini':
+            gemini_dict[img['Occupation']]['total_conf_man'] += img['Man']
+            gemini_dict[img['Occupation']]['total_conf_woman'] += img['Woman']
+            if img['dominant gender'] == 'Man':
+                gemini_dict[img['Occupation']]['num_men'] += 1
+            else:
+                gemini_dict[img['Occupation']]['num_women'] += 1
+
+    total_student = chatGPT_dict['Student']['num_men'] + chatGPT_dict['Student']['num_women']
+    total_lawyer = chatGPT_dict['Lawyer']['num_men'] + chatGPT_dict['Lawyer']['num_women']
+    total_nurse = chatGPT_dict['Nurse']['num_men'] + chatGPT_dict['Nurse']['num_women']
+
+    print("ChatGPT")
+    print("-------------------------------------------------------------------------------------")
+    print("Occupation, \tMan%, \tWoman%, \tAvg Confedence M, \tAvg Confedence W")
+    print(f"Studemt: \t{(chatGPT_dict['Student']['num_men']/total_student)*100}%, \t{(chatGPT_dict['Student']['num_women']/total_student)*100}%, \t{chatGPT_dict['Student']['total_conf_man']/total_student}, \t{chatGPT_dict['Student']['total_conf_woman']/total_student}")
+    print(f"Nurse: \t{(chatGPT_dict['Nurse']['num_men']/total_nurse)*100}%, \t{(chatGPT_dict['Nurse']['num_women']/total_nurse)*100}%, \t{chatGPT_dict['Nurse']['total_conf_man']/total_nurse}, \t{chatGPT_dict['Nurse']['total_conf_woman']/total_nurse}")
+    print(f"Lawyer: \t{(chatGPT_dict['Lawyer']['num_men']/total_lawyer)*100}%, \t{(chatGPT_dict['Lawyer']['num_women']/total_lawyer)*100}%, \t{chatGPT_dict['Lawyer']['total_conf_man']/total_lawyer}, \t{chatGPT_dict['Lawyer']['total_conf_woman']/total_lawyer}\n")
+
+    total_student = DALLE_dict['Student']['num_men'] + DALLE_dict['Student']['num_women']
+    total_lawyer = DALLE_dict['Lawyer']['num_men'] + DALLE_dict['Lawyer']['num_women']
+    total_nurse = DALLE_dict['Nurse']['num_men'] + DALLE_dict['Nurse']['num_women']
+
+
+    print("DALL-E")
+    print("-------------------------------------------------------------------------------------")
+    print("Occupation, \tMan%, \tWoman%, \tAvg Confedence M, \tAvg Confedence W")
+    print(f"Studemt: \t{(DALLE_dict['Student']['num_men']/total_student)*100}%, \t{(DALLE_dict['Student']['num_women']/total_student)*100}%, \t{DALLE_dict['Student']['total_conf_man']/total_student}, \t{DALLE_dict['Student']['total_conf_woman']/total_student}")
+    print(f"Nurse: \t{(DALLE_dict['Nurse']['num_men']/total_nurse)*100}%, \t{(DALLE_dict['Nurse']['num_women']/total_nurse)*100}%, \t{DALLE_dict['Nurse']['total_conf_man']/total_nurse}, \t{DALLE_dict['Nurse']['total_conf_woman']/total_nurse}")
+    print(f"Lawyer: \t{(DALLE_dict['Lawyer']['num_men']/total_lawyer)*100}%, \t{(DALLE_dict['Lawyer']['num_women']/total_lawyer)*100}%, \t{DALLE_dict['Lawyer']['total_conf_man']/total_lawyer}, \t{DALLE_dict['Lawyer']['total_conf_woman']/total_lawyer}\n")
+
+    total_student = gemini_dict['Student']['num_men'] + gemini_dict['Student']['num_women']
+    total_lawyer = gemini_dict['Lawyer']['num_men'] + gemini_dict['Lawyer']['num_women']
+    total_nurse = gemini_dict['Nurse']['num_men'] + gemini_dict['Nurse']['num_women']
+
+
+    print("Gemini")
+    print("-------------------------------------------------------------------------------------")
+    print("Occupation, \tMan%, \tWoman%, \tAvg Confedence M, \tAvg Confedence W")
+    print(f"Studemt: \t{(gemini_dict['Student']['num_men']/total_student)*100}%, \t{(gemini_dict['Student']['num_women']/total_student)*100}%, \t{gemini_dict['Student']['total_conf_man']/total_student}, \t{gemini_dict['Student']['total_conf_woman']/total_student}")
+    print(f"Nurse: \t{(gemini_dict['Nurse']['num_men']/total_nurse)*100}%, \t{(gemini_dict['Nurse']['num_women']/total_nurse)*100}%, \t{gemini_dict['Nurse']['total_conf_man']/total_nurse}, \t{gemini_dict['Nurse']['total_conf_woman']/total_nurse}")
+    print(f"Lawyer: \t{(gemini_dict['Lawyer']['num_men']/total_lawyer)*100}%, \t{(gemini_dict['Lawyer']['num_women']/total_lawyer)*100}%, \t{gemini_dict['Lawyer']['total_conf_man']/total_lawyer}, \t{gemini_dict['Lawyer']['total_conf_woman']/total_lawyer}\n")
+
+    print("Standard Deviations")
+    print("---------------------Dominant gender Man----------------------")
+    print("ChatGPT Student: "+str(image_data.loc[image_data['Model']=='ChatGPT'].loc[image_data['Occupation']=='Student'].loc[image_data['dominant gender']=='Man']['Man'].std()))
+    print("ChatGPT Nurse: "+str(image_data.loc[image_data['Model']=='ChatGPT'].loc[image_data['Occupation']=='Nurse'].loc[image_data['dominant gender']=='Man']['Man'].std()))
+    print("ChatGPT Lawyer: "+str(image_data.loc[image_data['Model']=='ChatGPT'].loc[image_data['Occupation']=='Lawyer'].loc[image_data['dominant gender']=='Man']['Man'].std()))
+    print("DALL-E Student: "+str(image_data.loc[image_data['Model']=='DALL-E'].loc[image_data['Occupation']=='Student'].loc[image_data['dominant gender']=='Man']['Man'].std()))
+    print("DALL-E Nurse: "+str(image_data.loc[image_data['Model']=='DALL-E'].loc[image_data['Occupation']=='Nurse'].loc[image_data['dominant gender']=='Man']['Man'].std()))
+    print("DALL-E Lawyer: "+str(image_data.loc[image_data['Model']=='DALL-E'].loc[image_data['Occupation']=='Lawyer'].loc[image_data['dominant gender']=='Man']['Man'].std()))
+    print("Gemini Student: "+str(image_data.loc[image_data['Model']=='Gemini'].loc[image_data['Occupation']=='Student'].loc[image_data['dominant gender']=='Man']['Man'].std()))
+    print("Gemini Nurse: "+str(image_data.loc[image_data['Model']=='Gemini'].loc[image_data['Occupation']=='Nurse'].loc[image_data['dominant gender']=='Man']['Man'].std()))
+    print("Gemini Lawyer: "+str(image_data.loc[image_data['Model']=='Gemini'].loc[image_data['Occupation']=='Lawyer'].loc[image_data['dominant gender']=='Man']['Man'].std()))
+    print("---------------------Dominant gender Woman--------------------")
+    print("ChatGPT Student: "+str(image_data.loc[image_data['Model']=='ChatGPT'].loc[image_data['Occupation']=='Student'].loc[image_data['dominant gender']=='Woman']['Woman'].std()))
+    print("ChatGPT Nurse: "+str(image_data.loc[image_data['Model']=='ChatGPT'].loc[image_data['Occupation']=='Nurse'].loc[image_data['dominant gender']=='Woman']['Woman'].std()))
+    print("ChatGPT Lawyer: "+str(image_data.loc[image_data['Model']=='ChatGPT'].loc[image_data['Occupation']=='Lawyer'].loc[image_data['dominant gender']=='Woman']['Woman'].std()))
+    print("DALL-E Student: "+str(image_data.loc[image_data['Model']=='DALL-E'].loc[image_data['Occupation']=='Student'].loc[image_data['dominant gender']=='Woman']['Woman'].std()))
+    print("DALL-E Nurse: "+str(image_data.loc[image_data['Model']=='DALL-E'].loc[image_data['Occupation']=='Nurse'].loc[image_data['dominant gender']=='Woman']['Woman'].std()))
+    print("DALL-E Lawyer: "+str(image_data.loc[image_data['Model']=='DALL-E'].loc[image_data['Occupation']=='Lawyer'].loc[image_data['dominant gender']=='Woman']['Woman'].std()))
+    print("Gemini Student: "+str(image_data.loc[image_data['Model']=='Gemini'].loc[image_data['Occupation']=='Student'].loc[image_data['dominant gender']=='Woman']['Woman'].std()))
+    print("Gemini Nurse: "+str(image_data.loc[image_data['Model']=='Gemini'].loc[image_data['Occupation']=='Nurse'].loc[image_data['dominant gender']=='Woman']['Woman'].std()))
+    print("Gemini Lawyer: "+str(image_data.loc[image_data['Model']=='Gemini'].loc[image_data['Occupation']=='Lawyer'].loc[image_data['dominant gender']=='Woman']['Woman'].std()))
+    #print(image_data.loc[image_data['Model']=='ChatGPT'].loc[image_data['Occupation']=='Student'].loc[image_data['dominant gender']=='Man']['Man'].std())
+
+    #["Man"].std()
+#
+if __name__ == "__main__":
+    main()
diff --git a/imagesToCSV.py b/imagesToCSV.py
@@ -0,0 +1,46 @@
+from deepface import DeepFace
+import csv
+import os
+
+def main():
+    with open("analyzed_images",'w') as csv_file:
+
+        field_names = ["Model","Occupation","File Name","asian","indian","black","white","middle eastern","latino hispanic","dominant race","age",
+                       "Woman","Man","dominant gender","angry","disgust","fear","happy","sad","surprise","neutral","dominant emotion"]
+
+        writer = csv.DictWriter(csv_file, fieldnames=field_names)
+
+        writer.writeheader()
+
+        for model in os.listdir("AI_Images"):
+            for occ in os.listdir("AI_Images/"+model):
+                for img in os.listdir("AI_Images/"+model+"/"+occ):
+                    result = DeepFace.analyze("AI_Images/"+model+"/"+occ+"/"+img, actions=['race','age','gender','emotion'],enforce_detection=False)[0]
+
+                    writer.writerow({"Model" : model,
+                                    "Occupation" : occ,
+                                    "File Name" : img,
+                                    "asian" : result["race"]["asian"],
+                                    "indian": result["race"]["indian"],
+                                    "black" : result["race"]["black"],
+                                    "white" : result["race"]["white"],
+                                    "middle eastern" : result["race"]["middle eastern"],
+                                    "latino hispanic" : result["race"]["latino hispanic"],
+                                    "dominant race" : result["dominant_race"],
+                                    "age" : result["age"],
+                                    "Woman" : result["gender"]["Woman"],
+                                    "Man" : result["gender"]["Man"],
+                                    "dominant gender" : result["dominant_gender"],
+                                    "angry" : result["emotion"]["angry"],
+                                    "disgust" : result["emotion"]["disgust"],
+                                    "fear" : result["emotion"]["fear"],
+                                    "happy" : result["emotion"]["happy"],
+                                    "sad" : result["emotion"]["sad"],
+                                    "surprise" : result["emotion"]["surprise"],
+                                    "neutral" : result["emotion"]["neutral"],
+                                    "dominant emotion" : result["dominant_emotion"]})
+
+    #print(DeepFace.analyze("image2.png", actions=['race','age','gender','emotion'])[0])
+
+if __name__ == "__main__":
+    main()
diff --git a/test.py b/test.py
@@ -0,0 +1,7 @@
+from deepface import DeepFace
+
+def main():
+    print(DeepFace.analyze("image.png", actions=['race','age','gender','emotion'])[0]["emotion"]["surprise"])
+
+if __name__ == "__main__":
+    main()