added sentiment.py and object_detection.py

safetycam/object_detection.py

@@ -0,0 +1,47 @@
+import cv2
+from deepface import DeepFace
+
+def get_dominant_emotion(dictionary_list):
+    dominant_emotion = dictionary_list[0]['dominant_emotion']
+    return dominant_emotion
+
+def obejct_detection():
+    cap=cv2.VideoCapture(0)
+    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
+    eye_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_eye.xml')
+    smile_casacde = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_smile.xml')
+    
+    while True:
+        ret, frame = cap.read()
+        try:
+            emotion=DeepFace.analyze(frame, actions= ['emotion'])
+        except:
+            print("No face detected.")
+        to_gray=cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+        faces=face_cascade.detectMultiScale(to_gray,1.3,5)
+        for (x,y,w,h) in faces:
+            cv2.rectangle(frame, (x,y), (x+w, y+h), (255,0,0), 2)
+            try:
+                dominant=get_dominant_emotion(emotion)
+                cv2.putText(frame, dominant, (x+10,y+50), cv2.FONT_HERSHEY_COMPLEX, 1 , (0,255,0),2)
+            except:
+                print("Face undetected.")
+            roi_gray=to_gray[y:y+w, x:x+w]
+            roi_color=frame[y:y+h, x:x+w]
+            smiles=smile_casacde.detectMultiScale(roi_gray,1.3,7)
+            for (x_s,y_s,w_s,h_s) in smiles:
+                cv2.rectangle(roi_color,(x_s, y_s),(x_s+w_s,y_s+h_s),(255,0,0),2)
+                cv2.putText(roi_color,"Smile", (x_s+10,y_s+50), cv2.FONT_HERSHEY_COMPLEX, 0.5 , (0,255,0),2)
+            eyes = eye_cascade.detectMultiScale(roi_gray, 1.3, 5)
+            for (ex, ey, ew, eh) in eyes:
+                cv2.rectangle(roi_color, (ex, ey), (ex + ew, ey + eh), (255, 0, 0), 5)
+                cv2.putText(roi_color,"Eye", (ex+10,ey+30), cv2.FONT_HERSHEY_COMPLEX, 0.5 , (0,255,0),2)
+        cv2.imshow('frame', frame)
+                
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+    cap.release()
+    cv2.destroyAllWindows()
+
+obejct_detection()

safetycam/sentiment.py

@@ -1,4 +1,38 @@
-#!/usr/bin/env python
+import pandas as pd
+from nltk.corpus import stopwords
+from textblob import Word, TextBlob
+stop_words=stopwords.words('english')
+from vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer
 
-def dummy(msg):
-    return msg.upper()
+def replace_non_alphanumeric(text):
+    result = ""
+    for char in text:
+        if char.isalnum() or char.isspace():
+            result += char
+    return result
+
+def preprocess_texts(text):
+    processed_text = replace_non_alphanumeric(text)
+    processed_text = " ".join(word for word in processed_text.split() if word not in stop_words)
+    processed_text = " ".join(Word(word).lemmatize() for word in processed_text.split())
+    return processed_text
+
+def get_polarity_subjectivity(preprocess_text):
+    processed_text=preprocess_texts(preprocess_text)
+    polarity = TextBlob(processed_text).sentiment[0]
+    subjectivity = TextBlob(processed_text).sentiment[1]
+    return polarity, subjectivity
+
+def sentiment_analysis(text):
+    processed_text=preprocess_texts(text)
+    sia=SentimentIntensityAnalyzer()
+    sentiment=sia.polarity_scores(text)
+    return sentiment
+
+# use microphone input in the future.
+text=input()
+
+dict_sentiment = (sentiment_analysis(text))
+score = dict_sentiment['compound']
+if score < -.3:
+    print("Alert")