updated: prosodic features

Presently/presently/users/emotiondetectionvideo.py

-# -*- coding: utf-8 -*-
-"""emotionDetection.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1o8opa083KfLohUfWs-oGfkdo9kuRN_eS
-
-**Importing dependencies**
-
-
-*   pip install fer
-*   List item
-"""
-
 from fer import Video
 from fer import FER
 import os
@@ -20,13 +5,9 @@ import sys
 import pandas as pd
 import numpy as np
 
-"""Upload the video to the Google colab and get the path of the video and xml file"""
-
 location_videofile = "F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\media\\video\\22\\testvideo.mp4"
 cascPath = "F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\models\\abc.xml"
 
-"""Get labels to each emotion"""
-
 def _get_labels():
   return {
             0: "angry",
@@ -38,10 +19,7 @@ def _get_labels():
             6: "neutral",
         }
 
-"""Build a square (around the face)"""
-
 def tosquare(bbox):
-        """Convert bounding box to square by elongating shorter side."""
         x, y, w, h = bbox
         if h > w:
             diff = h - w
@@ -56,15 +34,11 @@ def tosquare(bbox):
 
         return (x, y, w, h)
 
-"""Builded square around the face"""
-
 def __apply_offsets(face_coordinates):
   x, y, width, height = face_coordinates
   x_off, y_off = (10, 10)
   return (x - x_off, x + width + x_off, y - y_off, y + height + y_off)
 
-"""To correct numbers"""
-
 def __preprocess_input(x, v2=False):
         x = x.astype("float32")
         x = x / 255.0
@@ -73,8 +47,6 @@ def __preprocess_input(x, v2=False):
             x = x * 2.0
         return x
 
-"""Add padding to the image"""
-
 def pad(image):
         PADDING = 40
         row, col = image.shape[:2]
@@ -106,7 +78,6 @@ def detect_emotions(location_videofile, NumberofFrames):
 
   success,image = vidcap.read()
   frame_count = vidcap.get(cv2.CAP_PROP_FRAME_COUNT)
-  print("Frame Count: ", frame_count)
   count = 0
   faceCascade = cv2.CascadeClassifier(cascPath)
 
@@ -116,7 +87,7 @@ def detect_emotions(location_videofile, NumberofFrames):
 
       if success:
           if frame_count > NumberofFrames+1:
-            count += frame_count/(NumberofFrames+1) # i.e. at 30 fps, this advances one second
+            count += frame_count/(NumberofFrames+1) 
           else:
             count += 1
           vidcap.set(cv2.CAP_PROP_POS_FRAMES, count)
@@ -135,7 +106,6 @@ def detect_emotions(location_videofile, NumberofFrames):
                 face_coordinates = tosquare(face_coordinates)
                 x1, x2, y1, y2 = __apply_offsets(face_coordinates)
                 
-                # adjust for padding
                 x1 += PADDING
                 x2 += PADDING
                 y1 += PADDING
@@ -154,10 +124,8 @@ def detect_emotions(location_videofile, NumberofFrames):
                 try:
                   gray_face = cv2.resize(gray_face, model.input_shape[1:3])
                 except Exception as e:
-                  #print("Cannot resize")
                   continue
                 
-                # Local Keras model
                 gray_face = __preprocess_input(gray_face, True)
                 gray_face = np.expand_dims(np.expand_dims(gray_face, 0), -1)
 
@@ -170,9 +138,6 @@ def detect_emotions(location_videofile, NumberofFrames):
                 emotions.append(
                     dict(box=face_coordinates, emotions=labelled_emotions)
                 )
-            #print("Prediction : ", emotions[0]["emotions"])
-            #plt.imshow(gray_img, interpolation='nearest')
-            #plt.show()
             top_emotions  = [max(e["emotions"], key=lambda key: e["emotions"][key]) for e in emotions]
             if len(top_emotions):
               for top_emotion in emotions[0]["emotions"]:
@@ -190,4 +155,3 @@ def detect_emotions(location_videofile, NumberofFrames):
     return max(arry, key=arry.get), arry
 
 emo, arr1 = detect_emotions(location_videofile, 300)
-# print(emo, arr1)
\ No newline at end of file

Presently/presently/users/prosodydetectionaudio.py

-import myprosody as mysp
-import pickle
-
-p="happy" 
-c="F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\myprosody" 
-
-mysp.myspsyl(p,c)
-mysp.mysppaus(p,c)
-mysp.myspsr(p,c)
-mysp.myspatc(p,c)
-mysp.myspst(p,c)
-mysp.myspod(p,c)
-mysp.myspbala(p,c)
-mysp.myspf0mean(p,c)
-mysp.myspf0sd(p,c)
-mysp.myspf0med(p,c)
-mysp.myspf0min(p,c)
-mysp.myspf0max(p,c)
-mysp.myspf0q25(p,c)
-mysp.myspf0q75(p,c)
-
-mysp.mysptotal(p,c)
-mysp.myspgend(p,c)
-mysp.mysppron(p,c)
-mysp.myprosody(p,c)
-mysp.mysplev(p,c)

Presently/presently/users/templates/users/base.html

@@ -7,8 +7,7 @@
     <link rel="stylesheet" href="{% static 'users/css/main.css' %}" type="text/css">
     <link rel="stylesheet" href="{% static 'users/css/plugin.css' %}" type="text/css">
     <link rel="stylesheet" href="{% static 'users/css/style.css' %}" type="text/css">
-
-
+    <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/4.7.0/css/font-awesome.min.css">
     <link href="https://cdn.jsdelivr.net/npm/bootstrap@5.0.0-beta3/dist/css/bootstrap.min.css" rel="stylesheet" integrity="sha384-eOJMYsd53ii+scO/bJGFsiCZc+5NDVN2yr8+0RDqr0Ql0h+rP48ckxlpbzKgwra6" crossorigin="anonymous">
     <link rel="preconnect" href="https://fonts.gstatic.com">
     <link href="https://fonts.googleapis.com/css2?family=Montserrat:wght@500&family=Open+Sans:wght@800&display=swap" rel="stylesheet">

Presently/presently/users/templates/users/test.html

@@ -5,7 +5,7 @@
 {% block content %}
     <h2> {{ var1 }} {{ var2 }}</h2>
 
-    <!-- {% for key, value in predictions.items %}
+    <!-- {% for key, value in var2.items %}
             <h2>{{ forloop.counter }}. {{ key }} : {{ value }}</h2>
         {% endfor %} -->
     

Presently/presently/users/views.py

@@ -10,11 +10,16 @@ from .models import Video
 from django.template import Template, Context
 import datetime
 from . import emotiondetectionvideo
-from . import emotiondetectionaudio
+# from . import emotiondetectionaudio
+import myprosody as mysp
+import pickle
+from . import myprosody
 
 def test(request):
-    var1= emotiondetectionaudio.emo_list
-    var2= emotiondetectionaudio.total_predictions_np
+    p="happy" 
+    c="F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\myprosody" 
+    var2= myprosody.myspgend(p,c)
+    var1= myprosody.myprosody(p,c)
     context= {
         'var1': var1,
         'var2': var2
@@ -60,7 +65,46 @@ def emotionvideo(request):
     return render(request, 'users/emotionvideo.html',context) 
 
 def emotionaudioprosody(request):
-    return render(request, 'users/prosody.html')    
+    p="happy" 
+    c="F:\\CDAP-PRESENTLY\\21_22-j-02\\Presently\\presently\\users\\myprosody" 
+    myspsyl= myprosody.myspsyl(p,c)
+    mysppaus= myprosody.mysppaus(p,c)
+    myspsr= myprosody.myspsr(p,c)
+    myspatc= myprosody.myspatc(p,c)
+    myspst= myprosody.myspst(p,c)
+    myspod= myprosody.myspod(p,c)
+    myspbala= myprosody.myspbala(p,c)
+    myspf0mean= myprosody.myspf0mean(p,c)
+    myspf0sd= myprosody.myspf0sd(p,c)
+    myspf0med= myprosody.myspf0med(p,c)
+    myspf0min= myprosody.myspf0min(p,c)
+    myspf0max= myprosody.myspf0max(p,c)
+    myspf0q25= myprosody.myspf0q25(p,c)
+    myspf0q75= myprosody.myspf0q75(p,c)
+    myspgend= myprosody.myspgend(p,c)
+    mysppron= myprosody.mysppron(p,c)
+    prosody= myprosody.myprosody(p,c)
+    
+    context= {
+        'myspsyl': myspsyl,
+        'mysppaus': mysppaus,
+        'myspsr': myspsr,
+        'myspatc': myspatc,
+        'myspst': myspst,
+        'myspod': myspod,
+        'myspbala': myspbala,
+        'myspf0mean': myspf0mean,
+        'myspf0sd': myspf0sd,
+        'myspf0med': myspf0med,
+        'myspf0min': myspf0min,
+        'myspf0max': myspf0max,
+        'myspf0q25': myspf0q25,
+        'myspf0q75': myspf0q75,
+        'myspgend': myspgend,
+        'mysppron': mysppron,
+        'prosody': prosody
+    }
+    return render(request, 'users/prosody.html',context)    
 
 def overallfeedback(request):
     return render(request, 'users/overallfeedback.html')