Merge branch 'IT18121902' into 'master'

It18121902

See merge request !7

.idea/Updated.iml

@@ -2,7 +2,7 @@
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
     <content url="file://$MODULE_DIR$" />
-    <orderEntry type="inheritedJdk" />
+    <orderEntry type="jdk" jdkName="Python 3.7" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>
\ No newline at end of file

.idea/misc.xml

 <?xml version="1.0" encoding="UTF-8"?>
 <project version="4">
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.9" project-jdk-type="Python SDK" />
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.7" project-jdk-type="Python SDK" />
+  <component name="PyCharmProfessionalAdvertiser">
+    <option name="shown" value="true" />
+  </component>
 </project>
\ No newline at end of file

Emotion/textAnalyze.py

+import string
+from collections import Counter
+
+def textAnalyze(speech):
+    text = speech
+
+    # converting to lowercase
+    lower_case = text.lower()
+
+    # Removing punctuations of the full text
+    cleaned_text = lower_case.translate(str.maketrans('', '', string.punctuation))
+
+    # splitting text into words
+    tokenized_words = cleaned_text.split()
+
+    stop_words = ["i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours", "yourself",
+                  "yourselves", "he", "him", "his", "himself", "she", "her", "hers", "herself", "it", "its", "itself",
+                  "they", "them", "their", "theirs", "themselves", "what", "which", "who", "whom", "this", "that",
+                  "these",
+                  "those", "am", "is", "are", "was", "were", "be", "been", "being", "have", "has", "had", "having",
+                  "do",
+                  "does", "did", "doing", "a", "an", "the", "and", "but", "if", "or", "because", "as", "until", "while",
+                  "of", "at", "by", "for", "with", "about", "against", "between", "into", "through", "during", "before",
+                  "after", "above", "below", "to", "from", "up", "down", "in", "out", "on", "off", "over", "under",
+                  "again",
+                  "further", "then", "once", "here", "there", "when", "where", "why", "how", "all", "any", "both",
+                  "each",
+                  "few", "more", "most", "other", "some", "such", "no", "nor", "not", "only", "own", "same", "so",
+                  "than",
+                  "too", "very", "s", "t", "can", "will", "just", "don", "should", "now"]
+
+    # Removing stop words from the tokenized words list
+    final_words = []
+    for word in tokenized_words:
+        if word not in stop_words:
+            final_words.append(word)
+
+    emotion_list = []
+    with open('Emotion/emotions.txt', 'r') as file:
+        for line in file:
+            clear_line = line.replace("\n", '').replace(",", '').replace("'", '').strip()
+            word, emotion = clear_line.split(':')
+
+            if word in final_words:
+                emotion_list.append(emotion)
+
+    print(emotion_list)
+    w = Counter(emotion_list)
+    print(w)
+    return {
+        "message": w
+    }

Emotion/videoAnalyzing.py

+# from skimage import io
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import tensorflow as tf
+from keras.preprocessing import image
+
+Savedmodel = tf.keras.models.load_model('emotion_lts.h5')
+Savedmodel.summary()
+
+objects = ('Angry', 'Happy', 'Sad', 'Neutral')
+vid = cv2.VideoCapture(0)
+#
+# def run():
+#     while True:
+#
+#         _, frame = vid.read()
+#         frame = imutils.resize(frame, width=500)
+#
+#         # result = api(frame)
+#
+#         cv2.imshow("frame",frame)
+#         # getPrediction(frame)
+#
+#         # cv.waitKey(0)
+#         if cv2.waitKey(20) & 0XFF == ord('q'):
+#             break
+#
+#     vid.release()
+#     cv2.destroyAllWindows()
+def emotion_analysis(emotions):
+    objects = ['Angry', 'Happy', 'Sad', 'Neutral']
+    y_pos = np.arange(len(objects))
+    plt.bar(y_pos, emotions, align='center', alpha=0.9)
+    plt.tick_params(axis='x', which='both', pad=10, width=4, length=10)
+    plt.xticks(y_pos, objects)
+    plt.ylabel('percentage')
+    plt.title('emotion')
+
+def getEmotions(filePath):
+    cap = cv2.VideoCapture(filePath)
+
+    emotions = []
+
+    while (cap.isOpened()):
+        try:
+            ret, frame = cap.read()
+            # img = image.load_img(frame,grayscale=True, target_size=(48, 48))
+            frame = cv2.resize(frame, (48, 48))
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+            x = image.img_to_array(frame)
+            x = np.expand_dims(x, axis=0)
+
+            x /= 255
+
+            custom = Savedmodel.predict(x)
+            # print(custom[0])
+            emotion_analysis(custom[0])
+
+            x = np.array(x, 'float32')
+            x = x.reshape([48, 48]);
+
+            m = 0.000000000000000000001
+            a = custom[0]
+            for i in range(0, len(a)):
+                if a[i] > m:
+                    m = a[i]
+                    ind = i
+
+            print('Expression Prediction:', objects[ind])
+            emotions.append(objects[ind])
+
+            if cv2.waitKey(20) & 0XFF == ord('q'):
+                break
+        except:
+            print("Damaged frame")
+            break
+    return emotions
+
+getEmotions("speech.mp4")
+cv2.destroyAllWindows()

README.md

-<<<<<<< README.md
-# Main Objective
-
-The main objective of this research part is to provide a customized fully detailed feedback for the user (speaker) about the usage of correct facial expressions and usage of eye contact throughout the speech.
-=======
-**Main objective**
-
-Build the best modern solution for practice English Public Speaking in a proper way.
-
-
-**Main Research questions**
-
-What is the best modern solution for practice English Public Speaking in a proper way?
-
-
-**Individual research questions**
-
-
-* How to analyze content of the speech and give proper feedback for the speaker?
-* How to check grammar and richness of grammar use and give proper feedback for the speaker?
-* How to check the user’s facial expressions according to the speech content and give proper feedback for the speaker?
-* How to check the user’s eye contact continuity throughout the speech and give proper feedback for the speaker?
-* How to  check identify the user’s pause fillers, filler words, interjections, exclamations, etc?
-
-
-**Individual Objectives**
-
-* Analyze content of the speech and give proper feedback for the speaker.
-* Check grammar and richness of grammar use and give proper feedback for the speaker
-* Check the user’s facial expressions according to the speech content and give proper feedback for the speaker.
-* Check the user’s eye contact continuity throughout the speech and give proper feedback for the speaker
-* Check identify the user’s pause fillers, filler words, interjections, exclamations, etc.
-
-
->>>>>>> README.md

textAnalyze.py

-import string
-from collections import Counter
-
-# import matplotlib.pyplot as plt
-
-# reading text file
-text = open("read.txt", encoding="utf-8").read()
-
-# converting to lowercase
-lower_case = text.lower()
-
-# Removing punctuations of the full text
-cleaned_text = lower_case.translate(str.maketrans('', '', string.punctuation))
-
-# splitting text into words
-tokenized_words = cleaned_text.split()
-
-stop_words = ["i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours", "yourself",
-              "yourselves", "he", "him", "his", "himself", "she", "her", "hers", "herself", "it", "its", "itself",
-              "they", "them", "their", "theirs", "themselves", "what", "which", "who", "whom", "this", "that", "these",
-              "those", "am", "is", "are", "was", "were", "be", "been", "being", "have", "has", "had", "having", "do",
-              "does", "did", "doing", "a", "an", "the", "and", "but", "if", "or", "because", "as", "until", "while",
-              "of", "at", "by", "for", "with", "about", "against", "between", "into", "through", "during", "before",
-              "after", "above", "below", "to", "from", "up", "down", "in", "out", "on", "off", "over", "under", "again",
-              "further", "then", "once", "here", "there", "when", "where", "why", "how", "all", "any", "both", "each",
-              "few", "more", "most", "other", "some", "such", "no", "nor", "not", "only", "own", "same", "so", "than",
-              "too", "very", "s", "t", "can", "will", "just", "don", "should", "now"]
-
-# Removing stop words from the tokenized words list
-final_words = []
-for word in tokenized_words:
-    if word not in stop_words:
-        final_words.append(word)
-
-
-emotion_list = []
-with open('emotions.txt', 'r') as file:
-    for line in file:
-        clear_line = line.replace("\n", '').replace(",", '').replace("'", '').strip()
-        word, emotion = clear_line.split(':')
-
-        if word in final_words:
-            emotion_list.append(emotion)
-
-print(emotion_list)
-w = Counter(emotion_list)
-print(w)

videoAnalyzing(expressions).py

-import tensorflow as tf
-from keras.preprocessing import image
-import matplotlib.pyplot as plt
-import cv2
-import numpy as np
-from collections import Counter
-
-Savedmodel = tf.keras.models.load_model('./emotion_lts.h5')
-Savedmodel.summary()
-
-objects = ('Angry', 'Happy', 'Sad', 'Neutral')
-vid = cv2.VideoCapture(0)
-
-def emotion_analysis(emotions):
-    objects = ['Angry', 'Happy', 'Sad', 'Neutral']
-    y_pos = np.arange(len(objects))
-    plt.bar(y_pos, emotions, align='center', alpha=0.9)
-    plt.tick_params(axis='x', which='both', pad=10, width=4, length=10)
-    plt.xticks(y_pos, objects)
-    plt.ylabel('percentage')
-    plt.title('emotion')
-
-
-videoDir = './speechVideo'
-cap = cv2.VideoCapture('./speechVideo/speech.mp4')
-
-while(cap.isOpened()):
-  ret,frame=cap.read()
-  # img = image.load_img(frame,grayscale=True, target_size=(48, 48))
-  frame = cv2.resize(frame,(48,48))
-  frame = cv2.cvtColor(frame,cv2.COLOR_BGR2GRAY)
-  x = image.img_to_array(frame)
-  x = np.expand_dims(x, axis = 0)
-
-  x /= 255
-
-  custom = Savedmodel.predict(x)
-  #print(custom[0])
-  emotion_analysis(custom[0])
-
-  x = np.array(x, 'float32')
-  x = x.reshape([48, 48]);
-
-
-  m=0.000000000000000000001
-  a=custom[0]
-  for i in range(0,len(a)):
-      if a[i]>m:
-          m=a[i]
-          ind=i
-
-
-
-  print('Expression Prediction:',objects[ind])
-
-
-
-
-
-  if cv2.waitKey(20) & 0XFF == ord('q'):
-    break
-
-cap.release()
-cv2.destroyAllWindows()
-