expressions analyzing changers done

Emotion/convert_VideoToAudio.py

+import moviepy.editor
+
+video = moviepy.editor.VideoFileClip('speech.mp4')
+audio = video.audio
+audio.write_audiofile("sample.wav")
+print("Completed the Separates !")

Emotion/emotions.txt

+ 'afflicted': 'Sad',
+ 'agonized': 'Sad',
+ 'amused': 'Happy',
+ 'angry': 'Angry',
+ 'furious': 'Angry',
+ 'irritated': 'Angry',
+ 'upset': 'Angry',
+ 'sore': 'Angry',
+ 'disturbed': 'Angry',
+ 'incensed': 'Angry',
+ 'infuriated': 'Angry',
+ 'wrathful': 'Angry',
+ 'displeased': 'Angry',
+ 'inflamed': 'Angry',
+ 'outraged': 'Angry',
+ 'ill-tempered': 'Angry',
+ 'irritable': 'Angry',
+ 'berserk': 'Angry',
+ 'hateful': 'Angry',
+ 'oppressed': 'Angry',
+ 'anguished': 'Sad',
+ 'animated': 'Happy',
+ 'annoyed': 'Angry',
+ 'appalled': 'Angry',
+ 'beatific': 'Happy',
+ 'bereaved': 'Sad',
+ 'bitter': 'Angry',
+ 'blissful': 'Happy',
+ 'blithe': 'Happy',
+ 'blue': 'Sad',
+ 'boiling': 'Angry',
+ 'bright': 'Happy',
+ 'brisk': 'Happy',
+ 'cheerful': 'Happy',
+ 'cheerless': 'Sad',
+ 'cheery': 'Happy',
+ 'comfortable': 'Happy',
+ 'contented': 'Happy',
+ 'convivial': 'Happy',
+ 'crestfallen': 'Sad',
+ 'cross': 'Angry',
+ 'crushed': 'Sad',
+ 'dark': 'Sad',
+ 'dejected': 'Sad',
+ 'depressed': 'Sad',
+ 'despondent': 'Sad',
+ 'disconsolate': 'Sad',
+ 'discontented': 'Sad',
+ 'disgusted': 'Angry',
+ 'dismal': 'Sad',
+ 'displeased': 'Sad',
+ 'distressed': 'Sad',
+ 'dolorous': 'Sad',
+ 'down': 'Sad',
+ 'downhearted': 'Sad',
+ 'dreadful': 'Sad',
+ 'dreary': 'Sad',
+ 'dull': 'Sad',
+ 'ecstatic': 'Happy',
+ 'elated': 'Happy',
+ 'enraged': 'Angry',
+ 'enthusiastic': 'Happy',
+ 'excited': 'Happy',
+ 'exhilarated': 'Happy',
+ 'festive': 'Happy',
+ 'flat': 'Sad',
+ 'focused': 'Focused',
+ 'free & easy': 'Happy',
+ 'frisky': 'Happy',
+ 'frustrated': 'Angry',
+ 'fuming': 'Angry',
+ 'funereal': 'Sad',
+ 'furious': 'Angry',
+ 'genial': 'Happy',
+ 'glad': 'Happy',
+ 'gleeful': 'Happy',
+ 'gloomy': 'Sad',
+ 'glum': 'Sad',
+ 'grief-stricken': 'Sad',
+ 'grieved': 'Sad',
+ 'guilt': 'Sad',
+ 'happy': 'Happy',
+ 'heartbroken': 'Sad',
+ 'heavyhearted': 'Sad',
+ 'high-spirited': 'Happy',
+ 'hilarious': 'Happy',
+ 'humiliated': 'Sad',
+ 'humorous': 'Happy',
+ 'hurt': 'Sad',
+ 'ill at ease': 'Sad',
+ 'important': 'Happy',
+ 'in a huff': 'Angry',
+ 'in a stew': 'Angry',
+ 'in pain': 'Sad',
+ 'in the dumps': 'Sad',
+ 'incensed': 'Angry',
+ 'indignant': 'Angry',
+ 'inflamed': 'Angry',
+ 'injured': 'Sad',
+ 'irate': 'Angry',
+ 'irritated': 'Angry',
+ 'jaunty': 'Happy',
+ 'jocular': 'Happy',
+ 'jolly': 'Happy',
+ 'jovial': 'Happy',
+ 'joyful': 'Happy',
+ 'joyless': 'Sad',
+ 'joyous': 'Happy',
+ 'jubilant': 'Happy',
+ 'lighthearted': 'Happy',
+ 'lively': 'Happy',
+ 'livid': 'Angry',
+ 'low': 'Sad',
+ 'lucky': 'Happy',
+ 'lugubrious': 'Sad',
+ 'mad': 'Angry',
+ 'melancholy': 'Sad',
+ 'merry': 'Happy',
+ 'mirthful': 'Happy',
+ 'moody': 'Sad',
+ 'moping': 'Sad',
+ 'mournful': 'Sad',
+ 'offended': 'Angry',
+ 'oppressed': 'Sad',
+ 'out of sorts': 'Sad',
+ 'outraged': 'Angry',
+ 'overjoyed': 'Happy',
+ 'pathetic': 'Sad',
+ 'playful': 'Happy',
+ 'pleased': 'Happy',
+ 'proud': 'Happy',
+ 'provoked': 'Angry',
+ 'rageful': 'Angry',
+ 'rapturous': 'Happy',
+ 'regretful': 'Sad',
+ 'remorse': 'Sad',
+ 'resentful': 'Angry',
+ 'rueful': 'Sad',
+ 'sad': 'Sad',
+ 'satisfied': 'Happy',
+ 'saucy': 'Happy',
+ 'serene': 'Happy',
+ 'shamed': 'Sad',
+ 'somber': 'Sad',
+ 'sparkling': 'Happy',
+ 'spirited': 'Happy',
+ 'spiritless': 'Sad',
+ 'sprightly': 'Happy',
+ 'suffering': 'Sad',
+ 'sulky': 'Sad',
+ 'sullen': 'Angry',
+ 'sunny': 'Happy',
+ 'thankful': 'Happy',
+ 'tortured': 'Sad',
+ 'tragic': 'Sad',
+ 'tranquil': 'Happy',
+ 'transported': 'Happy',
+ 'up in arms': 'Angry',
+ 'victimized': 'Sad',
+ 'virulent': 'Angry',
+ 'vivacious': 'Happy',
+ 'woebegone': 'Sad',
+ 'woeful': 'Sad',
+ 'wrathful': 'Angry',
+ 'wrought up': 'Angry',
+ 'aching': 'Sad',
+ 'acrimonious': 'Angry',
+ 'airy': 'Happy',
+ 'buoyant': 'Happy',
+ 'clouded': 'Sad',
+ 'debonair': 'Happy',
+ 'despairing': 'Sad',
+ 'devastated': 'Angry',
+ 'disheartened': 'Sad',
+ 'downcast': 'Sad',
+ 'exultant': 'Happy',
+ 'frowning': 'Sad',
+ 'great': 'Happy',
+ 'hapless': 'Sad',
+ 'hated': 'Hated',
+ 'hostile': 'Angry',
+ 'in despair': 'Sad',
+ 'infuriated': 'Angry',
+ 'inspired': 'Happy',
+ 'laughting': 'Happy',
+ 'low': 'Sad',
+ 'mumpish': 'Sad',
+ 'peaceful': 'Happy',
+ 'piqued': 'Angry',
+ 'piteous': 'Sad',
+ 'self-satisfied': 'Happy',
+ 'sorrowful': 'Sad',
+ 'spirited': 'Sad',
+ 'terrific': 'Happy',
+ 'unhappy': 'Sad',
+ 'worked up': 'Angry'
\ 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 = []
+
+    for i in final_words:
+        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 i in word:
+                    final_emotions = emotion.replace("", '').strip()
+                    emotion_list.append(final_emotions)
+
+    print(emotion_list)
+    w = 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()