added python files for detect emotions

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()