├── README.md
└── emotion dectection.py


/README.md:
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1 | # emotion-detection


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/emotion dectection.py:
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 1 | import numpy as np
 2 | import cv2
 3 | import argparse
 4 | import matplotlib.pyplot as plt
 5 | import cv2
 6 | from tensorflow.keras.models import Sequential
 7 | from tensorflow.keras.layers import Dense, Dropout, Flatten
 8 | from tensorflow.keras.layers import Conv2D
 9 | from tensorflow.keras.optimizers import Adam
10 | from tensorflow.keras.layers import MaxPooling2D
11 | from tensorflow.keras.preprocessing.image import ImageDataGenerator
12 | import os
13 | os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
14 | 
15 | mode = "display"
16 | 
17 | # Create the model
18 | model = Sequential()
19 | 
20 | model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48,48,1)))
21 | model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
22 | model.add(MaxPooling2D(pool_size=(2, 2)))
23 | model.add(Dropout(0.25))
24 | 
25 | model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
26 | model.add(MaxPooling2D(pool_size=(2, 2)))
27 | model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
28 | model.add(MaxPooling2D(pool_size=(2, 2)))
29 | model.add(Dropout(0.25))
30 | 
31 | model.add(Flatten())
32 | model.add(Dense(1024, activation='relu'))
33 | model.add(Dropout(0.5))
34 | model.add(Dense(7, activation='softmax'))
35 | 
36 | 
37 | def emotion_recog(frame):
38 |     model.load_weights('model.h5')
39 | 
40 |     # prevents openCL usage and unnecessary logging messages
41 |     cv2.ocl.setUseOpenCL(False)
42 | 
43 |     # dictionary which assigns each label an emotion (alphabetical order)
44 |     emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
45 | 
46 |     # frame = cv2.imread("image1.jpg")
47 |     facecasc = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
48 |     gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
49 |     faces = facecasc.detectMultiScale(gray,scaleFactor=1.3, minNeighbors=5)
50 | 
51 |     for (x, y, w, h) in faces:
52 |         cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (255, 0, 255), 3)
53 |         roi_gray = gray[y:y + h, x:x + w]
54 |         cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray, (48, 48)), -1), 0)
55 |         prediction = model.predict(cropped_img)
56 |         maxindex = int(np.argmax(prediction))
57 |         cv2.putText(frame, emotion_dict[maxindex], (x+20, y-60), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
58 | 
59 |     # cv2_imshow(frame)
60 |     return frame
61 | input = cv2.imread("E:\elon musk.jpg")
62 | output = emotion_recog(input)
63 | cv2_imshow(output)


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