face sentiment analysis

Face Sentiment Analysis/TestEmotionDetectorcheck.py

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+import cv2
+import numpy as np
+from keras.models import model_from_json
+import tensorflow.compat.v1 as tf
+
+emotion_dict = {0: "Angry", 1: "Disgusted", 2: "Fearful", 3: "Happy", 4: "Neutral", 5: "Sad", 6: "Surprised"}
+
+# load json and create model
+json_file = open('model/emotion_model.json', 'r')
+loaded_model_json = json_file.read()
+json_file.close()
+emotion_model = model_from_json(loaded_model_json)
+
+# load weights into new model
+emotion_model.load_weights("model/emotion_model.h5")
+print("Loaded model from disk")
+
+# start the webcam feed
+#cap = cv2.VideoCapture(0)
+# start the video
+cap = cv2.VideoCapture("pexels-cottonbro-studio-3198345-1920x1080-25fps.mp4")
+
+while True:
+    # Find haar cascade to draw bounding box around face
+    ret, frame = cap.read()
+    frame = cv2.resize(frame, (1280, 720))
+    if not ret:
+        break
+    face_detector = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_default.xml')
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # detect faces available on camera
+    num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
+
+    # take each face available on the camera and Preprocess it
+    for (x, y, w, h) in num_faces:
+        cv2.rectangle(frame, (x, y-50), (x+w, y+h+10), (0, 255, 0), 4)
+        roi_gray_frame = gray_frame[y:y + h, x:x + w]
+        cropped_img = np.expand_dims(np.expand_dims(cv2.resize(roi_gray_frame, (48, 48)), -1), 0)
+
+        # predict the emotions
+        emotion_prediction = emotion_model.predict(cropped_img)
+        maxindex = int(np.argmax(emotion_prediction))
+        cv2.putText(frame, emotion_dict[maxindex], (x+5, y-20), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2, cv2.LINE_AA)
+
+    cv2.imshow('Emotion Detection', frame)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+cap.release()
+cv2.destroyAllWindows()

Face Sentiment Analysis/TrainEmotionDetectorcheck.py

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+import cv2
+from keras.models import Sequential
+from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten
+from keras.optimizers import Adam
+from keras.preprocessing.image import ImageDataGenerator
+import tensorflow.compat.v1 as tf
+
+# Initialize image data generator with rescaling
+train_data_gen = ImageDataGenerator(rescale=1./255)
+validation_data_gen = ImageDataGenerator(rescale=1./255)
+
+# Preprocess all test images
+train_generator = train_data_gen.flow_from_directory(
+        'data/train',
+        target_size=(48, 48),
+        batch_size=64,
+        color_mode="grayscale",
+        class_mode='categorical')
+
+# Preprocess all train images
+validation_generator = validation_data_gen.flow_from_directory(
+        'data/test',
+        target_size=(48, 48),
+        batch_size=64,
+        color_mode="grayscale",
+        class_mode='categorical')
+
+# create model structure
+emotion_model = Sequential()
+
+emotion_model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48, 48, 1)))
+emotion_model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
+emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
+emotion_model.add(Dropout(0.25))
+
+emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
+emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
+emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
+emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
+emotion_model.add(Dropout(0.25))
+
+emotion_model.add(Flatten())
+emotion_model.add(Dense(1024, activation='relu'))
+emotion_model.add(Dropout(0.5))
+emotion_model.add(Dense(7, activation='softmax'))
+
+cv2.ocl.setUseOpenCL(False)
+
+emotion_model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001, decay=1e-6), metrics=['accuracy'])
+
+# Train the neural network/model
+emotion_model_info = emotion_model.fit_generator(
+        train_generator,
+        steps_per_epoch=28709 // 64,
+        epochs=50,
+        validation_data=validation_generator,
+        validation_steps=7178 // 64)
+
+# save model structure in jason file
+model_json = emotion_model.to_json()
+with open("emotion_model.json", "w") as json_file:
+    json_file.write(model_json)
+
+# save trained model weight in .h5 file
+emotion_model.save_weights('emotion_model.h5')