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EvaluateEmotionDetector.py

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+
+import numpy as np
+from keras.models import model_from_json
+import matplotlib.pyplot as plt
+from keras.preprocessing.image import ImageDataGenerator
+from sklearn.metrics import confusion_matrix, classification_report,ConfusionMatrixDisplay
+
+
+emotion_dict = {0: "Happy", 1: "Neutral", 2: "Sad"}
+
+# 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")
+
+# Initialize image data generator with rescaling
+test_data_gen = ImageDataGenerator(rescale=1./255)
+
+# Preprocess all test images
+test_generator = test_data_gen.flow_from_directory(
+        'data/test',
+        target_size=(48, 48),
+        batch_size=64,
+        color_mode="grayscale",
+        class_mode='categorical',
+        classes=['Happy', 'Neutral', 'Sad'])
+
+# do prediction on test data
+predictions = emotion_model.predict(test_generator)
+
+# see predictions
+for result in predictions:
+    max_index = int(np.argmax(result))
+    print(emotion_dict[max_index])
+
+print("-----------------------------------------------------------------")
+# confusion matrix
+c_matrix = confusion_matrix(test_generator.classes, predictions.argmax(axis=1))
+print(c_matrix)
+cm_display = ConfusionMatrixDisplay(confusion_matrix=c_matrix, display_labels=list(emotion_dict.values()))
+cm_display.plot(cmap=plt.cm.Blues)
+plt.show()
+
+# Classification report
+print("-----------------------------------------------------------------")
+print(classification_report(test_generator.classes, predictions.argmax(axis=1)))
+

TrainEmotionDetector.py

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+
+# import required packages
+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
+from sklearn.utils.class_weight import compute_class_weight
+import numpy as np
+
+# 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')
+
+
+
+# Calculate class weights
+class_labels = train_generator.classes
+class_weights = compute_class_weight(class_weight = "balanced", classes= np.unique(class_labels), y= class_labels)
+class_weight_dict = dict(enumerate(class_weights))
+
+
+# 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(3, activation='softmax'))
+
+cv2.ocl.setUseOpenCL(False)
+
+emotion_model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=0.0001), metrics=['accuracy'])
+
+# Train the neural network/model
+emotion_model_info = emotion_model.fit_generator(
+        train_generator,
+        steps_per_epoch=len(train_generator) // 64,
+        epochs=100,
+        validation_data=validation_generator,
+        validation_steps=7178 // 64,
+        class_weight=class_weight_dict)
+
+
+# save model structure in jason file
+model_json = emotion_model.to_json()
+with open("model/emotion_model.json", "w") as json_file:
+    json_file.write(model_json)
+
+# save trained model weight in .h5 file
+emotion_model.save_weights('model/emotion_model.h5')

main.py

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+import cv2
+import numpy as np
+from keras.models import model_from_json
+from collections import Counter
+import time
+
+emotion_dict = {0: "Happy", 1: "Neutral/Sad", 2: "Sad"}
+detected_emotions = []  # List to store detected emotions
+
+# Function to reset the list of detected emotions
+def reset_detected_emotions():
+    global detected_emotions
+    detected_emotions = []
+
+# Function to process the frame and update the detected emotions
+def process_frame(cap2, emotion_model):
+    global detected_emotions
+    ret, frame = cap2.read()
+    frame = cv2.resize(frame, (1280, 720))
+
+    face_detector = cv2.CascadeClassifier('emotion/haarcascades/haarcascade_frontalface_default.xml')
+    gray_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    num_faces = face_detector.detectMultiScale(gray_frame, scaleFactor=1.3, minNeighbors=5)
+
+    for (x, y, w, h) in num_faces:
+        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)
+
+        emotion_prediction = emotion_model.predict(cropped_img)
+        maxindex = int(np.argmax(emotion_prediction))
+        detected_emotions.append(emotion_dict[maxindex])
+
+# Function to get the most common emotion from the list
+def get_most_common_emotion():
+    global detected_emotions
+    if detected_emotions:
+        counter = Counter(detected_emotions)
+        most_common_emotion = counter.most_common(1)[0][0]
+        return most_common_emotion
+    else:
+        return None
+
+def call_me():
+    # Load the emotion model
+    json_file = open('emotion/model/emotion_model.json', 'r')
+    loaded_model_json = json_file.read()
+    json_file.close()
+    emotion_model = model_from_json(loaded_model_json)
+    emotion_model.load_weights("emotion/model/emotion_model.h5")
+    print("Loaded model from disk")
+        
+    # Start the webcam feed
+    cap2 = cv2.VideoCapture(0)
+    
+    duration = 5  # seconds
+    end_time = time.time() + duration
+
+    # Example usage of the functions
+    while time.time() < end_time:
+        process_frame(cap2, emotion_model)
+
+    cap2.release()
+    # print(cap)
+    cv2.destroyAllWindows()
+
+    # Get the most common emotion detected during the session
+    most_common_emotion = get_most_common_emotion()
+    return most_common_emotion
+    # print("Most Common Emotion:", most_common_emotion)
+    # print("User's current Emotion:", most_common_emotion)
+   
+    

model/emotion_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:95033f96d0bbd3f695b4bd4ee95e442d3cd98f80c6bcef745d442238352adec2
+size 9397216

model/emotion_model.json

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