initial commit

README.md

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+# emotion_recognition

application.py

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+import cv2
+import time
+from keras.models import model_from_json
+
+
+emotion_labels = ['anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise']
+cascPath = "./haarcascade_frontalface_default.xml"
+
+faceCascade = cv2.CascadeClassifier(cascPath)
+
+# load json and create model arch
+json_file = open('./models/fer2013_Model.json', 'r')
+loaded_model_json = json_file.read()
+json_file.close()
+model = model_from_json(loaded_model_json)
+
+# load weights into new model
+model.load_weights('./models/fer2013_Model_weights.keras')
+
+
+def predict_emotion(face_image_gray):  # a single cropped face
+    resized_img = cv2.resize(face_image_gray, (48, 48), interpolation=cv2.INTER_AREA)
+    image = resized_img.reshape(1, 48, 48)
+    list_of_list = model.predict(image, batch_size=1, verbose=1)
+    anger, disgust, fear, joy, neutral, sadness, surprise = [prob for lst in list_of_list for prob in lst]
+    return [anger, disgust, fear, joy, neutral, sadness, surprise]
+
+
+video_capture = cv2.VideoCapture(0)
+while True:
+    # Capture frame-by-frame
+    ret, frame = video_capture.read()
+    img_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY, 1)
+
+    faces = faceCascade.detectMultiScale(
+        img_gray,
+        scaleFactor=1.1,
+        minNeighbors=5,
+        minSize=(48, 48),
+        flags=cv2.CASCADE_SCALE_IMAGE
+    )
+
+    # Draw a rectangle around the faces
+    for (x, y, w, h) in faces:
+        face_image_gray = img_gray[y:y+h, x:x+w]
+        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)
+
+        # Predict emotion probabilities
+        emotion_probs = predict_emotion(face_image_gray)
+        predicted_emotion = emotion_labels[emotion_probs.index(max(emotion_probs))]
+
+        # Embed predicted emotion label on the frame
+        label_text = f'Emotion: {predicted_emotion}'
+        cv2.putText(frame, label_text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)
+
+        with open('emotion.txt', 'a') as f:
+            f.write('{},{},{},{},{},{},{}\n'.format(time.time(), *emotion_probs))
+
+    # Display the resulting frame
+    cv2.imshow('Video', frame)
+
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+
+# When everything is done, release the capture
+video_capture.release()
+cv2.destroyAllWindows()

cleaning_data.py

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+import os
+import glob
+from PIL import Image
+import PIL
+
+
+# Function to get the class label from the filename
+def get_class_label(filename):
+    class_names = ['anger', 'disgust', 'fear', 'joy', 'neutral', 'sadness', 'surprise']
+    for class_name in class_names:
+        if class_name in filename:
+            return class_name
+    return None
+
+
+# Defining the source directories for train and test data
+train_directory = 'C:/Users/takav/PycharmProjects/realTime/data_split/train'
+test_directory = 'C:/Users/takav/PycharmProjects/realTime/data_split/test'
+
+
+# Function to remove unidentified images in a directory
+def remove_unidentified_images(directory):
+    for class_name in os.listdir(directory):
+        class_dir = os.path.join(directory, class_name)
+        if os.path.isdir(class_dir):
+            for image_file in glob.glob(os.path.join(class_dir, '*.png')):
+                try:
+                    # Open the image using PIL to check if it is a valid image
+                    img = Image.open(image_file)
+                except (IOError, OSError, PIL.UnidentifiedImageError):
+                    print(f"Removed invalid image: {image_file}")
+                    os.remove(image_file)
+
+
+# Remove unidentified images from the train and test directories
+remove_unidentified_images(train_directory)
+remove_unidentified_images(test_directory)