rename

README.md

@@ -1,13 +1,13 @@
-# IntrospectData's OpenSource FER Application
+# Facial Expression Recognition
 
-Give the function an input and it will return a dictionary of detected faces and emotion predictions.
+Give the function an input and it will return a dictionary of detected faces and expression predictions.
 
 
 ---
 
 ## About
 
-This is a python3 utility for Facial Detection/Emotion Recognition (FER) using Keras and OpenCV.
+This is a python3 cli for Facial Expression Recognition (FER) using Keras and OpenCV.
 
 This project uses the [haarcascade](https://github.com/opencv/opencv/blob/master/data/haarcascades/haarcascade_frontalface_default.xml) xml for facial detection.
 

fer_capture/main.py

@@ -12,7 +12,7 @@
 log = logging.getLogger("fer-capture-log")
 
 #global values
-emotion_dict = {0: "Angry", 1: "Disgust", 2: "Fear", 3: "Happy", 4: "Sad", 5: "Surprise", 6: "Neutral"}
+expression_dict = {0: "Angry", 1: "Disgust", 2: "Fear", 3: "Happy", 4: "Sad", 5: "Surprise", 6: "Neutral"}
 face_casc = os.path.join(os.path.dirname(__file__), "haarcascade_frontalface_default.xml")
 
 default_model_path = wget.download("https://storage.googleapis.com/id-public-read/model.h5")
@@ -56,13 +56,13 @@ def face_check(img, model, show=False):
         model (model): Keras model.
 
     Returns:
-        data (dict): Dictionary containing detected faces along with the predicted emotions.
+        data (dict): Dictionary containing detected faces along with the predicted expressions.
     """
     #begin analysis
     frame = img
     gray, faces = detect_faces(frame)
     data = {"faces" : []}
-    log.info("Beginning emotion recognition.")
+    log.info("Beginning expression recognition.")
 
     for i, (x, y, w, h) in enumerate(faces):
         roi_gray = gray[y:y + h, x:x + w]
@@ -119,7 +119,7 @@ def check_image(image_path, model_path=default_model_path, show=False):
         show (bool): Whether or not to display the media being analyzed.
 
     Returns:
-        data (dict): Dictionary containing detected faces along with the predicted emotions.
+        data (dict): Dictionary containing detected faces along with the predicted expressions.
     """
     img = path_to_img(image_path)
     model = tf.keras.models.load_model(model_path)
@@ -136,7 +136,7 @@ def check_stream(input=0, model_path=default_model_path, show=False):
             when filepath: stream from videofile.
         show (bool): Whether or not to display the media being analyzed.
     Returns:
-        data (list/dict): List of dictionaries containing detected faces along with the predicted emotions from each frame.
+        data (list/dict): List of dictionaries containing detected faces along with the predicted expressions from each frame.
     """
     cap = cv2.VideoCapture(input)
     model = tf.keras.models.load_model(model_path)