Add webcam support and example

Author: sinbycos

EmoPy/examples/fermodel_example_webcam.py

@@ -0,0 +1,41 @@
+#This file allows to perform Emotion detection on frames grabbed from the webcam using OpenCV-Python
+
+import cv2
+import sys
+sys.path.append('../')
+
+#Choose the type of Face Expression Model
+from src.fermodel import FERModel
+
+#Frame Number
+FRAME_NUM = 0
+
+#Choose the type of face detector cascade you want to use
+cascPath = "~/EmoPy/venv/lib/python3.5/site-packages/cv2/data/haarcascade_frontalface_default.xml"
+faceCascade = cv2.CascadeClassifier(cascPath)
+#Specify the camera which you want to use. The default argument is '0'
+video_capture = cv2.VideoCapture(0)
+
+while True:
+	#Capture frame-by-frame
+	ret, frame = video_capture.read()
+	#Save the captured frame on disk
+	file = '~/EmoPy/models/examples/image_data/image.jpg'
+	cv2.imwrite(file, frame)
+	#Can choose other target emotions from the emotion subset defined in fermodel.py in src directory. The function
+	# defined as `def _check_emotion_set_is_supported(self):`
+	target_emotions = ['calm', 'anger', 'happiness']
+	model = FERModel(target_emotions, verbose=True)
+	frameString = model.predict(file)
+	#Display frame number and emotion 
+	cv2.putText(frame, 'Frame:' + str(FRAME_NUM), (10, 40), cv2.FONT_HERSHEY_COMPLEX_SMALL, 3, (0, 0, 255), 2, cv2.LINE_AA)
+	cv2.putText(frame, frameString, (10,450), cv2.FONT_HERSHEY_COMPLEX_SMALL, 3, (0,255,0), 2, cv2.LINE_AA)
+	cv2.imshow('Video', frame)
+	cv2.waitKey(1)
+	FRAME_NUM += 1
+	#Press Esc to exit the window
+	if cv2.waitKey(1) & 0xFF == 27:
+		break
+#Closes all windows
+cv2.destroyAllWindows()
+

EmoPy/src/fermodel.py

@@ -3,7 +3,6 @@
 from scipy import misc
 import numpy as np
 import json
-from pkg_resources import resource_filename
 
 class FERModel:
     """
@@ -57,7 +56,9 @@ def predict(self, image_file):
         resized_image = cv2.resize(gray_image, self.target_dimensions, interpolation=cv2.INTER_LINEAR)
         final_image = np.array([np.array([resized_image]).reshape(list(self.target_dimensions)+[self.channels])])
         prediction = self.model.predict(final_image)
-        self._print_prediction(prediction[0])
+        ### Return the dominant expression
+        dominant_expression = self._print_prediction(prediction[0])
+        return dominant_expression
 
     def _check_emotion_set_is_supported(self):
         """
@@ -91,10 +92,11 @@ def _choose_model_from_target_emotions(self):
         model_indices = [self.emotion_index_map[emotion] for emotion in self.target_emotions]
         sorted_indices = [str(idx) for idx in sorted(model_indices)]
         model_suffix = ''.join(sorted_indices)
-        model_file = 'models/conv_model_%s.hdf5' % model_suffix
-        emotion_map_file = 'models/conv_emotion_map_%s.json' % model_suffix
-        emotion_map = json.loads(open(resource_filename('EmoPy',emotion_map_file)).read())
-        return load_model(resource_filename('EmoPy',model_file)), emotion_map
+        #Modify the path to choose the model file and the emotion map that you want to use
+        model_file = '~/EmoPy/models/conv_model_%s.hdf5' % model_suffix
+        emotion_map_file = '~/EmoPy/models/conv_emotion_map_%s.json' % model_suffix
+        emotion_map = json.loads(open(emotion_map_file).read())
+        return load_model(model_file), emotion_map
 
     def _print_prediction(self, prediction):
         normalized_prediction = [x/sum(prediction) for x in prediction]
@@ -105,5 +107,7 @@ def _print_prediction(self, prediction):
             if dominant_emotion_index == self.emotion_map[emotion]:
                 dominant_emotion = emotion
                 break
-        print('Dominant emotion: %s' % dominant_emotion)
-        print()
+        # print('Dominant emotion: %s' % dominant_emotion)
+        # print()
+        return dominant_emotion
+