def analysisEmotion():
logger.info('Emotion Request')
try:
imageData = request.get_json()['image']
emotion = prediction_path(imageData)
emotion_request_json = jsonify({'analysis_result': emotion})
logger.info('Result : ' + emotion)
return emotion_request_json
except Exception as e:
logger.error(e)
def video_emotions(path):
# load keras model
emotion_model_path = 'models/model.hdf5'
detection_model_path = 'haarcascades/haarcascade_frontalface_default.xml'
face_detection = cv2.CascadeClassifier(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
print('Model loaded')
# save location for image
save_loc = 'save_loc/1.jpg'
# numpy matrix for stroing prediction
result = np.array((1, 7))
# for knowing whether prediction has started or not
once = False
# list of given emotions
EMOTIONS = [
'Angry', 'Disgusted', 'Fearful', 'Happy', 'Sad', 'Surprised', 'Neutral'
]
# store the emoji coreesponding to different emotions
emoji_faces = []
for index, emotion in enumerate(EMOTIONS):
emoji_faces.append(cv2.imread('emojis/' + emotion.lower() + '.png',
-1))
# set video capture device , webcam in this case
video_capture = cv2.VideoCapture(path)
video_capture.set(3, 640) # WIDTH
video_capture.set(4, 480) # HEIGHT
# save current time
prev_time = time.time()
# start webcam feed
while True:
# Capture frame-by-frame
ret, frame = video_capture.read()
cv2.imwrite(save_loc, frame)
frame = prediction_path(save_loc)
# 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()
def analysisEmotionAndReturnRecommends():
logger.info('Recommendations Request')
try:
imageData = request.get_json()['image']
path = request.get_json()['path']
emotion = prediction_path(imageData)
recommended_list = recommend_by_emotion(emotion, path)
emotion_request_json = jsonify({
'header': 'EMOTIONAL_STATE',
'analysis_result': emotion,
'recommended_list': recommended_list
})
logger.info('Result : ' + emotion)
logger.info('Recommends : ' + recommended_list)
return emotion_request_json
except Exception as e:
logger.error(e)
def run_detection_path(path_img, path_text):
prediction_path(path_img, path_text)
from prediction_utils import prediction_path
import sys
# for running emotion detection
def read_emotion(path):
get_current_emotion(path)
# to run emotion detection on image saved on disk
def run_detection_path(path_img, path_text):
prediction_path(path_img, path_text)
if __name__ == '__main__':
if len(sys.argv) == 3:
prediction_path(sys.argv[1], sys.argv[2])
elif len(sys.argv) == 4:
prediction_path(sys.argv[1], sys.argv[2], sys.argv[3])
else:
print(
"--------------------\nEmotion Recognition:\n--------------------")
print("python ./__init__.py [path_img] [path_text]")
print(
"[path_img]: the path of the image of which emotion need be detected"
)
print(
"[path_text]: the path of the text file in which result will be stored\n"
)
print("[name_model]: (optional) the name of the model to use\n")
def run_detection_path(path):
prediction_path(path)
def run_detection_path(path):
img = prediction_path(path)
imgplot = plt.imshow(img)
plt.show()