def __init__(self,model_detection):
self.model_detection = model_detection
self.face_detection = load_detection_model(self.model_detection)
else:
print("----There is this folder! ----")
mkdir("pic_save")
detection_model_path = '../trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = '../trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.pb'
emotion_labels = get_labels('fer2013')
# hyper-parameters for bounding boxes shape
frame_window = 10
emotion_offsets = (20, 40)
# loading models
face_detection = load_detection_model(detection_model_path)
# emotion_classifier = load_model(emotion_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size =(64,64) #???1,3 represent what
# starting lists for calculating modes
emotion_window = []
# starting video streaming
cv2.namedWindow('window_frame',cv2.WINDOW_NORMAL)
cv2.resizeWindow('window_frame',500,500)
video_capture = cv2.VideoCapture(0)
count=0
emotion_mode=""
#
def process_image(image, filename, model_path):
try:
# parameters for loading data and images
detection_model_path = './trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = model_path + '/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
gender_model_path = model_path + '/gender_models/simple_CNN.81-0.96.hdf5'
emotion_labels = get_labels('fer2013')
gender_labels = get_labels('imdb')
font = cv2.FONT_HERSHEY_SIMPLEX
# hyper-parameters for bounding boxes shape
gender_offsets = (30, 60)
gender_offsets = (10, 10)
emotion_offsets = (20, 40)
emotion_offsets = (0, 0)
# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
gender_classifier = load_model(gender_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
gender_target_size = gender_classifier.input_shape[1:3]
# loading images
image_array = np.fromstring(image, np.uint8)
unchanged_image = cv2.imdecode(image_array, cv2.IMREAD_UNCHANGED)
rgb_image = cv2.cvtColor(unchanged_image, cv2.COLOR_BGR2RGB)
gray_image = cv2.cvtColor(unchanged_image, cv2.COLOR_BGR2GRAY)
faces = detect_faces(face_detection, gray_image)
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, gender_offsets)
rgb_face = rgb_image[y1:y2, x1:x2]
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
rgb_face = cv2.resize(rgb_face, (gender_target_size))
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
rgb_face = preprocess_input(rgb_face, False)
rgb_face = np.expand_dims(rgb_face, 0)
gender_prediction = gender_classifier.predict(rgb_face)
gender_label_arg = np.argmax(gender_prediction)
gender_text = gender_labels[gender_label_arg]
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
emotion_text = emotion_labels[emotion_label_arg]
if gender_text == gender_labels[0]:
color = (0, 0, 255)
else:
color = (255, 0, 0)
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, gender_text, color, 0, -20, 1, 2)
draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1, 2)
except Exception as err:
logging.error('Error in emotion gender processor: "{0}"'.format(err))
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
dirname = 'result'
if not os.path.exists(dirname):
os.mkdir(dirname)
cv2.imwrite(os.path.join(dirname, filename), bgr_image)
def image_emotion_gender(image):
bgr_image = image
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
detection_model_path = 'trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = 'trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
gender_model_path = 'trained_models/gender_models/simple_CNN.81-0.96.hdf5'
emotion_labels = get_labels('fer2013')
gender_labels = get_labels('imdb')
font = cv2.FONT_HERSHEY_SIMPLEX
# hyper-parameters for bounding boxes shape
gender_offsets = (30, 60)
gender_offsets = (10, 10)
emotion_offsets = (20, 40)
emotion_offsets = (0, 0)
# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
gender_classifier = load_model(gender_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
gender_target_size = gender_classifier.input_shape[1:3]
faces = detect_faces(face_detection, gray_image)
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, gender_offsets)
rgb_face = rgb_image[y1:y2, x1:x2]
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
rgb_face = cv2.resize(rgb_face, (gender_target_size))
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
rgb_face = preprocess_input(rgb_face, False)
rgb_face = np.expand_dims(rgb_face, 0)
gender_prediction = gender_classifier.predict(rgb_face)
gender_label_arg = np.argmax(gender_prediction)
gender_text = gender_labels[gender_label_arg]
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
emotion_text = emotion_labels[emotion_label_arg]
if gender_text == gender_labels[0]:
color = (0, 0, 255)
else:
color = (255, 0, 0)
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, gender_text, color, 0, -20, 1, 2)
draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1, 2)
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
return bgr_image
def process_image(image):
K.clear_session()
# parameters for loading data and images
if sys.path[1] == '/app':
#load model for heroku
detection_model_path = sys.path[
1] + '/trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = sys.path[
1] + '/trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
else:
detection_model_path = sys.path[
-1] + '/trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = sys.path[
-1] + '/trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
emotion_labels = get_labels('fer2013')
font = cv2.FONT_HERSHEY_SIMPLEX
# hyper-parameters for bounding boxes shape
emotion_offsets = (20, 40)
emotion_offsets = (0, 0)
# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
# loading images
image_array = np.fromstring(image, np.uint8)
unchanged_image = cv2.imdecode(image_array, cv2.IMREAD_UNCHANGED)
rgb_image = cv2.cvtColor(unchanged_image, cv2.COLOR_BGR2RGB)
gray_image = cv2.cvtColor(unchanged_image, cv2.COLOR_BGR2GRAY)
gray_image = np.squeeze(gray_image)
gray_image = gray_image.astype('uint8')
faces = detect_faces(face_detection, gray_image)
emotion_text_arr = []
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
continue
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
emotion_text = emotion_labels[emotion_label_arg]
emotion_text_arr.append(emotion_text)
color = (255, 0, 0)
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1,
2)
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
K.clear_session()
return (bgr_image, emotion_text_arr)
def race_emotion(image_path, save_path=None, task='save', faces):
image_path = image_path #'../test_images'#sys.argv[1]
detection_model_path = '../trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = '../trained_models/emotion_models/fer2013_mini_XCEPTION.102-0.66.hdf5'
gender_model_path = '../trained_models/gender_models/simple_CNN.81-0.96.hdf5'
emotion_labels = get_labels('../trained_models/fer2013')
gender_labels = get_labels('../trained_models/imdb')
font = cv2.FONT_HERSHEY_SIMPLEX
base = os.path.basename(image_path)
name = os.path.splitext(base)[0]
# hyper-parameters for bounding boxes shape
gender_offsets = (30, 60)
gender_offsets = (10, 10)
emotion_offsets = (20, 40)
emotion_offsets = (0, 0)
# loading models
face_detection = load_detection_model(detection_model_path)
emotion_classifier = load_model(emotion_model_path, compile=False)
gender_classifier = load_model(gender_model_path, compile=False)
# getting input model shapes for inference
emotion_target_size = emotion_classifier.input_shape[1:3]
gender_target_size = gender_classifier.input_shape[1:3]
# loading images
rgb_image = load_image(image_path, grayscale=False)
gray_image = load_image(image_path, grayscale=True)
gray_image = np.squeeze(gray_image)
gray_image = gray_image.astype('uint8')
#faces = detect_faces(face_detection, gray_image)
faces = faces
i = 0
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, gender_offsets)
rgb_face = rgb_image[y1:y2, x1:x2]
x1, x2, y1, y2 = apply_offsets(face_coordinates, emotion_offsets)
gray_face = gray_image[y1:y2, x1:x2]
try:
rgb_face = cv2.resize(rgb_face, (gender_target_size))
gray_face = cv2.resize(gray_face, (emotion_target_size))
except:
print '=' * 10 + 'exception in resize'
continue
#print 'exception in resize' #continue
rgb_face = preprocess_input(rgb_face, False)
rgb_face = np.expand_dims(rgb_face, 0)
gender_prediction = gender_classifier.predict(rgb_face)
gender_label_arg = np.argmax(gender_prediction)
gender_text = gender_labels[gender_label_arg]
gray_face = preprocess_input(gray_face, True)
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_label_arg = np.argmax(emotion_classifier.predict(gray_face))
emotion_text = emotion_labels[emotion_label_arg]
if gender_text == gender_labels[0]:
color = (0, 0, 255)
else:
color = (255, 0, 0)
crop = rgb_image[y1:y2, x1:x2]
crop = cv2.cvtColor(crop, cv2.COLOR_RGB2BGR)
#race=find_race(crop,gender_text)
print('emotion and race are:', emotion_text, race)
#cv2.imwrite('images/'+str(i)+'.png', crop)
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, color, 0, -20, 1, 2)
#draw_text(face_coordinates, rgb_image, gender_text, color, 0, -20, 1, 2)
draw_text(face_coordinates, rgb_image, emotion_text, color, 0, -50, 1,
2)
i = i + 1
if task == 'save':
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
cv2.imwrite('../OUT/' + name + '.png', bgr_image)