def frame_process(base64_string_list):
# starting lists for calculating modes
ls = []
###############################################
detection_model_path = os.getcwd(
) + '/trained_models/detection_models/haarcascade_frontalface_default.xml'
emotion_model_path = os.getcwd() + \
'/trained_models/emotion_models/emotion_modelsfer2013_mini_XCEPTION.82-0.80.hdf5'
emotion_labels = {0: 'confused', 1: 'neutral', 2: 'confident'}
# hyper-parameters for bounding boxes shape
frame_window = 10
emotion_offsets = (20, 40)
# loading models
face_detection = inference.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]
#################################################################
for base64_string in base64_string_list:
imgdata = base64.b64decode(base64_string)
image = Image.open(io.BytesIO(imgdata))
gray_image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2GRAY)
rgb_image = cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB)
faces = inference.detect_faces(face_detection, gray_image)
for face_coordinates in faces:
print(face_coordinates)
x1, x2, y1, y2 = inference.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_prediction = emotion_classifier.predict(gray_face)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
ls.append(emotion_text)
K.clear_session()
return ls
def predict_gender(name):
#loading data and images
image_path = source + name
m = re.search('.*(?=-)', name)
if m:
found = m.group(0)
else:
found = name
result = {'male': 0, 'female': 0, 'domain': found}
try:
rgb_image = load_image(image_path, grayscale=False)
except:
print('3. Doesn"t open')
if (os.path.isfile(image_path)):
os.remove(image_path)
to_remove.append(name)
return result
gray_image = load_image(image_path, grayscale=True)
gray_image = np.squeeze(gray_image)
gray_image = gray_image.astype('uint8')
#face and gender detection
faces = detect_faces(face_detection, gray_image)
if (len(faces) == 0):
print('no faces')
to_remove.append(name)
for face_coordinates in faces:
x1, x2, y1, y2 = apply_offsets(face_coordinates, gender_offsets)
rgb_face = rgb_image[y1:y2, x1:x2]
try:
rgb_face = cv2.resize(rgb_face, (gender_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]
if gender_text == gender_labels[0]:
result['female'] += 1
else:
result['male'] += 1
return result
def ExpressionDetection (image):
detection_model_path = "%s/%s/haarcascade_frontalface_default.xml"%(PATH,"haar_cascade")
emotion_model_path = '%s/vision/models/fer2013_mini_XCEPTION.102-0.66.hdf5'%(PATH)
emotion_labels = get_labels('fer2013')
# 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]
emotion_offsets = (20, 40)
#read image
# print 'dddddddd',gray_image
#gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# rgb_image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
gray_image=cv2.imread(image,0)
faces = detect_faces(face_detection,gray_image)
# print 'qqqqqqqq',faces
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)
# print 'eeeeee',gray_face
gray_face = np.expand_dims(gray_face, 0)
gray_face = np.expand_dims(gray_face, -1)
emotion_prediction = emotion_classifier.predict(gray_face)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
# print 'the emotion probability is:',emotion_probability
# print 'the emotion is:',emotion_label_arg
# print 'the predicted emotion is:',emotion_text
return emotion_text
for foo in camera.capture_continuous(stream,
format('bgr'),
use_video_port=True):
bgr_image = np.fromstring(stream.getvalue(), dtype=np.uint8)
# bgr_image = video_capture.read()[1]
#bgr_image = np.empty((480 * 640 * 3,), dtype=np.uint8)
#camera.capture(bgr_image, format='bgr', use_video_port=True)
bgr_image = bgr_image.reshape((480, 640, 3))
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
faces = detect_faces(face_detection, gray_image)
for face_coordinates in faces:
count = count + 1
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) # 类似于标准化的操作(64,64)
gray_face = np.expand_dims(gray_face, 0) # (1,64,64)
gray_face = np.expand_dims(gray_face, -1) # (1,64,64,1)
emotion_prediction = emotion_classifier.predict(gray_face)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
emotion_window.append(emotion_text)
if len(emotion_window) > frame_window:
def emotion():
# parameters for loading data and images
emotion_text = ""
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'
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 = emotion_classifier.input_shape[1:3]
# starting lists for calculating modes
emotion_window = []
# starting video streaming
cv2.namedWindow('window_frame')
video_capture = cv2.VideoCapture(0)
while True:
bgr_image = video_capture.read()[1]
gray_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2GRAY)
rgb_image = cv2.cvtColor(bgr_image, cv2.COLOR_BGR2RGB)
faces = detect_faces(face_detection, gray_image)
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_prediction = emotion_classifier.predict(gray_face)
emotion_probability = np.max(emotion_prediction)
emotion_label_arg = np.argmax(emotion_prediction)
emotion_text = emotion_labels[emotion_label_arg]
emotion_window.append(emotion_text)
if len(emotion_window) > frame_window:
emotion_window.pop(0)
try:
emotion_mode = mode(emotion_window)
except:
continue
if emotion_text == 'angry':
color = emotion_probability * np.asarray((255, 0, 0))
elif emotion_text == 'sad':
color = emotion_probability * np.asarray((0, 0, 255))
elif emotion_text == 'happy':
color = emotion_probability * np.asarray((255, 255, 0))
elif emotion_text == 'surprise':
color = emotion_probability * np.asarray((0, 255, 255))
else:
color = emotion_probability * np.asarray((0, 255, 0))
color = color.astype(int)
color = color.tolist()
draw_bounding_box(face_coordinates, rgb_image, color)
draw_text(face_coordinates, rgb_image, emotion_mode, color, 0, -45,
1, 1)
if (emotion_text):
return emotion_text
break
bgr_image = cv2.cvtColor(rgb_image, cv2.COLOR_RGB2BGR)
cv2.imshow('window_frame', bgr_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
