|
|
|
import os |
|
|
|
|
|
import gdown |
|
import numpy as np |
|
import cv2 |
|
|
|
|
|
from deepface.commons import package_utils, folder_utils |
|
from deepface.models.Demography import Demography |
|
from deepface.commons import logger as log |
|
|
|
logger = log.get_singletonish_logger() |
|
|
|
|
|
|
|
|
|
|
|
tf_version = package_utils.get_tf_major_version() |
|
|
|
if tf_version == 1: |
|
from keras.models import Sequential |
|
from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout |
|
else: |
|
from tensorflow.keras.models import Sequential |
|
from tensorflow.keras.layers import ( |
|
Conv2D, |
|
MaxPooling2D, |
|
AveragePooling2D, |
|
Flatten, |
|
Dense, |
|
Dropout, |
|
) |
|
|
|
|
|
|
|
labels = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"] |
|
|
|
|
|
class EmotionClient(Demography): |
|
""" |
|
Emotion model class |
|
""" |
|
|
|
def __init__(self): |
|
self.model = load_model() |
|
self.model_name = "Emotion" |
|
|
|
def predict(self, img: np.ndarray) -> np.ndarray: |
|
img_gray = cv2.cvtColor(img[0], cv2.COLOR_BGR2GRAY) |
|
img_gray = cv2.resize(img_gray, (48, 48)) |
|
img_gray = np.expand_dims(img_gray, axis=0) |
|
|
|
emotion_predictions = self.model.predict(img_gray, verbose=0)[0, :] |
|
return emotion_predictions |
|
|
|
|
|
def load_model( |
|
url="https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5", |
|
) -> Sequential: |
|
""" |
|
Consruct emotion model, download and load weights |
|
""" |
|
|
|
num_classes = 7 |
|
|
|
model = Sequential() |
|
|
|
|
|
model.add(Conv2D(64, (5, 5), activation="relu", input_shape=(48, 48, 1))) |
|
model.add(MaxPooling2D(pool_size=(5, 5), strides=(2, 2))) |
|
|
|
|
|
model.add(Conv2D(64, (3, 3), activation="relu")) |
|
model.add(Conv2D(64, (3, 3), activation="relu")) |
|
model.add(AveragePooling2D(pool_size=(3, 3), strides=(2, 2))) |
|
|
|
|
|
model.add(Conv2D(128, (3, 3), activation="relu")) |
|
model.add(Conv2D(128, (3, 3), activation="relu")) |
|
model.add(AveragePooling2D(pool_size=(3, 3), strides=(2, 2))) |
|
|
|
model.add(Flatten()) |
|
|
|
|
|
model.add(Dense(1024, activation="relu")) |
|
model.add(Dropout(0.2)) |
|
model.add(Dense(1024, activation="relu")) |
|
model.add(Dropout(0.2)) |
|
|
|
model.add(Dense(num_classes, activation="softmax")) |
|
|
|
|
|
|
|
home = folder_utils.get_deepface_home() |
|
|
|
if os.path.isfile(home + "/.deepface/weights/facial_expression_model_weights.h5") != True: |
|
logger.info("facial_expression_model_weights.h5 will be downloaded...") |
|
|
|
output = home + "/.deepface/weights/facial_expression_model_weights.h5" |
|
gdown.download(url, output, quiet=False) |
|
|
|
model.load_weights(home + "/.deepface/weights/facial_expression_model_weights.h5") |
|
|
|
return model |
|
|