130 lines
3.7 KiB
Python
130 lines
3.7 KiB
Python
# stdlib dependencies
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from typing import List, Union, Any
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# 3rd party dependencies
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import numpy as np
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from numpy.typing import NDArray
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import cv2
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# project dependencies
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from deepface.commons import package_utils, weight_utils
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from deepface.models.Demography import Demography
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from deepface.commons.logger import Logger
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# dependency configuration
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tf_version = package_utils.get_tf_major_version()
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if tf_version == 1:
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from keras.models import Sequential
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from keras.layers import Conv2D, MaxPooling2D, AveragePooling2D, Flatten, Dense, Dropout
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else:
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import (
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Conv2D,
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MaxPooling2D,
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AveragePooling2D,
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Flatten,
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Dense,
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Dropout,
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)
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# Labels for the emotions that can be detected by the model.
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labels = ["angry", "disgust", "fear", "happy", "sad", "surprise", "neutral"]
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logger = Logger()
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# pylint: disable=line-too-long, disable=too-few-public-methods
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WEIGHTS_URL = "https://github.com/serengil/deepface_models/releases/download/v1.0/facial_expression_model_weights.h5"
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class EmotionClient(Demography):
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"""
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Emotion model class
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"""
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def __init__(self) -> None:
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self.model = load_model()
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self.model_name = "Emotion"
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def _preprocess_image(self, img: NDArray[Any]) -> NDArray[Any]:
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"""
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Preprocess single image for emotion detection
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Args:
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img: Input image (224, 224, 3)
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Returns:
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Preprocessed grayscale image (48, 48)
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"""
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img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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img_gray = cv2.resize(img_gray, (48, 48))
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return img_gray
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def predict(self, img: Union[NDArray[Any], List[NDArray[Any]]]) -> NDArray[Any]:
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"""
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Predict emotion probabilities for single or multiple faces
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Args:
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img: Single image as np.ndarray (224, 224, 3) or
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List of images as List[np.ndarray] or
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Batch of images as np.ndarray (n, 224, 224, 3)
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Returns:
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np.ndarray (n, n_emotions)
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where n_emotions is the number of emotion categories
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"""
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# Preprocessing input image or image list.
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imgs = self._preprocess_batch_or_single_input(img)
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processed_imgs = np.expand_dims(
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np.array([self._preprocess_image(img) for img in imgs]), axis=-1
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)
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# Prediction
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predictions = self._predict_internal(processed_imgs)
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return predictions
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def load_model(
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url: str = WEIGHTS_URL,
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) -> Sequential:
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"""
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Consruct emotion model, download and load weights
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"""
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num_classes = 7
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model = Sequential()
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# 1st convolution layer
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model.add(Conv2D(64, (5, 5), activation="relu", input_shape=(48, 48, 1)))
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model.add(MaxPooling2D(pool_size=(5, 5), strides=(2, 2)))
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# 2nd convolution layer
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model.add(Conv2D(64, (3, 3), activation="relu"))
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model.add(Conv2D(64, (3, 3), activation="relu"))
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model.add(AveragePooling2D(pool_size=(3, 3), strides=(2, 2)))
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# 3rd convolution layer
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model.add(Conv2D(128, (3, 3), activation="relu"))
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model.add(Conv2D(128, (3, 3), activation="relu"))
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model.add(AveragePooling2D(pool_size=(3, 3), strides=(2, 2)))
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model.add(Flatten())
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# fully connected neural networks
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model.add(Dense(1024, activation="relu"))
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model.add(Dropout(0.2))
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model.add(Dense(1024, activation="relu"))
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model.add(Dropout(0.2))
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model.add(Dense(num_classes, activation="softmax"))
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# ----------------------------
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weight_file = weight_utils.download_weights_if_necessary(
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file_name="facial_expression_model_weights.h5", source_url=url
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)
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model = weight_utils.load_model_weights(model=model, weight_file=weight_file)
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return model
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