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129 строки
3.8 KiB
Python
129 строки
3.8 KiB
Python
import cv2
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import ipywidgets
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import numpy as np
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import os
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from tensorflow.keras.models import load_model
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from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
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from tensorflow.keras.preprocessing.image import img_to_array
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from IPython.display import display
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from deepface import DeepFace
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from retinaface import RetinaFace
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# Module-wide storage for lazily loaded mask detection model
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mask_detection_model = None
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def facial_expression_analysis(img_path):
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result = {"filename": img_path}
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# Find (multiple) faces in the image and cut them
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faces = RetinaFace.extract_faces(img_path)
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# If no faces are found, we return an empty dictionary
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if len(faces) == 0:
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return result
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# Find the biggest face image in the detected ones
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maxface = max(faces, key=lambda f: f.shape[0] * f.shape[1])
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# Determine whether the face wears a mask
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result["wears_mask"] = wears_mask(maxface)
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# Adapt the features we are looking for depending on whether a mask is
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# worn. White masks screw race detection, emotion detection is useless.
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actions = ["age", "gender"]
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if not result["wears_mask"]:
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actions = actions + ["race", "emotion"]
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# Run the full DeepFace analysis
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result["deepface_results"] = DeepFace.analyze(
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img_path=maxface,
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actions=actions,
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prog_bar=False,
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detector_backend="skip",
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)
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# We remove the region, as the data is not correct - after all we are
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# running the analysis on a subimage.
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del result["deepface_results"]["region"]
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return result
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def wears_mask(face):
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global mask_detection_model
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# Preprocess the face to match the assumptions of the face mask
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# detection model
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face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
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face = cv2.resize(face, (224, 224))
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face = img_to_array(face)
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face = preprocess_input(face)
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face = np.expand_dims(face, axis=0)
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# Lazily load the model
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if mask_detection_model is None:
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mask_detection_model = load_model(
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os.path.join(os.path.split(__file__)[0], "models", "mask_detector.model")
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)
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# Run the model (ignoring output)
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with ipywidgets.Output():
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mask, withoutMask = mask_detection_model.predict(face)[0]
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# Convert from np.bool_ to bool to later be able to serialize the result
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return bool(mask > withoutMask)
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class JSONContainer:
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"""Expose a Python dictionary as a JSON document in JupyterLab
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rich display rendering.
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"""
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def __init__(self, data={}):
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self._data = data
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def _repr_json_(self):
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return self._data
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def explore_face_recognition(image_paths):
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# Create an image selector widget
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image_select = ipywidgets.Select(
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options=image_paths, layout=ipywidgets.Layout(width="20%"), rows=20
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)
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# Set up the facial recognition output widget
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output = ipywidgets.Output(layout=ipywidgets.Layout(width="30%"))
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# Set up the image selection and display widget
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image_widget = ipywidgets.Box(
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children=[],
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layout=ipywidgets.Layout(width="50%"),
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)
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# Register the tab switch logic
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def switch(_):
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# Clear existing output
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image_widget.children = ()
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output.clear_output()
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# Create the new content
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image_widget.children = (ipywidgets.Image.from_file(image_select.value),)
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# This output widget absorbes print statements that are messing with
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# the widget output and cannot be disabled through the API.
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with ipywidgets.Output():
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analysis = facial_expression_analysis(image_select.value)
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with output:
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display(JSONContainer(analysis))
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# Register the handler and trigger it immediately
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image_select.observe(switch, names=("value",), type="change")
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switch(None)
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# Show the combined widget
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return ipywidgets.HBox([image_select, image_widget, output])
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