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76 строки
2.2 KiB
Python
76 строки
2.2 KiB
Python
import ipywidgets
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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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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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# 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=["age", "gender", "race", "emotion"],
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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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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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# 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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images = [ipywidgets.Image.from_file(p) for p in image_paths]
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image_widget = ipywidgets.Tab(
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children=images,
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titles=[f"#{i}" for i in range(len(image_paths))],
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layout=ipywidgets.Layout(width="70%"),
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)
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# Precompute all the results for a user experience without delay
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with ipywidgets.Output():
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results = [facial_expression_analysis(i) for i in image_paths]
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# Register the tab switch logic
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def tabswitch(_):
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output.clear_output()
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with output:
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display(JSONContainer(results[image_widget.selected_index]))
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# Register the handler and trigger it immediately
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image_widget.observe(tabswitch, names=("selected_index",), type="change")
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tabswitch(None)
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# Show the combined widget
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return ipywidgets.HBox([image_widget, output])
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