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* colors expression by KMean algorithm * object detection by imageai * object detection by cvlib * add encapsulation of object detection * remove encapsulation of objdetect v0 * objects expression to dict * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * added imageai to requirements * add objects to dictionary * update for AnalysisMethod baseline * add objects dection support explore_analysis display * extend python version of misinf to allow imageai * account for older python * use global functionality for dict to csv convert * update for docker build * docker will build now but ipywidgets still not working * test code * include test data folder in repo * add some sample images * load cvs labels to dict * add test data * retrigger checks * add map to human coding * get orders from dict, missing dep * add module to test accuracy * retrigger checks * retrigger checks * now removing imageai * removed imageai * move labelmanager to analyse * multiple faces in mydict * fix pre-commit issues * map mydict * hide imageai * objects default using cvlib, isolate and disable imageai * correct python version * refactor faces tests * refactor objects tests * sonarcloud issues * refactor utils tests * address code smells * update readme * update notebook without imageai Co-authored-by: Ma Xianghe <825074348@qq.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: iulusoy <inga.ulusoy@uni-heidelberg.de>
71 строка
2.2 KiB
Python
71 строка
2.2 KiB
Python
import ipywidgets
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from IPython.display import display
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import misinformation.faces as faces
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import misinformation.text as text
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import misinformation.objects as objects
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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_analysis(mydict, identify="faces"):
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# dictionary mapping the type of analysis to be explored
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identify_dict = {
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"faces": faces.EmotionDetector,
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"text-on-image": text.TextDetector,
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"objects": objects.ObjectDetector,
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}
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# create a list containing the image ids for the widget
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# image_paths = [mydict[key]["filename"] for key in mydict.keys()]
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image_ids = [key for key in mydict.keys()]
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# Create an image selector widget
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image_select = ipywidgets.Select(
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options=image_ids, 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 = (
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ipywidgets.Image.from_file(mydict[image_select.value]["filename"]),
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)
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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 faces.NocatchOutput():
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mydict[image_select.value] = identify_dict[identify](
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mydict[image_select.value]
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).analyse_image()
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with output:
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display(JSONContainer(mydict[image_select.value]))
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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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