AMMICO/misinformation/display.py

69 строки
2.1 KiB
Python

import ipywidgets
from IPython.display import display
import misinformation.faces as faces
import misinformation.text as text
class JSONContainer:
"""Expose a Python dictionary as a JSON document in JupyterLab
rich display rendering.
"""
def __init__(self, data={}):
self._data = data
def _repr_json_(self):
return self._data
def explore_analysis(mydict, identify="faces"):
# dictionary mapping the type of analysis to be explored
identify_dict = {
"faces": faces.facial_expression_analysis,
"text-on-image": text.detect_text,
}
# create a list containing the image ids for the widget
# image_paths = [mydict[key]["filename"] for key in mydict.keys()]
image_ids = [key for key in mydict.keys()]
# Create an image selector widget
image_select = ipywidgets.Select(
options=image_ids, layout=ipywidgets.Layout(width="20%"), rows=20
)
# Set up the facial recognition output widget
output = ipywidgets.Output(layout=ipywidgets.Layout(width="30%"))
# Set up the image selection and display widget
image_widget = ipywidgets.Box(
children=[],
layout=ipywidgets.Layout(width="50%"),
)
# Register the tab switch logic
def switch(_):
# Clear existing output
image_widget.children = ()
output.clear_output()
# Create the new content
image_widget.children = (
ipywidgets.Image.from_file(mydict[image_select.value]["filename"]),
)
# This output widget absorbes print statements that are messing with
# the widget output and cannot be disabled through the API.
with faces.NocatchOutput():
mydict[image_select.value] = identify_dict[identify](
mydict[image_select.value]
)
with output:
display(JSONContainer(mydict[image_select.value]))
# Register the handler and trigger it immediately
image_select.observe(switch, names=("value",), type="change")
switch(None)
# Show the combined widget
return ipywidgets.HBox([image_select, image_widget, output])