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e11d4c05f4
* updated notebooks for the new interface --------- Co-authored-by: Petr Andriushchenko <pitandmind@gmail.com>
533 строки
20 KiB
Python
533 строки
20 KiB
Python
import ammico.faces as faces
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import ammico.text as text
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import ammico.objects as objects
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import ammico.colors as colors
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from ammico.utils import is_interactive
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import ammico.summary as summary
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import dash_renderjson
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from dash import html, Input, Output, dcc, State
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import jupyter_dash
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from PIL import Image
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COLOR_SCHEMES = [
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"CIE 1976",
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"CIE 1994",
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"CIE 2000",
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"CMC",
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"ITP",
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"CAM02-LCD",
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"CAM02-SCD",
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"CAM02-UCS",
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"CAM16-LCD",
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"CAM16-SCD",
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"CAM16-UCS",
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"DIN99",
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]
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SUMMARY_ANALYSIS_TYPE = ["summary_and_questions", "summary", "questions"]
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SUMMARY_MODEL = ["base", "large"]
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class AnalysisExplorer:
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def __init__(self, mydict: dict) -> None:
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"""Initialize the AnalysisExplorer class to create an interactive
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visualization of the analysis results.
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Args:
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mydict (dict): A nested dictionary containing image data for all images.
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"""
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self.app = jupyter_dash.JupyterDash(__name__)
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self.mydict = mydict
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self.theme = {
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"scheme": "monokai",
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"author": "wimer hazenberg (http://www.monokai.nl)",
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"base00": "#272822",
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"base01": "#383830",
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"base02": "#49483e",
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"base03": "#75715e",
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"base04": "#a59f85",
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"base05": "#f8f8f2",
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"base06": "#f5f4f1",
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"base07": "#f9f8f5",
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"base08": "#f92672",
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"base09": "#fd971f",
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"base0A": "#f4bf75",
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"base0B": "#a6e22e",
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"base0C": "#a1efe4",
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"base0D": "#66d9ef",
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"base0E": "#ae81ff",
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"base0F": "#cc6633",
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}
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# Setup the layout
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app_layout = html.Div(
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[
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# Top
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html.Div(
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[self._top_file_explorer(mydict)],
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id="Div_top",
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style={
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"width": "30%",
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},
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),
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# Middle
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html.Div(
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[self._middle_picture_frame()],
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id="Div_middle",
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style={
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"width": "50%",
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"display": "inline-block",
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"verticalAlign": "top",
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},
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),
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# Right
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html.Div(
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[self._right_output_json()],
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id="Div_right",
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style={
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"width": "45%",
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"display": "inline-block",
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"verticalAlign": "top",
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},
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),
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],
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style={"width": "95%", "display": "inline-block"},
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)
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self.app.layout = app_layout
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# Add callbacks to the app
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self.app.callback(
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Output("img_middle_picture_id", "src"),
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Input("left_select_id", "value"),
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prevent_initial_call=True,
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)(self.update_picture)
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self.app.callback(
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Output("right_json_viewer", "data"),
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Input("button_run", "n_clicks"),
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State("left_select_id", "options"),
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State("left_select_id", "value"),
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State("Dropdown_select_Detector", "value"),
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State("setting_Text_analyse_text", "value"),
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State("setting_Text_model_names", "value"),
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State("setting_Text_revision_numbers", "value"),
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State("setting_Emotion_emotion_threshold", "value"),
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State("setting_Emotion_race_threshold", "value"),
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State("setting_Color_delta_e_method", "value"),
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State("setting_Summary_analysis_type", "value"),
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State("setting_Summary_model", "value"),
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State("setting_Summary_list_of_questions", "value"),
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prevent_initial_call=True,
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)(self._right_output_analysis)
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self.app.callback(
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Output("settings_TextDetector", "style"),
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Output("settings_EmotionDetector", "style"),
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Output("settings_ColorDetector", "style"),
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Output("settings_Summary_Detector", "style"),
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Input("Dropdown_select_Detector", "value"),
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)(self._update_detector_setting)
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# I split the different sections into subfunctions for better clarity
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def _top_file_explorer(self, mydict: dict) -> html.Div:
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"""Initialize the file explorer dropdown for selecting the file to be analyzed.
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Args:
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mydict (dict): A dictionary containing image data.
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Returns:
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html.Div: The layout for the file explorer dropdown.
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"""
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left_layout = html.Div(
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[
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dcc.Dropdown(
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options={value["filename"]: key for key, value in mydict.items()},
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id="left_select_id",
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)
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]
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)
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return left_layout
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def _middle_picture_frame(self) -> html.Div:
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"""Initialize the picture frame to display the image.
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Returns:
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html.Div: The layout for the picture frame.
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"""
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middle_layout = html.Div(
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[
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html.Img(
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id="img_middle_picture_id",
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style={
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"width": "80%",
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},
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)
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]
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)
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return middle_layout
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def _create_setting_layout(self):
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settings_layout = html.Div(
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[
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html.Div(
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id="settings_TextDetector",
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style={"display": "none"},
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children=[
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dcc.Checklist(
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["Analyse text"],
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["Analyse text"],
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id="setting_Text_analyse_text",
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),
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html.Div(
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[
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html.Div(
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"Select models for text_summary, text_sentiment, text_NER or leave blank for default:",
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style={
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"height": "30px",
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"margin-top": "5px",
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},
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),
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dcc.Input(
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type="text",
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id="setting_Text_model_names",
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style={"height": "auto", "margin-bottom": "auto"},
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),
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],
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style={
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"width": "33%",
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"display": "inline-block",
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"margin-top": "10px",
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},
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),
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html.Div(
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[
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html.Div(
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"Select model revision number for text_summary, text_sentiment, text_NER or leave blank for default:",
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style={
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"height": "30px",
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"margin-top": "5px",
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},
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),
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dcc.Input(
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type="text",
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id="setting_Text_revision_numbers",
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style={"height": "auto", "margin-bottom": "auto"},
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),
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],
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style={
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"width": "33%",
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"display": "inline-block",
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"margin-top": "10px",
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},
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),
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],
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),
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html.Div(
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id="settings_EmotionDetector",
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style={"display": "none"},
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children=[
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html.Div(
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[
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html.Div(
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"Emotion threshold",
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style={"height": "30px", "margin-top": "5px"},
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),
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dcc.Input(
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value=50,
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type="number",
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max=100,
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min=0,
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id="setting_Emotion_emotion_threshold",
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style={"height": "auto", "margin-bottom": "auto"},
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),
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],
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style={"width": "49%", "display": "inline-block"},
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),
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html.Div(
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[
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html.Div(
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"Race threshold",
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style={
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"height": "30px",
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"margin-top": "5px",
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},
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),
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dcc.Input(
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type="number",
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value=50,
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max=100,
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min=0,
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id="setting_Emotion_race_threshold",
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style={"height": "auto", "margin-bottom": "auto"},
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),
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],
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style={
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"width": "49%",
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"display": "inline-block",
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"margin-top": "10px",
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},
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),
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],
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),
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html.Div(
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id="settings_ColorDetector",
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style={"display": "none"},
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children=[
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html.Div(
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[
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dcc.Dropdown(
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options=COLOR_SCHEMES,
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value="CIE 1976",
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id="setting_Color_delta_e_method",
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)
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],
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style={
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"width": "49%",
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"display": "inline-block",
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"margin-top": "10px",
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},
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)
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],
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),
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html.Div(
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id="settings_Summary_Detector",
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style={"display": "none"},
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children=[
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html.Div(
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[
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dcc.Dropdown(
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options=SUMMARY_ANALYSIS_TYPE,
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value="summary_and_questions",
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id="setting_Summary_analysis_type",
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)
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],
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style={
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"width": "33%",
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"display": "inline-block",
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},
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),
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html.Div(
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[
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dcc.Dropdown(
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options=SUMMARY_MODEL,
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value="base",
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id="setting_Summary_model",
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)
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],
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style={
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"width": "33%",
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"display": "inline-block",
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"margin-top": "10px",
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},
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),
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html.Div(
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[
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html.Div(
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"Please enter a question",
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style={
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"height": "50px",
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"margin-top": "5px",
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},
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),
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dcc.Input(
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type="text",
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id="setting_Summary_list_of_questions",
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style={"height": "auto", "margin-bottom": "auto"},
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),
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],
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style={
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"width": "33%",
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"display": "inline-block",
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"margin-top": "10px",
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},
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),
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],
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),
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],
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)
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return settings_layout
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def _right_output_json(self) -> html.Div:
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"""Initialize the DetectorDropdown, argument Div and JSON viewer for displaying the analysis output.
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Returns:
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html.Div: The layout for the JSON viewer.
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"""
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right_layout = html.Div(
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[
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dcc.Loading(
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id="loading-2",
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children=[
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html.Div(
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[
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dcc.Dropdown(
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options=[
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"TextDetector",
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"ObjectDetector",
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"EmotionDetector",
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"SummaryDetector",
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"ColorDetector",
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],
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value="TextDetector",
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id="Dropdown_select_Detector",
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),
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html.Div(
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children=[self._create_setting_layout()],
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id="div_detector_args",
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),
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html.Button("Run Detector", id="button_run"),
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dash_renderjson.DashRenderjson(
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id="right_json_viewer",
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data={},
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max_depth=-1,
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theme=self.theme,
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invert_theme=True,
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),
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]
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)
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],
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type="circle",
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)
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]
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)
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return right_layout
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def run_server(self, port: int = 8050) -> None:
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"""Run the Dash server to start the analysis explorer.
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This method should only be called in an interactive environment like Jupyter notebooks.
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Raises an EnvironmentError if not called in an interactive environment.
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Args:
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port (int, optional): The port number to run the server on (default: 8050).
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"""
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if not is_interactive():
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raise EnvironmentError(
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"Dash server should only be called in an interactive environment like Jupyter notebooks."
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)
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self.app.run_server(debug=True, mode="inline", port=port)
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# Dash callbacks
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def update_picture(self, img_path: str):
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"""Callback function to update the displayed image.
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Args:
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img_path (str): The path of the selected image.
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Returns:
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Union[PIL.PngImagePlugin, None]: The image object to be displayed
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or None if the image path is
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"""
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if img_path is not None:
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image = Image.open(img_path)
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return image
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else:
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return None
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def _update_detector_setting(self, setting_input):
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# return settings_TextDetector -> style, settings_EmotionDetector -> style
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display_none = {"display": "none"}
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display_flex = {
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"display": "flex",
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"flexWrap": "wrap",
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"width": 400,
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"margin-top": "20px",
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}
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if setting_input == "TextDetector":
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return display_flex, display_none, display_none, display_none
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if setting_input == "EmotionDetector":
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return display_none, display_flex, display_none, display_none
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if setting_input == "ColorDetector":
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return display_none, display_none, display_flex, display_none
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if setting_input == "SummaryDetector":
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return display_none, display_none, display_none, display_flex
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else:
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return display_none, display_none, display_none, display_none
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def _right_output_analysis(
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self,
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n_clicks,
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all_img_options: dict,
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current_img_value: str,
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detector_value: str,
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settings_text_analyse_text: list,
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settings_text_model_names: str,
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settings_text_revision_numbers: str,
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setting_emotion_emotion_threshold: int,
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setting_emotion_race_threshold: int,
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setting_color_delta_e_method: str,
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setting_summary_analysis_type: str,
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setting_summary_model: str,
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setting_summary_list_of_questions: str,
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) -> dict:
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"""Callback function to perform analysis on the selected image and return the output.
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Args:
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all_options (dict): The available options in the file explorer dropdown.
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current_value (str): The current selected value in the file explorer dropdown.
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Returns:
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dict: The analysis output for the selected image.
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"""
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identify_dict = {
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"EmotionDetector": faces.EmotionDetector,
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"TextDetector": text.TextDetector,
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"ObjectDetector": objects.ObjectDetector,
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"SummaryDetector": summary.SummaryDetector,
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"ColorDetector": colors.ColorDetector,
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}
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# Get image ID from dropdown value, which is the filepath
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if current_img_value is None:
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return {}
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image_id = all_img_options[current_img_value]
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# copy image so prvious runs don't leave their default values in the dict
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image_copy = self.mydict[image_id].copy()
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identify_function = identify_dict[detector_value]
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if detector_value == "TextDetector":
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analyse_text = (
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True if settings_text_analyse_text == ["Analyse text"] else False
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)
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detector_class = identify_function(
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image_copy,
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analyse_text=analyse_text,
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model_names=[settings_text_model_names]
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if (settings_text_model_names is not None)
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else None,
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revision_numbers=[settings_text_revision_numbers]
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if (settings_text_revision_numbers is not None)
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else None,
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)
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elif detector_value == "EmotionDetector":
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detector_class = identify_function(
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image_copy,
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race_threshold=setting_emotion_race_threshold,
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emotion_threshold=setting_emotion_emotion_threshold,
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)
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elif detector_value == "ColorDetector":
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detector_class = identify_function(
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image_copy,
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delta_e_method=setting_color_delta_e_method,
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)
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elif detector_value == "SummaryDetector":
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detector_class = identify_function(
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image_copy,
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analysis_type=setting_summary_analysis_type,
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summary_model_type=setting_summary_model,
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list_of_questions=[setting_summary_list_of_questions]
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if (setting_summary_list_of_questions is not None)
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else None,
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)
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else:
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detector_class = identify_function(image_copy)
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return detector_class.analyse_image()
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