app.py

import gradio as gr
import pandas as pd
import os
import shutil
import matplotlib.pyplot as plt
import numpy as np
import japanize_matplotlib


class DataProcessor:
   def __init__(self, bodypart_names, x_max, y_max):
      self.bodypart_names = bodypart_names.split(',')
      self.x_max = x_max
      self.y_max = y_max
      self.output_folder = 'output_plots'

   def process_csv(self, file_path):
      df = pd.read_csv(file_path, header=[1, 2])
      df_likelihood = self.extract_likelihood(df)
      df = self.remove_first_column_and_likelihood(df)
      df = self.rename_bodyparts(df)
      return df, df_likelihood

   def remove_first_column_and_likelihood(self, df):
      df = df.drop(df.columns[0], axis=1)
      df = df[df.columns.drop(list(df.filter(regex='likelihood')))]
      return df

   def rename_bodyparts(self, df):
      current_names = df.columns.get_level_values(0).unique()
      if len(self.bodypart_names) != len(current_names):
         raise ValueError(
             "The length of bodypart_names must be equal to the number of bodyparts.")
      mapping = dict(zip(current_names, self.bodypart_names))
      new_columns = [(mapping[col[0]], col[1]) if col[0]
                     in mapping else col for col in df.columns]
      df.columns = pd.MultiIndex.from_tuples(new_columns)
      return df

   def extract_likelihood(self, df):
      # likelihood列のみを抽出する
      df = df[df.columns[df.columns.get_level_values(1) == 'likelihood']]
      df.drop(df.columns[0], axis=1)
      current_names = df.columns.get_level_values(0).unique()
      mapping = dict(zip(current_names, self.bodypart_names))
      new_columns = [(mapping[col[0]], col[1]) if col[0]
                     in mapping else col for col in df.columns]
      df.columns = pd.MultiIndex.from_tuples(new_columns)

      return df

   def plot_scatter(self, df):
      if not os.path.exists(self.output_folder):
         os.makedirs(self.output_folder)
      return self.plot_scatter_fixed(df, self.output_folder, self.x_max, self.y_max)

   def plot_scatter_fixed(self, df, output_folder, x_max, y_max):
      image_paths = []
      bodyparts = df.columns.get_level_values(0).unique()
      colors = plt.cm.rainbow(np.linspace(0, 1, len(bodyparts)))
      for i, bodypart in enumerate(bodyparts):
         x = df[bodypart]['x'].values
         y = df[bodypart]['y'].values
         plt.figure(figsize=(8, 6))
         plt.scatter(x, y, color=colors[i], label=bodypart)
         plt.scatter(x[0], y[0], color='black', marker='o', s=100)
         plt.text(x[0], y[0], ' Start', color='black',
                  fontsize=12, verticalalignment='bottom')
         plt.xlim(0, x_max)
         plt.ylim(0, y_max)
         plt.gca().invert_yaxis()
         plt.title(f'トラッキングの座標({bodypart})')
         plt.xlabel('X Coordinate(pixel)')
         plt.ylabel('Y Coordinate(pixel)')
         plt.legend(loc='upper right')
         plt.savefig(f'{output_folder}/{bodypart}.png')
         image_paths.append(f'{output_folder}/{bodypart}.png')
         plt.close()

      plt.figure(figsize=(8, 6))
      for i, bodypart in enumerate(bodyparts):
         x = df[bodypart]['x'].values
         y = df[bodypart]['y'].values
         plt.scatter(x, y, color=colors[i], label=bodypart)
      plt.xlim(0, x_max)
      plt.ylim(0, y_max)
      plt.gca().invert_yaxis()
      plt.title('トラッキングの座標(全付属肢)')
      plt.xlabel('X Coordinate(pixel)')
      plt.ylabel('Y Coordinate(pixel)')
      plt.legend(loc='upper right')
      plt.savefig(f'{output_folder}/all_plot.png')
      image_paths.append(f'{output_folder}/all_plot.png')
      plt.close()

      return image_paths

   def plot_trajectories(self, df):
      image_paths = []
      bodyparts = df.columns.get_level_values(0).unique()
      colors = plt.cm.rainbow(np.linspace(0, 1, len(bodyparts)))

      for i, bodypart in enumerate(bodyparts):
         x = df[bodypart]['x'].values
         y = df[bodypart]['y'].values
         plt.figure(figsize=(8, 6))
         plt.plot(x, color=colors[i], label=bodypart +
                  "(x座標)", linestyle='dashed')
         plt.plot(y, color=colors[i], label=bodypart + "(y座標)")
         plt.title(f'トラッキングの座標({bodypart})')
         plt.xlabel('Frames')
         plt.ylabel('Coordinate(pixel)')
         plt.legend(loc='upper right')
         plt.savefig(f'{self.output_folder}/{bodypart}_trajectories.png')
         image_paths.append(
             f'{self.output_folder}/{bodypart}_trajectories.png')
         plt.close()

      plt.figure(figsize=(8, 6))
      for i, bodypart in enumerate(bodyparts):
         x = df[bodypart]['x'].values
         y = df[bodypart]['y'].values
         plt.plot(x, color=colors[i], label=bodypart +
                  "(x座標)", linestyle='dashed')
         plt.plot(y, color=colors[i], label=bodypart + "(y座標)")
      plt.title(f'トラッキングの座標({bodypart})')
      plt.xlabel('Frames')
      plt.ylabel('Coordinate(pixel)')
      plt.legend(loc='upper right')
      plt.savefig(f'{self.output_folder}/all_trajectories.png')
      image_paths.append(f'{self.output_folder}/all_trajectories.png')
      plt.close()
      return image_paths

   def plot_likelihood(self, likelihood_df):
      image_paths = []
      plt.ylim(0, 1.0)
      bodyparts = likelihood_df.columns.get_level_values(0).unique()
      colors = plt.cm.rainbow(np.linspace(0, 1, len(bodyparts)))

      # 付属肢ごとに尤度をプロット
      for i, bodypart in enumerate(bodyparts):
         plt.figure(figsize=(8, 6))
         plt.ylim(0, 1.0)
         plt.plot(likelihood_df[bodypart], color=colors[i], label=bodypart)
         plt.xlabel('Frames')
         plt.ylabel('尤度')
         plt.title('フレーム別の尤度')
         # 凡例を右上の外側に表示
         plt.legend(bbox_to_anchor=(1.05, 1),
                    loc='upper left', borderaxespad=0)
         # 凡例がはみ出さないようにレイアウトを調整
         plt.tight_layout()
         plt.savefig(f'{self.output_folder}/{bodypart}_likelihood.png')
         image_paths.append(f'{self.output_folder}/{bodypart}_likelihood.png')
         plt.close()

      # 全ての付属肢の尤度をプロット
      plt.figure(figsize=(8, 6))
      plt.ylim(0, 1.0)
      for i, column in enumerate(likelihood_df.columns):
         plt.plot(likelihood_df[column], color=colors[i], label=column[0])
      plt.xlabel('Frames')
      plt.ylabel('尤度')
      plt.title('フレーム別の尤度')
      # 凡例を右上の外側に表示
      plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0)
      # 凡例がはみ出さないようにレイアウトを調整
      plt.tight_layout()
      plt.savefig(f'{self.output_folder}/likelihood_plot.png')
      plt.close()
      image_paths.append(f'{self.output_folder}/likelihood_plot.png')
      return image_paths

# 以下のGradioInterfaceクラスとメイン実行部分は変更なし


class GradioInterface:
   def __init__(self):
      self.interface = gr.Interface(
          fn=self.process_and_plot,
          inputs=[
              gr.File(label="CSVファイルをドラッグ＆ドロップ"),
              gr.Textbox(label="付属肢の名前（カンマ区切り）",
                         value="指節1, 指節2, 指節3, 指節4, 指節5, 指節6, 指節7, 指節8, 指節9,指節10, 指節11, 指節12, 指節13, 指節14, 触角(左), 触角(右), 頭部, 腹尾節"),
              gr.Number(label="X軸の最大値", value=1920),
              gr.Number(label="Y軸の最大値", value=1080),
              gr.CheckboxGroup(
                  label="プロットするグラフを選択",
                  choices=["散布図", "軌跡図", "尤度グラフ"],
                  value=["散布図", "軌跡図", "尤度グラフ"],
                  type="value"
              )
          ],
          outputs=[
              gr.Gallery(label="散布図"),
              gr.File(label="ZIPダウンロード")
          ],
          title="DeepLabCutグラフ出力ツール",
          description="CSVファイルからグラフを作成します。"
      )

   def process_and_plot(self, file, bodypart_names, x_max, y_max, graph_choices):
      processor = DataProcessor(bodypart_names, x_max, y_max)
      df, df_likelihood = processor.process_csv(file.name)

      all_image_paths = []
      if "散布図" in graph_choices:
         all_image_paths += processor.plot_scatter(df)
      if "軌跡図" in graph_choices:
         all_image_paths += processor.plot_trajectories(df)
      if "尤度グラフ" in graph_choices:
         all_image_paths += processor.plot_likelihood(df_likelihood)

      shutil.make_archive(processor.output_folder,
                          'zip', processor.output_folder)
      return all_image_paths, processor.output_folder + '.zip'

   def launch(self):
      self.interface.launch()


if __name__ == "__main__":
   gradio_app = GradioInterface()
   gradio_app.launch()