Create app.py

app.py

@@ -0,0 +1,225 @@
+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()