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, x_max, y_max): self.x_max = x_max self.y_max = y_max self.output_folder = 'output_plots' self.bodypart_names = None # 初期化時にはNoneに設定 def process_csv(self, file_path): df = pd.read_csv(file_path, header=[1, 2]) # CSVから自動的に付属肢名を抽出 self.bodypart_names = df.columns.get_level_values(0).unique().tolist() # 最初の列（通常はscorerなど）を除外 if len(self.bodypart_names) > 0: self.bodypart_names = self.bodypart_names[1:] df_likelihood = self.extract_likelihood(df) df = self.remove_first_column_and_likelihood(df) return df, df_likelihood, self.bodypart_names # 抽出した付属肢名も返す 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 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) 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.ylim(0, self.x_max) 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(全付属肢)') plt.xlabel('Frames') plt.ylabel('Coordinate(pixel)') plt.ylim(0, self.x_max) 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 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.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="グラフ"), gr.File(label="ZIPダウンロード"), gr.Textbox(label="検出された付属肢") # 検出された付属肢を表示するための出力を追加 ], 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。付属肢はCSVファイルから自動的に抽出されます。" ) def process_and_plot(self, file, x_max, y_max, graph_choices): processor = DataProcessor(x_max, y_max) df, df_likelihood, bodypart_names = 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) # 付属肢の名前を表示用に結合 bodyparts_text = ", ".join(bodypart_names) shutil.make_archive(processor.output_folder, 'zip', processor.output_folder) return all_image_paths, processor.output_folder + '.zip', bodyparts_text def launch(self): self.interface.launch() if __name__ == "__main__": gradio_app = GradioInterface() gradio_app.launch()