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app.py

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+import os
+import tempfile
+import shutil
+
+import dlc2kinematics as dlc
+import gradio as gr
+import matplotlib.cm as cm
+import pandas as pd
+from dlc2kinematics import compute_speed, load_data
+from matplotlib import pyplot as plt
+
+
+def all_plot(bodyparts, speed_csv, tmpdir):
+
+   # speed_csvからグラフを作成。
+   plt.figure()
+   for i in bodyparts:
+      plt.plot(speed_csv[i], label=i, color=cm.rainbow(int(255 * bodyparts.index(i) / len(bodyparts))))
+
+   plt.legend()
+   plt.xlabel('frame')
+   plt.ylabel('speed(px/frame)')
+
+   # 横軸の最大値を取得して設定
+   xmax = speed_csv.index.max()
+   plt.xlim(0, xmax)
+   plt.ylim(0, 100)
+
+   # グラフの凡例をグラフ外に表示。
+   plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0, fontsize=10)
+
+   # 凡例をすべて画像内に収める
+   plt.tight_layout()
+   plt.savefig(f'{tmpdir}/all_plot.png')
+
+
+def bodyparts_plot(bodyparts, speed_csv, tmpdir):
+   # speed_csvからグラフを作成。
+   for i in bodyparts:
+      plt.figure()
+      plt.plot(speed_csv[i], label=i, color=cm.rainbow(int(255 * bodyparts.index(i) / len(bodyparts))))
+      plt.legend()
+      plt.xlabel('frame')
+      plt.ylabel('speed')
+
+      # 横軸の最大値を取得して設定
+      xmax = speed_csv.index.max()
+      plt.xlim(0, xmax)
+      plt.ylim(0, 100)
+
+      # グラフの凡例をグラフ外に表示。
+      plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0, fontsize=10)
+      # 凡例がはみ出ないように
+      plt.tight_layout()
+
+      plt.savefig(f'{tmpdir}/{i}.png')
+
+
+def likelihood_plot(bodyparts, likelihood_csv, tmpdir):
+   plt.figure()
+   for i in bodyparts:
+      plt.plot(likelihood_csv[i], label=i, color=cm.rainbow(int(255 * bodyparts.index(i) / len(bodyparts))))
+
+   plt.legend()
+   plt.xlabel('frame')
+   plt.ylabel('likeloohood')
+
+   # 横軸の最大値を取得して設定
+   xmax = likelihood_csv.index.max()
+   plt.xlim(0, xmax)
+   plt.ylim(0, 1)
+
+   # グラフの凡例をグラフ外に表示。
+   plt.legend(bbox_to_anchor=(1.05, 1), loc='upper left', borderaxespad=0, fontsize=10)
+
+   # 凡例をすべて画像内に収める
+   plt.tight_layout()
+
+   plt.savefig(f'{tmpdir}/likelihood_plot.png')
+
+
+def main(h5_file):
+   h5_file_name = os.path.basename(h5_file.name)
+   h5_file_name = os.path.splitext(h5_file_name)[0]
+   df, bd, scorer = load_data(f'h5/{h5_file_name}.h5')
+   speed = compute_speed(df, ['all'])
+
+   # speedのカラムを取得
+   new_columns = speed.columns.droplevel([0, 2])
+   speed.columns = new_columns
+   # カラムを取得
+   bodyparts = df.columns.droplevel([0, 2]).to_list()
+   bodyparts = list(dict.fromkeys(bodyparts))
+   speed_csv = pd.DataFrame()
+   likelihood_csv = pd.DataFrame()
+   for i in bodyparts:
+      speed_csv[i] = speed[i].iloc[:, 0]
+      likelihood_csv[i] = speed[i].iloc[:, 1]
+
+   with tempfile.TemporaryDirectory(dir=".") as tmpdir:
+      bodyparts_plot(bodyparts, speed_csv, tmpdir)
+      all_plot(bodyparts, speed_csv, tmpdir)
+      likelihood_plot(bodyparts, likelihood_csv, tmpdir)
+      shutil.make_archive(f"{h5_file_name}", 'zip', root_dir=tmpdir)
+      return f"{h5_file_name}.zip"
+
+
+iface = gr.Interface(fn=main, inputs="file", outputs="file")
+iface.launch()

requirements.txt

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+dlc2kinematics
+gradio
+pandas
+matplotlib