feat: add gradio app to demonstrating segmentations by hrnet

README.md

@@ -4,7 +4,7 @@ emoji: 🌍
 colorFrom: red
 colorTo: pink
 sdk: gradio
-sdk_version: 5.11.0
+sdk_version: 5.12.0
 app_file: app.py
 pinned: false
 license: apache-2.0

app.py

@@ -0,0 +1,212 @@
+# from io import BytesIO
+import gradio as gr
+import pandas as pd
+import numpy as np
+import torch
+import os
+from aitiautils.model_loader import ModelLoader
+import tempfile
+import matplotlib.pyplot as plt
+import traceback as tb
+
+# True 이면, tmp directory 에 파일 존재 유무와 상관없이 항상 새로운 이미지 생성
+ALWAYS_RECREATE_IMAGE = os.getenv("ALWAYS_RECREATE_IMAGE", "False").lower() == "true"
+selected_columns = ["subject_id", "no_p", "Rhythm", "Electric axis of the heart", "Etc"]
+train_df = pd.read_csv("./res/ludb/dataset/train_for_public.csv").drop_duplicates(
+    subset=["subject_id"]
+)[selected_columns]
+valid_df = pd.read_csv("./res/ludb/dataset/valid_for_public.csv").drop_duplicates(
+    subset=["subject_id"]
+)[selected_columns]
+test_df = pd.read_csv("./res/ludb/dataset/test_for_public.csv").drop_duplicates(
+    subset=["subject_id"]
+)[selected_columns]
+cutoffs = [0.001163482666015625, 0.15087890625, -0.587890625]
+lead_names = ["I", "II", "III", "aVR", "aVL", "aVF", "V1", "V2", "V3", "V4", "V5", "V6"]
+
+
+def gen_seg(subject_id):
+    input = np.load(f"./res/ludb/ecg_np/{subject_id}.npy")
+    network = ModelLoader("./res/models/hrnetv2/checkpoint.pth").get_network()
+    output: torch.Tensor = network(torch.from_numpy(input)).detach().numpy()
+    seg = [(output[:, i, :] >= cutoffs[i]).astype(int) for i in range(len(cutoffs))]
+    return input, np.stack(seg, axis=1)
+
+
+def concat_short_interval(seg, th):
+    """seg에서 구간(1)과 구간(1) 사이에 th 보다 짧은 부분(0)을 이어 붙인다. (0 -> 1)"""
+    # seg 에서 같은 구간끼리 그룹을 만듦. ex: seg = [0, 0, 1, 1, 0, 1, 1, 1, 1] -> seg_groups=[[0, 0], [1, 1], [0], [1, 1, 1, 1]]]
+    seg_groups = np.split(seg, np.where(np.diff(seg) != 0)[0] + 1)
+    for i in range(1, len(seg_groups) - 1):  # 첫 번째와 마지막 그룹 제외
+        group = seg_groups[i]
+        if len(group) <= th and np.all(group == 0):
+            seg_groups[i] = np.ones_like(group)  # 0 -> 1
+
+    return np.concatenate(seg_groups)
+
+
+def remove_short_duration(seg, th):
+    """seg의 구간(1)중에 th 보다 짧은 구간은 제거 (1 -> 0)"""
+    seg_groups = np.split(seg, np.where(np.diff(seg) != 0)[0] + 1)
+    for i, group in enumerate(seg_groups):
+        if len(group) <= th and np.all(group == 1):
+            seg_groups[i] = np.zeros_like(group)  # 1 -> 0
+
+    return np.concatenate(seg_groups)
+
+
+def gen_each_image(input, seg, image_path, ths, pp=False):
+    fig = plt.figure(figsize=(15, 18))
+    plt.subplots_adjust(left=0.02, right=0.98, top=0.98, bottom=0.02, hspace=0.2)
+    for idx, (in_by_lead, seg_by_lead) in enumerate(zip(input, seg)):
+        sub_fig = fig.add_subplot(12, 1, idx + 1)
+        sub_fig.text(
+            0.02,
+            0.5,
+            f"{lead_names[idx]}",
+            fontsize=9,
+            fontweight="bold",
+            ha="center",
+            va="center",
+            rotation=90,
+            transform=sub_fig.transAxes,
+        )
+        sub_fig.set_xticks([])
+        sub_fig.set_yticks([])
+        sub_fig.plot(
+            range(len(in_by_lead[0])), in_by_lead[0], color="black", linewidth=1.0
+        )
+        p_seg = seg_by_lead[0]
+        qrs_seg = seg_by_lead[1]
+        t_seg = seg_by_lead[2]
+        if pp:
+            p_seg = remove_short_duration(concat_short_interval(p_seg, ths[0]), ths[1])
+            qrs_seg = remove_short_duration(
+                concat_short_interval(qrs_seg, ths[2]), ths[3]
+            )
+            t_seg = remove_short_duration(concat_short_interval(t_seg, ths[4]), ths[5])
+        sub_fig.plot(
+            range(len(p_seg)), p_seg / 2, label="P", color="red", linewidth=0.7
+        )
+        sub_fig.plot(
+            range(len(qrs_seg)), qrs_seg, label="QRS", color="green", linewidth=0.7
+        )
+        sub_fig.plot(
+            range(len(t_seg)), t_seg / 2, label="T", color="blue", linewidth=0.7
+        )
+    plt.savefig(image_path, dpi=150)
+    plt.close()
+
+
+def gen_image(subject_id, image_path, pp_image_path, ths):
+    try:
+        input, seg = gen_seg(subject_id)
+        gen_each_image(input, seg, image_path, ths)
+        gen_each_image(input, seg, pp_image_path, ths, True)
+        return True
+    except Exception:
+        print(tb.format_exc())
+        return False
+
+
+with gr.Blocks() as demo:
+    with gr.Tab("App"):
+        with gr.Row():
+            gr.Textbox(
+                "Welcome to visit ECG-Delineation space",
+                label="Information",
+                lines=1,
+            )
+        gr_dfs = []
+        with gr.Row():
+            gr_dfs.append(
+                gr.Dataframe(
+                    value=train_df,
+                    interactive=False,
+                    max_height=250,
+                    label="our train dataset. (source: ./res/ludb/dataset/train_for_public.csv)",
+                )
+            )
+
+        with gr.Row():
+            gr_dfs.append(
+                gr.Dataframe(
+                    value=valid_df,
+                    interactive=False,
+                    max_height=250,
+                    label="our valid dataset. (source: ./res/ludb/dataset/valid_for_public.csv)",
+                )
+            )
+
+        with gr.Row():
+            gr_dfs.append(
+                gr.Dataframe(
+                    value=test_df,
+                    interactive=False,
+                    max_height=250,
+                    label="our test dataset. (source: ./res/ludb/dataset/test_for_public.csv)",
+                )
+            )
+
+        with gr.Row():
+            gr_ths = [
+                gr.Textbox(
+                    label="Interval Threshold of P (ms)",
+                    lines=1,
+                    value="10",
+                ),
+                gr.Textbox(
+                    label="Duration Threshold of P (ms)",
+                    lines=1,
+                    value="50",
+                ),
+                gr.Textbox(
+                    label="Interval Threshold of QRS (ms)",
+                    lines=1,
+                    value="50",
+                ),
+                gr.Textbox(
+                    label="Duration Threshold of QRS (ms)",
+                    lines=1,
+                    value="50",
+                ),
+                gr.Textbox(
+                    label="Interval Threshold of T (ms)",
+                    lines=1,
+                    value="30",
+                ),
+                gr.Textbox(
+                    label="Duration Threshold of T (ms)",
+                    lines=1,
+                    value="50",
+                ),
+            ]
+
+        with gr.Row():
+            gr_image = gr.Image(type="filepath", label="Output")
+            gr_pp_image = gr.Image(type="filepath", label="PostProcessed Output")
+
+        def show_image(df: pd.DataFrame, evt: gr.SelectData, *ths):
+            subject_id = evt.row_value[0]
+
+            image_path = f"{tempfile.gettempdir()}/ludb_{subject_id}.png"
+            pp_image_path = f"{tempfile.gettempdir()}/ludb_{subject_id}_pp.png"
+            if not ALWAYS_RECREATE_IMAGE and (
+                os.path.exists(image_path) and os.path.exists(pp_image_path)
+            ):
+                return [image_path, pp_image_path]
+
+            gen_image(
+                subject_id, image_path, pp_image_path, [int(th) / 2 for th in ths]
+            )
+
+            return [image_path, pp_image_path]
+
+        for gr_df in gr_dfs:
+            gr_df.select(
+                fn=show_image,
+                inputs=[gr_df, *gr_ths],
+                outputs=[gr_image, gr_pp_image],
+            )
+
+demo.launch()

requirements.txt

@@ -0,0 +1,4 @@
+--extra-index-url https://download.pytorch.org/whl/cpu
+
+./res/whl/aitiautils-1.1.59-py3-none-any.whl[torch]
+gradio==5.12.0

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