add code

app.py

@@ -1,61 +1,83 @@
 import gradio as gr
 import numpy as np
 import plotly.graph_objs as go
-
+from scipy.ndimage import convolve
 
 def readRAW(path):
 
-  arr = np.fromfile(path, dtype=np.int16).reshape(96,240,256)
-  # 将最后一维重塑为 (-1, 2)，其中 -1 自动计算为 128
-  reshaped = arr.reshape(*arr.shape[:-1], -1, 2)
-  # 交换每一对中的两个元素
-  swapped = reshaped[..., :, ::-1]
-  # 恢复原始形状
-  histogram_data = swapped.reshape(arr.shape)
-  # 定义映射顺序：对每组8行进行调换
-  mapping = [0, 4, 1, 5, 2, 6, 3, 7]
-  # 每组包含的行数
-  group_size = 8
-  num_groups = 12  # 96/8
+    arr = np.fromfile(path, dtype=np.int16).reshape(96,240,256)
+    # 将最后一维重塑为 (-1, 2)，其中 -1 自动计算为 128
+    reshaped = arr.reshape(*arr.shape[:-1], -1, 2)
+    # 交换每一对中的两个元素
+    swapped = reshaped[..., :, ::-1]
+    # 恢复原始形状
+    histogram_data = swapped.reshape(arr.shape)
+    # 定义映射顺序：对每组8行进行调换
+    mapping = [0, 4, 1, 5, 2, 6, 3, 7]
+    # 每组包含的行数
+    group_size = 8
+    num_groups = 12  # 96/8
 
-  # 创建一个用于存储结果的数组（也可以原地修改）
-  output = np.empty_like(histogram_data)
+    # 创建一个用于存储结果的数组（也可以原地修改）
+    output = np.empty_like(histogram_data)
 
-  # 对每个 group 分别进行行重排
-  for g in range(num_groups):
-    start = g * group_size
-    end = start + group_size
-    output[start:end,:,:] = histogram_data[start:end,:,:][mapping,:,:]
+    # 对每个 group 分别进行行重排
+    for g in range(num_groups):
+        start = g * group_size
+        end = start + group_size
+        output[start:end,:,:] = histogram_data[start:end,:,:][mapping,:,:]
 
-  return output
+    return output
 
 
 # 解析bin文件，数据shape是 (H, W, T) = (96, 240, 256)
 def load_bin(file):
-    volume = readRAW(file.name)
+    raw_hist = readRAW(file.name)
     # 默认显示一张 sum 图像
-    img = np.sum(volume[:, :, :-2], axis=2)
+
+    multishot =  (raw_hist[...,254]*1024 + raw_hist[...,255])
+    normalize_data =  1 / multishot
+    nor_hist = (raw_hist) * normalize_data[...,np.newaxis]
+
+    img = np.sum(nor_hist[:, :, :-2], axis=2)
     norm_img = (img - img.min()) / (img.max() + 1e-8)
     img_uint8 = (norm_img * 255).astype(np.uint8)
 
     img_zoomed = np.repeat(np.repeat(img_uint8, 4, axis=0), 4, axis=1)  # (96→192, 240→480)
     
-    return img_zoomed, volume
+    return img_zoomed, raw_hist, nor_hist
 
 
-def plot_pixel_histogram(evt: gr.SelectData, volume):
+def plot_pixel_histogram(evt: gr.SelectData, raw_hist, nor_hist):
     # print("evt:", evt)
     x, y = evt.index  # Gradio SelectData 对象
     x = x // 4
     y = y // 4
-    values = volume[y, x, :]
+    raw_values = raw_hist[y, x, :]
+    nor_values = nor_hist[y, x, :]
+
+
+
+    # fig = go.Figure()
+    # fig.add_trace(go.Scatter(y=raw_values, mode="lines+markers"))
+    # fig.update_layout(
+    #     title=f"Pixel ({x}, {y}) 在所有 {values.shape[0]} 帧的强度变化",
+    #     xaxis_title="帧索引 (T)",
+    #     yaxis_title="强度值",
+    # )
+
     fig = go.Figure()
-    fig.add_trace(go.Scatter(y=values, mode="lines+markers"))
+    fig.add_trace(go.Scatter(y=raw_values, mode="lines", name="原始值", yaxis="y1"))
+    fig.add_trace(go.Scatter(y=nor_values, mode="lines", name="归一化", yaxis="y2"))
+
     fig.update_layout(
-        title=f"Pixel ({x}, {y}) 在所有 {values.shape[0]} 帧的强度变化",
+        title=f"Pixel ({x}, {y}) 双 Y 轴示意图",
         xaxis_title="帧索引 (T)",
-        yaxis_title="强度值",
+        yaxis=dict(title="原始值", titlefont=dict(color="blue"), tickfont=dict(color="blue")),
+        yaxis2=dict(title="归一化", overlaying="y", side="right", titlefont=dict(color="red"), tickfont=dict(color="red")),
     )
+
+
     return fig
 
 
@@ -65,9 +87,10 @@ with gr.Blocks() as demo:
     file_input = gr.File(label="上传 .raw/.bin 文件", file_types=[".raw", ".bin"])
     image_display = gr.Image(interactive=True, label="点击像素显示强度曲线")
     histogram = gr.Plot(label="像素强度曲线")
-    volume_state = gr.State()
+    raw_hist = gr.State()
+    nor_hist = gr.State()
 
-    file_input.change(load_bin, inputs=file_input, outputs=[image_display, volume_state])
-    image_display.select(plot_pixel_histogram, inputs=volume_state, outputs=histogram)
+    file_input.change(load_bin, inputs=file_input, outputs=[image_display,  raw_hist, nor_hist])
+    image_display.select(plot_pixel_histogram, inputs=[ raw_hist, nor_hist], outputs=histogram)
 
 demo.launch()