Add application file

app.py

@@ -4,49 +4,22 @@ import plotly.graph_objs as go
 from scipy.ndimage import convolve
 from gradio_imageslider import ImageSlider
 
-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
-
-    # 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,:,:]
-
-    return output
-
 
-# 解析bin文件，数据shape是 (H, W, T) = (96, 240, 256)
 def load_bin(file):
     raw_hist = readRAW(file.name)
-    # 默认显示一张 sum 图像
 
-    multishot =  (raw_hist[...,254]*1024 + raw_hist[...,255])
-    normalize_data =  1 / multishot * 12000
-    nor_hist = (raw_hist) * normalize_data[...,np.newaxis]
+    multishot = (raw_hist[..., 254] * 1024 + raw_hist[..., 255]).astype(np.float32)
+    normalize_data = np.where(multishot != 0, 12000 / multishot, 0)
+    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)
 
-    img_zoomed = np.repeat(np.repeat(img_uint8, 4, axis=0), 4, axis=1)  # (96→192, 240→480)
-    
-    return img_zoomed, raw_hist, nor_hist
+    depth_slider_imgs = plot_depth(nor_hist)  # 👈 直接在这里计算
+
+    return img_zoomed, raw_hist, nor_hist, depth_slider_imgs
 
 
 def plot_pixel_histogram(evt: gr.SelectData, raw_hist, nor_hist):
@@ -66,32 +39,8 @@ def plot_pixel_histogram(evt: gr.SelectData, raw_hist, nor_hist):
         xaxis_title="帧索引 (T)",
         yaxis_title="强度值",
     )
-
-    # fig = go.Figure()
-    # 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}) 双 Y 轴示意图",
-    #     xaxis_title="帧索引 (T)",
-        
-    #     yaxis=dict(
-    #         title=dict(text="原始值", font=dict(color="blue")),
-    #         tickfont=dict(color="blue")
-    #     ),
-        
-    #     yaxis2=dict(
-    #         title=dict(text="归一化", font=dict(color="red")),
-    #         tickfont=dict(color="red"),
-    #         overlaying="y",
-    #         side="right"
-    #     )
-    # )
-
-
     return fig
 
-
 def to_uint8_image(arr):
     norm = (arr - arr.min()) / (arr.ptp() + 1e-8)
     return (norm * 255).astype(np.uint8)
@@ -129,24 +78,56 @@ def plot_depth(nor_hist):
     return [(img_tof, img_filter)]
 
 
+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
+
+    # 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,:,:]
+
+    return output
+
+
 with gr.Blocks() as demo:
     gr.Markdown("## 上传 96×240×256 int16 `.bin/.raw` 文件，点击图像像素查看该像素的 256 帧直方图")
-    
+
     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")
 
-    depth_image_slider = ImageSlider(label="Filter Depth Map with Slider View", elem_id='img-display-output', position=0.5,)
-   
-
+    depth_image_slider = ImageSlider(label="Filter Depth Map with Slider View", elem_id='img-display-output', position=0.5)
 
     histogram = gr.Plot(label="像素强度曲线")
     raw_hist = gr.State()
     nor_hist = gr.State()
 
+    # 单一入口统一触发
+    file_input.change(
+        load_bin,
+        inputs=file_input,
+        outputs=[image_display, raw_hist, nor_hist, depth_image_slider]
+    )
 
-    file_input.change(load_bin, inputs=file_input, outputs=[image_display,  raw_hist, nor_hist])
-    file_input.change(plot_depth, inputs=nor_hist, outputs=[depth_image_slider])
-
-    image_display.select(plot_pixel_histogram, inputs=[ raw_hist, nor_hist], outputs=histogram)
+    image_display.select(
+        plot_pixel_histogram,
+        inputs=[raw_hist, nor_hist],
+        outputs=histogram
+    )
 
-demo.launch()
+demo.launch()