Add application file

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 import numpy as np
 import plotly.graph_objs as go
 from scipy.ndimage import convolve
+from gradio_imageslider import ImageSlider
 
 def readRAW(path):
 
@@ -91,16 +92,61 @@ def plot_pixel_histogram(evt: gr.SelectData, raw_hist, nor_hist):
     return fig
 
 
+def to_uint8_image(arr):
+    norm = (arr - arr.min()) / (arr.ptp() + 1e-8)
+    return (norm * 255).astype(np.uint8)
+
+def plot_depth(nor_hist):
+    kernel = np.ones((3,3))
+    output = np.zeros((96, 240, 254))
+
+    for i in range(254):
+        output[:, :, i] = convolve(nor_hist[:, :, i], kernel, mode='constant', cval=0)
+
+    modulate1 = np.arange(1, 181)
+    modulate = (modulate1 * modulate1) / (180 * 180)
+
+    arr = output[:, :, :180] * modulate[np.newaxis, np.newaxis, :]
+
+    tc_bin = np.sum(arr, axis=(0,1))
+    max_id = np.argmax(tc_bin[:-2])
+
+    pad_head = np.ones(max_id - 4)
+    expand_kernel = np.arange(1, 13) * 0.01
+    pad_tail = np.ones(180 - len(pad_head) - len(expand_kernel))
+    expand_filter = np.concatenate([pad_head, expand_kernel, pad_tail])
+
+    arr_expandfilter = arr * expand_filter[np.newaxis, np.newaxis, :]
+
+    tof = np.argmax(arr, axis=2)
+    tof_filter = np.argmax(arr_expandfilter, axis=2)
+
+
+        # 转uint8图像方便展示
+    img_tof = to_uint8_image(tof)
+    img_filter = to_uint8_image(tof_filter)
+
+    return [(img_tof, img_filter)]
+
+
 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,)
+   
+
+
     histogram = gr.Plot(label="像素强度曲线")
     raw_hist = gr.State()
     nor_hist = gr.State()
 
+
     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)
 
 demo.launch()