support packed

app.py

@@ -2,20 +2,55 @@ import gradio as gr
 import numpy as np
 import plotly.graph_objs as go
 from scipy.ndimage import convolve
-
+import os
 
 def readRAW(path):
 
-    output = np.fromfile(path, dtype=np.int16).reshape(31,40,64)
+    filesize = os.path.getsize(path)
+    print(filesize)
+    if filesize == 31*40*64*2:
+        output = np.fromfile(path, dtype=np.int16)
+    else:
+        with open(path, "rb") as f:
+            raw_data = f.read()
+
+
+        raw10 = np.frombuffer(raw_data, dtype=np.uint8)
+        n_blocks = raw10.shape[0] // 5
+
+        raw10 = raw10[:n_blocks * 5].reshape(-1, 5)
+
+        B0 = raw10[:, 0].astype(np.uint16)
+        B1 = raw10[:, 1].astype(np.uint16)
+        B2 = raw10[:, 2].astype(np.uint16)
+        B3 = raw10[:, 3].astype(np.uint16)
+        B4 = raw10[:, 4]
+
+        p0 = (B0 << 2) | ((B4 >> 0) & 0x03)
+        p1 = (B1 << 2) | ((B4 >> 2) & 0x03)
+        p2 = (B2 << 2) | ((B4 >> 4) & 0x03)
+        p3 = (B3 << 2) | ((B4 >> 6) & 0x03)
+
+        output = np.stack([p0, p1, p2, p3], axis=1).flatten()
     # output = np.fromfile(path, dtype=np.int16).reshape(31,40,64*2)
     # output = np.fromfile(path, dtype=np.int16).reshape(30,40,64)
 
-    return output
+    return output.reshape(31,40,64)
 
 
 def load_bin(file):
+
+    
+    
+
     # raw_hist = readRAW(file.name)[1:,...].astype(np.float32)
     raw_hist = readRAW(file.name).astype(np.float32)
+
+    print("raw_hist shape:", raw_hist[0,0,:])
+    # raw_hist = raw_hist[::-1, ::-1, :]
+
+    print("raw_hist shape:", raw_hist[0,0,:])
+
     # raw_hist = readRAW(file.name)
     # 默认显示一张 sum 图像
 
@@ -28,12 +63,19 @@ def load_bin(file):
     # nor_hist = (raw_hist) 
 
     img = np.sum(nor_hist[1:, :, :-2], axis=2)
-    norm_img = (img - img.min()) / (img.max() + 1e-8)
+    img = np.log(img +1)
+    norm_img = (img - img.min()) / (img.max())
     img_uint8 = (norm_img * 255).astype(np.uint8)
 
-    img_zoomed = np.repeat(np.repeat(img_uint8, 16, axis=0), 16, axis=1) 
+    img_tc_zoomed = np.repeat(np.repeat(img_uint8, 16, axis=0), 16, axis=1) 
+
+
+    img = np.argmax(nor_hist[1:, :, 5:-2], axis=2)
+    norm_img = (img - img.min()) / (img.max() + 1e-8)
+    img_uint8 = (norm_img * 255).astype(np.uint8)
+    img_tof_zoomed = np.repeat(np.repeat(img_uint8, 16, axis=0), 16, axis=1) 
     
-    return img_zoomed, raw_hist, nor_hist
+    return img_tc_zoomed,img_tof_zoomed, raw_hist, nor_hist
 
 
 def plot_pixel_histogram(evt: gr.SelectData, raw_hist, nor_hist):
@@ -41,8 +83,20 @@ def plot_pixel_histogram(evt: gr.SelectData, raw_hist, nor_hist):
     x, y = evt.index  # Gradio SelectData 对象
     x = x // 16
     y = y // 16
-    raw_values = raw_hist[y, x, :]
-    nor_values = nor_hist[y, x, :]
+    raw_values = raw_hist[y+1, x, :]
+    
+    tof = np.argmax(nor_hist[y+1, x, :-2])
+    range = 5
+    sim_values = nor_hist[y+1, x, tof-range:tof+range+1]
+    histogram_sim = raw_hist[1:, :, tof-range:tof+range+1]
+    print(sim_values.shape, histogram_sim.shape)
+
+    img = np.tensordot(sim_values,histogram_sim, axes=(0, 2))
+
+    # img = np.log(img +1)
+    norm_img = (img - img.min()) / (img.max() + 1e-8)
+    img_uint8 = (norm_img * 255).astype(np.uint8)
+    img_tof_zoomed = np.repeat(np.repeat(img_uint8, 16, axis=0), 16, axis=1) 
 
     vctEmbd = raw_hist[:1,:,:].flatten().astype(np.int32) >> 2
     fRX_Temp = (vctEmbd[15] << 3) + vctEmbd[14]
@@ -53,17 +107,20 @@ def plot_pixel_histogram(evt: gr.SelectData, raw_hist, nor_hist):
 
     # fTx_Temp = float(vctEmbd[61]+((vctEmbd[63] & 0xc0) << 2)) * 0.178 - 38.18
     # LDVCC = ((((vctEmbd[63]&0x30)<<4) + vctEmbd[60] - 110) * 13.7 + 5000) / 1000
-
+    y_min = np.min(raw_values[:-2]) - 10
+    y_max = np.max(raw_values[:-2]) + 10
     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 {raw_values.shape[0]} 帧的强度变化  {f'RX: {fRX_Temp}  °C'} {f'TX: {fTx_Temp:.2f} °C'} {f'LDVCC: {LDVCC:.2f} V'} {f'BVD: {BVD} V'}",
         xaxis_title="帧索引 (T)",
         yaxis_title="强度值",
+        yaxis=dict(
+        range=[y_min, y_max])  # Set the min and max for y-axis
     )
 
 
-    return fig
+    return fig, img_tof_zoomed
 
 # def plot_depth(nor_hist):
 
@@ -100,14 +157,17 @@ with gr.Blocks() as demo:
     gr.Markdown("## 上传 31,40,64 int16 `.bin/.raw` 文件，点击图像像素查看该像素的 64 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")
+    image_tc_display = gr.Image(interactive=True, label="tc")
+    image_tof_display = gr.Image(interactive=True, label="tof")
 
     histogram = gr.Plot(label="像素强度曲线")
     raw_hist = gr.State()
     nor_hist = gr.State()
+    image_sim_display = gr.Image(interactive=True, label="sim")
 
-    file_input.change(load_bin, inputs=file_input, outputs=[image_display,  raw_hist, nor_hist])
+    file_input.change(load_bin, inputs=file_input, outputs=[image_tc_display, image_tof_display, raw_hist, nor_hist])
 
-    image_display.select(plot_pixel_histogram, inputs=[ raw_hist, nor_hist], outputs=histogram)
+    image_tof_display.select(plot_pixel_histogram, inputs=[ raw_hist, nor_hist], outputs=[histogram,image_sim_display])
 
-demo.launch(share=True)
+# demo.launch(share=True)
+demo.launch(share=False)