add code

app.py

@@ -4,30 +4,84 @@ import plotly.graph_objs as go
 from scipy.ndimage import convolve
 from gradio_imageslider import ImageSlider
 import cv2
+import os
+
+# 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
+
+def mipi_raw10_to_raw8_scaled(raw10_data):
+    raw10_data = np.frombuffer(raw10_data, dtype=np.uint8)
+    n_blocks = len(raw10_data) // 5
+    raw10_data = raw10_data[:n_blocks * 5].reshape(-1, 5)
+
+    p0 = (raw10_data[:, 0].astype(np.uint16) << 2) | ((raw10_data[:, 4] >> 0) & 0x03)
+    p1 = (raw10_data[:, 1].astype(np.uint16) << 2) | ((raw10_data[:, 4] >> 2) & 0x03)
+    p2 = (raw10_data[:, 2].astype(np.uint16) << 2) | ((raw10_data[:, 4] >> 4) & 0x03)
+    p3 = (raw10_data[:, 3].astype(np.uint16) << 2) | ((raw10_data[:, 4] >> 6) & 0x03)
+
+    raw8_data = np.empty((n_blocks * 4 * 2,), dtype=np.uint8)
+    raw8_data[0::8] = p0 & 0xFF
+    raw8_data[1::8] = p0 >> 8
+    raw8_data[2::8] = p1 & 0xFF
+    raw8_data[3::8] = p1 >> 8
+    raw8_data[4::8] = p2 & 0xFF
+    raw8_data[5::8] = p2 >> 8
+    raw8_data[6::8] = p3 & 0xFF
+    raw8_data[7::8] = p3 >> 8
+
+    return raw8_data.tobytes()
 
 def readRAW(path):
+    filesize = os.path.getsize(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
+    with open(path, "rb") as f:
+        raw_data = f.read()
+
+    # Case 1: 如果是 MIPI RAW10 格式，大小为 7,372,800 字节
+    if filesize == 7372800:
+        raw_data = mipi_raw10_to_raw8_scaled(raw_data)
+
+    # 转换为 int16 并 reshape
+    arr = np.frombuffer(raw_data, dtype=np.int16).reshape(96, 240, 256)
+
+    # Byte Swap: [x,y,256] → [x,y,128,2] → swap last dim → [x,y,256]
     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
+    swapped = reshaped[..., ::-1]
     histogram_data = swapped.reshape(arr.shape)
-    # 定义映射顺序：对每组8行进行调换
+
+    # Line remapping (每组8行：0,4,1,5,...)
     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）
+    num_groups = 12  # 96 / 8
     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,:,:]
+        output[start:end, :, :] = histogram_data[start:end, :, :][mapping, :, :]
 
     return output