feat: HSAP platform v2 — modular navigation, quality review, audit log, world model simulation

Major changes:
- New frontend (platform/web/): Vite + React 18 + TypeScript + Tailwind
- 4-module navigation: 数据送标 / 模型管理 / 车队管理 / 系统管理
- Data catalog with charts (DMS/ADAS/Lane 3-tab view)
- Quality review workflow (标注质检): Good/Fine/Bad scoring with auto-advance
- Audit enhancements: batch operations, rejection categories, Feishu notifications
- Operation audit log (操作日志)
- World model simulation studio (仿真工坊)
- Dataset version management with snapshots and diff
- ADAS 7-class dataset integration (138K images organized + compressed)
- User management with Feishu integration and pagination
- CRUD/search/filter on all pages, card layout redesign
- PIL-optimized image overlay rendering
- Auto-snapshot on build, in_review workflow stage
- Removed embedded algorithm code (now in workspace)

algorithms/lane_ufld/code.embedded.bak/UFLD/lane_show.py

@@ -0,0 +1,139 @@
+iport cv2
+import math
+import cap
+import numpy as np
+
+
+def is_in_poly(p, poly):
+    """
+    对点进行筛选，选出符合ROI特定区域内的点
+    :param p: 待判断的点坐标， [x, y]
+    :param poly: 多边形顶点，[[x1,y1], [x2,y2], [x3,y3], [x4,y4], ...]
+    return: is_in若为True，则说明点在ROI区域，保留，反之则删除。
+    """
+    px, py = p[0], p[1]
+    is_in = False
+    for i, corner in enumerate(poly):
+        # len(poly) = 4    next_i=(0,1,2,3,0,1,2......)
+        next_i = i + 1 if i + 1 < len(poly) else 0
+        x1, y1 = corner
+        x2, y2 = poly[next_i]
+        if (x1 == px and y1 == py) or (x2 == px and y2 == py):  # if point is on vertex
+            is_in = True
+            break
+        if min(y1, y2) < py <= max(y1, y2):  # 判断y是否处于y1与y2之间
+            x = x1 + (py - y1) * (x2 - x1) / (y2 - y1)
+            if x == px:  # if point is on edge
+                is_in = True
+                break
+            elif x > px:  # if point is on left-side of line
+                is_in = True
+    return is_in
+
+
+def handle_point(x, y):
+    """
+    根据x的大小对 x,y 进行排序。再找到最大间隔，并据此把控制点分成两部分。
+    return: 返回的是左车道线的x,y坐标以及右车道线x,y的坐标
+    """
+    lx = [] # 存储左车道线x坐标
+    ly = [] # 存储左车道线y坐标
+    rx = [] # 存储右车道线x坐标
+    ry = [] # 存储右车道线y坐标
+    points = zip(x, y)
+    # 从小到大排序
+    sorted_points = sorted(points)
+    x = [point[0] for point in sorted_points]
+    y = [point[1] for point in sorted_points]
+    # 分割
+    Max = 0
+    k = 0
+    # 找出x坐标最大间隔，分出左车道和右车道
+    for i in range(len(x) - 1):
+        # 计算欧几里得距离
+        d = np.int(math.hypot(x[i + 1] - x[i], y[i + 1] - y[i]))
+        if d > Max:
+            Max = d
+            k = i
+    for i in range(len(x)):
+        # 坐车道点
+        if i < k + 1:
+            lx.append(x[i])
+            ly.append(y[i])
+        # 右车道点
+        else:
+            rx.append(x[i])
+            ry.append(y[i])
+    return lx, ly, rx, ry
+
+
+def poly_fitting(lx, ly, rx, ry):
+    """
+    分别对两部分控制点进行二次多项式拟合
+    """
+    lx = np.array(lx)
+    ly = np.array(ly)
+    rx = np.array(rx)
+    ry = np.array(ry)
+    fl = np.polyfit(lx, ly, 2)  # 用2次多项式拟合
+    fr = np.polyfit(rx, ry, 2)  # 用2次多项式拟合
+
+    ploty = np.linspace(0, 719, 720)
+    leftx = fl[0]*ploty**2 + fl[1]*ploty + fl[2]
+    rightx = fr[0]*ploty**2 + fr[1]*ploty + fr[2]
+    # 定义从像素空间到米的x和y转换
+    ym_per_pix = 30/720  # meters per pixel in y dimension
+    xm_per_pix = 3.7/700  # meters per pixel in x dimension
+    y_eval = np.max(ploty) # 719
+
+    # 将新多项式拟合到世界空间中的x，y
+    left_fit_cr = np.polyfit(ploty*ym_per_pix, leftx*xm_per_pix, 2)
+    right_fit_cr = np.polyfit(ploty*ym_per_pix, rightx*xm_per_pix, 2)
+
+    # 计算新的曲率半径
+    left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
+    right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
+    curvature = ((left_curverad + right_curverad) / 2)  # 曲率
+
+    lane_width = np.absolute(leftx[719] - rightx[719])
+    lane_xm_per_pix = 3.7 / lane_width
+    # 车辆应该保持偏移的距离
+    veh_pos = (((leftx[719] + rightx[719]) * lane_xm_per_pix) / 2.)
+    # 当前车辆偏移的距离
+    cen_pos = ((1280 * lane_xm_per_pix) / 2.)
+    # cen_pos = ((cap.get(3) * lane_xm_per_pix) / 2.)
+    # 计算车辆偏移距离
+    distance_from_center = cen_pos - veh_pos
+    return curvature, distance_from_center
+
+
+def draw_values(img,curvature,distance_from_center):
+    """
+    将曲率和车道偏移距离里显示在图片上
+    """
+    font = cv2.FONT_HERSHEY_SIMPLEX
+    radius_text = "Radius of Curvature: %sm"%(round(curvature))
+    if distance_from_center > 0:
+        pos_flag = 'right'
+    else:
+        pos_flag = 'left'
+    cv2.putText(img, radius_text, (100, 100), font, 1, (255, 255, 255), 2)
+    center_text = "Vehicle is %.3fm %s of center"%(abs(distance_from_center), pos_flag)
+    cv2.putText(img, center_text, (100, 150), font, 1, (255, 255, 255), 2)
+    return img
+
+
+# if __name__ == "__main__":
+#     poly = [[0, 0], [0, 719], [1279, 0], [1279, 719]]
+#     lane_x = []
+#     lane_y = []
+#     is_in = is_in_poly(ppp, poly)
+#     if is_in == True:
+#         # 将处理后的点坐标添如一个空列表做拟合用
+#         lane_x.append(ppp[0])
+#         lane_y.append(ppp[1])
+#         cv2.circle(frame, ppp, 5, (0, 255, 0), -1)
+#
+#     lx, ly, rx, ry = handle_point(lane_x, lane_y)
+#     curvature, distance_from_center = poly_fitting(lx, ly, rx, ry)
+#     draw_values(frame, curvature, distance_from_center)