feat: CVAT 标注引擎、我的标注收件箱与 ADAS Cuboid 送标
- 统一标注引擎为 CVAT：客户端/配置/格式转换、iframe 标注页、docker-compose.cvat.yml 与 no_auth 补丁 - 移除 Label Studio 相关配置与构建脚本，清理 embedded.bak 备份与误提交的 node_modules - 新增「我的标注」：跨 Campaign 收件箱、逐张清单、CVAT frame 跳转 - 飞书任务分配：通讯录同步选人、按量分配、分配后 DM 通知（含 my-tasks 链接） - ADAS cuboid_7cls 数据湖接入：workflow 路径、register-batch、开标上传与标注同步 - 数据湖挂载 AS_DATA_LAKE_ROOT、datasets/adas 符号链接、reset_labeling 运维脚本 - 补充 docs/HANDOVER.md 项目交接文档 Co-authored-by: Cursor <cursoragent@cursor.com>
This commit is contained in:
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platform/as_platform/labeling/format_converter.py
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625
platform/as_platform/labeling/format_converter.py
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"""标注格式转换器：KITTI / CVAT JSON / YOLO / COCO / HSAP quaternion 互转。"""
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from __future__ import annotations
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import json
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import math
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from pathlib import Path
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from typing import Any
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# ═══════════════════════════════════════════════════════
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# Quaternion ↔ Euler 辅助
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# ═══════════════════════════════════════════════════════
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def quat_to_rot_y(qw: float, qx: float, qy: float, qz: float) -> float:
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"""四元数 → 绕 Y 轴旋转角 (KITTI rot_y)。"""
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# rot_y = atan2(2*(qw*qy + qx*qz), 1 - 2*(qy^2 + qx^2))
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sin_y = 2.0 * (qw * qy + qx * qz)
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cos_y = 1.0 - 2.0 * (qy * qy + qx * qx)
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return math.atan2(sin_y, cos_y)
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def rot_y_to_quat(rot_y: float) -> tuple[float, float, float, float]:
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"""绕 Y 轴旋转角 → 四元数 (qw, qx, qy, qz)。"""
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half = rot_y / 2.0
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return (math.cos(half), 0.0, math.sin(half), 0.0)
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# ═══════════════════════════════════════════════════════
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# 原始 quaternion 格式 → KITTI label_2
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# ═══════════════════════════════════════════════════════
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def quaternion_line_to_kitti(line: str, calib_bbox_fn=None) -> str | None:
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"""将一行 quaternion 格式转为 KITTI label_2 行。
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输入格式 (空格分隔):
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Class x y z w l h qw qx qy qz class_id truncation [bbox_2d_8values] [extra...]
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输出格式 (KITTI label_2):
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Class truncated occluded alpha bbox_x1 bbox_y1 bbox_x2 bbox_y2 h w l x y z rot_y [score]
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"""
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parts = line.strip().split()
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if len(parts) < 13:
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return None
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label = parts[0]
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x = float(parts[1])
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y = float(parts[2])
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z = float(parts[3])
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w = float(parts[4])
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l = float(parts[5])
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h_dim = float(parts[6])
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qw = float(parts[7])
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qx = float(parts[8])
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qy = float(parts[9])
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qz = float(parts[10])
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class_id = int(parts[11]) if len(parts) > 11 else 0
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truncation = int(parts[12]) if len(parts) > 12 else 0
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rot_y = quat_to_rot_y(qw, qx, qy, qz)
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# KITTI: alpha = rot_y - arctan(center_x / center_z), 简化处理
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alpha = rot_y
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# 2D bbox (如果存在: 后续8个值)
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bbox_2d = None
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if len(parts) >= 21:
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try:
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bbox_2d = [float(p) for p in parts[13:21]]
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except ValueError:
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pass
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# 截断和遮挡
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occluded = 0 # quaternion 格式没有直接对应
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# KITTI 位置是 camera coordinate: x(right), y(down), z(forward)
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# quaternion 格式是 LiDAR coordinate: x(forward), y(left), z(up)
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# 简化转换：x_kitti = -y_lidar, y_kitti = -z_lidar, z_kitti = x_lidar
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kitti_x = -y
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kitti_y = -z
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kitti_z = x
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# KITTI 3D 尺寸: height, width, length
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if bbox_2d and len(bbox_2d) == 8:
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x1, y1, x2, y2 = bbox_2d[0:4]
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else:
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x1 = y1 = x2 = y2 = 0
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# Format: Class truncated occluded alpha x1 y1 x2 y2 h w l x y z rot_y
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return (
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f"{label} {truncation} {occluded} {alpha:.6f} "
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f"{x1:.2f} {y1:.2f} {x2:.2f} {y2:.2f} "
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f"{h_dim:.6f} {w:.6f} {l:.6f} "
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f"{kitti_x:.6f} {kitti_y:.6f} {kitti_z:.6f} "
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f"{rot_y:.6f}"
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)
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# ═══════════════════════════════════════════════════════
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# KITTI → 原始 quaternion 格式
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# ═══════════════════════════════════════════════════════
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def kitti_line_to_quaternion(line: str) -> str | None:
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"""KITTI label_2 → quaternion 格式（回传HSAP）。"""
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parts = line.strip().split()
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if len(parts) < 15:
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return None
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label = parts[0]
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alpha = float(parts[3])
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bbox = [float(p) for p in parts[4:8]]
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h_dim = float(parts[8])
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w = float(parts[9])
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l = float(parts[10])
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kx = float(parts[11])
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ky = float(parts[12])
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kz = float(parts[13])
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rot_y = float(parts[14])
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# 逆转换
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x = kz # LiDAR X = KITTI Z
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y = -kx # LiDAR Y = -KITTI X
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z = -ky # LiDAR Z = -KITTI Y
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qw, qx, qy, qz = rot_y_to_quat(rot_y)
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# 输出 quaternion 格式
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return (
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f"{label} {x:.6f} {y:.6f} {z:.6f} {w:.6f} {l:.6f} {h_dim:.6f} "
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f"{qw:.6f} {qx:.6f} {qy:.6f} {qz:.6f} 0 0 "
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f"{bbox[0]:.2f} {bbox[1]:.2f} {bbox[2]:.2f} {bbox[3]:.2f} "
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f"0 0 0 0 0 0 1"
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)
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# ═══════════════════════════════════════════════════════
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# CVAT Job API shapes → HSAP / YOLO
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# ═══════════════════════════════════════════════════════
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def cvat_shape_to_result_item(
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shape: dict[str, Any],
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label_map: dict[int, str],
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) -> dict[str, Any]:
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"""CVAT Job annotations API 单条 shape → HSAP result 条目。"""
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label = label_map.get(shape.get("label_id"), "unknown")
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stype = shape.get("type", "")
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item: dict[str, Any] = {
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"type": stype,
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"label": label,
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"source": "cvat",
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"cvat_id": shape.get("id"),
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"frame": shape.get("frame", 0),
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}
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if stype == "rectangle":
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item["points"] = [
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shape.get("xtl", 0),
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shape.get("ytl", 0),
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shape.get("xbr", 0),
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shape.get("ybr", 0),
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]
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elif stype == "cuboid":
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for key in (
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"xtl1", "ytl1", "xtr1", "ytr1", "xbl1", "ybl1", "xbr1", "ybr1",
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"xtl2", "ytl2", "xtr2", "ytr2", "xbl2", "ybl2", "xbr2", "ybr2",
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):
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if key in shape:
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item[key] = shape[key]
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if shape.get("points"):
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item["points"] = shape["points"]
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elif stype in ("polyline", "polygon", "points"):
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item["points"] = shape.get("points", [])
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return item
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def cvat_job_shapes_to_yolo_lines(
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shapes: list[dict[str, Any]],
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label_map: dict[int, str],
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class_map: dict[str, int],
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img_width: int,
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img_height: int,
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) -> list[str]:
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lines: list[str] = []
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for shape in shapes:
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if shape.get("type") != "rectangle":
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continue
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label = label_map.get(shape.get("label_id"), "")
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class_id = class_map.get(label)
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if class_id is None:
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# 尝试大小写不敏感匹配
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for name, cid in class_map.items():
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if name.lower() == label.lower():
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class_id = cid
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break
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if class_id is None:
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continue
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x1, y1, x2, y2 = (
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float(shape.get("xtl", 0)),
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float(shape.get("ytl", 0)),
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float(shape.get("xbr", 0)),
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float(shape.get("ybr", 0)),
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)
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if img_width <= 0 or img_height <= 0:
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continue
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cx = ((x1 + x2) / 2) / img_width
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cy = ((y1 + y2) / 2) / img_height
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bw = (x2 - x1) / img_width
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bh = (y2 - y1) / img_height
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lines.append(f"{class_id} {cx:.6f} {cy:.6f} {bw:.6f} {bh:.6f}")
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return lines
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def group_cvat_job_shapes_by_frame(
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job_annotations: dict[str, Any],
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) -> dict[int, list[dict[str, Any]]]:
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grouped: dict[int, list[dict[str, Any]]] = {}
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for shape in job_annotations.get("shapes") or []:
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frame = int(shape.get("frame", 0))
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grouped.setdefault(frame, []).append(shape)
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return grouped
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def cvat_shapes_to_export_regions(
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shapes: list[dict[str, Any]],
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label_map: dict[int, str],
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img_width: int,
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img_height: int,
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) -> list[dict[str, Any]]:
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"""CVAT Job shapes → HSAP 导出链兼容的 result[]（原 Label Studio 字段布局）。"""
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if img_width <= 0 or img_height <= 0:
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return []
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regions: list[dict[str, Any]] = []
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for shape in shapes:
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stype = shape.get("type") or ""
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label = label_map.get(shape.get("label_id"), "unknown")
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base = {
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"id": str(shape.get("id", "")),
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"original_width": img_width,
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"original_height": img_height,
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}
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if stype == "rectangle":
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xtl = float(shape.get("xtl", 0))
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ytl = float(shape.get("ytl", 0))
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xbr = float(shape.get("xbr", 0))
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ybr = float(shape.get("ybr", 0))
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regions.append({
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**base,
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"type": "rectanglelabels",
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"value": {
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"x": xtl / img_width * 100.0,
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"y": ytl / img_height * 100.0,
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"width": (xbr - xtl) / img_width * 100.0,
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"height": (ybr - ytl) / img_height * 100.0,
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"rotation": 0,
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"rectanglelabels": [label],
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},
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})
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elif stype == "points":
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pts = shape.get("points") or []
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if len(pts) < 2:
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continue
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regions.append({
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**base,
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"type": "keypointlabels",
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"value": {
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"x": float(pts[0]) / img_width * 100.0,
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"y": float(pts[1]) / img_height * 100.0,
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"width": 0.5,
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"keypointlabels": [label],
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},
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})
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elif stype in ("polyline", "polygon"):
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regions.append({
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**base,
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"type": "polyline",
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"label": label,
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"points": list(shape.get("points") or []),
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})
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elif stype == "cuboid":
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item = cvat_shape_to_result_item(shape, label_map)
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item["original_width"] = img_width
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item["original_height"] = img_height
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regions.append(item)
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return regions
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# ═══════════════════════════════════════════════════════
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# CVAT JSON → YOLO bbox
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# ═══════════════════════════════════════════════════════
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def cvat_json_to_yolo(
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cvat_annotations: dict[str, Any],
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class_map: dict[str, int],
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img_width: int = 1920,
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img_height: int = 1080,
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) -> dict[str, list[str]]:
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"""CVAT annotations JSON → YOLO 格式文件内容。
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返回 {image_name: [yolo_line, ...]} 的字典。
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"""
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result: dict[str, list[str]] = {}
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for img_ann in cvat_annotations.get("annotations", []):
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frame = img_ann.get("frame", 0)
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img_name = _resolve_image_name(cvat_annotations, img_ann)
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lines: list[str] = []
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for shape in img_ann.get("shapes", []):
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shape_type = shape.get("type", "")
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label_name = shape.get("label", "")
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class_id = class_map.get(label_name)
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if class_id is None:
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continue
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if shape_type == "rectangle":
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# YOLO: class_id cx cy w h (归一化 0-1)
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x1, y1, x2, y2 = (shape.get(p, 0) for p in ("xtl", "ytl", "xbr", "ybr"))
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cx = ((x1 + x2) / 2) / img_width
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cy = ((y1 + y2) / 2) / img_height
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bw = (x2 - x1) / img_width
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bh = (y2 - y1) / img_height
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lines.append(f"{class_id} {cx:.6f} {cy:.6f} {bw:.6f} {bh:.6f}")
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if lines:
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result[img_name] = lines
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return result
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# ═══════════════════════════════════════════════════════
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# CVAT JSON → COCO keypoints
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# ═══════════════════════════════════════════════════════
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def cvat_json_to_coco_keypoints(
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cvat_annotations: dict[str, Any],
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keypoint_labels: list[str],
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image_dir: Path | None = None,
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) -> dict[str, Any]:
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"""提取 CVAT 关键点标注 → COCO keypoints 格式。"""
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images: list[dict[str, Any]] = []
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annotations: list[dict[str, Any]] = []
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# 构建 keypoint_label → id 映射
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kp_map = {name: i for i, name in enumerate(keypoint_labels)}
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ann_id = 0
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for img_idx, img_ann in enumerate(cvat_annotations.get("annotations", [])):
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img_name = _resolve_image_name(cvat_annotations, img_ann)
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img_w = img_ann.get("width", 1920)
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img_h = img_ann.get("height", 1080)
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img_id = img_idx + 1
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images.append({"id": img_id, "file_name": img_name, "width": img_w, "height": img_h})
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for shape in img_ann.get("shapes", []):
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if shape.get("type") != "points":
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continue
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points = shape.get("points", [])
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if not points:
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continue
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# points 格式: [[x1,y1], [x2,y2], ...]
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keypoints_list: list[float] = []
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num_keypoints = 0
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for kp_label in keypoint_labels:
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kp_data = next((p for p in points if p.get("label") == kp_label), None)
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if kp_data:
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keypoints_list.extend([kp_data.get("x", 0), kp_data.get("y", 0), 2]) # visible
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num_keypoints += 1
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else:
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keypoints_list.extend([0, 0, 0]) # not labeled
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annotations.append({
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"id": ann_id,
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"image_id": img_id,
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"category_id": 1,
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"keypoints": keypoints_list,
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"num_keypoints": num_keypoints,
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"bbox": _keypoint_bbox(keypoints_list, img_w, img_h),
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})
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ann_id += 1
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return {
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"images": images,
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"annotations": annotations,
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"categories": [{"id": 1, "name": "person", "keypoints": keypoint_labels, "skeleton": []}],
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}
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# ═══════════════════════════════════════════════════════
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# CVAT JSON → HSAP Lane polyline
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# ═══════════════════════════════════════════════════════
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def cvat_json_to_lane_polylines(
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cvat_annotations: dict[str, Any],
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) -> dict[str, list[dict[str, Any]]]:
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"""提取 CVAT 折线标注 → HSAP 车道线格式。"""
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result: dict[str, list[dict[str, Any]]] = {}
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for img_ann in cvat_annotations.get("annotations", []):
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img_name = _resolve_image_name(cvat_annotations, img_ann)
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polylines: list[dict[str, Any]] = []
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for shape in img_ann.get("shapes", []):
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if shape.get("type") not in ("polyline", "polygon"):
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continue
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points = shape.get("points", [])
|
||||
if not points:
|
||||
continue
|
||||
attrs = {a.get("name"): a.get("value") for a in (shape.get("attributes") or [])}
|
||||
polylines.append({
|
||||
"label": shape.get("label", "lane_line"),
|
||||
"attributes": attrs,
|
||||
"points": [[p.get("x", 0), p.get("y", 0)] for p in points],
|
||||
})
|
||||
|
||||
if polylines:
|
||||
result[img_name] = polylines
|
||||
|
||||
return result
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════
|
||||
# 辅助函数
|
||||
# ═══════════════════════════════════════════════════════
|
||||
|
||||
def _resolve_image_name(annotations: dict[str, Any], img_ann: dict[str, Any]) -> str:
|
||||
"""从 CVAT annotation JSON 中解析图像文件名。"""
|
||||
frame = img_ann.get("frame", 0)
|
||||
images = annotations.get("images", [])
|
||||
if isinstance(images, list) and frame < len(images):
|
||||
img_info = images[frame]
|
||||
if isinstance(img_info, dict):
|
||||
return img_info.get("file_name", f"frame_{frame}")
|
||||
return img_ann.get("name", f"frame_{frame}")
|
||||
|
||||
|
||||
def _keypoint_bbox(kpts: list[float], img_w: int, img_h: int) -> list[float]:
|
||||
"""从 keypoints 列表计算 bbox [x, y, w, h]。"""
|
||||
xs = [kpts[i] for i in range(0, len(kpts), 3) if kpts[i + 2] > 0]
|
||||
ys = [kpts[i + 1] for i in range(0, len(kpts), 3) if kpts[i + 2] > 0]
|
||||
if not xs or not ys:
|
||||
return [0, 0, 0, 0]
|
||||
x_min, x_max = min(xs), max(xs)
|
||||
y_min, y_max = min(ys), max(ys)
|
||||
return [x_min, y_min, x_max - x_min, y_max - y_min]
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════
|
||||
# 批量 KITTI 转换
|
||||
# ═══════════════════════════════════════════════════════
|
||||
|
||||
def convert_quaternion_dir_to_kitti(label_dir: Path, output_dir: Path) -> int:
|
||||
"""将 quaternion 格式目录批量转换为 KITTI label_2 格式。"""
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
count = 0
|
||||
for txt_file in sorted(label_dir.rglob("*.txt")):
|
||||
kitti_lines: list[str] = []
|
||||
for line in txt_file.read_text(encoding="utf-8").strip().split("\n"):
|
||||
if not line.strip():
|
||||
continue
|
||||
kitti_line = quaternion_line_to_kitti(line)
|
||||
if kitti_line:
|
||||
kitti_lines.append(kitti_line)
|
||||
if kitti_lines:
|
||||
out_file = output_dir / txt_file.name
|
||||
out_file.write_text("\n".join(kitti_lines) + "\n", encoding="utf-8")
|
||||
count += 1
|
||||
return count
|
||||
|
||||
|
||||
def convert_cvat_kitti_export_to_hsap(kitti_data: bytes, output_dir: Path) -> int:
|
||||
"""将 CVAT KITTI 导出（zip 字节）解压并转为 HSAP quaternion 格式。"""
|
||||
import io
|
||||
import zipfile
|
||||
|
||||
output_dir.mkdir(parents=True, exist_ok=True)
|
||||
count = 0
|
||||
with zipfile.ZipFile(io.BytesIO(kitti_data)) as zf:
|
||||
for name in zf.namelist():
|
||||
if not name.endswith(".txt") or "label_2" not in name:
|
||||
continue
|
||||
content = zf.read(name).decode("utf-8")
|
||||
hsap_lines: list[str] = []
|
||||
for line in content.strip().split("\n"):
|
||||
if not line.strip():
|
||||
continue
|
||||
hsap_line = kitti_line_to_quaternion(line)
|
||||
if hsap_line:
|
||||
hsap_lines.append(hsap_line)
|
||||
if hsap_lines:
|
||||
fname = Path(name).name
|
||||
(output_dir / fname).write_text("\n".join(hsap_lines) + "\n", encoding="utf-8")
|
||||
count += 1
|
||||
return count
|
||||
|
||||
|
||||
# ═══════════════════════════════════════════════════════
|
||||
# ADAS 3D Quaternion JSON → CVAT cuboid XML
|
||||
# ═══════════════════════════════════════════════════════
|
||||
|
||||
from xml.etree.ElementTree import Element, SubElement, tostring
|
||||
from xml.dom import minidom
|
||||
from datetime import datetime, timezone
|
||||
|
||||
|
||||
def _get_np():
|
||||
"""Lazy numpy import."""
|
||||
import numpy as np
|
||||
return np
|
||||
|
||||
|
||||
def _quat_to_rotation_matrix(qw: float, qx: float, qy: float, qz: float):
|
||||
np = _get_np()
|
||||
return np.array([
|
||||
[1 - 2*qy**2 - 2*qz**2, 2*qx*qy - 2*qz*qw, 2*qx*qz + 2*qy*qw],
|
||||
[2*qx*qy + 2*qz*qw, 1 - 2*qx**2 - 2*qz**2, 2*qy*qz - 2*qx*qw],
|
||||
[2*qx*qz - 2*qy*qw, 2*qy*qz + 2*qx*qw, 1 - 2*qx**2 - 2*qy**2],
|
||||
])
|
||||
|
||||
|
||||
def _get_3d_corners(center, w, l, h, qw, qx, qy, qz):
|
||||
"""Compute 8 corners in camera coordinates.
|
||||
Object frame: x=forward(±l/2), y=left(±w/2), z=up(±h/2)."""
|
||||
np = _get_np()
|
||||
ox = np.array([-l/2, -l/2, -l/2, -l/2, l/2, l/2, l/2, l/2])
|
||||
oy = np.array([-w/2, w/2, w/2, -w/2, -w/2, w/2, w/2, -w/2])
|
||||
oz = np.array([-h/2, -h/2, h/2, h/2, -h/2, -h/2, h/2, h/2])
|
||||
corners_obj = np.stack([ox, oy, oz], axis=1)
|
||||
R = _quat_to_rotation_matrix(qw, qx, qy, qz)
|
||||
return (R @ corners_obj.T).T + np.array(center)
|
||||
|
||||
|
||||
def _project_2d(pts_3d, K):
|
||||
pts = pts_3d @ K.T
|
||||
return pts[:, :2] / pts[:, 2:]
|
||||
|
||||
|
||||
def quaternion_json_to_cvat_cuboid_xml(
|
||||
json_dir: str | Path,
|
||||
image_names: list[str],
|
||||
task_id: int | None = None,
|
||||
) -> str:
|
||||
"""将 ADAS 3D quaternion JSON 标注转换为 CVAT cuboid XML。
|
||||
|
||||
Args:
|
||||
json_dir: 包含 .json 标注文件的目录
|
||||
image_names: 图像文件名列表（与 CVAT task 中的 frame 顺序对应）
|
||||
task_id: 可选 CVAT task ID
|
||||
|
||||
Returns:
|
||||
CVAT for images 1.1 XML 字符串
|
||||
"""
|
||||
json_dir = Path(json_dir)
|
||||
root = Element("annotations")
|
||||
SubElement(root, "version").text = "1.1"
|
||||
meta = SubElement(root, "meta")
|
||||
te = SubElement(meta, "task")
|
||||
SubElement(te, "id").text = str(task_id or 0)
|
||||
SubElement(te, "name").text = "ADAS 3D"
|
||||
SubElement(te, "size").text = str(len(image_names))
|
||||
SubElement(te, "mode").text = "annotation"
|
||||
SubElement(te, "overlap").text = "0"
|
||||
now = datetime.now(timezone.utc).isoformat()
|
||||
SubElement(te, "created").text = now
|
||||
SubElement(te, "updated").text = now
|
||||
le = SubElement(te, "labels")
|
||||
for lbl in ["car", "pedestrian", "truck", "bus", "motorcycle", "tricycle", "traffic cone"]:
|
||||
l = SubElement(le, "label"); SubElement(l, "name").text = lbl; SubElement(l, "attributes")
|
||||
se = SubElement(te, "segments"); s = SubElement(se, "segment")
|
||||
SubElement(s, "id").text = "1"; SubElement(s, "start").text = "0"
|
||||
SubElement(s, "stop").text = str(len(image_names) - 1)
|
||||
ow = SubElement(te, "owner"); SubElement(ow, "username").text = "platform"; SubElement(ow, "email").text = ""
|
||||
SubElement(meta, "dumped").text = now
|
||||
|
||||
total = 0
|
||||
for fid, img_name in enumerate(image_names):
|
||||
stem = Path(img_name).stem
|
||||
jp = json_dir / f"{stem}.json"
|
||||
if not jp.is_file():
|
||||
continue
|
||||
|
||||
ann = json.loads(jp.read_text(encoding="utf-8"))
|
||||
np = _get_np()
|
||||
K = np.array(ann["K"])
|
||||
img_w, img_h = ann["image_size"]
|
||||
|
||||
ie = SubElement(root, "image")
|
||||
ie.set("id", str(fid))
|
||||
ie.set("name", Path(img_name).name)
|
||||
ie.set("width", str(img_w))
|
||||
ie.set("height", str(img_h))
|
||||
|
||||
for det in ann.get("detections", []):
|
||||
w, l, h = det["dimensions_wlh"]
|
||||
c3d = _get_3d_corners(det["center_3d"], w, l, h, *det["quaternion_wxyz"])
|
||||
if _get_np().any(c3d[:, 2] <= 0):
|
||||
continue
|
||||
c2d = _project_2d(c3d, K)
|
||||
|
||||
# 4 edge-pairs: (rear, front) × (tl, tr, bl, br)
|
||||
pairs = [(3, 7), (2, 6), (0, 4), (1, 5)]
|
||||
pd = []
|
||||
for ri, fi in pairs:
|
||||
mid = (c2d[ri] + c2d[fi]) / 2.0
|
||||
f1_i, f2_i = (fi, ri) if c3d[fi, 2] <= c3d[ri, 2] else (ri, fi)
|
||||
pd.append({"mid": mid, "f1_i": f1_i, "f2_i": f2_i})
|
||||
|
||||
pd.sort(key=lambda p: p["mid"][1])
|
||||
top = sorted(pd[:2], key=lambda p: p["mid"][0])
|
||||
bot = sorted(pd[2:], key=lambda p: p["mid"][0])
|
||||
tl, tr = top[0], top[1]
|
||||
bl, br = bot[0], bot[1]
|
||||
|
||||
cub = SubElement(ie, "cuboid")
|
||||
cub.set("label", det["class_name"]); cub.set("source", "manual"); cub.set("occluded", "0")
|
||||
cub.set("xtl1", f"{c2d[tl['f1_i']][0]:.2f}"); cub.set("ytl1", f"{c2d[tl['f1_i']][1]:.2f}")
|
||||
cub.set("xtr1", f"{c2d[tr['f1_i']][0]:.2f}"); cub.set("ytr1", f"{c2d[tr['f1_i']][1]:.2f}")
|
||||
cub.set("xbl1", f"{c2d[bl['f1_i']][0]:.2f}"); cub.set("ybl1", f"{c2d[bl['f1_i']][1]:.2f}")
|
||||
cub.set("xbr1", f"{c2d[br['f1_i']][0]:.2f}"); cub.set("ybr1", f"{c2d[br['f1_i']][1]:.2f}")
|
||||
cub.set("xtl2", f"{c2d[tl['f2_i']][0]:.2f}"); cub.set("ytl2", f"{c2d[tl['f2_i']][1]:.2f}")
|
||||
cub.set("xtr2", f"{c2d[tr['f2_i']][0]:.2f}"); cub.set("ytr2", f"{c2d[tr['f2_i']][1]:.2f}")
|
||||
cub.set("xbl2", f"{c2d[bl['f2_i']][0]:.2f}"); cub.set("ybl2", f"{c2d[bl['f2_i']][1]:.2f}")
|
||||
cub.set("xbr2", f"{c2d[br['f2_i']][0]:.2f}"); cub.set("ybr2", f"{c2d[br['f2_i']][1]:.2f}")
|
||||
cub.set("z_order", "0")
|
||||
total += 1
|
||||
|
||||
xml_str = minidom.parseString(tostring(root, 'utf-8')).toprettyxml(indent=" ")
|
||||
return xml_str
|
||||
Reference in New Issue
Block a user