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# py_vision_demo
# py_vision_demo — Python 图像话题(cv_bridge + OpenCV)
ROS2 图像传感器演示包(Python)。属于 Level 1 基础机制第 9 块。
> ROS2 图像数据流:`fake_camera` 模拟相机 → 发布 Image → `image_processor` 用 OpenCV 处理。
>
> 预计学习时间:1-2 小时。
## 功能
---
- **`fake_camera`**: 周期性发布 640x480 bgr8 合成图像(渐变 + 帧号 + 动态圆)
- **`image_processor`**: 订阅图像,转 OpenCV,计算平均亮度
## 这是什么?
## 关键概念
ROS2 图像话题跟普通 Topic 一样,**只是消息类型用 `sensor_msgs/Image`**。本包演示:
| 概念 | 用途 |
|---|---|
| `sensor_msgs/Image` | 图像消息(height/width/encoding/step/data) |
| `cv_bridge` | ROS Image ↔ OpenCV numpy 转换 |
| `bgr8` / `rgb8` / `mono8` | 像素编码 |
| `REP-105 frame_id` | `camera_optical_frame`(光心系) |
1. **fake_camera**:周期性发布合成图像(640x480,带渐变背景 + 帧号文本 + 中心圆)
2. **image_processor**:订阅图像 → 转 numpy → OpenCV 处理 → 再发回
## 运行
**关键工具**: `cv_bridge`(ROS Image ↔ OpenCV numpy array)。
```bash
# 终端 1:启 camera + processor
ros2 launch py_vision_demo vision_launch.py
---
# 终端 2:实时看图像(需要 RViz 或 image_view)
ros2 run rqt_image_view rqt_image_view /image_raw
## 🎯 学完之后你能做什么?
# CLI 验证
ros2 topic info /image_raw -v
ros2 topic hz /image_raw
1. ✅ 理解 `sensor_msgs/Image` 消息结构(height/width/encoding/data)
2. ✅ 用 `cv_bridge.imgmsg_to_cv2()` 把 ROS Image 转 OpenCV 数组
3. ✅ 用 OpenCV 处理图像(灰度、画框、滤波)
4. ✅ 用 `cv_bridge.cv2_to_imgmsg()` 转回 ROS Image 发布
5. ✅ 用 `image_transport` 压缩传输(可选)
---
## 📁 文件结构
```
src/py_vision_demo/
├── py_vision_demo/
│ ├── fake_camera.py # 模拟相机 Publisher
│ └── image_processor.py # 图像处理 Subscriber
├── launch/vision_launch.py # 一键启动
├── test/
│ ├── conftest.py
│ ├── test_fake_camera.py
│ ├── test_image_processor.py
│ └── test_vision.py # 端到端测试
└── setup.py
```
## 测试
---
## 🚀 跑起来
### 终端 1:启动 fake_camera + image_processor
```bash
source /opt/ros/humble/setup.bash
source /root/ros2_ws/install/setup.bash
ros2 launch py_vision_demo vision_launch.py
```
**预期输出**:
```
[INFO] [fake_camera]: FakeCamera started: 640x480 @ 1Hz, topic="/image_raw"
[INFO] [image_processor]: ImageProcessor started: topic="/image_processed"
[INFO] [image_processor]: processing frame=0, mean_brightness=127.5
...
```
### 终端 2:看图像话题列表
```bash
ros2 topic list
```
**预期输出**:
```
/image_raw
/image_processed
/parameter_events
/rosout
```
### 终端 3:运行时改参数
```bash
# 改分辨率
ros2 param set fake_camera image_width 320
ros2 param set fake_camera image_height 240
# 改帧率
ros2 param set fake_camera publish_rate_hz 5.0
# 改处理模式(下游 image_processor)
ros2 param set image_processor mode 'edges' # 或 'gray' 或 'raw'
```
---
## 📖 核心代码解读
### fake_camera.py(发布图像)
```python
import cv2
import numpy as np
from sensor_msgs.msg import Image
class FakeCamera(Node):
def __init__(self):
super().__init__('fake_camera')
# 1) 声明参数
self.declare_parameter('image_width', 640, ...)
self.declare_parameter('image_height', 480, ...)
self.declare_parameter('publish_rate_hz', 1.0, ...)
# 2) 读参数
self._width = self.get_parameter('image_width').value
self._height = self.get_parameter('image_height').value
# 3) 创建 Image Publisher
self.publisher_ = self.create_publisher(Image, '/image_raw', 10)
# 4) 定时器
self.timer_ = self.create_timer(1.0/rate, self._publish_frame)
def _publish_frame(self):
# 1) 用 numpy + OpenCV 生成合成图像
frame = np.zeros((self._height, self._width, 3), dtype=np.uint8)
frame[:, :, 0] = np.linspace(0, 255, self._width, dtype=np.uint8) # B
frame[:, :, 1] = np.linspace(0, 255, self._height, dtype=np.uint8) # G
cv2.putText(frame, f'frame={self._frame_count}', (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255,255,255), 2)
# 2) 转 ROS Image 消息
msg = Image()
msg.height = self._height
msg.width = self._width
msg.encoding = 'bgr8' # OpenCV 默认 BGR!
msg.step = self._width * 3 # 每行字节数(width * 3 channels)
msg.data = frame.tobytes() # numpy → bytes
# 3) 发布
self.publisher_.publish(msg)
```
**关键**:`encoding='bgr8'`(OpenCV 用 BGR 而非 RGB!),`step = width * 3`(每行字节数)。
### image_processor.py(订阅 + 处理 + 再发布)
```python
import cv2
from cv_bridge import CvBridge
class ImageProcessor(Node):
def __init__(self):
super().__init__('image_processor')
self._bridge = CvBridge() # 关键:cv_bridge 实例
# 订阅图像
self.subscription = self.create_subscription(
Image, '/image_raw', self._on_image, 10)
# 发布处理后的图像
self.publisher_ = self.create_publisher(Image, '/image_processed', 10)
def _on_image(self, msg):
# 1) ROS Image → OpenCV numpy
cv_image = self._bridge.imgmsg_to_cv2(msg, desired_encoding='bgr8')
# 2) OpenCV 处理
mode = self.get_parameter('mode').value
if mode == 'gray':
processed = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
processed = cv2.cvtColor(processed, cv2.COLOR_GRAY2BGR) # 转回 3 通道
elif mode == 'edges':
gray = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY)
processed = cv2.cvtColor(cv2.Canny(gray, 50, 150), cv2.COLOR_GRAY2BGR)
else:
processed = cv_image
# 3) numpy → ROS Image
out_msg = self._bridge.cv2_to_imgmsg(processed, encoding='bgr8')
out_msg.header = msg.header # 保留 timestamp + frame_id
# 4) 发布
self.publisher_.publish(out_msg)
```
---
## 🧪 跑测试
```bash
colcon test --packages-select py_vision_demo
colcon test-result --all --verbose
```
测试覆盖:
**预期**:`py_vision_demo: pytest 13/13 ✓` 全部通过。
| 文件 | 用例数 | 内容 |
---
## 🔧 实战:接真实相机
把 `fake_camera` 换成 `usb_cam` 或 `realsense2_camera`:
```bash
sudo apt install ros-humble-usb-cam
ros2 launch usb_cam camera.launch.py
```
下游 `image_processor` 完全不用改 — 它只关心 `/image_raw` 是不是 `sensor_msgs/Image`。
---
## 📚 深入学习
- [sensor_msgs/Image 文档](https://docs.ros.org/en/humble/p/sensor_msgs/msg/Image.html)
- [cv_bridge 教程](https://wiki.ros.org/cv_bridge/Tutorials/ConvertingBetweenROSImagesAndOpenCVImagesPython)
---
## ⏭️ 下一个包
继续学 **[cpp_robot_tf2](../cpp_robot_tf2/README.md)** — TF2 坐标变换 + URDF 机械臂模型(机器人入门必学)。
---
## 📍 学习路径导航
| ⏮ 上一个 | 🏠 当前位置 | ⏭ 下一个 |
|---|---|---|
| `test_fake_camera.py` | 5 | 节点名 + 参数 + frame_id(REP-105) + publisher |
| `test_image_processor.py` | 5 | 节点名 + 参数 + subscription + 全白/全黑处理 |
| `test_vision_pipeline.py` | 1 | 同进程 fake_camera → image_processor 端到端 |
| **总计** | **11** | **目标 11/11 100% 通过** |
| [cpp_custom_interface — C++ 自定义接口](../cpp_custom_interface/README.md) | **py_vision_demo — Python 图像** | [cpp_robot_tf2 — C++ TF2 + URDF](../cpp_robot_tf2/README.md) |
## 深度学习
- 编程规范:[`doc/CODING_STYLE.md`](../doc/CODING_STYLE.md)
- 视觉 + cv_bridge:[`doc/30-vision.md`](../doc/30-vision.md)(下一阶段新增)
- ROS 图像消息: <https://docs.ros.org/en/humble/p/sensor_msgs/msg/Image.html>
## 进阶(下一阶段)
- 接 RealSense / Azure Kinect 真相机
- 加 YOLO / GraspNet 等视觉模型
- 接 MoveIt2 做视觉抓取
📍 完整 12 包学习顺序见 [主 README](../../README.md#-12-包推荐学习顺序)