2023RT-Thread开发者大会

news2024/9/26 3:23:27

参加了一次RT-Thread的开发者大会，相当有意思，虽然一天奔波挺累，但睡了半天之后简单剪了下22号的视频，也就有时间写自己的参会笔记了。
RT-Thread开发者大会图
RTT上午会场的座位

与openEuler社区不同，RT-Thread社区更专注于嵌入式，与硬件厂商结合较为紧密。我是在openEuler的嵌入式SIG的引导下来的，openEuler社区的Embedded发行版是Yocto架构为主与服务器端的发行版本并不一样。很明显在ARM-M系列的场景下是跑不了openEuler的，这时RTOS（实时操作系统）显然更为适合这类场景，正如RT-Thread开发者大会上演讲者所说，RT-Thread也在做一些填补大型操作系统与硬件之间关系，可以这么理解。
会议议程
我们是到的最早的那一批，坐了无人车，到了会场外，边收集着开发板，边看着一家家公司布着展，看着demo, 看着有意思的东西，开发板、系统、小样。虽然都在说今年裁员压力大，但各家公司的产品都还看起来不错。

开发者在交流
瑞萨的展台

RTT上午现场的参会者们

早上，是开场，然后小睡了会儿，主办方RTT在说社区的人更多了，软件包更多了，但也更为完善了，更新放缓的阶段。英飞凌介绍了PSoc MCU, 瑞萨给了几个行业应用的例子，比较让我惊喜的是给了很多新能源行业的解决方案。给我不少做课设的启发，如果自己的成果能解决行业问题，我觉得也是挺好的方向。

Psoc

下午，听了场瑞萨和恩智普的边缘计算分会场的讲座。

一个在做MCU的e-AI模型迁移部署实验–强调了
MPU与MCU的界限逐渐模糊
Cheak MOTOR 电机检测

算力检测–平台
HVAC风机检测
13帧的视觉检测的RA8 MCU

RA8

一个在做相似的方向（emmm, 没咋听全，有个老哥打我电话，喊我搬砖）

机器学习
控制器
产品

对了还有一家做车载AI视觉的大宋汽车技术合作方——黑芝麻
车载MCU

动手实践也比较简单，但挺有趣的。

现场发布的 RA8 MCU 开发板

用的 OpenMV IDE, 界面也比较简单（与PR相比），挺有意思的，demo如下

DEMO RTT RA8
Blog就到这了，Bye 2023RT-Thread开发者大会。

这里是用到的代码

# Fast Linear Regression Example
#
# This example shows off how to use the get_regression() method on your OpenMV Cam
# to get the linear regression of a ROI. Using this method you can easily build
# a robot which can track lines which all point in the same general direction
# but are not actually connected. Use find_blobs() on lines that are nicely
# connected for better filtering options and control.
#
# This is called the fast linear regression because we use the least-squares
# method to fit the line. However, this method is NOT GOOD FOR ANY images that
# have a lot (or really any) outlier points which corrupt the line fit...

import sensor
import time

THRESHOLD = (0, 100)  # Grayscale threshold for dark things.
BINARY_VISIBLE = True  # Binary pass first to see what linear regression is running on.

sensor.reset()
sensor.set_pixformat(sensor.GRAYSCALE)
sensor.set_framesize(sensor.QQVGA)
sensor.skip_frames(time=2000)
clock = time.clock()

while True:
    clock.tick()
    img = sensor.snapshot().binary([THRESHOLD]) if BINARY_VISIBLE else sensor.snapshot()

    # Returns a line object similar to line objects returned by find_lines() and
    # find_line_segments(). You have x1(), y1(), x2(), y2(), length(),
    # theta() (rotation in degrees), rho(), and magnitude().
    #
    # magnitude() represents how well the linear regression worked. It goes from
    # (0, INF] where 0 is returned for a circle. The more linear the
    # scene is the higher the magnitude.
    line = img.get_regression([(255, 255) if BINARY_VISIBLE else THRESHOLD])

    print(
        "FPS %f, mag = %s" % (clock.fps(), str(line.magnitude()) if (line) else "N/A")
    )

# About negative rho values:
#
# A [theta+0:-rho] tuple is the same as [theta+180:+rho].

# Automatic RGB565 Color Tracking Example
#
# This example shows off single color automatic RGB565 color tracking using the OpenMV Cam.

import sensor
import time

print("请勿在相机前放置任何物品")

sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)
sensor.skip_frames(time=2000)
sensor.set_auto_gain(False)  # must be turned off for color tracking
sensor.set_auto_whitebal(False)  # must be turned off for color tracking
clock = time.clock()

# Capture the color thresholds for whatever was in the center of the image.
r = [(320 // 2) - (50 // 2), (240 // 2) - (50 // 2), 50, 50]  # 50x50 center of QVGA.

print(
    "将要跟踪的物体放在相机前面的框中"
)
print(
    "确保您要追踪的物体的颜色完全被框住！"
)
for i in range(60):
    img = sensor.snapshot()
    img.draw_rectangle(r)

print("开始学习颜色  ")
threshold = [50, 50, 0, 0, 0, 0]  # Middle L, A, B values.
for i in range(60):
    img = sensor.snapshot()
    hist = img.get_histogram(roi=r)
    lo = hist.get_percentile(
        0.01
    )  # Get the CDF of the histogram at the 1% range (ADJUST AS NECESSARY)!
    hi = hist.get_percentile(
        0.99
    )  # Get the CDF of the histogram at the 99% range (ADJUST AS NECESSARY)!
    # Average in percentile values.
    threshold[0] = (threshold[0] + lo.l_value()) // 2
    threshold[1] = (threshold[1] + hi.l_value()) // 2
    threshold[2] = (threshold[2] + lo.a_value()) // 2
    threshold[3] = (threshold[3] + hi.a_value()) // 2
    threshold[4] = (threshold[4] + lo.b_value()) // 2
    threshold[5] = (threshold[5] + hi.b_value()) // 2
    for blob in img.find_blobs(
        [threshold], pixels_threshold=100, area_threshold=100, merge=True, margin=10
    ):
        img.draw_rectangle(blob.rect())
        img.draw_cross(blob.cx(), blob.cy())
        img.draw_rectangle(r)

print("Thresholds learned...")
print("Tracking colors...")

while True:
    clock.tick()
    img = sensor.snapshot()
    for blob in img.find_blobs(
        [threshold], pixels_threshold=100, area_threshold=100, merge=True, margin=10
    ):
        img.draw_rectangle(blob.rect())
        img.draw_cross(blob.cx(), blob.cy())
    print(clock.fps())