2023RT-Thread开发者大会

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
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。

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

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())