MaixCam视觉识别实战：色块与直线检测

Maixcam学习笔记：寻址色块和直线

硬件准备与环境搭建

确保Maixcam开发板已连接电脑并安装最新固件安装MaixPy IDE开发环境，建议使用v0.5.0以上版本准备彩色物体和直线场景用于测试，建议使用高对比度颜色

色块识别实现方法

初始化摄像头模块并设置合适分辨率，推荐使用QVGA(320x240)

import sensor
sensor.reset()
sensor.set_pixformat(sensor.RGB565)
sensor.set_framesize(sensor.QVGA)

定义颜色阈值范围，可通过IDE中的阈值编辑器获取

red_threshold = (30, 100, 15, 127, 15, 127)  # (L_min, L_max, A_min, A_max, B_min, B_max)

使用find_blobs函数检测色块并绘制识别结果

blobs = img.find_blobs([red_threshold], area_threshold=100)
for blob in blobs:
    img.draw_rectangle(blob.rect())
    img.draw_cross(blob.cx(), blob.cy())

直线检测技术要点

启用边缘检测算法前需将图像转换为灰度

img.to_grayscale()

使用find_lines函数检测直线，可调节roi区域和阈值参数

lines = img.find_lines(threshold=1000, roi=(10,10,100,100))
for line in lines:
    img.draw_line(line.line())

优化直线检测效果可尝试高斯滤波预处理

img.gaussian(1)

性能优化技巧

降低图像分辨率可显著提高处理速度

sensor.set_framesize(sensor.QQVGA)  # 160x120

合理设置ROI区域减少处理数据量

blobs = img.find_blobs([threshold], roi=(50,50,200,150))

使用硬件加速功能提升处理效率

sensor.set_windowing((224, 224))  # 适配KPU的输入尺寸

实际应用案例

色块追踪机器人控制逻辑示例

if len(blobs) > 0:
    largest_blob = max(blobs, key=lambda b: b.area())
    if largest_blob.cx() < WIDTH//3:
        turn_left()
    elif largest_blob.cx() > 2*WIDTH//3:
        turn_right()
    else:
        move_forward()

基于直线检测的巡线算法实现

for line in lines:
    if line.theta() < 90:  # 左侧线
        left_lines.append(line)
    else:                  # 右侧线
        right_lines.append(line)
adjust_steering(left_lines, right_lines)

常见问题解决

颜色阈值不准确时建议重新校准环境光源直线检测碎片化可尝试合并相邻线段

import image
image.merge_lines(lines, 10)  # 合并10像素内的线段

处理帧率过低时可关闭图像预览功能

sensor.skip_frames(time=2000)  # 跳过初始不稳定帧

进阶开发方向

结合KPU实现深度学习目标检测多传感器数据融合提升识别鲁棒性开发无线图传功能实现远程监控通过MicroPython扩展模块实现更多计算机视觉功能

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