霍夫圆检测显示问题

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k230

镜像版本：CanMV v1.3-91-g1ea9b3b(based on Micropython e00a144) on 2025-07-28; k230_canmv_01studio with K230

我想实现类似这个连接的代码示例显示效果：https://wiki.01studio.cc/docs/canmv_k230/machine_vision/image_detection/find_circles

image.png

但我基于霍夫圆检测示例无法实现，测试只能用800x480得统一尺寸显示在屏上，我得代码如下：

# ============================================================
# MicroPython 基于 cv_lite 的 RGB888 霍夫圆检测测试代码
# RGB888 Hough Circle Detection Test using cv_lite extension
# ============================================================

import time, os, sys, gc
from machine import Pin
from media.sensor import *    # 导入摄像头接口 / Camera interface
from media.display import *   # 导入显示接口 / Display interface
from media.media import *     # 导入媒体资源管理器 / Media manager
import _thread
import cv_lite                # 导入 cv_lite 扩展模块 / cv_lite extension
import ulab.numpy as np       # MicroPython 类 NumPy 库

# -------------------------------
# 图像尺寸设置 / Image resolution
# -------------------------------
image_shape = [480, 800]  # 高 x 宽 / Height x Width

# -------------------------------
# 初始化摄像头（RGB888格式） / Initialize camera (RGB888 format)
# -------------------------------
sensor = Sensor(id=2, width=1280, height=960)
sensor.reset()
sensor.set_framesize(width=320, height=240) #设置帧大小，默认通道0
sensor.set_pixformat(Sensor.RGB888)  # 设置 RGB888 像素格式 / Set RGB888 pixel format

# -------------------------------
# 初始化显示器（IDE虚拟显示） / Initialize display (IDE virtual output)
# -------------------------------
Display.init(Display.ST7701, width=image_shape[1], height=image_shape[0], to_ide=True, quality=100)

# -------------------------------
# 初始化媒体资源管理器 / Initialize media manager
# -------------------------------
MediaManager.init()
sensor.run()

# -------------------------------
# 启动帧率计时器 / Start FPS timer
# -------------------------------
clock = time.clock()

# -------------------------------
# 霍夫圆检测参数 / Hough Circle parameters
# -------------------------------
dp = 1           # 累加器分辨率与图像分辨率的反比 / Inverse ratio of accumulator resolution
minDist = 30     # 检测到的圆心最小距离 / Minimum distance between detected centers
param1 = 80      # Canny边缘检测高阈值 / Higher threshold for Canny edge detection
param2 = 20      # 霍夫变换圆心检测阈值 / Threshold for center detection in accumulator
minRadius = 10   # 检测圆最小半径 / Minimum circle radius
maxRadius = 50   # 检测圆最大半径 / Maximum circle radius

# -------------------------------
# 主循环 / Main loop
# -------------------------------
while True:
    clock.tick()

    # 拍摄一帧图像 / Capture a frame
    img = sensor.snapshot()
    img_np = img.to_numpy_ref()  # 获取 RGB888 ndarray 引用

    # 调用 cv_lite 扩展的霍夫圆检测函数，返回圆参数列表 [x, y, r, ...]
    circles = cv_lite.rgb888_find_circles(
        image_shape, img_np, dp, minDist, param1, param2, minRadius, maxRadius
    )

    # 遍历检测到的圆形，绘制圆形框
    for i in range(0, len(circles), 3):
        x = circles[i]
        y = circles[i + 1]
        r = circles[i + 2]
        img.draw_circle(x, y, r, color=(255, 0, 0), thickness=2)  # 红色圆圈

    # 显示带有检测圆的图像 / Display image with circles drawn
    Display.show_image(img)

    # 垃圾回收 / Garbage collect
    gc.collect()

    # 打印帧率 / Print FPS
    print("findcircles:", clock.fps())

# -------------------------------
# 程序退出时释放资源 / Cleanup on exit
# -------------------------------
sensor.stop()
Display.deinit()
os.exitpoint(os.EXITPOINT_ENABLE_SLEEP)
time.sleep_ms(100)
MediaManager.deinit()
xd_v13
4 Answers

你好，参考这个。

# ============================================================
# MicroPython 基于 cv_lite 的 RGB888 霍夫圆检测测试代码
# RGB888 Hough Circle Detection Test using cv_lite extension
# ============================================================

import time, os, sys, gc
from machine import Pin
from media.sensor import *    # 导入摄像头接口 / Camera interface
from media.display import *   # 导入显示接口 / Display interface
from media.media import *     # 导入媒体资源管理器 / Media manager
import _thread
import cv_lite                # 导入 cv_lite 扩展模块 / cv_lite extension
import ulab.numpy as np       # MicroPython 类 NumPy 库

# -------------------------------
# 图像尺寸设置 / Image resolution
# -------------------------------
image_shape = [480, 640]  # 高 x 宽 / Height x Width

def calculate_crop(sensor_width, sensor_height, target_width, target_height):
    """
    Calculate center crop rectangle from sensor resolution to match target resolution
    with preserved aspect ratio.

    Returns:
        (crop_x, crop_y, crop_width, crop_height)
    """
    scale = min(sensor_width // target_width, sensor_height // target_height)
    crop_width = int(target_width * scale)
    crop_height = int(target_height * scale)
    crop_x = (sensor_width - crop_width) // 2
    crop_y = (sensor_height - crop_height) // 2
    return (crop_x, crop_y, crop_width, crop_height)

# -------------------------------
# 初始化摄像头（灰度图模式） / Initialize camera (grayscale mode)
# -------------------------------
sensor = Sensor(id=2, fps = 90)
sensor.reset()

sensor_width = sensor.width(None)
sensor_height = sensor.height(None)
sensor.set_framesize(width=image_shape[1], height=image_shape[0], crop = calculate_crop(sensor_width,sensor_height,image_shape[1],image_shape[0]))
sensor.set_pixformat(Sensor.RGB888)  # 灰度图格式 / Grayscale format

# -------------------------------
# 初始化显示器（IDE虚拟显示） / Initialize display (IDE virtual output)
# -------------------------------
Display.init(Display.ST7701, to_ide=True, quality=50)

# -------------------------------
# 初始化媒体资源管理器 / Initialize media manager
# -------------------------------
MediaManager.init()
sensor.run()

# -------------------------------
# 启动帧率计时器 / Start FPS timer
# -------------------------------
clock = time.clock()

# -------------------------------
# 霍夫圆检测参数 / Hough Circle parameters
# -------------------------------
dp = 1           # 累加器分辨率与图像分辨率的反比 / Inverse ratio of accumulator resolution
minDist = 30     # 检测到的圆心最小距离 / Minimum distance between detected centers
param1 = 80      # Canny边缘检测高阈值 / Higher threshold for Canny edge detection
param2 = 20      # 霍夫变换圆心检测阈值 / Threshold for center detection in accumulator
minRadius = 10   # 检测圆最小半径 / Minimum circle radius
maxRadius = 50   # 检测圆最大半径 / Maximum circle radius

# -------------------------------
# 主循环 / Main loop
# -------------------------------
while True:
    clock.tick()

    # 拍摄一帧图像 / Capture a frame
    img = sensor.snapshot()
    img_np = img.to_numpy_ref()  # 获取 RGB888 ndarray 引用

    # 调用 cv_lite 扩展的霍夫圆检测函数，返回圆参数列表 [x, y, r, ...]
    circles = cv_lite.rgb888_find_circles(
        image_shape, img_np, dp, minDist, param1, param2, minRadius, maxRadius
    )

    # 遍历检测到的圆形，绘制圆形框
    for i in range(0, len(circles), 3):
        x = circles[i]
        y = circles[i + 1]
        r = circles[i + 2]
        img.draw_circle(x, y, r, color=(255, 0, 0), thickness=2)  # 红色圆圈

    # 显示带有检测圆的图像 / Display image with circles drawn
    Display.show_image(img)

    # 垃圾回收 / Garbage collect
    gc.collect()

    # 打印帧率 / Print FPS
    print("findcircles:", clock.fps())

# -------------------------------
# 程序退出时释放资源 / Cleanup on exit
# -------------------------------
sensor.stop()
Display.deinit()
os.exitpoint(os.EXITPOINT_ENABLE_SLEEP)
time.sleep_ms(100)
MediaManager.deinit()
xelll2040

参考这个代码
circles = cv_lite.rgb888_find_circles(
frame_shape, img_np, dp, minDist, param1, param2, minRadius, maxRadius
主要是之前这个函数的第一个参数不对导致出错了，把第一个参数改成你送进来的图像大小就可以了

# ============================================================
# MicroPython 基于 cv_lite 的 RGB888 霍夫圆检测测试代码
# RGB888 Hough Circle Detection Test using cv_lite extension
# ============================================================

import time, os, sys, gc
from machine import Pin
from media.sensor import *    # 导入摄像头接口 / Camera interface
from media.display import *   # 导入显示接口 / Display interface
from media.media import *     # 导入媒体资源管理器 / Media manager
import _thread
import cv_lite                # 导入 cv_lite 扩展模块 / cv_lite extension
import ulab.numpy as np       # MicroPython 类 NumPy 库

# -------------------------------
# 图像尺寸设置 / Image resolution
# -------------------------------
image_shape = [480, 800]  # 高 x 宽 / Height x Width
frame_shape = [240, 320]  # 高 x 宽 / Height x Width

# -------------------------------
# 初始化摄像头（RGB888格式） / Initialize camera (RGB888 format)
# -------------------------------
sensor = Sensor(id=2, width=1280, height=720)
sensor.reset()
sensor.set_framesize(width=320, height=240) #设置帧大小，默认通道0
sensor.set_pixformat(Sensor.RGB888)  # 设置 RGB888 像素格式 / Set RGB888 pixel format

# -------------------------------
# 初始化显示器（IDE虚拟显示） / Initialize display (IDE virtual output)
# -------------------------------
Display.init(Display.ST7701, width=image_shape[1], height=image_shape[0], to_ide=True, quality=100)

# -------------------------------
# 初始化媒体资源管理器 / Initialize media manager
# -------------------------------
MediaManager.init()
sensor.run()

# -------------------------------
# 启动帧率计时器 / Start FPS timer
# -------------------------------
clock = time.clock()

# -------------------------------
# 霍夫圆检测参数 / Hough Circle parameters
# -------------------------------
dp = 1           # 累加器分辨率与图像分辨率的反比 / Inverse ratio of accumulator resolution
minDist = 30     # 检测到的圆心最小距离 / Minimum distance between detected centers
param1 = 80      # Canny边缘检测高阈值 / Higher threshold for Canny edge detection
param2 = 20      # 霍夫变换圆心检测阈值 / Threshold for center detection in accumulator
minRadius = 10   # 检测圆最小半径 / Minimum circle radius
maxRadius = 50   # 检测圆最大半径 / Maximum circle radius

# -------------------------------
# 主循环 / Main loop
# -------------------------------
while True:
    clock.tick()


    # 拍摄一帧图像 / Capture a frame
    img = sensor.snapshot()
    img_np = img.to_numpy_ref()  # 获取 RGB888 ndarray 引用

    # 调用 cv_lite 扩展的霍夫圆检测函数，返回圆参数列表 [x, y, r, ...]
    circles = cv_lite.rgb888_find_circles(
        frame_shape, img_np, dp, minDist, param1, param2, minRadius, maxRadius
    )
    # 遍历检测到的圆形，绘制圆形框
    for i in range(0, len(circles), 3):
        x = circles[i]
        y = circles[i + 1]
        r = circles[i + 2]
        img.draw_circle(x, y, r, color=(255, 0, 0), thickness=2)  # 红色圆圈
    # 显示带有检测圆的图像 / Display image with circles drawn
    Display.show_image(img, x=240, y=120)
    # 垃圾回收 / Garbage collect
    gc.collect()

    # 打印帧率 / Print FPS
    print("findcircles:", clock.fps())

# -------------------------------
# 程序退出时释放资源 / Cleanup on exit
# -------------------------------
sensor.stop()
Display.deinit()
os.exitpoint(os.EXITPOINT_ENABLE_SLEEP)
time.sleep_ms(100)
MediaManager.deinit()
lily94

参考这个代码

# ============================================================
# MicroPython 基于 cv_lite 的 RGB888 霍夫圆检测测试代码
# RGB888 Hough Circle Detection Test using cv_lite extension
# ============================================================

import time, os, sys, gc
from machine import Pin
from media.sensor import *    # 导入摄像头接口 / Camera interface
from media.display import *   # 导入显示接口 / Display interface
from media.media import *     # 导入媒体资源管理器 / Media manager
import _thread
import cv_lite                # 导入 cv_lite 扩展模块 / cv_lite extension
import ulab.numpy as np       # MicroPython 类 NumPy 库

# -------------------------------
# 图像尺寸设置 / Image resolution
# -------------------------------
image_shape = [240, 320]  # 高 x 宽 / Height x Width

# -------------------------------
# 初始化摄像头（RGB888格式） / Initialize camera (RGB888 format)
# -------------------------------
sensor = Sensor(id=2)
sensor.reset()
sensor.set_framesize(width=320, height=240) #设置帧大小，默认通道0
sensor.set_pixformat(Sensor.RGB888)  # 设置 RGB888 像素格式 / Set RGB888 pixel format


# -------------------------------
# 初始化显示器（IDE虚拟显示） / Initialize display (IDE virtual output)
# -------------------------------
Display.init(Display.ST7701, width=800, height=480, to_ide=True, quality=100)

# -------------------------------
# 初始化媒体资源管理器 / Initialize media manager
# -------------------------------
MediaManager.init()
sensor.run()

# -------------------------------
# 启动帧率计时器 / Start FPS timer
# -------------------------------
clock = time.clock()

# -------------------------------
# 霍夫圆检测参数 / Hough Circle parameters
# -------------------------------
dp = 1           # 累加器分辨率与图像分辨率的反比 / Inverse ratio of accumulator resolution
minDist = 30     # 检测到的圆心最小距离 / Minimum distance between detected centers
param1 = 80      # Canny边缘检测高阈值 / Higher threshold for Canny edge detection
param2 = 20      # 霍夫变换圆心检测阈值 / Threshold for center detection in accumulator
minRadius = 10   # 检测圆最小半径 / Minimum circle radius
maxRadius = 50   # 检测圆最大半径 / Maximum circle radius

# -------------------------------
# 主循环 / Main loop
# -------------------------------
while True:
    clock.tick()

    # 拍摄一帧图像 / Capture a frame
    img = sensor.snapshot()
    img_np = img.to_numpy_ref()  # 获取 RGB888 ndarray 引用

    # 调用 cv_lite 扩展的霍夫圆检测函数，返回圆参数列表 [x, y, r, ...]
    circles = cv_lite.rgb888_find_circles(
        image_shape, img_np, dp, minDist, param1, param2, minRadius, maxRadius
    )

    # 遍历检测到的圆形，绘制圆形框
    for i in range(0, len(circles), 3):
        x = circles[i]
        y = circles[i + 1]
        r = circles[i + 2]
        img.draw_circle(x, y, r, color=(255, 0, 0), thickness=2)  # 红色圆圈

    # 显示带有检测圆的图像 / Display image with circles drawn
    Display.show_image(img,x=(800-image_shape[1])//2,y=(480-image_shape[0])//2)

    # 垃圾回收 / Garbage collect
    gc.collect()

    # 打印帧率 / Print FPS
    print("findcircles:", clock.fps())

# -------------------------------
# 程序退出时释放资源 / Cleanup on exit
# -------------------------------
sensor.stop()
Display.deinit()
os.exitpoint(os.EXITPOINT_ENABLE_SLEEP)
time.sleep_ms(100)
MediaManager.deinit()
Wy0013018

image_shape = [480, 800] # 高 x 宽 / Height x Width

-------------------------------

初始化摄像头（RGB888格式） / Initialize camera (RGB888 format)

-------------------------------

sensor = Sensor(id=2, width=1280, height=960)
sensor.reset()
sensor.set_framesize(width=320, height=240) #设置帧大小，默认通道0
sensor.set_pixformat(Sensor.RGB888) # 设置 RGB888 像素格式 / Set RGB888 pixel format

-------------------------------

初始化显示器（IDE虚拟显示） / Initialize display (IDE virtual output)

-------------------------------

Display.init(Display.ST7701, width=image_shape[1], height=image_shape[0], to_ide=True, quality=100)
需要根据你的图片和显示的情况调整这些参数吧

lily94

只能统一480X800不然屏幕显示不了，黑屏，我之前是可以这样写法，然后能成功像01教程那样显示，现在这个只能一个800X480来用

xd_v1•

要小于800x480，或者大于这个尺寸的图片，做一个resize来显示，不然放不下

lily•

那这个怎么整，之前矩形检测我都可以800X480中 显示320x240 用了这个cv模块反而不可了

xd_v1•