opencv—常用函数学习_“干货“

图像轮廓
findContours	drawContours	arcLength	contourArea
检测图像中的轮廓	绘制图像中的轮廓	计算轮廓的周长	计算轮廓的面积
minAreaRect	boundingRect	minEnclosingCircle	minEnclosingTriangle
找到面积最小的旋转矩形	计算包围轮廓的矩形	计算包围轮廓的最小圆	计算包围轮廓的最小三角形
approxPolyDP	convexHull	convexityDefects	fitLine
使用多边形逼近曲线或轮廓	计算轮廓的凸包	计算轮廓的凸缺陷	用一条直线拟合轮廓
fitEllipse	fitEllipseAMS	fitEllipseDirect	fillPoly
用一个椭圆拟合轮廓	用AMS方法拟合椭圆	用直接方法拟合椭圆	填充多边形
fillConvexPoly	pointPolygonTest	isContourConvex	intersectConvexConvex
填充凸多边形	测试点与轮廓的相对位置	测试轮廓是否是凸的	计算两个凸多边形的交集

检测并绘制轮廓 (`findContours` 和 `drawContours`)

import cv2
import numpy as np

# 读取图像并转换为灰度图
image = cv2.imread('path_to_image.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 应用阈值处理
_, binary = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)

# 检测轮廓
contours, _ = cv2.findContours(binary, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

# 绘制轮廓
cv2.drawContours(image, contours, -1, (0, 255, 0), 2)

cv2.imshow('Contours', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

计算轮廓的周长和面积 (`arcLength` 和 `contourArea`)

for contour in contours:
    perimeter = cv2.arcLength(contour, True)
    area = cv2.contourArea(contour)
    print(f"Perimeter: {perimeter}, Area: {area}")

计算包围轮廓的最小矩形和最小圆 (`minAreaRect` 和 `minEnclosingCircle`)

for contour in contours:
    # 最小旋转矩形
    rect = cv2.minAreaRect(contour)
    box = cv2.boxPoints(rect)
    box = np.int0(box)
    cv2.drawContours(image, [box], 0, (0, 0, 255), 2)

    # 最小包围圆
    (x, y), radius = cv2.minEnclosingCircle(contour)
    center = (int(x), int(y))
    radius = int(radius)
    cv2.circle(image, center, radius, (255, 0, 0), 2)

cv2.imshow('Min Area Rect and Enclosing Circle', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

多边形逼近 (`approxPolyDP`)

for contour in contours:
    epsilon = 0.02 * cv2.arcLength(contour, True)
    approx = cv2.approxPolyDP(contour, epsilon, True)
    cv2.drawContours(image, [approx], -1, (0, 255, 255), 2)

cv2.imshow('Approx Poly DP', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

计算轮廓的凸包 (`convexHull`)

for contour in contours:
    hull = cv2.convexHull(contour)
    cv2.drawContours(image, [hull], -1, (255, 255, 0), 2)

cv2.imshow('Convex Hull', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

用椭圆拟合轮廓 (`fitEllipse`)

for contour in contours:
    if len(contour) >= 5:  # 拟合椭圆要求至少有5个点
        ellipse = cv2.fitEllipse(contour)
        cv2.ellipse(image, ellipse, (0, 255, 255), 2)

cv2.imshow('Fit Ellipse', image)
cv2.waitKey(0)
cv2.destroyAllWindows()

测试点与轮廓的相对位置 (`pointPolygonTest`)

point = (50, 50)
for contour in contours:
    dist = cv2.pointPolygonTest(contour, point, True)
    print(f"Distance from point {point} to contour: {dist}")

这些示例展示了如何使用OpenCV中的各种轮廓检测和处理函数来处理图像。根据具体的应用需求，可以灵活运用这些函数来实现复杂的图像轮廓检测和分析任务。

十八、图像大小

在OpenCV中，调整图像大小是一个常见的操作，主要通过以下几个函数实现：pyrDown、pyrUp、resize、reduce 和 reshape。下面介绍这些函数及其使用示例。

图像大小调整函数
pyrDown	pyrUp	resize	reduce	reshape
降低图像分辨率（降采样）	提高图像分辨率（升采样）	调整图像大小	沿某个轴缩小图像	改变图像的形状

降采样 (`pyrDown`)

import cv2
import numpy as np

# 读取图像
image = cv2.imread('path_to_image.jpg')

# 降采样
pyr_down_image = cv2.pyrDown(image)
cv2.imshow('PyrDown Image', pyr_down_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

升采样 (`pyrUp`)

# 升采样
pyr_up_image = cv2.pyrUp(image)
cv2.imshow('PyrUp Image', pyr_up_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

调整图像大小 (`resize`)

# 调整图像大小
resized_image = cv2.resize(image, (300, 300))  # 指定新的宽和高
cv2.imshow('Resized Image', resized_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

# 按比例缩放
resized_image_aspect = cv2.resize(image, None, fx=0.5, fy=0.5)  # 宽和高分别缩小一半
cv2.imshow('Resized Image with Aspect Ratio', resized_image_aspect)
cv2.waitKey(0)
cv2.destroyAllWindows()

沿某个轴缩小图像 (`reduce`)

# 沿列方向缩小图像（求和）
reduced_image = cv2.reduce(image, dim=1, rtype=cv2.REDUCE_SUM)
cv2.imshow('Reduced Image', reduced_image)
cv2.waitKey(0)
cv2.destroyAllWindows()

改变图像的形状 (`reshape`)

# 改变图像的形状
reshaped_image = image.reshape((-1, 3))  # 改为一行三通道的图像
print("Reshaped Image Shape:", reshaped_image.shape)

这些示例展示了如何使用OpenCV中的各种函数来调整图像的大小和形状。根据具体的应用需求，可以灵活运用这些函数来实现复杂的图像处理任务。

opencv—常用函数学习_“干货“_6

十七、图像轮廓