opencv-python提取特定颜色、面积、圆度、惯性和凸度的代码

以下是使用OpenCV-Python库提取特定颜色、面积、圆度、惯性和凸度的代码示例：

1. 提取特定颜色的特征点

import cv2
import numpy as np

# 读取图像
img = cv2.imread('image.jpg')

# 将图像转换为HSV颜色空间
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)

# 设定颜色范围
lower_color = np.array([0, 100, 100])
upper_color = np.array([10, 255, 255])

# 提取特定颜色区域
mask = cv2.inRange(hsv, lower_color, upper_color)

# 寻找特定颜色的特征点
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
keypoints = []
for contour in contours:
    x, y, w, h = cv2.boundingRect(contour)
    keypoints.append(cv2.KeyPoint(x=x, y=y, _size=w*h))

2. 提取特定面积、圆度、惯性和凸度的特征点

import cv2

# 读取图像
img = cv2.imread('image.jpg')

# 灰度化
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

# 二值化
_, thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)

# 寻找轮廓
contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 提取特定面积、圆度、惯性和凸度的特征点
keypoints = []
for contour in contours:
    area = cv2.contourArea(contour)
    if area < 100 or area > 1000:
        continue
    perimeter = cv2.arcLength(contour, True)
    circularity = 4*np.pi*(area/(perimeter**2))
    if circularity < 0.7:
        continue
    x, y, w, h = cv2.boundingRect(contour)
    if w/h > 2 or h/w > 2:
        continue
    M = cv2.moments(contour)
    if M['m00'] == 0:
        continue
    cx = int(M['m10']/M['m00'])
    cy = int(M['m01']/M['m00'])
    inertia = cv2.moments(contour, True)
    if inertia['nu20'] + inertia['nu02'] < 0.1:
        continue
    hull = cv2.convexHull(contour)
    hull_area = cv2.contourArea(hull)
    if area/hull_area < 0.5:
        continue
    keypoints.append(cv2.KeyPoint(x=cx, y=cy, _size=area))

这些代码示例仅供参考，实际应用中需要根据具体的需求进行调整。