我编写了下面的代码来检测图像中的3D形状,并且可以正常工作。
现在,我需要检测形状内的颜色并进行计算。
谁能指出我应该从哪里进行色彩检测?
下面的形状检测代码,也许会有用:
import cv2
import numpy as np
cv2.imshow('Original Image',rawImage)
cv2.waitKey(0)
hsv = cv2.cvtColor(rawImage, cv2.COLOR_BGR2HSV)
cv2.imshow('HSV Image',hsv)
cv2.waitKey(0)
hue ,saturation ,value = cv2.split(hsv)
cv2.imshow('Saturation Image',saturation)
cv2.waitKey(0)
retval, thresholded = cv2.threshold(saturation, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
cv2.imshow('Thresholded Image',thresholded)
cv2.waitKey(0)
medianFiltered = cv2.medianBlur(thresholded,5)
cv2.imshow('Median Filtered Image',medianFiltered)
cv2.waitKey(0)
cnts, hierarchy = cv2.findContours(medianFiltered, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
for c in cnts:
# compute the center of the contour
M = cv2.moments(c)
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
first = cv2.drawContours(rawImage, [c], -1, (0, 255, 0), 2)
second =cv2.circle(rawImage, (cX, cY),1 , (255, 255, 255), -1)
cv2.imshow('Objects Detected',rawImage)
cv2.waitKey(0)
答案 0 :(得分:2)
基本思路:
(1) Convert the image to HSV color space;
(2) Threahold the `S` to find color regions;
(3) Calculate average hsv for each color-region-maskin HSV, then convert into BGR.
对于图片:
(1) Convert the image to HSV color space:
(2) Threahold the `S` to find color regions:
(3) Calculate average hsv for each color-region-maskin HSV, then convert into BGR.
某些链接可能有用:
1. just detect color regions:
(1)How to detect colored patches in an image using OpenCV?
(2) OpenCV C++/Obj-C: Detecting a sheet of paper / Square Detection
2. detect specific color in HSV:
(1)为绿色:How to define a threshold value to detect only green colour objects in an image :Opencv
(2)代表橙色:Choosing the correct upper and lower HSV boundaries for color detection with`cv::inRange` (OpenCV)
(3)取红色的H
:How to find the RED color regions using OpenCV?
3. If you want to crop polygon mask: