在opencv中查找并绘制特定颜色的最大轮廓（Python） [英] Find and draw the largest contour in opencv on a specific color (Python)

问题描述

我试图获得一本红皮书的最大轮廓。
我的代码有一个小问题，因为它得到的是最小对象（斑点）而不是最大对象的轮廓，我似乎无法弄清楚为什么会发生这种情况

我使用的代码：

  camera = cv2.VideoCapture（0）
内核= np.ones（（（2,2），np.uint8）
 
而True：
 #Loading Camera 
 ret，frame = camera.read（）
 
 blurd = cv2.pyrMeanShiftFiltering（frame，3，3）
 hsv = cv2.cvtColor（blurred，cv2.COLOR_BGR2HSV）
 
 lower_range = np.array（[150， 10、10]）
 upper_range = np.array（[180，255，255]）
 mask = cv2.inRange（hsv，lower_range，upper_range）
 
膨胀= cv2 .dilate（掩码，内核，迭代次数= 1）
 
关闭= cv2.morphologyEx（膨胀，cv2.MORPH_GRADIENT，内核）
关闭= cv2.morphologyEx（膨胀，cv2.MORPH_CLOSE，内核）
 
＃获取形态学的边缘
 edge = cv2.Canny（closing，175，175）
 _，轮廓线，层次结构= cv2.findContours（edge，cv2.RETR_TREE，cv2.CHAIN_APPROX_SIMPLE）
 
＃查找最大轮廓线的索引
区域= [cv2.contourArea（c）为轮廓中的c] 
 max_index = np.argmax（areas）
 cnt = contours [max_index] 
 
x，y，w，h = cv2.boundingRect（cnt）
 cv2.rectangle（frame，（x，y），（x + w，y + h），（0,255,0），2）
 
 
 cv2.imshow（'threshold' ，frame）
 cv2.imshow（'edge'，edge）
 
如果cv2.waitKey（1）== 27：
中断
 
 
 camera.release（）
 cv2.destroyAllWindows（）
  

如果您想旋转书本您可以使用 rect = cv2.minAreaRect（cnt）找到它

具体来说，在OpenCV中，色相范围为 [0,179] 。在下图中（由

Im trying to get the largest contour of a red book. I've got a little problem with the code because its getting the contours of the smallest objects (blobs) instead of the largest one and I can't seem to figure out why this is happening

The code I use:

camera = cv2.VideoCapture(0)
kernel = np.ones((2,2),np.uint8)

while True:
    #Loading Camera
    ret, frame = camera.read()

    blurred = cv2.pyrMeanShiftFiltering(frame, 3, 3)
    hsv = cv2.cvtColor(blurred, cv2.COLOR_BGR2HSV)

    lower_range = np.array([150, 10, 10])
    upper_range = np.array([180, 255, 255])
    mask = cv2.inRange(hsv, lower_range, upper_range)

    dilation = cv2.dilate(mask,kernel,iterations = 1)

    closing = cv2.morphologyEx(dilation, cv2.MORPH_GRADIENT, kernel)
    closing = cv2.morphologyEx(dilation, cv2.MORPH_CLOSE, kernel)

    #Getting the edge of morphology
    edge = cv2.Canny(closing, 175, 175)
    _, contours,hierarchy = cv2.findContours(edge, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

    # Find the index of the largest contour
    areas = [cv2.contourArea(c) for c in contours]
    max_index = np.argmax(areas)
    cnt=contours[max_index]

    x,y,w,h = cv2.boundingRect(cnt)
    cv2.rectangle(frame,(x,y),(x+w,y+h),(0,255,0),2)


    cv2.imshow('threshold', frame)
    cv2.imshow('edge', edge)

    if cv2.waitKey(1) == 27:
        break


camera.release()
cv2.destroyAllWindows()

As you can see on this picture

Hopefully there is someone who can help

解决方案

You can start by defining a mask in the range of the red tones of the book you are looking for.

Then you can just find the contour with the biggest area and draw the rectangular shape of the book.

import numpy as np
import cv2

# load the image
image = cv2.imread("path_to_your_image.png", 1)

# red color boundaries [B, G, R]
lower = [1, 0, 20]
upper = [60, 40, 220]

# create NumPy arrays from the boundaries
lower = np.array(lower, dtype="uint8")
upper = np.array(upper, dtype="uint8")

# find the colors within the specified boundaries and apply
# the mask
mask = cv2.inRange(image, lower, upper)
output = cv2.bitwise_and(image, image, mask=mask)

ret,thresh = cv2.threshold(mask, 40, 255, 0)
if (cv2.__version__[0] > 3):
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
else:
    im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

if len(contours) != 0:
    # draw in blue the contours that were founded
    cv2.drawContours(output, contours, -1, 255, 3)

    # find the biggest countour (c) by the area
    c = max(contours, key = cv2.contourArea)
    x,y,w,h = cv2.boundingRect(c)

    # draw the biggest contour (c) in green
    cv2.rectangle(output,(x,y),(x+w,y+h),(0,255,0),2)

# show the images
cv2.imshow("Result", np.hstack([image, output]))

cv2.waitKey(0)

Using your image:

If you want the book to rotate you can use rect = cv2.minAreaRect(cnt) as you can find it here.

Edit:

You should also consider other colour spaces beside the RGB, as the HSV or HLS. Usually, people use the HSV since the H channel stays fairly consistent in shadow or excessive brightness. In other words, you should get better results if you use the HSV colourspace.

In specific, in OpenCV the Hue range is [0,179]. In the following figure (made by @Knight), you can find a 2D slice of that cylinder, in V = 255, where the horizontal axis is the H and the vertical axis the S. As you can see from that figure to capture the red you need both to include the lower (e.g., H=0 to H=10) and upper region (e.g., H=170 to H=179) of the Hue values.

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