在Opencv Python中的BitWise掩码中查找最高点 [英] Finding Highest Point In A BitWise Mask In Opencv Python
本文介绍了在Opencv Python中的BitWise掩码中查找最高点的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有一个对象的按位掩码，我想从该对象中检测其最高点.如何使用OpenCV Python做到这一点?
I have a bitwise mask of a object from which I want to detect its highest point. How can I do this using OpenCV Python?
| 我的面具 | 预期结果 |
|---|---|
原始图片:
这是该代码
import cv2
import numpy as np
def line(im, pt1, pt2, color, thickness):
im = cv2.line(im, pt1, pt2, color=color, thickness=thickness, lineType=cv2.LINE_AA)
return im
def empty(a):
pass
path = 'images/m_1.jpg'
global img
img = cv2.imread(path)
img = cv2.resize(img, (640, 480))
org_img = img.copy()
copy_img = img.copy()
img_blur = cv2.blur(img,(5,5))
imgHSV = cv2.cvtColor(img_blur, cv2.COLOR_BGR2HSV)
h_min = 0
h_max = 179
s_min = 0
s_max = 255
v_min = 48
v_max = 166
lower = np.array([h_min, s_min, v_min])
upper = np.array([h_max, s_max, v_max])
mask = cv2.inRange(imgHSV, lower, upper)
global imgResult
imgResult = cv2.bitwise_and(img, img, mask=mask)
imgResult = cv2.resize(imgResult, (640, 480))
cont, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
c = max(cont, key=cv2.contourArea)
x, y, w, h = cv2.boundingRect(c)
cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
image = cv2.line(img, (int(w/2),y), (int(w/2),y+h), (0,0,255), 2)
global cropped_img
cropped_img = copy_img[y:y + h, x:x + w]
draw_img = np.zeros((img.shape[0], img.shape[1], img.shape[2]), dtype="uint8")
cv2.drawContours(draw_img,[c],-1,(255,255,255),thickness=-1)
img_bitwise = cv2.bitwise_and(org_img, draw_img)
margin = 90
draw_img = draw_img[margin:-margin]
imgResult = imgResult[margin:-margin]
img = img[margin:-margin]
img_bitwise = img_bitwise[margin:-margin]
print(draw_img.shape[:2])
has_white = print(np.any(draw_img, axis=1))
print(np.argmin(has_white))
result_i = np.argmin(has_white)
print(np.argmin(img_bitwise[result_i,:]))
cv2.imwrite('bit_img.png',img_bitwise)
cv2.imshow("Track Images", imgResult)
cv2.imshow("Result Images", img)
cv2.imshow("Cropped Image", cropped_img)
cv2.imshow("Draw Image", draw_img)
cv2.imshow("bit_img", img_bitwise)
k = cv2.waitKey(0)
if k == 27: # wait for ESC key to exit
cv2.destroyAllWindows()
elif k == ord('s'): # wait for 's' key to save and exit
cv2.imwrite(r'C:\Users\Anush\PycharmProjects\WeldPoolAnalysis\resultImages\imgResult.png', img)
cv2.imwrite(r'C:\Users\Anush\PycharmProjects\WeldPoolAnalysis\resultImages\imgCropped.png', cropped_img)
cv2.destroyAllWindows()
脚本输出
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在以下程序中将原始图像用作主图像，然后可以轻松调试问题
use the original image as the main image in the following program and then you can debug the problem easily
推荐答案
假设图片看起来像这样:
Let's say the image looks like this:
0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0
0 0 0 0 1 0 0 0 0
0 0 0 1 1 1 0 0 0
0 1 1 1 1 1 1 0 0
1是白色，0是黑色.假设这个numpy数组称为 mask .让我们打印形状:
the 1 is white, the 0 is black.
let's say this numpy array is called mask.
Let's print the shape:
import numpy as np
print(mask.shape)
应该给你
(5,9)
在这种情况下，您应该得到(image_width，image_height)
in this case, you should get (image_width, image_height)
然后，我们将首先检查是否有任何行是全黑的，或者是否有一些像素为白色的 np.any ，这样做是:'沿轴的每一行，是否有任何"字样?值不为0?如果是，则为该行返回True，否则为该行返回False".所以:
Then we'll first check if any rows are either fully black, or there is some pixel that is white with np.any, which does this: 'for every row along axis, is there "any" value which is not 0? if yes return True for that row, otherwise return False for that row'. So:
has_white = print(np.any(mask, axis=1))
收益
array([False, False, True, True, True])
因此每行的 True/False 值.
现在我们取最小值，因为它是图像中的最高行:
Now we take the smallest value, as it is the highest row in your image:
np.argmin(has_white)
收益
2
是从上到下的索引
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