python - 收集所有不同轮廓的非零像素

Question

我一直在尝试根据序列对轮廓进行排序（序列在这里无关紧要）。我有一个非常小的问题，这是我应该在下面的代码片段中传递的正确的 numpy 数组，我可以同时获得正确的行/列（非零）像素。

row_pixels=cv2.countNonZero(blur[cy][:])

col_pixels=cv2.countNonZero(blur[:,cx])

我所做的结果如下所示：对于所有 5 个轮廓，我得到几乎相同数量的非零像素，我意识到这是因为我正在传递“整个”图像（如您在上面看到的那样，模糊是整个图像）为用于计算错误像素的 numpy 数组，我意识到这一点。

当前输入：下图（无标记）

预期输出：对于所有 5 个轮廓，行/列（非零）像素。

我目前正在做的是：

import cv2
import numpy as np
from imutils import perspective
from imutils import contours 
import imutils 
from scipy.spatial import distance as dist
import argparse
import pandas as pd
import time

parser = argparse.ArgumentParser(description='Object Detection and Tracking using YOLO in OPENCV')
parser.add_argument('--image', help='Path to image file.')

args = parser.parse_args()
font=cv2.FONT_HERSHEY_SIMPLEX


start=time.time()
im_in = cv2.imread(args.image, 0)
_, thres2=cv2.threshold(im_in, 140, 255,cv2.THRESH_BINARY_INV)
dilate = cv2.dilate(thres2,None)
erode = cv2.erode(dilate,None)

im_3=erode.copy()

blur=cv2.medianBlur(im_3,5)

a=[]
r=[] 
row_col_pixel_values=[]
cl=[]
data=[]
global mainar
#find contours 
_,contour2,_=cv2.findContours(blur,cv2.RETR_CCOMP,cv2.CHAIN_APPROX_NONE)
# print(contour2)
for c in contour2:
    area=cv2.contourArea(c)
    if area>10000 and area <30000:
        a.append(area)

        cv2.drawContours(blur, [c], 0, (128, 128, 128), 1)


        M=cv2.moments(c)
        cx=int((M["m10"]/M["m00"]))
        cy=int((M["m01"]/M["m00"]))
        center =(cx,cy)
        data.append((cx,cy))
        cv2.circle(blur,(cx,cy), 5,(128,128,128),-1)
        print("",cx,cy)
        print(len(blur[cy][:])) 
        # one=blur[c]
        row_pixels=cv2.countNonZero(blur[cy][:])

        col_pixels=cv2.countNonZero(blur[:,cx])
        comb=(row_pixels,col_pixels)
        cl.append(comb)

nparea=np.array(a)
npcentercoord=np.array(data)

row_col_pixel_values=np.array(cl)
print("Area of 5 contours :",nparea)
print("Center coordinates of 5 contours:",npcentercoord)

print("Row and Column pixel values of 5 contours:",row_col_pixel_values)

mainar=np.column_stack((nparea,npcentercoord,row_col_pixel_values))
# print(mainar)

mainar[:,[1]] = (mainar[:,[1]]).astype(int)

MinX = int(min([_[1] for _ in mainar]))
MinlowerX = (MinX - 10) 
MinupperX = (MinX + 10)
MinY = int(min([_[2] for _ in mainar]))
MinlowerY = (MinY - 10) 
MinupperY = (MinY + 10)
MaxX = int(max([_[1] for _ in mainar]))
MaxlowerX = (MaxX - 10) 
MaxupperX = (MaxX + 10)
MaxY = int(max([_[2] for _ in mainar]))
MaxlowerY = (MaxY - 10)
MaxupperY = (MaxY + 10)

print("", MinX,MinY,MaxX,MaxY)


def PixeltoNumeric(channel,rowMM,colMM):

    if channel=="4S":
        for i in range(0, len(mainar[:,1])):
            cx=mainar[i,1]
            cy=mainar[i,2]
            if (cx in range(MinlowerX,MinupperX+1)) and (cy in range(MinlowerY,MinupperY+1)):
                rowp=mainar[i,3]
                colp=mainar[i,4]
                print("The center coordinates(x,y) and (Row/Col) pixels of 4Schannel: ")
                print("1 pixel has {:.5f} many mm in row:".format(rowMM/rowp))
                print("1 pixel has {:.5f} many mm in col:".format(colMM/colp))
                print(cx,cy,rowp,colp)

    if channel == '1':
            for i in range(0, len(mainar[:,1])):
                cx=mainar[i,1]
                cy=mainar[i,2]
                if (cx in range(MaxlowerX,MaxupperX+1)) and (cy in range(MaxlowerY,MaxupperY+1)):
                    rowp=mainar[i,3]
                    colp=mainar[i,4]
                    print("The center coordinates(x,y) and (Row/Col) pixels of 1Channel: ")
                    print("1 pixel has {:.5f} many mm in row:".format(rowMM/rowp))
                    print("1 pixel has {:.5f} many mm in col:".format(colMM/colp))
                    print(cx,cy,rowp,colp)

    if channel == '2':
        for i in range(0, len(mainar[:,1])):
            cx=mainar[i,1]
            cy=mainar[i,2]
            if (cx in range(MinlowerX,MinupperX+1)) and (cy in range(MaxlowerY,MaxupperY+1)):
                rowp=mainar[i,3]
                colp=mainar[i,4]
                print("The center coordinates(x,y) and (Row/Col) pixels of 2Channel: ")
                print("1 pixel has {:.5f} many mm in row:".format(rowMM/rowp))
                print("1 pixel has {:.5f} many mm in col:".format(colMM/colp))
                print(cx,cy,rowp,colp)

    if channel == '3':
        for i in range(0, len(mainar[:,1])):
            cx=mainar[i,1]
            cy=mainar[i,2]
            if (cx in range(((MinlowerX+MaxlowerX)//2),((MinupperX+MaxupperX+1)//2)) and (cy in range(((MinlowerY+MaxlowerY)//2),((MinupperY+MaxupperY+1)//2)))):
                rowp=mainar[i,3]
                colp=mainar[i,4]
                print("The center coordinates(x,y) and (Row/Col) pixels of 3Channel: ")
                print("1 pixel has {:.5f} many mm in row:".format(rowMM/rowp))
                print("1 pixel has {:.5f} many mm in col:".format(colMM/colp))
                print(cx,cy,rowp,colp)

    if channel == '4N':
        for i in range(0, len(mainar[:,1])):
            cx=mainar[i,1]
            cy=mainar[i,2]
            if (cx in range(MaxlowerX,MaxupperX+1)) and (cy in range(MinlowerY,MinupperY+1)):
                rowp=mainar[i,3]
                colp=mainar[i,4]
                print("The center coordinates(x,y) and (Row/Col) pixels of 4NChannel: ")
                print("1 pixel has {:.5f} many mm in row:".format(rowMM/rowp))
                print("1 pixel has {:.5f} many mm in col:".format(colMM/colp))
                print(cx,cy,rowp,colp)

    return (cv2.imshow("4",blur))


cv2.waitKey(0)
cv2.destroyAllWindows()

标签： pythonopencvimage-processing

Answer 1

我不完全确定我是否正确理解你，但我的理解是你想找到所有轮廓内像素的所有坐标（x，y）。如果这是您的问题，您可以使用以下代码实现：

import cv2
import matplotlib.pyplot as plt
import numpy as np

im_in = cv2.imread(r'image.png', 0)
_, thres2 = cv2.threshold(im_in, 140, 255, cv2.THRESH_BINARY_INV)
dilate = cv2.dilate(thres2, None)
erode = cv2.erode(dilate, None)
im_3 = erode.copy()
blur = cv2.medianBlur(im_3, 5)

# I am using OpenCV 4 therefore it returns only 4 parameters
contour2, _ = cv2.findContours(blur, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
extracted = np.zeros(blur.shape, np.uint8)

for c in contour2:
    area = cv2.contourArea(c)
    # I have modified these values to make it work for attached picture
    if 10000 < area < 300000: 
        cv2.drawContours(extracted, [c], 0, (255), cv2.FILLED)

contour_x, contour_y = np.nonzero(extracted)

plt.imshow(extracted, 'gray')
plt.show()

这是提取的图像。

更新 1

经过您的解释，我了解到您想要计算每个单独轮廓的宽度和高度。根据您提供的示例代码，我假设您想使用穿过轮廓中心的线来测量宽度和高度。您可以通过在清晰图像上绘制和测量轮廓来实现。请看下面的代码：

# I am using OpenCV 4 therefore it returns only 4 parameters
contour2, _ = cv2.findContours(blur, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
extracted = np.zeros(blur.shape, np.uint8)
contoursSize = []
for c in contour2:
    area = cv2.contourArea(c)
    # I have modified these values to make it work for attached picture
    if 10000 < area < 300000:
        M = cv2.moments(c)
        cx = int((M["m10"] / M["m00"]))
        cy = int((M["m01"] / M["m00"]))
        extracted.fill(0) 
        cv2.drawContours(extracted, [c], 0, 255, cv2.FILLED)
        width = cv2.countNonZero(extracted[cy][:])
        height = cv2.countNonZero(extracted[:, cx])
        contoursSize.append((width, height))