问用opencv远心单色相机拍摄金属零件孔直径图

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import diplib as dip
import numpy as np

img = dip.ImageRead('geriausias.bmp')
img.SetPixelSize(1,'um') # Usually this info is in the image file
bin, thresh = dip.Threshold(img)
bin = dip.EdgeObjectsRemove(bin)
bin = dip.Label(bin)
msr = dip.MeasurementTool.Measure(bin, features=['Size','Radius'])
print(msr)
d1 = np.sqrt(np.array(msr['Size'])[:,0] * 4 / np.pi)
print("method 1:", d1)
d2 = np.array(msr['Radius'])[:,1] * 2
print("method 2:", d2)

bin = dip.Dilation(bin, 10) # we need larger regions to average over so we take all of the light
                            # coming through the hole into account.
img = (dip.ErfClip(img, thresh, thresh/4, "range") - (thresh*7/8)) / (thresh/4)
msr = dip.MeasurementTool.Measure(bin, img, features=['Mass'])
d3 = np.sqrt(np.array(msr['Mass'])[:,0] * 4 / np.pi)
print("method 3:", d3)

  |       Size |                                            Radius | 
- | ---------- | ------------------------------------------------- | 
  |            |        Max |       Mean |        Min |     StdDev | 
  |      (µm²) |       (µm) |       (µm) |       (µm) |       (µm) | 
- | ---------- | ---------- | ---------- | ---------- | ---------- | 
1 |  6.282e+04 |      143.9 |      141.4 |      134.4 |      1.628 | 
2 |  9.110e+04 |      171.5 |      170.3 |      168.3 |     0.5643 | 
3 |  6.303e+04 |      143.5 |      141.6 |      133.9 |      1.212 | 
4 |  9.103e+04 |      171.6 |      170.2 |      167.3 |     0.6292 | 
5 |  6.306e+04 |      143.9 |      141.6 |      126.5 |      2.320 | 
6 |  2.495e+05 |      283.5 |      281.8 |      274.4 |     0.9805 | 
7 |  1.176e+05 |      194.4 |      193.5 |      187.1 |     0.6303 | 
8 |  1.595e+05 |      226.7 |      225.3 |      219.8 |     0.8629 | 
9 |  9.063e+04 |      171.0 |      169.8 |      167.6 |     0.5457 | 

method 1: [282.8250363  340.57242408 283.28834869 340.45277017 283.36249824
 563.64770132 386.9715443  450.65294139 339.70023023]
method 2: [282.74577033 340.58808144 283.24878097 340.43862835 283.1641869
 563.59706479 386.95245928 450.65392268 339.68617582]
method 3: [282.74836803 340.56787463 283.24627163 340.39568372 283.31396961
 563.601641   386.89884807 450.62167913 339.68954136]

bin = dip.Label(bin, 2, 500) # Imposing minimum object size rather than filtering

  |       Size |                                            Radius | 
- | ---------- | ------------------------------------------------- | 
  |            |        Max |       Mean |        Min |     StdDev | 
  |      (µm²) |       (µm) |       (µm) |       (µm) |       (µm) | 
- | ---------- | ---------- | ---------- | ---------- | ---------- | 
1 |  4.023e+06 |      1133. |      1132. |      1125. |     0.4989 | 

method 1: [2263.24621554]
method 2: [2263.22724164]
method 3: [2262.90068056]

import cv2 as cv
import math
import numpy as np
from matplotlib import pyplot as pl

gray = cv.imread('geriausias.bmp', cv.IMREAD_GRAYSCALE)
_,mask = cv.threshold(gray, 127, 255, cv.THRESH_BINARY)
contours,_ = cv.findContours(mask, cv.RETR_LIST, cv.CHAIN_APPROX_NONE)
contours = [contour for contour in contours if len(contour) > 15]
circles = [cv.minEnclosingCircle(contour) for contour in contours]
areas = [cv.contourArea(contour) for contour in contours]
radiuses = [math.sqrt(area / math.pi) for area in areas]

# Render contours blue and circles green.
canvas = cv.cvtColor(mask, cv.COLOR_GRAY2BGR)
cv.drawContours(canvas, contours, -1, (255, 0, 0), 10)
for circle, radius_from_area in zip(circles, radiuses):
    if 0.9 <= circle[1] / radius_from_area <= 1.1:  # Only allow 10% error in radius.
        p = (round(circle[0][0]), round(circle[0][1]))
        r = round(circle[1])
        cv.circle(canvas, p, r, (0, 255, 0), 10)
cv.imwrite('geriausias_circles.png', canvas)

canvas_small = cv.resize(canvas, None, None, 0.25, 0.25, cv.INTER_AREA)
cv.imwrite('geriausias_circles_small.png', canvas_small)

import cv2

image = cv2.imread('1.bmp')

# Gray, blur, adaptive threshold
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (3,3), 0)
thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]

# Morphological transformations
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5,5))
opening = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)

# Find contours
cnts = cv2.findContours(opening, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]

for c in cnts:
    # Find perimeter of contour
    perimeter = cv2.arcLength(c, True)
    # Perform contour approximation
    approx = cv2.approxPolyDP(c, 0.04 * perimeter, True)

    # We assume that if the contour has more than a certain
    # number of verticies, we can make the assumption
    # that the contour shape is a circle
    if len(approx) > 6:

        # Obtain bounding rectangle to get measurements
        x,y,w,h = cv2.boundingRect(c)

        # Find measurements
        diameter = w
        radius = w/2

        # Find centroid
        M = cv2.moments(c)
        cX = int(M["m10"] / M["m00"])
        cY = int(M["m01"] / M["m00"])

        # Draw the contour and center of the shape on the image
        cv2.rectangle(image,(x,y),(x+w,y+h),(0,255,0),4)
        cv2.drawContours(image,[c], 0, (36,255,12), 4)
        cv2.circle(image, (cX, cY), 15, (320, 159, 22), -1) 

        # Draw line and diameter information 
        cv2.line(image, (x, y + int(h/2)), (x + w, y + int(h/2)), (156, 188, 24), 3)
        cv2.putText(image, "Diameter: {}".format(diameter), (cX - 50, cY - 50), cv2.FONT_HERSHEY_SIMPLEX, 3, (156, 188, 24), 3)

cv2.imwrite('image.png', image)
cv2.imwrite('thresh.png', thresh)
cv2.imwrite('opening.png', opening)