来源OpenCV的官方教程：https://github.com/dloperab/PyImageSearch-CV-DL-CrashCourse#day-6-measuring-size-of-objects-in-an-image-with-opencv

使用方法：

将下面的代码保存为 object_size.py 文件

新建一个文件main.py

import os
os.system("python object_size.py --image 图片路径 --width 0.955")
这里width后面的参数含义是图像最左边的物体离边缘的距离
在命令行中输入 python object_size.py --image 图片路径 --width 0.955

核心代码：object_size.py

# import the necessary packages
from scipy.spatial import distance as dist
from imutils import perspective
from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import osdef midpoint(ptA, ptB):return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,help="path to the input image")
ap.add_argument("-w", "--width", type=float, required=True,help="width of the left-most object in the image (in inches)")
args = vars(ap.parse_args())# load the image, convert it to grayscale, and blur it slightly
image = cv2.imread(args["image"])
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)# perform edge detection, then perform a dilation + erosion to
# close gaps in between object edges
edged = cv2.Canny(gray, 50, 100)
edged = cv2.dilate(edged, None, iterations=1)
edged = cv2.erode(edged, None, iterations=1)# find contours in the edge map
cnts = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)# sort the contours from left-to-right and initialize the
# 'pixels per metric' calibration variable
(cnts, _) = contours.sort_contours(cnts)
pixelsPerMetric = None# loop over the contours individually
for c in cnts:# if the contour is not sufficiently large, ignore itif cv2.contourArea(c) < 100:continue# compute the rotated bounding box of the contourorig = image.copy()box = cv2.minAreaRect(c)box = cv2.cv.BoxPoints(box) if imutils.is_cv2() else cv2.boxPoints(box)box = np.array(box, dtype="int")# order the points in the contour such that they appear# in top-left, top-right, bottom-right, and bottom-left# order, then draw the outline of the rotated bounding# boxbox = perspective.order_points(box)cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)# loop over the original points and draw themfor (x, y) in box:cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)# unpack the ordered bounding box, then compute the midpoint# between the top-left and top-right coordinates, followed by# the midpoint between bottom-left and bottom-right coordinates(tl, tr, br, bl) = box(tltrX, tltrY) = midpoint(tl, tr)(blbrX, blbrY) = midpoint(bl, br)# compute the midpoint between the top-left and top-right points,# followed by the midpoint between the top-righ and bottom-right(tlblX, tlblY) = midpoint(tl, bl)(trbrX, trbrY) = midpoint(tr, br)# draw the midpoints on the imagecv2.circle(orig, (int(tltrX), int(tltrY)), 5, (255, 0, 0), -1)cv2.circle(orig, (int(blbrX), int(blbrY)), 5, (255, 0, 0), -1)cv2.circle(orig, (int(tlblX), int(tlblY)), 5, (255, 0, 0), -1)cv2.circle(orig, (int(trbrX), int(trbrY)), 5, (255, 0, 0), -1)# draw lines between the midpointscv2.line(orig, (int(tltrX), int(tltrY)), (int(blbrX), int(blbrY)),(255, 0, 255), 2)cv2.line(orig, (int(tlblX), int(tlblY)), (int(trbrX), int(trbrY)),(255, 0, 255), 2)# compute the Euclidean distance between the midpointsdA = dist.euclidean((tltrX, tltrY), (blbrX, blbrY))dB = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))# if the pixels per metric has not been initialized, then# compute it as the ratio of pixels to supplied metric# (in this case, inches)if pixelsPerMetric is None:pixelsPerMetric = dB / args["width"]# compute the size of the objectdimA = dA / pixelsPerMetricdimB = dB / pixelsPerMetric# draw the object sizes on the imagecv2.putText(orig, "{:.1f}in".format(dimA),(int(tltrX - 15), int(tltrY - 10)), cv2.FONT_HERSHEY_SIMPLEX,0.65, (255, 255, 255), 2)cv2.putText(orig, "{:.1f}in".format(dimB),(int(trbrX + 10), int(trbrY)), cv2.FONT_HERSHEY_SIMPLEX,0.65, (255, 255, 255), 2)# show the output imagecv2.imshow("Image", orig)cv2.waitKey(0)

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OpenCV检验物体尺寸

使用方法：

核心代码：object_size.py

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