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
cv2
...
  
def midpoint(ptA, ptB):
    return ((ptA[0] + ptB[0]) * 0.5, (ptA[1] + ptB[1]) * 0.5)
 
# construct the argument parse and parse the arguments
 
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())
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 = cnts[0] if imutils.is_cv2() else cnts[1]
 
sort the contours from left-to-right and initialize the
 
'pixels per metric' calibration variable
 
(cnts, _) = contours.sort_contours(cnts)
pixelsPerMetric = None
for c in cnts:
    # if the contour is not sufficiently large, ignore it
    if cv2.contourArea(c) < 100:
        continue
 
# compute the rotated bounding box of the contour
orig = 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
# box
box = perspective.order_points(box)
cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)

# loop over the original points and draw them
for (x, y) in box:
    cv2.circle(orig, (int(x), int(y)), 5, (0, 0, 255), -1)
...
  
(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 image
 
cv2.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 midpoints
 
cv2.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)
dA = 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"]
dimA = dA / pixelsPerMetric
dimB = dB / pixelsPerMetric
 
draw the object sizes on the image
 
cv2.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 image
 
cv2.imshow("Image", orig)
cv2.waitKey(0)