python - OpenCV2 测量图像中物体之间的距离

Question

所以我有一个程序可以测量图像中两个对象之间的距离。但是，程序会自动将最左边的对象作为“参考图像”。例如，我一直在努力将这个参考对象更改为中心的正方形。

我可以通过图像来做到这一点的唯一方法是使用 Python Pillow 来计算图像的中心坐标并使用这些坐标来创建“方形”参考对象。然而，我一直在努力做到这一点。我在下面包含了未更改的代码。

任何帮助将不胜感激。

# USAGE
# python distance_between.py --image images/example_01.png --width 0.955
# python distance_between.py --image images/example_02.png --width 0.955
# python distance_between.py --image images/example_03.png --width 3.5
# python distance_between.py --image images/example_04.jpg --width 1.0

# 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

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
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, then initialize the
# distance colors and reference object
(cnts, _) = contours.sort_contours(cnts)
colors = ((0, 0, 255), (240, 0, 159), (0, 165, 255), (255, 255, 0),
    (255, 0, 255))
refObj = None

# loop over the contours individually
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
    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)

    # compute the center of the bounding box
    cX = np.average(box[:, 0])
    cY = np.average(box[:, 1])

    # if this is the first contour we are examining (i.e.,
    # the left-most contour), we presume this is the
    # reference object
    if refObj is None:
        # unpack the ordered bounding box, then compute the
        # midpoint between the top-left and top-right points,
        # followed by the midpoint between the top-right and
        # bottom-right
        (tl, tr, br, bl) = box
        (tlblX, tlblY) = midpoint(tl, bl)
        (trbrX, trbrY) = midpoint(tr, br)

        # compute the Euclidean distance between the midpoints,
        # then construct the reference object
        D = dist.euclidean((tlblX, tlblY), (trbrX, trbrY))
        refObj = (box, (cX, cY), D / args["width"])
        continue

    # draw the contours on the image
    orig = image.copy()
    cv2.drawContours(orig, [box.astype("int")], -1, (0, 255, 0), 2)
    cv2.drawContours(orig, [refObj[0].astype("int")], -1, (0, 255, 0), 2)

    # stack the reference coordinates and the object coordinates
    # to include the object center
    refCoords = np.vstack([refObj[0], refObj[1]])
    objCoords = np.vstack([box, (cX, cY)])
       
    # loop over the original points
    for ((xA, yA), (xB, yB), color) in zip(refCoords, objCoords, colors):
        # draw circles corresponding to the current points and
        # connect them with a line
        cv2.circle(orig, (int(xA), int(yA)), 5, color, -1)
        cv2.circle(orig, (int(xB), int(yB)), 5, color, -1)
        cv2.line(orig, (int(xA), int(yA)), (int(xB), int(yB)),
            color, 2)
        # compute the Euclidean distance between the coordinates,
        # and then convert the distance in pixels to distance in
        # units
        D = dist.euclidean((xA, yA), (xB, yB)) / refObj[2]
        (mX, mY) = midpoint((xA, yA), (xB, yB))
        cv2.putText(orig, "{:.1f}in".format(D), (int(mX), int(mY - 10)),
            cv2.FONT_HERSHEY_SIMPLEX, 0.55, color, 2)

        # show the output image
        cv2.imshow("Image", orig)
        cv2.waitKey(0)

标签： pythonlistobjectcomputer-visionopencv3.0