在进行结核病测试以准确确定尺寸后，我正在尝试对某人的硬结（皮肤上大小不一的红色斑点）进行图像分析。
当前，我正在使用NodeJS作为后端/ Web服务器，提供仪表板界面，并将图像推送到使用OpenCV来获取最左边的对象和给定的参考尺寸参数（例如0.955英寸）的Python脚本四分之一并使用该比例尺可以测量出其他检测到的物体，例如硬结。
我遇到的问题是未正确检测到红点（指示），但四分之一（参考物体）被检测到。通过在硬结周围划一条线并重新运行该程序，我做了a脚的修复工作，很明显，这一次它正确地识别了尺寸正确的硬结。
由于这不是一门图像分析课程，而且我确实没有进一步优化该程序的线索，因此我试图找出为用户提供标记硬结的界面（例如可调整大小的圆圈）的方法可以紧紧包裹住他们的硬结。
我还没有看到任何使用客户端JS上的getUserMedia（）功能直接执行此操作的示例，并且想知道如何执行此操作或查看是否已有库可以帮助实现此目的。另一种选择是让用户使用硬结和四分之一来拍照，然后在仪表板上的下一步测试中，用户可以用某种方式在其图像上绘制（在硬结周围画一个快速圆圈）。
我只看到了通过动态调整大小并在图像上拖动对象来静态地在画布上画东西的方法。
感谢任何输入
编辑： This是示例图片。
这是当前的代码：
from __future__ import print_function
from scipy.spatial import distance as dist
from imutils import perspective
from imutils import contours
#from mysql.connector import errorcode
from datetime import date, datetime, timedelta
import numpy as np
import argparse
import imutils
import cv2
#import mysql.connector
import os
# 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)")
ap.add_argument("-u", "--user", required=True,
help="Username of ")
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
countourCount = 0
# loop over the contours individually
for c in cnts:
# if the contour is not sufficiently large, ignore it
if cv2.contourArea(c) < 200:
continue
countourCount=countourCount+1
print(countourCount)
if countourCount == 1:
print('This is the scaling reference, should be: '+ str(args["width"]))
# 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)
# 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
print(box)
(tltrX, tltrY) = midpoint(tl, tr)
(blbrX, blbrY) = midpoint(bl, br)
# compute the midpoint between the top-left and bottom-left 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)
# compute the Euclidean distance between the midpoints
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"]
# compute the size of the object
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.imwrite(os.path.join('/Users/Ooga/Desktop/Skin-Analyzer/pythonServer/components/'+str(args["user"]) , 'component'+str(countourCount)+'.jpg'), orig)
cv2.waitKey(0)
# Make sure data is committed to the database
# cnx.commit()