Need some help. I need to get length of the arc from Point A to Point B in centimeters as marked in image. Also, we need to extract on the only leg nothing else that might be there in the image or might not be. This is using Python and OpenCV. I have been able to get only 2 largest contours from many of them but struggling to keep only to the largest one. Below given is the current code.
# import the necessary packages
from __future__ import print_function
from skimage.feature import peak_local_max
from skimage.morphology import watershed
from scipy import ndimage
import numpy as np
import imutils
import cv2
image_dir = "/home/rahul/Desktop/img-708/"
img = cv2.imread(image_dir+'side_left.jpg')
lower = np.array([0, 48, 80], dtype = "uint8")
upper = np.array([20, 255, 255], dtype = "uint8")
# keep looping over the frames in the video
# resize the frame, convert it to the HSV color space,
# and determine the HSV pixel intensities that fall into
# the speicifed upper and lower boundaries
frame = imutils.resize(img, width = 400)
converted = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
skinMask = cv2.inRange(converted, lower, upper)
# apply a series of erosions and dilations to the mask
# using an elliptical kernel
kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11))
skinMask = cv2.erode(skinMask, kernel, iterations = 2)
skinMask = cv2.dilate(skinMask, kernel, iterations = 2)
# blur the mask to help remove noise, then apply the
# mask to the frame
skinMask = cv2.GaussianBlur(skinMask, (3, 3), 0)
skin = cv2.bitwise_and(frame, frame, mask = skinMask)
# show the skin in the image along with the mask
cv2.imwrite(image_dir+'output.jpg', np.hstack([skin]))
#image = cv2.imshow(np.hstack([skin])
image_dir = "/home/rahul/Desktop/img-708/"
image = cv2.imread(image_dir+'output.jpg')
#image = cv2.imread(pass)
shifted = cv2.pyrMeanShiftFiltering(image, 21, 51)
cv2.imshow("Input", image)
# convert the mean shift image to grayscale, then apply
# Otsu's thresholding
gray = cv2.cvtColor(shifted, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255,
cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1]
cv2.imshow("Thresh", thresh)
# find contours in the thresholded image
cnts = cv2.findContours(thresh.copy(), cv2.RETR_EXTERNAL,
cv2.CHAIN_APPROX_SIMPLE)[-2]
print("[INFO] {} unique contours found".format(len(cnts)))
# loop over the contours
for (i, c) in enumerate(cnts):
# draw the contour
((x, y), _) = cv2.minEnclosingCircle(c)
cv2.putText(image, "#{}".format(i), (int(x) - 10, int(y)),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2)
cv2.drawContours(image, [c], -1, (0, 255, 0), 2)
# show the output image
cv2.imshow("Image", image)
cv2.waitKey(0)
Original Image
Output image of my code:
