I want to mask the hand from the picture and want to get the binary image from it. Also want to measure the euclidian distance from the masked region to the entire image. I have coded so far:

-- coding: utf-8 --

""" Created on Sun Feb 9 00:09:31 2020

@author: Shrouti """

import cv2 import numpy as np from PIL import Image, ImageCms from skimage import color

# Read input image, and create output image

src = cv2.imread("D:\SHROUTI\Testpictures\hand_gesture3.jpg") src = cv2.resize(src, (640, 480))

    # convert image to gray scale

gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)

    # blur the image

blur = cv2.blur(gray, (3, 3))

    # binary thresholding of the image

ret, thresh = cv2.threshold(blur, 200, 255, cv2.THRESH_BINARY) # ret, thresh = cv2.threshold(gray, 127, 255,0)

    # find contours
    # contours, hierarchy = cv2.findContours(thresh,2,1)

contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE) # cc cnt = sorted(contours, key=cv2.contourArea, reverse=True) # ROI will be object with biggest contour mask = contours[0] # Know the coordinates of the bounding box of the ROI x, y, w, h = cv2.boundingRect(mask)

    # Convert to Lab colourspace

Lab = color.rgb2lab(dst) L, A, B = cv2.split(Lab) # cv2.imshow("L_Channel", L) # For L Channel # cv2.imshow("A_Channel", A) # For A Channel # cv2.imshow("B_Channel", B) # For B Channel LMean = L.mean() AMean = A.mean() BMean = B.mean() cv2.waitKey(0) cv2.destroyAllWindows() #print(Lab) #print(LMean) #print(AMean) #print(BMean) [row,column,no_of_ColorBands]= src.shape #make uniform images with lab colors LStandard = LMeannp.ones([row, column], dtype = int) AStandard = AMeannp.ones([row, column], dtype = int) BStandard = BMeannp.ones([row, column], dtype = int) #determine delta values DeltaL = L-LStandard DeltaA = A- AStandard DeltaB = B - BStandard DeltaE = np.sqrt(pow(DeltaA,2)+pow(DeltaB,2)+pow(DeltaL,2)) print(DeltaE) print(mask) #euclidian distance for only the masked region maskedDeltaE = DeltaEmask

I want to mask the hand from the picture and want to get the binary image from it. Also want to measure the euclidian distance from the masked region to the entire image. I have coded so far:

color
# Read input image, and create output image

480))
 # convert image to gray scale

cv2.COLOR_BGR2GRAY)
 # blur the image

3))
 # binary thresholding of the image

255,0)
 # find contours
 # contours, hierarchy = cv2.findContours(thresh,2,1)

cv2.boundingRect(mask)
 # Convert to Lab colourspace

I want to mask the hand from the picture and want to get the binary image from it. Also want to measure the euclidian distance from the masked region to the entire image. I have coded so far:

# -*- coding: utf-8 -*-
"""
Created on Sun Feb  9 00:09:31 2020

@author: Shrouti
"""

import cv2
import numpy as np
from PIL import Image, ImageCms
from skimage import color

    # Read input image, and create output image
 src = cv2.imread("D:\SHROUTI\Testpictures\hand_gesture3.jpg")
 src = cv2.resize(src, (640, 480))


        # convert image to gray scale
 gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)

        # blur the image
 blur = cv2.blur(gray, (3, 3))

        # binary thresholding of the image
 ret, thresh = cv2.threshold(blur, 200, 255, cv2.THRESH_BINARY)
        # ret, thresh = cv2.threshold(gray, 127, 255,0)

        # find contours
        # contours, hierarchy = cv2.findContours(thresh,2,1)
contours, hierarchy = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
        # cc
cnt = sorted(contours, key=cv2.contourArea, reverse=True)
        # ROI will be object with biggest contour
 mask = contours[0]
        # Know the coordinates of the bounding box of the ROI
x, y, w, h = cv2.boundingRect(mask)

        # Convert to Lab colourspace
Lab = color.rgb2lab(dst)
L, A, B = cv2.split(Lab)
       # cv2.imshow("L_Channel", L)  # For L Channel
       # cv2.imshow("A_Channel", A)  # For A Channel
       # cv2.imshow("B_Channel", B)  # For B Channel
LMean = L.mean()
AMean = A.mean()
BMean = B.mean()
cv2.waitKey(0)
cv2.destroyAllWindows()
        #print(Lab)
        #print(LMean)
        #print(AMean)
        #print(BMean)
[row,column,no_of_ColorBands]= src.shape
        #make uniform images with lab colors
LStandard = LMean*np.ones([row, column], dtype = int)
AStandard = AMean*np.ones([row, column], dtype = int)
BStandard = BMean*np.ones([row, column], dtype = int)
        #determine delta values
DeltaL = L-LStandard
DeltaA = A- AStandard
DeltaB = B - BStandard
DeltaE = np.sqrt(pow(DeltaA,2)+pow(DeltaB,2)+pow(DeltaL,2))
print(DeltaE)
print(mask)
        #euclidian distance for only the masked region
maskedDeltaE = DeltaE*mask