Opencv Python Hand Detection and Tracking

Aim of the project is to move a robotic hand, mimicking humand hand based on a camera feed.The project is divided into

• Software (i'm using opencv to detect human hand and find the distance between palm center and finger tips. Popular method of convex hull and convexity defect is used to detect the movement of hand.)
• Hardware (pc/raspberry pi)

python code you can get it from here

import cv2
import numpy as np
import copy
import math
import os</p><p>def calculateFingers(res, drawing):
    #  convexity defect
    hull = cv2.convexHull(res, returnPoints=False)
    if len(hull) > 3:
        defects = cv2.convexityDefects(res, hull)
        if defects is not None:
            cnt = 0
            for i in range(defects.shape[0]):  # calculate the angle
                s, e, f, d = defects[i][0]
                start = tuple(res[s][0])
                end = tuple(res[e][0])
                far = tuple(res[f][0])
                a = math.sqrt((end[0] - start[0]) ** 2 + (end[1] - start[1]) ** 2)
                b = math.sqrt((far[0] - start[0]) ** 2 + (far[1] - start[1]) ** 2)
                c = math.sqrt((end[0] - far[0]) ** 2 + (end[1] - far[1]) ** 2)
                angle = math.acos((b ** 2 + c ** 2 - a ** 2) / (2 * b * c))  # cosine theorem
                if angle <= math.pi / 2:  # angle less than 90 degree, treat as fingers
                    cnt += 1
                    cv2.circle(drawing, far, 8, [211, 84, 0], -1)
            if cnt > 0:
                return True, cnt+1
            else:
                return True, 0
    return False, 0

# Open Camera
camera = cv2.VideoCapture(0)
camera.set(10, 200)</p><p>#while True:
while camera.isOpened():
    #Main Camera
    ret, frame = camera.read()
    frame = cv2.bilateralFilter(frame, 5, 50, 100)  # Smoothing
    frame = cv2.flip(frame, 1)  #Horizontal Flip
    cv2.imshow('original', frame)
       #Background Removal
    bgModel = cv2.createBackgroundSubtractorMOG2(0, 50)
    fgmask = bgModel.apply(frame)</p><p>    kernel = np.ones((3, 3), np.uint8)
    fgmask = cv2.erode(fgmask, kernel, iterations=1)
    img = cv2.bitwise_and(frame, frame, mask=fgmask)
    
    # Skin detect and thresholding
    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
    lower = np.array([0, 48, 80], dtype="uint8")
    upper = np.array([20, 255, 255], dtype="uint8")
    skinMask = cv2.inRange(hsv, lower, upper)
    cv2.imshow('Threshold Hands', skinMask)</p><p>     # Getting the contours and convex hull
    skinMask1 = copy.deepcopy(skinMask)
    contours, hierarchy = cv2.findContours(skinMask1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    length = len(contours)
    maxArea = -1
    if length > 0:
        for i in xrange(length):
            temp = contours[i]
            area = cv2.contourArea(temp)
            if area > maxArea:
                maxArea = area
                ci = i</p><p>        res = contours[ci]
        hull = cv2.convexHull(res)
        drawing = np.zeros(img.shape, np.uint8)
        cv2.drawContours(drawing, [res], 0, (0, 255, 0), 2)
        cv2.drawContours(drawing, [hull], 0, (0, 0, 255), 3)

	isFinishCal, cnt = calculateFingers(res, drawing)
        print "Fingers", cnt
        cv2.imshow('output', drawing)
        k = cv2.waitKey(10)
    if k == 27:  # press ESC to exit
        break

output should be

you can find the Opencv and Python Installation Instructions here