Hand Recognition Example

import numpy as np
import cv2
import math
import socket
import time

UDP_IP = "127.0.0.1"
UDP_PORT = 5065

sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)

last = []

# Open Camera
try:
    default = 0 # Try Changing it to 1 if webcam not found
    capture = cv2.VideoCapture(default)
except:
    print("No Camera Source Found!")

while capture.isOpened():

    # Capture frames from the camera
    ret, frame = capture.read()

    # Get hand data from the rectangle sub window
    cv2.rectangle(frame,(100,100),(300,300),(0,255,0),0)
    crop_image = frame[100:500, 100:500]

    # Apply Gaussian blur
    blur = cv2.GaussianBlur(crop_image, (3,3), 0)

    # Change color-space from BGR -> HSV
    hsv = cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)

    # Create a binary image with where white will be skin colors and rest is black
    mask2 = cv2.inRange(hsv, np.array([2,0,0]), np.array([20,255,255]))

    # Kernel for morphological transformation
    kernel = np.ones((5,5))

    # Apply morphological transformations to filter out the background noise
    dilation = cv2.dilate(mask2, kernel, iterations = 1)
    erosion = cv2.erode(dilation, kernel, iterations = 1)

    # Apply Gaussian Blur and Threshold
    filtered = cv2.GaussianBlur(erosion, (3,3), 0)
    ret,thresh = cv2.threshold(filtered, 127, 255, 0)

    # Show threshold image
    # cv2.imshow("Thresholded", thresh)

    # Find contours
    contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE )

    try:
        # Find contour with maximum area
        contour = max(contours, key = lambda x: cv2.contourArea(x))

        # Create bounding rectangle around the contour
        x,y,w,h = cv2.boundingRect(contour)
        cv2.rectangle(crop_image,(x,y),(x+w,y+h),(0,0,255),0)

        # Find convex hull
        hull = cv2.convexHull(contour)

        # Draw contour
        drawing = np.zeros(crop_image.shape, np.uint8)
        cv2.drawContours(drawing,[contour],-1,(0,255,0),0)
        cv2.drawContours(drawing,[hull],-1,(0,0,255),0)

        # Find convexity defects
        hull = cv2.convexHull(contour, returnPoints=False)
        defects = cv2.convexityDefects(contour,hull)

        # Use cosine rule to find angle of the far point from the start and end point i.e. the convex points (the finger
        # tips) for all defects
        count_defects = 0

        for i in range(defects.shape[0]):
            s,e,f,d = defects[i,0]
            start = tuple(contour[s][0])
            end = tuple(contour[e][0])
            far = tuple(contour[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))*180)/3.14

            # if angle > 90 draw a circle at the far point
            if angle <= 90:
                count_defects += 1
                cv2.circle(crop_image,far,1,[0,0,255],-1)

            cv2.line(crop_image,start,end,[0,255,0],2)

        # Print number of fingers

        print("Defects : ", count_defects)
        if count_defects == 0:
            cv2.putText(frame,"ZERO", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
        elif count_defects == 1:
            cv2.putText(frame,"TWO", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
        elif count_defects == 2:
            cv2.putText(frame, "THREE", (5,50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
        elif count_defects == 3:
            cv2.putText(frame,"FOUR", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
        elif count_defects == 4:
            cv2.putText(frame,"FIVE", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
        else:
            pass

        # Show required images
        cv2.imshow("Full Frame", frame)
        all_image = np.hstack((drawing, crop_image))
        cv2.imshow('Recognition', all_image)

        last.append(count_defects)
        if(len(last) > 5):
            last = last[-5:]

        # print(last)

        # Check if previously hand was wide open (3/4 fingers in previous frames), and is now a fist (0 fingers)
        if(count_defects == 0 and 4 in last):
            last = []
            sock.sendto( ("JUMP!").encode(), (UDP_IP, UDP_PORT) )
            print("_"*10, "Jump Action Triggered!", "_"*10)
    except:
        pass

    # Close the camera if 'q' is pressed
    if cv2.waitKey(1) == ord('q'):
        break

capture.release()
cv2.destroyAllWindows()