#------------------------------------------------------------
# SEGMENT, RECOGNIZE and COUNT fingers from a video sequence
#------------------------------------------------------------
# organize imports
import cv2
import imutils
import numpy as np
from sklearn.metrics import pairwise
# global variables
bg = None
#--------------------------------------------------
# To find the running average over the background
#--------------------------------------------------
def run_avg(image, accumWeight):
global bg
# initialize the background
if bg is None:
bg = image.copy().astype("float")
return
# compute weighted average, accumulate it and update the background
cv2.accumulateWeighted(image, bg, accumWeight)
#---------------------------------------------
# To segment the region of hand in the image
#---------------------------------------------
def segment(image, threshold=25):
global bg
# find the absolute difference between background and current frame
diff = cv2.absdiff(bg.astype("uint8"), image)
# threshold the diff image so that we get the foreground
thresholded = cv2.threshold(diff, threshold, 255, cv2.THRESH_BINARY)[1]
# get the contours in the thresholded image
(_, cnts, _) = cv2.findContours(thresholded.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
# return None, if no contours detected
if len(cnts) == 0:
return
else:
# based on contour area, get the maximum contour which is the hand
segmented = max(cnts, key=cv2.contourArea)
return (thresholded, segmented)
#--------------------------------------------------------------
# To count the number of fingers in the segmented hand region
#--------------------------------------------------------------
def count(thresholded, segmented):
# find the convex hull of the segmented hand region
chull = cv2.convexHull(segmented)
# find the most extreme points in the convex hull
extreme_top = tuple(chull[chull[:, :, 1].argmin()][0])
extreme_bottom = tuple(chull[chull[:, :, 1].argmax()][0])
extreme_left = tuple(chull[chull[:, :, 0].argmin()][0])
extreme_right = tuple(chull[chull[:, :, 0].argmax()][0])
# find the center of the palm
cX = int((extreme_left[0] + extreme_right[0]) / 2)
cY = int((extreme_top[1] + extreme_bottom[1]) / 2)
# find the maximum euclidean distance between the center of the palm
# and the most extreme points of the convex hull
distance = pairwise.euclidean_distances([(cX, cY)], Y=[extreme_left, extreme_right, extreme_top, extreme_bottom])[0]
maximum_distance = distance[distance.argmax()]
# calculate the radius of the circle with 80% of the max euclidean distance obtained
radius = int(0.8 * maximum_distance)
# find the circumference of the circle
circumference = (2 * np.pi * radius)
# take out the circular region of interest which has
# the palm and the fingers
circular_roi = np.zeros(thresholded.shape[:2], dtype="uint8")
# draw the circular ROI
cv2.circle(circular_roi, (cX, cY), radius, 255, 1)
# take bit-wise AND between thresholded hand using the circular ROI as the mask
# which gives the cuts obtained using mask on the thresholded hand image
circular_roi = cv2.bitwise_and(thresholded, thresholded, mask=circular_roi)
# compute the contours in the circular ROI
(_, cnts, _) = cv2.findContours(circular_roi.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
# initalize the finger count
count = 0
# loop through the contours found
for c in cnts:
# compute the bounding box of the contour
(x, y, w, h) = cv2.boundingRect(c)
# increment the count of fingers only if -
# 1. The contour region is not the wrist (bottom area)
# 2. The number of points along the contour does not exceed
# 25% of the circumference of the circular ROI
if ((cY + (cY * 0.25)) > (y + h)) and ((circumference * 0.25) > c.shape[0]):
count += 1
return count
#-----------------
# MAIN FUNCTION
#-----------------
if __name__ == "__main__":
# initialize accumulated weight
accumWeight = 0.5
# get the reference to the webcam
camera = cv2.VideoCapture(0)
# region of interest (ROI) coordinates
top, right, bottom, left = 10, 350, 225, 590
# initialize num of frames
num_frames = 0
# calibration indicator
calibrated = False
# keep looping, until interrupted
while(True):
# get the current frame
(grabbed, frame) = camera.read()
# resize the frame
frame = imutils.resize(frame, width=700)
# flip the frame so that it is not the mirror view
frame = cv2.flip(frame, 1)
# clone the frame
clone = frame.copy()
# get the height and width of the frame
(height, width) = frame.shape[:2]
# get the ROI
roi = frame[top:bottom, right:left]
# convert the roi to grayscale and blur it
gray = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
# to get the background, keep looking till a threshold is reached
# so that our weighted average model gets calibrated
if num_frames < 30:
run_avg(gray, accumWeight)
if num_frames == 1:
print("[STATUS] please wait! calibrating...")
elif num_frames == 29:
print("[STATUS] calibration successfull...")
else:
# segment the hand region
hand = segment(gray)
# check whether hand region is segmented
if hand is not None:
# if yes, unpack the thresholded image and
# segmented region
(thresholded, segmented) = hand
# draw the segmented region and display the frame
cv2.drawContours(clone, [segmented + (right, top)], -1, (0, 0, 255))
# count the number of fingers
fingers = count(thresholded, segmented)
cv2.putText(clone, str(fingers), (70, 45), cv2.FONT_HERSHEY_SIMPLEX, 1, (0,0,255), 2)
# show the thresholded image
cv2.imshow("Thesholded", thresholded)
# draw the segmented hand
cv2.rectangle(clone, (left, top), (right, bottom), (0,255,0), 2)
# increment the number of frames
num_frames += 1
# display the frame with segmented hand
cv2.imshow("Video Feed", clone)
# observe the keypress by the user
keypress = cv2.waitKey(1) & 0xFF
# if the user pressed "q", then stop looping
if keypress == ord("q"):
break
# free up memory
camera.release()
cv2.destroyAllWindows()
