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main_prg.py
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main_prg.py
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import cv2
import numpy as np
from sklearn.metrics import pairwise
background = None
accumulated_weight = 0.5
roi_top = 20
roi_bottom = 300
roi_right = 300
roi_left = 600
def calc_accum_avg(frame,accumulated_weight):
global background
if background is None:
background = frame.copy().astype('float')
return None
cv2.accumulateWeighted(frame,background,accumulated_weight)
def segment(frame,threshold_min=25):
diff = cv2.absdiff(background.astype('uint8'),frame)
ret,thresholded = cv2.threshold(diff,threshold_min,255,cv2.THRESH_BINARY)
image,contours,hierarchy = cv2.findContours(thresholded.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)
if len(contours) == 0:
return None
else:
# ASSUMING THE LARGEST EXTERNAL CONTOUR IN ROI, IS THE HAND
hand_segment = max(contours,key=cv2.contourArea)
return (thresholded,hand_segment)
def count_fingers(thresholded,hand_segment):
conv_hull =cv2.convexHull(hand_segment)
# TOP
top = tuple(conv_hull[conv_hull[:, :, 1].argmin()][0])
bottom = tuple(conv_hull[conv_hull[:, :, 1].argmax()][0])
left = tuple(conv_hull[conv_hull[:, :, 0].argmin()][0])
right = tuple(conv_hull[conv_hull[:, :, 0].argmax()][0])
cX = (left[0] + right[0]) // 2
cY = (top[1] + bottom[1]) // 2
distance = pairwise.euclidean_distances([cX,cY],Y=[left,right,top,bottom])[0]
max_distance = distance.max()
radius = int(0.9*max_distance)
circumfrence = (2*np.pi*radius)
circular_roi = np.zeros(thresholded[:2],dtype='uint8')
cv2.circle(circular_roi,(cX,cY),radius,255,10)
circular_roi = cv2.bitwise_and(thresholded,thresholded,mask=circular_roi)
image,contours,hierarchy = cv2.findContours(circular_roi.copy(),cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
count = 0
for cnt in contours:
(x,y,w,h) = cv2.boundingRect(cnt)
out_of_wrist = (cY + (cY*0.25)) > (y+h)
limit_points = ((circumfrence*0.25) > cnt.shape[0])
if out_of_wrist and limit_points:
count += 1
return count
cam = cv2.VideoCapture(0)
num_frames = 0
while True:
ret, frame = cam.read()
frame_copy = frame.copy()
roi = frame[roi_top:roi_bottom,roi_right:roi_left]
gray = cv2.cvtColor(roi,cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray,(7,7),0)
if num_frames < 60:
calc_accum_avg(gray,accumulated_weight)
if num_frames <= 59:
cv2.putText(frame_copy,'WAIT. GETTING BACKGROUND',(200,300),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255),2)
cv2.imshow('Finger Count',frame_copy)
else:
hand = segment(gray)
if hand is not None:
thresholded , hand_segment = hand
# DRAWS CONTOURS AROUND REAL HAND IN LIVE STREAM
cv2.drawContours(frame_copy,[hand_segment+(roi_right,roi_top)],-1,(255,0,0),5)
fingers = count_fingers(thresholded,hand_segment)
cv2.putText(frame_copy,str(fingers),(70,50),cv2.FONT_HERSHEY_SIMPLEX,1,(0,0,255),2)
cv2.imshow('Thresholded',thresholded)
cv2.rectangle(frame_copy,(roi_left,roi_top),(roi_right,roi_bottom),(0,0,255),5)
num_frames += 1
cv2.imshow('Finger Count',frame_copy)
k = cv2.waitKey(1) & 0xFF
if k == 27:
break
cam.release()
cv2.destroyAllWindows()