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Readability, pretty printing #3

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Readability, pretty printing #3

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58 changes: 26 additions & 32 deletions gesture.py
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Original file line number Diff line number Diff line change
@@ -1,72 +1,67 @@
import cv2
import numpy as np
import math

# pass source as 0 (zero) for inbuilt webcam, 1 for external camera
cap = cv2.VideoCapture(0)

while(1):
while(True):

try:
#an error comes if it does not find anything in window as it cannot find contour of max area
#therefore this try error statement

try: #an error comes if it does not find anything in window as it cannot find contour of max area
#therefore this try error statement

#obtain frame and kernel matrix
ret, frame = cap.read()
frame=cv2.flip(frame,1)
kernel = np.ones((3,3),np.uint8)

#define region of interest
roi=frame[100:300, 100:300]


#convert to HSV
cv2.rectangle(frame,(100,100),(300,300),(0,255,0),0)
hsv = cv2.cvtColor(roi, cv2.COLOR_BGR2HSV)



# define range of skin color in HSV
#define range of skin color in HSV
lower_skin = np.array([0,20,70], dtype=np.uint8)
upper_skin = np.array([20,255,255], dtype=np.uint8)

#extract skin colur imagw
#extract skin colur image
mask = cv2.inRange(hsv, lower_skin, upper_skin)



#extrapolate the hand to fill dark spots within

#extrapolate the hand to fill dark spots within
mask = cv2.dilate(mask,kernel,iterations = 4)

#blur the image
#blur the image
mask = cv2.GaussianBlur(mask,(5,5),100)



#find contours
#find contours
_,contours,hierarchy= cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
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contours,hierarchy= cv2.findContours(mask,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE


#find contour of max area(hand)
#find contour of max area(hand)
cnt = max(contours, key = lambda x: cv2.contourArea(x))

#approx the contour a little
#approx the contour a little
epsilon = 0.0005*cv2.arcLength(cnt,True)
approx= cv2.approxPolyDP(cnt,epsilon,True)


#make convex hull around hand
#make convex hull around hand
hull = cv2.convexHull(cnt)

#define area of hull and area of hand
#define area of hull and area of hand
areahull = cv2.contourArea(hull)
areacnt = cv2.contourArea(cnt)

#find the percentage of area not covered by hand in convex hull
#find the percentage of area not covered by hand in convex hull
arearatio=((areahull-areacnt)/areacnt)*100

#find the defects in convex hull with respect to hand
#find the defects in convex hull with respect to hand
hull = cv2.convexHull(approx, returnPoints=False)
defects = cv2.convexityDefects(approx, hull)

# l = no. of defects
#finding number of defects due to fingers (= l)
l=0

#code for finding no. of defects due to fingers
for i in range(defects.shape[0]):
s,e,f,d = defects[i,0]
start = tuple(approx[s][0])
Expand All @@ -75,7 +70,7 @@
pt= (100,180)


# find length of all sides of triangle
#find length of all sides of triangle
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)
Expand All @@ -85,19 +80,18 @@
#distance between point and convex hull
d=(2*ar)/a

# apply cosine rule here
#apply cosine rule here
angle = math.acos((b**2 + c**2 - a**2)/(2*b*c)) * 57


# ignore angles > 90 and ignore points very close to convex hull(they generally come due to noise)
#ignore angles > 90 and ignore points very close to convex hull (generally noise induced points)
if angle <= 90 and d>30:
l += 1
cv2.circle(roi, far, 3, [255,0,0], -1)

#draw lines around hand
cv2.line(roi,start, end, [0,255,0], 2)

#minimum one defect for hand
l+=1

#print corresponding gestures which are in their ranges
Expand Down