class HandRecognition():
def __init__(self, camera,window):
self.camera = camera
self.window = window
self.draw = Draw()
def hand_threshold(self,frame,frame_original,fg_frame,hand_hist):
self.new_frame = frame
self.new_frame = cv2.medianBlur(self.new_frame,3)
hsv = cv2.cvtColor(self.new_frame,cv2.COLOR_BGR2HSV)
hsv[0:int(cap_region_y_end*hsv.shape[0]),0:int(cap_region_x_begin*hsv.shape[1])]=0 # Right half screen only
hsv[int(cap_region_y_end*hsv.shape[0]):hsv.shape[0],0:hsv.shape[1]]=0
back_projection = cv2.calcBackProject([hsv], [0,1],hand_hist, [00,180,0,256], 1)
disc = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (morph_elem_size,morph_elem_size))
cv2.filter2D(back_projection, -1, disc, back_projection)
back_projection=cv2.GaussianBlur(back_projection,(gaussian_ksize,gaussian_ksize), gaussian_sigma)
back_projection=cv2.medianBlur(back_projection,median_ksize)
ret, thresh = cv2.threshold(back_projection, hsv_thresh_lower, 255, 0)
contour_frame = np.copy(thresh)
image,contours,hierarchy = cv2.findContours(contour_frame,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
found , hand_contours = self.find_hand_contour(contours)
if(found):
hand_convex_hull = cv2.convexHull(hand_contours)
self.new_frame,hand_center,hand_radius,hand_size_score = self.mark_hand_center(frame_original,hand_contours)
if(hand_size_score):
self.new_frame,finger,palm = self.mark_fingers(self.new_frame,hand_convex_hull,hand_center,hand_radius)
else:
self.new_frame = frame_original
return self.new_frame,image
def mark_hand_center(self,frame_original,hand_contour):
max_d=0
pt=(0,0)
x,y,w,h = cv2.boundingRect(hand_contour)
for ind_y in xrange(int(y+0.3*h),int(y+0.8*h)): #around 0.25 to 0.6 region of height (Faster calculation with ok results)
for ind_x in xrange(int(x+0.3*w),int(x+0.6*w)): #around 0.3 to 0.6 region of width (Faster calculation with ok results)
dist= cv2.pointPolygonTest(hand_contour,(ind_x,ind_y),True)
if(dist>max_d):
max_d=dist
pt=(ind_x,ind_y)
if(max_d>radius_thresh*frame_original.shape[1]):
thresh_score=True
cv2.circle(frame_original,pt,int(max_d),(255,0,0),2)
else:
thresh_score=False
return frame_original,pt,max_d,thresh_score
def mark_fingers(self,frame_in,hull,pt,radius):
global first_iteration
global finger_ct_history
global previous
finger=[(hull[0][0][0],hull[0][0][1])]
j=0
cx = pt[0]
cy = pt[1]
for i in range(len(hull)):
dist = np.sqrt((hull[-i][0][0] - hull[-i+1][0][0])**2 + (hull[-i][0][1] - hull[-i+1][0][1])**2)
if (dist>18):
if(j==0):
finger=[(hull[-i][0][0],hull[-i][0][1])]
else:
finger.append((hull[-i][0][0],hull[-i][0][1]))
j=j+1
temp_len=len(finger)
i=0
while(i<temp_len):
dist = np.sqrt( (finger[i][0]- cx)**2 + (finger[i][1] - cy)**2)
if(dist<finger_thresh_l*radius or dist>finger_thresh_u*radius or finger[i][1]>cy+radius):
finger.remove((finger[i][0],finger[i][1]))
temp_len=temp_len-1
else:
i=i+1
if(temp_len>5):
for i in range(1,temp_len+1-5):
finger.remove((finger[temp_len-i][0],finger[temp_len-i][1]))
palm=[(cx,cy),radius]
if(first_iteration):
finger_ct_history[0]=finger_ct_history[1]=len(finger)
first_iteration=False
else:
finger_ct_history[0]=0.34*(finger_ct_history[0]+finger_ct_history[1]+len(finger))
if((finger_ct_history[0]-int(finger_ct_history[0]))>0.8):
finger_count=int(finger_ct_history[0])+1
else:
finger_count=int(finger_ct_history[0])
finger_ct_history[1]=len(finger)
count_text="FINGERS:"+str(finger_count)
cv2.putText(frame_in,count_text,(int(0.62*frame_in.shape[1]),int(0.88*frame_in.shape[0])),cv2.FONT_HERSHEY_DUPLEX,1,(0,255,255),1,8)
for k in range(len(finger)):
cv2.circle(frame_in,finger[k],10,255,2)
cv2.line(frame_in,finger[k],(cx,cy),255,2)
print hull, ' ', finger, ' ', palm
self.draw.drawPattern(self.window,palm,previous)
previous = palm
return frame_in,finger,palm
def find_hand_contour(self,contours):
max_area=0
largest_contour=-1
for i in range(len(contours)):
cont=contours[i]
area=cv2.contourArea(cont)
if(area>max_area):
max_area=area
largest_contour=i
if(largest_contour==-1):
return False,0
else:
h_contour=contours[largest_contour]
return True,h_contour
def hand_capture(self,frame_in,box_x,box_y):
hsv = cv2.cvtColor(frame_in, cv2.COLOR_BGR2HSV)
ROI = np.zeros([capture_box_dim*capture_box_count,capture_box_dim,3], dtype=hsv.dtype)
for i in xrange(capture_box_count):
ROI[i*capture_box_dim:i*capture_box_dim+capture_box_dim,0:capture_box_dim] = hsv[box_y[i]:box_y[i]+capture_box_dim,box_x[i]:box_x[i]+capture_box_dim]
hand_hist = cv2.calcHist([ROI],[0, 1], None, [180, 256], [0, 180, 0, 256])
cv2.normalize(hand_hist,hand_hist, 0, 255, cv2.NORM_MINMAX)
print hand_hist
return hand_hist
