def main():
#Define flags for face detection and background detection
face_flag = 0
bg_flag = 1
start_flag = 0
stop_flag = 0
#Set arbitrary limits
wait_limit = 45 # number of frames before ending action
area_limit = 0 # pixels squared
#About a delay with a threshold of 50 (intensity)
fgbg = cv2.createBackgroundSubtractorMOG2(500,50)
bgLim = 10000
#Parse all of the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
help = "path to the (optional) video file")
args = vars(ap.parse_args())
# if no video, use webcam
if not args.get("video", False):
nome = 0
else:
nome = args["video"]
# Capture first frame
cap = cv2.VideoCapture(nome)
ret, frame = cap.read()
if cap.isOpened():
print "Success\n"
else:
print "Unable to open file/webcam"
return
# Create some random colors
color = np.random.randint(0,255,(100,3))
#Find face if flagged
if face_flag == 1:
minSkin,maxSkin = findMinMaxHand(frame)#findMinMaxSkin(frame)
#print minSkin
#print maxSkin
#Initialize arrays, counters, masks, etc
objectx_old = []
objecty_old = []
bg_cnt = 0
ct1 = 0
ct2 = 0
start_cnt = 0
stop_cnt = 0
sz = np.shape(frame)
mask = np.zeros(shape=(sz[0],sz[1],3,20), dtype = np.uint8)
mask_old = np.copy(mask)
tim = np.zeros(20)
#Loop through each frame
while(cap.isOpened()):
#If background subtraction is on, do that
if bg_flag == 1:
#if bg_cnt <= bgLim:
fgmask = fgbg.apply(frame)
#bg_cnt = bg_cnt+1;
#print "Apply BG Sub"
masked_frame = cv2.bitwise_and(frame,frame,mask = fgmask)
else:
masked_frame = frame
#If face detection is on, use personalized skin hue for binary conversion
if face_flag == 1:
binary_frame = personalSkin2BW(masked_frame,minSkin,maxSkin)
#else:
# binary_frame = skin2BW(masked_frame)
frame = cv2.line(frame,(40,200),(600,200),(0,255,0),2)
#crop_img = frame[200:600, 40:600]
binary_frame = skin2BW(frame)
binary_frame[0:199][:] = 0
cv2.imshow('BW Frame',binary_frame)
#Find blobs in binary image
__ , contours, contoursOut, defects = blob2(binary_frame,frame,area_limit)
if not defects == []:
#print "Defects Exist\n"
#Check flags to see what text should be displayed...
if start_flag == 0 and stop_flag == 0:
#print "Looking for start"
start_flag = startGest(frame,contoursOut,defects)
if start_flag == 1 and start_cnt < 10:
start_cnt = start_cnt+1
start_flag = 0
else:
start_cnt = 0
stop_flag = 0
elif start_flag == 1 and ct1<10:
cv2.putText(frame,"Start", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
print "Start"
ct1 = ct1+1
ct2 = 0
elif start_flag == 1 and ct1>=10:
#print "Looking for stop"
stop_flag = stopGest(frame,contoursOut,defects)
if stop_flag == 1 and stop_cnt < 10:
stop_cnt = stop_cnt+1
stop_flag = 0
else:
stop_cnt = 0
(frame,objectx_old,objecty_old,mask,mask_old) = trackObj2(frame,contours,objectx_old,objecty_old,mask,mask_old,area_limit)
if stop_flag == 1 and ct2 < 10:
cv2.putText(frame,"Stop", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
print "Stop"
start_flag = 0
#Increment frame counter
ct1 = 0
ct2 = ct2+1
elif stop_flag == 1 and ct2 >= 10:
start_flag = 0
stop_flag = 0
ct1 = 0
ct2 = 0
#Clear out all flags after being stopped for X amount of time
start_flag = 0
stop_flag = 0
ct1 = 0
ct2 = 0
print "Output mask created. \n"
mask_output = mask_old
#print mask_output
objectx_old = []
objecty_old = []
else:
print "Defects do not exist\n"
#Show frame with magic applied
cv2.imshow('frame',frame)
#Check for break code
if cv2.waitKey(1) & 0xFF == ord('q'):
break
ret, frame = cap.read()
cap.release()
cv2.destroyAllWindows()
def main():
#Define flags for face detection and background detection
face_flag = 0
bg_flag = 1
start_flag = 0
stop_flag = 0
#Set arbitrary limits
wait_limit = 500 # number of frames before ending action
area_limit = 500 # pixels squared
#About a delay with a threshold of 50 (intensity)
fgbg = cv2.createBackgroundSubtractorMOG2(50000,100)
#Parse all of the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
help = "path to the (optional) video file")
args = vars(ap.parse_args())
# if no video, use webcam
if not args.get("video", False):
nome = 0
else:
nome = args["video"]
# Capture first frame
cap = cv2.VideoCapture(nome)
ret, frame = cap.read()
#Find face if flagged
if face_flag == 1:
minSkin,maxSkin = findMinMaxHand(frame)#findMinMaxSkin(frame)
print minSkin
print maxSkin
#Initialize arrays, counters, masks, etc
objectx_old = []
objecty_old = []
ct = 0
sz = np.shape(frame)
mask = np.zeros(shape=(sz[0],sz[1],3,20), dtype = np.uint8)
mask_old = np.copy(mask)
mask_out = np.copy(mask)
tim = np.zeros(20)
#Build filters
n = 4
#filters = build_filters(n)
#Loop through gestures to store histograms
ct = 0
ct1 = 0
ct2 = 0
ct3 = 0
start_cnt = 0
stop_cnt = 0
gest_dir = 'C:\Users\jonat_000\Desktop\ECE5554\project\opencvimplemet\masks'
path, dirs, files = os.walk(gest_dir).next()
m = len(files)
gest = np.zeros([n,m])
fnome = os.listdir(gest_dir)
while(cap.isOpened()):
#If background subtraction is on, do that
if bg_flag == 1:
fgmask = fgbg.apply(frame)
masked_frame = cv2.bitwise_and(frame,frame,mask = fgmask)
else:
masked_frame = frame
#If face detection is on, use personalized skin hue for binary conversion
if face_flag == 1:
binary_frame = personalSkin2BW(masked_frame,minSkin,maxSkin)
else:
binary_frame = skin2BW(masked_frame)
cv2.imshow('BW Frame',binary_frame)
#Find blobs in binary image
frame, contours, contoursOut, defects = blob2(binary_frame,frame,area_limit)
img = np.copy(frame)
#Show binary image
#cv2.imshow('frame',binary_frame)
#Check if a frame was found
if not defects == []:
#print "Defects Exist\n"
#Check flags to see what text should be displayed...
if start_flag == 0 and stop_flag == 0:
print "Looking for start"
start_flag = startGest(frame,contoursOut,defects)
if start_flag == 1 and start_cnt < 10:
start_cnt = start_cnt+1
start_flag = 0
else:
start_cnt = 0
stop_flag = 0
elif start_flag == 1 and ct1<10:
cv2.putText(frame,"Start", (50,50), cv2.FONT_HERSHEY_SIMPLEX, 2, 2)
print "Start"
ct1 = ct1+1
ct2 = 0
elif start_flag == 1 and ct1>=10:
#print "Looking for stop"
stop_flag = stopGest(frame,contoursOut,defects)
if stop_flag == 1 and stop_cnt < 45:
stop_cnt = stop_cnt+1
stop_flag = 0
else:
stop_cnt = 0
if start_flag == 1:
#Find centroids
objectx, objecty = fcent(contours, area_limit)
#Calculate distance to each previous object
objectloc, objectval = objdist(objectx, objecty, objectx_old, objecty_old)
img = np.copy(frame)
#Check if any objects were found to match previous frame objects
if not objectloc == []:
#Ensure only one object in current frame is mapped to previous frame object
objectval2 = prevmap(objectloc, objectval)
mask, mask_check, tim = obj2mask(objectloc, objectval2, objectx, objecty, objectx_old, objecty_old, mask_old, wait_limit, tim)
#frame2, mask_all = addmask2frame(mask, frame)
mask_old = np.copy(mask)
img = np.copy(frame2)
objectx_old = np.copy(objectx)
objecty_old = np.copy(objecty)
if stop_flag == 1:
start_flag = 0
stop_flag = 0
ct1 = 0
ct2 = 0
#Clear out all flags after being stopped for X amount of time
start_flag = 0
stop_flag = 0
ct1 = 0
ct2 = 0
print "Output mask created. \n"
sim, pat = coHi3(m,fnome,gest_dir,mask_all,ct3,0)
cv2.imshow('Drawn mask',mask_all)
cv2.imshow('Nearest pattern',pat)
ct3 = ct3+1
mask_output = mask_old
objectx_old = []
objecty_old = []
for i in range(0, 19):
mask[:,:,:,i] = 0
mask_old = np.copy(mask)
ct = ct+1
#Show frame with magic applied
frame2, mask_all = addmask2frame(mask, frame)
cv2.imshow('frame',frame2)
cv2.imshow('mask',mask_all)
#Check for break code
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if cv2.waitKey(1) & 0xFF == ord('s'):
start_flag = 1
stop_flag = 0
if cv2.waitKey(1) & 0xFF == ord('z'):
start_flag = 0
stop_flag = 1
ret, frame = cap.read()
cap.release()
cv2.destroyAllWindows()
def main():
#Define flags for face detection and background detection
face_flag = 0
bg_flag = 1
gest_flag = 1
#Flags used for collection of data
start_flag = 0
stop_flag = 0
#Set arbitrary limits
wait_limit = 45 # number of frames before ending action
area_limit = 500 # pixels squared
#About a delay with a threshold of 50 (intensity)
fgbg = cv2.createBackgroundSubtractorMOG2(50000,100)
#Parse all of the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
help = "path to the (optional) video file")
args = vars(ap.parse_args())
# if no video, use webcam
if not args.get("video", False):
nome = 0
else:
nome = args["video"]
# Capture first frame
cap = cv2.VideoCapture(nome)
ret, frame = cap.read()
if cap.isOpened():
print "Success\n"
else:
print "Unable to open file/webcam"
return
# Create some random colors
color = np.random.randint(0,255,(100,3))
#Find face if flagged
if face_flag == 1:
minSkin,maxSkin = findMinMaxHand(frame)#findMinMaxSkin(frame)
#print minSkin
#print maxSkin
#Initialize arrays, counters, masks, etc
objectx_old = []
objecty_old = []
#Starting all of the counters
ct = 0
ct1 = 0
ct2 = 0
start_cnt = 0
stop_cnt = 0
#Creating mask arrays
sz = np.shape(frame)
mask = np.zeros(shape=(sz[0],sz[1],3,20), dtype = np.uint8)
mask_old = np.copy(mask)
tim = np.zeros(20)
#Loop through each frame
while(cap.isOpened()):
#If background subtraction is on, do that
if bg_flag == 1:
fgmask = fgbg.apply(frame)
masked_frame = cv2.bitwise_and(frame,frame,mask = fgmask)
else:
masked_frame = frame
#If face detection is on, use personalized skin hue for binary conversion
if face_flag == 1:
binary_frame = personalSkin2BW(masked_frame,minSkin,maxSkin)
#elif gest_flag == 1:
# frame = cv2.line(frame,(40,200),(600,200),(0,255,0),2)
# crop_img = frame[200:600, 40:600]
# binary_frame = skin2BW(crop_img)
else:
binary_frame = skin2BW(masked_frame)
#Find blobs in binary image
if gest_flag == 1:
__ , contours, contoursOut, defects = blob2(binary_frame,frame,area_limit)
else:
frame, contours = blob(binary_frame,frame,area_limit)
#Show binary image
cv2.imshow('frame',binary_frame)
#Check if a frame was found
if not ret == False:
if gest_flag == 1:
if not defects == []:
if start_flag == 0 and stop_flag == 0:
print "Looking for start..."
#cv2.waitKey(10)
#os.system("pause")
start_flag = startGest(frame,contoursOut,defects)
(start_flag,start_cnt,ct2) = testGest(start_flag,start_cnt,ct2)
stop_flag = 0
img = frame
elif start_flag == 1 and ct1<10:
cv2.putText(frame,"Start",(50,50),cv2.FONT_HERSHEY_SIMPLEX,2,2)
print "Start"
ct1 = ct1+1
ct2 = 0
stop_flag = 0
#print "ct1"
#print ct1
#cv2.waitKey(27)
(img,objectx_old,objecty_old,mask,mask_old) = trackObj2(frame,contours,objectx_old,objecty_old,mask,mask_old, area_limit)
elif start_flag == 1 and ct1 >= 10:
#Looking for stop...
#print "Looking for stop ..."
stop_flag = stopGest(frame,contoursOut,defects)
(stop_flag,stop_cnt,ct1) = testGest(stop_flag,stop_cnt,ct1)
#print stop_cnt
if stop_flag == 1:
start_flag = 0
(img,objectx_old,objecty_old,mask,mask_old) = trackObj2(frame,contours,objectx_old,objecty_old,mask,mask_old,area_limit)
elif stop_flag == 1 and ct2 < 1:
#Stop Gesture collection
cv2.putText(frame,"Stop",(50,50),cv2.FONT_HERSHEY_SIMPLEX,2,2)
print "Stop"
#start_flag = 0
ct1 = 0
ct2 = ct2+1
img = frame
elif stop_flag == 1 and ct2 >= 10:
#Clear out all flags after being stopped for X amount of time
start_flag = 0
stop_flag = 0
ct1 = 0
ct2 = 0
print "Output mask created. \n"
mask_output = mask_old
objectx_old = []
objecty_old = []
img = frame
else: #If the defects aren't found, do nothing
#print "Defects do not exist. \n"
img = frame
else: #If not using the gestures to start and stop the function...
#Initialize variable which change with each frame
(img,objectx_old,objecty_old,mask,mask_old,tim) = trackObj(frame,contours,objectx_old,objecty_old,mask,mask_old,area_limit,tim)
#If the frame was not found o' so long ago...
else:
break
#Increment frame counter
ct = ct+1
#Show frame with magic applied
cv2.imshow('frame',img)
#Check for break code
if cv2.waitKey(1) & 0xFF == ord('q'):
break
ret, frame = cap.read()
cap.release()
cv2.destroyAllWindows()
def main():
#Define flags for face detection and background detection
face_flag = 0
bg_flag = 1
#Set arbitrary limits
wait_limit = 45 # number of frames before ending action
area_limit = 500 # pixels squared
#About a delay with a threshold of 50 (intensity)
fgbg = cv2.createBackgroundSubtractorMOG2(50000,100)
#Parse all of the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video",
help = "path to the (optional) video file")
args = vars(ap.parse_args())
# if no video, use webcam
if not args.get("video", False):
nome = 0
else:
nome = args["video"]
# Capture first frame
cap = cv2.VideoCapture(nome)
ret, frame = cap.read()
if cap.isOpened():
print "Success"
else:
print "Unable to open file/webcam"
return
# Create some random colors
color = np.random.randint(0,255,(100,3))
#Find face if flagged
if face_flag == 1:
minSkin,maxSkin = findMinMaxHand(frame)#findMinMaxSkin(frame)
print minSkin
print maxSkin
#Initialize arrays, counters, masks, etc
objectx_old = []
objecty_old = []
ct = 0
sz = np.shape(frame)
mask = np.zeros(shape=(sz[0],sz[1],3,20), dtype = np.uint8)
mask_old = np.copy(mask)
tim = np.zeros(20)
#Loop through each frame
while(cap.isOpened()):
#If background subtraction is on, do that
if bg_flag == 1:
fgmask = fgbg.apply(frame)
masked_frame = cv2.bitwise_and(frame,frame,mask = fgmask)
else:
masked_frame = frame
#If face detection is on, use personalized skin hue for binary conversion
if face_flag == 1:
binary_frame = personalSkin2BW(masked_frame,minSkin,maxSkin)
else:
binary_frame = skin2BW(masked_frame)
#Find blobs in binary image
frame, contours = blob(binary_frame,frame,area_limit)
#Show binary image
cv2.imshow('frame',binary_frame)
#Check if a frame was found
if not ret == False:
#Initialize variable which change with each frame
objectx = []
objecty = []
objectloc = []
objectval = []
x = []
y = []
img = np.copy(frame)
#Find contours around blobs
for i, c in enumerate(contours):
area = cv2.contourArea(c)
if area > area_limit:
cent = cv2.moments(c)
temp = cent['m00']
if not temp == 0:
cx = int(cent['m10']/cent['m00'])
cy = int(cent['m01']/cent['m00'])
objectx.append(cx)
objecty.append(cy)
#Check if any objects were found
if not objectx_old == []:
#Loop through each object in current frame and compute distance
#to each object in previous frame
for i, j in zip(objectx, objecty):
x_dist = np.array(objectx_old)
x_dist = x_dist - i
y_dist = np.array(objecty_old)
y_dist = y_dist - j
y_dist = np.square(y_dist)
x_dist = np.square(x_dist)
dist = np.sqrt(x_dist+y_dist)
if len(dist) > 0:
minloc = np.argmin(dist)
minval = dist[minloc]
objectloc.append(minloc)
objectval.append(minval)
x.append(i)
y.append(j)
#Check if any objects were found to match previous frame objects
if not objectloc == []:
#Initialize parameters for loop over individual objects
mx = np.amax(objectloc)
mn = np.amin(objectloc)
objs = unique(objectloc)
vals = np.zeros_like(objs)+999
locs = np.zeros_like(objs)-1
co = len(objectloc)
co2 = len(objs)
#Ensure at most only 1 current object mapped to previous object
for i in range(0, co2):
for j in range(0, co):
if objs[i] == objectloc[j]:
if objectval[j] < vals[i]:
vals[i] = objectval[j]
locs[i] = j
objectval2 = np.zeros_like(objectval)-1
for i in range(0, co2):
if locs[i] > -1:
if locs[i] < co2:
objectval2[locs[i]] = objectval[locs[i]]
#Initialize mask parameters
img = cv2.add(frame,mask[:,:,:,0])
mask_check = np.zeros(20)
frame2 = np.copy(frame)
#Loop through each matched object and add to corresponding mask
for i in range(0, co):
if objectval2[i] > -1:
hihi = 1
a = np.copy(objectx_old[objectloc[i]])
b = np.copy(objecty_old[objectloc[i]])
c = np.copy(objectx[i])
d = np.copy(objecty[i])
dist = abs(((a-c)^2+(b-d)^2)^(1/2))
mask_check[i] = 1
if dist < 5:
tim[i] = tim[i]+1
if tim[i] > wait_limit:
mask[:,:,:,i] = 0
tim[i] = 0
mask_check[i] = 0
else:
tim[i] = 0
temp1 = np.copy(mask_old[:,:,:,objectloc[i]])
if np.sum(mask[:,:,:,i]) > 0:
#Connect the dots
mask[:,:,:,i] = cv2.line(temp1, (a,b),(c,d), color[i].tolist(), 2)
else:
#Start point
mask[:,:,:,i] = cv2.circle(temp1,(a,b),5,color[i].tolist(),-1)
#Current point/End pointt
frame2 = cv2.circle(frame2,(a,b),5,color[i].tolist(),-1)
objectx_old[objectloc[i]] = np.copy(objectx[i])
objecty_old[objectloc[i]] = np.copy(objecty[i])
#Check if a mask disappeared
for i in range(0, 19):
if mask_check[i] == 0:
#to add: if summing to nonzero, send as an output mask
mask[:,:,:,i] = 0
else:
frame2 = cv2.add(frame2,mask[:,:,:,i])
mask_old = np.copy(mask)
img = np.copy(frame2)
else:
for i in range(0, 19):
mask[:,:,:,i] = 0
mask_old = np.copy(mask)
objectx_old = np.copy(objectx)
objecty_old = np.copy(objecty)
else:
break
#Increment frame counter
ct = ct+1
#Show frame with magic applied
cv2.imshow('frame',img)
#Check for break code
if cv2.waitKey(1) & 0xFF == ord('q'):
break
ret, frame = cap.read()
cap.release()
cv2.destroyAllWindows()
def main():
#Define flags for face detection and background detection
face_flag = 0
bg_flag = 1
start_flag = 0
stop_flag = 0
#Set arbitrary limits
wait_limit = 500 # number of frames before ending action
area_limit = 500 # pixels squared
#About a delay with a threshold of 50 (intensity)
fgbg = cv2.createBackgroundSubtractorMOG2(50000, 100)
#Parse all of the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-v", "--video", help="path to the (optional) video file")
args = vars(ap.parse_args())
# if no video, use webcam
if not args.get("video", False):
nome = 0
else:
nome = args["video"]
# Capture first frame
cap = cv2.VideoCapture(nome)
ret, frame = cap.read()
#Find face if flagged
if face_flag == 1:
minSkin, maxSkin = findMinMaxHand(frame) #findMinMaxSkin(frame)
print minSkin
print maxSkin
#Initialize arrays, counters, masks, etc
objectx_old = []
objecty_old = []
ct = 0
sz = np.shape(frame)
mask = np.zeros(shape=(sz[0], sz[1], 3, 20), dtype=np.uint8)
mask_old = np.copy(mask)
mask_out = np.copy(mask)
tim = np.zeros(20)
#Build filters
n = 4
#filters = build_filters(n)
#Loop through gestures to store histograms
ct = 0
ct1 = 0
ct2 = 0
ct3 = 0
start_cnt = 0
stop_cnt = 0
gest_dir = 'C:\Users\jonat_000\Desktop\ECE5554\project\opencvimplemet\masks'
path, dirs, files = os.walk(gest_dir).next()
m = len(files)
gest = np.zeros([n, m])
fnome = os.listdir(gest_dir)
while (cap.isOpened()):
#If background subtraction is on, do that
if bg_flag == 1:
fgmask = fgbg.apply(frame)
masked_frame = cv2.bitwise_and(frame, frame, mask=fgmask)
else:
masked_frame = frame
#If face detection is on, use personalized skin hue for binary conversion
if face_flag == 1:
binary_frame = personalSkin2BW(masked_frame, minSkin, maxSkin)
else:
binary_frame = skin2BW(masked_frame)
cv2.imshow('BW Frame', binary_frame)
#Find blobs in binary image
frame, contours, contoursOut, defects = blob2(binary_frame, frame,
area_limit)
img = np.copy(frame)
#Show binary image
#cv2.imshow('frame',binary_frame)
#Check if a frame was found
if not defects == []:
#print "Defects Exist\n"
#Check flags to see what text should be displayed...
if start_flag == 0 and stop_flag == 0:
print "Looking for start"
start_flag = startGest(frame, contoursOut, defects)
if start_flag == 1 and start_cnt < 10:
start_cnt = start_cnt + 1
start_flag = 0
else:
start_cnt = 0
stop_flag = 0
elif start_flag == 1 and ct1 < 10:
cv2.putText(frame, "Start", (50, 50), cv2.FONT_HERSHEY_SIMPLEX,
2, 2)
print "Start"
ct1 = ct1 + 1
ct2 = 0
elif start_flag == 1 and ct1 >= 10:
#print "Looking for stop"
stop_flag = stopGest(frame, contoursOut, defects)
if stop_flag == 1 and stop_cnt < 45:
stop_cnt = stop_cnt + 1
stop_flag = 0
else:
stop_cnt = 0
if start_flag == 1:
#Find centroids
objectx, objecty = fcent(contours, area_limit)
#Calculate distance to each previous object
objectloc, objectval = objdist(objectx, objecty, objectx_old,
objecty_old)
img = np.copy(frame)
#Check if any objects were found to match previous frame objects
if not objectloc == []:
#Ensure only one object in current frame is mapped to previous frame object
objectval2 = prevmap(objectloc, objectval)
mask, mask_check, tim = obj2mask(objectloc, objectval2,
objectx, objecty, objectx_old,
objecty_old, mask_old,
wait_limit, tim)
#frame2, mask_all = addmask2frame(mask, frame)
mask_old = np.copy(mask)
img = np.copy(frame2)
objectx_old = np.copy(objectx)
objecty_old = np.copy(objecty)
if stop_flag == 1:
start_flag = 0
stop_flag = 0
ct1 = 0
ct2 = 0
#Clear out all flags after being stopped for X amount of time
start_flag = 0
stop_flag = 0
ct1 = 0
ct2 = 0
print "Output mask created. \n"
sim, pat = coHi3(m, fnome, gest_dir, mask_all, ct3, 0)
cv2.imshow('Drawn mask', mask_all)
cv2.imshow('Nearest pattern', pat)
ct3 = ct3 + 1
mask_output = mask_old
objectx_old = []
objecty_old = []
for i in range(0, 19):
mask[:, :, :, i] = 0
mask_old = np.copy(mask)
ct = ct + 1
#Show frame with magic applied
frame2, mask_all = addmask2frame(mask, frame)
cv2.imshow('frame', frame2)
cv2.imshow('mask', mask_all)
#Check for break code
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if cv2.waitKey(1) & 0xFF == ord('s'):
start_flag = 1
stop_flag = 0
if cv2.waitKey(1) & 0xFF == ord('z'):
start_flag = 0
stop_flag = 1
ret, frame = cap.read()
cap.release()
cv2.destroyAllWindows()