def __init__(self,callback,**kwargs):
fig, axes = plt.subplots(1,2)
self.axes = dict(zip(['raw','backprojection'],axes.ravel()))
for k,ax in self.axes.items(): ax.set_title(k)
VideoApp.__init__(self,fig,**kwargs)
img = self._cap.read()
self.axes['raw'].imshow(img)
self.axes['backprojection'].imshow(img[...,0],cmap=mpl.cm.get_cmap('gray'))
fig.tight_layout()
cid = fig.canvas.mpl_connect('button_press_event', self.on_press)
cid = fig.canvas.mpl_connect('button_release_event', self.on_release)
self.SELECTING = False
self.bbox = None
self._bbox = None
self.callback = callback
self.show = self._fig.show
def __init__(self, callback, **kwargs):
fig, axes = plt.subplots(1, 2)
self.axes = dict(zip(['raw', 'backprojection'], axes.ravel()))
for k, ax in self.axes.items():
ax.set_title(k)
VideoApp.__init__(self, fig, **kwargs)
img = self._cap.read()
self.axes['raw'].imshow(img)
self.axes['backprojection'].imshow(img[..., 0],
cmap=mpl.cm.get_cmap('gray'))
fig.tight_layout()
cid = fig.canvas.mpl_connect('button_press_event', self.on_press)
cid = fig.canvas.mpl_connect('button_release_event', self.on_release)
self.SELECTING = False
self.bbox = None
self._bbox = None
self.callback = callback
self.show = self._fig.show
from gestures.utils.gui import VideoApp
import cv2
get_imdisp = lambda ax: ax.get_images()[0]
blur = lambda x: cv2.blur(x,(9,9),borderType=cv2.BORDER_REFLECT)
fig, axes = plt.subplots(1,2)
axes = dict(zip(['raw','skin'],axes.ravel()))
for k,ax in axes.items(): ax.set_title(k)
cap = FrameBuffer.from_argv()
curr = cap.read()
axes['raw'].imshow(curr)
axes['skin'].imshow(curr)
fig.tight_layout()
app = VideoApp(fig,cap)
coseg = GaussianSkinSegmenter()
fig.show()
while app:
curr = blur(cap.read())
skin = coseg(curr)
dispimg = curr.copy()
dispimg *= skin[...,None]
app.update_artists((get_imdisp(axes['raw']), curr[:,:,::-1])
,(get_imdisp(axes['skin']), dispimg[:,:,::-1]))
cap = FrameBuffer.from_argv()
curr = cap.read()
axes['raw'].imshow(curr)
axes['moving'].imshow(curr,cmap=mpl.cm.get_cmap('gray'))
axes['skin'].imshow(curr,cmap=mpl.cm.get_cmap('gray'))
axes['fused'].imshow(curr,cmap=mpl.cm.get_cmap('gray'))
fig.tight_layout()
prev = blur(curr)
curr = blur(cap.read())
prevg = cv2.cvtColor(prev,cv2.COLOR_BGR2GRAY)
currg = cv2.cvtColor(curr,cv2.COLOR_BGR2GRAY)
smseg = SkinMotionSegmenter(prevg,currg)
app = VideoApp(fig,cap)
fig.show()
while app:
curr = blur(cap.read())
mask = smseg(curr)
dispimg = curr.copy()
if smseg.bbox is not None:
x,y,w,h = smseg.bbox
cv2.circle(dispimg,tuple(smseg.com),5,color=(0,255,0),thickness=-1)
cv2.rectangle(dispimg,(x,y),(x+w,y+h),color=(0,204,255),thickness=2)
app.update_artists((get_imdisp(axes['raw']), dispimg[:,:,::-1])
,(get_imdisp(axes['moving']), smseg.motion*255)
,(get_imdisp(axes['skin']), smseg.skin*255)
,(get_imdisp(axes['fused']), smseg.backprojection*255))
get_imdisp = lambda ax: ax.get_images()[0]
fig, axes = plt.subplots(1,2)
axes = dict(zip(['raw','skin'],axes.ravel()))
for k,ax in axes.items(): ax.set_title(k)
cap = FrameBuffer.from_argv()
curr = cap.read()
axes['raw'].imshow(curr)
axes['skin'].imshow(curr,cmap=mpl.cm.get_cmap('gray'))
fig.tight_layout()
hdetect = ConvexityHandDetector()
smseg = GaussianSkinSegmenter()
app = VideoApp(fig,cap)
fig.show()
while app:
curr = blur(cap.read())
dispimg = curr.copy()
mask = smseg(curr)
detected = hdetect(mask)
if detected:
hull_pts = hdetect.hull
com = (np.sum(hull_pts[:,0,:],axis=0,dtype=float)/len(hull_pts)).astype(int)
cv2.circle(dispimg,tuple(com),5,color=(0,255,0),thickness=-1)
color = (255,0,0) if detected else (0,0,255)
cv2.drawContours(dispimg,[hdetect.hull],0,color,3)
cap = FrameBuffer.from_argv()
prev = blur(cap.read())
prevg = cv2.cvtColor(prev, cv2.COLOR_BGR2GRAY)
currg = cv2.cvtColor(blur(cap.read()), cv2.COLOR_BGR2GRAY)
fig, axes = plt.subplots(2, 2)
axes = dict(zip(["raw", "bkgnd", "thresh", "moving"], axes.ravel()))
for k, ax in axes.items():
ax.set_title(k)
axes["raw"].imshow(prev)
axes["bkgnd"].imshow(prevg, cmap=mpl.cm.get_cmap("gray"))
axes["moving"].imshow(prevg, cmap=mpl.cm.get_cmap("gray"))
axes["thresh"].imshow(prevg, cmap=mpl.cm.get_cmap("gray"))
fig.tight_layout()
app = VideoApp(fig, cap=cap)
moseg = MotionSegmenter(prevg, currg, dict(alpha=0.1))
fig.show()
while app:
curr = blur(cap.read())
moving = moseg(cv2.cvtColor(curr, cv2.COLOR_BGR2GRAY))
dispimg = curr
# estimate centroid and bounding box
if moseg.bbox is not None:
x, y, w, h = moseg.bbox
cv2.circle(dispimg, (x, y), 5, color=(0, 255, 0), thickness=-1)
cv2.rectangle(dispimg, (x, y), (x + w, y + h), color=(0, 204, 255), thickness=2)
app.update_artists(
cap = FrameBuffer.from_argv()
prev = blur(cap.read())
prevg = cv2.cvtColor(prev,cv2.COLOR_BGR2GRAY)
currg = cv2.cvtColor(blur(cap.read()),cv2.COLOR_BGR2GRAY)
fig, axes = plt.subplots(2,2)
axes = dict(zip(['raw','bkgnd','thresh','moving'],axes.ravel()))
for k,ax in axes.items(): ax.set_title(k)
axes['raw'].imshow(prev)
axes['bkgnd'].imshow(prevg,cmap=mpl.cm.get_cmap('gray'))
axes['moving'].imshow(prevg,cmap=mpl.cm.get_cmap('gray'))
axes['thresh'].imshow(prevg,cmap=mpl.cm.get_cmap('gray'))
fig.tight_layout()
app = VideoApp(fig,cap=cap)
moseg = MotionSegmenter(prevg,currg,dict(alpha=0.1))
fig.show()
while app:
curr = blur(cap.read())
moving = moseg(cv2.cvtColor(curr,cv2.COLOR_BGR2GRAY))
dispimg = curr
# estimate centroid and bounding box
if moseg.bbox is not None:
x,y,w,h = moseg.bbox
cv2.circle(dispimg,(x,y),5,color=(0,255,0),thickness=-1)
cv2.rectangle(dispimg,(x,y),(x+w,y+h),color=(0,204,255),thickness=2)
app.update_artists((get_imdisp(axes['raw']),dispimg[...,::-1].copy())