def gray_frame(self, backsub = True): if backsub: (mask, ctrs, fd) = findObjectThreshold(self.frame, threshold = self.threshold) frame = np.multiply(mask, self.grayframe) else: frame = self.grayframe return frame
def gray_frame(self, backsub = True): if backsub: (mask, ctrs, fd) = findObjectThreshold(self.frame, threshold = self.threshold) frame = np.multiply(mask, self.grayframe) else: frame = self.grayframe return frame
def current_frame(self, backsub = True): if backsub: (mask, ctrs, fd) = findObjectThreshold(self.frame, threshold = self.threshold) frame = np.multiply(np.dstack((mask, mask, mask)), self.frame) else: frame = self.frame return frame
def current_frame(self, backsub = True): if backsub: (mask, ctrs, fd) = findObjectThreshold(self.frame, threshold = self.threshold) frame = np.multiply(np.dstack((mask, mask, mask)), self.frame) else: frame = self.frame return frame
def backsub(self, im = None): (mask, ctrs, fd) = findObjectThreshold(self.frame, threshold = self.threshold) if im is None: return mask, ctrs, fd else: if len(im.shape) == 2: return np.multiply(mask, im) #Probably a better way to do this... elif im.shape[2] == 2: return np.multiply(np.dstack((mask, mask)), im) elif im.shape[2] == 3: return np.multiply(np.dstack((mask, mask, mask)), im)
def backsub(self, im = None): (mask, ctrs, fd) = findObjectThreshold(self.frame, threshold = self.threshold) if im is None: return mask, ctrs, fd else: if len(im.shape) == 2: return np.multiply(mask, im) #Probably a better way to do this... elif im.shape[2] == 2: return np.multiply(np.dstack((mask, mask)), im) elif im.shape[2] == 3: return np.multiply(np.dstack((mask, mask, mask)), im)
def read(self, backsub = True): mask = None try: ret = True self.frame = self.gen.next() except: ret = False self.frame = None return ret, self.frame, mask if not backsub: return ret, self.frame, mask else: (mask, ctrs, fd) = findObjectThreshold(self.frame, threshold = self.threshold) #Apply mask backframe = np.multiply(mask, self.frame) return ret, backframe, mask
def projectmask(self, y_m): print '-- projecting outliers onto contour' #Project vertices onto boundary ddeps = 1e-1 (mask2, ctrs, fd) = findObjectThreshold(y_m, threshold = 0.5) p = self.state.vertices() p_orig = p.copy() d = fd(p) ix = d>1 for idx in range(10): if ix.any(): dgradx = (fd(p[ix]+[ddeps,0])-d[ix])/ddeps # Numerical dgrady = (fd(p[ix]+[0,ddeps])-d[ix])/ddeps # gradient dgrad2 = dgradx**2 + dgrady**2 p[ix] -= (d[ix]*np.vstack((dgradx, dgrady))/dgrad2).T # Project self.state.X[0:(2*self.N)] = np.reshape(p, (-1,1)) #Update velocities also... or else it might crash... self.state.X[(2*self.N):] += np.reshape(p - p_orig, (-1,1))
def read(self, backsub = True): try: ret, frame = self.cap.read() self.frame = frame self.grayframe = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY) except: ret = False frame = None grayframe = None mask = None return ret, frame, grayframe, mask if not backsub: return ret, self.frame, self.grayframe else: (mask, ctrs, fd) = findObjectThreshold(self.frame, threshold = self.threshold) #Apply mask backframe = np.multiply(np.dstack((mask, mask, mask)), self.frame) backgrayframe = np.multiply(mask, self.grayframe) return ret, backframe, backgrayframe, mask
def read(self, backsub = True): try: ret, frame = self.cap.read() self.frame = frame self.grayframe = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY) except: ret = False frame = None grayframe = None mask = None return ret, frame, grayframe, mask if not backsub: return ret, self.frame, self.grayframe else: (mask, ctrs, fd) = findObjectThreshold(self.frame, threshold = self.threshold) #Apply mask backframe = np.multiply(np.dstack((mask, mask, mask)), self.frame) backgrayframe = np.multiply(mask, self.grayframe) return ret, backframe, backgrayframe, mask
cuda = True gridsize = 30 threshold = 9 name = 'test_data' #Grab test data video, flow = test_data(680, 680) #video, flow = test_data_texture(680, 680) #video, flow = test_data_image() flowframe = flow[:,:,:,0] frame = video[:,:,0] #Make mesh distmesh = DistMesh(frame, h0 = gridsize) mask, ctrs, h = findObjectThreshold(frame, threshold = threshold) distmesh.createMesh(ctrs, h, frame, plot=False) nx = 680 start = nx//3 end = 2*nx//3 kf = IteratedMSKalmanFilter(distmesh, frame, flowframe, cuda) #Now perturb the positions a bit... kf.state.X = kf.state.X*1.2 kf.state.refresh() rend = kf.state.renderer cuda = kf.state.renderer.cudagl kf.state.render()
nx = capture.nx nF = capture.nframes vid = np.zeros((nx, nx, nF)) masks = np.zeros((nx, nx, nF)) for idx in range(nF): print 'Loading frame', idx ret, frame, mask = capture.read() if idx == nref: refframe = frame.copy() vid[:,:,idx] = frame masks[:,:,idx] = mask #Generate ctrs and fd (mask, ctrs, fd) = findObjectThreshold(tracking_mask, threshold = threshold) #distmesh = DistMesh(refframe, h0 = gridsize) #distmesh.createMesh(ctrs, fd, refframe, plot = True) #Save this distmesh and reload it for quicker testing #distmesh.save(dm_out) distmesh = DistMesh(refframe, h0 = gridsize) distmesh.load(dm_out) refpositions = distmesh.p #Create dummy input for flow frame flowframe = np.zeros((nx, nx, 2)) #Create Kalman Filter object to store mesh and make use of plotting functions kf = IteratedMSKalmanFilter(distmesh, refframe, flowframe, cuda = cuda)
def projectmask(self, y_m):
print '-- projecting outliers onto contour'
logging.info("--KF projection")
#Project vertices onto boundary
ddeps = 1e-1
(mask2, ctrs, fd) = findObjectThreshold(y_m, threshold=0.5)
p = self.state.vertices()
p_orig = p.copy()
#Find vertices outside of mask and project these onto contour
for idx in range(10):
d = fd(p)
ix = d > 1
if ix.any():
#First order differencing
#dgradx = (fd(p[ix]+[ddeps,0])-d[ix])/(ddeps) # Numerical
#dgrady = (fd(p[ix]+[0,ddeps])-d[ix])/(ddeps) # gradient
#Central differencing
dgradx = (fd(p[ix] + [ddeps, 0]) - fd(p[ix] - [ddeps, 0])) / (
2 * ddeps) # Numerical
dgrady = (fd(p[ix] + [0, ddeps]) - fd(p[ix] - [0, ddeps])) / (
2 * ddeps) # gradient
dgrad2 = dgradx**2 + dgrady**2
p[ix] -= (d[ix] * np.vstack(
(dgradx, dgrady)) / dgrad2).T # Project
#Find _outer_ vertices inside of mask and project these onto contour
#Get rendered mask
rend_mask = self.state.renderer.rendermask()[:, :, 2]
#rend_mask = np.flipud(rend_mask)
#For each vertex, find if its on the border of the mask...
border = np.zeros(self.N, dtype=bool)
for idx, pi in enumerate(p):
pii = pi.astype(int)
i = np.zeros(8, dtype=bool)
i[0] = rend_mask[pii[0, 1] + 2, pii[0, 0] + 2]
i[1] = rend_mask[pii[0, 1] + 2, pii[0, 0] - 2]
i[2] = rend_mask[pii[0, 1] - 2, pii[0, 0] + 2]
i[3] = rend_mask[pii[0, 1] - 2, pii[0, 0] - 2]
i[4] = rend_mask[pii[0, 1], pii[0, 0] + 2]
i[5] = rend_mask[pii[0, 1], pii[0, 0] - 2]
i[6] = rend_mask[pii[0, 1] + 2, pii[0, 0]]
i[7] = rend_mask[pii[0, 1] - 2, pii[0, 0]]
if (np.sum(i) < 8) and (np.sum(i) > 0):
border[idx] = 1
#Plot the mask...
#prend_mask = rend_mask.copy()
#for pi in p:
# pii = pi.astype(int)
# prend_mask[pii[0,1], pii[0,0]] = 300
# prend_mask[pii[0,1]+1, pii[0,0]] = 300
# prend_mask[pii[0,1], pii[0,0]+1] = 300
# prend_mask[pii[0,1]+1, pii[0,0]+1] = 300
#plt.imshow(prend_mask)
#plt.colorbar()
#plt.show()
d = fd(p)
ix = (d < -1) * border
for idx in range(1):
if ix.any():
#First order differencing
#dgradx = (fd(p[ix]+[ddeps,0])-d[ix])/(ddeps) # Numerical
#dgrady = (fd(p[ix]+[0,ddeps])-d[ix])/(ddeps) # gradient
#Central differencing
dgradx = (fd(p[ix] + [ddeps, 0]) - fd(p[ix] - [ddeps, 0])) / (
2 * ddeps) # Numerical
dgrady = (fd(p[ix] + [0, ddeps]) - fd(p[ix] - [0, ddeps])) / (
2 * ddeps) # gradient
dgrad2 = dgradx**2 + dgrady**2
p[ix] -= (d[ix] * np.vstack(
(dgradx, dgrady)) / dgrad2).T # Project
#Write changes
self.state.X[0:(2 * self.N)] = np.reshape(p, (-1, 1))
#Update velocities also... or else it might crash...
self.state.X[(2 * self.N):] += np.reshape(p - p_orig, (-1, 1))
def backsub(self): (mask, ctrs, fd) = findObjectThreshold(self.img, threshold = self.threshold) return mask, ctrs, fd
