def calc_post_pos(movieinfo):
threshed_img = nim.threshold(movieinfo.background, 0, 35)
blob = nim.find_biggest_blob(threshed_img)
center, radius = nim.find_circle(blob,
npts=200,
navg=30,
nstart=0,
plot=False)
movieinfo.post_pos = center
movieinfo.post_radius_in_pixels = radius
def get_active_frames(npmovie):
# series of frames where position is not (0,0)
try:
frame_of_landing = npmovie.frame_of_landing
except:
frame_of_landing = -1
frames = np.nonzero(npmovie.obj.bool[0:frame_of_landing] > 50)[0]
framediff = np.diff(frames) == 1
blobs = nim.find_biggest_blob(framediff)
try:
contframes = frames[np.where(blobs == 1)[0].tolist()].tolist()
except:
contframes = []
return contframes
def background_subtraction(self, raw, save_raw=False):
mask_center_0 = int(self.mask_center[0])
mask_center_1 = int(self.mask_center[1])
mask_0_lo = max(0, mask_center_0 - self.mask_radius)
mask_0_hi = min(self.width, mask_center_0 + self.mask_radius)
mask_1_lo = max(0, mask_center_1 - self.mask_radius)
mask_1_hi = min(self.width, mask_center_1 + self.mask_radius)
if self.tracking_mask is not None:
tracking_mask = copy.copy(self.tracking_mask)
tracking_mask[mask_0_lo:mask_0_hi, mask_1_lo:mask_1_hi] = 1
if self.dynamic_tracking_mask is True:
# TODO: currently dynamic tracking and such only works for square format cameras
#print 'dynamic tracking'
masked_img = raw[mask_0_lo:mask_0_hi, mask_1_lo:mask_1_hi]
masked_background = self.background[mask_0_lo:mask_0_hi, mask_1_lo:mask_1_hi]
else:
masked_img = raw
masked_background = self.background
'''
if masked_img.shape[0] < 100 or masked_img.shape[1] < 100:
#print 'no uframe'
self.dynamic_tracking_mask = False
uframe = uFrame()
return uframe, None
'''
absdiff = nim.absdiff(masked_img, masked_background)
if self.dynamic_tracking_mask is False and self.tracking_mask is not None:
absdiff *= tracking_mask
#absdiff = nim.auto_adjust_levels(absdiff)
#print 'shape: ', np.shape(absdiff)
#threshold = max( 10, absdiff.max() - THRESHRANGE )
#print 'dynamic threshold: ', threshold
#diffthresh = nim.threshold(absdiff, threshold)
#print 'max absdiff: ', absdiff.max()
diffthresh = nim.threshold(absdiff, 15, threshold_hi=255)
# abort early if there is no info:
s = np.sum(diffthresh)
if s < 10:
uframe = uFrame()
#print 'no uframe, early abort, sum: ', s
self.dynamic_tracking_mask = False
return uframe, None
blobs = nim.find_blobs(diffthresh, self.blob_size_range)
if blobs is None:
self.dynamic_tracking_mask = False
masked_img = raw
masked_background = self.background
absdiff = nim.absdiff(masked_img, masked_background)
diffthresh = nim.threshold(absdiff, threshold)
if self.dynamic_tracking_mask is False and self.tracking_mask is not None:
diffthresh *= tracking_mask
blobs = nim.find_blobs(diffthresh, self.blob_size_range)
if blobs is None:
uframe = uFrame()
if save_raw is False:
#print 'no uframe'
self.dynamic_tracking_mask = False
return uframe, None
else:
frame = Frame(raw, absdiff, diffthresh)
return uframe, frame
nblobs = blobs.max()
#print 'n blobs: ', nblobs
if nblobs > 1:
blobs = nim.find_biggest_blob(blobs)
center = nim.center_of_blob(blobs)
#print 'center: ', center
if np.isnan(center)[0] or np.isnan(center)[1]:
uframe = uFrame()
#print 'no uframe, NaN center!'
self.dynamic_tracking_mask = False
return uframe, None
if center[0] < 1 or center[1] < 1:
uframe = uFrame()
#print 'no uframe, NaN center!'
self.dynamic_tracking_mask = False
return uframe, None
#print 'center found'
if 1:
limlo_x = max( int(center[0])-ROI_RADIUS, 0 )
limlo_y = max( int(center[1])-ROI_RADIUS, 0 )
limhi_x = min( int(center[0])+ROI_RADIUS, masked_img.shape[0] )
limhi_y = min( int(center[1])+ROI_RADIUS, masked_img.shape[1] )
# TODO: right now object entering or leaving frame doesnt work perfectly
uimg = masked_img[limlo_x:limhi_x, limlo_y:limhi_y]
uimg = copy.copy(uimg)
uimg_absdiff = absdiff[limlo_x:limhi_x, limlo_y:limhi_y]
uimg_absdiff = copy.copy(uimg_absdiff)
uimg_diffthresh = blobs[limlo_x:limhi_x, limlo_y:limhi_y]
uimg_diffthresh = copy.copy(uimg_diffthresh)
if self.dynamic_tracking_mask is True:
tmp = np.array([masked_img.shape[0]/2., masked_img.shape[1]/2.])
center = np.array(self.mask_center) + np.array(center) - tmp
uimg_indices = [mask_0_lo+limlo_x, mask_0_lo+limhi_x, mask_1_lo+limlo_y, mask_1_lo+limhi_y]
else:
uimg_indices = [limlo_x, limhi_x, limlo_y, limhi_y]
self.dynamic_tracking_mask = True
self.mask_center = center
uframe = uFrame(center, uimg, uimg_absdiff, uimg_diffthresh)
uframe.blobsize = blobs.sum()
uframe.indices = uimg_indices
if save_raw is False:
del(raw)
del(absdiff)
del(diffthresh)
del(blobs)
return uframe, None
frame = Frame(masked_img, absdiff, diffthresh)
return uframe, frame
