def crop_by_region(self, frame, region, clip_flow=True):
thermal = region.subimage(frame.thermal)
filtered = region.subimage(frame.filtered)
if frame.flow is not None:
flow_h = region.subimage(frame.flow_h)
flow_v = region.subimage(frame.flow_v)
if clip_flow and not frame.flow_clipped:
flow_h = get_clipped_flow(flow_h)
flow_v = get_clipped_flow(flow_v)
else:
flow_h = None
flow_v = None
mask = region.subimage(frame.mask).copy()
# make sure only our pixels are included in the mask.
mask[mask != region.id] = 0
mask[mask > 0] = 1
# stack together into a numpy array.
# by using int16 we lose a little precision on the filtered frames, but not much (only 1 bit)
if flow_h is not None and flow_v is not None:
return np.int16(
np.stack((thermal, filtered, flow_h, flow_v, mask), axis=0))
else:
empty = np.zeros(filtered.shape)
return np.int16(
np.stack((thermal, filtered, empty, empty, mask), axis=0))
return frame
def add_frame(self, frame):
self.open()
frames = self.db["frames"]
frame_group = frames.create_group(str(frame.frame_number))
frame_group.attrs["ffc_affected"] = frame.ffc_affected
height, width = frame.thermal.shape
chunks = (1, height, width)
dims = (5, height, width)
frame_node = frame_group.create_dataset("frame",
dims,
chunks=chunks,
dtype=np.float16)
scaled_flow = get_clipped_flow(frame.flow)
frame_val = (
np.float16(frame.thermal),
np.float16(frame.filtered),
np.float16(scaled_flow[:, :, 0]),
np.float16(scaled_flow[:, :, 1]),
np.float16(frame.mask),
)
frame_node[:, :, :] = frame_val
if not self.keep_open:
self.close()
def get_flow_split(self, clip_flow=False):
if self.flow is not None:
if self.clip_flow and not self.flow_clipped:
flow_c = get_clipped_flow(self.flow)
return flow_c[:, :, 0], flow_c[:, :, 1]
else:
return self.flow_h, self.flow_v
return None, None
def add_frame(self, frame):
self.open()
frames = self.db["frames"]
frame_group = frames.create_group(str(frame.frame_number))
frame_group.attrs["ffc_affected"] = frame.ffc_affected
height, width = frame.thermal.shape
chunks = (1, height, width)
channels = []
dims = 0
data = []
if frame.thermal is not None:
channels.append(TrackChannels.thermal)
dims += 1
data.append(np.float32(frame.thermal))
if frame.filtered is not None:
channels.append(TrackChannels.filtered)
dims += 1
data.append(np.float32(frame.filtered))
if frame.flow is not None:
channels.append(TrackChannels.flow)
scaled_flow = get_clipped_flow(frame.flow)
scaled_flow_h = np.float32(scaled_flow[:, :, 0])
scaled_flow_v = np.float32(scaled_flow[:, :, 1])
data.append(scaled_flow_h)
data.append(scaled_flow_v)
dims += 2
if frame.mask is not None:
channels.append(TrackChannels.mask)
data.append(np.float32(frame.mask))
dims += 1
frame_group.attrs["channels"] = np.uint8(channels)
dims = (dims, height, width)
frame_node = frame_group.create_dataset("frame",
dims,
chunks=chunks,
dtype=np.float32)
frame_node[:, :, :] = data
if not self.keep_open:
self.close()
def clip_flow(self):
if self.flow is not None:
self.flow = get_clipped_flow(self.flow)
self.flow_clipped = True
def clip_flow(self):
self.flow = get_clipped_flow(self.flow)
self.flow_clipped = True
