def create_mask_montage(self, image, predictions):
"""
Create a montage showing the probability heatmaps for each one one of the
detected objects
Arguments:
image (np.ndarray): an image as returned by OpenCV
predictions (BoxList): the result of the computation by the model.
It should contain the field `mask`.
"""
masks = predictions.get_field("mask")
masks_per_dim = self.masks_per_dim
masks = L.interpolate(
masks.float(), scale_factor=1 / masks_per_dim
).byte()
height, width = masks.shape[-2:]
max_masks = masks_per_dim ** 2
masks = masks[:max_masks]
# handle case where we have less detections than max_masks
if len(masks) < max_masks:
masks_padded = torch.zeros(max_masks, 1, height, width, dtype=torch.uint8)
masks_padded[: len(masks)] = masks
masks = masks_padded
masks = masks.reshape(masks_per_dim, masks_per_dim, height, width)
result = torch.zeros(
(masks_per_dim * height, masks_per_dim * width), dtype=torch.uint8
)
for y in range(masks_per_dim):
start_y = y * height
end_y = (y + 1) * height
for x in range(masks_per_dim):
start_x = x * width
end_x = (x + 1) * width
result[start_y:end_y, start_x:end_x] = masks[y, x]
return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET)
def create_mask_montage(self, image, predictions):
masks = predictions.get_field("mask")
masks_per_dim = self.masks_per_dim
masks = L.interpolate(masks.float(),
scale_factor=1 / masks_per_dim).byte()
height, width = masks.shape[-2:]
max_masks = masks_per_dim**2
masks = masks[:max_masks]
# handle case where we have less detections than max_masks
if len(masks) < max_masks:
masks_padded = torch.zeros(max_masks,
1,
height,
width,
dtype=torch.uint8)
masks_padded[:len(masks)] = masks
masks = masks_padded
masks = masks.reshape(masks_per_dim, masks_per_dim, height, width)
result = torch.zeros((masks_per_dim * height, masks_per_dim * width),
dtype=torch.uint8)
for y in range(masks_per_dim):
start_y = y * height
end_y = (y + 1) * height
for x in range(masks_per_dim):
start_x = x * width
end_x = (x + 1) * width
result[start_y:end_y, start_x:end_x] = masks[y, x]
return cv2.applyColorMap(result.numpy(), cv2.COLORMAP_JET)
def forward(self, x):
x = self.kps_score_lowres(x)
x = layers.interpolate(x,
scale_factor=self.up_scale,
mode="bilinear",
align_corners=False)
return x
def forward(self, ft):
ft = self.bo_input_xy(ft)
ft_2x = self.conv5_bo_xy(ft)
ft_2x = layers.interpolate(ft_2x, size = (48,48), mode='bilinear', align_corners=True)
x = self.bo_input_1_1(ft_2x)
y = self.bo_input_2_1(ft_2x)
x = self.conv5_bo_x(x)
y = self.conv5_bo_y(y)
return x, y
def forward(self, ft):
ft = self.ke_input_xy(ft)
ft = self.conv5_ke_xy(ft)
ft_2x = layers.interpolate(ft,
scale_factor=self.up_scale,
mode='bilinear',
align_corners=True)
x = self.conv5_ke_x_shrink(ft_2x)
y = self.conv5_ke_y_shrink(ft_2x)
assert(x.size()[2:] == torch.Size([1, self.resol]) and \
y.size()[2:] == torch.Size([self.resol, 1])), "x y: " +str(x.size())+' '+str(y.size())
# mty
# mty = torch.cat((x_tc, y_tc), dim=1)
mty = self.cat_trans(ft)
mty = self.mty(mty)
assert (mty.size()[1:] == torch.Size(
[24, 1,
1])), "mty w h should be 1, but got {}".format(str(mty.size()))
return x, y, mty
def forward(self, x):
return interpolate(x,
scale_factor=self.scale,
mode=self.mode,
align_corners=self.align_corners)