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import torch
def generate_heatmap(bboxes, patch_size=320, stride=16):
"""
Generate ground truth heatmap same as CenterNet
Args:
bboxes (torch.Tensor): shape of [num_search, bs, 4]
Returns:
gaussian_maps: list of generated heatmap
"""
gaussian_maps = []
heatmap_size = patch_size // stride
for single_patch_bboxes in bboxes:
bs = single_patch_bboxes.shape[0]
gt_scoremap = torch.zeros(bs, heatmap_size, heatmap_size)
classes = torch.arange(bs).to(torch.long)
bbox = single_patch_bboxes * heatmap_size
wh = bbox[:, 2:]
centers_int = (bbox[:, :2] + wh / 2).round()
CenterNetHeatMap.generate_score_map(gt_scoremap, classes, wh, centers_int, 0.7)
gaussian_maps.append(gt_scoremap.to(bbox.device))
return gaussian_maps
class CenterNetHeatMap(object):
@staticmethod
def generate_score_map(fmap, gt_class, gt_wh, centers_int, min_overlap):
radius = CenterNetHeatMap.get_gaussian_radius(gt_wh, min_overlap)
radius = torch.clamp_min(radius, 0)
radius = radius.type(torch.int).cpu().numpy()
for i in range(gt_class.shape[0]):
channel_index = gt_class[i]
CenterNetHeatMap.draw_gaussian(fmap[channel_index], centers_int[i], radius[i])
@staticmethod
def get_gaussian_radius(box_size, min_overlap):
"""
copyed from CornerNet
box_size (w, h), it could be a torch.Tensor, numpy.ndarray, list or tuple
notice: we are using a bug-version, please refer to fix bug version in CornerNet
"""
# box_tensor = torch.Tensor(box_size)
box_tensor = box_size
width, height = box_tensor[..., 0], box_tensor[..., 1]
a1 = 1
b1 = height + width
c1 = width * height * (1 - min_overlap) / (1 + min_overlap)
sq1 = torch.sqrt(b1 ** 2 - 4 * a1 * c1)
r1 = (b1 + sq1) / 2
a2 = 4
b2 = 2 * (height + width)
c2 = (1 - min_overlap) * width * height
sq2 = torch.sqrt(b2 ** 2 - 4 * a2 * c2)
r2 = (b2 + sq2) / 2
a3 = 4 * min_overlap
b3 = -2 * min_overlap * (height + width)
c3 = (min_overlap - 1) * width * height
sq3 = torch.sqrt(b3 ** 2 - 4 * a3 * c3)
r3 = (b3 + sq3) / 2
return torch.min(r1, torch.min(r2, r3))
@staticmethod
def gaussian2D(radius, sigma=1):
# m, n = [(s - 1.) / 2. for s in shape]
m, n = radius
y, x = np.ogrid[-m: m + 1, -n: n + 1]
gauss = np.exp(-(x * x + y * y) / (2 * sigma * sigma))
gauss[gauss < np.finfo(gauss.dtype).eps * gauss.max()] = 0
return gauss
@staticmethod
def draw_gaussian(fmap, center, radius, k=1):
diameter = 2 * radius + 1
gaussian = CenterNetHeatMap.gaussian2D((radius, radius), sigma=diameter / 6)
gaussian = torch.Tensor(gaussian)
x, y = int(center[0]), int(center[1])
height, width = fmap.shape[:2]
left, right = min(x, radius), min(width - x, radius + 1)
top, bottom = min(y, radius), min(height - y, radius + 1)
masked_fmap = fmap[y - top: y + bottom, x - left: x + right]
masked_gaussian = gaussian[radius - top: radius + bottom, radius - left: radius + right]
if min(masked_gaussian.shape) > 0 and min(masked_fmap.shape) > 0:
masked_fmap = torch.max(masked_fmap, masked_gaussian * k)
fmap[y - top: y + bottom, x - left: x + right] = masked_fmap
# return fmap
def compute_grids(features, strides):
"""
grids regret to the input image size
"""
grids = []
for level, feature in enumerate(features):
h, w = feature.size()[-2:]
shifts_x = torch.arange(
0, w * strides[level],
step=strides[level],
dtype=torch.float32, device=feature.device)
shifts_y = torch.arange(
0, h * strides[level],
step=strides[level],
dtype=torch.float32, device=feature.device)
shift_y, shift_x = torch.meshgrid(shifts_y, shifts_x)
shift_x = shift_x.reshape(-1)
shift_y = shift_y.reshape(-1)
grids_per_level = torch.stack((shift_x, shift_y), dim=1) + \
strides[level] // 2
grids.append(grids_per_level)
return grids
def get_center3x3(locations, centers, strides, range=3):
'''
Inputs:
locations: M x 2
centers: N x 2
strides: M
'''
range = (range - 1) / 2
M, N = locations.shape[0], centers.shape[0]
locations_expanded = locations.view(M, 1, 2).expand(M, N, 2) # M x N x 2
centers_expanded = centers.view(1, N, 2).expand(M, N, 2) # M x N x 2
strides_expanded = strides.view(M, 1, 1).expand(M, N, 2) # M x N
centers_discret = ((centers_expanded / strides_expanded).int() * strides_expanded).float() + \
strides_expanded / 2 # M x N x 2
dist_x = (locations_expanded[:, :, 0] - centers_discret[:, :, 0]).abs()
dist_y = (locations_expanded[:, :, 1] - centers_discret[:, :, 1]).abs()
return (dist_x <= strides_expanded[:, :, 0] * range) & \
(dist_y <= strides_expanded[:, :, 0] * range)
def get_pred(score_map_ctr, size_map, offset_map, feat_size):
max_score, idx = torch.max(score_map_ctr.flatten(1), dim=1, keepdim=True)
idx = idx.unsqueeze(1).expand(idx.shape[0], 2, 1)
size = size_map.flatten(2).gather(dim=2, index=idx).squeeze(-1)
offset = offset_map.flatten(2).gather(dim=2, index=idx).squeeze(-1)
return size * feat_size, offset
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