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import torch
from maskrcnn_benchmark.config import cfg
# transpose
FLIP_LEFT_RIGHT = 0
FLIP_TOP_BOTTOM = 1
class Keypoints(object):
def __init__(self, keypoints, size, mode=None):
# FIXME remove check once we have better integration with device
# in my version this would consistently return a CPU tensor
device = keypoints.device if isinstance(keypoints, torch.Tensor) else torch.device("cpu")
keypoints = torch.as_tensor(keypoints, dtype=torch.float32, device=device)
num_keypoints = keypoints.shape[0]
if num_keypoints:
keypoints = keypoints.view(num_keypoints, -1, 3)
# TODO should I split them?
# self.visibility = keypoints[..., 2]
self.keypoints = keypoints # [..., :2]
self.size = size
self.mode = mode
self.extra_fields = {}
def crop(self, box):
raise NotImplementedError()
def resize(self, size, *args, **kwargs):
ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(size, self.size))
ratio_w, ratio_h = ratios
resized_data = self.keypoints.clone()
resized_data[..., 0] *= ratio_w
resized_data[..., 1] *= ratio_h
keypoints = type(self)(resized_data, size, self.mode)
for k, v in self.extra_fields.items():
keypoints.add_field(k, v)
return keypoints
def transpose(self, method):
if method not in (FLIP_LEFT_RIGHT,):
raise NotImplementedError("Only FLIP_LEFT_RIGHT implemented")
flip_inds = self.FLIP_INDS
flipped_data = self.keypoints[:, flip_inds]
width = self.size[0]
TO_REMOVE = 1
# Flip x coordinates
flipped_data[..., 0] = width - flipped_data[..., 0] - TO_REMOVE
# Maintain COCO convention that if visibility == 0, then x, y = 0
inds = flipped_data[..., 2] == 0
flipped_data[inds] = 0
keypoints = type(self)(flipped_data, self.size, self.mode)
for k, v in self.extra_fields.items():
keypoints.add_field(k, v)
return keypoints
def to(self, *args, **kwargs):
keypoints = type(self)(self.keypoints.to(*args, **kwargs), self.size, self.mode)
for k, v in self.extra_fields.items():
if hasattr(v, "to"):
v = v.to(*args, **kwargs)
keypoints.add_field(k, v)
return keypoints
def __getitem__(self, item):
keypoints = type(self)(self.keypoints[item], self.size, self.mode)
for k, v in self.extra_fields.items():
keypoints.add_field(k, v[item])
return keypoints
def add_field(self, field, field_data):
self.extra_fields[field] = field_data
def get_field(self, field):
return self.extra_fields[field]
def __repr__(self):
s = self.__class__.__name__ + "("
s += "num_instances={}, ".format(len(self.keypoints))
s += "image_width={}, ".format(self.size[0])
s += "image_height={})".format(self.size[1])
return s
class PersonKeypoints(Keypoints):
_NAMES = [
"nose",
"left_eye",
"right_eye",
"left_ear",
"right_ear",
"left_shoulder",
"right_shoulder",
"left_elbow",
"right_elbow",
"left_wrist",
"right_wrist",
"left_hip",
"right_hip",
"left_knee",
"right_knee",
"left_ankle",
"right_ankle",
]
_FLIP_MAP = {
"left_eye": "right_eye",
"left_ear": "right_ear",
"left_shoulder": "right_shoulder",
"left_elbow": "right_elbow",
"left_wrist": "right_wrist",
"left_hip": "right_hip",
"left_knee": "right_knee",
"left_ankle": "right_ankle",
}
def __init__(self, *args, **kwargs):
super(PersonKeypoints, self).__init__(*args, **kwargs)
if len(cfg.MODEL.ROI_KEYPOINT_HEAD.KEYPOINT_NAME) > 0:
self.NAMES = cfg.MODEL.ROI_KEYPOINT_HEAD.KEYPOINT_NAME
self.FLIP_MAP = {
l: r for l, r in PersonKeypoints._FLIP_MAP.items() if l in cfg.MODEL.ROI_KEYPOINT_HEAD.KEYPOINT_NAME
}
else:
self.NAMES = PersonKeypoints._NAMES
self.FLIP_MAP = PersonKeypoints._FLIP_MAP
self.FLIP_INDS = self._create_flip_indices(self.NAMES, self.FLIP_MAP)
self.CONNECTIONS = self._kp_connections(self.NAMES)
def to_coco_format(self):
coco_result = []
for i in range(self.keypoints.shape[0]):
coco_kps = [0] * len(PersonKeypoints._NAMES) * 3
for ki, name in enumerate(self.NAMES):
coco_kps[3 * PersonKeypoints._NAMES.index(name)] = self.keypoints[i, ki, 0].item()
coco_kps[3 * PersonKeypoints._NAMES.index(name) + 1] = self.keypoints[i, ki, 1].item()
coco_kps[3 * PersonKeypoints._NAMES.index(name) + 2] = self.keypoints[i, ki, 2].item()
coco_result.append(coco_kps)
return coco_result
def _create_flip_indices(self, names, flip_map):
full_flip_map = flip_map.copy()
full_flip_map.update({v: k for k, v in flip_map.items()})
flipped_names = [i if i not in full_flip_map else full_flip_map[i] for i in names]
flip_indices = [names.index(i) for i in flipped_names]
return torch.tensor(flip_indices)
def _kp_connections(self, keypoints):
CONNECTIONS = [
["left_eye", "right_eye"],
["left_eye", "nose"],
["right_eye", "nose"],
["right_eye", "right_ear"],
["left_eye", "left_ear"],
["right_shoulder", "right_elbow"],
["right_elbow", "right_wrist"],
["left_shoulder", "left_elbow"],
["left_elbow", "left_wrist"],
["right_hip", "right_knee"],
["right_knee", "right_ankle"],
["left_hip", "left_knee"],
["left_knee", "left_ankle"],
["right_shoulder", "left_shoulder"],
["right_hip", "left_hip"],
]
kp_lines = [
[keypoints.index(conn[0]), keypoints.index(conn[1])]
for conn in CONNECTIONS
if conn[0] in self.NAMES and conn[1] in self.NAMES
]
return kp_lines
# TODO make this nicer, this is a direct translation from C2 (but removing the inner loop)
def keypoints_to_heat_map(keypoints, rois, heatmap_size):
if rois.numel() == 0:
return rois.new().long(), rois.new().long()
offset_x = rois[:, 0]
offset_y = rois[:, 1]
scale_x = heatmap_size / (rois[:, 2] - rois[:, 0])
scale_y = heatmap_size / (rois[:, 3] - rois[:, 1])
offset_x = offset_x[:, None]
offset_y = offset_y[:, None]
scale_x = scale_x[:, None]
scale_y = scale_y[:, None]
x = keypoints[..., 0]
y = keypoints[..., 1]
x_boundary_inds = x == rois[:, 2][:, None]
y_boundary_inds = y == rois[:, 3][:, None]
x = (x - offset_x) * scale_x
x = x.floor().long()
y = (y - offset_y) * scale_y
y = y.floor().long()
x[x_boundary_inds] = heatmap_size - 1
y[y_boundary_inds] = heatmap_size - 1
valid_loc = (x >= 0) & (y >= 0) & (x < heatmap_size) & (y < heatmap_size)
vis = keypoints[..., 2] > 0
valid = (valid_loc & vis).long()
lin_ind = y * heatmap_size + x
heatmaps = lin_ind * valid
return heatmaps, valid
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