# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from copy import deepcopy
from typing import Optional
import numpy as np
import torch
from mmengine.config import Config
from mmengine.dataset import pseudo_collate
from mmengine.structures import InstanceData, PixelData
from mmpose.structures import MultilevelPixelData, PoseDataSample
from mmpose.structures.bbox import bbox_xyxy2cs
def get_coco_sample(
img_shape=(240, 320),
img_fill: Optional[int] = None,
num_instances=1,
with_bbox_cs=True,
with_img_mask=False,
random_keypoints_visible=False,
non_occlusion=False):
"""Create a dummy data sample in COCO style."""
rng = np.random.RandomState(0)
h, w = img_shape
if img_fill is None:
img = np.random.randint(0, 256, (h, w, 3), dtype=np.uint8)
else:
img = np.full((h, w, 3), img_fill, dtype=np.uint8)
if non_occlusion:
bbox = _rand_bboxes(rng, num_instances, w / num_instances, h)
for i in range(num_instances):
bbox[i, 0::2] += w / num_instances * i
else:
bbox = _rand_bboxes(rng, num_instances, w, h)
keypoints = _rand_keypoints(rng, bbox, 17)
if random_keypoints_visible:
keypoints_visible = np.random.randint(0, 2, (num_instances,
17)).astype(np.float32)
else:
keypoints_visible = np.full((num_instances, 17), 1, dtype=np.float32)
upper_body_ids = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
lower_body_ids = [11, 12, 13, 14, 15, 16]
flip_pairs = [[2, 1], [1, 2], [4, 3], [3, 4], [6, 5], [5, 6], [8, 7],
[7, 8], [10, 9], [9, 10], [12, 11], [11, 12], [14, 13],
[13, 14], [16, 15], [15, 16]]
flip_indices = [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
dataset_keypoint_weights = np.array([
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5,
1.5
]).astype(np.float32)
data = {
'img': img,
'img_shape': img_shape,
'ori_shape': img_shape,
'bbox': bbox,
'keypoints': keypoints,
'keypoints_visible': keypoints_visible,
'upper_body_ids': upper_body_ids,
'lower_body_ids': lower_body_ids,
'flip_pairs': flip_pairs,
'flip_indices': flip_indices,
'dataset_keypoint_weights': dataset_keypoint_weights,
'invalid_segs': [],
}
if with_bbox_cs:
data['bbox_center'], data['bbox_scale'] = bbox_xyxy2cs(data['bbox'])
if with_img_mask:
data['img_mask'] = np.random.randint(0, 2, (h, w), dtype=np.uint8)
return data
def get_packed_inputs(batch_size=2,
num_instances=1,
num_keypoints=17,
num_levels=1,
img_shape=(256, 192),
input_size=(192, 256),
heatmap_size=(48, 64),
simcc_split_ratio=2.0,
with_heatmap=True,
with_reg_label=True,
with_simcc_label=True):
"""Create a dummy batch of model inputs and data samples."""
rng = np.random.RandomState(0)
inputs_list = []
for idx in range(batch_size):
inputs = dict()
# input
h, w = img_shape
image = rng.randint(0, 255, size=(3, h, w), dtype=np.uint8)
inputs['inputs'] = torch.from_numpy(image)
# meta
img_meta = {
'id': idx,
'img_id': idx,
'img_path': '<demo>.png',
'img_shape': img_shape,
'input_size': input_size,
'flip': False,
'flip_direction': None,
'flip_indices': list(range(num_keypoints))
}
np.random.shuffle(img_meta['flip_indices'])
data_sample = PoseDataSample(metainfo=img_meta)
# gt_instance
gt_instances = InstanceData()
gt_instance_labels = InstanceData()
bboxes = _rand_bboxes(rng, num_instances, w, h)
bbox_centers, bbox_scales = bbox_xyxy2cs(bboxes)
keypoints = _rand_keypoints(rng, bboxes, num_keypoints)
keypoints_visible = np.ones((num_instances, num_keypoints),
dtype=np.float32)
# [N, K] -> [N, num_levels, K]
# keep the first dimension as the num_instances
if num_levels > 1:
keypoint_weights = np.tile(keypoints_visible[:, None],
(1, num_levels, 1))
else:
keypoint_weights = keypoints_visible.copy()
gt_instances.bboxes = bboxes
gt_instances.bbox_centers = bbox_centers
gt_instances.bbox_scales = bbox_scales
gt_instances.bbox_scores = np.ones((num_instances, ), dtype=np.float32)
gt_instances.keypoints = keypoints
gt_instances.keypoints_visible = keypoints_visible
gt_instance_labels.keypoint_weights = torch.FloatTensor(
keypoint_weights)
if with_reg_label:
gt_instance_labels.keypoint_labels = torch.FloatTensor(keypoints /
input_size)
if with_simcc_label:
len_x = np.around(input_size[0] * simcc_split_ratio)
len_y = np.around(input_size[1] * simcc_split_ratio)
gt_instance_labels.keypoint_x_labels = torch.FloatTensor(
_rand_simcc_label(rng, num_instances, num_keypoints, len_x))
gt_instance_labels.keypoint_y_labels = torch.FloatTensor(
_rand_simcc_label(rng, num_instances, num_keypoints, len_y))
# gt_fields
if with_heatmap:
if num_levels == 1:
gt_fields = PixelData()
# generate single-level heatmaps
W, H = heatmap_size
heatmaps = rng.rand(num_keypoints, H, W)
gt_fields.heatmaps = torch.FloatTensor(heatmaps)
else:
# generate multilevel heatmaps
heatmaps = []
for _ in range(num_levels):
W, H = heatmap_size
heatmaps_ = rng.rand(num_keypoints, H, W)
heatmaps.append(torch.FloatTensor(heatmaps_))
# [num_levels*K, H, W]
gt_fields = MultilevelPixelData()
gt_fields.heatmaps = heatmaps
data_sample.gt_fields = gt_fields
data_sample.gt_instances = gt_instances
data_sample.gt_instance_labels = gt_instance_labels
inputs['data_samples'] = data_sample
inputs_list.append(inputs)
packed_inputs = pseudo_collate(inputs_list)
return packed_inputs
def _rand_keypoints(rng, bboxes, num_keypoints):
n = bboxes.shape[0]
relative_pos = rng.rand(n, num_keypoints, 2)
keypoints = relative_pos * bboxes[:, None, :2] + (
1 - relative_pos) * bboxes[:, None, 2:4]
return keypoints
def _rand_simcc_label(rng, num_instances, num_keypoints, len_feats):
simcc_label = rng.rand(num_instances, num_keypoints, int(len_feats))
return simcc_label
def _rand_bboxes(rng, num_instances, img_w, img_h):
cx, cy = rng.rand(num_instances, 2).T
bw, bh = 0.2 + 0.8 * rng.rand(num_instances, 2).T
tl_x = ((cx * img_w) - (img_w * bw / 2)).clip(0, img_w)
tl_y = ((cy * img_h) - (img_h * bh / 2)).clip(0, img_h)
br_x = ((cx * img_w) + (img_w * bw / 2)).clip(0, img_w)
br_y = ((cy * img_h) + (img_h * bh / 2)).clip(0, img_h)
bboxes = np.vstack([tl_x, tl_y, br_x, br_y]).T
return bboxes
def get_repo_dir():
"""Return the path of the MMPose repo directory."""
try:
# Assume the function in invoked is the source mmpose repo
repo_dir = osp.dirname(osp.dirname(osp.dirname(__file__)))
except NameError:
# For IPython development when __file__ is not defined
import mmpose
repo_dir = osp.dirname(osp.dirname(mmpose.__file__))
return repo_dir
def get_config_file(fn: str):
"""Return full path of a config file from the given relative path."""
repo_dir = get_repo_dir()
if fn.startswith('configs'):
fn_config = osp.join(repo_dir, fn)
else:
fn_config = osp.join(repo_dir, 'configs', fn)
if not osp.isfile(fn_config):
raise FileNotFoundError(f'Cannot find config file {fn_config}')
return fn_config
def get_pose_estimator_cfg(fn: str):
"""Load model config from a config file."""
fn_config = get_config_file(fn)
config = Config.fromfile(fn_config)
return deepcopy(config.model)