Source code for common.vision.datasets.keypoint_detection.human36m
"""
@author: Junguang Jiang
@contact: [email protected]
"""
import os
import json
import tqdm
from PIL import ImageFile
import torch
from .keypoint_dataset import Body16KeypointDataset
from ...transforms.keypoint_detection import *
from .util import *
ImageFile.LOAD_TRUNCATED_IMAGES = True
[docs]class Human36M(Body16KeypointDataset):
"""`Human3.6M Dataset <http://vision.imar.ro/human3.6m/description.php>`_
Args:
root (str): Root directory of dataset
split (str, optional): The dataset split, supports ``train``, ``test``, or ``all``.
Default: ``train``.
task (str, optional): Placeholder.
download (bool, optional): Placeholder.
transforms (callable, optional): A function/transform that takes in a dict (which contains PIL image and
its labels) and returns a transformed version. E.g, :class:`~common.vision.transforms.keypoint_detection.Resize`.
image_size (tuple): (width, height) of the image. Default: (256, 256)
heatmap_size (tuple): (width, height) of the heatmap. Default: (64, 64)
sigma (int): sigma parameter when generate the heatmap. Default: 2
.. note:: You need to download Human36M manually.
Ensure that there exist following files in the `root` directory before you using this class.
::
annotations/
Human36M_subject11_joint_3d.json
...
images/
.. note::
We found that the original Human3.6M image is in high resolution while most part in an image is background,
thus we crop the image and keep only the surrounding area of hands (1.5x bigger than hands) to speed up training.
In `root`, there will exist following files after crop.
::
Human36M_crop/
annotations/
keypoints2d_11.json
...
"""
def __init__(self, root, split='train', task='all', download=True, **kwargs):
assert split in ['train', 'test', 'all']
self.split = split
samples = []
if self.split == 'train':
parts = [1, 5, 6, 7, 8]
elif self.split == 'test':
parts = [9, 11]
else:
parts = [1, 5, 6, 7, 8, 9, 11]
for part in parts:
annotation_file = os.path.join(root, 'annotations/keypoints2d_{}.json'.format(part))
if not os.path.exists(annotation_file):
self.preprocess(part, root)
print("loading", annotation_file)
with open(annotation_file) as f:
samples.extend(json.load(f))
# decrease the number of test samples to decrease the time spent on test
random.seed(42)
if self.split == 'test':
samples = random.choices(samples, k=3200)
super(Human36M, self).__init__(root, samples, **kwargs)
def __getitem__(self, index):
sample = self.samples[index]
image_name = sample['name']
image_path = os.path.join(self.root, "crop_images", image_name)
image = Image.open(image_path)
keypoint3d_camera = np.array(sample['keypoint3d']) # NUM_KEYPOINTS x 3
keypoint2d = np.array(sample['keypoint2d']) # NUM_KEYPOINTS x 2
intrinsic_matrix = np.array(sample['intrinsic_matrix'])
Zc = keypoint3d_camera[:, 2]
image, data = self.transforms(image, keypoint2d=keypoint2d, intrinsic_matrix=intrinsic_matrix)
keypoint2d = data['keypoint2d']
intrinsic_matrix = data['intrinsic_matrix']
keypoint3d_camera = keypoint2d_to_3d(keypoint2d, intrinsic_matrix, Zc)
# noramlize 2D pose:
visible = np.ones((self.num_keypoints, ), dtype=np.float32)
visible = visible[:, np.newaxis]
# 2D heatmap
target, target_weight = generate_target(keypoint2d, visible, self.heatmap_size, self.sigma, self.image_size)
target = torch.from_numpy(target)
target_weight = torch.from_numpy(target_weight)
# normalize 3D pose:
# put middle finger metacarpophalangeal (MCP) joint in the center of the coordinate system
# and make distance between wrist and middle finger MCP joint to be of length 1
keypoint3d_n = keypoint3d_camera - keypoint3d_camera[9:10, :]
keypoint3d_n = keypoint3d_n / np.sqrt(np.sum(keypoint3d_n[0, :] ** 2))
meta = {
'image': image_name,
'keypoint2d': keypoint2d, # （NUM_KEYPOINTS x 2）
'keypoint3d': keypoint3d_n, # （NUM_KEYPOINTS x 3）
}
return image, target, target_weight, meta
def preprocess(self, part, root):
body_index = [3, 2, 1, 4, 5, 6, 0, 11, 8, 10, 16, 15, 14, 11, 12, 13]
image_size = 512
print("preprocessing part", part)
camera_json = os.path.join(root, "annotations", "Human36M_subject{}_camera.json".format(part))
data_json = os.path.join(root, "annotations", "Human36M_subject{}_data.json".format(part))
joint_3d_json = os.path.join(root, "annotations", "Human36M_subject{}_joint_3d.json".format(part))
with open(camera_json, "r") as f:
cameras = json.load(f)
with open(data_json, "r") as f:
data = json.load(f)
images = data['images']
with open(joint_3d_json, "r") as f:
joints_3d = json.load(f)
data = []
for i, image_data in enumerate(tqdm.tqdm(images)):
# downsample
if i % 5 == 0:
keypoint3d = np.array(joints_3d[str(image_data["action_idx"])][str(image_data["subaction_idx"])][
str(image_data["frame_idx"])])
keypoint3d = keypoint3d[body_index, :]
keypoint3d[7, :] = 0.5 * (keypoint3d[12, :] + keypoint3d[13, :])
camera = cameras[str(image_data["cam_idx"])]
R, T = np.array(camera["R"]), np.array(camera['t'])[:, np.newaxis]
extrinsic_matrix = np.concatenate([R, T], axis=1)
keypoint3d_camera = np.matmul(extrinsic_matrix, np.hstack(
(keypoint3d, np.ones((keypoint3d.shape[0], 1)))).T) # (3 x NUM_KEYPOINTS)
Z_c = keypoint3d_camera[2:3, :] # 1 x NUM_KEYPOINTS
f, c = np.array(camera["f"]), np.array(camera['c'])
intrinsic_matrix = np.zeros((3, 3))
intrinsic_matrix[0, 0] = f[0]
intrinsic_matrix[1, 1] = f[1]
intrinsic_matrix[0, 2] = c[0]
intrinsic_matrix[1, 2] = c[1]
intrinsic_matrix[2, 2] = 1
keypoint2d = np.matmul(intrinsic_matrix, keypoint3d_camera) # (3 x NUM_KEYPOINTS)
keypoint2d = keypoint2d[0: 2, :] / Z_c
keypoint2d = keypoint2d.T
src_image_path = os.path.join(root, "images", image_data['file_name'])
tgt_image_path = os.path.join(root, "crop_images", image_data['file_name'])
os.makedirs(os.path.dirname(tgt_image_path), exist_ok=True)
image = Image.open(src_image_path)
bounding_box = get_bounding_box(keypoint2d)
w, h = image.size
left, upper, right, lower = scale_box(bounding_box, w, h, 1.5)
image, keypoint2d = crop(image, upper, left, lower-upper+1, right-left+1, keypoint2d)
Z_c = Z_c.T
# Calculate XYZ from uvz
uv1 = np.concatenate([np.copy(keypoint2d), np.ones((16, 1))],
axis=1) # NUM_KEYPOINTS x 3
uv1 = uv1 * Z_c # NUM_KEYPOINTS x 3
keypoint3d_camera = np.matmul(np.linalg.inv(intrinsic_matrix), uv1.T).T
# resize image will change camera intrinsic matrix
w, h = image.size
image = image.resize((image_size, image_size))
image.save(tgt_image_path)
zoom_factor = float(w) / float(image_size)
keypoint2d /= zoom_factor
intrinsic_matrix[0, 0] /= zoom_factor
intrinsic_matrix[1, 1] /= zoom_factor
intrinsic_matrix[0, 2] /= zoom_factor
intrinsic_matrix[1, 2] /= zoom_factor
data.append({
"name": image_data['file_name'],
'keypoint2d': keypoint2d.tolist(),
'keypoint3d': keypoint3d_camera.tolist(),
'intrinsic_matrix': intrinsic_matrix.tolist(),
})
with open(os.path.join(root, "annotations", "keypoints2d_{}.json".format(part)), "w") as f:
json.dump(data, f)
