Source code for

@author: Junguang Jiang
import os
import json
from PIL import ImageFile
import torch
from ...transforms.keypoint_detection import *
from .util import *
from .._util import download as download_data, check_exits
from .keypoint_dataset import Body16KeypointDataset


[docs]class SURREAL(Body16KeypointDataset): """`Surreal Dataset <>`_ 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): If true, downloads the dataset from the internet and puts it \ in root directory. If dataset is already downloaded, it is not downloaded again. 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:``. 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:: We found that the original Surreal 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. .. note:: In `root`, there will exist following files after downloading. :: train/ test/ val/ """ def __init__(self, root, split='train', task='all', download=True, **kwargs): assert split in ['train', 'test', 'val'] self.split = split if download: download_data(root, "train/run0", "train0.tgz", "") download_data(root, "train/run1", "train1.tgz", "") download_data(root, "train/run1", "train2.tgz", "") download_data(root, "val", "val.tgz", "") download_data(root, "test", "test.tgz", "") else: check_exits(root, "train/run0") check_exits(root, "train/run1") check_exits(root, "train/run2") check_exits(root, "val") check_exits(root, "test") all_samples = [] for part in [0, 1, 2]: annotation_file = os.path.join(root, split, 'run{}.json'.format(part)) print("loading", annotation_file) with open(annotation_file) as f: samples = json.load(f) for sample in samples: sample["image_path"] = os.path.join(root, self.split, 'run{}'.format(part), sample['name']) all_samples.extend(samples) random.seed(42) random.shuffle(all_samples) samples_len = len(all_samples) samples_split = min(int(samples_len * 0.2), 3200) if self.split == 'train': all_samples = all_samples[samples_split:] elif self.split == 'test': all_samples = all_samples[:samples_split] self.joints_index = (7, 4, 1, 2, 5, 8, 0, 9, 12, 15, 20, 18, 13, 14, 19, 21) super(SURREAL, self).__init__(root, all_samples, **kwargs) def __getitem__(self, index): sample = self.samples[index] image_name = sample['name'] image_path = sample['image_path'] image = keypoint3d_camera = np.array(sample['keypoint3d'])[self.joints_index, :] # NUM_KEYPOINTS x 3 keypoint2d = np.array(sample['keypoint2d'])[self.joints_index, :] # 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.array([1.] * 16, 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 __len__(self): return len(self.samples)


Access comprehensive documentation for Transfer Learning Library

View Docs


Get started for Transfer Learning Library

Get Started

Paper List

Get started for transfer learning

View Resources