cseproj154.cse.iitk.ac.in:/dataset1/EPIC-KITCHENS
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Located at: cseproj154.cse.iitk.ac.in:/dataset1/EPIC-KITCHENS
The largest dataset in first-person (egocentric) vision; multi-faceted non-scripted recordings in native environments - i.e. the wearers' homes, capturing all daily activities in the kitchen over multiple days. Annotations are collected using a novel `live' audio commentary approach.
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Located at: cseproj154.cse.iitk.ac.in:/dataset1/Audioset
This dataset has been downloaded and segregated into classes further. The files are as mentioned in the Audioset dataset website and they have been segregated into 24 classes. It's a dataset for audio event research. It has approximately 0.5 million video clips with classes like Accordion, Cricket, Meow, etc.
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Located at: cseproj154.cse.iitk.ac.in:/dataset2/ms_coco_2014
COCO is a large-scale object detection, segmentation, and captioning dataset. COCO has several features:
Arrangement:
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Located at: cseproj154.cse.iitk.ac.in:/dataset2/SURREAL/data
SURREAL/data/ ------------- cmu/ # using MoCap from CMU dataset -------------------- train/ -------------------- val/ # small subset of test -------------------- test/ ---------------------------- run0/ #50% overlap ---------------------------- run1/ #30% overlap ---------------------------- run2/ #70% overlap ------------------------------------ <sequenceName>/ #e.g. 01_01 -------------------------------------------------- <sequenceName>_c%04d.mp4 # RGB - 240x320 resolution video -------------------------------------------------- <sequenceName>_c%04d_depth.mat # Depth # depth_1, depth_2, ... depth_T [240x320 single] - in meters -------------------------------------------------- <sequenceName>_c%04d_segm.mat # Segmentation # segm_1, segm_2, ... segm_T [240x320 uint8] - 0 for background and 1..24 for SMPL body parts -------------------------------------------------- <sequenceName>_c%04d_gtflow.mat # Ground truth optical flow # gtflow_1, gtflow_2, ... gtflow_T [240x320x2 single] -------------------------------------------------- <sequenceName>_c%04d_info.mat # Remaining annotation # bg [1xT cell] - names of background image files # camDist [1 single] - camera distance # camLoc [3x1 single] - camera location # clipNo [1 double] - clip number of the full sequence (corresponds to the c%04d part of the file) # cloth [1xT cell] - names of texture image files # gender [Tx1 uint8] - gender (0: 'female', 1: 'male') # joints2D [2x24xT single] - 2D coordinates of 24 SMPL body joints on the image pixels # joints3D [3x24xT single] - 3D coordinates of 24 SMPL body joints in real world meters # light [9x100 single] - spherical harmonics lighting coefficients # pose [72xT single] - SMPL parameters (axis-angle) # sequence [char] - <sequenceName>_c%04d # shape [10xT single] - body shape parameters # source [char] - 'cmu' # stride [1 uint8] - percent overlap between clips, 30 or 50 or 70 # zrot [Tx1 single] - rotation in Z (euler angle) # *** T is the number of frames, mostly 100.
.mat
filesimport scipy import scipy.io import numpy as np DATA_PREFIX = '/nfs/154/dataset2/SURREAL/data' RUN_PREFIX = 'cmu/train/run1' SEQUENCE_NAME = 'ung_132_07' CLIP = 1 FILENAME_PREFIX = f'{DATA_PREFIX}/{RUN_PREFIX}/{SEQUENCE_NAME}/{SEQUENCE_NAME}_c{CLIP:04d}' INFO = 'depth' FILENAME = f'{FILENAME_PREFIX}_{INFO}.mat' # eg. './ung_132_07_c0001_depth.mat' depth = scipy.io.loadmat(FILENAME) depth_array = np.stack(list(map( lambda key: depth[key], sorted( filter( lambda s:isinstance(depth[s], np.ndarray), depth.keys() ), key=lambda s: int(s.split('_')[-1]) ) )))
joints2D
dataimport scipy import scipy.io import numpy as np DATA_PREFIX = '/nfs/154/dataset2/SURREAL/data' RUN_PREFIX = 'cmu/train/run1' SEQUENCE_NAME = 'ung_132_07' CLIP = 1 FILENAME_PREFIX = f'{DATA_PREFIX}/{RUN_PREFIX}/{SEQUENCE_NAME}/{SEQUENCE_NAME}_c{CLIP:04d}' INFO = 'info' FILENAME = f'{FILENAME_PREFIX}_{INFO}.mat' info = scipy.io.loadmat(FILENAME) KEY = 'joints2D' joints2D_array = info[KEY] # permute axes to arrange num_framesXnum_jointsXnum_coords joints2D_array = joints2D_array.transpose(2,1,0) # The resulting values are integral (cast into float) bounded by: IMAGE_SIZE = (240,320) # H, W