single DHG dataset pickle file
Fazlik995 opened this issue · 2 comments
Fazlik995 commented
Hi,
Thank you for sharing your awesome work.
Do you know how to downlaod ready-to-use DHG-14/28 dataset, pre-loaded into a single pickle file dhg_data.pckl?
I donwloaded for shrec dataset, however in your repository you did not mention about DHG dataset
guillaumephd commented
Hi,
Thanks for your kind comment.
I am not the creator of the DHG-14/28 dataset and it looks like you should register on the dataset homepage before downloading the dataset: http://www-rech.telecom-lille.fr/DHGdataset/
I don't plan to host the data myself, however creating a pickle file with the pre-resized gestures is quite straightforward. Moreover, using the original author's data rather than a derived version is also a good practice.
Here is what you can do, on your computer on a fresh Google Colab Notebook. If you work inside a colab/notebook, enter a ! before shell commands (e.g. !mkdir dataset_shrec2017 instead of mkdir dataset_shrec2017) to call the shell. On the left panel of google colab there is a tab where you can see (and download) the files you have downloaded or created.
First, download and extract the dataset. In a shell:
# ---------------------------------------------------------
# Step 1. Download hand gesture datasets
# ---------------------------------------------------------
# --------------------------
# SHREC2017 dataset
# http://www-rech.telecom-lille.fr/shrec2017-hand/
# --------------------------
mkdir dataset_shrec2017
wget http://www-rech.telecom-lille.fr/shrec2017-hand/HandGestureDataset_SHREC2017.tar.gz -O SHREC2017.tar.gz
tar -xzf SHREC2017.tar.gz -C dataset_shrec2017
# --------------------------
# DHG14/28 dataset
# http://www-rech.telecom-lille.fr/DHGdataset/
# --------------------------
# Note: you should register on http://www-rech.telecom-lille.fr/DHGdataset/ before downloading the dataset
mkdir dataset_dhg1428
wget http://www-rech.telecom-lille.fr/DHGdataset/DHG2016.zip
unzip DHG2016.zip -d dataset_dhg1428
# --------------------------
# Online DHG dataset
# http://www-rech.telecom-lille.fr/shrec2017-hand/
# --------------------------
mkdir dataset_onlinedhg
wget http://www-rech.telecom-lille.fr/shrec2017-hand/OnlineDHG.zip
unzip OnlineDHG.zip -d dataset_onlinedhgSecond, in a jupyter notebook, a google colab or a python file, you can read the data like this:
# ---------------------------------------------------------
# Step 2. Utils
# ---------------------------------------------------------
import glob
import numpy
import pickle
from scipy import ndimage as ndimage
from sklearn.model_selection import train_test_split
def resize_gestures(input_gestures, final_length=100):
"""
Resize the time series by interpolating them to the same length
Input:
- input_gestures: list of numpy.ndarray tensors.
Each tensor represents a single gesture.
Gestures can have variable durations.
Each tensor has a shape: (duration, channels)
where duration is the duration of the individual gesture
channels = 44 = 2 * 22 if recorded in 2D and
channels = 66 = 3 * 22 if recorded in 3D
Output:
- output_gestures: one numpy.ndarray tensor.
The output tensor has a shape: (records, final_length, channels)
where records = len(input_gestures)
final_length is the common duration of all gestures
channels is the same as above
"""
# please use python3. if you still use python2, important note: redefine the classic division operator / by importing it from the __future__ module
output_gestures = numpy.array([numpy.array([ndimage.zoom(x_i.T[j], final_length / len(x_i), mode='reflect') for j in range(numpy.size(x_i, 1))]).T for x_i in input_gestures])
return output_gestures
def load_gestures(dataset='dhg', root='./dataset_dhg1428', version_x='3D', version_y='both', resize_gesture_to_length=100):
"""
Get the 3D or 2D pose gestures sequences, and their associated labels.
Ouput:
- a tuple of (gestures, labels) or (gestures, labels_14, labels_28)
where gestures is either a numpy.ndarray tensor or
a list of numpy.ndarray tensors,
depending on if the gestures have been resized or not.
Each tensor represents a single gesture.
Gestures can have variable durations.
Each tensor has a shape: (duration, channels) where channels is either 44 (= 2 * 22) or 66 (=3 * 22)
"""
# SHREC 2017 (on Google Colab):
# root = '/content/dataset_shrec2017/HandGestureDataset_SHREC2017'
# DHG 14/28 (on Google Colab):
# root = '/content/dataset_dhg1428'
if dataset == 'dhg':
assert 'dataset_dhg' in root
if dataset == 'shrec':
assert 'dataset_shrec' in root
if version_x == '3D':
if dataset == 'dhg':
pattern = root + '/gesture_*/finger_*/subject_*/essai_*/skeleton_world.txt'
elif dataset == 'shrec':
pattern = root + '/gesture_*/finger_*/subject_*/essai_*/skeletons_world.txt'
else:
if dataset == 'dhg':
pattern = root + '/gesture_*/finger_*/subject_*/essai_*/skeleton_image.txt'
elif dataset == 'shrec':
pattern = root + '/gesture_*/finger_*/subject_*/essai_*/skeletons_image.txt'
gestures_filenames = sorted(glob.glob(pattern))
gestures = [numpy.genfromtxt(f) for f in gestures_filenames]
if resize_gesture_to_length is not None:
gestures = resize_gestures(gestures, final_length=resize_gesture_to_length)
labels_14 = [int(filename.split('/')[-5].split('_')[1]) for filename in gestures_filenames]
labels_28 = [int(filename.split('/')[-4].split('_')[1]) for filename in gestures_filenames]
labels_28 = [labels_14[idx_gesture] if n_fingers_used == 1 else 14 + labels_14[idx_gesture] for idx_gesture, n_fingers_used in enumerate(labels_28)]
if version_y == '14' or version_y == 14:
return gestures, labels_14
elif version_y == '28' or version_y == 28:
return gestures, labels_28
elif version_y == 'both':
return gestures, labels_14, labels_28
def write_data(data, filepath):
"""Save the dataset to a file. Note: data is a dict with keys 'x_train', ..."""
with open(filepath, 'wb') as output_file:
pickle.dump(data, output_file)
def load_data(filepath='./shrec_data.pckl'):
"""
Returns hand gesture sequences (X) and their associated labels (Y).
Each sequence has two different labels.
The first label Y describes the gesture class out of 14 possible gestures (e.g. swiping your hand to the right).
The second label Y describes the gesture class out of 28 possible gestures (e.g. swiping your hand to the right with your index pointed, or not pointed).
"""
file = open(filepath, 'rb')
data = pickle.load(file, encoding='latin1') # <<---- change to 'latin1' to 'utf8' if the data does not load
file.close()
return data['x_train'], data['x_test'], data['y_train_14'], data['y_train_28'], data['y_test_14'], data['y_test_28']Third, generate the dhg_data.pckl you want:
# ---------------------------------------------------------
# Step 3. Save the dataset(s) you need
# ---------------------------------------------------------
# Example A: 2D version of the SHREC gestures, untouched, and only the 14-label version of the labels
# x_2d_shrec, y_shrec_14 = load_gestures(dataset='shrec',
# root='/tmp/dataset_shrec2017/HandGestureDataset_SHREC2017/',
# version_x='2D',
# version_y='14',
# resize_gesture_to_length=None)
# Example B: 3D version of the DHG gestures, resized to 100 timesteps
gestures, labels_14, labels_28 = load_gestures(dataset='dhg',
root='/tmp/dataset_dhg1428/',
version_x='3D',
version_y='both',
resize_gesture_to_length=100)
# Split the dataset into train and test sets if you want:
x_train, x_test, y_train_14, y_train_28, y_test_14, y_test_28 = train_test_split(gestures, labels_14, labels_28, test_size=0.30)
# Save the dataset
data = {
'x_train': x_train,
'x_test': x_test,
'y_train_14': y_train_14,
'y_train_28': y_train_28,
'y_test_14': y_test_14,
'y_test_28': y_test_28
}
write_data(data, filepath='dhg_data.pckl')You can now use the dataset you created:
# ---------------------------------------------------------
# Step 4. Use the dataset(s)
# ---------------------------------------------------------
x_train, x_test, y_train_14, y_train_28, y_test_14, y_test_28 = load_data('dhg_data.pckl')This should likely do the work. If you discover any error (e.g. regarding the labels, the filenames, and so on) feel free to open the issue again.
Best regards
Prothoma2001 commented
Hello.
Can I use the same code for a different gesture dataset? How can I use it?
The dataset I want to use is in my drive.