def get(self):
from tf_pose.estimator import TfPoseEstimator
from tf_pose.networks import get_graph_path
from skvideo.io import vread
import sldatasets as sld
dataset = sld.get("boston").data
data = dataset.__next__()[0]
data = vread()
n, h, w, _c = data.shape
e = TfPoseEstimator(get_graph_path('cmu'), target_size=(w, h))
for j in range(n):
img = data[j:]
humans = e.inference(img, True, 4.0)
image = TfPoseEstimator.draw_humans(img, humans, imgcopy=False)
from tf_pose.estimator import TfPoseEstimator
from tf_pose.networks import get_graph_path
from matplotlib import pyplot as plt
import sldatasets as sld
dataset = sld.get("lsa64", version="cut", index=4)
fps = 30.0
data = dataset.__next__()[0]
e = TfPoseEstimator(get_graph_path('cmu'), target_size=(432, 368))
for tup in data:
img = tup[0]
humans = e.inference(img, True, 4.0)
image = TfPoseEstimator.draw_humans(img, humans, imgcopy=False)
plt.imshow(image)
plt.title(f'Frame {j}')
plt.pause(1.0 / fps)
import positions
import sldatasets as sld
from pathlib import Path
import os.path as osp
example_path = osp.join(Path.home(), '.sldatasets')
# must provide the path of 'lsa64_positions.mat', in this example
# ~/.sldatasets/LSA&$_pre
# a_path = osp.join(example_path, 'LSA64_pre')
# positions.positions_mat_to_npz(osp.join(example_path, 'LSA64_pre'))
# # can provide the destiny path of the npz else current dir will be used, in this example
# # ~/.sldatasets/LSA64_raw
a_path = osp.join(example_path, 'LSA64_raw')
positions.get_humans_from_dataset(sld.get('lsa64', version='raw').data, a_path)
a_path = osp.join(example_path, 'LSA64_cut')
positions.get_humans_from_dataset(sld.get('lsa64', version='cut').data, a_path)
a_path = osp.join(example_path, 'ASLLVD_pre')
positions.get_humans_from_dataset(sld.get('asllvd').data, a_path)
import sldatasets as sld
# in progress
video_dataset = sld.get('lsa64', version='raw')
print("test loading lsa64 raw npz positions file")
#npzfile = np.load(video_dataset.get_positions())
# print(npzfile.files)
print("Done")
import sldatasets as sld
dataset = sld.get("boston")
dataset.summary()
video, description = dataset.__next__()
print(video[0][0].shape)
print(description)
dataset = sld.get("lsa64", version="raw")
dataset.summary()
video, description = dataset.__next__()
print(video[0][0].shape)
print(description)
import sldatasets as sld
dataset = sld.get("asllvd", word='Twenty', consultant='liz')
print("dataset summary:")
dataset.summary()
for video, description in dataset:
print('video description:')
print(description)
frame, frame_annot = video[3]
print('frame shape: ', frame.shape)
print('estimated body parts (x,y,score): ')
# this method pretty print a Dictionary that returns the method frame_annot.get()
frame_annot.pretty()
face = frame_annot.h.get_face_box(640, 480)
print('face box: ', face)
print('upper body box: ', frame_annot.h.get_upper_body_box(640, 480))
print('\n testing lsa64...')
dataset = sld.get("lsa64", version='cut', word=25,
consultant=10, repetition=5)
dataset.summary()
for video, description in dataset:
print(description)
frame, frame_annot = video[3]
print('frame shape: ', frame.shape)
print('estimated body parts (x,y,score): ')
frame_annot.pretty()