Mediapipe to Signal

module for processing mediapipe's holistic model for sign languages.

The module allows extracting pure movement features by:

• It provides pose normalization in 3D space using body proportions
• It rotates the pose toward the camera
• Sign movement is presented in a series of signals (directional cosine angles per each joint)
• Sign movement is easily stored, modified, processed, and generated
• Sign movement can be processed as a signal or as an image
• It has a "human-readable" representation for quick analysis and intuition.
• Simplified data representation speeds up processing and reduces storage.

Converting videos to a series of signals that are easy to read, store, and generate:

Install

pip install mp2signal

Usage

importing the module:

import mp2signal.mp2s as mp2s

Using MediaPipe we process the video and save the normalized signal:

m = mp2s.Movement('path_to_your_signlanguage_video.mp4')

The joint signals can be plotted:

import matplotlib.pyplot as plt

human_redable_posegram = m.posegram(human=True)
print(human_redable_posegram.dtype, human_redable_posegram.shape)
plt.imshow(human_redable_posegram)
plt.show()

for processing, a two-dimensional version is available in uint8 and float:

machine_uint8_posegram = m.posegram(human=False,signal=False)
print(machine_uint8_posegram.dtype, machine_uint8_posegram.shape)
plt.imshow(machine_uint8_posegram,cmap='brg')
plt.show()

machine_float_posegram = m.posegram(human=False,signal=True)
print(machine_float_posegram.dtype, machine_float_posegram.shape)
plt.imshow(machine_float_posegram,cmap='brg')
plt.show()

Storing or Generating Movement Data

Sign Language movement is easy to store and generate in the signal form.

Movement() can be initialized with the stored or generated sample:

import numpy as np
np.save('mov_human.npy',human_redable_posegram)
np.save('mov_uin8.npy',machine_uint8_posegram)
np.save('mov_float.npy',machine_float_posegram)

human_redable = np.load('mov_human.npy')
machine_uint8 = np.load('mov_uin8.npy')
machine_float = np.load('mov_float.npy')

hum_restore = mp2s.Movement(human_redable)
hum_rest_gram = hum_restore.posegram(human=True)
print(hum_rest_gram.dtype)
plt.imshow(hum_rest_gram,cmap='brg')
plt.show()

uin8_restore = mp2s.Movement(machine_uint8)
uin8_rest_gram = uin8_restore.posegram(human=False,signal=False)
print(uin8_rest_gram.dtype)
plt.imshow(uin8_rest_gram,cmap='brg')
plt.show()

float_restore_float = mp2s.Movement(machine_float)
float_rest_gram = float_restore_float.posegram(human=False,signal=True)
print(float_rest_gram.dtype)
plt.imshow(float_rest_gram,cmap='brg')
plt.show()

Signals to Video

You can generate a video with your stored/generated movement signals:

video_out = uin8_restore.make_a_video('video_from_signal.mp4')

Usage with Camera

This script takes input from the web camera and shows rotated and normalized skeleton:

python examples/mp_live_rot_demo.py