معرفی شرکت ها
camera-z-transition-1.0
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
This package provides quick and easy way to estimate the camera transition on the z axis given an optical flow.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | camera-z-transition-1.0 |
| نام | camera-z-transition |
| نسخه کتابخانه | 1.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Tamas Suveges |
| ایمیل نویسنده | stamas01@gmail.com |
| آدرس صفحه اصلی | https://github.com/stamas02/camera_z_transition |
| آدرس اینترنتی | https://pypi.org/project/camera-z-transition/ |
| مجوز | MIT |
# camera_z_transition
#### Description
This is a small python package to estimate the camera motion/zoom
on the z axis.
what it is:
- Estimates the camera motion/zoom on the z axis
what it is NOT:
- It does not calcualte optical flow (although you can see example of that in
the example.py)
- It is not a metric estimation. It will not tell you the exact amount
of transition on the z axis in meter/feet
- It does not estimate any other camera motion such az tilling, panning,
tracking etc.
#### Install
```
pip install cam-motion-field
```
#### Usage
```python
from camera_z_transition import estimate_z_transition
import numpy as np
# This is the width and height of the image on which you perform the
# optical flow calculation
width = 1080
height = 720
# You should change this to some real optical flow calculation! see example
# in example.py. Both origins and displacements should be a list of 2D vectors.
origins = np.ones((10,2))*(1080//2)+10
displacements = np.ones((10,2))*(1080//2)+20
# Normalize and scale the optical flow first!
origins[:, 0] = (origins[:, 0] / width) - 0.5
origins[:, 1] = (origins[:, 1] / height) - 0.5
displacements[:, 0] = (displacements[:, 0] / width) - 0.5
displacements[:, 1] = (displacements[:, 1] / height) - 0.5
# Estimate transition on the z axis
z = estimate_z_transition(origins, displacements)
print(z)
```
Parameters for estimate_z_transition()
----------
origins: numpy array,
Set of origin vectors. Shape must be (nr_of_points, 2). MUST BE ZERO CENTERED AND SCALED!
displacements: numpy array,
Set of displacement vectors (coordinates). Shape must be (nr_of_points, 2)
focal_length
#### How it works
Given any point in the image plane (X,Y) we can estimate their new position
on the image plane (X',Y') given a transition on the x axis [[1]](#1).
<img src="https://render.githubusercontent.com/render/math?math=X' = f[tan^{-1}\frac{X}{f}](1+\frac{X^2}{f^2})\beta">
<br>
<img src="https://render.githubusercontent.com/render/math?math=Y' = f[tan^{-1}\frac{Y}{f}](1+\frac{Y^2}{f^2})\beta">
Where parameter X and Y are coordinates on the image plane
and <img src="https://render.githubusercontent.com/render/math?math=\beta">
is the transition on the z axis. X' and Y' are the new coordinates
on the image plane given the transition parameter.
Notice that both equations require a parameter *f* which is the focal lenght
if the camera. Interestingly it does not seem to have a noticeable effect on the
output. It seams that until *f* and (X,Y) has sensible values the
exact value of *f* does not matter. It is important that I do not have any
mathematical proof on this. It is purely come from visually observing
the behaviour of the function with different *f* and (X,Y). You can have a
look here: [link to Plot](https://www.desmos.com/calculator/hpo4u5xdkb)
Given an observed optical flow it is easy to perform a parameter search
for <img src="https://render.githubusercontent.com/render/math?math=\beta">
using the equations above.
#### Results
Given two consecutive images from a camera:
<img src="https://github.com/stamas02/camera_z_transition/blob/master/data/image_anim.gif" width="400"/>
We first obtain the optical flow using opencv
<img src="https://github.com/stamas02/camera_z_transition/blob/master/data/optical_flow.jpg" width="400"/>
The estimated <img src="https://render.githubusercontent.com/render/math?math=\beta">
parameter value for the images above is 0.053796382332247594
The gif below shows the original optical flow and the one artificially
generated using the estimated <img src="https://render.githubusercontent.com/render/math?math=\beta">
parameter.
<img src="https://github.com/stamas02/camera_z_transition/blob/master/data/op_flow_anim.gif" width="400"/>
#### References
<a id="1">[1]</a>
Srinivasan, M.V., Venkatesh, S., Hosie, R.: Qualitative estimation of camera motion
parameters from video sequences. Pattern Recognition 30(4), 593–606 (1997)
#### Bib
@article{Srinivasan at al.,
title={Qualitative estimation of camera motion parameters from video sequences},
author={Srinivasan, Mandyam V and Venkatesh, Svetha and Hosie, Robin},
journal={Pattern Recognition},
volume={30},
number={4},
pages={593--606},
year={1997},
publisher={Elsevier}
}
نیازمندی
| مقدار | نام |
|---|---|
| - | scipy |
| - | numpy |
نحوه نصب
نصب پکیج whl camera-z-transition-1.0:
pip install camera-z-transition-1.0.whl
نصب پکیج tar.gz camera-z-transition-1.0:
pip install camera-z-transition-1.0.tar.gz