樹莓派、樹莓派之學習、樹莓派之教育

樹莓派相機︰ PiCamera 《二》

然而程式整合常常卻沒那麼簡單︰

scikit-video

Installing scikit-video is easy!

$ sudo pip install sk-video

If you have any installation issues, consult the FAQ. If you still have trouble, please report the issue on github.

Scikit-video is designed for easy video processing using Python. It is modeled in the spirit of other successful scikits such as scikit-learn and scikit-image. The developers of scikit-video know libraries exist for manipulating videos, such as PyFFmpeg, MoviePy, PyAV, imageIO, and opencv. However, no libraries have been found to provide an all-in-one solution for research-level video processing tools.

This project aims to make video algorithms easy to access for students, engineers, instructors, and researchers. Choose a category below to start learning how to use scikit-video!

………

比方說添加

【派生科學程式庫安裝】

sudo apt-get install python-numpy
sudo apt-get install python-numpy
sudo apt-get install python-scipy
sudo apt-get install python3-scipy
sudo apt-get install python-skimage 
sudo apt-get install python3-skimage

【派生總變差程式庫安裝】

sudo apt-get install python-cffi python3-cffi
sudo apt-get install liblapacke-dev
sudo apt-get install libblas-dev
sudo apt-get install liblapack-dev
sudo pip install prox-tv
sudo pip3 install prox-tv

借著

proxTV

Matlab and Python toolbox for fast Total Variation proximity operators.

For an up-to-date version, check https://github.com/albarji/proxTV .

Introduction

proxTV is a toolbox implementing blazing fast implementations of Total Variation proximity operators. While the core algorithms are implemented in C to achieve high efficiency, Matlab and Python interfaces are provided for ease of use.

The library provides efficient solvers for the following Total Variation proximity problems:

Problem Formulation
Standard (l1) Total Variation on a 1-dimensional signal alt tag
Quadratic (l2) Total Variation on a 1-dimensional signal alt tag
lp-norm Total Variation on a 1-dimensional signal alt tag
Weighted Total Variation on a 1-dimensional signal alt tag
Anisotropic Total Variation on a 2-dimensional signal alt tag
lp-norm Anisotropic Total Variation on a 2-dimensional signal alt tag
Weighted Anisotropic Total Variation on a 2-dimensional signal alt tag
Anisotropic Total Variation on a 3-dimensional signal alt tag
Generalized N-dimensional Anisotropic Total Variation alt tag, with X(di) every possible 1-dimensional slice of X following dimension di.

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可以探索

Total variation

In mathematics, the total variation identifies several slightly different concepts, related to the (local or global) structure of the codomain of a function or a measure. For a real-valued continuous function f, defined on an interval [a, b] ⊂ ℝ, its total variation on the interval of definition is a measure of the one-dimensional arclength of the curve with parametric equation x ↦ f(x), for x ∈ [a, b].

Total_variation

As the green ball travels on the graph of the given function, the length of the path travelled by that ball’s projection on the y-axis, shown as a red ball, is the total variation of the function.

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之旨趣。甚或能得『雜訊處理』之門徑耶！！

───《W!o+ 的《小伶鼬工坊演義》︰神經網絡【轉折點】六》

 

利用

4. Advanced Recipes

The following recipes involve advanced techniques and may not be “beginner friendly”. Please feel free to suggest enhancements or additional recipes.

4.1. Capturing to a numpy array

Since 1.11, picamera can capture directly to any object which supports Python’s buffer protocol (including numpy’s ndarray). Simply pass the object as the destination of the capture and the image data will be written directly to the object. The target object must fulfil various requirements (some of which are dependent on the version of Python you are using):

  1. The buffer object must be writeable (e.g. you cannot capture to a bytes object as it is immutable).
  2. The buffer object must be large enough to receive all the image data.
  3. (Python 2.x only) The buffer object must be 1-dimensional.
  4. (Python 2.x only) The buffer object must have byte-sized items.

For example, to capture directly to a three-dimensional numpy ndarray (Python 3.x only):

import time
import picamera
import numpy as np

with picamera.PiCamera() as camera:
    camera.resolution = (320, 240)
    camera.framerate = 24
    time.sleep(2)
    output = np.empty((240, 320, 3), dtype=np.uint8)
    camera.capture(output, 'rgb')

It is also important to note that when outputting to unencoded formats, the camera rounds the requested resolution. The horizontal resolution is rounded up to the nearest multiple of 32 pixels, while the vertical resolution is rounded up to the nearest multiple of 16 pixels. For example, if the requested resolution is 100×100, the capture will actually contain 128×112 pixels worth of data, but pixels beyond 100×100 will be uninitialized.

So, to capture a 100×100 image we first need to provide a 128×112 array, then strip off the uninitialized pixels afterward. The following example demonstrates this along with the re-shaping necessary under Python 2.x:

import time
import picamera
import numpy as np

with picamera.PiCamera() as camera:
    camera.resolution = (100, 100)
    camera.framerate = 24
    time.sleep(2)
    output = np.empty((112 * 128 * 3,), dtype=np.uint8)
    camera.capture(output, 'rgb')
    output = output.reshape((112, 128, 3))
    output = output[:100, :100, :]
 Warning

Under certain circumstances (non-resized, non-YUV, video-port captures), the resolution is rounded to 16×16 blocks instead of 32×16. Adjust your resolution rounding accordingly.

New in version 1.11.

numpy array 溝通不同程式庫之間的資料轉換。

此事想必廚師早知道的吧☆