معرفی شرکت ها

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 بیشتر

توضیحات

Optical Flow video tools

ویژگی	مقدار
سیستم عامل	-
نام فایل	flowvid-0.4.1
نام	flowvid
نسخه کتابخانه	0.4.1
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Diego Royo
ایمیل نویسنده	740388@unizar.es
آدرس صفحه اصلی	https://github.com/diegoroyo/flowvid
آدرس اینترنتی	https://pypi.org/project/flowvid/
مجوز	-

<a href="https://github.com/diegoroyo/flowvid"> <img src="https://raw.githubusercontent.com/diegoroyo/flowvid/master/examples/images/logo_square.png" alt="Logo" width=250 height=250> </a> <a href="https://badge.fury.io/py/flowvid"><img src="https://badge.fury.io/py/flowvid.svg" alt="PyPI version" height="18"></a> <a href="https://pepy.tech/project/flowvid/month"><img src="https://pepy.tech/badge/flowvid/month" alt="PyPI monthly downloads" height="18"></a> <a href="https://github.com/diegoroyo/flowvid/issues"><img alt="GitHub issues" src="https://img.shields.io/github/issues/diegoroyo/flowvid" height="18"></a> Toolkit to generate customized visualizations related to optical flow <a href="https://pypi.org/project/flowvid/">PyPI page</a> · <a href="https://github.com/diegoroyo/flowvid/blob/master/README.md#installation">Installation</a> · <a href="https://github.com/diegoroyo/flowvid/blob/master/examples">Examples</a> · <a href="https://github.com/diegoroyo/flowvid/blob/master/flowvid/presets">Presets</a> <table align="center"> <tr> <td align="center"><img src="https://raw.githubusercontent.com/diegoroyo/flowvid/master/examples/images/color_flow.png" alt="color_flow result"></td> <td align="center"><img src="https://raw.githubusercontent.com/diegoroyo/flowvid/master/examples/images/color_epe.png" alt="color_epe result"></td> <td align="center"><img src="https://raw.githubusercontent.com/diegoroyo/flowvid/master/examples/images/flow_arrows_1.png" alt="flow_arrows result"></td> </tr> </table> ## Table of contents `flowvid` is a toolkit for all things related to optical flow. It comes with many visualization presets you can generate with no effort (see below), but it also allows for more complex data manipulation that doesn't have to imply generating a visualization (see [examples](https://github.com/diegoroyo/flowvid/blob/master/examples)). - [Installation](#installation) - [Quick start](#quick-start) - [Python library](#python-library) - [Acknowledgements](#acknowledgements) ## Installation `flowvid` is available as a PyPI package: ``` pip3 install flowvid ``` Or you can install it directly from Github: ``` pip3 install git+https://github.com/diegoroyo/flowvid/ ``` or you can clone the repo: ``` git clone https://github.com/diegoroyo/flowvid.git cd flowvid scripts/local_install.sh ``` You might need to install the following dependencies: ```bash pip3 install imageio imageio-ffmpeg numpy matplotlib Pillow # or if you use the provided requirements.txt pip3 install -r requirements.txt ``` ## Quick start Flowvid is a python library for video generation, but it also contains several video presets with an user-friendly assistant: * Usage: `python3 -m flowvid `_`<preset>`_ `[ <config-params> | --config <config-file> ]` * Presets can be listed using `python3 -m flowvid -h` ``` Preset can be one of: color_flow: Convert flow data to RGB using the Middlebury representation color_epe: Calculate endpoint error and generate a video representation flow_arrows: Draw arrows representing optical flow over a video plot_epe: Generate a pyplot plot with the EPE distribution in all frames track_points: Place points in a image and see how flow moves them track_side_by_side: Place points in a image and see how flow can track them ``` * Example: converting flow files to rgb and saving into a video ```bash $ python3 -m flowvid color_flow # \/ option names are shown here [--flo-dir] Flow files directory (default: flo): path/to/flo/dir [--norm-type] Vector normalization type (video, [frame], none): video [--norm-clamp] Normalization clamp percentage (default: 1.0): 0.8 [--norm-gamma] Normalization gamma curve exponent (default: 1.0): 1.5 [--output-type] Output type ([video], pyplot): pyplot [--output-framerate] Video framerate (default: 10): 10 ``` You can specify its parameters via the command line. The following is equivalent: ```bash # Option names can be listed with python3 -m flowvid --help $ python3 -m flowvid color_flow --flo-dir path/to/flo/dir --norm-type video --norm-clamp 0.8 --norm-gamma 1.5 --output-type pyplot --output-framerate 10 ``` Configuration can also be saved in a file so you don't have to type it always: ```bash # Store configuration after use $ python3 -m flowvid color_flow --flo-dir path/to/flo/dir (...) Save configuration in a file? (y, [n]): y Output configuration filename (default: preset_color_flow.yaml): path/to/config.yaml Saved configuration file in path/to/config.yaml (...) # Load from a file $ python3 -m flowvid color_flow --config path/to/config.yaml ``` <img src="https://raw.githubusercontent.com/diegoroyo/flowvid/master/examples/images/color_flow.png" alt="color_flow result"> Here are some examples illustrating the other presets' results: | **`color_epe`** | **`plot_epe`** | |---|---| | <img src="https://raw.githubusercontent.com/diegoroyo/flowvid/master/examples/images/color_epe.png" alt="color_epe result"> | <img src="https://raw.githubusercontent.com/diegoroyo/flowvid/master/examples/images/plot_epe.png" alt="plot_epe result"> | | **`track_points`** | **`track_side_by_side`** | |---|---| | <img src="https://raw.githubusercontent.com/diegoroyo/flowvid/master/examples/images/track_points.png" alt="track_points result"> | <img src="https://raw.githubusercontent.com/diegoroyo/flowvid/master/examples/images/track_side_by_side.png" alt="track_side_by_side result"> | | **`flow_arrows`** (1) | **`flow_arrows`** (2) | |---|---| | <img src="https://raw.githubusercontent.com/diegoroyo/flowvid/master/examples/images/flow_arrows_1.png" alt="flow_arrows result"> | <img src="https://raw.githubusercontent.com/diegoroyo/flowvid/master/examples/images/flow_arrows_2.png" alt="flow_arrows result"> | ### Python library If you want to generate more complex or customized videos, you can easily use `flowvid`'s tools: * You can check the [documentation and example usages here](https://github.com/diegoroyo/flowvid/blob/master/examples). * You can check the [source code for the given presets here](https://github.com/diegoroyo/flowvid/blob/master/flowvid/presets). ```python import flowvid as fv # Convert multiple optical flow files to their color representation, # and save them to a video (.mp4) file. # Similar to `color_flow` preset, without the extra options # Read flow files data flo_data = fv.input.flo('path/to/flo/dir') # Normalize each file (so max flow's module is 1), # necessary to convert to RGB flo_data = fv.normalize_frame(flo_data) rgb_data = fv.flow_to_rgb(flo_data) # Output as video out = fv.output.video(filename='output.mp4', framerate=24) out.add_all(rgb_data) ``` The given code's structure and information flows (`flo`/`rgb` data) can be represented with the following nodes: <img src="https://raw.githubusercontent.com/diegoroyo/flowvid/master/examples/images/diagram_example_color_flow.png" alt="color_flow code representation using nodes"> ## Acknowledgements * Logo made by Github user [Aeri (see profile)](https://github.com/aeri). * Flow to RGB conversion is based on [C. Liu's work](https://people.csail.mit.edu/celiu/OpticalFlow/). > C. Liu. Beyond Pixels: Exploring New Representations and Applications for Motion Analysis. Doctoral Thesis. Massachusetts Institute of Technology. May 2009.

زبان مورد نیاز

مقدار	نام
>=3.6	Python

نحوه نصب

نصب پکیج whl flowvid-0.4.1:

pip install flowvid-0.4.1.whl

نصب پکیج tar.gz flowvid-0.4.1:

pip install flowvid-0.4.1.tar.gz