Search and filter videos based on objects that appear in them using convolutional neural networks

Overview

Thingscoop: Utility for searching and filtering videos based on their content

Description

Thingscoop is a command-line utility for analyzing videos semantically - that means searching, filtering, and describing videos based on objects, places, and other things that appear in them.

When you first run thingscoop on a video file, it uses a convolutional neural network to create an "index" of what's contained in the every second of the input by repeatedly performing image classification on a frame-by-frame basis. Once an index for a video file has been created, you can search (i.e. get the start and end times of the regions in the video matching the query) and filter (i.e. create a supercut of the matching regions) the input using arbitrary queries. Thingscoop uses a very basic query language that lets you to compose queries that test for the presence or absence of labels with the logical operators ! (not), || (or) and && (and). For example, to search a video the presence of the sky and the absence of the ocean: thingscoop search 'sky && !ocean' <file>.

Right now two models are supported by thingscoop: vgg_imagenet uses the architecture described in "Very Deep Convolutional Networks for Large-Scale Image Recognition" to recognize objects from the ImageNet database, and googlenet_places uses the architecture described in "Going Deeper with Convolutions" to recognize settings and places from the MIT Places database. You can specify which model you'd like to use by running thingscoop models use <model>, where <model> is either vgg_imagenet or googlenet_places. More models will be added soon.

Thingscoop is based on Caffe, an open-source deep learning framework.

Installation

  1. Install ffmpeg, imagemagick, and ghostscript: brew install ffmpeg imagemagick ghostscript (Mac OS X) or apt-get install ffmpeg imagemagick ghostscript (Ubuntu).
  2. Follow the installation instructions on the Caffe Installation page.
  3. Make sure you build the Python bindings by running make pycaffe (on Caffe's directory).
  4. Set the environment variable CAFFE_ROOT to point to Caffe's directory: export CAFFE_ROOT=[Caffe's directory].
  5. Install thingscoop: easy_install thingscoop or pip install thingscoop.

Usage

thingscoop search <query> <files...>

Print the start and end times (in seconds) of the regions in <files> that match <query>. Creates an index for <file> using the current model if it does not exist.

Example output:

$ thingscoop search violin waking_life.mp4
/Users/anastasis/Downloads/waking_life.mp4 148.000000 162.000000
/Users/anastasis/Downloads/waking_life.mp4 176.000000 179.000000
/Users/anastasis/Downloads/waking_life.mp4 180.000000 186.000000
/Users/anastasis/Downloads/waking_life.mp4 189.000000 190.000000
/Users/anastasis/Downloads/waking_life.mp4 192.000000 200.000000
/Users/anastasis/Downloads/waking_life.mp4 211.000000 212.000000
/Users/anastasis/Downloads/waking_life.mp4 222.000000 223.000000
/Users/anastasis/Downloads/waking_life.mp4 235.000000 243.000000
/Users/anastasis/Downloads/waking_life.mp4 247.000000 249.000000
/Users/anastasis/Downloads/waking_life.mp4 251.000000 253.000000
/Users/anastasis/Downloads/waking_life.mp4 254.000000 258.000000

####thingscoop filter <query> <files...>

Generate a video compilation of the regions in the <files> that match <query>. Creates index for <file> using the current model if it does not exist.

Example output:

thingscoop sort <file>

Create a compilation video showing examples for every label recognized in the video (in alphabetic order). Creates an index for <file> using the current model if it does not exist.

Example output:

thingscoop describe <file>

Print every label that appears in <file> along with the number of times it appears. Creates an index for <file> using the current model if it does not exist.

thingscoop preview <file>

Create a window that plays the input video <file> while also displaying the labels the model recognizes on every frame.

$ thingscoop describe koyaanisqatsi.mp4 -m googlenet_places
sky 405
skyscraper 363
canyon 141
office_building 130
highway 78
lighthouse 66
hospital 64
desert 59
shower 49
volcano 45
underwater 44
airport_terminal 43
fountain 39
runway 36
assembly_line 35
aquarium 34
fire_escape 34
music_studio 32
bar 28
amusement_park 28
stage 26
wheat_field 25
butchers_shop 25
engine_room 24
slum 20
butte 20
igloo 20
...etc

thingscoop index <file>

Create an index for <file> using the current model if it does not exist.

thingscoop models list

List all models currently available in Thingscoop.

$ thingscoop models list
googlenet_imagenet            Model described in the paper "Going Deeper with Convolutions" trained on the ImageNet database
googlenet_places              Model described in the paper "Going Deeper with Convolutions" trained on the MIT Places database
vgg_imagenet                  16-layer model described in the paper "Return of the Devil in the Details: Delving Deep into Convolutional Nets" trained on the ImageNet database

thingscoop models info <model>

Print more detailed information about <model>.

$ thingscoop models info googlenet_places
Name: googlenet_places
Description: Model described in the paper "Going Deeper with Convolutions" trained on the MIT Places database
Dataset: MIT Places

thingscoop models freeze

List all models that have already been downloaded.

$ thingscoop models freeze
googlenet_places
vgg_imagenet

thingscoop models current

Print the model that is currently in use.

$ thingscoop models current
googlenet_places

thingscoop models use <model>

Set the current model to <model>. Downloads that model locally if it hasn't been downloaded already.

thingscoop models download <model>

Download the model <model> locally.

thingscoop models remove <model>

Remove the model <model> locally.

thingscoop models clear

Remove all models stored locally.

thingscoop labels list

Print all the labels used by the current model.

$ thingscoop labels list
abacus
abaya
abstraction
academic gown
accessory
accordion
acorn
acorn squash
acoustic guitar
act
actinic radiation
action
activity
adhesive bandage
adjudicator
administrative district
admiral
adornment
adventurer
advocate
...

thingscoop labels search <regexp>

Print all the labels supported by the current model that match the regular expression <regexp>.

$ thingscoop labels search instrument$
beating-reed instrument
bowed stringed instrument
double-reed instrument
free-reed instrument
instrument
keyboard instrument
measuring instrument
medical instrument
musical instrument
navigational instrument
negotiable instrument
optical instrument
percussion instrument
scientific instrument
stringed instrument
surveying instrument
wind instrument
...

Full usage options

thingscoop - Command-line utility for searching and filtering videos based on their content

Usage:
  thingscoop filter <query> <files>... [-o <output_path>] [-m <model>] [-s <sr>] [-c <mc>] [--recreate-index] [--gpu-mode] [--open]
  thingscoop search <query> <files>... [-o <output_path>] [-m <model>] [-s <sr>] [-c <mc>] [--recreate-index] [--gpu-mode] 
  thingscoop describe <file> [-n <words>] [-m <model>] [--recreate-index] [--gpu-mode] [-c <mc>]
  thingscoop index <files> [-m <model>] [-s <sr>] [-c <mc>] [-r <ocr>] [--recreate-index] [--gpu-mode] 
  thingscoop sort <file> [-m <model>] [--gpu-mode] [--min-confidence <ct>] [--max-section-length <ms>] [-i <ignore>] [--open]
  thingscoop preview <file> [-m <model>] [--gpu-mode] [--min-confidence <ct>]
  thingscoop labels list [-m <model>]
  thingscoop labels search <regexp> [-m <model>]
  thingscoop models list
  thingscoop models info <model>
  thingscoop models freeze
  thingscoop models current
  thingscoop models use <model>
  thingscoop models download <model>
  thingscoop models remove <model>
  thingscoop models clear

Options:
  --version                       Show version.
  -h --help                       Show this screen.
  -o --output <dst>               Output file for supercut
  -s --sample-rate <sr>           How many frames to classify per second (default = 1)
  -c --min-confidence <mc>        Minimum prediction confidence required to consider a label (default depends on model)
  -m --model <model>              Model to use (use 'thingscoop models list' to see all available models)
  -n --number-of-words <words>    Number of words to describe the video with (default = 5)
  -t --max-section-length <ms>    Max number of seconds to show examples of a label in the sorted video (default = 5)
  -r --min-occurrences <ocr>      Minimum number of occurrences of a label in video required for it to be shown in the sorted video (default = 2)
  -i --ignore-labels <labels>     Labels to ignore when creating the sorted video video
  --title <title>                 Title to show at the beginning of the video (sort mode only)
  --gpu-mode                      Enable GPU mode
  --recreate-index                Recreate object index for file if it already exists
  --open                          Open filtered video after creating it (OS X only)

CHANGELOG

0.2 (8/16/2015)

  • Added sort option for creating a video compilation of all labels appearing in a video
  • Now using JSON for the index files

0.1 (8/5/2015)

  • Conception

License

MIT

You might also like...
Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks

Spontaneous Facial Micro Expression Recognition using 3D Spatio-Temporal Convolutional Neural Networks Abstract Facial expression recognition in video

A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks
A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks

SVHNClassifier-PyTorch A PyTorch implementation of Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks If

Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors
Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors

-IEEE-TIM-2021-1-Shallow-CNN-for-HAR [IEEE TIM 2021-1] Shallow Convolutional Neural Networks for Human Activity Recognition using Wearable Sensors All

Densely Connected Search Space for More Flexible Neural Architecture Search (CVPR2020)
Densely Connected Search Space for More Flexible Neural Architecture Search (CVPR2020)

DenseNAS The code of the CVPR2020 paper Densely Connected Search Space for More Flexible Neural Architecture Search. Neural architecture search (NAS)

An implementation of paper `Real-time Convolutional Neural Networks for Emotion and Gender Classification` with PaddlePaddle.
An implementation of paper `Real-time Convolutional Neural Networks for Emotion and Gender Classification` with PaddlePaddle.

简介 通过PaddlePaddle框架复现了论文 Real-time Convolutional Neural Networks for Emotion and Gender Classification 中提出的两个模型,分别是SimpleCNN和MiniXception。利用 imdb_crop

PyTorch framework, for reproducing experiments from the paper Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks
PyTorch framework, for reproducing experiments from the paper Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks

Implicit Regularization in Hierarchical Tensor Factorization and Deep Convolutional Neural Networks. Code, based on the PyTorch framework, for reprodu

The official implementation of the CVPR2021 paper: Decoupled Dynamic Filter Networks
The official implementation of the CVPR2021 paper: Decoupled Dynamic Filter Networks

Decoupled Dynamic Filter Networks This repo is the official implementation of CVPR2021 paper: "Decoupled Dynamic Filter Networks". Introduction DDF is

Global Filter Networks for Image Classification
Global Filter Networks for Image Classification

Global Filter Networks for Image Classification Created by Yongming Rao, Wenliang Zhao, Zheng Zhu, Jiwen Lu, Jie Zhou This repository contains PyTorch

Deep Text Search is an AI-powered multilingual text search and recommendation engine with state-of-the-art transformer-based multilingual text embedding (50+ languages).
Deep Text Search is an AI-powered multilingual text search and recommendation engine with state-of-the-art transformer-based multilingual text embedding (50+ languages).

Deep Text Search - AI Based Text Search & Recommendation System Deep Text Search is an AI-powered multilingual text search and recommendation engine w

Comments
  • Error in finding input video file

    Error in finding input video file

    Whenever I run thingscoop for any video input do I need to store that input file explicitly at some location,if yes what should be the location,currently I saved one video koyaanisqatsi.mp4 in thingscoop directory then after running thingscoop describe koyaanisqatsi.mp4 -m googlenet_places query in terminal I am getting following error:- preksha@preksha:~/thingscoop$ thingscoop describe high.mp4 -m googlenet_places Traceback (most recent call last): File "/usr/local/bin/thingscoop", line 52, in sys.exit(thingscoop.main(args)) File "/home/preksha/thingscoop/thingscoop/init.py", line 104, in main recreate_index=recreate_index File "/home/preksha/thingscoop/thingscoop/search.py", line 107, in label_videos recreate_index=recreate_index File "/home/preksha/thingscoop/thingscoop/search.py", line 80, in label_video temp_frame_dir, frames = extract_frames(filename, sample_rate=sample_rate) File "/home/preksha/thingscoop/thingscoop/utils.py", line 39, in extract_frames subprocess.check_output(["ffmpeg", "-i", filename, "-vf", "fps="+str(sample_rate), dest]) File "/usr/lib/python2.7/subprocess.py", line 566, in check_output process = Popen(stdout=PIPE, _popenargs, *_kwargs) File "/usr/lib/python2.7/subprocess.py", line 710, in init errread, errwrite) File "/usr/lib/python2.7/subprocess.py", line 1327, in _execute_child raise child_exception OSError: [Errno 2] No such file or directory

    Kindly provide me guidance for solving this error.

    opened by preksha12 4
  • Error when running pip install thingscoop

    Error when running pip install thingscoop

    This is the output when running pip install thingscoop

    Collecting thingscoop
      Downloading https://files.pythonhosted.org/packages/f3/53/33d5edb3c029aa2b8e599dcefc6ab7a8bad7b21c982159b2abdd4633ce92/thingscoop-0.2.tar.gz
    Collecting pyPEG2>=2.15.1 (from thingscoop)
      Downloading https://files.pythonhosted.org/packages/f9/bd/10398e2c2d2070cc8a9c7153abfbd4ddb2895a2c52a32722ab8689e0cc7d/pyPEG2-2.15.2.tar.gz (40kB)
    Requirement already satisfied: requests>=2.7.0 in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from thingscoop) (2.18.4)
    Requirement already satisfied: moviepy>=0.2.2.11 in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from thingscoop) (0.2.3.5)
    Collecting docopt>=0.6.2 (from thingscoop)
      Downloading https://files.pythonhosted.org/packages/a2/55/8f8cab2afd404cf578136ef2cc5dfb50baa1761b68c9da1fb1e4eed343c9/docopt-0.6.2.tar.gz
    Requirement already satisfied: progressbar>=2.3 in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from thingscoop) (2.5)
    Requirement already satisfied: numpy>=1.9.2 in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from thingscoop) (1.15.0)
    Collecting pattern>=2.6 (from thingscoop)
      Downloading https://files.pythonhosted.org/packages/1e/07/b0e61b6c818ed4b6145fe01d1c341223aa6cfbc3928538ad1f2b890924a3/Pattern-3.6.0.tar.gz (22.2MB)
    Requirement already satisfied: termcolor>=1.1.0 in c:\users\jmorales\appdata\roaming\python\python35\site-packages (from thingscoop) (1.1.0)
    Collecting urllib3<1.23,>=1.21.1 (from requests>=2.7.0->thingscoop)
      Using cached https://files.pythonhosted.org/packages/63/cb/6965947c13a94236f6d4b8223e21beb4d576dc72e8130bd7880f600839b8/urllib3-1.22-py2.py3-none-any.whl
    Requirement already satisfied: chardet<3.1.0,>=3.0.2 in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from requests>=2.7.0->thingscoop) (3.0.4)
    Requirement already satisfied: certifi>=2017.4.17 in c:\users\jmorales\appdata\roaming\python\python35\site-packages (from requests>=2.7.0->thingscoop) (2018.4.16)
    Collecting idna<2.7,>=2.5 (from requests>=2.7.0->thingscoop)
      Using cached https://files.pythonhosted.org/packages/27/cc/6dd9a3869f15c2edfab863b992838277279ce92663d334df9ecf5106f5c6/idna-2.6-py2.py3-none-any.whl
    Requirement already satisfied: tqdm<5.0,>=4.11.2 in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from moviepy>=0.2.2.11->thingscoop) (4.23.4)
    Requirement already satisfied: decorator<5.0,>=4.0.2 in c:\users\jmorales\appdata\roaming\python\python35\site-packages (from moviepy>=0.2.2.11->thingscoop) (4.3.0)
    Requirement already satisfied: imageio<3.0,>=2.1.2 in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from moviepy>=0.2.2.11->thingscoop) (2.2.0)
    Requirement already satisfied: future in c:\users\jmorales\appdata\roaming\python\python35\site-packages (from pattern>=2.6->thingscoop) (0.16.0)
    Collecting backports.csv (from pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/71/f7/5db9136de67021a6dce4eefbe50d46aa043e59ebb11c83d4ecfeb47b686e/backports.csv-1.0.6-py2.py3-none-any.whl
    Collecting mysqlclient (from pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/ec/fd/83329b9d3e14f7344d1cb31f128e6dbba70c5975c9e57896815dbb1988ad/mysqlclient-1.3.13.tar.gz (90kB)
    Requirement already satisfied: beautifulsoup4 in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from pattern>=2.6->thingscoop) (4.3.2)
    Requirement already satisfied: lxml in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from pattern>=2.6->thingscoop) (4.0.0)
    Collecting feedparser (from pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/91/d8/7d37fec71ff7c9dbcdd80d2b48bcdd86d6af502156fc93846fb0102cb2c4/feedparser-5.2.1.tar.bz2 (192kB)
    Collecting pdfminer.six (from pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/a5/4c/dfc462296ff431aa072c952560ee8e43304523b5e7be82e220fae72dc61c/pdfminer.six-20170720.tar.gz (12.0MB)
    Requirement already satisfied: scipy in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from pattern>=2.6->thingscoop) (1.0.0)
    Requirement already satisfied: nltk in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from pattern>=2.6->thingscoop) (3.2.5)
    Collecting python-docx (from pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/00/ed/dc8d859eb32980ccf0e5a9b1ab3311415baf55de208777d85826a7fb0b65/python-docx-0.8.7.tar.gz (5.4MB)
    Collecting cherrypy (from pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/0c/20/e239c3afdeed57203cef5a5bf326234d9e17b449023ea504cd8ef1555833/CherryPy-17.3.0-py2.py3-none-any.whl (421kB)
    Requirement already satisfied: pillow in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from imageio<3.0,>=2.1.2->moviepy>=0.2.2.11->thingscoop) (5.2.0)
    Requirement already satisfied: six in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from pdfminer.six->pattern>=2.6->thingscoop) (1.11.0)
    Collecting pycryptodome (from pdfminer.six->pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/fe/ca/470bc3edb5b9c54391ca689f7922f3af01cb1c20e197b4789d5a3e7c74b2/pycryptodome-3.6.6-cp35-cp35m-win_amd64.whl (7.9MB)
    Collecting pywin32; sys_platform == "win32" and python_version != "3.4" (from cherrypy->pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/73/48/42e18ab94651c28d21f626ce1b5feaf60e5fb2aae3d264138aa768532a5f/pywin32-223-cp35-cp35m-win_amd64.whl (9.0MB)
    Requirement already satisfied: contextlib2 in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from cherrypy->pattern>=2.6->thingscoop) (0.5.5)
    Collecting zc.lockfile (from cherrypy->pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/f5/fe/efb94907d8b2b81c3beab1bd628ff67e310d82816b94aa00b52062727ea9/zc.lockfile-1.3.0.tar.gz
    Requirement already satisfied: more-itertools in c:\users\jmorales\appdata\roaming\python\python35\site-packages (from cherrypy->pattern>=2.6->thingscoop) (4.2.0)
    Collecting cheroot>=6.2.4 (from cherrypy->pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/9b/57/c36a8edec77f1a19bf5fefc4b7bd4d7b36556c593992b4aa08202687e71d/cheroot-6.4.0-py2.py3-none-any.whl (68kB)
    Collecting portend>=2.1.1 (from cherrypy->pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/81/43/21afd5914b74d4271184ee76f4093b45aa6a580dc6627d72dfc33664c6ac/portend-2.3-py2.py3-none-any.whl
    Requirement already satisfied: setuptools in c:\users\jmorales\appdata\roaming\python\python35\site-packages (from zc.lockfile->cherrypy->pattern>=2.6->thingscoop) (39.1.0)
    Collecting backports.functools-lru-cache (from cheroot>=6.2.4->cherrypy->pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/03/8e/2424c0e65c4a066e28f539364deee49b6451f8fcd4f718fefa50cc3dcf48/backports.functools_lru_cache-1.5-py2.py3-none-any.whl
    Collecting tempora>=1.8 (from portend>=2.1.1->cherrypy->pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/d8/25/bbb1b0785e7fb2f6bf871fdce2b2039b3336076927501e0dad61781d0830/tempora-1.13-py2.py3-none-any.whl
    Collecting jaraco.functools>=1.20 (from tempora>=1.8->portend>=2.1.1->cherrypy->pattern>=2.6->thingscoop)
      Downloading https://files.pythonhosted.org/packages/c2/f3/ce1bd8d5434227d26c2c2e07a9f89229a6b81122437452871d7e67f0ec76/jaraco.functools-1.20-py2.py3-none-any.whl
    Requirement already satisfied: pytz in c:\users\jmorales\appdata\local\programs\python\python35\lib\site-packages (from tempora>=1.8->portend>=2.1.1->cherrypy->pattern>=2.6->thingscoop) (2015.7)
    Building wheels for collected packages: thingscoop, pyPEG2, docopt, pattern, mysqlclient, feedparser, pdfminer.six, python-docx, zc.lockfile
      Running setup.py bdist_wheel for thingscoop: started
      Running setup.py bdist_wheel for thingscoop: finished with status done
      Stored in directory: C:\Users\jmorales\AppData\Local\pip\Cache\wheels\a4\5f\b5\9a6afeeff00db2460518f46db75c3e369385704fb137ab2bca
      Running setup.py bdist_wheel for pyPEG2: started
      Running setup.py bdist_wheel for pyPEG2: finished with status done
      Stored in directory: C:\Users\jmorales\AppData\Local\pip\Cache\wheels\4a\57\58\03557d6f87c87f518b56561f8ee036d46554824e08832e06f3
      Running setup.py bdist_wheel for docopt: started
      Running setup.py bdist_wheel for docopt: finished with status done
      Stored in directory: C:\Users\jmorales\AppData\Local\pip\Cache\wheels\9b\04\dd\7daf4150b6d9b12949298737de9431a324d4b797ffd63f526e
      Running setup.py bdist_wheel for pattern: started
      Running setup.py bdist_wheel for pattern: finished with status done
      Stored in directory: C:\Users\jmorales\AppData\Local\pip\Cache\wheels\dc\9a\0e\5fb1a603ed4e3aa8722a88e9cf4a82da7d1b63e3d2cc34bee5
      Running setup.py bdist_wheel for mysqlclient: started
      Running setup.py bdist_wheel for mysqlclient: finished with status error
      Complete output from command c:\users\jmorales\appdata\local\programs\python\python35\python.exe -u -c "import setuptools, tokenize;__file__=C:\\Users\\jmorales\\AppData\\Local\\Temp\\pip-install-femg8cxb\\mysqlclient\\setup.py;f=getattr(tokenize, open, open)(__file__);code=f.read().replace(\r\n, \n);f.close();exec(compile(code, __file__, exec))" bdist_wheel -d C:\Users\jmorales\AppData\Local\Temp\pip-wheel-d6byf8yj --python-tag cp35:
      c:\users\jmorales\appdata\local\programs\python\python35\lib\distutils\dist.py:261: UserWarning: Unknown distribution option: long_description_content_type
        warnings.warn(msg)
      running bdist_wheel
      running build
      running build_py
      creating build
      creating build\lib.win-amd64-3.5
      copying _mysql_exceptions.py -> build\lib.win-amd64-3.5
      creating build\lib.win-amd64-3.5\MySQLdb
      copying MySQLdb\__init__.py -> build\lib.win-amd64-3.5\MySQLdb
      copying MySQLdb\compat.py -> build\lib.win-amd64-3.5\MySQLdb
      copying MySQLdb\connections.py -> build\lib.win-amd64-3.5\MySQLdb
      copying MySQLdb\converters.py -> build\lib.win-amd64-3.5\MySQLdb
      copying MySQLdb\cursors.py -> build\lib.win-amd64-3.5\MySQLdb
      copying MySQLdb\release.py -> build\lib.win-amd64-3.5\MySQLdb
      copying MySQLdb\times.py -> build\lib.win-amd64-3.5\MySQLdb
      creating build\lib.win-amd64-3.5\MySQLdb\constants
      copying MySQLdb\constants\__init__.py -> build\lib.win-amd64-3.5\MySQLdb\constants
      copying MySQLdb\constants\CLIENT.py -> build\lib.win-amd64-3.5\MySQLdb\constants
      copying MySQLdb\constants\CR.py -> build\lib.win-amd64-3.5\MySQLdb\constants
      copying MySQLdb\constants\ER.py -> build\lib.win-amd64-3.5\MySQLdb\constants
      copying MySQLdb\constants\FIELD_TYPE.py -> build\lib.win-amd64-3.5\MySQLdb\constants
      copying MySQLdb\constants\FLAG.py -> build\lib.win-amd64-3.5\MySQLdb\constants
      copying MySQLdb\constants\REFRESH.py -> build\lib.win-amd64-3.5\MySQLdb\constants
      running build_ext
      building _mysql extension
      creating build\temp.win-amd64-3.5
      creating build\temp.win-amd64-3.5\Release
      C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\x86_amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Dversion_info=(1,3,13,final,0) -D__version__=1.3.13 "-IC:\Program Files (x86)\MySQL\MySQL Connector C 6.1\include" -Ic:\users\jmorales\appdata\local\programs\python\python35\include -Ic:\users\jmorales\appdata\local\programs\python\python35\include "-IC:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10240.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tc_mysql.c /Fobuild\temp.win-amd64-3.5\Release\_mysql.obj /Zl
      _mysql.c
      _mysql.c(29): fatal error C1083: Cannot open include file: mysql.h: No such file or directory
      error: command C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\BIN\\x86_amd64\\cl.exe failed with exit status 2
    
      ----------------------------------------
      Failed building wheel for mysqlclient
      Running setup.py clean for mysqlclient
      Running setup.py bdist_wheel for feedparser: started
      Running setup.py bdist_wheel for feedparser: finished with status done
      Stored in directory: C:\Users\jmorales\AppData\Local\pip\Cache\wheels\8c\69\b7\f52763c41c5471df57703a0ef718a32a5e81ee35dcf6d4f97f
      Running setup.py bdist_wheel for pdfminer.six: started
      Running setup.py bdist_wheel for pdfminer.six: finished with status done
      Stored in directory: C:\Users\jmorales\AppData\Local\pip\Cache\wheels\86\02\3a\b283a32dc7d89b140de8580ec12dff081d84052decb5c9c6c6
      Running setup.py bdist_wheel for python-docx: started
      Running setup.py bdist_wheel for python-docx: finished with status done
      Stored in directory: C:\Users\jmorales\AppData\Local\pip\Cache\wheels\c2\c7\a7\4cb54d79bf82f9bb4be1780647700f7ec311f0f98a7a017c17
      Running setup.py bdist_wheel for zc.lockfile: started
      Running setup.py bdist_wheel for zc.lockfile: finished with status done
      Stored in directory: C:\Users\jmorales\AppData\Local\pip\Cache\wheels\73\ba\20\9f4fc6a70cf61b6e9ec1a3f7a421a4226afa016509c8ca07d2
    Successfully built thingscoop pyPEG2 docopt pattern feedparser pdfminer.six python-docx zc.lockfile
    Failed to build mysqlclient
    py-webdriver-manager 0.0.1 has requirement tqdm==4.23.1, but youll have tqdm 4.23.4 which is incompatible.
    httpie 0.9.9 has requirement Pygments>=2.1.3, but youll have pygments 1.6 which is incompatible.
    Installing collected packages: pyPEG2, docopt, backports.csv, mysqlclient, feedparser, pycryptodome, pdfminer.six, python-docx, pywin32, zc.lockfile, backports.functools-lru-cache, cheroot, jaraco.functools, tempora, portend, cherrypy, pattern, thingscoop, urllib3, idna
      Found existing installation: docopt 0.6.1
        Uninstalling docopt-0.6.1:
          Successfully uninstalled docopt-0.6.1
      Running setup.py install for mysqlclient: started
        Running setup.py install for mysqlclient: finished with status error
        Complete output from command c:\users\jmorales\appdata\local\programs\python\python35\python.exe -u -c "import setuptools, tokenize;__file__=C:\\Users\\jmorales\\AppData\\Local\\Temp\\pip-install-femg8cxb\\mysqlclient\\setup.py;f=getattr(tokenize, open, open)(__file__);code=f.read().replace(\r\n, \n);f.close();exec(compile(code, __file__, exec))" install --record C:\Users\jmorales\AppData\Local\Temp\pip-record-l6e1v6vd\install-record.txt --single-version-externally-managed --compile:
        c:\users\jmorales\appdata\local\programs\python\python35\lib\distutils\dist.py:261: UserWarning: Unknown distribution option: long_description_content_type
          warnings.warn(msg)
        running install
        running build
        running build_py
        creating build
        creating build\lib.win-amd64-3.5
        copying _mysql_exceptions.py -> build\lib.win-amd64-3.5
        creating build\lib.win-amd64-3.5\MySQLdb
        copying MySQLdb\__init__.py -> build\lib.win-amd64-3.5\MySQLdb
        copying MySQLdb\compat.py -> build\lib.win-amd64-3.5\MySQLdb
        copying MySQLdb\connections.py -> build\lib.win-amd64-3.5\MySQLdb
        copying MySQLdb\converters.py -> build\lib.win-amd64-3.5\MySQLdb
        copying MySQLdb\cursors.py -> build\lib.win-amd64-3.5\MySQLdb
        copying MySQLdb\release.py -> build\lib.win-amd64-3.5\MySQLdb
        copying MySQLdb\times.py -> build\lib.win-amd64-3.5\MySQLdb
        creating build\lib.win-amd64-3.5\MySQLdb\constants
        copying MySQLdb\constants\__init__.py -> build\lib.win-amd64-3.5\MySQLdb\constants
        copying MySQLdb\constants\CLIENT.py -> build\lib.win-amd64-3.5\MySQLdb\constants
        copying MySQLdb\constants\CR.py -> build\lib.win-amd64-3.5\MySQLdb\constants
        copying MySQLdb\constants\ER.py -> build\lib.win-amd64-3.5\MySQLdb\constants
        copying MySQLdb\constants\FIELD_TYPE.py -> build\lib.win-amd64-3.5\MySQLdb\constants
        copying MySQLdb\constants\FLAG.py -> build\lib.win-amd64-3.5\MySQLdb\constants
        copying MySQLdb\constants\REFRESH.py -> build\lib.win-amd64-3.5\MySQLdb\constants
        running build_ext
        building _mysql extension
        creating build\temp.win-amd64-3.5
        creating build\temp.win-amd64-3.5\Release
        C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\BIN\x86_amd64\cl.exe /c /nologo /Ox /W3 /GL /DNDEBUG /MD -Dversion_info=(1,3,13,final,0) -D__version__=1.3.13 "-IC:\Program Files (x86)\MySQL\MySQL Connector C 6.1\include" -Ic:\users\jmorales\appdata\local\programs\python\python35\include -Ic:\users\jmorales\appdata\local\programs\python\python35\include "-IC:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\INCLUDE" "-IC:\Program Files (x86)\Windows Kits\10\include\10.0.10240.0\ucrt" "-IC:\Program Files (x86)\Windows Kits\NETFXSDK\4.6.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\shared" "-IC:\Program Files (x86)\Windows Kits\8.1\include\um" "-IC:\Program Files (x86)\Windows Kits\8.1\include\winrt" /Tc_mysql.c /Fobuild\temp.win-amd64-3.5\Release\_mysql.obj /Zl
        _mysql.c
        _mysql.c(29): fatal error C1083: Cannot open include file: mysql.h: No such file or directory
        error: command C:\\Program Files (x86)\\Microsoft Visual Studio 14.0\\VC\\BIN\\x86_amd64\\cl.exe failed with exit status 2
    
        ----------------------------------------
    Command "c:\users\jmorales\appdata\local\programs\python\python35\python.exe -u -c "import setuptools, tokenize;__file__=C:\\Users\\jmorales\\AppData\\Local\\Temp\\pip-install-femg8cxb\\mysqlclient\\setup.py;f=getattr(tokenize, open, open)(__file__);code=f.read().replace(\r\n, \n);f.close();exec(compile(code, __file__, exec))" install --record C:\Users\jmorales\AppData\Local\Temp\pip-record-l6e1v6vd\install-record.txt --single-version-externally-managed --compile" failed with error code 1 in C:\Users\jmorales\AppData\Local\Temp\pip-install-femg8cxb\mysqlclient\
    

    This is my setup:

    Python version: 3.5 OS: Windows 10 Home System Type: 64-bit Operating System, x64-based processor Processor: AMD A10-8700P Radeon R6

    opened by jafetmorales 1
Owner
Anastasis Germanidis
🎭
Anastasis Germanidis
This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of Coordinate Independent Convolutional Networks.

Orientation independent Möbius CNNs This repository implements and evaluates convolutional networks on the Möbius strip as toy model instantiations of

Maurice Weiler 59 Dec 9, 2022
A python bot to move your mouse every few seconds to appear active on Skype, Teams or Zoom as you go AFK. 🐭 🤖

PyMouseBot If you're from GT and annoyed with SGVPN idle timeouts while working on development laptop, You might find this useful. A python cli bot to

Oaker Min 6 Oct 24, 2022
Differentiable architecture search for convolutional and recurrent networks

Differentiable Architecture Search Code accompanying the paper DARTS: Differentiable Architecture Search Hanxiao Liu, Karen Simonyan, Yiming Yang. arX

Hanxiao Liu 3.7k Jan 9, 2023
A DeepStack custom model for detecting common objects in dark/night images and videos.

DeepStack_ExDark This repository provides a custom DeepStack model that has been trained and can be used for creating a new object detection API for d

MOSES OLAFENWA 98 Dec 24, 2022
Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning

Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning This repository is official Tensorflow implementation of paper: Ensemb

Seunghyun Lee 12 Oct 18, 2022
Display, filter and search log messages in your terminal

Textualog Display, filter and search logging messages in the terminal. This project is powered by rich and textual. Some of the ideas and code in this

Rik Huygen 24 Dec 10, 2022
DeepHyper: Scalable Asynchronous Neural Architecture and Hyperparameter Search for Deep Neural Networks

What is DeepHyper? DeepHyper is a software package that uses learning, optimization, and parallel computing to automate the design and development of

DeepHyper Team 214 Jan 8, 2023
[ICCV2021] Learning to Track Objects from Unlabeled Videos

Unsupervised Single Object Tracking (USOT) ?? Learning to Track Objects from Unlabeled Videos Jilai Zheng, Chao Ma, Houwen Peng and Xiaokang Yang 2021

null 53 Dec 28, 2022
Learning and Building Convolutional Neural Networks using PyTorch

Image Classification Using Deep Learning Learning and Building Convolutional Neural Networks using PyTorch. Models, selected are based on number of ci

Mayur 126 Dec 22, 2022
PyTorch implementation of convolutional neural networks-based text-to-speech synthesis models

Deepvoice3_pytorch PyTorch implementation of convolutional networks-based text-to-speech synthesis models: arXiv:1710.07654: Deep Voice 3: Scaling Tex

Ryuichi Yamamoto 1.8k Jan 8, 2023