updated packages, reformatting bits of the notebooks

This commit is contained in:
aj 2021-01-01 22:05:49 +00:00
parent 6ad5fab2a0
commit c7292cc00a
7 changed files with 323 additions and 297 deletions

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computer vision testing
computer vision
==================
Jupyter scratchpad for playing with computer vision
(Using [MSRCv2 Set](https://www.microsoft.com/en-us/research/project/image-understanding/))

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<h2><center>average RGB</center></h2>"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.4"
}
},
"nbformat": 4,
"nbformat_minor": 4
}

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@ -4,7 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3><center>arrays</center></h3>"
"# arrays"
]
},
{
@ -115,7 +115,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<h3><center>inserting</center></h3>"
"# inserting"
]
},
{

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@ -1,55 +1,78 @@
attrs==19.3.0
backcall==0.1.0
bleach==3.1.4
anyio==2.0.2
argon2-cffi==20.1.0
async-generator==1.10
attrs==20.3.0
Babel==2.9.0
backcall==0.2.0
bleach==3.2.1
certifi==2020.12.5
cffi==1.14.4
chardet==4.0.0
colorama==0.4.4
cycler==0.10.0
decorator==4.4.1
decorator==4.4.2
defusedxml==0.6.0
entrypoints==0.3
importlib-metadata==1.2.0
ipykernel==5.1.3
ipython==7.10.1
idna==2.10
importlib-metadata==3.3.0
ipykernel==5.4.2
ipython==7.19.0
ipython-genutils==0.2.0
jedi==0.15.1
Jinja2==2.10.3
joblib==0.14.0
json5==0.8.5
jedi==0.18.0
Jinja2==2.11.2
joblib==1.0.0
json5==0.9.5
jsonschema==3.2.0
jupyter-client==5.3.4
jupyter-core==4.6.1
jupyterlab==1.2.3
jupyterlab-server==1.0.6
kiwisolver==1.1.0
jupyter-client==6.1.7
jupyter-core==4.7.0
jupyter-server==1.1.3
jupyterlab==3.0.0
jupyterlab-pygments==0.1.2
jupyterlab-server==2.0.0
kiwisolver==1.3.1
MarkupSafe==1.1.1
matplotlib==3.1.2
matplotlib==3.3.3
mistune==0.8.4
more-itertools==8.0.2
nbconvert==5.6.1
nbformat==4.4.0
notebook==6.1.5
numpy==1.17.4
opencv-python==4.1.2.30
pandas==0.25.3
pandocfilters==1.4.2
parso==0.5.1
pexpect==4.7.0
more-itertools==8.6.0
nbclassic==0.2.5
nbclient==0.5.1
nbconvert==6.0.7
nbformat==5.0.8
nest-asyncio==1.4.3
notebook==6.1.6
numpy==1.19.4
opencv-python==4.4.0.46
packaging==20.8
pandas==1.2.0
pandocfilters==1.4.3
parso==0.8.1
pexpect==4.8.0
pickleshare==0.7.5
prometheus-client==0.7.1
prompt-toolkit==3.0.2
ptyprocess==0.6.0
Pygments==2.5.2
pyparsing==2.4.5
pyrsistent==0.15.6
Pillow==8.0.1
prometheus-client==0.9.0
prompt-toolkit==3.0.8
ptyprocess==0.7.0
pycparser==2.20
Pygments==2.7.3
pyparsing==2.4.7
pyrsistent==0.17.3
python-dateutil==2.8.1
pytz==2019.3
pyzmq==18.1.1
scikit-learn==0.22
scipy==1.3.3
pytz==2020.5
pywin32==300
pywinpty==0.5.7
pyzmq==20.0.0
requests==2.25.1
scikit-learn==0.24.0
scipy==1.6.0
Send2Trash==1.5.0
six==1.13.0
terminado==0.8.3
six==1.15.0
sniffio==1.2.0
terminado==0.9.1
testpath==0.4.4
tornado==6.0.3
traitlets==4.3.3
wcwidth==0.1.7
threadpoolctl==2.1.0
tornado==6.1
traitlets==5.0.5
urllib3==1.26.2
wcwidth==0.2.5
webencodings==0.5.1
zipp==0.6.0
zipp==3.4.0

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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"\n",
"import vision.io\n",
"from vision.visualsearch import run_query\n",
"from vision.descriptor.avg_RGB import extract_average_rgb\n",
"import vision.descriptor.spatial as spatial"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### vision"
"# vision"
]
},
{
@ -36,12 +52,6 @@
}
],
"source": [
"from matplotlib import pyplot as plt\n",
"\n",
"import vision.io\n",
"from vision.descriptor.avg_RGB import extract_average_rgb\n",
"from vision.visualsearch import run_query\n",
"\n",
"x = vision.io.load_msrc('msrc/Images')\n",
"extract_average_rgb(images=x)\n",
"results = run_query(x)\n",
@ -104,12 +114,6 @@
}
],
"source": [
"import cv2\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"\n",
"import vision.descriptor.spatial as spatial\n",
"\n",
"img = cv2.imread('sheep.png')[:,:,::-1]\n",
"\n",
"segments = spatial.grid_image(2, 2, img)\n",
@ -123,12 +127,6 @@
"metadata": {},
"outputs": [],
"source": [
"import cv2\n",
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"\n",
"import vision.descriptor.spatial as spatial\n",
"\n",
"img = cv2.imread('sheep.png')[:,:,::-1]\n",
"\n",
"segments = spatial.extract_spatial_average_rgb(2, 2, img)"