зеркало из
https://github.com/ssciwr/AMMICO.git
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5569 строки
176 KiB
Plaintext
5569 строки
176 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "dcaa3da1",
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"metadata": {},
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"source": [
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"# Text extraction on image\n",
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"Inga Ulusoy, SSC, July 2022"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "f43f327c",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-05-16T08:55:09.293084Z",
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"iopub.status.busy": "2023-05-16T08:55:09.292436Z",
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"iopub.status.idle": "2023-05-16T08:55:09.305860Z",
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"shell.execute_reply": "2023-05-16T08:55:09.305058Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"# if running on google colab\n",
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"# flake8-noqa-cell\n",
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"import os\n",
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"\n",
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"if \"google.colab\" in str(get_ipython()):\n",
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" # update python version\n",
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" # install setuptools\n",
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" !pip install setuptools==61 -qqq\n",
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" # install ammico\n",
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" !pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
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" # mount google drive for data and API key\n",
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" from google.colab import drive\n",
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"\n",
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" drive.mount(\"/content/drive\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "cf362e60",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-05-16T08:55:09.310291Z",
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"iopub.status.busy": "2023-05-16T08:55:09.309569Z",
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"iopub.status.idle": "2023-05-16T08:55:26.492276Z",
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"shell.execute_reply": "2023-05-16T08:55:26.491489Z"
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},
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"tags": []
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},
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"outputs": [],
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"source": [
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"import ammico\n",
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"from ammico import utils as mutils\n",
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"from ammico import display as mdisplay"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "27675810",
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"metadata": {
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"execution": {
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"iopub.execute_input": "2023-05-16T08:55:26.497189Z",
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"iopub.status.busy": "2023-05-16T08:55:26.496344Z",
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"iopub.status.idle": "2023-05-16T08:55:40.011532Z",
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"shell.execute_reply": "2023-05-16T08:55:40.010231Z"
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},
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Collecting en-core-web-md==3.5.0\r\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_md-3.5.0/en_core_web_md-3.5.0-py3-none-any.whl (42.8 MB)\r\n",
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"\u001b[?25l"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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" \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/42.8 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r",
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"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.1/42.8 MB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:19\u001b[0m"
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"\r",
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"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.9/42.8 MB\u001b[0m \u001b[31m176.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m"
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"\u001b[2K \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m27.9/42.8 MB\u001b[0m \u001b[31m174.1 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r",
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"Requirement already satisfied: spacy<3.6.0,>=3.5.0 in /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages (from en-core-web-md==3.5.0) (3.5.3)\r\n"
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"Requirement already satisfied: confection<1.0.0,>=0.0.1 in /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages (from thinc<8.2.0,>=8.1.8->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (0.0.4)\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Requirement already satisfied: click<9.0.0,>=7.1.1 in /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages (from typer<0.8.0,>=0.3.0->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (8.1.3)\r\n",
|
||
"Requirement already satisfied: MarkupSafe>=2.0 in /opt/hostedtoolcache/Python/3.9.16/x64/lib/python3.9/site-packages (from jinja2->spacy<3.6.0,>=3.5.0->en-core-web-md==3.5.0) (2.1.2)\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Installing collected packages: en-core-web-md\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Successfully installed en-core-web-md-3.5.0\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\r\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m22.0.4\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m23.1.2\u001b[0m\r\n",
|
||
"\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"\u001b[38;5;2m✔ Download and installation successful\u001b[0m\r\n",
|
||
"You can now load the package via spacy.load('en_core_web_md')\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[nltk_data] Downloading package brown to /home/runner/nltk_data...\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[nltk_data] Unzipping corpora/brown.zip.\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[nltk_data] Downloading package punkt to /home/runner/nltk_data...\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[nltk_data] Unzipping tokenizers/punkt.zip.\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[nltk_data] Downloading package wordnet to /home/runner/nltk_data...\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[nltk_data] Downloading package averaged_perceptron_tagger to\r\n",
|
||
"[nltk_data] /home/runner/nltk_data...\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[nltk_data] Unzipping taggers/averaged_perceptron_tagger.zip.\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[nltk_data] Downloading package conll2000 to /home/runner/nltk_data...\r\n",
|
||
"[nltk_data] Unzipping corpora/conll2000.zip.\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[nltk_data] Downloading package movie_reviews to\r\n",
|
||
"[nltk_data] /home/runner/nltk_data...\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"[nltk_data] Unzipping corpora/movie_reviews.zip.\r\n"
|
||
]
|
||
},
|
||
{
|
||
"name": "stdout",
|
||
"output_type": "stream",
|
||
"text": [
|
||
"Finished.\r\n"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"# download the models if they are not there yet\n",
|
||
"!python -m spacy download en_core_web_md\n",
|
||
"!python -m textblob.download_corpora"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 4,
|
||
"id": "6da3a7aa",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-05-16T08:55:40.016445Z",
|
||
"iopub.status.busy": "2023-05-16T08:55:40.015897Z",
|
||
"iopub.status.idle": "2023-05-16T08:55:40.020769Z",
|
||
"shell.execute_reply": "2023-05-16T08:55:40.020119Z"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"images = mutils.find_files(path=\"data\", limit=10)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 5,
|
||
"id": "8b32409f",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-05-16T08:55:40.024660Z",
|
||
"iopub.status.busy": "2023-05-16T08:55:40.024103Z",
|
||
"iopub.status.idle": "2023-05-16T08:55:40.029378Z",
|
||
"shell.execute_reply": "2023-05-16T08:55:40.028614Z"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"mydict = mutils.initialize_dict(images)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "7b8b929f",
|
||
"metadata": {},
|
||
"source": [
|
||
"## google cloud vision API\n",
|
||
"First 1000 images per month are free."
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "0891b795-c7fe-454c-a45d-45fadf788142",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Inspect the elements per image"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 6,
|
||
"id": "7c6ecc88",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-05-16T08:55:40.032984Z",
|
||
"iopub.status.busy": "2023-05-16T08:55:40.032400Z",
|
||
"iopub.status.idle": "2023-05-16T08:55:41.419926Z",
|
||
"shell.execute_reply": "2023-05-16T08:55:41.419071Z"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"ename": "AttributeError",
|
||
"evalue": "module 'ammico.display' has no attribute 'explore_analysis'",
|
||
"output_type": "error",
|
||
"traceback": [
|
||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
"\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)",
|
||
"Cell \u001b[0;32mIn[6], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mmdisplay\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexplore_analysis\u001b[49m(mydict, identify\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtext-on-image\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
|
||
"\u001b[0;31mAttributeError\u001b[0m: module 'ammico.display' has no attribute 'explore_analysis'"
|
||
]
|
||
}
|
||
],
|
||
"source": [
|
||
"mdisplay.explore_analysis(mydict, identify=\"text-on-image\")"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "9c3e72b5-0e57-4019-b45e-3e36a74e7f52",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Or directly analyze for further processing"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 7,
|
||
"id": "365c78b1-7ff4-4213-86fa-6a0a2d05198f",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-05-16T08:55:41.423867Z",
|
||
"iopub.status.busy": "2023-05-16T08:55:41.423329Z",
|
||
"iopub.status.idle": "2023-05-16T08:56:59.076383Z",
|
||
"shell.execute_reply": "2023-05-16T08:56:59.068404Z"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "9c6335e81a714abc82ebcc41f68a6ed3",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading (…)/a4f8f3e/config.json: 0%| | 0.00/1.80k [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "c48876385e164469ae93bd899744fc1f",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading pytorch_model.bin: 0%| | 0.00/1.22G [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "6a04a643e5ac4efbb8613aed3ae04a09",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading (…)okenizer_config.json: 0%| | 0.00/26.0 [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "422b6c6aa97148489d5a707c422fcc83",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading (…)e/a4f8f3e/vocab.json: 0%| | 0.00/899k [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "8760f156ee6c4b01a3fb8bb513dad93d",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading (…)e/a4f8f3e/merges.txt: 0%| | 0.00/456k [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "8b363d1bc7e84480a9c2de83d23018e1",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading (…)/af0f99b/config.json: 0%| | 0.00/629 [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "06fe271bf0f34ddc839c2e97fb1df48a",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading pytorch_model.bin: 0%| | 0.00/268M [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "5bb7ee5d8f174c1f89e66c608e18b09f",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading (…)okenizer_config.json: 0%| | 0.00/48.0 [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "9c8df02c5cf9402ea8a42a86c80ca7bb",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading (…)ve/af0f99b/vocab.txt: 0%| | 0.00/232k [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "a300fba1b7294b88820174cc2836c917",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading (…)/f2482bf/config.json: 0%| | 0.00/998 [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "1d5c2c6b5f7c4eee9816f5734eea00d7",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading pytorch_model.bin: 0%| | 0.00/1.33G [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "7f3d4c2ae78a4aa5b810d114643a1312",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading (…)okenizer_config.json: 0%| | 0.00/60.0 [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
},
|
||
{
|
||
"data": {
|
||
"application/vnd.jupyter.widget-view+json": {
|
||
"model_id": "940b6df8a53c4a9da58fe9dd105e3db1",
|
||
"version_major": 2,
|
||
"version_minor": 0
|
||
},
|
||
"text/plain": [
|
||
"Downloading (…)ve/f2482bf/vocab.txt: 0%| | 0.00/213k [00:00<?, ?B/s]"
|
||
]
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data"
|
||
}
|
||
],
|
||
"source": [
|
||
"for key in mydict:\n",
|
||
" mydict[key] = ammico.text.TextDetector(\n",
|
||
" mydict[key], analyse_text=True\n",
|
||
" ).analyse_image()"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "markdown",
|
||
"id": "3c063eda",
|
||
"metadata": {},
|
||
"source": [
|
||
"## Convert to dataframe and write csv"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 8,
|
||
"id": "5709c2cd",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-05-16T08:56:59.109002Z",
|
||
"iopub.status.busy": "2023-05-16T08:56:59.108315Z",
|
||
"iopub.status.idle": "2023-05-16T08:56:59.206624Z",
|
||
"shell.execute_reply": "2023-05-16T08:56:59.205776Z"
|
||
},
|
||
"tags": []
|
||
},
|
||
"outputs": [],
|
||
"source": [
|
||
"outdict = mutils.append_data_to_dict(mydict)\n",
|
||
"df = mutils.dump_df(outdict)"
|
||
]
|
||
},
|
||
{
|
||
"cell_type": "code",
|
||
"execution_count": 9,
|
||
"id": "c4f05637",
|
||
"metadata": {
|
||
"execution": {
|
||
"iopub.execute_input": "2023-05-16T08:56:59.210332Z",
|
||
"iopub.status.busy": "2023-05-16T08:56:59.209802Z",
|
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