locsec/AMMICO
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зеркало из https://github.com/ssciwr/AMMICO.git synced 2025-11-01 22:46:06 +02:00
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AMMICO/build/html/notebooks/Example text.ipynb

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177 KiB
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{
"cells": [
{
"cell_type": "markdown",
"id": "dcaa3da1",
"metadata": {},
"source": [
"# Text extraction on image\n",
"Inga Ulusoy, SSC, July 2022"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "f43f327c",
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-16T09:08:26.707566Z",
"iopub.status.busy": "2023-05-16T09:08:26.707316Z",
"iopub.status.idle": "2023-05-16T09:08:26.717048Z",
"shell.execute_reply": "2023-05-16T09:08:26.716417Z"
},
"tags": []
},
"outputs": [],
"source": [
"# if running on google colab\n",
"# flake8-noqa-cell\n",
"import os\n",
"\n",
"if \"google.colab\" in str(get_ipython()):\n",
" # update python version\n",
" # install setuptools\n",
" !pip install setuptools==61 -qqq\n",
" # install ammico\n",
" !pip install git+https://github.com/ssciwr/ammico.git -qqq\n",
" # mount google drive for data and API key\n",
" from google.colab import drive\n",
"\n",
" drive.mount(\"/content/drive\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cf362e60",
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-16T09:08:26.720421Z",
"iopub.status.busy": "2023-05-16T09:08:26.719810Z",
"iopub.status.idle": "2023-05-16T09:08:40.370747Z",
"shell.execute_reply": "2023-05-16T09:08:40.370085Z"
},
"tags": []
},
"outputs": [],
"source": [
"import ammico\n",
"from ammico import utils as mutils\n",
"from ammico import display as mdisplay"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "27675810",
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-16T09:08:40.374794Z",
"iopub.status.busy": "2023-05-16T09:08:40.373905Z",
"iopub.status.idle": "2023-05-16T09:08:52.515286Z",
"shell.execute_reply": "2023-05-16T09:08:52.514423Z"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting en-core-web-md==3.5.0\r\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
" 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",
"\u001b[?25l \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/42.8 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m"
]
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"name": "stdout",
"output_type": "stream",
"text": [
"\r",
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"\r",
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"\r",
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"\r",
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"\r",
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},
{
"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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"[nltk_data] Unzipping corpora/conll2000.zip.\r\n",
"[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": "2023-05-16T09:08:52.518905Z",
"iopub.status.busy": "2023-05-16T09:08:52.518477Z",
"iopub.status.idle": "2023-05-16T09:08:52.522981Z",
"shell.execute_reply": "2023-05-16T09:08:52.522340Z"
},
"tags": []
},
"outputs": [],
"source": [
"images = mutils.find_files(path=\"data\", limit=10)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8b32409f",
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-16T09:08:52.526352Z",
"iopub.status.busy": "2023-05-16T09:08:52.525742Z",
"iopub.status.idle": "2023-05-16T09:08:52.529106Z",
"shell.execute_reply": "2023-05-16T09:08:52.528489Z"
},
"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": "2023-05-16T09:08:52.531866Z",
"iopub.status.busy": "2023-05-16T09:08:52.531506Z",
"iopub.status.idle": "2023-05-16T09:08:53.654748Z",
"shell.execute_reply": "2023-05-16T09:08:53.654046Z"
},
"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": "2023-05-16T09:08:53.657856Z",
"iopub.status.busy": "2023-05-16T09:08:53.657475Z",
"iopub.status.idle": "2023-05-16T09:09:59.591809Z",
"shell.execute_reply": "2023-05-16T09:09:59.583764Z"
},
"tags": []
},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "85348f51b89d44bc9d388c6245776881",
"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": "790e70e6e03c48dcb230b4d5e362e153",
"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": "0c6518b708714ef5b1f1f32d0d155e37",
"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": "ab138321b24d4f5eb81b16a37a4820cb",
"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": "105bafaad2e64afc98e4a03f49229a37",
"version_major": 2,
"version_minor": 0
},
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3c29faee81334bd2b9ba7113e8f9425d",
"version_major": 2,
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "bf21d055ce73424b85dff3f0b2ad7412",
"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": "1126541c47bb47488279b63ce8fdc90d",
"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": "d6baaa052e8d4e1da44cc2124cab0708",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "7022f0de529e4d7aae1ff363666065ac",
"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": "9f50fa3e11c04991ab3a58afa7d1304e",
"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": "cebf4a7897264c91b92edaff0b943865",
"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": "47a9a44e748e48e3983464df04406951",
"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": "2023-05-16T09:09:59.617453Z",
"iopub.status.busy": "2023-05-16T09:09:59.616178Z",
"iopub.status.idle": "2023-05-16T09:09:59.699559Z",
"shell.execute_reply": "2023-05-16T09:09:59.698123Z"
},
"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": "2023-05-16T09:09:59.707566Z",
"iopub.status.busy": "2023-05-16T09:09:59.706229Z",
"iopub.status.idle": "2023-05-16T09:09:59.807480Z",
"shell.execute_reply": "2023-05-16T09:09:59.806264Z"
},
"tags": []
},
"outputs": [
{
"data": {
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" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <td>en</td>\n",
" <td>400 DEATHS GET E-BOOK X AN Corporation ncy Ser...</td>\n",
" <td>DEATHS GET E - BOOK X AN Corporation Services ...</td>\n",
" <td>400 DEATHS GET E-BOOK X of Corporation ney Ser...</td>\n",
" <td>-0.125000</td>\n",
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" <td>CORONAVIRUS QUARANTINE CORONAVIRUS OUTBREAK BE...</td>\n",
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" <td>data/106349S_por.png</td>\n",
" <td>NEWS URGENTE SAMSUNG AO VIVO Rio de Janeiro NO...</td>\n",
" <td>pt</td>\n",
" <td>NEWS URGENT SAMSUNG LIVE Rio de Janeiro NEW CO...</td>\n",
" <td>NEWS URGENT SAMSUNG LIVE Rio de Janeiro NEW CO...</td>\n",
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},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# check the dataframe\n",
"df.head(10)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "bf6c9ddb",
"metadata": {
"execution": {
"iopub.execute_input": "2023-05-16T09:09:59.810862Z",
"iopub.status.busy": "2023-05-16T09:09:59.810313Z",
"iopub.status.idle": "2023-05-16T09:09:59.837785Z",
"shell.execute_reply": "2023-05-16T09:09:59.837121Z"
},
"tags": []
},
"outputs": [],
"source": [
"# Write the csv\n",
"df.to_csv(\"./data_out.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9012544e-f818-46ea-b087-3e150850a5d5",
"metadata": {},
"outputs": [],
"source": []
}
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