locsec/AMMICO
1
Π€ΠΎΡ€ΠΊΠ½ΡƒΡ‚ΡŒ 0
Π·Π΅Ρ€ΠΊΠ°Π»ΠΎ ΠΈΠ· https://github.com/ssciwr/AMMICO.git synced 2025-10-30 13:36:04 +02:00
Код Π—Π°Π΄Π°Ρ‡ΠΈ ΠŸΠ°ΠΊΠ΅Ρ‚Ρ‹ ΠŸΡ€ΠΎΠ΅ΠΊΡ‚Ρ‹ Π Π΅Π»ΠΈΠ·Ρ‹ Π’ΠΈΠΊΠΈ ΠΠΊΡ‚ΠΈΠ²Π½ΠΎΡΡ‚ΡŒ

Deploying to gh-pages from @ ssciwr/AMMICO@844ade70e2 πŸš€

ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€ исходного ΠΊΠΎΠ΄Π°
Π­Ρ‚ΠΎΡ‚ ΠΊΠΎΠΌΠΌΠΈΡ‚ содСрТится Π²:
piterand 2023-12-13 22:25:28 +00:00
Ρ€ΠΎΠ΄ΠΈΡ‚Π΅Π»ΡŒ 72b31fa1f1
ΠšΠΎΠΌΠΌΠΈΡ‚ 53fe822400
20 ΠΈΠ·ΠΌΠ΅Π½Ρ‘Π½Π½Ρ‹Ρ… Ρ„Π°ΠΉΠ»ΠΎΠ²: 16352 Π΄ΠΎΠ±Π°Π²Π»Π΅Π½ΠΈΠΉ ΠΈ 3275 ΡƒΠ΄Π°Π»Π΅Π½ΠΈΠΉ
ΠŸΠΎΠΊΠ°Π·Π°Ρ‚ΡŒ статистику Π‘ΠΊΠ°Ρ‡Π°Ρ‚ΡŒ Patch Ρ„Π°ΠΉΠ» Π‘ΠΊΠ°Ρ‡Π°Ρ‚ΡŒ Diff Ρ„Π°ΠΉΠ» ΠŸΠΎΠΊΠ°Π·Π°Ρ‚ΡŒ всС Ρ„Π°ΠΉΠ»Ρ‹ Π‘Π²Π΅Ρ€Π½ΡƒΡ‚ΡŒ всС Ρ„Π°ΠΉΠ»Ρ‹

Π”Π²ΠΎΠΈΡ‡Π½Ρ‹Π΅ Π΄Π°Π½Π½Ρ‹Π΅
build/doctrees/environment.pickle
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

Π”Π²ΠΎΠΈΡ‡Π½Ρ‹ΠΉ Ρ„Π°ΠΉΠ» Π½Π΅ отобраТаСтся.

4840
build/doctrees/nbsphinx/notebooks/DemoNotebook_ammico.ipynb
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

Π Π°Π·Π½ΠΈΡ†Π° ΠΌΠ΅ΠΆΠ΄Ρƒ Ρ„Π°ΠΉΠ»Π°ΠΌΠΈ Π½Π΅ ΠΏΠΎΠΊΠ°Π·Π°Π½Π° ΠΈΠ·-Π·Π° своСго большого Ρ€Π°Π·ΠΌΠ΅Ρ€Π° Π—Π°Π³Ρ€ΡƒΠ·ΠΈΡ‚ΡŒ Ρ€Π°Π·Π½ΠΈΡ†Ρƒ

102
build/doctrees/nbsphinx/notebooks/Example colors.ipynb
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

@ -21,10 +21,10 @@
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:38.050307Z",
"iopub.status.busy": "2023-12-13T10:00:38.050113Z",
"iopub.status.idle": "2023-12-13T10:00:38.057744Z",
"shell.execute_reply": "2023-12-13T10:00:38.057124Z"
"iopub.execute_input": "2023-12-13T22:21:56.272598Z",
"iopub.status.busy": "2023-12-13T22:21:56.272405Z",
"iopub.status.idle": "2023-12-13T22:21:56.280051Z",
"shell.execute_reply": "2023-12-13T22:21:56.279436Z"
}
},
"outputs": [],
@ -50,10 +50,10 @@
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:38.060128Z",
"iopub.status.busy": "2023-12-13T10:00:38.059900Z",
"iopub.status.idle": "2023-12-13T10:00:51.760685Z",
"shell.execute_reply": "2023-12-13T10:00:51.760058Z"
"iopub.execute_input": "2023-12-13T22:21:56.282590Z",
"iopub.status.busy": "2023-12-13T22:21:56.282244Z",
"iopub.status.idle": "2023-12-13T22:22:09.766029Z",
"shell.execute_reply": "2023-12-13T22:22:09.765425Z"
}
},
"outputs": [],
@ -75,10 +75,10 @@
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:51.764523Z",
"iopub.status.busy": "2023-12-13T10:00:51.764074Z",
"iopub.status.idle": "2023-12-13T10:00:51.769014Z",
"shell.execute_reply": "2023-12-13T10:00:51.768506Z"
"iopub.execute_input": "2023-12-13T22:22:09.769832Z",
"iopub.status.busy": "2023-12-13T22:22:09.769143Z",
"iopub.status.idle": "2023-12-13T22:22:09.774091Z",
"shell.execute_reply": "2023-12-13T22:22:09.773612Z"
}
},
"outputs": [],
@ -104,10 +104,10 @@
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:51.771479Z",
"iopub.status.busy": "2023-12-13T10:00:51.771113Z",
"iopub.status.idle": "2023-12-13T10:00:51.798285Z",
"shell.execute_reply": "2023-12-13T10:00:51.797326Z"
"iopub.execute_input": "2023-12-13T22:22:09.776426Z",
"iopub.status.busy": "2023-12-13T22:22:09.776045Z",
"iopub.status.idle": "2023-12-13T22:22:09.802440Z",
"shell.execute_reply": "2023-12-13T22:22:09.801381Z"
}
},
"outputs": [
@ -126,7 +126,7 @@
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f9f549037c0>"
"<IPython.lib.display.IFrame at 0x7fbbd51b9dc0>"
]
},
"metadata": {},
@ -150,10 +150,10 @@
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:51.801733Z",
"iopub.status.busy": "2023-12-13T10:00:51.801166Z",
"iopub.status.idle": "2023-12-13T10:00:57.146255Z",
"shell.execute_reply": "2023-12-13T10:00:57.145448Z"
"iopub.execute_input": "2023-12-13T22:22:09.806616Z",
"iopub.status.busy": "2023-12-13T22:22:09.805666Z",
"iopub.status.idle": "2023-12-13T22:22:15.232694Z",
"shell.execute_reply": "2023-12-13T22:22:15.231945Z"
}
},
"outputs": [],
@ -174,10 +174,10 @@
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:57.150305Z",
"iopub.status.busy": "2023-12-13T10:00:57.149910Z",
"iopub.status.idle": "2023-12-13T10:00:57.153495Z",
"shell.execute_reply": "2023-12-13T10:00:57.152991Z"
"iopub.execute_input": "2023-12-13T22:22:15.236384Z",
"iopub.status.busy": "2023-12-13T22:22:15.235823Z",
"iopub.status.idle": "2023-12-13T22:22:15.242299Z",
"shell.execute_reply": "2023-12-13T22:22:15.241675Z"
}
},
"outputs": [],
@ -197,10 +197,10 @@
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:57.156601Z",
"iopub.status.busy": "2023-12-13T10:00:57.156195Z",
"iopub.status.idle": "2023-12-13T10:00:57.168780Z",
"shell.execute_reply": "2023-12-13T10:00:57.168151Z"
"iopub.execute_input": "2023-12-13T22:22:15.245534Z",
"iopub.status.busy": "2023-12-13T22:22:15.245147Z",
"iopub.status.idle": "2023-12-13T22:22:15.257572Z",
"shell.execute_reply": "2023-12-13T22:22:15.256901Z"
}
},
"outputs": [
@ -243,19 +243,19 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>data/106349S_por.png</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>data/102730_eng.png</td>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.22</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.03</td>\n",
" <td>0.68</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.66</td>\n",
" <td>0.02</td>\n",
" <td>0.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
@ -275,19 +275,19 @@
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>data/102730_eng.png</td>\n",
" <td>data/106349S_por.png</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" <td>0.22</td>\n",
" <td>0.0</td>\n",
" <td>0.03</td>\n",
" <td>0.68</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.66</td>\n",
" <td>0.02</td>\n",
" <td>0.27</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@ -295,14 +295,14 @@
],
"text/plain": [
" filename red green blue yellow cyan orange purple \\\n",
"0 data/106349S_por.png 0.01 0.01 0.05 0.0 0 0 0.22 \n",
"0 data/102730_eng.png 0.05 0.00 0.00 0.0 0 0 0.00 \n",
"1 data/102141_2_eng.png 0.04 0.03 0.02 0.0 0 0 0.01 \n",
"2 data/102730_eng.png 0.05 0.00 0.00 0.0 0 0 0.00 \n",
"2 data/106349S_por.png 0.01 0.01 0.05 0.0 0 0 0.22 \n",
"\n",
" pink brown grey white black \n",
"0 0.0 0.03 0.68 0.00 0.00 \n",
"0 0.0 0.00 0.66 0.02 0.27 \n",
"1 0.0 0.32 0.23 0.31 0.05 \n",
"2 0.0 0.00 0.66 0.02 0.27 "
"2 0.0 0.03 0.68 0.00 0.00 "
]
},
"execution_count": 7,
@ -326,10 +326,10 @@
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:57.171066Z",
"iopub.status.busy": "2023-12-13T10:00:57.170759Z",
"iopub.status.idle": "2023-12-13T10:00:57.175058Z",
"shell.execute_reply": "2023-12-13T10:00:57.174570Z"
"iopub.execute_input": "2023-12-13T22:22:15.259962Z",
"iopub.status.busy": "2023-12-13T22:22:15.259649Z",
"iopub.status.idle": "2023-12-13T22:22:15.263655Z",
"shell.execute_reply": "2023-12-13T22:22:15.263167Z"
}
},
"outputs": [],

45
build/doctrees/nbsphinx/notebooks/Example cropposts.ipynb
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

@ -25,10 +25,10 @@
"id": "70ffb7e2",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:01:01.830270Z",
"iopub.status.busy": "2023-12-13T10:01:01.830072Z",
"iopub.status.idle": "2023-12-13T10:01:01.845484Z",
"shell.execute_reply": "2023-12-13T10:01:01.844879Z"
"iopub.execute_input": "2023-12-13T22:22:20.064865Z",
"iopub.status.busy": "2023-12-13T22:22:20.064477Z",
"iopub.status.idle": "2023-12-13T22:22:20.079570Z",
"shell.execute_reply": "2023-12-13T22:22:20.079080Z"
}
},
"outputs": [],
@ -61,10 +61,10 @@
"id": "5ae02c45",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:01:01.848176Z",
"iopub.status.busy": "2023-12-13T10:01:01.847669Z",
"iopub.status.idle": "2023-12-13T10:01:11.258636Z",
"shell.execute_reply": "2023-12-13T10:01:11.257935Z"
"iopub.execute_input": "2023-12-13T22:22:20.081917Z",
"iopub.status.busy": "2023-12-13T22:22:20.081567Z",
"iopub.status.idle": "2023-12-13T22:22:29.578312Z",
"shell.execute_reply": "2023-12-13T22:22:29.577641Z"
}
},
"outputs": [],
@ -92,10 +92,10 @@
"id": "d04d0e86",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:01:11.261782Z",
"iopub.status.busy": "2023-12-13T10:01:11.261179Z",
"iopub.status.idle": "2023-12-13T10:01:11.950510Z",
"shell.execute_reply": "2023-12-13T10:01:11.949848Z"
"iopub.execute_input": "2023-12-13T22:22:29.581616Z",
"iopub.status.busy": "2023-12-13T22:22:29.580901Z",
"iopub.status.idle": "2023-12-13T22:22:30.213877Z",
"shell.execute_reply": "2023-12-13T22:22:30.213167Z"
}
},
"outputs": [
@ -153,10 +153,10 @@
"id": "71850d9d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:01:11.954042Z",
"iopub.status.busy": "2023-12-13T10:01:11.953689Z",
"iopub.status.idle": "2023-12-13T10:01:14.486752Z",
"shell.execute_reply": "2023-12-13T10:01:14.486127Z"
"iopub.execute_input": "2023-12-13T22:22:30.218102Z",
"iopub.status.busy": "2023-12-13T22:22:30.217709Z",
"iopub.status.idle": "2023-12-13T22:22:32.668655Z",
"shell.execute_reply": "2023-12-13T22:22:32.667999Z"
}
},
"outputs": [
@ -227,10 +227,10 @@
"id": "eef89291",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:01:14.489493Z",
"iopub.status.busy": "2023-12-13T10:01:14.489090Z",
"iopub.status.idle": "2023-12-13T10:01:19.014733Z",
"shell.execute_reply": "2023-12-13T10:01:19.014028Z"
"iopub.execute_input": "2023-12-13T22:22:32.671664Z",
"iopub.status.busy": "2023-12-13T22:22:32.671197Z",
"iopub.status.idle": "2023-12-13T22:22:37.130390Z",
"shell.execute_reply": "2023-12-13T22:22:37.129776Z"
}
},
"outputs": [
@ -238,14 +238,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Doing file data/106349S_por.png\n"
"Doing file data/102730_eng.png\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not enough matches are found - 1/6\n",
"Doing file data/102141_2_eng.png\n"
]
},
@ -253,7 +252,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Doing file data/102730_eng.png\n"
"Doing file data/106349S_por.png\n"
]
},
{

4682
build/doctrees/nbsphinx/notebooks/Example multimodal.ipynb
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

Π Π°Π·Π½ΠΈΡ†Π° ΠΌΠ΅ΠΆΠ΄Ρƒ Ρ„Π°ΠΉΠ»Π°ΠΌΠΈ Π½Π΅ ΠΏΠΎΠΊΠ°Π·Π°Π½Π° ΠΈΠ·-Π·Π° своСго большого Ρ€Π°Π·ΠΌΠ΅Ρ€Π° Π—Π°Π³Ρ€ΡƒΠ·ΠΈΡ‚ΡŒ Ρ€Π°Π·Π½ΠΈΡ†Ρƒ

Π”Π²ΠΎΠΈΡ‡Π½Ρ‹Π΅ Π΄Π°Π½Π½Ρ‹Π΅
build/doctrees/notebooks/DemoNotebook_ammico.doctree
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

Π”Π²ΠΎΠΈΡ‡Π½Ρ‹ΠΉ Ρ„Π°ΠΉΠ» Π½Π΅ отобраТаСтся.

Π”Π²ΠΎΠΈΡ‡Π½Ρ‹Π΅ Π΄Π°Π½Π½Ρ‹Π΅
build/doctrees/notebooks/Example colors.doctree
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

Π”Π²ΠΎΠΈΡ‡Π½Ρ‹ΠΉ Ρ„Π°ΠΉΠ» Π½Π΅ отобраТаСтся.

Π”Π²ΠΎΠΈΡ‡Π½Ρ‹Π΅ Π΄Π°Π½Π½Ρ‹Π΅
build/doctrees/notebooks/Example cropposts.doctree
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

Π”Π²ΠΎΠΈΡ‡Π½Ρ‹ΠΉ Ρ„Π°ΠΉΠ» Π½Π΅ отобраТаСтся.

Π”Π²ΠΎΠΈΡ‡Π½Ρ‹Π΅ Π΄Π°Π½Π½Ρ‹Π΅
build/doctrees/notebooks/Example multimodal.doctree
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

Π”Π²ΠΎΠΈΡ‡Π½Ρ‹ΠΉ Ρ„Π°ΠΉΠ» Π½Π΅ отобраТаСтся.

148
build/html/notebooks/DemoNotebook_ammico.html
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

@ -234,7 +234,7 @@ directly on the right next to the image. This way, the user can directly inspect
<div class="highlight"><pre>
Collecting en-core-web-md==3.7.1
Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_md-3.7.1/en_core_web_md-3.7.1-py3-none-any.whl (42.8 MB)
<span class="ansi-black-intense-fg">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-green-fg">42.8/42.8 MB</span> <span class="ansi-red-fg">62.2 MB/s</span> eta <span class="ansi-cyan-fg">0:00:00</span>
<span class="ansi-black-intense-fg">━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━</span> <span class="ansi-green-fg">42.8/42.8 MB</span> <span class="ansi-red-fg">64.7 MB/s</span> eta <span class="ansi-cyan-fg">0:00:00</span>
Requirement already satisfied: spacy&lt;3.8.0,&gt;=3.7.2 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from en-core-web-md==3.7.1) (3.7.2)
Requirement already satisfied: spacy-legacy&lt;3.1.0,&gt;=3.0.11 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (3.0.12)
Requirement already satisfied: spacy-loggers&lt;2.0.0,&gt;=1.0.0 in /opt/hostedtoolcache/Python/3.9.18/x64/lib/python3.9/site-packages (from spacy&lt;3.8.0,&gt;=3.7.2-&gt;en-core-web-md==3.7.1) (1.0.5)
@ -280,19 +280,19 @@ You can now load the package via spacy.load(&#39;en_core_web_md&#39;)
<span class="ansi-bold">[</span><span class="ansi-blue-fg">notice</span><span class="ansi-bold">]</span> A new release of pip is available: <span class="ansi-red-fg">23.0.1</span> -&gt; <span class="ansi-green-fg">23.3.1</span>
<span class="ansi-bold">[</span><span class="ansi-blue-fg">notice</span><span class="ansi-bold">]</span> To update, run: <span class="ansi-green-fg">pip install --upgrade pip</span>
config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.80k/1.80k [00:00&lt;00:00, 1.00MB/s]
pytorch_model.bin: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.22G/1.22G [00:04&lt;00:00, 252MB/s]
tokenizer_config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 26.0/26.0 [00:00&lt;00:00, 37.3kB/s]
vocab.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 899k/899k [00:00&lt;00:00, 9.27MB/s]
merges.txt: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 456k/456k [00:00&lt;00:00, 46.5MB/s]
config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 629/629 [00:00&lt;00:00, 331kB/s]
pytorch_model.bin: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 268M/268M [00:01&lt;00:00, 257MB/s]
tokenizer_config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 48.0/48.0 [00:00&lt;00:00, 63.1kB/s]
vocab.txt: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 232k/232k [00:00&lt;00:00, 136MB/s]
config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 998/998 [00:00&lt;00:00, 575kB/s]
pytorch_model.bin: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.33G/1.33G [00:43&lt;00:00, 30.9MB/s]
tokenizer_config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 60.0/60.0 [00:00&lt;00:00, 77.7kB/s]
vocab.txt: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 213k/213k [00:00&lt;00:00, 86.2MB/s]
config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.80k/1.80k [00:00&lt;00:00, 992kB/s]
pytorch_model.bin: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.22G/1.22G [00:04&lt;00:00, 256MB/s]
tokenizer_config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 26.0/26.0 [00:00&lt;00:00, 29.5kB/s]
vocab.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 899k/899k [00:00&lt;00:00, 4.99MB/s]
merges.txt: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 456k/456k [00:00&lt;00:00, 2.57MB/s]
config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 629/629 [00:00&lt;00:00, 751kB/s]
pytorch_model.bin: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 268M/268M [00:01&lt;00:00, 249MB/s]
tokenizer_config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 48.0/48.0 [00:00&lt;00:00, 59.0kB/s]
vocab.txt: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 232k/232k [00:00&lt;00:00, 3.96MB/s]
config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 998/998 [00:00&lt;00:00, 1.17MB/s]
pytorch_model.bin: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.33G/1.33G [00:04&lt;00:00, 286MB/s]
tokenizer_config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 60.0/60.0 [00:00&lt;00:00, 68.9kB/s]
vocab.txt: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 213k/213k [00:00&lt;00:00, 1.79MB/s]
Downloading data from &#39;https://github.com/serengil/deepface_models/releases/download/v1.0/retinaface.h5&#39; to file &#39;/home/runner/.cache/pooch/3be32af6e4183fa0156bc33bda371147-retinaface.h5&#39;.
Downloading data from &#39;https://github.com/chandrikadeb7/Face-Mask-Detection/raw/v1.0.0/mask_detector.model&#39; to file &#39;/home/runner/.cache/pooch/865b4b1e20f798935b70082440d5fb21-mask_detector.model&#39;.
</pre></div></div>
@ -302,7 +302,7 @@ Downloading data from &#39;https://github.com/chandrikadeb7/Face-Mask-Detection/
</div>
<div class="output_area docutils container">
<div class="highlight"><pre>
1/1 [==============================] - 1s 501ms/step
1/1 [==============================] - 1s 516ms/step
</pre></div></div>
</div>
<div class="nboutput docutils container">
@ -321,16 +321,16 @@ Downloading data from &#39;https://github.com/serengil/deepface_models/releases/
</div>
<div class="output_area docutils container">
<div class="highlight"><pre>
1/1 [==============================] - 0s 310ms/step
1/1 [==============================] - 0s 314ms/step
1/1 [==============================] - 0s 469ms/step
1/1 [==============================] - 0s 219ms/step
1/1 [==============================] - 0s 214ms/step
1/1 [==============================] - 0s 491ms/step
1/1 [==============================] - 0s 213ms/step
1/1 [==============================] - 0s 212ms/step
1/1 [==============================] - 0s 310ms/step
1/1 [==============================] - 0s 65ms/step
1/1 [==============================] - 0s 360ms/step
1/1 [==============================] - 0s 332ms/step
1/1 [==============================] - 0s 331ms/step
1/1 [==============================] - 0s 75ms/step
1/1 [==============================] - 1s 988ms/step
1/1 [==============================] - 0s 222ms/step
1/1 [==============================] - 0s 216ms/step
1/1 [==============================] - 1s 518ms/step
1/1 [==============================] - 0s 222ms/step
1/1 [==============================] - 0s 210ms/step
</pre></div></div>
</div>
<p>For the computationally demanding <code class="docutils literal notranslate"><span class="pre">SummaryDetector</span></code>, it is best to initialize the model first and then analyze each image while passing the model explicitly. This can be done in a separate loop or in the same loop as for text and emotion detection.</p>
@ -353,11 +353,11 @@ Downloading data from &#39;https://github.com/serengil/deepface_models/releases/
</div>
<div class="output_area stderr docutils container">
<div class="highlight"><pre>
vocab.txt: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 232k/232k [00:00&lt;00:00, 34.0MB/s]
tokenizer_config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 28.0/28.0 [00:00&lt;00:00, 41.5kB/s]
config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 570/570 [00:00&lt;00:00, 865kB/s]
100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2.50G/2.50G [00:18&lt;00:00, 146MB/s]
100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.35G/1.35G [00:08&lt;00:00, 169MB/s]
vocab.txt: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 232k/232k [00:00&lt;00:00, 50.8MB/s]
tokenizer_config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 28.0/28.0 [00:00&lt;00:00, 37.3kB/s]
config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 570/570 [00:00&lt;00:00, 755kB/s]
100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 2.50G/2.50G [00:49&lt;00:00, 54.3MB/s]
100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.35G/1.35G [00:27&lt;00:00, 52.9MB/s]
</pre></div></div>
</div>
<p>This can be done in a separate loop or in the same loop as for text and emotion detection.</p>
@ -432,27 +432,27 @@ config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 570/570 [00:00&lt;00:00, 865kB
<tbody>
<tr>
<th>0</th>
<td>data/106349S_por.png</td>
<td>NEWS URGENTE SAMSUNG AO VIVO Rio de Janeiro NO...</td>
<td>pt</td>
<td>NEWS URGENT SAMSUNG LIVE Rio de Janeiro NEW CO...</td>
<td>NEWS URGENT SAMSUNG LIVE Rio de Janeiro NEW CO...</td>
<td>NEW COUNTING METHOD RJ City HALL EXCLUDES 1,1...</td>
<td>data/102730_eng.png</td>
<td>400 DEATHS GET E-BOOK X AN Corporation ncy Ser...</td>
<td>en</td>
<td>400 DEATHS GET E-BOOK X AN Corporation ncy Ser...</td>
<td>DEATHS GET E - BOOK X AN Corporation Services ...</td>
<td>A municipal worker sprays disinfectant on his...</td>
<td>NEGATIVE</td>
<td>0.99</td>
<td>[Rio de Janeiro, C, ##IT, ##Y, PLANALTO]</td>
<td>[LOC, ORG, LOC, ORG, LOC]</td>
<td>[AN Corporation ncy Services, Ahmedabad, RE, #...</td>
<td>[ORG, LOC, PER, ORG]</td>
<td>...</td>
<td>No</td>
<td>1</td>
<td>[Yes]</td>
<td>[24]</td>
<td>[No]</td>
<td>[27]</td>
<td>[Man]</td>
<td>[None]</td>
<td>[None]</td>
<td>[None]</td>
<td>a man wearing a face mask while looking at a c...</td>
<td>[a news anchor holding a mobile phone with a m...</td>
<td>[asian]</td>
<td>[sad]</td>
<td>[Negative]</td>
<td>two people in blue coats spray disinfection a van</td>
<td>[two men in blue scrubbing suits and yellow gl...</td>
</tr>
<tr>
<th>1</th>
@ -476,31 +476,31 @@ config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 570/570 [00:00&lt;00:00, 865kB
<td>[None]</td>
<td>[None]</td>
<td>a collage of images including a corona sign, a...</td>
<td>[the left hand is holding an orange tube, with...</td>
<td>[some signs and pictures are shown with blood,...</td>
</tr>
<tr>
<th>2</th>
<td>data/102730_eng.png</td>
<td>400 DEATHS GET E-BOOK X AN Corporation ency Se...</td>
<td>en</td>
<td>400 DEATHS GET E-BOOK X AN Corporation ency Se...</td>
<td>DEATHS GET E - BOOK X AN Corporation Services ...</td>
<td>A municipal worker sprays disinfectant on his...</td>
<td>data/106349S_por.png</td>
<td>NEWS URGENTE SAMSUNG AO VIVO Rio de Janeiro NO...</td>
<td>pt</td>
<td>NEWS URGENT SAMSUNG LIVE Rio de Janeiro NEW CO...</td>
<td>NEWS URGENT SAMSUNG LIVE Rio de Janeiro NEW CO...</td>
<td>NEW COUNTING METHOD RJ City HALL EXCLUDES 1,1...</td>
<td>NEGATIVE</td>
<td>0.99</td>
<td>[AN Corporation ency Services, Ahmedabad, RE]</td>
<td>[ORG, LOC, PER]</td>
<td>[Rio de Janeiro, C, ##IT, ##Y, PLANALTO]</td>
<td>[LOC, ORG, LOC, ORG, LOC]</td>
<td>...</td>
<td>No</td>
<td>1</td>
<td>[No]</td>
<td>[27]</td>
<td>[Yes]</td>
<td>[24]</td>
<td>[Man]</td>
<td>[asian]</td>
<td>[sad]</td>
<td>[Negative]</td>
<td>two people in blue coats spray disinfection a van</td>
<td>[two men dressed in blue and holding the back ...</td>
<td>[None]</td>
<td>[None]</td>
<td>[None]</td>
<td>a man wearing a face mask while looking at a c...</td>
<td>[television screen with a man wearing a mask o...</td>
</tr>
</tbody>
</table>
@ -521,7 +521,7 @@ config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 570/570 [00:00&lt;00:00, 865kB
<section id="The-detector-modules">
<h1>The detector modules<a class="headerlink" href="#The-detector-modules" title="Link to this heading"></a></h1>
<p>The different detector modules with their options are explained in more detail in this section. ## Text detector Text on the images can be extracted using the <code class="docutils literal notranslate"><span class="pre">TextDetector</span></code> class (<code class="docutils literal notranslate"><span class="pre">text</span></code> module). The text is initally extracted using the Google Cloud Vision API and then translated into English with googletrans. The translated text is cleaned of whitespace, linebreaks, and numbers using Python syntax and spaCy.</p>
<p><img alt="0cd865b9a545483d8fed10c60fd24e80" class="no-scaled-link" src="../_images/text_detector.png" style="width: 800px;" /></p>
<p><img alt="fbaca8f7c93e4c93a90889bc1f0d1e8d" class="no-scaled-link" src="../_images/text_detector.png" style="width: 800px;" /></p>
<p>The user can set if the text should be further summarized, and analyzed for sentiment and named entity recognition, by setting the keyword <code class="docutils literal notranslate"><span class="pre">analyse_text</span></code> to <code class="docutils literal notranslate"><span class="pre">True</span></code> (the default is <code class="docutils literal notranslate"><span class="pre">False</span></code>). If set, the transformers pipeline is used for each of these tasks, with the default models as of 03/2023. Other models can be selected by setting the optional keyword <code class="docutils literal notranslate"><span class="pre">model_names</span></code> to a list of selected models, on for each task:
<code class="docutils literal notranslate"><span class="pre">model_names=[&quot;sshleifer/distilbart-cnn-12-6&quot;,</span> <span class="pre">&quot;distilbert-base-uncased-finetuned-sst-2-english&quot;,</span> <span class="pre">&quot;dbmdz/bert-large-cased-finetuned-conll03-english&quot;]</span></code> for summary, sentiment, and ner. To be even more specific, revision numbers can also be selected by specifying the optional keyword <code class="docutils literal notranslate"><span class="pre">revision_numbers</span></code> to a list of revision numbers for each model, for example <code class="docutils literal notranslate"><span class="pre">revision_numbers=[&quot;a4f8f3e&quot;,</span> <span class="pre">&quot;af0f99b&quot;,</span> <span class="pre">&quot;f2482bf&quot;]</span></code>.</p>
<p>Please note that for the Google Cloud Vision API (the TextDetector class) you need to set a key in order to process the images. This key is ideally set as an environment variable using for example:</p>
@ -592,7 +592,7 @@ config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 570/570 [00:00&lt;00:00, 865kB
<section id="Image-summary-and-query">
<h2>Image summary and query<a class="headerlink" href="#Image-summary-and-query" title="Link to this heading"></a></h2>
<p>The <code class="docutils literal notranslate"><span class="pre">SummaryDetector</span></code> can be used to generate image captions (<code class="docutils literal notranslate"><span class="pre">summary</span></code>) as well as visual question answering (<code class="docutils literal notranslate"><span class="pre">VQA</span></code>).</p>
<p><img alt="020c1ffe4fc24eacbe231e7f3ac7bcc1" class="no-scaled-link" src="../_images/summary_detector.png" style="width: 800px;" /></p>
<p><img alt="6841a82722104527912ce112f09350cb" class="no-scaled-link" src="../_images/summary_detector.png" style="width: 800px;" /></p>
<p>This module is based on the <a class="reference external" href="https://github.com/salesforce/LAVIS">LAVIS</a> library. Since the models can be quite large, an initial object is created which will load the necessary models into RAM/VRAM and then use them in the analysis. The user can specify the type of analysis to be performed using the <code class="docutils literal notranslate"><span class="pre">analysis_type</span></code> keyword. Setting it to <code class="docutils literal notranslate"><span class="pre">summary</span></code> will generate a caption (summary), <code class="docutils literal notranslate"><span class="pre">questions</span></code> will prepare answers (VQA) to a list of questions as set by the user,
<code class="docutils literal notranslate"><span class="pre">summary_and_questions</span></code> will do both. Note that the desired analysis type needs to be set here in the initialization of the detector object, and not when running the analysis for each image; the same holds true for the selected model.</p>
<div class="nbinput nblast docutils container">
@ -696,7 +696,7 @@ config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 570/570 [00:00&lt;00:00, 865kB
<section id="Detection-of-faces-and-facial-expression-analysis">
<h2>Detection of faces and facial expression analysis<a class="headerlink" href="#Detection-of-faces-and-facial-expression-analysis" title="Link to this heading"></a></h2>
<p>Faces and facial expressions are detected and analyzed using the <code class="docutils literal notranslate"><span class="pre">EmotionDetector</span></code> class from the <code class="docutils literal notranslate"><span class="pre">faces</span></code> module. Initially, it is detected if faces are present on the image using RetinaFace, followed by analysis if face masks are worn (Face-Mask-Detection). The detection of age, gender, race, and emotions is carried out with deepface.</p>
<p><img alt="88edcfaa3855477bae8261b667004b19" class="no-scaled-link" src="../_images/emotion_detector.png" style="width: 800px;" /></p>
<p><img alt="8a55434816df43c09a97832700b756a1" class="no-scaled-link" src="../_images/emotion_detector.png" style="width: 800px;" /></p>
<p>Depending on the features found on the image, the face detection module returns a different analysis content: If no faces are found on the image, all further steps are skipped and the result <code class="docutils literal notranslate"><span class="pre">&quot;face&quot;:</span> <span class="pre">&quot;No&quot;,</span> <span class="pre">&quot;multiple_faces&quot;:</span> <span class="pre">&quot;No&quot;,</span> <span class="pre">&quot;no_faces&quot;:</span> <span class="pre">0,</span> <span class="pre">&quot;wears_mask&quot;:</span> <span class="pre">[&quot;No&quot;],</span> <span class="pre">&quot;age&quot;:</span> <span class="pre">[None],</span> <span class="pre">&quot;gender&quot;:</span> <span class="pre">[None],</span> <span class="pre">&quot;race&quot;:</span> <span class="pre">[None],</span> <span class="pre">&quot;emotion&quot;:</span> <span class="pre">[None],</span> <span class="pre">&quot;emotion</span> <span class="pre">(category)&quot;:</span> <span class="pre">[None]</span></code> is returned. If one or several faces are found, up to three faces are analyzed if they are partially concealed by a face mask. If
yes, only age and gender are detected; if no, also race, emotion, and dominant emotion are detected. In case of the latter, the output could look like this: <code class="docutils literal notranslate"><span class="pre">&quot;face&quot;:</span> <span class="pre">&quot;Yes&quot;,</span> <span class="pre">&quot;multiple_faces&quot;:</span> <span class="pre">&quot;Yes&quot;,</span> <span class="pre">&quot;no_faces&quot;:</span> <span class="pre">2,</span> <span class="pre">&quot;wears_mask&quot;:</span> <span class="pre">[&quot;No&quot;,</span> <span class="pre">&quot;No&quot;],</span> <span class="pre">&quot;age&quot;:</span> <span class="pre">[27,</span> <span class="pre">28],</span> <span class="pre">&quot;gender&quot;:</span> <span class="pre">[&quot;Man&quot;,</span> <span class="pre">&quot;Man&quot;],</span> <span class="pre">&quot;race&quot;:</span> <span class="pre">[&quot;asian&quot;,</span> <span class="pre">None],</span> <span class="pre">&quot;emotion&quot;:</span> <span class="pre">[&quot;angry&quot;,</span> <span class="pre">&quot;neutral&quot;],</span> <span class="pre">&quot;emotion</span> <span class="pre">(category)&quot;:</span> <span class="pre">[&quot;Negative&quot;,</span> <span class="pre">&quot;Neutral&quot;]</span></code>, where for the two faces that are detected (given by <code class="docutils literal notranslate"><span class="pre">no_faces</span></code>), some of the values are returned as a list
with the first item for the first (largest) face and the second item for the second (smaller) face (for example, <code class="docutils literal notranslate"><span class="pre">&quot;emotion&quot;</span></code> returns a list <code class="docutils literal notranslate"><span class="pre">[&quot;angry&quot;,</span> <span class="pre">&quot;neutral&quot;]</span></code> signifying the first face expressing anger, and the second face having a neutral expression).</p>
@ -719,17 +719,17 @@ default is set to 50%, so that a confidence above 0.5 results in an emotion bein
</div>
<div class="output_area docutils container">
<div class="highlight"><pre>
1/1 [==============================] - 1s 516ms/step
1/1 [==============================] - 0s 225ms/step
1/1 [==============================] - 0s 224ms/step
1/1 [==============================] - 0s 493ms/step
1/1 [==============================] - 0s 223ms/step
1/1 [==============================] - 0s 213ms/step
1/1 [==============================] - 1s 505ms/step
1/1 [==============================] - 0s 219ms/step
1/1 [==============================] - 0s 215ms/step
1/1 [==============================] - 0s 214ms/step
1/1 [==============================] - 1s 543ms/step
1/1 [==============================] - 0s 243ms/step
1/1 [==============================] - 0s 230ms/step
1/1 [==============================] - 0s 233ms/step
1/1 [==============================] - 0s 15ms/step
1/1 [==============================] - 1s 510ms/step
1/1 [==============================] - 0s 223ms/step
1/1 [==============================] - 0s 215ms/step
1/1 [==============================] - 1s 523ms/step
1/1 [==============================] - 0s 277ms/step
1/1 [==============================] - 0s 214ms/step
</pre></div></div>
</div>
<p>The thresholds can be adapted interactively in the notebook interface and the optimal value can then be used in a subsequent analysis of the whole data set.</p>

4840
build/html/notebooks/DemoNotebook_ammico.ipynb
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

Π Π°Π·Π½ΠΈΡ†Π° ΠΌΠ΅ΠΆΠ΄Ρƒ Ρ„Π°ΠΉΠ»Π°ΠΌΠΈ Π½Π΅ ΠΏΠΎΠΊΠ°Π·Π°Π½Π° ΠΈΠ·-Π·Π° своСго большого Ρ€Π°Π·ΠΌΠ΅Ρ€Π° Π—Π°Π³Ρ€ΡƒΠ·ΠΈΡ‚ΡŒ Ρ€Π°Π·Π½ΠΈΡ†Ρƒ

28
build/html/notebooks/Example colors.html
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

@ -228,19 +228,19 @@ server.</p>
<tbody>
<tr>
<th>0</th>
<td>data/106349S_por.png</td>
<td>0.01</td>
<td>0.01</td>
<td>data/102730_eng.png</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.0</td>
<td>0</td>
<td>0</td>
<td>0.22</td>
<td>0.00</td>
<td>0.0</td>
<td>0.03</td>
<td>0.68</td>
<td>0.00</td>
<td>0.00</td>
<td>0.66</td>
<td>0.02</td>
<td>0.27</td>
</tr>
<tr>
<th>1</th>
@ -260,19 +260,19 @@ server.</p>
</tr>
<tr>
<th>2</th>
<td>data/102730_eng.png</td>
<td>data/106349S_por.png</td>
<td>0.01</td>
<td>0.01</td>
<td>0.05</td>
<td>0.00</td>
<td>0.00</td>
<td>0.0</td>
<td>0</td>
<td>0</td>
<td>0.00</td>
<td>0.22</td>
<td>0.0</td>
<td>0.03</td>
<td>0.68</td>
<td>0.00</td>
<td>0.00</td>
<td>0.66</td>
<td>0.02</td>
<td>0.27</td>
</tr>
</tbody>
</table>

102
build/html/notebooks/Example colors.ipynb
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

@ -21,10 +21,10 @@
"execution_count": 1,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:38.050307Z",
"iopub.status.busy": "2023-12-13T10:00:38.050113Z",
"iopub.status.idle": "2023-12-13T10:00:38.057744Z",
"shell.execute_reply": "2023-12-13T10:00:38.057124Z"
"iopub.execute_input": "2023-12-13T22:21:56.272598Z",
"iopub.status.busy": "2023-12-13T22:21:56.272405Z",
"iopub.status.idle": "2023-12-13T22:21:56.280051Z",
"shell.execute_reply": "2023-12-13T22:21:56.279436Z"
}
},
"outputs": [],
@ -50,10 +50,10 @@
"execution_count": 2,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:38.060128Z",
"iopub.status.busy": "2023-12-13T10:00:38.059900Z",
"iopub.status.idle": "2023-12-13T10:00:51.760685Z",
"shell.execute_reply": "2023-12-13T10:00:51.760058Z"
"iopub.execute_input": "2023-12-13T22:21:56.282590Z",
"iopub.status.busy": "2023-12-13T22:21:56.282244Z",
"iopub.status.idle": "2023-12-13T22:22:09.766029Z",
"shell.execute_reply": "2023-12-13T22:22:09.765425Z"
}
},
"outputs": [],
@ -75,10 +75,10 @@
"execution_count": 3,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:51.764523Z",
"iopub.status.busy": "2023-12-13T10:00:51.764074Z",
"iopub.status.idle": "2023-12-13T10:00:51.769014Z",
"shell.execute_reply": "2023-12-13T10:00:51.768506Z"
"iopub.execute_input": "2023-12-13T22:22:09.769832Z",
"iopub.status.busy": "2023-12-13T22:22:09.769143Z",
"iopub.status.idle": "2023-12-13T22:22:09.774091Z",
"shell.execute_reply": "2023-12-13T22:22:09.773612Z"
}
},
"outputs": [],
@ -104,10 +104,10 @@
"execution_count": 4,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:51.771479Z",
"iopub.status.busy": "2023-12-13T10:00:51.771113Z",
"iopub.status.idle": "2023-12-13T10:00:51.798285Z",
"shell.execute_reply": "2023-12-13T10:00:51.797326Z"
"iopub.execute_input": "2023-12-13T22:22:09.776426Z",
"iopub.status.busy": "2023-12-13T22:22:09.776045Z",
"iopub.status.idle": "2023-12-13T22:22:09.802440Z",
"shell.execute_reply": "2023-12-13T22:22:09.801381Z"
}
},
"outputs": [
@ -126,7 +126,7 @@
" "
],
"text/plain": [
"<IPython.lib.display.IFrame at 0x7f9f549037c0>"
"<IPython.lib.display.IFrame at 0x7fbbd51b9dc0>"
]
},
"metadata": {},
@ -150,10 +150,10 @@
"execution_count": 5,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:51.801733Z",
"iopub.status.busy": "2023-12-13T10:00:51.801166Z",
"iopub.status.idle": "2023-12-13T10:00:57.146255Z",
"shell.execute_reply": "2023-12-13T10:00:57.145448Z"
"iopub.execute_input": "2023-12-13T22:22:09.806616Z",
"iopub.status.busy": "2023-12-13T22:22:09.805666Z",
"iopub.status.idle": "2023-12-13T22:22:15.232694Z",
"shell.execute_reply": "2023-12-13T22:22:15.231945Z"
}
},
"outputs": [],
@ -174,10 +174,10 @@
"execution_count": 6,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:57.150305Z",
"iopub.status.busy": "2023-12-13T10:00:57.149910Z",
"iopub.status.idle": "2023-12-13T10:00:57.153495Z",
"shell.execute_reply": "2023-12-13T10:00:57.152991Z"
"iopub.execute_input": "2023-12-13T22:22:15.236384Z",
"iopub.status.busy": "2023-12-13T22:22:15.235823Z",
"iopub.status.idle": "2023-12-13T22:22:15.242299Z",
"shell.execute_reply": "2023-12-13T22:22:15.241675Z"
}
},
"outputs": [],
@ -197,10 +197,10 @@
"execution_count": 7,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:57.156601Z",
"iopub.status.busy": "2023-12-13T10:00:57.156195Z",
"iopub.status.idle": "2023-12-13T10:00:57.168780Z",
"shell.execute_reply": "2023-12-13T10:00:57.168151Z"
"iopub.execute_input": "2023-12-13T22:22:15.245534Z",
"iopub.status.busy": "2023-12-13T22:22:15.245147Z",
"iopub.status.idle": "2023-12-13T22:22:15.257572Z",
"shell.execute_reply": "2023-12-13T22:22:15.256901Z"
}
},
"outputs": [
@ -243,19 +243,19 @@
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>data/106349S_por.png</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>data/102730_eng.png</td>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.22</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0.03</td>\n",
" <td>0.68</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.66</td>\n",
" <td>0.02</td>\n",
" <td>0.27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
@ -275,19 +275,19 @@
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>data/102730_eng.png</td>\n",
" <td>data/106349S_por.png</td>\n",
" <td>0.01</td>\n",
" <td>0.01</td>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0.00</td>\n",
" <td>0.22</td>\n",
" <td>0.0</td>\n",
" <td>0.03</td>\n",
" <td>0.68</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.66</td>\n",
" <td>0.02</td>\n",
" <td>0.27</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
@ -295,14 +295,14 @@
],
"text/plain": [
" filename red green blue yellow cyan orange purple \\\n",
"0 data/106349S_por.png 0.01 0.01 0.05 0.0 0 0 0.22 \n",
"0 data/102730_eng.png 0.05 0.00 0.00 0.0 0 0 0.00 \n",
"1 data/102141_2_eng.png 0.04 0.03 0.02 0.0 0 0 0.01 \n",
"2 data/102730_eng.png 0.05 0.00 0.00 0.0 0 0 0.00 \n",
"2 data/106349S_por.png 0.01 0.01 0.05 0.0 0 0 0.22 \n",
"\n",
" pink brown grey white black \n",
"0 0.0 0.03 0.68 0.00 0.00 \n",
"0 0.0 0.00 0.66 0.02 0.27 \n",
"1 0.0 0.32 0.23 0.31 0.05 \n",
"2 0.0 0.00 0.66 0.02 0.27 "
"2 0.0 0.03 0.68 0.00 0.00 "
]
},
"execution_count": 7,
@ -326,10 +326,10 @@
"execution_count": 8,
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:00:57.171066Z",
"iopub.status.busy": "2023-12-13T10:00:57.170759Z",
"iopub.status.idle": "2023-12-13T10:00:57.175058Z",
"shell.execute_reply": "2023-12-13T10:00:57.174570Z"
"iopub.execute_input": "2023-12-13T22:22:15.259962Z",
"iopub.status.busy": "2023-12-13T22:22:15.259649Z",
"iopub.status.idle": "2023-12-13T22:22:15.263655Z",
"shell.execute_reply": "2023-12-13T22:22:15.263167Z"
}
},
"outputs": [],

5
build/html/notebooks/Example cropposts.html
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

@ -230,10 +230,9 @@
</div>
<div class="output_area docutils container">
<div class="highlight"><pre>
Doing file data/106349S_por.png
Not enough matches are found - 1/6
Doing file data/102141_2_eng.png
Doing file data/102730_eng.png
Doing file data/102141_2_eng.png
Doing file data/106349S_por.png
Batch cropping images done
</pre></div></div>
</div>

45
build/html/notebooks/Example cropposts.ipynb
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

@ -25,10 +25,10 @@
"id": "70ffb7e2",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:01:01.830270Z",
"iopub.status.busy": "2023-12-13T10:01:01.830072Z",
"iopub.status.idle": "2023-12-13T10:01:01.845484Z",
"shell.execute_reply": "2023-12-13T10:01:01.844879Z"
"iopub.execute_input": "2023-12-13T22:22:20.064865Z",
"iopub.status.busy": "2023-12-13T22:22:20.064477Z",
"iopub.status.idle": "2023-12-13T22:22:20.079570Z",
"shell.execute_reply": "2023-12-13T22:22:20.079080Z"
}
},
"outputs": [],
@ -61,10 +61,10 @@
"id": "5ae02c45",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:01:01.848176Z",
"iopub.status.busy": "2023-12-13T10:01:01.847669Z",
"iopub.status.idle": "2023-12-13T10:01:11.258636Z",
"shell.execute_reply": "2023-12-13T10:01:11.257935Z"
"iopub.execute_input": "2023-12-13T22:22:20.081917Z",
"iopub.status.busy": "2023-12-13T22:22:20.081567Z",
"iopub.status.idle": "2023-12-13T22:22:29.578312Z",
"shell.execute_reply": "2023-12-13T22:22:29.577641Z"
}
},
"outputs": [],
@ -92,10 +92,10 @@
"id": "d04d0e86",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:01:11.261782Z",
"iopub.status.busy": "2023-12-13T10:01:11.261179Z",
"iopub.status.idle": "2023-12-13T10:01:11.950510Z",
"shell.execute_reply": "2023-12-13T10:01:11.949848Z"
"iopub.execute_input": "2023-12-13T22:22:29.581616Z",
"iopub.status.busy": "2023-12-13T22:22:29.580901Z",
"iopub.status.idle": "2023-12-13T22:22:30.213877Z",
"shell.execute_reply": "2023-12-13T22:22:30.213167Z"
}
},
"outputs": [
@ -153,10 +153,10 @@
"id": "71850d9d",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:01:11.954042Z",
"iopub.status.busy": "2023-12-13T10:01:11.953689Z",
"iopub.status.idle": "2023-12-13T10:01:14.486752Z",
"shell.execute_reply": "2023-12-13T10:01:14.486127Z"
"iopub.execute_input": "2023-12-13T22:22:30.218102Z",
"iopub.status.busy": "2023-12-13T22:22:30.217709Z",
"iopub.status.idle": "2023-12-13T22:22:32.668655Z",
"shell.execute_reply": "2023-12-13T22:22:32.667999Z"
}
},
"outputs": [
@ -227,10 +227,10 @@
"id": "eef89291",
"metadata": {
"execution": {
"iopub.execute_input": "2023-12-13T10:01:14.489493Z",
"iopub.status.busy": "2023-12-13T10:01:14.489090Z",
"iopub.status.idle": "2023-12-13T10:01:19.014733Z",
"shell.execute_reply": "2023-12-13T10:01:19.014028Z"
"iopub.execute_input": "2023-12-13T22:22:32.671664Z",
"iopub.status.busy": "2023-12-13T22:22:32.671197Z",
"iopub.status.idle": "2023-12-13T22:22:37.130390Z",
"shell.execute_reply": "2023-12-13T22:22:37.129776Z"
}
},
"outputs": [
@ -238,14 +238,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Doing file data/106349S_por.png\n"
"Doing file data/102730_eng.png\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Not enough matches are found - 1/6\n",
"Doing file data/102141_2_eng.png\n"
]
},
@ -253,7 +252,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Doing file data/102730_eng.png\n"
"Doing file data/106349S_por.png\n"
]
},
{

90
build/html/notebooks/Example multimodal.html
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

@ -151,9 +151,9 @@
</div>
<div class="output_area docutils container">
<div class="highlight"><pre>
{&#39;106349S_por&#39;: {&#39;filename&#39;: &#39;data/106349S_por.png&#39;},
{&#39;102730_eng&#39;: {&#39;filename&#39;: &#39;data/102730_eng.png&#39;},
&#39;102141_2_eng&#39;: {&#39;filename&#39;: &#39;data/102141_2_eng.png&#39;},
&#39;102730_eng&#39;: {&#39;filename&#39;: &#39;data/102730_eng.png&#39;}}
&#39;106349S_por&#39;: {&#39;filename&#39;: &#39;data/106349S_por.png&#39;}}
</pre></div></div>
</div>
<section id="Indexing-and-extracting-features-from-images-in-selected-folder">
@ -195,9 +195,9 @@
</div>
<div class="output_area docutils container">
<div class="highlight"><pre>
{&#39;106349S_por&#39;: {&#39;filename&#39;: &#39;data/106349S_por.png&#39;},
{&#39;102730_eng&#39;: {&#39;filename&#39;: &#39;data/102730_eng.png&#39;},
&#39;102141_2_eng&#39;: {&#39;filename&#39;: &#39;data/102141_2_eng.png&#39;},
&#39;102730_eng&#39;: {&#39;filename&#39;: &#39;data/102730_eng.png&#39;}}
&#39;106349S_por&#39;: {&#39;filename&#39;: &#39;data/106349S_por.png&#39;}}
</pre></div></div>
</div>
<div class="nbinput docutils container">
@ -223,10 +223,10 @@
</div>
<div class="output_area stderr docutils container">
<div class="highlight"><pre>
vocab.txt: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 232k/232k [00:00&lt;00:00, 31.3MB/s]
tokenizer_config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 28.0/28.0 [00:00&lt;00:00, 17.1kB/s]
config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 570/570 [00:00&lt;00:00, 1.11MB/s]
100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.97G/1.97G [00:07&lt;00:00, 281MB/s]
vocab.txt: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 232k/232k [00:00&lt;00:00, 42.2MB/s]
tokenizer_config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 28.0/28.0 [00:00&lt;00:00, 16.5kB/s]
config.json: 100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 570/570 [00:00&lt;00:00, 962kB/s]
100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.97G/1.97G [00:41&lt;00:00, 50.8MB/s]
</pre></div></div>
</div>
<div class="nbinput docutils container">
@ -507,13 +507,13 @@ tensor([[0.1135, 0.1063, 0.0490],
</div>
<div class="output_area docutils container">
<div class="highlight"><pre>
{&#39;106349S_por&#39;: {&#39;filename&#39;: &#39;data/106349S_por.png&#39;,
&#39;rank politician press conference&#39;: 0,
&#39;politician press conference&#39;: 0.16655388474464417,
&#39;rank a world map&#39;: 2,
&#39;a world map&#39;: 0.09352904558181763,
&#39;rank a dog&#39;: 0,
&#39;a dog&#39;: 0.10862946510314941},
{&#39;102730_eng&#39;: {&#39;filename&#39;: &#39;data/102730_eng.png&#39;,
&#39;rank politician press conference&#39;: 1,
&#39;politician press conference&#39;: 0.14405086636543274,
&#39;rank a world map&#39;: 0,
&#39;a world map&#39;: 0.13108763098716736,
&#39;rank a dog&#39;: 1,
&#39;a dog&#39;: 0.10083308815956116},
&#39;102141_2_eng&#39;: {&#39;filename&#39;: &#39;data/102141_2_eng.png&#39;,
&#39;rank politician press conference&#39;: 2,
&#39;politician press conference&#39;: 0.11350078880786896,
@ -521,13 +521,13 @@ tensor([[0.1135, 0.1063, 0.0490],
&#39;a world map&#39;: 0.10633410513401031,
&#39;rank a dog&#39;: 2,
&#39;a dog&#39;: 0.04904009774327278},
&#39;102730_eng&#39;: {&#39;filename&#39;: &#39;data/102730_eng.png&#39;,
&#39;rank politician press conference&#39;: 1,
&#39;politician press conference&#39;: 0.14405086636543274,
&#39;rank a world map&#39;: 0,
&#39;a world map&#39;: 0.13108763098716736,
&#39;rank a dog&#39;: 1,
&#39;a dog&#39;: 0.10083308815956116}}
&#39;106349S_por&#39;: {&#39;filename&#39;: &#39;data/106349S_por.png&#39;,
&#39;rank politician press conference&#39;: 0,
&#39;politician press conference&#39;: 0.16655388474464417,
&#39;rank a world map&#39;: 2,
&#39;a world map&#39;: 0.09352904558181763,
&#39;rank a dog&#39;: 0,
&#39;a dog&#39;: 0.10862946510314941}}
</pre></div></div>
</div>
<p>After launching <code class="docutils literal notranslate"><span class="pre">multimodal_search</span></code> function, the results of each query will be added to the source dictionary.</p>
@ -717,7 +717,7 @@ simultaneously. With the parameter <code class="docutils literal notranslate"><s
</div>
<div class="output_area stderr docutils container">
<div class="highlight"><pre>
100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.78G/1.78G [00:09&lt;00:00, 200MB/s]
100%|β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆ| 1.78G/1.78G [00:58&lt;00:00, 32.6MB/s]
</pre></div></div>
</div>
<p>Then using the same output function you can add the <code class="docutils literal notranslate"><span class="pre">ITM=True</span></code> arguments to output the new image order. You can also add the <code class="docutils literal notranslate"><span class="pre">image_gradcam_with_itm</span></code> argument to output the heat maps of the calculated images.</p>
@ -910,19 +910,19 @@ simultaneously. With the parameter <code class="docutils literal notranslate"><s
<tbody>
<tr>
<th>0</th>
<td>data/106349S_por.png</td>
<td>data/102730_eng.png</td>
<td>1</td>
<td>0.144051</td>
<td>0</td>
<td>0.166554</td>
<td>2</td>
<td>0.093529</td>
<td>0.131088</td>
<td>1</td>
<td>0.100833</td>
<td>0.001856</td>
<td>1</td>
<td>0.004548</td>
<td>1</td>
<td>0.000812</td>
<td>0</td>
<td>0.108629</td>
<td>0.058296</td>
<td>0</td>
<td>0.000794</td>
<td>2</td>
<td>0.000091</td>
<td>2</td>
</tr>
<tr>
<th>1</th>
@ -942,19 +942,19 @@ simultaneously. With the parameter <code class="docutils literal notranslate"><s
</tr>
<tr>
<th>2</th>
<td>data/102730_eng.png</td>
<td>1</td>
<td>0.144051</td>
<td>data/106349S_por.png</td>
<td>0</td>
<td>0.131088</td>
<td>1</td>
<td>0.100833</td>
<td>0.001856</td>
<td>1</td>
<td>0.004548</td>
<td>1</td>
<td>0.000812</td>
<td>0.166554</td>
<td>2</td>
<td>0.093529</td>
<td>0</td>
<td>0.108629</td>
<td>0.058296</td>
<td>0</td>
<td>0.000794</td>
<td>2</td>
<td>0.000091</td>
<td>2</td>
</tr>
</tbody>
</table>

4682
build/html/notebooks/Example multimodal.ipynb
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

Π Π°Π·Π½ΠΈΡ†Π° ΠΌΠ΅ΠΆΠ΄Ρƒ Ρ„Π°ΠΉΠ»Π°ΠΌΠΈ Π½Π΅ ΠΏΠΎΠΊΠ°Π·Π°Π½Π° ΠΈΠ·-Π·Π° своСго большого Ρ€Π°Π·ΠΌΠ΅Ρ€Π° Π—Π°Π³Ρ€ΡƒΠ·ΠΈΡ‚ΡŒ Ρ€Π°Π·Π½ΠΈΡ†Ρƒ

2
build/html/searchindex.js
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

Различия Ρ„Π°ΠΉΠ»ΠΎΠ² скрыты, ΠΏΠΎΡ‚ΠΎΠΌΡƒ Ρ‡Ρ‚ΠΎ ΠΎΠ΄Π½Π° ΠΈΠ»ΠΈ нСсколько строк слишком Π΄Π»ΠΈΠ½Π½Ρ‹

4
source/notebooks/data/data_out.csv
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

@ -1,4 +1,4 @@
,filename,rank politician press conference,politician press conference,rank a world map,a world map,rank a dog,a dog,itm politician press conference,itm_rank politician press conference,itm a world map,itm_rank a world map,itm a dog,itm_rank a dog
0,data/106349S_por.png,0,0.166553884745,2,0.0935290455818,0,0.108629465103,0.0582960061729,0,0.000794103252701,2,9.0896646725e-05,2
0,data/102730_eng.png,1,0.144050866365,0,0.131087630987,1,0.10083308816,0.00185553671326,1,0.0045476439409,1,0.000811598263681,0
1,data/102141_2_eng.png,2,0.113500788808,1,0.106334105134,2,0.0490400977433,0.00126223196276,2,0.0857630595565,0,0.000174979766598,1
2,data/102730_eng.png,1,0.144050866365,0,0.131087630987,1,0.10083308816,0.00185553671326,1,0.0045476439409,1,0.000811598263681,0
2,data/106349S_por.png,0,0.166553884745,2,0.0935290455818,0,0.108629465103,0.0582960061729,0,0.000794103252701,2,9.0896646725e-05,2

1 filename rank politician press conference politician press conference rank a world map a world map rank a dog a dog itm politician press conference itm_rank politician press conference itm a world map itm_rank a world map itm a dog itm_rank a dog
2 0 data/106349S_por.png data/102730_eng.png 0 1 0.166553884745 0.144050866365 2 0 0.0935290455818 0.131087630987 0 1 0.108629465103 0.10083308816 0.0582960061729 0.00185553671326 0 1 0.000794103252701 0.0045476439409 2 1 9.0896646725e-05 0.000811598263681 2 0
3 1 data/102141_2_eng.png 2 0.113500788808 1 0.106334105134 2 0.0490400977433 0.00126223196276 2 0.0857630595565 0 0.000174979766598 1
4 2 data/102730_eng.png data/106349S_por.png 1 0 0.144050866365 0.166553884745 0 2 0.131087630987 0.0935290455818 1 0 0.10083308816 0.108629465103 0.00185553671326 0.0582960061729 1 0 0.0045476439409 0.000794103252701 1 2 0.000811598263681 9.0896646725e-05 0 2

4
source/notebooks/data_out.csv
ΠŸΡ€ΠΎΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ Ρ„Π°ΠΉΠ»

@ -1,4 +1,4 @@
,filename,red,green,blue,yellow,cyan,orange,purple,pink,brown,grey,white,black
0,data/106349S_por.png,0.01,0.01,0.05,0.0,0,0,0.22,0.0,0.03,0.68,0.0,0.0
0,data/102730_eng.png,0.05,0.0,0.0,0.0,0,0,0.0,0.0,0.0,0.66,0.02,0.27
1,data/102141_2_eng.png,0.04,0.03,0.02,0.0,0,0,0.01,0.0,0.32,0.23,0.31,0.05
2,data/102730_eng.png,0.05,0.0,0.0,0.0,0,0,0.0,0.0,0.0,0.66,0.02,0.27
2,data/106349S_por.png,0.01,0.01,0.05,0.0,0,0,0.22,0.0,0.03,0.68,0.0,0.0

1 filename red green blue yellow cyan orange purple pink brown grey white black
2 0 data/106349S_por.png data/102730_eng.png 0.01 0.05 0.01 0.0 0.05 0.0 0.0 0 0 0.22 0.0 0.0 0.03 0.0 0.68 0.66 0.0 0.02 0.0 0.27
3 1 data/102141_2_eng.png 0.04 0.03 0.02 0.0 0 0 0.01 0.0 0.32 0.23 0.31 0.05
4 2 data/102730_eng.png data/106349S_por.png 0.05 0.01 0.0 0.01 0.0 0.05 0.0 0 0 0.0 0.22 0.0 0.0 0.03 0.66 0.68 0.02 0.0 0.27 0.0