1523 lines
4.3 MiB
Text
1523 lines
4.3 MiB
Text
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b12b091f-11e4-4572-a326-3656ff85fbde",
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"metadata": {},
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"outputs": [],
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"source": [
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"import json"
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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": null,
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"id": "d0336432-6451-4b46-b198-b1e6ff2caf38",
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"metadata": {},
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"outputs": [],
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"source": [
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"import re\n",
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"def extract_total_sqm(input_str: str):\n",
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" sqmregex = r\"(\\d+\\.\\d*) ?(sq ?m|sq. ?m)\"\n",
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" matches = re.findall(sqmregex, input_str.lower())\n",
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" if len(matches) == 0:\n",
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" return None\n",
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" sqms = [float(m[0]) for m in matches]\n",
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" return max(sqms)"
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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": 7,
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"id": "cefd4cd0-82b7-4fc7-b863-238c67fba6e4",
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"metadata": {},
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"outputs": [],
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"source": [
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"with open('data/rs/86807694/floorplan_ocr.json') as f:\n",
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" ocred = json.load(f)"
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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": 27,
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"id": "c697bcaf-e40c-4218-b226-f0481f2221a9",
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"metadata": {},
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"outputs": [],
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"source": [
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"def extract_total_sqm(input_str: str):\n",
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" sqmregex = r\"(\\d+\\.?\\d*) ?(sq ?m|sq. ?m)\"\n",
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" matches = re.findall(sqmregex, input_str.lower())\n",
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" if len(matches) == 0:\n",
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" return None\n",
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" sqms = [float(m[0]) for m in matches]\n",
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" filtered = [sqm for sqm in sqms if 30 < sqm < 160]\n",
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" return max(filtered)"
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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": 21,
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"id": "5c9688d5-273e-4fb1-a806-2ab2ccd4efa9",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"['2.36', '1.56', '79', '51', '5.30', '2.34', '175', '78', '2', '172', '16']"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"text = ocred[0]['text'].lower()\n",
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"sqmregex = r\"(\\d+\\.\\d*) ?(sq ?m|sq. ?m)\"\n",
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"sqmregex = r\"(\\d+\\.?\\d*)\"\n",
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"matches = re.findall(sqmregex, text.lower())\n",
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"\n",
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"matches"
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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": "57833889-c71c-42f8-a2f0-12fd284eca3d",
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"metadata": {},
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"outputs": [],
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"source": [
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"fix = [135577163,\n",
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"139692791,\n",
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"148661672,\n",
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"149763596,\n",
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"145384067,\n",
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"150394943,\n",
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"146826371,\n",
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"151149578,\n",
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"147682598,\n",
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"148826510,\n",
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"86922567,\n",
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"147740009,\n",
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"142128437,\n",
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"86648925,\n",
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"148609136,\n",
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"145712417,\n",
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"150133154,\n",
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"86807694,\n",
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"148345610,\n",
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"140016896,\n",
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"147800474,\n",
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"148319222,\n",
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"150066749,\n",
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"148401890,\n",
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"149307794,\n",
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"146923850,\n",
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"149822654,\n",
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"149803352,\n",
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"150019004,\n",
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"148078598,\n",
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"145801379,\n",
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"149453036,\n",
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"147421130,\n",
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"150806240,\n",
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"144411161,\n",
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"149352407,\n",
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"145816121,\n",
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"150267824,\n",
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"129764555,\n",
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"141653678,\n",
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"142919210,\n",
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"147722849,\n",
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"150753473,\n",
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"151045490,\n",
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"60761856,\n",
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"139696430,\n",
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"142793525,\n",
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"150270113,\n",
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"150578588,\n",
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"150975599,\n",
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"134499296,\n",
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"149746589,\n",
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"84129467,\n",
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"150757094,\n",
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|
"148034210,\n",
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"137157644]"
|
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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": 12,
|
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"id": "3a07c174-3f0a-40f9-ad64-b9b6e2b16c6b",
|
||
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"metadata": {},
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"outputs": [],
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"source": [
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|
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"from data_access import Listing\n",
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"from rec.floorplan import extract_total_sqm\n",
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"import json"
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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": 6,
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"id": "5b5c5826-1462-427d-9c8e-d65ac20f57b2",
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"metadata": {},
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"outputs": [],
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"source": [
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"from tqdm import tqdm"
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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": 8,
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"id": "84f9a356-ab29-4bcd-8214-f1f59c75f5a2",
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"metadata": {},
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"outputs": [],
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"source": [
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"listings = Listing.get_all_listings()"
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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": 20,
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"id": "9fc45893-08ec-4f7b-9c47-871399e2ce8e",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 75982/75982 [00:11<00:00, 6631.57it/s]\n"
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]
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}
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],
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"source": [
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"c = 0\n",
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"for listing in tqdm(listings):\n",
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|
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" with open(listing.path_floorplan_ocr_json()) as f:\n",
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" objs = json.load(f)\n",
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|
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" for obj in objs:\n",
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" old = obj['estimated_sqm']\n",
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" sqm = extract_total_sqm(obj['text'])\n",
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" obj['estimated_sqm'] = sqm\n",
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|
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" if old != sqm:\n",
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" c+=1\n",
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|
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" with open(floorplan_path, 'w') as f:\n",
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" objs = json.dump(objs, f)"
|
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]
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},
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{
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"cell_type": "code",
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||
|
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"execution_count": 21,
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||
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"id": "3f72a3d6-db81-403a-a773-b33ac776f71c",
|
||
|
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"metadata": {},
|
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"outputs": [
|
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{
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||
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"data": {
|
||
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"text/plain": [
|
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"7646"
|
||
|
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]
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},
|
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"execution_count": 21,
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||
|
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"metadata": {},
|
||
|
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"output_type": "execute_result"
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|
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}
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],
|
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"source": [
|
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|
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"c"
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]
|
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},
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{
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"cell_type": "code",
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|
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"execution_count": null,
|
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"id": "b2a8f7f1-beb9-40ab-b2c2-d27fefc310fe",
|
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"metadata": {},
|
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "markdown",
|
||
|
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"id": "0cf00989-b013-4b4e-ac65-bbdec1da5e9a",
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"metadata": {},
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"source": [
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|
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"# testing ocr"
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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": 45,
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"id": "b9f4a649-e417-4078-a2c2-bd105642ee8e",
|
||
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"metadata": {},
|
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"outputs": [],
|
||
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"source": [
|
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|
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"from data_access import Listing\n",
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|
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"import json"
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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": 7,
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||
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"id": "867e1d67-3c22-42f3-a6af-22c3722b6ce4",
|
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"metadata": {},
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"outputs": [],
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"source": [
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"l = Listing(101369066)"
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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": 8,
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"id": "aa618056-fc5b-411b-a5a1-bbf257bc926e",
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"metadata": {},
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"outputs": [],
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"source": [
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"l.calculate_sqm_ocr(True)"
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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": 9,
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||
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"id": "8f194b0a-1e40-41d6-8539-08b69116f052",
|
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"metadata": {},
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"outputs": [],
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"source": [
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"with open(l.path_floorplan_ocr_json()) as f:\n",
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" js = json.load(f)"
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]
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},
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{
|
||
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"cell_type": "code",
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||
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"execution_count": 10,
|
||
|
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"id": "5d2e51bb-2d02-46e8-a853-7d385e097b69",
|
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|
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"metadata": {},
|
||
|
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"outputs": [
|
||
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{
|
||
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"data": {
|
||
|
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"text/plain": [
|
||
|
|
"[{'floorplan_path': 'data/rs/101369066/floorplans/0_57956_LAD180253_FLP_00_0000.gif',\n",
|
||
|
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" 'estimated_sqm': None,\n",
|
||
|
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" 'text': 'Dalgarno Gardens\\n\\n: Reception/ z E g\\nDining Room 5 © ®\\n27\\'8\" x 19\\'5\" ExXx8\\npossesses One\\n; 8.43 x 5.92m 8 5\\n\\nKitchen|\\n\\n12\\'3\" x 8\\'4 Bedroom\\n3.73 x 2.6 15\\'4\" x 12\\'6\"\\n4.67 x 3.81m\\n\\nBedroom\\n13\\'5\" x 10\\'11\"\\n4.09 x 3.33m\\n\\nBedroom\\n12\\'6\" x 12\\'\\n3.81 x 3.66m\\n\\nGround Floor\\nFor Illustration Purposes Only - Not To Seale\\n\\nThis floor plan should be used as a general outline for guidance only and does not constitute in whole or in part an offer or contract.\\nAny intending purchaser or lessee should satisfy themselves by inspection, searches, enquiries and full survey as to the correctness of each statement.\\nAny areas, measurements or distances quoted are approximate and should not be used to value a property or be the basis of any sale or let.\\n'}]"
|
||
|
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]
|
||
|
|
},
|
||
|
|
"execution_count": 10,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"js"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "24813a78-9c39-49f4-973a-696261e23c4f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 48,
|
||
|
|
"id": "54ebc983-e98d-41e8-b0b1-6488f1ba6020",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "error",
|
||
|
|
"evalue": "OpenCV(4.11.0) :-1: error: (-5:Bad argument) in function 'imread'\n> Overload resolution failed:\n> - Expected 'filename' to be a str or path-like object\n> - Expected 'filename' to be a str or path-like object\n> - Expected 'filename' to be a str or path-like object\n",
|
||
|
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"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[0;31merror\u001b[0m Traceback (most recent call last)",
|
||
|
|
"Cell \u001b[0;32mIn[48], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mcv2\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mimread\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimg\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
|
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"\u001b[0;31merror\u001b[0m: OpenCV(4.11.0) :-1: error: (-5:Bad argument) in function 'imread'\n> Overload resolution failed:\n> - Expected 'filename' to be a str or path-like object\n> - Expected 'filename' to be a str or path-like object\n> - Expected 'filename' to be a str or path-like object\n"
|
||
|
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]
|
||
|
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}
|
||
|
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],
|
||
|
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"source": [
|
||
|
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"\n"
|
||
|
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]
|
||
|
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},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "c8928641-39b3-44ab-9dd5-9e45b44aefcb",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Improve OCR"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 2,
|
||
|
|
"id": "ee30258e-64d9-4d76-ae5b-f43677aaf865",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"import cv2\n",
|
||
|
|
"from PIL import Image\n",
|
||
|
|
"import re\n",
|
||
|
|
"import pytesseract\n",
|
||
|
|
"import numpy as np\n",
|
||
|
|
"\n",
|
||
|
|
"def extract_total_sqm(input_str: str):\n",
|
||
|
|
" sqmregex = r\"(\\d+\\.?\\d*) ?(sq ?m|sq. ?m)\"\n",
|
||
|
|
" matches = re.findall(sqmregex, input_str.lower())\n",
|
||
|
|
" sqms = [float(m[0]) for m in matches]\n",
|
||
|
|
" filtered = [sqm for sqm in sqms if 30 < sqm < 160]\n",
|
||
|
|
" if len(filtered) == 0:\n",
|
||
|
|
" return None\n",
|
||
|
|
" return max(filtered)\n",
|
||
|
|
"\n",
|
||
|
|
"def improve_img_for_ocr(img: Image):\n",
|
||
|
|
" img2 = np.array(img.convert('L'))\n",
|
||
|
|
" cv2.resize(img2, None, fx=1.2, fy=1.2, interpolation=cv2.INTER_CUBIC)\n",
|
||
|
|
" thresh = cv2.adaptiveThreshold(img2,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY,11,2)\n",
|
||
|
|
" return Image.fromarray(thresh)\n",
|
||
|
|
"\n",
|
||
|
|
"def new_ocr(path) -> tuple[float, str]:\n",
|
||
|
|
" img = Image.open(path)\n",
|
||
|
|
" img = improve_img_for_ocr(img)\n",
|
||
|
|
" text = pytesseract.image_to_string(img)\n",
|
||
|
|
" estimated_sqm = extract_total_sqm(text)\n",
|
||
|
|
" return estimated_sqm, text\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"def old_ocr(image_path):\n",
|
||
|
|
" img = Image.open(image_path)\n",
|
||
|
|
" text = pytesseract.image_to_string(img)\n",
|
||
|
|
" estimated_sqm = extract_total_sqm(text)\n",
|
||
|
|
" return estimated_sqm, text"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "80be9ffc-ee71-4a27-b13c-ad1e5f798899",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 27,
|
||
|
|
"id": "bae6347e-98c3-4cbd-ab97-2118c92d97ad",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(None, '<SeDROOU\\ncz UMASS.\\nASH\\n\\nerie\\nRoce. ae\\n\\n')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 27,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"path = \"/Users/kadir/code/realestate/crawler/data/rs/101369066/floorplans/0_57956_LAD180253_FLP_00_0000.gif\"\n",
|
||
|
|
"path2 = \"/Users/kadir/code/realestate/crawler/data/rs/126212030/floorplans/0_136139_2442224_FLP_00_0000.jpeg\"\n",
|
||
|
|
"\n",
|
||
|
|
"text = new_ocr(path2)\n",
|
||
|
|
"text"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "76867230",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"(None,\n",
|
||
|
|
" 'Dalgarno Gardens\\n\\n: Reception/ z E g\\nDining Room 5 © ®\\n27\\'8\" x 19\\'5\" ExXx8\\npossesses One\\n; 8.43 x 5.92m 8 5\\n\\nKitchen|\\n\\n12\\'3\" x 8\\'4 Bedroom\\n3.73 x 2.6 15\\'4\" x 12\\'6\"\\n4.67 x 3.81m\\n\\nBedroom\\n13\\'5\" x 10\\'11\"\\n4.09 x 3.33m\\n\\nBedroom\\n12\\'6\" x 12\\'\\n3.81 x 3.66m\\n\\nGround Floor\\nFor Illustration Purposes Only - Not To Seale\\n\\nThis floor plan should be used as a general outline for guidance only and does not constitute in whole or in part an offer or contract.\\nAny intending purchaser or lessee should satisfy themselves by inspection, searches, enquiries and full survey as to the correctness of each statement.\\nAny areas, measurements or distances quoted are approximate and should not be used to value a property or be the basis of any sale or let.\\n')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 32,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"new_ocr(\"data/rs/101369066/floorplans/0_57956_LAD180253_FLP_00_0000.gif\")\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "f3308c35-6e28-4307-8686-879e95368a0c",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# util"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "97897634",
|
||
|
|
"metadata": {},
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"id": "c509521a-7a34-4346-aaf5-d571d01c06ce",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def showpics(path):\n",
|
||
|
|
" img = Image.open(path)\n",
|
||
|
|
" display(img)\n",
|
||
|
|
" img2 = improve_img_for_ocr(img)\n",
|
||
|
|
" display(img2)\n",
|
||
|
|
" "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "e7b7ea6d-1fec-4886-b620-4f3f906e01f6",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# Compare against previous results"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"id": "34e71c93-06fc-4ef7-bc70-ed3a42a893bc",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"/Users/kadir/code/realestate/crawler/venv/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
|
||
|
|
" from .autonotebook import tqdm as notebook_tqdm\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"total pre filter: 106160\n",
|
||
|
|
"Only ones with floor plan pictures: 101162\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from data_access import Listing\n",
|
||
|
|
"\n",
|
||
|
|
"listings = Listing.get_all_listings()\n",
|
||
|
|
"print(\"total pre filter:\", len(listings))\n",
|
||
|
|
"\n",
|
||
|
|
"listings = [l for l in listings if len(l.list_floorplans()) > 0]\n",
|
||
|
|
"print(\"Only ones with floor plan pictures:\", len(listings))"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"id": "160c624a-49d4-4c3f-a757-e6e5a9aad7bc",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<style scoped>\n",
|
||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
||
|
|
" vertical-align: middle;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe tbody tr th {\n",
|
||
|
|
" vertical-align: top;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe thead th {\n",
|
||
|
|
" text-align: right;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>listing</th>\n",
|
||
|
|
" <th>sqmbefore</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>0</th>\n",
|
||
|
|
" <td>Listing(identifier=100938761, _cached=None)</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>1</th>\n",
|
||
|
|
" <td>Listing(identifier=101149631, _cached=None)</td>\n",
|
||
|
|
" <td>67.2</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>2</th>\n",
|
||
|
|
" <td>Listing(identifier=101369066, _cached=None)</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>3</th>\n",
|
||
|
|
" <td>Listing(identifier=101817179, _cached=None)</td>\n",
|
||
|
|
" <td>53.2</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>4</th>\n",
|
||
|
|
" <td>Listing(identifier=101939660, _cached=None)</td>\n",
|
||
|
|
" <td>56.5</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" listing sqmbefore\n",
|
||
|
|
"0 Listing(identifier=100938761, _cached=None) NaN\n",
|
||
|
|
"1 Listing(identifier=101149631, _cached=None) 67.2\n",
|
||
|
|
"2 Listing(identifier=101369066, _cached=None) NaN\n",
|
||
|
|
"3 Listing(identifier=101817179, _cached=None) 53.2\n",
|
||
|
|
"4 Listing(identifier=101939660, _cached=None) 56.5"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 5,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import pandas as pd\n",
|
||
|
|
"\n",
|
||
|
|
"df = pd.DataFrame(pd.Series(listings), columns=[\"listing\"])\n",
|
||
|
|
"# now add the column sqm from before\n",
|
||
|
|
"df.loc[:, \"sqmbefore\"] = df.listing.apply(lambda x: x.sqm_ocr)\n",
|
||
|
|
"df.head()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 9,
|
||
|
|
"id": "560255e4",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"l = listings[2]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 20,
|
||
|
|
"id": "71539963-2472-444e-b994-c45577577525",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"ename": "TypeError",
|
||
|
|
"evalue": "'Listing' object is not iterable",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
|
||
|
|
"Cell \u001b[0;32mIn[20], line 15\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mmax\u001b[39m(sqms)\n\u001b[0;32m---> 15\u001b[0m \u001b[43mnew_ocr_from_listing\u001b[49m\u001b[43m(\u001b[49m\u001b[43ml\u001b[49m\u001b[43m)\u001b[49m\n",
|
||
|
|
"Cell \u001b[0;32mIn[20], line 6\u001b[0m, in \u001b[0;36mnew_ocr_from_listing\u001b[0;34m(paths)\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mnew_ocr_from_listing\u001b[39m(paths: List[Path]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mtuple\u001b[39m[\u001b[38;5;28mfloat\u001b[39m, \u001b[38;5;28mstr\u001b[39m]:\n\u001b[1;32m 5\u001b[0m sqms \u001b[38;5;241m=\u001b[39m []\n\u001b[0;32m----> 6\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mfloorplan\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mpaths\u001b[49m\u001b[43m:\u001b[49m\n\u001b[1;32m 7\u001b[0m \u001b[43m \u001b[49m\u001b[43msqm\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtext\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mnew_ocr\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfloorplan\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 8\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;66;43;03m# print(sqm, text)\u001b[39;49;00m\n",
|
||
|
|
"\u001b[0;31mTypeError\u001b[0m: 'Listing' object is not iterable"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"from typing import List\n",
|
||
|
|
"from pathlib import Path\n",
|
||
|
|
"\n",
|
||
|
|
"def new_ocr_from_listing(paths: List[Path]) -> tuple[float, str]:\n",
|
||
|
|
" sqms = []\n",
|
||
|
|
" for floorplan in paths:\n",
|
||
|
|
" sqm, text = new_ocr(floorplan)\n",
|
||
|
|
" # print(sqm, text)\n",
|
||
|
|
" sqms.append(sqm)\n",
|
||
|
|
" sqms = [sqm for sqm in sqms if sqm is not None]\n",
|
||
|
|
" if len(sqms) == 0:\n",
|
||
|
|
" return None\n",
|
||
|
|
" return max(sqms)\n",
|
||
|
|
" \n",
|
||
|
|
"new_ocr_from_listing(l)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 24,
|
||
|
|
"id": "d0d419b2-2595-483a-a253-1c662698c91f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stderr",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"Process SpawnProcess-68:\n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n",
|
||
|
|
" self.run()\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/process.py\", line 108, in run\n",
|
||
|
|
" self._target(*self._args, **self._kwargs)\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/concurrent/futures/process.py\", line 246, in _process_worker\n",
|
||
|
|
" call_item = call_queue.get(block=True)\n",
|
||
|
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/queues.py\", line 122, in get\n",
|
||
|
|
" return _ForkingPickler.loads(res)\n",
|
||
|
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
||
|
|
"AttributeError: Can't get attribute 'new_ocr_from_listing' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>\n",
|
||
|
|
"Process SpawnProcess-69:\n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n",
|
||
|
|
" self.run()\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/process.py\", line 108, in run\n",
|
||
|
|
" self._target(*self._args, **self._kwargs)\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/concurrent/futures/process.py\", line 246, in _process_worker\n",
|
||
|
|
" call_item = call_queue.get(block=True)\n",
|
||
|
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/queues.py\", line 122, in get\n",
|
||
|
|
" return _ForkingPickler.loads(res)\n",
|
||
|
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
||
|
|
"AttributeError: Can't get attribute 'new_ocr_from_listing' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>\n",
|
||
|
|
"Process SpawnProcess-70:\n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n",
|
||
|
|
" self.run()\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/process.py\", line 108, in run\n",
|
||
|
|
" self._target(*self._args, **self._kwargs)\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/concurrent/futures/process.py\", line 246, in _process_worker\n",
|
||
|
|
" call_item = call_queue.get(block=True)\n",
|
||
|
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/queues.py\", line 122, in get\n",
|
||
|
|
" return _ForkingPickler.loads(res)\n",
|
||
|
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
||
|
|
"AttributeError: Can't get attribute 'new_ocr_from_listing' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>\n",
|
||
|
|
"Process SpawnProcess-71:\n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n",
|
||
|
|
" self.run()\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/process.py\", line 108, in run\n",
|
||
|
|
" self._target(*self._args, **self._kwargs)\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/concurrent/futures/process.py\", line 246, in _process_worker\n",
|
||
|
|
" call_item = call_queue.get(block=True)\n",
|
||
|
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/queues.py\", line 122, in get\n",
|
||
|
|
" return _ForkingPickler.loads(res)\n",
|
||
|
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
||
|
|
"AttributeError: Can't get attribute 'new_ocr_from_listing' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>\n",
|
||
|
|
"Process SpawnProcess-72:\n",
|
||
|
|
"Traceback (most recent call last):\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n",
|
||
|
|
" self.run()\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/process.py\", line 108, in run\n",
|
||
|
|
" self._target(*self._args, **self._kwargs)\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/concurrent/futures/process.py\", line 246, in _process_worker\n",
|
||
|
|
" call_item = call_queue.get(block=True)\n",
|
||
|
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
||
|
|
" File \"/Users/kadir/.pyenv/versions/3.12.0/lib/python3.12/multiprocessing/queues.py\", line 122, in get\n",
|
||
|
|
" return _ForkingPickler.loads(res)\n",
|
||
|
|
" ^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
|
||
|
|
"AttributeError: Can't get attribute 'new_ocr_from_listing' on <module '__main__' (<class '_frozen_importlib.BuiltinImporter'>)>\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"ename": "BrokenProcessPool",
|
||
|
|
"evalue": "A child process terminated abruptly, the process pool is not usable anymore",
|
||
|
|
"output_type": "error",
|
||
|
|
"traceback": [
|
||
|
|
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||
|
|
"\u001b[0;31mBrokenProcessPool\u001b[0m Traceback (most recent call last)",
|
||
|
|
"Cell \u001b[0;32mIn[24], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m concurrent\u001b[38;5;241m.\u001b[39mfutures\u001b[38;5;241m.\u001b[39mProcessPoolExecutor() \u001b[38;5;28;01mas\u001b[39;00m executor:\n\u001b[1;32m 4\u001b[0m paths \u001b[38;5;241m=\u001b[39m df\u001b[38;5;241m.\u001b[39mlisting\u001b[38;5;241m.\u001b[39miloc[:\u001b[38;5;241m100\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(\u001b[38;5;28;01mlambda\u001b[39;00m x: new_ocr_from_listing(x\u001b[38;5;241m.\u001b[39mlist_floorplans()))\n\u001b[0;32m----> 5\u001b[0m sqmafter \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[43mexecutor\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mnew_ocr_from_listing\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mpaths\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 8\u001b[0m \u001b[38;5;66;03m# df['sqmafter'] = sqmafter\u001b[39;00m\n\u001b[1;32m 10\u001b[0m df\u001b[38;5;241m.\u001b[39mhead()\n",
|
||
|
|
"File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/concurrent/futures/process.py:822\u001b[0m, in \u001b[0;36mProcessPoolExecutor.map\u001b[0;34m(self, fn, timeout, chunksize, *iterables)\u001b[0m\n\u001b[1;32m 819\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m chunksize \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m 820\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mchunksize must be >= 1.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 822\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmap\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpartial\u001b[49m\u001b[43m(\u001b[49m\u001b[43m_process_chunk\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfn\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 823\u001b[0m \u001b[43m \u001b[49m\u001b[43m_get_chunks\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43miterables\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mchunksize\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mchunksize\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 824\u001b[0m \u001b[43m \u001b[49m\u001b[43mtimeout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtimeout\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 825\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _chain_from_iterable_of_lists(results)\n",
|
||
|
|
"File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/concurrent/futures/_base.py:608\u001b[0m, in \u001b[0;36mExecutor.map\u001b[0;34m(self, fn, timeout, chunksize, *iterables)\u001b[0m\n\u001b[1;32m 605\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m timeout \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 606\u001b[0m end_time \u001b[38;5;241m=\u001b[39m timeout \u001b[38;5;241m+\u001b[39m time\u001b[38;5;241m.\u001b[39mmonotonic()\n\u001b[0;32m--> 608\u001b[0m fs \u001b[38;5;241m=\u001b[39m [\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msubmit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfn\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m args \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39miterables)]\n\u001b[1;32m 610\u001b[0m \u001b[38;5;66;03m# Yield must be hidden in closure so that the futures are submitted\u001b[39;00m\n\u001b[1;32m 611\u001b[0m \u001b[38;5;66;03m# before the first iterator value is required.\u001b[39;00m\n\u001b[1;32m 612\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mresult_iterator\u001b[39m():\n",
|
||
|
|
"File \u001b[0;32m~/.pyenv/versions/3.12.0/lib/python3.12/concurrent/futures/process.py:776\u001b[0m, in \u001b[0;36mProcessPoolExecutor.submit\u001b[0;34m(self, fn, *args, **kwargs)\u001b[0m\n\u001b[1;32m 774\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shutdown_lock:\n\u001b[1;32m 775\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_broken:\n\u001b[0;32m--> 776\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m BrokenProcessPool(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_broken)\n\u001b[1;32m 777\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_shutdown_thread:\n\u001b[1;32m 778\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcannot schedule new futures after shutdown\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
|
||
|
|
"\u001b[0;31mBrokenProcessPool\u001b[0m: A child process terminated abruptly, the process pool is not usable anymore"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"import concurrent.futures\n",
|
||
|
|
"\n",
|
||
|
|
"with concurrent.futures.ProcessPoolExecutor() as executor:\n",
|
||
|
|
" paths = df.listing.iloc[:100].apply(lambda x: new_ocr_from_listing(x.list_floorplans()))\n",
|
||
|
|
" sqmafter = list(executor.map(new_ocr_from_listing, paths))\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"# df['sqmafter'] = sqmafter\n",
|
||
|
|
"\n",
|
||
|
|
"df.head()\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 26,
|
||
|
|
"id": "aab58c46",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"843.0166666666667"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 26,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "90653983-1d9c-4e66-a80f-6bf435d7b332",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 40,
|
||
|
|
"id": "da633fab-de64-4567-a370-6aacf52352e5",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"data/rs/101149631/floorplans/0_75997_thgrth_FLP_00_0000.jpeg\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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"image/png": "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
|
||
|
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"text/plain": [
|
||
|
|
"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1704x1276>"
|
||
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]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
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"data": {
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|
|
"image/jpeg": "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
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.Image.Image image mode=L size=1704x1276>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(l.list_floorplans()[0])\n",
|
||
|
|
"showpics(l.list_floorplans()[0])"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"id": "3aa1e2f0-00c4-429b-822a-a266ee5c5263",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"PosixPath('data/rs/100938761/floorplans/0_159428_21croydon_FLP_00_0000.png')"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"l.list_floorplans()[0]"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"id": "b214c167-492f-4bfb-b4ec-fb34527c50bc",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"# increasing percentage rate of ocr detection"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 19,
|
||
|
|
"id": "c3da3539-96dc-41ba-a177-9dd36f01deeb",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"from PIL import Image\n",
|
||
|
|
"import pandas as pd\n",
|
||
|
|
"from pathlib import PosixPath\n",
|
||
|
|
"from data_access import Listing\n",
|
||
|
|
"pd.set_option('display.float_format', lambda x: '%.0f' % x)\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 4,
|
||
|
|
"id": "deddeafc-d04b-414e-9836-22d22489eca3",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"# find the percentage of floorplans we calculated\n",
|
||
|
|
"listings = Listing.get_all_listings()"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 5,
|
||
|
|
"id": "160957e3-7190-4bb7-be33-f371643c287f",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"total pre filter: 106160\n",
|
||
|
|
"Only ones with floor plan pictures: 101162\n",
|
||
|
|
"With sqm calculated: 74212\n",
|
||
|
|
"percentage: 0.7335956189082857\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(\"total pre filter:\", len(listings))\n",
|
||
|
|
"listings2 = [l for l in listings if len(l.list_floorplans()) > 0]\n",
|
||
|
|
"print(\"Only ones with floor plan pictures:\", len(listings2))\n",
|
||
|
|
"\n",
|
||
|
|
"withsqm = []\n",
|
||
|
|
"withoutsqm = []\n",
|
||
|
|
"for l in listings2:\n",
|
||
|
|
" if l.sqm_ocr is None:\n",
|
||
|
|
" withoutsqm.append(l)\n",
|
||
|
|
" else:\n",
|
||
|
|
" withsqm.append(l)\n",
|
||
|
|
" \n",
|
||
|
|
"print(\"With sqm calculated:\", len(withsqm))\n",
|
||
|
|
"percentage = len(withsqm) / len(listings2)\n",
|
||
|
|
"print(\"percentage:\", percentage)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "26c57483-51a2-4aae-8da5-90685ff827c3",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "b10b753b-7787-47e8-bc29-27b6d193511c",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 22,
|
||
|
|
"id": "6690f19b-cd5c-48ee-91d4-27ec351dbe10",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"def analysis(path: PosixPath):\n",
|
||
|
|
" try:\n",
|
||
|
|
" filetype = path.suffix.lower()\n",
|
||
|
|
" img = Image.open(path)\n",
|
||
|
|
" pixels = img.width * img.height\n",
|
||
|
|
" x_dpi, y_dpi = img.info.get('dpi', (-1, -1))\n",
|
||
|
|
" except Exception as e:\n",
|
||
|
|
" print(path, e)\n",
|
||
|
|
" return None\n",
|
||
|
|
" return {\n",
|
||
|
|
" \"ftype\": filetype,\n",
|
||
|
|
" \"height\": img.width,\n",
|
||
|
|
" \"width\": img.width,\n",
|
||
|
|
" \"pixels\": pixels,\n",
|
||
|
|
" \"dpix\": x_dpi,\n",
|
||
|
|
" \"dpiy\": y_dpi,\n",
|
||
|
|
" }"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 23,
|
||
|
|
"id": "69a469d7-beba-40d2-8c44-b3d55eb3de35",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"analysiswith = [analysis(l.list_floorplans()[0]) for l in withsqm]\n",
|
||
|
|
"analysiswithout = [analysis(l.list_floorplans()[0]) for l in withoutsqm]\n",
|
||
|
|
"wth = pd.DataFrame(analysiswith)\n",
|
||
|
|
"wthout = pd.DataFrame(analysiswithout)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 16,
|
||
|
|
"id": "b71bd8b3-7f25-48d2-ace6-28d79a8fb6ba",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/html": [
|
||
|
|
"<div>\n",
|
||
|
|
"<style scoped>\n",
|
||
|
|
" .dataframe tbody tr th:only-of-type {\n",
|
||
|
|
" vertical-align: middle;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe tbody tr th {\n",
|
||
|
|
" vertical-align: top;\n",
|
||
|
|
" }\n",
|
||
|
|
"\n",
|
||
|
|
" .dataframe thead th {\n",
|
||
|
|
" text-align: right;\n",
|
||
|
|
" }\n",
|
||
|
|
"</style>\n",
|
||
|
|
"<table border=\"1\" class=\"dataframe\">\n",
|
||
|
|
" <thead>\n",
|
||
|
|
" <tr style=\"text-align: right;\">\n",
|
||
|
|
" <th></th>\n",
|
||
|
|
" <th>ftype</th>\n",
|
||
|
|
" <th>pixels</th>\n",
|
||
|
|
" <th>dpix</th>\n",
|
||
|
|
" <th>dpiy</th>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </thead>\n",
|
||
|
|
" <tbody>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>count</th>\n",
|
||
|
|
" <td>74212</td>\n",
|
||
|
|
" <td>74212</td>\n",
|
||
|
|
" <td>74212</td>\n",
|
||
|
|
" <td>74212</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>unique</th>\n",
|
||
|
|
" <td>4</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>134</td>\n",
|
||
|
|
" <td>135</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>top</th>\n",
|
||
|
|
" <td>.jpeg</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>96</td>\n",
|
||
|
|
" <td>96</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>freq</th>\n",
|
||
|
|
" <td>56516</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>17230</td>\n",
|
||
|
|
" <td>17230</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>mean</th>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>2062357</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>std</th>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>980308</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>min</th>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>55870</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>25%</th>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>1047552</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>50%</th>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>2134016</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>75%</th>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>2961408</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" <tr>\n",
|
||
|
|
" <th>max</th>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>4194304</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" <td>NaN</td>\n",
|
||
|
|
" </tr>\n",
|
||
|
|
" </tbody>\n",
|
||
|
|
"</table>\n",
|
||
|
|
"</div>"
|
||
|
|
],
|
||
|
|
"text/plain": [
|
||
|
|
" ftype pixels dpix dpiy\n",
|
||
|
|
"count 74212 74212 74212 74212\n",
|
||
|
|
"unique 4 NaN 134 135\n",
|
||
|
|
"top .jpeg NaN 96 96\n",
|
||
|
|
"freq 56516 NaN 17230 17230\n",
|
||
|
|
"mean NaN 2062357 NaN NaN\n",
|
||
|
|
"std NaN 980308 NaN NaN\n",
|
||
|
|
"min NaN 55870 NaN NaN\n",
|
||
|
|
"25% NaN 1047552 NaN NaN\n",
|
||
|
|
"50% NaN 2134016 NaN NaN\n",
|
||
|
|
"75% NaN 2961408 NaN NaN\n",
|
||
|
|
"max NaN 4194304 NaN NaN"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 16,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"wth.describe(include=\"all\")"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 36,
|
||
|
|
"id": "0ee4772e-6fbc-47c6-aa5b-e67e0b1ba69e",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"with\n",
|
||
|
|
"ftype\n",
|
||
|
|
".jpeg 76\n",
|
||
|
|
".png 18\n",
|
||
|
|
".gif 6\n",
|
||
|
|
".jpg 0\n",
|
||
|
|
"Name: count, dtype: float64\n",
|
||
|
|
"\n",
|
||
|
|
"without\n",
|
||
|
|
"ftype\n",
|
||
|
|
".jpeg 80\n",
|
||
|
|
".png 17\n",
|
||
|
|
".gif 3\n",
|
||
|
|
".jpg 0\n",
|
||
|
|
"Name: count, dtype: float64\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"print(\"with\")\n",
|
||
|
|
"print(wth.ftype.value_counts() / wth.shape[0] * 100)\n",
|
||
|
|
"\n",
|
||
|
|
"print(\"\")\n",
|
||
|
|
"\n",
|
||
|
|
"print(\"without\")\n",
|
||
|
|
"print(wthout.ftype.value_counts() / wthout.shape[0] * 100)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 25,
|
||
|
|
"id": "b7c2d54f-cc14-4f09-abc0-0f4032bd7f08",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Axes: >"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 25,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"wthout.pixels.hist(bins=20)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 28,
|
||
|
|
"id": "5582ffc9-c949-43bd-855b-ee28b7698c65",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"<Axes: >"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"execution_count": 28,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<Figure size 640x480 with 1 Axes>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"wth.pixels.hist(bins=20)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "8a0499c2-445b-48b6-9558-217ddc3e2afe",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 29,
|
||
|
|
"id": "c4e46a78-c9a4-48b1-a4d4-24826d3311d3",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": [
|
||
|
|
"maxsize = 700\n",
|
||
|
|
"def showpic(imgpath, resize=True):\n",
|
||
|
|
" img = Image.open(imgpath)\n",
|
||
|
|
" if resize:\n",
|
||
|
|
" ratio = min(maxsize/img.width, maxsize/img.height)\n",
|
||
|
|
" new_size = (int(img.width * ratio), int(img.height * ratio))\n",
|
||
|
|
" \n",
|
||
|
|
" # Resize and display\n",
|
||
|
|
" img = img.resize(new_size, Image.Resampling.LANCZOS)\n",
|
||
|
|
" display(img)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 37,
|
||
|
|
"id": "867ee3d2-6534-4491-90d2-19429afd9f9c",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
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]
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},
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"metadata": {},
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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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|
"[{'floorplan_path': 'data/rs/100938761/floorplans/0_159428_21croydon_FLP_00_0000.png', 'estimated_sqm': None, 'text': 'ee | | TET | |\\n\\no fo}\\nJ\\n° ) | Enfuite\\nBathrobm\\nry\\n<>\\nServices/Store Hall Batcony\\nr\\n© Kitchen/Living/Dining\\n\\nBedroom | | |\\n\\n'}]\n",
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"text/plain": [
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"<PIL.GifImagePlugin.GifImageFile image mode=P size=1128x1585>"
|
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]
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},
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"metadata": {},
|
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"output_type": "display_data"
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},
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{
|
||
|
|
"name": "stdout",
|
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|
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"output_type": "stream",
|
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"text": [
|
||
|
|
"[{'floorplan_path': 'data/rs/101369066/floorplans/0_57956_LAD180253_FLP_00_0000.gif', 'estimated_sqm': None, 'text': 'Dalgarno Gardens\\n\\n: Reception/ z E g\\nDining Room 5 © ®\\n27\\'8\" x 19\\'5\" ExXx8\\npossesses One\\n; 8.43 x 5.92m 8 5\\n\\nKitchen|\\n\\n12\\'3\" x 8\\'4 Bedroom\\n3.73 x 2.6 15\\'4\" x 12\\'6\"\\n4.67 x 3.81m\\n\\nBedroom\\n13\\'5\" x 10\\'11\"\\n4.09 x 3.33m\\n\\nBedroom\\n12\\'6\" x 12\\'\\n3.81 x 3.66m\\n\\nGround Floor\\nFor Illustration Purposes Only - Not To Seale\\n\\nThis floor plan should be used as a general outline for guidance only and does not constitute in whole or in part an offer or contract.\\nAny intending purchaser or lessee should satisfy themselves by inspection, searches, enquiries and full survey as to the correctness of each statement.\\nAny areas, measurements or distances quoted are approximate and should not be used to value a property or be the basis of any sale or let.\\n'}]\n",
|
||
|
|
"#######################################\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
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|
},
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||
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{
|
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|
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x1048>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[{'floorplan_path': 'data/rs/102360773/floorplans/0_104911_30366605_FLP_00_0000.jpeg', 'estimated_sqm': None, 'text': 'SECOND FLOOR\\n\\nBALCONY\\n\\nLOUNGE/KITCHEN Be\\n\\n7.81m x 3.42m\\n25\\'7\" x 11\\'3\"\\n\\nS\\nPig BATHROOM\\n\\nBEDROOM Ee\\n3.80m x 4.10m x\\n126\" x 13\\'5\\' RY\\nHALLWAY < CUPBOARI\\n\\n\\\\\\n\\n/ARDROBI 5 \\\\._ CUPBOAR\\n'}]\n",
|
||
|
|
"#######################################\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
|
||
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"text/plain": [
|
||
|
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"<PIL.PngImagePlugin.PngImageFile image mode=RGBA size=580x746>"
|
||
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]
|
||
|
|
},
|
||
|
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"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[{'floorplan_path': 'data/rs/106419218/floorplans/0_159428_206QH_FLP_00_0000.png', 'estimated_sqm': None, 'text': 'UVING AREA, ey\\n\\nAREA,\\n>\\nBEDROOM\\nmenu BATHROOM\\nApartment Eis S0bsam\\nKicheniLvngDinng 10x27\" 335x840\\n\\nBedroom | 1010\" 1re 331x350\\n\\n'}]\n",
|
||
|
|
"#######################################\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
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|
},
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||
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{
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"data": {
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"text/plain": [
|
||
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"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1182x1631>"
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]
|
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},
|
||
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"metadata": {},
|
||
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"output_type": "display_data"
|
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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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"[{'floorplan_path': 'data/rs/109211666/floorplans/0_568_LEY210167_FLP_00_0001.jpeg', 'estimated_sqm': None, 'text': 'es)\\nes)\\nDining/Kitchen\\n3.78 m x 29.41m\\n125\" x 966\"\\n\\nLiving Room\\n5.08 m x 419m\\n168\" x 139\"\\n\\nGround Floor\\n\\nThis plan is illustrative only,\\nits details cannot be relied upon and no liability is taken for any errors.\\n\\nFirst Floor\\n\\nTotal approx floor area: 609.6 ft? (56.6 m?)\\nGround Floor: 31.5 ft? (29 m?)\\nFirst Floor: 578.0 ft? (53.7 m?)\\n\\n'}]\n",
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"#######################################\n",
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"\n",
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"\n",
|
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"\n",
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"\n",
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"\n"
|
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]
|
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},
|
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{
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"data": {
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|
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|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=856x613>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[{'floorplan_path': 'data/rs/109399676/floorplans/0_74302_LATYM_000716_FLP_00_0000.jpeg', 'estimated_sqm': None, 'text': 'TOTAL APPROX FLOOR AREA 888 SQ.FT. (830 SQ.M)\\n\\nKITCHEN / LIVING ROOM\\n\\nBEDROOM\\n197 x 122 219% 139\\n6.0m x3.7m 6.6m x4.2m\\n\\n7x6\\n2.1m x 1.8m,\\n\\nBEDROOM\\n1411 x94\\n4.5m x2.8m\\n\\n191475\\n3.4m x 2.3m\\n\\n'}]\n",
|
||
|
|
"#######################################\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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"image/jpeg": "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
|
||
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"text/plain": [
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"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=664x720>"
|
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]
|
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},
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|
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"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
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"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[{'floorplan_path': 'data/rs/109595123/floorplans/0_74302_LATYM_000722_FLP_00_0000.jpeg', 'estimated_sqm': None, 'text': 'omnes\\ne--\\n\\note 2 iieene\\n= =\\n© enon? _\\no ° @\\nco\\n\\npartment) 250 (a\\nFoor o: [ae\\nLiving Room\\nKotchen\\n\\nMaster Bedroom\\n\\nfirm x 0\\n25m x 279m\\n\\nsexe?\\n'}]\n",
|
||
|
|
"#######################################\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
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},
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{
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"data": {
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"text/plain": [
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"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=664x720>"
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]
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},
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"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
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},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[{'floorplan_path': 'data/rs/109599476/floorplans/0_74302_LATYM_000725_FLP_00_0000.jpeg', 'estimated_sqm': None, 'text': 'omnes\\ne--\\n\\note 2 iieene\\n= =\\n© enon? _\\no ° @\\nco\\n\\npartment) 250 (a\\nFoor o: [ae\\nLiving Room\\nKotchen\\n\\nMaster Bedroom\\n\\nfirm x 0\\n25m x 279m\\n\\nsexe?\\n'}]\n",
|
||
|
|
"#######################################\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
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|
},
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{
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"data": {
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|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=853x613>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[{'floorplan_path': 'data/rs/109612856/floorplans/0_74302_LATYM_000727_FLP_00_0000.jpeg', 'estimated_sqm': None, 'text': 'BEDROOM 91x75\\n1411 x94 3.4m x 2.3m\\n4.5m x2.8m\\n\\nED) 7x6\\n2.1m x 1.8m\\n\\n=\\n\\nKITCHEN / LIVING ROOM\\n29x 139\\n6.6m x4.2m\\n\\nBEDROOM\\n197 x 122\\n6.0m x3.7m\\n\\nBALCONY\\n120% 414\\n3.9m x 1.3m\\n\\nTOTAL APPROX. FLOOR AREA 888 SQ.FT. (830SQ.M.)\\n'}]\n",
|
||
|
|
"#######################################\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"data": {
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|
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"image/jpeg": "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
|
||
|
|
"image/png": "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
|
||
|
|
"text/plain": [
|
||
|
|
"<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=893x1201>"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "display_data"
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"name": "stdout",
|
||
|
|
"output_type": "stream",
|
||
|
|
"text": [
|
||
|
|
"[{'floorplan_path': 'data/rs/109648052/floorplans/0_74302_LATYM_000728_FLP_00_0000.jpeg', 'estimated_sqm': None, 'text': 'TYPE 2D\\n\\n2 BEDROOM APARTMENT\\n\\nAPARTMENT E= |\\n\\nPLooR 2\\nLivinid id Git SES7MM x S27 SM IF A= K 14? O*\\nKT KEM 7OMM x 319MM ie ae x 7! OF\\nBEDROOM 1 S6220M x 27SaMM Wwen= «a2\\nBEDROOM > TIOSMM x Fe GeM lor a= x Nr ae\\nBALCONY WOIMM xX 44Q0mm xa\\nTOTAL ANEA me som a2760 FT\\nBALCONY aan io 26 oF\\n\\nREDROOM I\\n\\n{I\\n\\ninewne\\n\\nENTRANCE\\n\\n¥\\n'}]\n",
|
||
|
|
"#######################################\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n",
|
||
|
|
"\n"
|
||
|
|
]
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"\n",
|
||
|
|
"for l in withoutsqm[:10]:\n",
|
||
|
|
" imgpath = l.list_floorplans()[0]\n",
|
||
|
|
" showpic(imgpath, False)\n",
|
||
|
|
" with open(l.path_floorplan_ocr_json()) as f:\n",
|
||
|
|
" js = json.load(f)\n",
|
||
|
|
" print(js)\n",
|
||
|
|
" print(\"#######################################\\n\\n\\n\\n\\n\")\n",
|
||
|
|
"\n",
|
||
|
|
" "
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": null,
|
||
|
|
"id": "a6c7c047-616f-4930-bda9-7c9285b1affa",
|
||
|
|
"metadata": {},
|
||
|
|
"outputs": [],
|
||
|
|
"source": []
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"metadata": {
|
||
|
|
"kernelspec": {
|
||
|
|
"display_name": "venv",
|
||
|
|
"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": "3.12.0"
|
||
|
|
}
|
||
|
|
},
|
||
|
|
"nbformat": 4,
|
||
|
|
"nbformat_minor": 5
|
||
|
|
}
|