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"""Processes a CSV file using AutoML's object detection format into tfrecords. | |
This script will accept a CSV file path or URL and write tfrecord files at the | |
provided output path. Example usage: | |
``` | |
mkdir -p /tmp/salad_dataset | |
python tfrecords_from_dataset_descriptor.py \ | |
--csv_path=gs://cloud-ml-data/img/openimage/csv/salads_ml_use.csv \ | |
--output_path=/tmp/salad_dataset | |
``` |
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{ | |
"embeddings": [ | |
{ | |
"tensorName": "My tensor", | |
"tensorShape": [ | |
1000, | |
50 | |
], | |
"tensorPath": "https://gist.githubusercontent.com/owahltinez/eab8d4c2afc6966857fea7cbb5add900/raw/8bf1fd142644b1f1a7b139c9754e6ffbc130e33c/tensors.tsv", | |
"metadataPath": "https://gist.githubusercontent.com/owahltinez/3781ae090f317371103a72022916c173/raw/0bafaf1ff3e1934417b2d5a0704db077ce9d39d2/metadata.tsv" |
We can make this file beautiful and searchable if this error is corrected: No tabs found in this TSV file in line 0.
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Asru70 | |
Asru56 | |
Asru76 | |
Asru50 | |
Asru58 | |
Asru56 | |
Asru55 | |
Asru70 | |
Asru58 | |
Asru76 |
We can't make this file beautiful and searchable because it's too large.
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-2.247926779091358185e-02 4.126220196485519409e-02 2.201553061604499817e-02 4.435282200574874878e-02 4.219446703791618347e-02 7.351647363975644112e-04 -1.420196741819381714e-01 3.519088402390480042e-02 7.742006331682205200e-02 1.293895300477743149e-02 7.603470981121063232e-02 -1.221615076065063477e-01 -2.842330746352672577e-02 -6.303963717073202133e-03 -3.483119606971740723e-02 2.308848127722740173e-02 8.119910210371017456e-02 1.293514519929885864e-01 1.212856918573379517e-01 -7.862001657485961914e-02 -1.989656686782836914e-01 -1.172609031200408936e-01 1.417047232389450073e-01 5.320393666625022888e-02 -4.523742944002151489e-02 -1.421208977699279785e-01 -1.612507738173007965e-02 -8.112639933824539185e-02 -1.910826005041599274e-02 1.615460962057113647e-01 -4.172783717513084412e-02 1.076077520847320557e-01 1.423008143901824951e-01 -9.967331588268280029e-02 -1.324560940265655518e-01 7.485345005989074707e-02 -1.712135504931211472e-03 -7.529520243406295776e-02 4.165450483560562134e-02 -3.049586899578571320e-02 -7.7 |
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import json | |
import os | |
import sys | |
import time | |
def list_files(opts: str, marker: str = None) -> list: | |
fpath = f"/tmp/{int(1E9 * time.monotonic())}.json" | |
marker_opt = f'--marker "{marker}"' if marker else "" | |
os.system(f'az storage fs file list {opts} --show-next-marker {marker_opt} > "{fpath}"') |
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key | city_wikidata | city_name | population | |
---|---|---|---|---|
AD_02 | Q24413 | El Tarter | 747 | |
AE_AJ | Q530171 | Ajman | 238119 | |
AE_AZ | Q1519 | Abu Dhabi | 1000000 | |
AE_AZ | Q234600 | Al Ain | 631005 | |
AE_AZ | Q234600 | Al Ain | 766936 | |
AE_AZ | Q12241857 | Madinat Zayed | 29095 | |
AE_DU | Q612 | Dubai | 2502715 | |
AE_FU | Q4045 | Fujairah | 93673 | |
AE_RK | Q2126436 | Ras al-Khaimah | 115949 |
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key | country_code | country_name | subregion1_code | subregion1_name | subregion2_code | subregion2_name | locality_code | locality_name | match_string | aggregate_report_offset | |
---|---|---|---|---|---|---|---|---|---|---|---|
BR_AC_1200013 | BR | Brazil | AC | Acre | 1200013 | Acrelândia | |||||
BR_AC_1200054 | BR | Brazil | AC | Acre | 1200054 | Assis Brasil | |||||
BR_AC_1200104 | BR | Brazil | AC | Acre | 1200104 | Brasiléia | |||||
BR_AC_1200138 | BR | Brazil | AC | Acre | 1200138 | Bujari | |||||
BR_AC_1200179 | BR | Brazil | AC | Acre | 1200179 | Capixaba | |||||
BR_AC_1200203 | BR | Brazil | AC | Acre | 1200203 | Cruzeiro do Sul | |||||
BR_AC_1200252 | BR | Brazil | AC | Acre | 1200252 | Epitaciolândia | |||||
BR_AC_1200302 | BR | Brazil | AC | Acre | 1200302 | Feijó | |||||
BR_AC_1200328 | BR | Brazil | AC | Acre | 1200328 | Jordão |
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val session: CameraCaptureSession = ... // from CameraCaptureSession.StateCallback | |
// We will be using the preview capture template for the combined streams, because | |
// it is optimized for low latency; for high-quality images use | |
// TEMPLATE_STILL_CAPTURE and for a steady frame rate use | |
// TEMPLATE_RECORD | |
val requestTemplate = CameraDevice.TEMPLATE_PREVIEW | |
val combinedRequest = session.device.createCaptureRequest(requestTemplate) | |
// Link the Surface targets with the combined request |
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class SmartSize(width: Int, height: Int) { | |
var size = Size(width, height) | |
var long = max(size.width, size.height) | |
var short = min(size.width, size.height) | |
override fun toString() = "SmartSize(${long}x${short})" | |
} | |
val SIZE_1080P: SmartSize = SmartSize(1920, 1080) | |
fun getDisplaySmartSize(display: Display): SmartSize { |
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