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This commit is contained in:
Glenn Jocher 2019-12-13 18:52:08 -08:00
parent a4bdb8ce2e
commit 0465500b37
24 changed files with 148 additions and 122431 deletions
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classes=80
train=../coco/trainvalno5k.txt
valid=../coco/5k.txt
names=data/coco.names
backup=backup/
eval=coco

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classes=80
train=data/coco16.txt
valid=data/coco16.txt
names=data/coco.names

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classes=1
train=data/coco1cls.txt
valid=data/coco1cls.txt
names=data/coco.names

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classes=80
train=../coco/train2017.txt
valid=../coco/val2017.txt
names=data/coco.names

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classes=80
train=data/coco64.txt
valid=data/coco64.txt
names=data/coco.names

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../coco/images/train2017/000000318783.jpg
../coco/images/train2017/000000337760.jpg
../coco/images/train2017/000000298197.jpg
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classes=80
train=./data/coco_16img.txt
valid=./data/coco_16img.txt
names=data/coco.names
backup=backup/
eval=coco

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classes=1
train=./data/coco_1cls.txt
valid=./data/coco_1cls.txt
names=data/coco.names
backup=backup/
eval=coco

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classes=80
train=./data/coco_64img.txt
valid=./data/coco_64img.txt
names=data/coco.names
backup=backup/
eval=coco

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data/get_coco2014.sh Executable file
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#!/bin/bash
# Zip coco folder
# zip -r coco.zip coco
# tar -czvf coco.tar.gz coco
# Download labels from Google Drive, accepting presented query
filename="coco2014labels.zip"
fileid="1s6-CmF5_SElM28r52P1OUrCcuXZN-SFo"
curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" > /dev/null
curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}" -o ${filename}
rm ./cookie
# Unzip labels
unzip -q ${filename} # for coco.zip
# tar -xzf ${filename} # for coco.tar.gz
rm ${filename}
# Download images
cd coco/images
wget -c http://images.cocodataset.org/zips/train2014.zip
wget -c http://images.cocodataset.org/zips/val2014.zip
# Unzip images
unzip -q train2014.zip
unzip -q val2014.zip
# (optional) Delete zip files
rm -rf *.zip
# cd out
cd ../..

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data/get_coco_dataset.sh
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#!/bin/bash
# CREDIT: https://github.com/pjreddie/darknet/tree/master/scripts/get_coco_dataset.sh
# Clone COCO API
git clone https://github.com/pdollar/coco && cd coco
# Download Images
mkdir images && cd images
wget -c https://pjreddie.com/media/files/train2014.zip
wget -c https://pjreddie.com/media/files/val2014.zip
# Unzip
unzip -q train2014.zip
unzip -q val2014.zip
# (optional) Delete zip files
rm -rf *.zip
cd ..
# Download COCO Metadata
wget -c https://pjreddie.com/media/files/instances_train-val2014.zip
wget -c https://pjreddie.com/media/files/coco/5k.part
wget -c https://pjreddie.com/media/files/coco/trainvalno5k.part
wget -c https://pjreddie.com/media/files/coco/labels.tgz
tar xzf labels.tgz
unzip -q instances_train-val2014.zip
# Set Up Image Lists
paste <(awk "{print \"$PWD\"}" <5k.part) 5k.part | tr -d '\t' > 5k.txt
paste <(awk "{print \"$PWD\"}" <trainvalno5k.part) trainvalno5k.part | tr -d '\t' > trainvalno5k.txt
# get xview training data
# wget -O train_images.tgz 'https://d307kc0mrhucc3.cloudfront.net/train_images.tgz?Expires=1530124049&Signature=JrQoxipmsETvb7eQHCfDFUO-QEHJGAayUv0i-ParmS-1hn7hl9D~bzGuHWG82imEbZSLUARTtm0wOJ7EmYMGmG5PtLKz9H5qi6DjoSUuFc13NQ-~6yUhE~NfPaTnehUdUMCa3On2wl1h1ZtRG~0Jq1P-AJbpe~oQxbyBrs1KccaMa7FK4F4oMM6sMnNgoXx8-3O77kYw~uOpTMFmTaQdHln6EztW0Lx17i57kK3ogbSUpXgaUTqjHCRA1dWIl7PY1ngQnLslkLhZqmKcaL-BvWf0ZGjHxCDQBpnUjIlvMu5NasegkwD9Jjc0ClgTxsttSkmbapVqaVC8peR0pO619Q__&Key-Pair-Id=APKAIKGDJB5C3XUL2DXQ'
# tar -xvzf train_images.tgz
# sudo rm -rf train_images/._*
# lastly convert each .tif to a .bmp for faster loading in cv2
# ./coco/images/train2014/COCO_train2014_000000167126.jpg # corrupted image

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data/get_coco_dataset_gdrive.sh
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#!/bin/bash
# Zip coco folder
# zip -r coco.zip coco
# tar -czvf coco.tar.gz coco
# Set fileid and filename
filename="coco.zip"
fileid="1WQT6SOktSe8Uw6r10-2JhbEhMY5DJaph" # coco.zip
# Download from Google Drive, accepting presented query
curl -c ./cookie -s -L "https://drive.google.com/uc?export=download&id=${fileid}" > /dev/null
curl -Lb ./cookie "https://drive.google.com/uc?export=download&confirm=`awk '/download/ {print $NF}' ./cookie`&id=${fileid}" -o ${filename}
rm ./cookie
# Unzip
unzip -q ${filename} # for coco.zip
# tar -xzf ${filename} # for coco.tar.gz

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utils/datasets.py
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@ -282,7 +282,7 @@ class LoadImagesAndLabels(Dataset): # for training/testing
# Rectangular Training https://github.com/ultralytics/yolov3/issues/232 # Rectangular Training https://github.com/ultralytics/yolov3/issues/232
if self.rect: if self.rect:
# Read image shapes # Read image shapes
sp = 'data' + os.sep + path.replace('.txt', '.shapes').split(os.sep)[-1] # shapefile path sp = path.replace('.txt', '.shapes') # shapefile path
try: try:
with open(sp, 'r') as f: # read existing shapefile with open(sp, 'r') as f: # read existing shapefile
s = [x.split() for x in f.read().splitlines()] s = [x.split() for x in f.read().splitlines()]

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utils/utils.py
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print(i, len(files)) print(i, len(files))
def coco_only_people(path='../coco/labels/val2014/'): def coco_only_people(path='../coco/labels/train2017/'): # from utils.utils import *; coco_only_people()
# Find images with only people # Find images with only people
files = sorted(glob.glob('%s/*.*' % path)) files = sorted(glob.glob('%s/*.*' % path))
for i, file in enumerate(files): for i, file in enumerate(files):