Add optional dataset.yaml path attribute (#3753)
* Add optional dataset.yaml `path` attribute @KalenMike * pass locals to python scripts * handle lists * update coco128.yaml * Capitalize first letter * add test key * finalize GlobalWheat2020.yaml * finalize objects365.yaml * finalize SKU-110K.yaml * finalize SKU-110K.yaml * finalize VisDrone.yaml * NoneType fix * update download comment * voc to VOC * update * update VOC.yaml * update VOC.yaml * remove dashes * delete get_voc.sh * force coco and coco128 to ../datasets * Capitalize Argoverse_HD.yaml * Capitalize Objects365.yaml * update Argoverse_HD.yaml * coco segments fix * VOC single-thread * update Argoverse_HD.yaml * update data_dict in test handling * create root
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Glenn Jocher authored and GitHub committed
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@@ -1,61 +0,0 @@
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#!/bin/bash
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# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/
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# Download command: bash data/scripts/get_argoverse_hd.sh
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# Train command: python train.py --data argoverse_hd.yaml
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# Default dataset location is next to YOLOv5:
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# /parent_folder
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# /argoverse
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# /yolov5
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# Download/unzip images
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d='../argoverse/' # unzip directory
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mkdir $d
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url=https://argoverse-hd.s3.us-east-2.amazonaws.com/
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f=Argoverse-HD-Full.zip
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curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &# download, unzip, remove in background
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wait # finish background tasks
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cd ../argoverse/Argoverse-1.1/
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ln -s tracking images
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cd ../Argoverse-HD/annotations/
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python3 - "$@" <<END
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import json
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from pathlib import Path
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annotation_files = ["train.json", "val.json"]
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print("Converting annotations to YOLOv5 format...")
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for val in annotation_files:
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a = json.load(open(val, "rb"))
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label_dict = {}
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for annot in a['annotations']:
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img_id = annot['image_id']
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img_name = a['images'][img_id]['name']
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img_label_name = img_name[:-3] + "txt"
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cls = annot['category_id'] # instance class id
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x_center, y_center, width, height = annot['bbox']
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x_center = (x_center + width / 2) / 1920. # offset and scale
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y_center = (y_center + height / 2) / 1200. # offset and scale
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width /= 1920. # scale
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height /= 1200. # scale
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img_dir = "./labels/" + a['seq_dirs'][a['images'][annot['image_id']]['sid']]
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Path(img_dir).mkdir(parents=True, exist_ok=True)
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if img_dir + "/" + img_label_name not in label_dict:
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label_dict[img_dir + "/" + img_label_name] = []
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label_dict[img_dir + "/" + img_label_name].append(f"{cls} {x_center} {y_center} {width} {height}\n")
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for filename in label_dict:
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with open(filename, "w") as file:
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for string in label_dict[filename]:
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file.write(string)
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END
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mv ./labels ../../Argoverse-1.1/
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@@ -1,116 +0,0 @@
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#!/bin/bash
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# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC/
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# Download command: bash data/scripts/get_voc.sh
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# Train command: python train.py --data voc.yaml
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# Default dataset location is next to YOLOv5:
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# /parent_folder
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# /VOC
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# /yolov5
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start=$(date +%s)
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mkdir -p ../tmp
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cd ../tmp/
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# Download/unzip images and labels
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d='.' # unzip directory
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url=https://github.com/ultralytics/yolov5/releases/download/v1.0/
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f1=VOCtrainval_06-Nov-2007.zip # 446MB, 5012 images
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f2=VOCtest_06-Nov-2007.zip # 438MB, 4953 images
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f3=VOCtrainval_11-May-2012.zip # 1.95GB, 17126 images
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for f in $f3 $f2 $f1; do
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echo 'Downloading' $url$f '...'
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curl -L $url$f -o $f && unzip -q $f -d $d && rm $f & # download, unzip, remove in background
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done
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wait # finish background tasks
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end=$(date +%s)
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runtime=$((end - start))
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echo "Completed in" $runtime "seconds"
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echo "Splitting dataset..."
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python3 - "$@" <<END
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import os
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import xml.etree.ElementTree as ET
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from os import getcwd
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sets = [('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
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classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog",
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"horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
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def convert_box(size, box):
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dw = 1. / (size[0])
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dh = 1. / (size[1])
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x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
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return x * dw, y * dh, w * dw, h * dh
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def convert_annotation(year, image_id):
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in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml' % (year, image_id))
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out_file = open('VOCdevkit/VOC%s/labels/%s.txt' % (year, image_id), 'w')
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tree = ET.parse(in_file)
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root = tree.getroot()
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size = root.find('size')
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w = int(size.find('width').text)
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h = int(size.find('height').text)
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for obj in root.iter('object'):
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difficult = obj.find('difficult').text
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cls = obj.find('name').text
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if cls not in classes or int(difficult) == 1:
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continue
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cls_id = classes.index(cls)
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xmlbox = obj.find('bndbox')
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b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text),
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float(xmlbox.find('ymax').text))
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bb = convert_box((w, h), b)
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out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
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cwd = getcwd()
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for year, image_set in sets:
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if not os.path.exists('VOCdevkit/VOC%s/labels/' % year):
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os.makedirs('VOCdevkit/VOC%s/labels/' % year)
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image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt' % (year, image_set)).read().strip().split()
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list_file = open('%s_%s.txt' % (year, image_set), 'w')
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for image_id in image_ids:
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list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n' % (cwd, year, image_id))
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convert_annotation(year, image_id)
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list_file.close()
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END
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cat 2007_train.txt 2007_val.txt 2012_train.txt 2012_val.txt >train.txt
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cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt
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mkdir ../VOC ../VOC/images ../VOC/images/train ../VOC/images/val
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mkdir ../VOC/labels ../VOC/labels/train ../VOC/labels/val
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python3 - "$@" <<END
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import os
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print(os.path.exists('../tmp/train.txt'))
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with open('../tmp/train.txt', 'r') as f:
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for line in f.readlines():
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line = "/".join(line.split('/')[-5:]).strip()
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if os.path.exists("../" + line):
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os.system("cp ../" + line + " ../VOC/images/train")
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line = line.replace('JPEGImages', 'labels').replace('jpg', 'txt')
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if os.path.exists("../" + line):
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os.system("cp ../" + line + " ../VOC/labels/train")
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print(os.path.exists('../tmp/2007_test.txt'))
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with open('../tmp/2007_test.txt', 'r') as f:
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for line in f.readlines():
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line = "/".join(line.split('/')[-5:]).strip()
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if os.path.exists("../" + line):
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os.system("cp ../" + line + " ../VOC/images/val")
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line = line.replace('JPEGImages', 'labels').replace('jpg', 'txt')
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if os.path.exists("../" + line):
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os.system("cp ../" + line + " ../VOC/labels/val")
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END
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rm -rf ../tmp # remove temporary directory
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echo "VOC download done."
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