From 93619ce4a752e53b0a8d87b55220b06389df0575 Mon Sep 17 00:00:00 2001 From: freeneuro <923237475@qq.com> Date: Wed, 5 Jan 2022 22:48:15 +0800 Subject: [PATCH] update --- src/.gitignore | 1 - src/data/GlobalWheat2020.yaml | 55 ++++++++++++ src/data/SKU-110K.yaml | 52 ++++++++++++ src/data/VisDrone.yaml | 61 ++++++++++++++ src/data/argoverse_hd.yaml | 21 +++++ src/data/carla.yaml | 26 ++++++ src/data/coco.yaml | 35 ++++++++ src/data/coco128.yaml | 28 +++++++ src/data/hyp.finetune.yaml | 38 +++++++++ src/data/hyp.finetune_objects365.yaml | 28 +++++++ src/data/hyp.scratch.yaml | 33 ++++++++ src/data/images/bus.jpg | Bin 0 -> 487438 bytes src/data/images/zidane.jpg | Bin 0 -> 168949 bytes src/data/objects365.yaml | 102 ++++++++++++++++++++++ src/data/scripts/get_argoverse_hd.sh | 61 ++++++++++++++ src/data/scripts/get_coco.sh | 27 ++++++ src/data/scripts/get_coco128.sh | 17 ++++ src/data/scripts/get_voc.sh | 116 ++++++++++++++++++++++++++ src/data/voc.yaml | 21 +++++ 19 files changed, 721 insertions(+), 1 deletion(-) create mode 100644 src/data/GlobalWheat2020.yaml create mode 100644 src/data/SKU-110K.yaml create mode 100644 src/data/VisDrone.yaml create mode 100644 src/data/argoverse_hd.yaml create mode 100644 src/data/carla.yaml create mode 100644 src/data/coco.yaml create mode 100644 src/data/coco128.yaml create mode 100644 src/data/hyp.finetune.yaml create mode 100644 src/data/hyp.finetune_objects365.yaml create mode 100644 src/data/hyp.scratch.yaml create mode 100644 src/data/images/bus.jpg create mode 100644 src/data/images/zidane.jpg create mode 100644 src/data/objects365.yaml create mode 100644 src/data/scripts/get_argoverse_hd.sh create mode 100644 src/data/scripts/get_coco.sh create mode 100644 src/data/scripts/get_coco128.sh create mode 100644 src/data/scripts/get_voc.sh create mode 100644 src/data/voc.yaml diff --git a/src/.gitignore b/src/.gitignore index 91ce33f..503a229 100644 --- a/src/.gitignore +++ b/src/.gitignore @@ -25,7 +25,6 @@ storage.googleapis.com runs/* -data/* !data/images/zidane.jpg !data/images/bus.jpg !data/coco.names diff --git a/src/data/GlobalWheat2020.yaml b/src/data/GlobalWheat2020.yaml new file mode 100644 index 0000000..ca2a49e --- /dev/null +++ b/src/data/GlobalWheat2020.yaml @@ -0,0 +1,55 @@ +# Global Wheat 2020 dataset http://www.global-wheat.com/ +# Train command: python train.py --data GlobalWheat2020.yaml +# Default dataset location is next to YOLOv3: +# /parent_folder +# /datasets/GlobalWheat2020 +# /yolov3 + + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: # 3422 images + - ../datasets/GlobalWheat2020/images/arvalis_1 + - ../datasets/GlobalWheat2020/images/arvalis_2 + - ../datasets/GlobalWheat2020/images/arvalis_3 + - ../datasets/GlobalWheat2020/images/ethz_1 + - ../datasets/GlobalWheat2020/images/rres_1 + - ../datasets/GlobalWheat2020/images/inrae_1 + - ../datasets/GlobalWheat2020/images/usask_1 + +val: # 748 images (WARNING: train set contains ethz_1) + - ../datasets/GlobalWheat2020/images/ethz_1 + +test: # 1276 images + - ../datasets/GlobalWheat2020/images/utokyo_1 + - ../datasets/GlobalWheat2020/images/utokyo_2 + - ../datasets/GlobalWheat2020/images/nau_1 + - ../datasets/GlobalWheat2020/images/uq_1 + +# number of classes +nc: 1 + +# class names +names: [ 'wheat_head' ] + + +# download command/URL (optional) -------------------------------------------------------------------------------------- +download: | + from utils.general import download, Path + + # Download + dir = Path('../datasets/GlobalWheat2020') # dataset directory + urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/GlobalWheat2020_labels.zip'] + download(urls, dir=dir) + + # Make Directories + for p in 'annotations', 'images', 'labels': + (dir / p).mkdir(parents=True, exist_ok=True) + + # Move + for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \ + 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1': + (dir / p).rename(dir / 'images' / p) # move to /images + f = (dir / p).with_suffix('.json') # json file + if f.exists(): + f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations diff --git a/src/data/SKU-110K.yaml b/src/data/SKU-110K.yaml new file mode 100644 index 0000000..f4aea8b --- /dev/null +++ b/src/data/SKU-110K.yaml @@ -0,0 +1,52 @@ +# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 +# Train command: python train.py --data SKU-110K.yaml +# Default dataset location is next to YOLOv3: +# /parent_folder +# /datasets/SKU-110K +# /yolov3 + + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../datasets/SKU-110K/train.txt # 8219 images +val: ../datasets/SKU-110K/val.txt # 588 images +test: ../datasets/SKU-110K/test.txt # 2936 images + +# number of classes +nc: 1 + +# class names +names: [ 'object' ] + + +# download command/URL (optional) -------------------------------------------------------------------------------------- +download: | + import shutil + from tqdm import tqdm + from utils.general import np, pd, Path, download, xyxy2xywh + + # Download + datasets = Path('../datasets') # download directory + urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz'] + download(urls, dir=datasets, delete=False) + + # Rename directories + dir = (datasets / 'SKU-110K') + if dir.exists(): + shutil.rmtree(dir) + (datasets / 'SKU110K_fixed').rename(dir) # rename dir + (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir + + # Convert labels + names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names + for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv': + x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations + images, unique_images = x[:, 0], np.unique(x[:, 0]) + with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f: + f.writelines(f'./images/{s}\n' for s in unique_images) + for im in tqdm(unique_images, desc=f'Converting {dir / d}'): + cls = 0 # single-class dataset + with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f: + for r in x[images == im]: + w, h = r[6], r[7] # image width, height + xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance + f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label diff --git a/src/data/VisDrone.yaml b/src/data/VisDrone.yaml new file mode 100644 index 0000000..59f1cd5 --- /dev/null +++ b/src/data/VisDrone.yaml @@ -0,0 +1,61 @@ +# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset +# Train command: python train.py --data VisDrone.yaml +# Default dataset location is next to YOLOv3: +# /parent_folder +# /VisDrone +# /yolov3 + + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../VisDrone/VisDrone2019-DET-train/images # 6471 images +val: ../VisDrone/VisDrone2019-DET-val/images # 548 images +test: ../VisDrone/VisDrone2019-DET-test-dev/images # 1610 images + +# number of classes +nc: 10 + +# class names +names: [ 'pedestrian', 'people', 'bicycle', 'car', 'van', 'truck', 'tricycle', 'awning-tricycle', 'bus', 'motor' ] + + +# download command/URL (optional) -------------------------------------------------------------------------------------- +download: | + from utils.general import download, os, Path + + def visdrone2yolo(dir): + from PIL import Image + from tqdm import tqdm + + def convert_box(size, box): + # Convert VisDrone box to YOLO xywh box + dw = 1. / size[0] + dh = 1. / size[1] + return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh + + (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory + pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}') + for f in pbar: + img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size + lines = [] + with open(f, 'r') as file: # read annotation.txt + for row in [x.split(',') for x in file.read().strip().splitlines()]: + if row[4] == '0': # VisDrone 'ignored regions' class 0 + continue + cls = int(row[5]) - 1 + box = convert_box(img_size, tuple(map(int, row[:4]))) + lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n") + with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl: + fl.writelines(lines) # write label.txt + + + # Download + dir = Path('../VisDrone') # dataset directory + urls = ['https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-train.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-val.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-dev.zip', + 'https://github.com/ultralytics/yolov5/releases/download/v1.0/VisDrone2019-DET-test-challenge.zip'] + download(urls, dir=dir) + + # Convert + for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev': + visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels diff --git a/src/data/argoverse_hd.yaml b/src/data/argoverse_hd.yaml new file mode 100644 index 0000000..29d49b8 --- /dev/null +++ b/src/data/argoverse_hd.yaml @@ -0,0 +1,21 @@ +# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ +# Train command: python train.py --data argoverse_hd.yaml +# Default dataset location is next to YOLOv3: +# /parent_folder +# /argoverse +# /yolov3 + + +# download command/URL (optional) +download: bash data/scripts/get_argoverse_hd.sh + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../argoverse/Argoverse-1.1/images/train/ # 39384 images +val: ../argoverse/Argoverse-1.1/images/val/ # 15062 iamges +test: ../argoverse/Argoverse-1.1/images/test/ # Submit to: https://eval.ai/web/challenges/challenge-page/800/overview + +# number of classes +nc: 8 + +# class names +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'bus', 'truck', 'traffic_light', 'stop_sign' ] diff --git a/src/data/carla.yaml b/src/data/carla.yaml new file mode 100644 index 0000000..f52434e --- /dev/null +++ b/src/data/carla.yaml @@ -0,0 +1,26 @@ +# COCO 2017 dataset http://cocodataset.org - first 128 training images +# Train command: python train.py --data coco128.yaml +# Default dataset location is next to YOLOv5: +# /parent +# /datasets/coco128 +# /yolov5 + + +# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..] +path: F:/PythonPro/DualAttentionAttack/data/phy_attack # dataset root dir +train: train_new # train_new train_label_new +val: train_new # val images (relative to 'path') 128 images +test: # test images (optional) + +# Classes +nc: 80 # number of classes +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush' ] # class names + diff --git a/src/data/coco.yaml b/src/data/coco.yaml new file mode 100644 index 0000000..8714e6a --- /dev/null +++ b/src/data/coco.yaml @@ -0,0 +1,35 @@ +# COCO 2017 dataset http://cocodataset.org +# Train command: python train.py --data coco.yaml +# Default dataset location is next to YOLOv3: +# /parent_folder +# /coco +# /yolov3 + + +# download command/URL (optional) +download: bash data/scripts/get_coco.sh + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../coco/train2017.txt # 118287 images +val: ../coco/val2017.txt # 5000 images +test: ../coco/test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794 + +# number of classes +nc: 80 + +# class names +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush' ] + +# Print classes +# with open('data/coco.yaml') as f: +# d = yaml.safe_load(f) # dict +# for i, x in enumerate(d['names']): +# print(i, x) diff --git a/src/data/coco128.yaml b/src/data/coco128.yaml new file mode 100644 index 0000000..6d8f1b2 --- /dev/null +++ b/src/data/coco128.yaml @@ -0,0 +1,28 @@ +# COCO 2017 dataset http://cocodataset.org - first 128 training images +# Train command: python train.py --data coco128.yaml +# Default dataset location is next to YOLOv3: +# /parent_folder +# /coco128 +# /yolov3 + + +# download command/URL (optional) +download: https://github.com/ultralytics/yolov5/releases/download/v1.0/coco128.zip + +# train and val data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: F:/DataSource/coco128/images/train2017/ # 128 images +val: F:/DataSource/coco128/images/train2017/ # 128 images + +# number of classes +nc: 80 + +# class names +names: [ 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light', + 'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow', + 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee', + 'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard', + 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', + 'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', + 'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', + 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', + 'hair drier', 'toothbrush' ] diff --git a/src/data/hyp.finetune.yaml b/src/data/hyp.finetune.yaml new file mode 100644 index 0000000..1b84cff --- /dev/null +++ b/src/data/hyp.finetune.yaml @@ -0,0 +1,38 @@ +# Hyperparameters for VOC finetuning +# python train.py --batch 64 --weights yolov5m.pt --data voc.yaml --img 512 --epochs 50 +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + + +# Hyperparameter Evolution Results +# Generations: 306 +# P R mAP.5 mAP.5:.95 box obj cls +# Metrics: 0.6 0.936 0.896 0.684 0.0115 0.00805 0.00146 + +lr0: 0.0032 +lrf: 0.12 +momentum: 0.843 +weight_decay: 0.00036 +warmup_epochs: 2.0 +warmup_momentum: 0.5 +warmup_bias_lr: 0.05 +box: 0.0296 +cls: 0.243 +cls_pw: 0.631 +obj: 0.301 +obj_pw: 0.911 +iou_t: 0.2 +anchor_t: 2.91 +# anchors: 3.63 +fl_gamma: 0.0 +hsv_h: 0.0138 +hsv_s: 0.664 +hsv_v: 0.464 +degrees: 0.373 +translate: 0.245 +scale: 0.898 +shear: 0.602 +perspective: 0.0 +flipud: 0.00856 +fliplr: 0.5 +mosaic: 1.0 +mixup: 0.243 diff --git a/src/data/hyp.finetune_objects365.yaml b/src/data/hyp.finetune_objects365.yaml new file mode 100644 index 0000000..2b104ef --- /dev/null +++ b/src/data/hyp.finetune_objects365.yaml @@ -0,0 +1,28 @@ +lr0: 0.00258 +lrf: 0.17 +momentum: 0.779 +weight_decay: 0.00058 +warmup_epochs: 1.33 +warmup_momentum: 0.86 +warmup_bias_lr: 0.0711 +box: 0.0539 +cls: 0.299 +cls_pw: 0.825 +obj: 0.632 +obj_pw: 1.0 +iou_t: 0.2 +anchor_t: 3.44 +anchors: 3.2 +fl_gamma: 0.0 +hsv_h: 0.0188 +hsv_s: 0.704 +hsv_v: 0.36 +degrees: 0.0 +translate: 0.0902 +scale: 0.491 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +mosaic: 1.0 +mixup: 0.0 diff --git a/src/data/hyp.scratch.yaml b/src/data/hyp.scratch.yaml new file mode 100644 index 0000000..44f26b6 --- /dev/null +++ b/src/data/hyp.scratch.yaml @@ -0,0 +1,33 @@ +# Hyperparameters for COCO training from scratch +# python train.py --batch 40 --cfg yolov5m.yaml --weights '' --data coco.yaml --img 640 --epochs 300 +# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials + + +lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3) +lrf: 0.2 # final OneCycleLR learning rate (lr0 * lrf) +momentum: 0.937 # SGD momentum/Adam beta1 +weight_decay: 0.0005 # optimizer weight decay 5e-4 +warmup_epochs: 3.0 # warmup epochs (fractions ok) +warmup_momentum: 0.8 # warmup initial momentum +warmup_bias_lr: 0.1 # warmup initial bias lr +box: 0.05 # box loss gain +cls: 0.5 # cls loss gain +cls_pw: 1.0 # cls BCELoss positive_weight +obj: 1.0 # obj loss gain (scale with pixels) +obj_pw: 1.0 # obj BCELoss positive_weight +iou_t: 0.20 # IoU training threshold +anchor_t: 4.0 # anchor-multiple threshold +# anchors: 3 # anchors per output layer (0 to ignore) +fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5) +hsv_h: 0.015 # image HSV-Hue augmentation (fraction) +hsv_s: 0.7 # image HSV-Saturation augmentation (fraction) +hsv_v: 0.4 # image HSV-Value augmentation (fraction) +degrees: 0.0 # image rotation (+/- deg) +translate: 0.1 # image translation (+/- fraction) +scale: 0.5 # image scale (+/- gain) +shear: 0.0 # image shear (+/- deg) +perspective: 0.0 # image perspective (+/- fraction), range 0-0.001 +flipud: 0.0 # image flip up-down (probability) +fliplr: 0.5 # image flip left-right (probability) +mosaic: 1.0 # image mosaic (probability) +mixup: 0.0 # image mixup (probability) diff --git a/src/data/images/bus.jpg 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data as 1) directory: path/images/, 2) file: path/images.txt, or 3) list: [path1/images/, path2/images/] +train: ../datasets/objects365/images/train # 1742289 images +val: ../datasets/objects365/images/val # 5570 images + +# number of classes +nc: 365 + +# class names +names: [ 'Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp', 'Glasses', 'Bottle', 'Desk', 'Cup', + 'Street Lights', 'Cabinet/shelf', 'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet', 'Book', + 'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower', 'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag', + 'Pillow', 'Boots', 'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt', 'Monitor/TV', + 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker', 'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle', + 'Stool', 'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Basket', 'Drum', 'Pen/Pencil', 'Bus', 'Wild Bird', + 'High Heels', 'Motorcycle', 'Guitar', 'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck', + 'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy', 'Candle', 'Sailboat', 'Laptop', 'Awning', + 'Bed', 'Faucet', 'Tent', 'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner', 'Knife', + 'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork', 'Traffic Sign', 'Balloon', 'Tripod', 'Dog', 'Spoon', 'Clock', + 'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger', 'Blackboard/Whiteboard', 'Napkin', 'Other Fish', + 'Orange/Tangerine', 'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle', 'Fan', + 'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard', + 'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone', 'Sports Car', 'Stop Sign', + 'Dessert', 'Scooter', 'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat', + 'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard', + 'Gun', 'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot', 'Toilet', 'Kite', 'Strawberry', + 'Other Balls', 'Shovel', 'Pepper', 'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks', + 'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board', 'Coffee Table', 'Side Table', 'Scissors', + 'Marker', 'Pie', 'Ladder', 'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball', 'Zebra', 'Grape', + 'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin', 'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck', + 'Billiards', 'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase', 'Cucumber', 'Cigar/Cigarette', + 'Paint Brush', 'Pear', 'Heavy Truck', 'Hamburger', 'Extractor', 'Extension Cord', 'Tong', 'Tennis Racket', + 'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis', 'Ship', 'Swing', 'Coffee Machine', + 'Slide', 'Carriage', 'Onion', 'Green beans', 'Projector', 'Frisbee', 'Washing Machine/Drying Machine', + 'Chicken', 'Printer', 'Watermelon', 'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hot-air balloon', + 'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog', 'Blender', 'Peach', 'Rice', 'Wallet/Purse', + 'Volleyball', 'Deer', 'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple', 'Golf Ball', + 'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle', 'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin', + 'Megaphone', 'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion', 'Sandwich', 'Nuts', + 'Speed Limit Sign', 'Induction Cooker', 'Broom', 'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit', + 'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese', 'Notepaper', 'Cherry', 'Pliers', 'CD', + 'Pasta', 'Hammer', 'Cue', 'Avocado', 'Hamimelon', 'Flask', 'Mushroom', 'Screwdriver', 'Soap', 'Recorder', + 'Bear', 'Eggplant', 'Board Eraser', 'Coconut', 'Tape Measure/Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips', + 'Steak', 'Crosswalk Sign', 'Stapler', 'Camel', 'Formula 1', 'Pomegranate', 'Dishwasher', 'Crab', + 'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba', 'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal', + 'Butterfly', 'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill', 'Hair Dryer', 'Egg tart', + 'Jellyfish', 'Treadmill', 'Lighter', 'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target', 'French', + 'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak', 'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell', + 'Scallop', 'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Tennis paddle', 'Cosmetics Brush/Eyeliner Pencil', + 'Chainsaw', 'Eraser', 'Lobster', 'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling', 'Table Tennis' ] + + +# download command/URL (optional) -------------------------------------------------------------------------------------- +download: | + from pycocotools.coco import COCO + from tqdm import tqdm + + from utils.general import download, Path + + # Make Directories + dir = Path('../datasets/objects365') # dataset directory + for p in 'images', 'labels': + (dir / p).mkdir(parents=True, exist_ok=True) + for q in 'train', 'val': + (dir / p / q).mkdir(parents=True, exist_ok=True) + + # Download + url = "https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/train/" + download([url + 'zhiyuan_objv2_train.tar.gz'], dir=dir, delete=False) # annotations json + download([url + f for f in [f'patch{i}.tar.gz' for i in range(51)]], dir=dir / 'images' / 'train', + curl=True, delete=False, threads=8) + + # Move + train = dir / 'images' / 'train' + for f in tqdm(train.rglob('*.jpg'), desc=f'Moving images'): + f.rename(train / f.name) # move to /images/train + + # Labels + coco = COCO(dir / 'zhiyuan_objv2_train.json') + names = [x["name"] for x in coco.loadCats(coco.getCatIds())] + for cid, cat in enumerate(names): + catIds = coco.getCatIds(catNms=[cat]) + imgIds = coco.getImgIds(catIds=catIds) + for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'): + width, height = im["width"], im["height"] + path = Path(im["file_name"]) # image filename + try: + with open(dir / 'labels' / 'train' / path.with_suffix('.txt').name, 'a') as file: + annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None) + for a in coco.loadAnns(annIds): + x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner) + x, y = x + w / 2, y + h / 2 # xy to center + file.write(f"{cid} {x / width:.5f} {y / height:.5f} {w / width:.5f} {h / height:.5f}\n") + + except Exception as e: + print(e) diff --git a/src/data/scripts/get_argoverse_hd.sh b/src/data/scripts/get_argoverse_hd.sh new file mode 100644 index 0000000..ad36976 --- /dev/null +++ b/src/data/scripts/get_argoverse_hd.sh @@ -0,0 +1,61 @@ +#!/bin/bash +# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ +# Download command: bash data/scripts/get_argoverse_hd.sh +# Train command: python train.py --data argoverse_hd.yaml +# Default dataset location is next to YOLOv3: +# /parent_folder +# /argoverse +# /yolov3 + +# Download/unzip images +d='../argoverse/' # unzip directory +mkdir $d +url=https://argoverse-hd.s3.us-east-2.amazonaws.com/ +f=Argoverse-HD-Full.zip +curl -L $url$f -o $f && unzip -q $f -d $d && rm $f &# download, unzip, remove in background +wait # finish background tasks + +cd ../argoverse/Argoverse-1.1/ +ln -s tracking images + +cd ../Argoverse-HD/annotations/ + +python3 - "$@" <train.txt +cat 2007_train.txt 2007_val.txt 2007_test.txt 2012_train.txt 2012_val.txt >train.all.txt + +mkdir ../VOC ../VOC/images ../VOC/images/train ../VOC/images/val +mkdir ../VOC/labels ../VOC/labels/train ../VOC/labels/val + +python3 - "$@" <