63 lines
2.0 KiB
Bash
63 lines
2.0 KiB
Bash
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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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obj_class = annot['category_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"{obj_class} {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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