SAHI

Published by onesixx on 22-09-0422-09-04

Convert any dataset to COCO object detection format with SAHI – Fatih Cagatay Akyon

kitty-tiny to Coco

# https://www.immersivelimit.com/tutorials/create-coco-annotations-from-scratch
# https://github.com/obss/sahi/blob/main/docs/coco.md
# from distutils.log import info
# import inspect

# ****** Import required classes
from sahi.utils.coco import Coco, CocoCategory, CocoImage, CocoAnnotation
from sahi.utils.file import save_json

import mmcv
import os
from PIL import Image

path_root = './kitti_tiny/'
path_img  = os.path.join(path_root, 'training/image_2')
path_anno = os.path.join(path_root, 'training/label_2')

fullNm_trainSet = os.path.join(path_root,'train.txt')

# ****** Init Coco object
coco = Coco()

# ****** Add Category (id from 0)
CLASSES = ('Car', 'Truck', 'Pedestrian', 'Cyclist')

for i, cls in enumerate(CLASSES):
    coco.add_category(
        CocoCategory(id=i, name=cls)
    )

# ****** Add Image
image_list = mmcv.list_from_file(fullNm_trainSet)
cate2anno = {c:i for i, c in enumerate(CLASSES)}

for id, image_id in enumerate(image_list):
    #image_id = image_list[0]

    filenNm = f'{image_id}.jpeg' 
    img_fullNm = os.path.join(path_img, filenNm)
    width, height = Image.open(img_fullNm).size
    coco_image = CocoImage(
        height=height,
        width=width, 
        id = id,
        file_name = filenNm
    )

    # ****** Add Annotation 
    anno4Img = mmcv.list_from_file(os.path.join(path_anno, image_id+'.txt'))
    # annoList = [line.strip().split(' ') for line in anno4Img]
    # bbox_labels = [row[0] for row in annoList]
    # bboxes = [ [float(info) for info in row[4:8]] for row in annoList]

    for i, line in enumerate(anno4Img):
        # line = lines[1]
        row = line.strip().split(' ')
        bbox_label = row[0]
        if bbox_label in cate2anno:
            cateId = cate2anno[bbox_label]
            #cateNm = CLASSES[cateId]
            # [x_min, y_min, width, height],
            pnt = [float(info) for info in row[4:8]] 
            bboxes = [pnt[0], pnt[1], pnt[2]-pnt[0], pnt[3]-pnt[1]]

            coco_image.add_annotation(
                CocoAnnotation( 
                    image_id     = id,
                    bbox         = bboxes, 
                    segmentation = [],   
                    category_id  = cateId
                )
            )
        else:
            pass
            # "segmentation" =[],
            # "attribute"
            # "id" auto 
    # ****** add coco image to coco Object
    coco.add_image(coco_image)

coco_json = coco.json
coco_json["licenses"] = [dict()]
coco_json["info"] = [dict(items="x", dictionary ='x')]

fullNm_result = os.path.join(path_root,'anno_cc.json')
save_json(data=coco_json, save_path=fullNm_result)

xView to COCO conversion script
VisDrone to COCO conversion script
GitHub – obss/sahi: A lightweight vision library for performing large scale object detection
Object detection and instance segmentation are by far the most important fields of applications in Computer Vision…

SAHI

Convert any dataset to COCO object detection format with SAHI – Fatih Cagatay Akyon

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Convert any dataset to COCO object detection format with SAHI – Fatih Cagatay Akyon

kitty-tiny to Coco

onesixx

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