问题描述
from numpy import random data = {} import json data["images"] = [] data["annotations"] = [] # Define the classes (out of the 81) which you want to see. Others will not be shown. category_ids = [1] category_id_to_name = {1: 'benign_1',2: 'malignant_2'} bBoxes =[] NUM_OF_AUG = 3 # Number of augmentations for every image # Fetch class IDs only corresponding to the filterClasses catIds = coco.getCatIds() # Get all images containing the above Category IDs imgIds = coco.getImgIds(catIds=0) print("Number of images containing all the classes:",len(imgIds)) # load and display a random image img = coco.loadImgs(ids=2) I = io.imread(os.path.join(dataDir,img[0]['file_name']))/255.0 # get annotations from corrosponding img annIds = coco.getAnnIds(imgIds=img[0]['id'],catIds=catIds,iscrowd=None) anns = coco.loadAnns(annIds) bBoxes.append(anns[0]['bBox']) # save the bBox values new_img = img.copy() new_anns = anns.copy() for i in range(NUM_OF_AUG): augmentations = transform(image=I,bBoxes=bBoxes,category_ids=category_ids) augmented_img = augmentations["image"] augmented_bBox = augmentations["bBoxes"] #visualize(augmented_img,augmented_bBox,category_ids,category_id_to_name) new_anns[0]['bBox'] = augmented_bBox new_img[0]['id'] = i x = random.rand()*10 name = "augmented"+ str(x)+'.jpg' new_img[0]['file_name'] = name data["images"].append(new_img) data["annotations"].append(new_anns) {'annotations': [[{'area': 6305,'bBox': [(118.70517461626747,249.2004033678208,147.24821066225996,131.68045778464267)],'category_id': 1,'id': 1,'image_id': 1,'iscrowd': 0,'segmentation': []}],[{'area': 6305,'segmentation': []}]],'images': [[{'date_captured': '2021-01-31T14:09:29+00:00','file_name': 'benign_6.611640031287464.jpg','height': 416,'id': 4,'license': 1,'width': 416}],[{'date_captured': '2021-01-31T14:09:29+00:00','width': 416}]]} {'annotations': [[{'area': 6305,'images': [[{'date_captured': '2021-01-31T14:09:29+00:00',
@H_502_3@我想在转换后创建增强版本的图像和相应的边界框,并将其保存为 json coco 格式。但是数据总是存储与最后增强的 bBox 和图像相同的值
如何获取注释和图像的顺序版本
输出
"images": [ { "id": 0,"license": 1,"file_name": "benign--184-_png.rf.0835133a4b3867b2503f7afb080d6ef7.jpg","height": 416,"width": 416,"date_captured": "2021-01-31T14:09:29+00:00" },{ "id": 1,"file_name": "malignant--147-_png.rf.04e0a71de64594f0a3a65e6ac7b4794b.jpg","annotations": [ { "id": 0,"image_id": 0,"category_id": 1,"bBox": [ 73,65,336,101.5 ],"area": 34104,"segmentation": [],"iscrowd": 0 },"image_id": 1,"category_id": 2,"bBox": [ 165,74,189,168.5 ],"area": 31846.5,
@H_502_3@解决方法
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