Python+Torch+FasterCNN网络目标检测识别

前言

这篇博客针对《Python+Torch+FasterCNN网络目标检测识别》编写代码，代码整洁，规则，易读。 学习与应用推荐首选。

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运行结果

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文章目录

一、所需工具软件
二、使用步骤
       1. 主要代码
       2. 运行结果
三、在线协助

一、所需工具软件

       1. VS2019, Qt
       2. C++

二、使用步骤

代码如下（示例）：

# coding:utf-8

'''


import json
import shutil

data_root = './coco/'
with open(data_root+"annotations/instances_val2014.json", 'r') as f:
    annos = json.load(f)
images = annos["images"]
imageid2image = {}
for image in images:
    imageid2image[image['id']]=image
categories = annos['categories']
cateid2name={}
for cate in categories:
    cateid2name[cate['id']] = cate['name']
image_final=[]
annos_final=[]
categories_final=[{
"id":1,
"name": "airplane"}]
image_id=1
anno_id=1
imgid2airplane={}
for anno in annos['annotations']:
    if cateid2name[anno['category_id']] == 'airplane':
        image_id_t = anno['image_id']
        if image_id_t not in imgid2airplane:
            imgid2airplane[image_id_t] = []
        imgid2airplane[image_id_t].append(anno)
'mini_airplane/' + imagename)
    image =  imageid2image[imgid]
    image['id'] = image_id
    image_final.append(image)
    for anno in annos:
        anno["id"] = anno_id
        anno["image_id"] = image_id
        anno["category_id"] = 1
        anno_id+=1
        annos_final.append(anno)
    image_id+=1
    if image_id >=100:
        break
instance = {"images": image_final,
            "annotations":annos_final,
            "categories": categories_final}
with open('mini_airplane_train.json', 'w') as f:
    json.dump(instance, f, indent=1)

运行结果

三、在线协助：

如需安装运行环境或远程调试，见文章底部个人 QQ 名片，由专业技术人员远程协助！

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