安裝:
1. 首先解壓數據文件:
  - 圖像數據下載到 coco/images/ 文件夾中
  - 標籤數據下載到 coco/ 文件夾中.
2. matlab, 在 matlab 的默認路徑中添加 coco/MatlabApi
3. Python. 打開終端,將路徑切換到 coco/PythonAPI下,輸入 make
COCO數據集的標註信息

COCO的數據標註信息包括:

類別標誌
類別數量區分
像素級的分割

import sys
sys.path.append('E:/xinlib')
from data import cocox
import zipfile

查看 coco/images/ 文件夾下的數據：

image_names = cocox.get_image_names()
image_names

['E:/Data/coco/images/test2017.zip',
 'E:/Data/coco/images/train2017.zip',
 'E:/Data/coco/images/unlabeled2017.zip',
 'E:/Data/coco/images/val2017.zip']

查看 coco/ 文件夾的文件：

import os
dataDir = cocox.root

os.listdir(dataDir)

['annotations',
 'annotations_trainval2017.zip',
 'cocoapi',
 'images',
 'image_info_test2017.zip',
 'image_info_unlabeled2017.zip',
 'stuff_annotations_trainval2017.zip']

我們只需要獲取 annotations 的信息（這裏都是以 .zip 結尾）：

annDir = [z_name for z_name in os.listdir(dataDir) if z_name.endswith('.zip')]
annDir

['annotations_trainval2017.zip',
 'image_info_test2017.zip',
 'image_info_unlabeled2017.zip',
 'stuff_annotations_trainval2017.zip']

解壓 annotations 的文件：

for ann_name in annDir:
    z = zipfile.ZipFile(dataDir + '/' + ann_name)
    # 全部解壓
    z.extractall(dataDir)

# 封裝爲函數
cocox.unzip_annotations()

# 刪除標籤的壓縮文件
cocox.del_annotations()

由於圖片數據比較大，我就不解壓了，不過可以通過 MXNet + zipfile 來直接獲取圖片信息。

獲取圖片數據

我以 test2017.zip 爲例：

image_names

['E:/Data/coco/images/test2017.zip',
 'E:/Data/coco/images/train2017.zip',
 'E:/Data/coco/images/unlabeled2017.zip',
 'E:/Data/coco/images/val2017.zip']

z = zipfile.ZipFile(image_names[0])

# 測試集的圖片名稱列表
z.namelist()

['test2017/',
 'test2017/000000259564.jpg',
 'test2017/000000344475.jpg',
 ...]

我們可以看出，第一個是目錄名，之後的纔是圖片。下面我們來看看第一張圖片：

from mxnet import image

r = z.read(z.namelist()[1])    # bytes
data = image.imdecode(r)       # 轉換爲 NDArray 數組，可以做數值運算
data

[[[ 87  94  78]
  [ 85  94  77]
  [ 87  96  79]
  ..., 
  [108  63  44]
  [252 244 233]
  [253 253 253]]

 [[ 86  95  76]
  [ 88  97  78]
  [ 85  94  75]
  ..., 
  [ 55  14   0]
  [150  94  81]
  [252 245 216]]

 [[ 90  99  78]
  [ 89  98  77]
  [ 89  98  77]
  ..., 
  [ 63  37  12]
  [ 90  30   6]
  [149  83  61]]

 ..., 
 [[ 86 104  82]
  [ 89 102  82]
  [ 84 102  80]
  ..., 
  [ 50  62  40]
  [ 50  61  45]
  [ 51  58  50]]

 [[ 89 101  77]
  [ 87  96  75]
  [ 89 104  83]
  ..., 
  [ 54  63  42]
  [ 49  53  39]
  [ 53  54  48]]

 [[ 96 100  77]
  [ 94  97  76]
  [ 88 103  82]
  ..., 
  [ 44  58  32]
  [ 45  57  37]
  [ 49  57  42]]]
<NDArray 480x640x3 @cpu(0)>

x = data.asnumpy()   # 轉換爲 array

# 顯示圖片
%pylab inline 
plt.imshow(x)

爲此，我們可以將其封裝爲一個迭代器：cocox.data_iter(dataType)

獲取標籤信息（利用官方給定教程）

安裝 python API：

pip install -U pycocotools

Windows （一般需要安裝 visual studio）下有許多的坑：Windows 10 編譯 Pycocotools 踩坑記

%pylab inline
from pycocotools.coco import COCO
import numpy as np
import skimage.io as io
import matplotlib.pyplot as plt
import pylab
pylab.rcParams['figure.figsize'] = (8.0, 10.0)

這裏有一個坑 (由 PIL 引發) import skimage.io as io 在 Windows 下可能會報錯，我的解決辦法是：

先卸載 Pillow，然後重新安裝即可。
插曲：PIL(Python Imaging Library)是Python一個強大方便的圖像處理庫，名氣也比較大。Pillow 是 PIL 的一個派生分支，但如今已經發展成爲比 PIL 本身更具活力的圖像處理庫。

dataDir = cocox.root
dataType = 'val2017'
annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataType)

# initialize COCO api for instance annotations
coco=COCO(annFile)

loading annotations into memory...
Done (t=0.93s)
creating index...
index created!

COCO??

COCO 是一個類：

Constructor of Microsoft COCO helper class for reading and visualizing annotations.
:param annotation_file (str): location of annotation file
:param image_folder (str): location to the folder that hosts images.

display COCO categories and supercategories

cats = coco.loadCats(coco.getCatIds())
nms = [cat['name'] for cat in cats]
print('COCO categories: \n{}\n'.format(' '.join(nms)))

nms = set([cat['supercategory'] for cat in cats])
print('COCO supercategories: \n{}'.format(' '.join(nms)))

COCO categories: 
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

COCO supercategories: 
appliance sports person indoor vehicle food electronic furniture animal outdoor accessory kitchen

# get all images containing given categories, select one at random
catIds = coco.getCatIds(catNms=['person', 'dog', 'skateboard'])
imgIds = coco.getImgIds(catIds=catIds)
imgIds = coco.getImgIds(imgIds=[335328])
img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]

img

{'license': 4,
 'file_name': '000000335328.jpg',
 'coco_url': 'http://images.cocodataset.org/val2017/000000335328.jpg',
 'height': 640,
 'width': 512,
 'date_captured': '2013-11-20 19:29:37',
 'flickr_url': 'http://farm3.staticflickr.com/2079/2128089396_ddd988a59a_z.jpg',
 'id': 335328}

官方給的這個代碼需要將圖片數據集解壓：

# load and display image
# use url to load image
# I = io.imread(img['coco_url'])
I = io.imread('%s/images/%s/%s' % (dataDir, dataType, img['file_name']))
plt.axis('off')
plt.imshow(I)
plt.show()

我們可以使用 zipfile 模塊直接讀取圖片，而無須解壓：

image_names[-1]

'E:/Data/coco/images/val2017.zip'

val_z = zipfile.ZipFile(image_names[-1])
I = image.imdecode(val_z.read('%s/%s' % (dataType, img['file_name']))).asnumpy()
plt.axis('off')
plt.imshow(I)
plt.show()

load and display instance annotations

plt.imshow(I)
plt.axis('off')
annIds = coco.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco.loadAnns(annIds)
coco.showAnns(anns)

initialize COCO api for person keypoints annotations

annFile = '{}/annotations/person_keypoints_{}.json'.format(dataDir, dataType)
coco_kps = COCO(annFile)

loading annotations into memory...
Done (t=0.43s)
creating index...
index created!

load and display keypoints annotations

plt.imshow(I)
plt.axis('off')
ax = plt.gca()
annIds = coco_kps.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco_kps.loadAnns(annIds)
coco_kps.showAnns(anns)

initialize COCO api for caption annotations

annFile = '{}/annotations/captions_{}.json'.format(dataDir, dataType)
coco_caps = COCO(annFile)

loading annotations into memory...
Done (t=0.06s)
creating index...
index created!

load and display caption annotations

annIds = coco_caps.getAnnIds(imgIds=img['id'])
anns = coco_caps.loadAnns(annIds)
coco_caps.showAnns(anns)
plt.imshow(I)
plt.axis('off')
plt.show()

A couple of people riding waves on top of boards.
a couple of people that are surfing in water
A man and a young child in wet suits surfing in the ocean.
a man and small child standing on a surf board  and riding some waves
A young boy on a surfboard being taught to surf.

GitHub 展示

你也可以在線編輯：https://mybinder.org/v2/gh/q735613050/dataLoader/master

COCO 數據集的使用，以及下載鏈接

一、下載鏈接

二、使用

１．Note：

獲取圖片數據

獲取標籤信息（利用官方給定教程）

display COCO categories and supercategories

load and display instance annotations

initialize COCO api for person keypoints annotations

load and display keypoints annotations

initialize COCO api for caption annotations

load and display caption annotations

self.info["photoshop"] = photoshop UnboundLocalError: local variable 'photoshop' referenced before

這個要看呀！！！！：中間層提取特徵的可視化，以及熱圖的可視化

maskrcnn_benchmark-----Step-by-step tutorial 如何訓練自己的數據集以及網絡的finetune

人體姿態估計數據集整理（Pose Estimation/Keypoint）：MSCOCO（逐年）、LSP、FLIC、MPII、AI Challenge及打分標準

關於卷積的非常形象的圖

https://yachay.unat.edu.pe/blog/index.php?comment_area=format_blog&comment_component=blog&comment_co

linux以太網驅動總結