pycocotools模块

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from pycocotools.coco import COCO

dataDir = '..'
dataType = 'val2017'
annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataType)
coco = COCO(annFile)  # initialize COCO api for instance annotations

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loading annotations into memory...
Done (t=0.69s)
creating index...
index created!

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from pycocotools.coco import COCO

dataDir = '..'
dataType = 'val2017'
annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataType)
coco = COCO(annFile)  # initialize COCO api for instance annotations

# 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)))

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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 furniture accessory animal sports kitchen 
    electronic indoor person vehicle outdoor food

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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)

dataDir = '..'
dataType = 'val2017'
annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataType)
coco = COCO(annFile)  # initialize COCO api for instance annotations

# 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=[324158])
img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]

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

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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)

dataDir = '..'
dataType = 'val2017'
annFile = '{}/annotations/instances_{}.json'.format(dataDir, dataType)
coco = COCO(annFile)  # initialize COCO api for instance annotations

# 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=[324158])
img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]

I = io.imread(img['coco_url'])
# 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)
plt.show()

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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)
dataDir = '..'
dataType = 'val2017'
# initialize COCO api for person keypoints annotations
annFile = '{}/annotations/person_keypoints_{}.json'.format(dataDir, dataType)
coco_kps = COCO(annFile)
# get all images containing given categories, select one at random
catIds = coco_kps.getCatIds(catNms=['person', 'dog', 'skateboard'])
imgIds = coco_kps.getImgIds(catIds=catIds)
imgIds = coco_kps.getImgIds(imgIds=[324158])
img = coco_kps.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
I = io.imread(img['coco_url'])
plt.imshow(I)
annIds = coco_kps.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
anns = coco_kps.loadAnns(annIds)
print(anns)
coco_kps.showAnns(anns)
plt.show()

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[{
    'segmentation': [
        [228.43, 247.9, 229.63, 206.62, 224.24, 191.07, 220.65,
         179.7, 207.49, 169.53, 202.71, 163.55, 205.7, 133.04,
         218.86, 121.68, 213.47, 104.33, 225.44, 96.55, 236.8,
         106.12, 236.8, 116.29, 254.15, 127.06, 263.72, 150.39,
         274.49, 166.54, 271.5, 177.31, 266.12, 181.5, 257.14,
         159.96, 254.75, 177.91, 261.93, 192.27, 262.53, 216.79,
         261.33, 234.14, 268.51, 249.1, 247.57, 246.11, 245.78,
         249.69, 229.03, 248.5]],
    'num_keypoints': 12,
    'area': 5999.5445,
    'iscrowd': 0,
    'keypoints': [
        0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 212, 135, 2,
        241, 125, 2, 209, 162, 2, 257, 146, 2, 218, 172, 2, 267,
        167, 2, 225, 177, 2, 247, 176, 2, 235, 203, 2, 254, 204,
        2, 236, 240, 2, 254, 238, 2],
    'image_id': 324158,
    'bbox': [202.71, 96.55, 71.78, 153.14],
    'category_id': 1,
    'id': 2162813
}]

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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)
dataDir = '..'
dataType = 'val2017'
# initialize COCO api for caption annotations
annFile = '{}/annotations/captions_{}.json'.format(dataDir, dataType)
coco = COCO(annFile)
imgIds = coco.getImgIds(imgIds=[324158])
img = coco.loadImgs(imgIds[np.random.randint(0, len(imgIds))])[0]
# load and display caption annotations
annIds = coco.getAnnIds(imgIds=img['id'])
anns = coco.loadAnns(annIds)
coco.showAnns(anns)

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A man is skate boarding down a path and a dog is running by his side.
A man on a skateboard with a dog outside.
A person riding a skate board with a dog following beside.
This man is riding a skateboard behind a dog.
A man walking his dog on a quiet country road.

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from pycocotools.coco import COCO
import csv

dataDir = '..'
dataType = 'train2017'
# initialize COCO api for person keypoints annotations
annFile = '{}/annotations/person_keypoints_{}.json'.format(dataDir, dataType)
coco_kps = COCO(annFile)

# display COCO categories and supercategories
cats = coco_kps.loadCats(coco_kps.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)))

# get all images containing given categories, select one at random
catIds = coco_kps.getCatIds(catNms=['person'])
imgIds = coco_kps.getImgIds(catIds=catIds)
print('there are %d images containing human' % len(imgIds))

def getBndboxKeypointsGT():
    csvFile = open('./KeypointBndboxGT.csv', 'w')
    keypointsWriter = csv.writer(csvFile)
    firstRow = [
        'imageName', 'personNumber', 'bndbox', 'nose',
        'left_eye', 'right_eye', 'left_ear', 'right_ear', 'left_shoulder', 'right_shoulder',
        'left_elbow', 'right_elbow', 'left_wrist', 'right_wrist', 'left_hip', 'right_hip',
        'left_knee', 'right_knee', 'left_ankle', 'right_ankle']

    keypointsWriter.writerow(firstRow)

    for i in range(len(imgIds)):
        imageNameTemp = coco_kps.loadImgs(imgIds[i])[0]
        imageName = imageNameTemp['file_name'].encode('raw_unicode_escape')
        img = coco_kps.loadImgs(imgIds[i])[0]
        annIds = coco_kps.getAnnIds(imgIds=img['id'], catIds=catIds, iscrowd=None)
        anns = coco_kps.loadAnns(annIds)
        personNumber = len(anns)

        for j in range(personNumber):
            bndbox = anns[j]['bbox']
            keyPoints = anns[j]['keypoints']
            keypointsRow = [
                imageName, str(personNumber),
                str(bndbox[0]) + '_' + str(bndbox[1]) + '_' + str(bndbox[2]) + '_' + str(bndbox[3]),
                str(keyPoints[0]) + '_' + str(keyPoints[1]) + '_' + str(keyPoints[2]),
                str(keyPoints[3]) + '_' + str(keyPoints[4]) + '_' + str(keyPoints[5]),
                str(keyPoints[6]) + '_' + str(keyPoints[7]) + '_' + str(keyPoints[8]),
                str(keyPoints[9]) + '_' + str(keyPoints[10]) + '_' + str(keyPoints[11]),
                str(keyPoints[12]) + '_' + str(keyPoints[13]) + '_' + str(keyPoints[14]),
                str(keyPoints[15]) + '_' + str(keyPoints[16]) + '_' + str(keyPoints[17]),
                str(keyPoints[18]) + '_' + str(keyPoints[19]) + '_' + str(keyPoints[20]),
                str(keyPoints[21]) + '_' + str(keyPoints[22]) + '_' + str(keyPoints[23]),
                str(keyPoints[24]) + '_' + str(keyPoints[25]) + '_' + str(keyPoints[26]),
                str(keyPoints[27]) + '_' + str(keyPoints[28]) + '_' + str(keyPoints[29]),
                str(keyPoints[30]) + '_' + str(keyPoints[31]) + '_' + str(keyPoints[32]),
                str(keyPoints[33]) + '_' + str(keyPoints[34]) + '_' + str(keyPoints[35]),
                str(keyPoints[36]) + '_' + str(keyPoints[37]) + '_' + str(keyPoints[38]),
                str(keyPoints[39]) + '_' + str(keyPoints[40]) + '_' + str(keyPoints[41]),
                str(keyPoints[42]) + '_' + str(keyPoints[43]) + '_' + str(keyPoints[44]),
                str(keyPoints[45]) + '_' + str(keyPoints[46]) + '_' + str(keyPoints[47]),
                str(keyPoints[48]) + '_' + str(keyPoints[49]) + '_' + str(keyPoints[50]),]

            keypointsWriter.writerow(keypointsRow)

    csvFile.close()

if __name__ == "__main__":
    print('Writing bndbox and keypoints to csv files..."')
    getBndboxKeypointsGT()