plot_coco_inst_label.py

#!/usr/bin/python
from pycocotools.coco import COCO
import numpy as np
import skimage.io as io
import matplotlib.pyplot as plt
import pylab

# If necessary, pre-define category and its id
#  PRE_DEFINE_CATEGORIES = {"aeroplane": 1, "bicycle": 2, "bird": 3, "boat": 4,
#  "bottle":5, "bus": 6, "car": 7, "cat": 8, "chair": 9,
#  "cow": 10, "diningtable": 11, "dog": 12, "horse": 13,
#  "motorbike": 14, "person": 15, "pottedplant": 16,
#  "sheep": 17, "sofa": 18, "train": 19, "tvmonitor": 20}

image_directory = '/data/VOCdevkit/VOC2012/JPEGImages/'
annotation_file = 'voc_inst_val.json'
example_coco = COCO(annotation_file)

categories = example_coco.loadCats(example_coco.getCatIds())
category_names = [category['name'] for category in categories]
print('Custom COCO categories: \n{}\n'.format(' '.join(category_names)))

category_names = set([category['supercategory'] for category in categories])
print('Custom COCO supercategories: \n{}'.format(' '.join(category_names)))

category_ids = example_coco.getCatIds(catNms=['person'])
image_ids = example_coco.getImgIds(catIds=category_ids)
for i in range(len(image_ids)):
    image_data = example_coco.loadImgs(image_ids[i])[0]

    # load and display instance annotations
    image = io.imread(image_directory + image_data['file_name'])
    plt.imshow(image); plt.axis('off')
    pylab.rcParams['figure.figsize'] = (8.0, 10.0)
    annotation_ids = example_coco.getAnnIds(imgIds=image_data['id'], catIds=category_ids, iscrowd=None)
    annotations = example_coco.loadAnns(annotation_ids)
    example_coco.showAnns(annotations, draw_bbox=False)
    plt.savefig('./inst_example/test%05d.png'%i)
    plt.close()