def visualize_dataset(imageDir, annFile):
coco = COCO(annFile)
# Compute area if area is not set in annotations.
coco.computeArea()
# 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)))
super_nms = set([cat['supercategory'] for cat in cats])
print('coco supercategories: \n{}\n'.format(' '.join(super_nms)))
coco.printCatNums()
# get all images containing given categories, select one at random
catIds = coco.getCatIds(catNms=nms)
print(len(catIds))
imgIds = coco.getImgIdsUnion(catIds=catIds)
# imgIds = coco.getImgIdsUnion(catIds=coco.getCatIds());
# imgIds = coco.getImgIds(catIds=catIds);
# imgIds= coco.getImgIds();
imgIds = sorted(imgIds)
print(len(imgIds))
# show image and annotations
while True:
# randomly pick one image
imgId = imgIds[np.random.randint(len(imgIds))]
# print(imgId)
img = coco.loadImgs(imgId)[0]
# I = io.imread(img['file_name'])
# print(I.shape)
# I = io.imread('%s/images/%s/%s'%(dataDir,dataType,img['file_name']))
# I = io.imread("{}".format(img['file_name']))
# print(img['file_name'])
# print(os.path.join(imageDir, img['file_name']))
I = Image.open(os.path.join(imageDir, img['file_name']))
print(img['file_name'])
# load and display instance annotations
plt.imshow(I)
plt.axis('off')
annIds = coco.getAnnIds(imgIds=imgId, catIds=catIds)
print(annIds)
anns = coco.loadAnns(annIds)
coco.showAnns(anns, draw_bbox=True)
# plt.show()
plt.draw() # need to use draw() instead of show() when use waitforbutttonpress()
while not plt.waitforbuttonpress():
continue
plt.clf()
# -----------------------------------------------------
import sys
import json
sys.path.insert(0, sys.path[0] + '/..')
import os
from pycocotools.coco import COCO
annFile = sys.argv[1]
ratios = [0.6, 0.2, 0.2]
subsets_names = ['train', 'val', 'test']
coco = COCO(annFile)
# Compute area if area is not set in annotations.
coco.computeArea()
# catIds = coco.getCatIds() # all cats
catNms = ['yin_hua_qing_xie']
catIds = coco.getCatIds(catNms=catNms)
print("image num all: {}".format(len(coco.getImgIds(catIds=catIds))))
json_subsets = coco.splitAnno(catIds, ratios)
output_dir = os.path.dirname(annFile)
for name, json_dict in zip(subsets_names, json_subsets):
print("image num {}: {}".format(name, len(json_dict['images'])))
json_file = os.path.join(output_dir, name + ".json")
# os.makedirs(os.path.dirname(json_file), exist_ok=True)
assert not os.path.exists(json_file)
with open(json_file, 'w', encoding='utf-8') as f:
import sys
sys.path.insert(0, sys.path[0] + '/..')
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
annType = ['segm', 'bbox', 'keypoints']
annType = annType[1] # specify type here
prefix = 'person_keypoints' if annType == 'keypoints' else 'instances'
print('Running demo for *%s* results.'.format(annType))
# initialize COCO ground truth api
annFile = 'test_set.json'
cocoGt = COCO(annFile)
# Compute area if area is not set in annotations.
cocoGt.computeArea()
# initialize COCO detections api
resFile = 'test_set_results.json'
cocoDt = cocoGt.loadRes(resFile)
# running evaluation
cocoEval = COCOeval(cocoGt, cocoDt, annType)
# cocoEval.params.imgIds = imgIds
# area_seq = [400, 800, 1200, 1600, 2000, 1e10]
# area_rng = [[0,area_seq[-1]]]
# for i in range(len(area_seq))
# cocoEval.params.areaRng = [[0 ** 2, 10e5 ** 2], [0 ** 2, 32 ** 2],
# [32 ** 2, 64 ** 2],
# [64 ** 2, 96 ** 2],
# [96 ** 2, 1e5 ** 2]]
# cocoEval.params.areaRngLbl = ['all', 'small', 'sm', 'medium', 'large']
# area_seq = [32**2, 96**2]
