def sort_imgs(annotation_folder,
reqcat=5,
to_break=False): #reqcat = 5 for airplanes
#get airplane paths
global plane_name
global n
co = coco.COCO(annotation_folder)
plane_ids = co.getImgIds(catIds=[reqcat])
n = 0
#iterate over airplane IDs
while n < len(plane_ids):
#for plane_id in plane_ids:
plane_img = co.loadImgs(ids=plane_ids[n])[0]
plane_name = plane_img['file_name'] # '000000221184.jpg'
img_path = os.path.join(img_dir, plane_name)
# set windows size and read plane path
fig, ax = plt.subplots(1)
mngr = plt.get_current_fig_manager()
mngr.window.setGeometry(250, 100, 1280, 824)
image = cv2.imread(img_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
#get contour
annIds = co.getAnnIds(imgIds=plane_ids[n])
for annId in annIds:
if co.anns[annId]['category_id'] == reqcat:
try:
seg = co.anns[annId]['segmentation'][0]
poly = np.array(seg).reshape(
(int(len(seg) / 2), 1,
2)).astype(int) #arr(points,1,2)
image_con = image
image_con = cv2.drawContours(image_con, [poly], 0,
(0, 255, 0), 3)
except:
print(co.imgs[plane_ids[n]]['file_name'], plane_ids[n],
annId)
#show images and connect event
ax.imshow(image_con)
plt.connect('key_press_event', onkeypress)
plt.show()
n += 1
if n % 50 == 0:
print(n)
np.save('good_pic_name', good_full_plane_ids)
if to_break:
if n > 5:
break
def create_coco(val_json_file, use_cpp_extension=True):
"""Creates Microsoft COCO helper class object and return it."""
if val_json_file.startswith('gs://'):
_, local_val_json = tempfile.mkstemp(suffix='.json')
tf.gfile.Remove(local_val_json)
tf.gfile.Copy(val_json_file, local_val_json)
atexit.register(tf.gfile.Remove, local_val_json)
else:
local_val_json = val_json_file
if use_cpp_extension:
coco_gt = coco.COCO(local_val_json, False)
else:
coco_gt = COCO(local_val_json)
return coco_gt
def __init__(self, filename, use_cpp_extension=False):
"""Constructs COCO evaluation class.
The class provides the interface to metrics_fn in TPUEstimator. The
_update_op() takes detections from each image and push them to
self.detections. The _evaluate() loads a JSON file in COCO annotation format
as the groundtruths and runs COCO evaluation.
Args:
filename: Ground truth JSON file name. If filename is None, use
groundtruth data passed from the dataloader for evaluation.
use_cpp_extension: use cocoeval C++ library.
"""
if filename:
if filename.startswith('gs://'):
_, local_val_json = tempfile.mkstemp(suffix='.json')
tf.gfile.Remove(local_val_json)
tf.gfile.Copy(filename, local_val_json)
atexit.register(tf.gfile.Remove, local_val_json)
else:
local_val_json = filename
self.use_cpp_extension = use_cpp_extension
if self.use_cpp_extension:
self.coco_gt = coco.COCO(local_val_json, use_mask=True)
else:
self.coco_gt = MaskCOCO(local_val_json)
self.filename = filename
self.metric_names = [
'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'ARmax1', 'ARmax10',
'ARmax100', 'ARs', 'ARm', 'ARl'
]
mask_metric_names = ['mask_' + x for x in self.metric_names]
self.metric_names.extend(mask_metric_names)
self._reset()
trainorval = 'train'
reqcat = 5
savedir = 'cut_planes_{}'.format(trainorval)
if not os.path.isdir(savedir):
os.mkdir(savedir)
file__path = os.path.dirname(__file__)
ann_folder = 'instances_{}2017.json'.format(trainorval)
annotation_folder = os.path.join(file__path, 'annotations', ann_folder)
img_dir = os.path.join(file__path, '{}2017'.format(trainorval))
good_plane_names = np.load('good_plane_names.npy')
n = 0
co = coco.COCO(annotation_folder)
plane_ids = co.getImgIds(catIds=[reqcat])
adjusted_good_cuts = []
while n < len(good_plane_names):
plane_name_plus = good_plane_names[n]
plane_img = co.loadImgs(ids=plane_ids[n])[0]
plane_name_one = plane_img['file_name'] # '000000221184.jpg'
#print(n,adjusted_good_cuts)
value, plane_name = plane_name_plus.split(' ')
if value == 'ok' and plane_name_one == plane_name:
fig, ax = plt.subplots(1)
img_path = os.path.join(img_dir, plane_name)
"""
import coco
import os
import cv2
import numpy as np
from lxml import etree
import xml.etree.cElementTree as ET
#from generate_xml import write_xml
trainorval='val'
reqcat=5
annotation_file=os.path.join(os.path.join(os.path.dirname(__file__),'annotations'),'instances_{}2017.json'.format(trainorval))
co=coco.COCO(annotation_file)
print(co.createIndex())
plane_ids=co.getImgIds(catIds=[reqcat])
planeann_ids=co.getAnnIds(catIds=[reqcat])
planeann_ids2=co.getAnnIds(imgIds=plane_ids)
anns=co.loadAnns(ids=planeann_ids)
#co.showAnns(anns[0])
#anns[0]['bbox']
#anns[0]['image_id']
#anns[0]['segmentation']
#co.loadImgs(ids=[anns[0]['image_id']])[0]['file_name']
#300659
#for i in anns:print(i['image_id'])
imagefile = "/asm/project_mvtec/mvtec_d2s/images"
imageurl = imagefile + Imgs['file_name']
#print imageurl
I = io.imread(imageurl)
plt.imshow(I)
plt.show()
return Imgs, I ,Imgs['file_name']
dataDir='..'
dataType='val2017'
annFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)
annFile = "/asm/project_mvtec/mvtec_d2s/annotations/D2S_training.json"
coco=coco.COCO(annFile)
#print coco.loadAnns(coco.getAnnIds())
# print (len(coco.getCatIds()))
# print coco.getAnnIds()[0])
cats = coco.loadCats(coco.getCatIds())
nms=[cat['name'] for cat in cats]
# for i in range(len(nms)):
# print nms[i]
#time.sleep(100)
I_w = 1920.0
I_h = 1440.0
#print coco.loadAnns(coco.getAnnIds()[0])