labelnames[77] = "cell phone"
labelnames[78] = "microwave"
labelnames[79] = "oven"
labelnames[80] = "toaster"
labelnames[81] = "sink"
labelnames[82] = "refrigerator"
labelnames[84] = "book"
labelnames[85] = "clock"
labelnames[86] = "vase"
labelnames[87] = "scissors"
labelnames[88] = "teddy bear"
labelnames[89] = "hair drier"
labelnames[90] = "toothbrush"
neededlabels = ["person", "dog", "car", "bicycle"]
coco = COCO(annFile)
cats = coco.loadCats(coco.getCatIds())
nms = [cat['name'] for cat in cats]
imgIds = coco.getImgIds()
takeXml = 0
directory = './annotations_pascalformat/'
if not os.path.exists(directory):
os.makedirs(directory)
for n in range(len(imgIds)):
img = coco.loadImgs(imgIds[n])[0]
annIds = coco.getAnnIds(imgIds=img['id'], iscrowd=None)
anns = coco.loadAnns(annIds)
xml = '<annotation>\n<folder>\nCOCO2014pascalformat\n</folder>\n<filename>'
annFile = '/nightowls/annotations/nightowls_training.json'
image_directory = '/nightowls/images'
cocoGt = COCO(annFile)
imgIds = sorted(cocoGt.getImgIds())
print('There are %d images in the training set' % len(imgIds))
annotations = cocoGt.getAnnIds()
print('There are %d annotations in the training set' % len(annotations))
# Select random annotation
anno_id = annotations[random.randint(0, len(annotations))]
anno = cocoGt.loadAnns(ids=anno_id)[0]
print('Annotation (id=%d): %s' % (anno_id, anno))
cat = cocoGt.loadCats(ids=anno['category_id'])[0]
category_name = cat['name']
print('Object type %s' % category_name)
# Show the annotation in its image
image = cocoGt.loadImgs(ids=anno['image_id'])[0]
file_path = path.join(image_directory, image['file_name'])
fig, ax = plt.subplots(1)
img = mpimg.imread(file_path)
ax.imshow(img)
bbox = anno['bbox']
rect = patches.Rectangle((bbox[0], bbox[1]),
bbox[2],
class COCOTest():
def __init__(self, image_set, year):
#imdb.__init__(self, 'coco_' + year + '_' + image_set)
# COCO specific config options
self.config = {
'top_k': 2000,
'use_salt': True,
'cleanup': True,
'crowd_thresh': 0.7,
'min_size': 2
}
# name, paths
self._year = year
self._image_set = image_set
self._data_path = osp.join(cfg.DATA_DIR, 'coco')
# load COCO API, classes, class <-> id mappings
self._COCO = COCO(self._get_ann_file())
cats = self._COCO.loadCats(self._COCO.getCatIds())
#print self._COCO.anns[185487]
# anns = [self._COCO.anns[185487]]
#self._COCO.showAnns(anns)
#image_ids = self._COCO.getImgIds()
#print image_ids
self.test()
def _get_ann_file(self):
# prefix = 'instances' if self._image_set.find('test') == -1 \
# else 'image_info'
# return osp.join(self._data_path, 'annotations',
# prefix + '_' + self._image_set + self._year + '.json')
return osp.join(self._data_path, 'annotations',
'person_keypoints_train2014.json')
def get_img_file(self, im_ann):
return osp.join(self._data_path, 'train2014', im_ann['file_name'])
def test(self):
image_ids = self._COCO.getImgIds()
# print image_ids,'\n,len:',len(image_ids)
for i in xrange(len(image_ids)):
im_ann = self._COCO.loadImgs(image_ids[i])[0]
print '\n:', i
width = im_ann['width']
height = im_ann['height']
# print im_ann
# print self.get_img_file(im_ann)
annIds = self._COCO.getAnnIds(imgIds=image_ids[i], iscrowd=None)
objs = self._COCO.loadAnns(annIds)
# print annIds,objs
im = cv2.imread(self.get_img_file(im_ann))
# Sanitize bboxes -- some are invalid
valid_objs = []
im = im[:, :, (2, 1, 0)]
im[:, :, :] = (0, 0, 0)
#im = cv2.cvtColor(im,cv2.COLOR_BGR2GRAY)
fig, ax = plt.subplots(figsize=(12, 12))
for obj in objs:
print obj
mask = self._COCO.annToMask(obj)
#im[mask==0,:]=(0,0,0);
im[mask == 1, :] = (255, 255, 255)
# for i in range(width):
# for j in range(height):
# if(mask[i][j] == 0)
x1 = np.max((0, obj['bbox'][0]))
y1 = np.max((0, obj['bbox'][1]))
x2 = np.min((width - 1, x1 + np.max((0, obj['bbox'][2] - 1))))
y2 = np.min((height - 1, y1 + np.max((0, obj['bbox'][3] - 1))))
# print mask.shape,x1,y1,x2,y2,width,height
# print 'mask.shape[0]:mask_shape[1]',mask.shape[0],mask.shape[1]
start_h = np.round(np.max((1, y1))).astype(np.int)
end_h = np.round(np.min((height, y2))).astype(np.int)
start_w = np.round(np.max((1, x1))).astype(np.int)
end_w = np.round(np.min((width, x2))).astype(np.int)
cropped_mask = mask[start_h:end_h, start_w:end_w]
# print cropped_mask.shape
# resize_mask = cv2.resize(cropped_mask, (28, 28), interpolation=cv2.INTER_NEAREST)
# print resize_mask.shape,resize_mask
if 'keypoints' in obj:
print 'category_id', obj['category_id']
print '\nkeypoints', obj['keypoints']
print '\nlens', obj['num_keypoints']
