def processing(args):
ann_path = args.ann_path
json_path = args.json_path
mask_dir = args.mask_dir
coco = COCO(ann_path)
ids = list(coco.imgs.keys())
lists = []
flielist_fp = open(args.filelist_path, 'w')
masklist_fp = open(args.masklist_path, 'w')
for i, img_id in enumerate(ids):
ann_ids = coco.getAnnIds(imgIds=img_id)
img_anns = coco.loadAnns(ann_ids)
numPeople = len(img_anns)
name = coco.imgs[img_id]['file_name']
height = coco.imgs[img_id]['height']
width = coco.imgs[img_id]['width']
persons = []
person_centers = []
for p in range(numPeople):
if img_anns[p]['num_keypoints'] < 5 or img_anns[p]['area'] < 32 * 32:
continue
kpt = img_anns[p]['keypoints']
dic = dict()
# person center
person_center = [img_anns[p]['bbox'][0] + img_anns[p]['bbox'][2] / 2.0, img_anns[p]['bbox'][1] + img_anns[p]['bbox'][3] / 2.0]
scale = img_anns[p]['bbox'][3] / 368.0
# skip this person if the distance to exiting person is too small
flag = 0
for pc in person_centers:
dis = math.sqrt((person_center[0] - pc[0]) * (person_center[0] - pc[0]) + (person_center[1] - pc[1]) * (person_center[1] - pc[1]))
if dis < pc[2] * 0.3:
flag = 1;
break
if flag == 1:
continue
dic['objpos'] = person_center
dic['keypoints'] = np.zeros((17, 3)).tolist()
dic['scale'] = scale
for part in range(17):
dic['keypoints'][part][0] = kpt[part * 3]
dic['keypoints'][part][1] = kpt[part * 3 + 1]
# visiable is 1, unvisiable is 0 and not labeled is 2
if kpt[part * 3 + 2] == 2:
dic['keypoints'][part][2] = 1
elif kpt[part * 3 + 2] == 1:
dic['keypoints'][part][2] = 0
else:
dic['keypoints'][part][2] = 2
persons.append(dic)
person_centers.append(np.append(person_center, max(img_anns[p]['bbox'][2], img_anns[p]['bbox'][3])))
if len(persons) > 0:
filelist_fp.write(name + '\n')
info = dict()
info['filename'] = name
info['info'] = []
cnt = 1
for person in persons:
dic = dict()
dic['pos'] = person['objpos']
dic['keypoints'] = np.zeros((18,3)).tolist()
dic['scale'] = person['scale']
for i in range(17):
dic['keypoints'][COCO_TO_OURS[i]][0] = person['keypoints'][i][0]
dic['keypoints'][COCO_TO_OURS[i]][1] = person['keypoints'][i][1]
dic['keypoints'][COCO_TO_OURS[i]][2] = person['keypoints'][i][2]
dic['keypoints'][1][0] = (person['keypoints'][5][0] + person['keypoints'][6][0]) * 0.5
dic['keypoints'][1][1] = (person['keypoints'][5][1] + person['keypoints'][6][1]) * 0.5
if person['keypoints'][5][2] == person['keypoints'][6][2]:
dic['keypoints'][1][2] = person['keypoints'][5][2]
elif person['keypoints'][5][2] == 2 or person['keypoints'][6][2] == 2:
dic['keypoints'][1][2] = 2
else:
dic['keypoints'][1][2] = 0
info['info'].append(dic)
lists.append(info)
mask_all = np.zeros((height, width), dtype=np.uint8)
mask_miss = np.zeros((height, width), dtype=np.uint8)
flag = 0
for p in img_anns:
if p['iscrowd'] == 1:
mask_crowd = coco.annToMask(p)
temp = np.bitwise_and(mask_all, mask_crowd)
mask_crowd = mask_crowd - temp
flag += 1
continue
else:
mask = coco.annToMask(p)
mask_all = np.bitwise_or(mask, mask_all)
if p['num_keypoints'] <= 0:
mask_miss = np.bitwise_or(mask, mask_miss)
if flag < 1:
mask_miss = np.logical_not(mask_miss)
elif flag == 1:
mask_miss = np.logical_not(np.bitwise_or(mask_miss, mask_crowd))
mask_all = np.bitwise_or(mask_all, mask_crowd)
else:
raise Exception('crowd segments > 1')
np.save(os.path.join(mask_dir, name.split('.')[0] + '.npy'), mask_miss)
masklist_fp.write(os.path.join(mask_dir, name.split('.')[0] + '.npy') + '\n')
if i % 1000 == 0:
print "Processed {} of {}".format(i, len(ids))
masklist_fp.close()
filelist_fp.close()
print 'write json file'
fp = open(json_path, 'w')
fp.write(json.dumps(lists))
fp.close()
print 'done!'
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']
