from crowdposetools.coco import COCO # pycocotools
import numpy as np
import skimage.io as io
import matplotlib.pyplot as plt
import pylab
pylab.rcParams['figure.figsize'] = (8.0, 10.0)
# dataDir= '/home/andrew/datasets/MSCOCO/coco2017'
# dataType='train2017'
# annFile='{}/annotations/instances_{}.json'.format(dataDir,dataType)
annFile = 'data/crowdpose/person_keypoints_train2017.json'
# 初始化标注数据的 COCO api
coco = COCO(annFile)
imgIds = coco.getImgIds(imgIds=[100000])
img = coco.loadImgs(imgIds[0])[0]
I = io.imread('data/crowdpose/%s' % (img['file_name']))
plt.figure()
plt.imshow(I)
plt.axis('off')
ax = plt.gca()
annIds = coco.getAnnIds(imgIds=img['id'], iscrowd=None)
anns = coco.loadAnns(annIds)
coco.showAnns(anns)
plt.show()
# plt.savefig('coco4.png')
class HIECPDataset(CPJointsDataset):
'''
['nose', 'chest',
'right_shoulder', 'right_elbow', 'right_wrist',
'left_shoulder','left_elbow', 'left_wrist',
'right_hip','right_knee','right_ankle',
'left_hip', 'left_knee', 'left_ankle']
'''
def __init__(self, cfg, root, image_set, is_train, transform=None):
super().__init__(cfg, root, image_set, is_train, transform)
self.nms_thre = cfg.TEST.NMS_THRE
self.image_thre = cfg.TEST.IMAGE_THRE
self.soft_nms = cfg.TEST.SOFT_NMS
self.oks_thre = cfg.TEST.OKS_THRE
self.in_vis_thre = cfg.TEST.IN_VIS_THRE
self.bbox_file = cfg.TEST.COCO_BBOX_FILE
self.use_gt_bbox = cfg.TEST.USE_GT_BBOX
self.image_width = cfg.MODEL.IMAGE_SIZE[0]
self.image_height = cfg.MODEL.IMAGE_SIZE[1]
self.aspect_ratio = self.image_width * 1.0 / self.image_height
self.pixel_std = 200
self.coco = COCO(self._get_ann_file_keypoint())
# deal with class names
cats = [
cat['name'] for cat in self.coco.loadCats(self.coco.getCatIds())
]
self.classes = ['__background__'] + cats
logger.info('=> classes: {}'.format(self.classes))
self.num_classes = len(self.classes)
self._class_to_ind = dict(zip(self.classes, range(self.num_classes)))
self._class_to_coco_ind = dict(zip(cats, self.coco.getCatIds()))
self._coco_ind_to_class_ind = dict([(self._class_to_coco_ind[cls],
self._class_to_ind[cls])
for cls in self.classes[1:]])
# load image file names
self.image_set_index = self._load_image_set_index()
self.num_images = len(self.image_set_index)
logger.info('=> num_images: {}'.format(self.num_images))
self.num_joints = 14
self.flip_pairs = [[2, 5], [3, 6], [4, 7], [8, 11], [9, 12], [10, 13]]
self.parent_ids = None
self.upper_body_ids = (0, 1, 2, 3, 4, 5, 6, 7)
self.lower_body_ids = (8, 9, 10, 11, 12, 13)
self.joints_weight = np.array([
1., 1., 1.2, 1.5, 1.5, 1.2, 1.5, 1.5, 1.2, 1.5, 1.5, 1.2, 1.5, 1.5
],
dtype=np.float32).reshape(
(self.num_joints, 1))
self.nms_sigmas = np.array([
.79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89, .35,
.35
]) / 10.0
self.db = self._get_db()
if is_train and cfg.DATASET.SELECT_DATA:
self.db = self.select_data(self.db)
logger.info('=> load {} samples'.format(len(self.db)))
def _get_ann_file_keypoint(self):
""" self.root / annotations / hie_person_keypoints_train2020.json """
prefix = 'hie_person_keypoints'
return os.path.join(self.root, 'annotations',
prefix + '_' + self.image_set + '.json')
def _load_image_set_index(self):
""" image id: int """
image_ids = self.coco.getImgIds()
# print('image id:',image_ids[0])
return image_ids
def _get_db(self):
if self.is_train or self.use_gt_bbox:
# use ground truth bbox
gt_db = self._load_coco_keypoint_annotations()
else:
# use bbox from detection
gt_db = self._load_coco_person_detection_results()
return gt_db
def _load_coco_keypoint_annotations(self):
""" ground truth bbox and keypoints """
gt_db = []
for index in self.image_set_index:
gt_db.extend(self._load_coco_keypoint_annotation_kernal(index))
return gt_db
def _load_coco_keypoint_annotation_kernal(self, index):
"""
coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id']
iscrowd:
crowd instances are handled by marking their overlaps with all categories to -1
and later excluded in training
bbox:
[x1, y1, w, h]
:param index: coco image id
:return: db entry
"""
im_ann = self.coco.loadImgs([index])[0]
width = im_ann['width']
height = im_ann['height']
annIds = self.coco.getAnnIds(imgIds=[index], iscrowd=False)
objs = self.coco.loadAnns(annIds)
# sanitize bboxes
valid_objs = []
for obj in objs:
x, y, w, h = obj['bbox']
x1 = np.max((0, x))
y1 = np.max((0, y))
x2 = np.min((width - 1, x1 + np.max((0, w - 1))))
y2 = np.min((height - 1, y1 + np.max((0, h - 1))))
if x2 >= x1 and y2 >= y1:
obj['clean_bbox'] = [x1, y1, x2 - x1, y2 - y1]
valid_objs.append(obj)
objs = valid_objs
rec = []
for obj in objs:
cls = self._coco_ind_to_class_ind[obj['category_id']]
if cls != 1:
continue
# ignore objs without keypoints annotation
if max(obj['keypoints']) == 0:
continue
joints_3d = np.zeros((self.num_joints, 3), dtype=np.float)
joints_3d_vis = np.zeros((self.num_joints, 3), dtype=np.float)
for ipt in range(self.num_joints):
joints_3d[ipt, 0] = obj['keypoints'][ipt * 3 + 0]
joints_3d[ipt, 1] = obj['keypoints'][ipt * 3 + 1]
joints_3d[ipt, 2] = 0
t_vis = obj['keypoints'][ipt * 3 + 2]
if t_vis > 1:
t_vis = 1
joints_3d_vis[ipt, 0] = t_vis
joints_3d_vis[ipt, 1] = t_vis
joints_3d_vis[ipt, 2] = 0
center, scale = self._box2cs(obj['clean_bbox'][:4])
rec.append({
'image': self.image_path_from_index(index),
'center': center,
'scale': scale,
'joints_3d': joints_3d,
'joints_3d_vis': joints_3d_vis,
'filename': '',
'imgnum': 0,
})
return rec
def _box2cs(self, box):
x, y, w, h = box[:4]
return self._xywh2cs(x, y, w, h)
def _xywh2cs(self, x, y, w, h):
center = np.zeros((2), dtype=np.float32)
center[0] = x + w * 0.5
center[1] = y + h * 0.5
if w > self.aspect_ratio * h:
h = w * 1.0 / self.aspect_ratio
elif w < self.aspect_ratio * h:
w = h * self.aspect_ratio
scale = np.array([w * 1.0 / self.pixel_std, h * 1.0 / self.pixel_std],
dtype=np.float32)
if center[0] != -1:
scale = scale * 1.25
return center, scale
def image_path_from_index(self, index):
""" example: images / train2017 / 000000119993.jpg """
file_name = index
prefix = 'train' if ('train' in self.image_set
or 'val' in self.image_set) else self.image_set
image_path = os.path.join(self.root, 'images', prefix, file_name)
return image_path
def _load_coco_person_detection_results(self):
all_boxes = None
with open(self.bbox_file, 'r') as f:
all_boxes = json.load(f)
if not all_boxes:
logger.error('=> Load %s fail!' % self.bbox_file)
return None
logger.info('=> Total boxes: {}'.format(len(all_boxes)))
kpt_db = []
num_boxes = 0
for n_img in range(0, len(all_boxes)):
det_res = all_boxes[n_img]
if det_res['category_id'] != 1:
continue
img_name = self.image_path_from_index(det_res['image_id'])
box = det_res['bbox']
score = det_res['score']
if score < self.image_thre:
continue
num_boxes = num_boxes + 1
center, scale = self._box2cs(box)
joints_3d = np.zeros((self.num_joints, 3), dtype=np.float)
joints_3d_vis = np.ones((self.num_joints, 3), dtype=np.float)
kpt_db.append({
'image': img_name,
'center': center,
'scale': scale,
'score': score,
'joints_3d': joints_3d,
'joints_3d_vis': joints_3d_vis,
})
logger.info('=> Total boxes after fliter low score@{}: {}'.format(
self.image_thre, num_boxes))
return kpt_db
def evaluate(self, cfg, preds, output_dir, all_boxes, img_path, *args,
**kwargs):
rank = cfg.RANK
res_folder = os.path.join(output_dir, 'results')
if not os.path.exists(res_folder):
try:
os.makedirs(res_folder)
except Exception:
logger.error('Fail to make {}'.format(res_folder))
res_file = os.path.join(
res_folder,
'keypoints_{}_results_{}.json'.format(self.image_set, rank))
# person x (keypoints)
_kpts = []
for idx, kpt in enumerate(preds):
_kpts.append({
'keypoints': kpt,
'center': all_boxes[idx][0:2],
'scale': all_boxes[idx][2:4],
'area': all_boxes[idx][4],
'score': all_boxes[idx][5],
'image': img_path[idx][-13:-4]
})
# image x person x (keypoints)
kpts = defaultdict(list)
for kpt in _kpts:
kpts[kpt['image']].append(kpt)
# rescoring and oks nms
num_joints = self.num_joints
in_vis_thre = self.in_vis_thre
oks_thre = self.oks_thre
oks_nmsed_kpts = []
for img in kpts.keys():
img_kpts = kpts[img]
for n_p in img_kpts:
box_score = n_p['score']
kpt_score = 0
valid_num = 0
for n_jt in range(0, num_joints):
t_s = n_p['keypoints'][n_jt][2]
if t_s > in_vis_thre:
kpt_score = kpt_score + t_s
valid_num = valid_num + 1
if valid_num != 0:
kpt_score = kpt_score / valid_num
# rescoring
n_p['score'] = kpt_score * box_score
if self.soft_nms:
keep = soft_oks_nms(
[img_kpts[i] for i in range(len(img_kpts))], oks_thre)
else:
keep = oks_nms([img_kpts[i] for i in range(len(img_kpts))],
oks_thre, self.nms_sigmas)
if len(keep) == 0:
oks_nmsed_kpts.append(img_kpts)
else:
oks_nmsed_kpts.append([img_kpts[_keep] for _keep in keep])
self._write_coco_keypoint_results(oks_nmsed_kpts, res_file)
if 'test' not in self.image_set:
info_str = self._do_python_keypoint_eval(res_file, res_folder)
name_value = OrderedDict(info_str)
return name_value, name_value['AP']
else:
return {'Null': 0}, 0
def _write_coco_keypoint_results(self, keypoints, res_file):
data_pack = [{
'cat_id': self._class_to_coco_ind[cls],
'cls_ind': cls_ind,
'cls': cls,
'ann_type': 'keypoints',
'keypoints': keypoints
} for cls_ind, cls in enumerate(self.classes)
if not cls == '__background__']
results = self._coco_keypoint_results_one_category_kernel(data_pack[0])
logger.info('=> writing results json to %s' % res_file)
with open(res_file, 'w') as f:
json.dump(results, f, sort_keys=True, indent=4)
try:
json.load(open(res_file))
except Exception:
content = []
with open(res_file, 'r') as f:
for line in f:
content.append(line)
content[-1] = ']'
with open(res_file, 'w') as f:
for c in content:
f.write(c)
def _coco_keypoint_results_one_category_kernel(self, data_pack):
cat_id = data_pack['cat_id']
keypoints = data_pack['keypoints']
cat_results = []
for img_kpts in keypoints:
if len(img_kpts) == 0:
continue
_key_points = np.array(
[img_kpts[k]['keypoints'] for k in range(len(img_kpts))])
key_points = np.zeros((_key_points.shape[0], self.num_joints * 3),
dtype=np.float)
for ipt in range(self.num_joints):
key_points[:, ipt * 3 + 0] = _key_points[:, ipt, 0]
key_points[:, ipt * 3 + 1] = _key_points[:, ipt, 1]
key_points[:, ipt * 3 + 2] = _key_points[:, ipt,
2] # keypoints score.
result = [{
'image_id': img_kpts[k]['image'],
'category_id': cat_id,
'keypoints': list(key_points[k]),
'score': img_kpts[k]['score'],
'center': list(img_kpts[k]['center']),
'scale': list(img_kpts[k]['scale'])
} for k in range(len(img_kpts))]
cat_results.extend(result)
return cat_results
def _do_python_keypoint_eval(self, res_file, res_folder):
coco_dt = self.coco.loadRes(res_file)
coco_eval = COCOeval(self.coco, coco_dt, 'keypoints')
coco_eval.params.useSegm = None
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
stats_names = [
'AP', 'Ap .5', 'AP .75', 'AP (M)', 'AP (L)', 'AR', 'AR .5',
'AR .75', 'AR (M)', 'AR (L)'
]
info_str = []
for ind, name in enumerate(stats_names):
info_str.append((name, coco_eval.stats[ind]))
return info_str
class CrowdPoseRLDataset(CPRelationJointsDataset):
"""`CrowdPose`_ Dataset.
Args:
root (string): Root directory where dataset is located to.
dataset (string): Dataset name(train2017, val2017, test2017).
data_format(string): Data format for reading('jpg', 'zip')
transform (callable, optional): A function/transform that takes in an opencv image
and returns a transformed version. E.g, ``transforms.ToTensor``
target_transform (callable, optional): A function/transform that takes in the
target and transforms it.
"""
'''
keypoints: ['left_shoulder', 'right_shoulder',
'left_elbow', 'right_elbow',
'left_wrist', 'right_wrist',
'left_hip', 'right_hip',
'left_knee', 'right_knee',
'left_ankle', 'right_ankle',
'head', 'neck']
'''
def __init__(self, cfg, root, image_set, is_train, transform=None):
self.name = 'CROWDPOSE'
super().__init__(cfg, root, image_set, is_train, transform)
self.nms_thre = cfg.TEST.NMS_THRE
self.image_thre = cfg.TEST.IMAGE_THRE
self.soft_nms = cfg.TEST.SOFT_NMS
self.oks_thre = cfg.TEST.OKS_THRE
self.in_vis_thre = cfg.TEST.IN_VIS_THRE
self.bbox_file = cfg.TEST.COCO_BBOX_FILE
self.use_gt_bbox = cfg.TEST.USE_GT_BBOX
self.image_width = cfg.MODEL.IMAGE_SIZE[0]
self.image_height = cfg.MODEL.IMAGE_SIZE[1]
self.aspect_ratio = self.image_width * 1.0 / self.image_height
self.pixel_std = 200
self.nms_sigmas = np.array([
.79, .79, .72, .72, .62, .62, 1.07, 1.07, .87, .87, .89, .89, .35,
.35
]) / 10.0
self.coco = COCO(self._get_anno_file_name())
# deal with class names
cats = [
cat['name'] for cat in self.coco.loadCats(self.coco.getCatIds())
]
self.classes = ['__background__'] + cats
logger.info('=> classes: {}'.format(self.classes))
self.num_classes = len(self.classes)
self._class_to_ind = dict(zip(self.classes, range(self.num_classes)))
self._class_to_coco_ind = dict(zip(cats, self.coco.getCatIds()))
self._coco_ind_to_class_ind = dict([(self._class_to_coco_ind[cls],
self._class_to_ind[cls])
for cls in self.classes[1:]])
# load image file names
self.image_set_index = list(self.coco.imgs.keys())
self.num_images = len(self.image_set_index)
logger.info('=> num_images: {}'.format(self.num_images))
self.num_joints = 14
self.flip_pairs = [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]]
self.parent_ids = None
self.upper_body_ids = (0, 1, 2, 3, 4, 5, 12, 13)
self.lower_body_ids = (6, 7, 8, 9, 10, 11)
self.joints_weight = np.array(
[1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1.],
dtype=np.float32).reshape((self.num_joints, 1))
self.db = self._get_db()
if is_train and cfg.DATASET.SELECT_DATA:
self.db = self.select_data(self.db)
def _get_anno_file_name(self):
# example: root/json/crowdpose_{train,val,test}.json
return os.path.join(self.root, 'json',
'crowdpose_{}.json'.format(self.image_set))
def _get_image_path(self, file_name):
images_dir = os.path.join(self.root, 'images')
if self.data_format == 'zip':
return images_dir + '.zip@' + file_name
else:
return os.path.join(images_dir, file_name)
def _load_image_set_index(self):
""" image id: int """
image_ids = self.coco.getImgIds()
return image_ids
def _get_db(self):
if self.is_train or self.use_gt_bbox:
# use ground truth bbox
gt_db = self._load_coco_keypoint_annotations()
else:
# use bbox from detection
gt_db = self._load_coco_person_detection_results()
return gt_db
def _load_coco_keypoint_annotations(self):
""" ground truth bbox and keypoints """
gt_db = []
for index in self.image_set_index:
gt_db.extend(self._load_coco_keypoint_annotation_kernal(index))
return gt_db
def _load_coco_keypoint_annotation_kernal(self, index):
"""
coco ann: [u'segmentation', u'area', u'iscrowd', u'image_id', u'bbox', u'category_id', u'id']
iscrowd:
crowd instances are handled by marking their overlaps with all categories to -1
and later excluded in training
bbox:
[x1, y1, w, h]
:param index: coco image id
:return: db entry
"""
im_ann = self.coco.loadImgs(index)[0]
data_numpy = cv2.imread(
self.image_path_from_index(index),
cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
# bug in crowdpose
# height = im_ann['width']
# width = im_ann['height']
height = im_ann['height']
width = im_ann['width']
if data_numpy.shape[1] == height and data_numpy.shape[
0] == width and height != width:
# print('image size mismatched:',(width, height), data_numpy.shape[:2])
height = data_numpy.shape[0]
width = data_numpy.shape[1]
annIds = self.coco.getAnnIds(imgIds=index, iscrowd=False)
objs = self.coco.loadAnns(annIds)
# sanitize bboxes
valid_objs = []
for obj in objs:
x, y, w, h = obj['bbox']
x1 = np.max((0, x))
y1 = np.max((0, y))
x2 = np.min((width - 1, x1 + np.max((0, w - 1))))
y2 = np.min((height - 1, y1 + np.max((0, h - 1))))
if x2 >= x1 and y2 >= y1:
obj['clean_bbox'] = [x1, y1, x2 - x1, y2 - y1]
valid_objs.append(obj)
objs = valid_objs
rec = []
for obj in objs:
cls = self._coco_ind_to_class_ind[obj['category_id']]
if cls != 1:
continue
# ignore objs without keypoints annotation
if max(obj['keypoints']) == 0:
continue
joints_3d = np.zeros((self.num_joints, 3), dtype=np.float)
joints_3d_vis = np.zeros((self.num_joints, 3), dtype=np.float)
for ipt in range(self.num_joints):
joints_3d[ipt, 0] = obj['keypoints'][ipt * 3 + 0]
joints_3d[ipt, 1] = obj['keypoints'][ipt * 3 + 1]
joints_3d[ipt, 2] = 0
t_vis = obj['keypoints'][ipt * 3 + 2]
if t_vis > 1:
t_vis = 1
joints_3d_vis[ipt, 0] = t_vis
joints_3d_vis[ipt, 1] = t_vis
joints_3d_vis[ipt, 2] = 0
center, scale = self._box2cs(obj['clean_bbox'][:4])
obj_size = obj['clean_bbox'][2:4]
rec.append({
'image': self.image_path_from_index(index),
'center': center,
'scale': scale,
'obj_size': obj_size,
'joints_3d': joints_3d,
'joints_3d_vis': joints_3d_vis,
'filename': '',
'imgnum': 0,
})
for i in range(len(rec)):
interference = []
interfenrece_vis = []
for j in range(len(rec)):
if i == j:
continue
interference.append(rec[j]['joints_3d'])
interfenrece_vis.append(rec[j]['joints_3d_vis'])
rec[i]['interference'] = interference
rec[i]['interference_vis'] = interfenrece_vis
return rec
def _box2cs(self, box):
x, y, w, h = box[:4]
return self._xywh2cs(x, y, w, h)
def _xywh2cs(self, x, y, w, h):
center = np.zeros((2), dtype=np.float32)
center[0] = x + w * 0.5
center[1] = y + h * 0.5
if w > self.aspect_ratio * h:
h = w * 1.0 / self.aspect_ratio
elif w < self.aspect_ratio * h:
w = h * self.aspect_ratio
scale = np.array([w * 1.0 / self.pixel_std, h * 1.0 / self.pixel_std],
dtype=np.float32)
if center[0] != -1:
scale = scale * 1 #.25
return center, scale
def image_path_from_index(self, index, prefix=''):
""" example: images / 109979.jpg """
file_name = '%d.jpg' % index
image_path = os.path.join(self.root, 'images', file_name)
return image_path
def _load_coco_person_detection_results(self):
all_boxes = None
with open(self.bbox_file, 'r') as f:
all_boxes = json.load(f)
if not all_boxes:
logger.error('=> Load %s fail!' % self.bbox_file)
return None
logger.info('=> Total boxes: {}'.format(len(all_boxes)))
kpt_db = []
num_boxes = 0
for n_img in range(0, len(all_boxes)):
det_res = all_boxes[n_img]
if det_res['category_id'] != 1:
continue
img_name = self.image_path_from_index(det_res['image_id'])
box = det_res['bbox']
obj_size = det_res['bbox'][2:4]
score = det_res['score']
if score < self.image_thre:
continue
num_boxes = num_boxes + 1
center, scale = self._box2cs(box)
joints_3d = np.zeros((self.num_joints, 3), dtype=np.float)
joints_3d_vis = np.ones((self.num_joints, 3), dtype=np.float)
kpt_db.append({
'image': img_name,
'center': center,
'scale': scale,
'score': score,
'joints_3d': joints_3d,
'joints_3d_vis': joints_3d_vis,
'obj_size': obj_size,
})
logger.info('=> Total boxes after fliter low score@{}: {}'.format(
self.image_thre, num_boxes))
return kpt_db
def evaluate(self, cfg, preds, output_dir, all_boxes, img_path, *args,
**kwargs):
rank = cfg.RANK
res_folder = os.path.join(output_dir, 'results')
if not os.path.exists(res_folder):
try:
os.makedirs(res_folder)
except Exception:
logger.error('Fail to make {}'.format(res_folder))
res_file = os.path.join(
res_folder,
'keypoints_{}_results_{}.json'.format(self.image_set, rank))
# person x (keypoints)
_kpts = []
for idx, kpt in enumerate(preds):
_kpts.append({
'keypoints': kpt,
'center': all_boxes[idx][0:2],
'scale': all_boxes[idx][2:4],
'area': all_boxes[idx][4],
'score': all_boxes[idx][5],
'image': int(img_path[idx].split('/')[-1][:-4])
})
# image x person x (keypoints)
kpts = defaultdict(list)
for kpt in _kpts:
kpts[kpt['image']].append(kpt)
# rescoring and oks nms
num_joints = self.num_joints
in_vis_thre = self.in_vis_thre
oks_thre = self.oks_thre
oks_nmsed_kpts = []
for img in kpts.keys():
img_kpts = kpts[img]
for n_p in img_kpts:
box_score = n_p['score']
kpt_score = 0
valid_num = 0
for n_jt in range(0, num_joints):
t_s = n_p['keypoints'][n_jt][2]
if t_s > in_vis_thre:
kpt_score = kpt_score + t_s
valid_num = valid_num + 1
if valid_num != 0:
kpt_score = kpt_score / valid_num
# rescoring
n_p['score'] = kpt_score * box_score
if self.soft_nms:
keep = soft_oks_nms(
[img_kpts[i] for i in range(len(img_kpts))], oks_thre,
self.nms_sigmas)
else:
keep = oks_nms([img_kpts[i] for i in range(len(img_kpts))],
oks_thre, self.nms_sigmas)
if len(keep) == 0:
oks_nmsed_kpts.append(img_kpts)
else:
oks_nmsed_kpts.append([img_kpts[_keep] for _keep in keep])
self._write_coco_keypoint_results(oks_nmsed_kpts, res_file)
if 'test' not in self.image_set:
info_str = self._do_python_keypoint_eval(res_file, res_folder)
name_value = OrderedDict(info_str)
return name_value, name_value['AP']
else:
info_str = self._do_python_keypoint_eval(res_file, res_folder)
name_value = OrderedDict(info_str)
return name_value, name_value['AP']
# return {'Null': 0}, 0
def _write_coco_keypoint_results(self, keypoints, res_file):
data_pack = [{
'cat_id': self._class_to_coco_ind[cls],
'cls_ind': cls_ind,
'cls': cls,
'ann_type': 'keypoints',
'keypoints': keypoints
} for cls_ind, cls in enumerate(self.classes)
if not cls == '__background__']
results = self._coco_keypoint_results_one_category_kernel(data_pack[0])
logger.info('=> writing results json to %s' % res_file)
with open(res_file, 'w') as f:
json.dump(results, f, sort_keys=True, indent=4)
try:
json.load(open(res_file))
except Exception:
content = []
with open(res_file, 'r') as f:
for line in f:
content.append(line)
content[-1] = ']'
with open(res_file, 'w') as f:
for c in content:
f.write(c)
def _coco_keypoint_results_one_category_kernel(self, data_pack):
cat_id = data_pack['cat_id']
keypoints = data_pack['keypoints']
cat_results = []
for img_kpts in keypoints:
if len(img_kpts) == 0:
continue
_key_points = np.array(
[img_kpts[k]['keypoints'] for k in range(len(img_kpts))])
key_points = np.zeros((_key_points.shape[0], self.num_joints * 3),
dtype=np.float)
for ipt in range(self.num_joints):
key_points[:, ipt * 3 + 0] = _key_points[:, ipt, 0]
key_points[:, ipt * 3 + 1] = _key_points[:, ipt, 1]
key_points[:, ipt * 3 + 2] = _key_points[:, ipt,
2] # keypoints score.
result = [{
'image_id': img_kpts[k]['image'],
'category_id': cat_id,
'keypoints': list(key_points[k]),
'score': img_kpts[k]['score'],
'center': list(img_kpts[k]['center']),
'scale': list(img_kpts[k]['scale'])
} for k in range(len(img_kpts))]
cat_results.extend(result)
return cat_results
def _do_python_keypoint_eval(self, res_file, res_folder):
coco_dt = self.coco.loadRes(res_file)
coco_eval = COCOeval(self.coco, coco_dt, 'keypoints')
coco_eval.params.useSegm = None
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
stats_names = [
'AP', 'Ap .5', 'AP .75', 'AR', 'AR .5', 'AR .75', 'AP (easy)',
'AP (medium)', 'AP (hard)'
]
stats_index = [0, 1, 2, 5, 6, 7, 8, 9, 10]
info_str = []
for ind, name in enumerate(stats_names):
info_str.append((name, coco_eval.stats[stats_index[ind]]))
# info_str.append(coco_eval.stats[ind])
return info_str
