def _load_all(self, anno_file, shuffle):
"""
initialize all entries given annotation json file
Parameters:
----------
anno_file: str
annotation json file
shuffle: bool
whether to shuffle image list
"""
image_set_index = []
labels = []
coco = COCO(anno_file)
img_ids = coco.getImgIds()
for img_id in img_ids:
# filename
image_info = coco.loadImgs(img_id)[0]
filename = image_info["file_name"]
subdir = filename.split('_')[1]
height = image_info["height"]
width = image_info["width"]
# label
anno_ids = coco.getAnnIds(imgIds=img_id)
annos = coco.loadAnns(anno_ids)
label = []
for anno in annos:
cat_id = int(anno["category_id"])
bbox = anno["bbox"]
assert len(bbox) == 4
xmin = float(bbox[0]) / width
ymin = float(bbox[1]) / height
xmax = xmin + float(bbox[2]) / width
ymax = ymin + float(bbox[3]) / height
label.append([cat_id, xmin, ymin, xmax, ymax, 0])
if label:
labels.append(np.array(label))
image_set_index.append(os.path.join(subdir, filename))
if shuffle:
import random
indices = list(range(len(image_set_index)))
random.shuffle(indices)
image_set_index = [image_set_index[i] for i in indices]
labels = [labels[i] for i in indices]
# store the results
self.image_set_index = image_set_index
self.labels = labels
class CocoDataset(Dataset):
"""COCO Custom Dataset compatible with torch.utils.data.DataLoader."""
def __init__(self, root, json, vocab, transform=None):
"""Set the path for images, captions and vocabulary wrapper.
Args:
root: image directory.
json: coco annotation file path.
vocab: vocabulary wrapper.
transform: image transformer.
"""
self.root = root
self.coco = COCO(json)
self.ids = list(self.coco.anns.keys())
self.vocab = vocab
self.transform = transform
def __getitem__(self, index):
"""Returns one data pair (image and caption)."""
ann_id = self.ids[index]
img_id = self.coco.anns[ann_id]['image_id']
caption = self.coco.anns[ann_id]['caption']
path = self.coco.loadImgs(img_id)[0]['file_name']
# Load image from disk and perform required transformations
image = Image.open(os.path.join(self.root, path)).convert('RGB')
if self.transform is not None:
image = self.transform(image)
# numericalize: convert caption to token ids.
tokens = tokenizer(str(caption)) # nltk.tokenize.word_tokenize(str(caption).lower())
caption = []
caption.append(self.vocab('<BOS>'))
caption.extend([self.vocab(token) for token in tokens])
caption.append(self.vocab('<EOS>'))
target = torch.Tensor(caption)
return image, target
def __len__(self):
return len(self.ids)
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],
bbox[3],
linewidth=2,
edgecolor='g',
facecolor='none')
ax.add_patch(rect)
def plot(data, gt_file, img_path, save_path,
link_pairs, ring_color, save=True):
# joints
coco = COCO(gt_file)
coco_dt = coco.loadRes(data)
coco_eval = COCOeval(coco, coco_dt, 'keypoints')
coco_eval._prepare()
gts_ = coco_eval._gts
dts_ = coco_eval._dts
p = coco_eval.params
p.imgIds = list(np.unique(p.imgIds))
if p.useCats:
p.catIds = list(np.unique(p.catIds))
p.maxDets = sorted(p.maxDets)
# loop through images, area range, max detection number
catIds = p.catIds if p.useCats else [-1]
threshold = 0
joint_thres = 0.1
imgs = coco.loadImgs(p.imgIds)
mean_rmse_list = []
mean_rmse_mask_list = []
for catId in catIds:
for imgId in imgs[:3]:
# dimension here should be Nxm
gts = gts_[imgId['id'], catId]
dts = dts_[imgId['id'], catId]
if len(gts) != 0 and len(dts) != 0:
npgt = np.array(gts[0]["keypoints"])
npdt = np.array(dts[0]["keypoints"])
mask = npdt[2::3] >= joint_thres
RMSE = np.sqrt((npgt[0::3] - npdt[0::3]) ** 2 + (npgt[1::3] - npdt[1::3]) ** 2)
RMSE_mask = RMSE[mask]
mean_rmse = np.round(np.nanmean(RMSE.flatten()), 2)
mean_rmse_mask = np.round(np.nanmean(RMSE_mask.flatten()), 2)
print(f"mean rmse: {mean_rmse}")
print(f"mean rmse mask: {mean_rmse_mask}")
mean_rmse_list.append(mean_rmse)
mean_rmse_mask_list.append(mean_rmse_mask)
inds = np.argsort([-d['score'] for d in dts], kind='mergesort')
dts = [dts[i] for i in inds]
if len(dts) > p.maxDets[-1]:
dts = dts[0:p.maxDets[-1]]
if len(gts) == 0 or len(dts) == 0:
continue
sum_score = 0
num_box = 0
# Read Images
img_file = os.path.join(img_path, imgId["file_name"])
# img_file = img_path + img_name + '.jpg'
data_numpy = cv2.imread(img_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
h = data_numpy.shape[0]
w = data_numpy.shape[1]
# Plot
fig = plt.figure(figsize=(w / 100, h / 100), dpi=100)
ax = plt.subplot(1, 1, 1)
bk = plt.imshow(data_numpy[:, :, ::-1])
bk.set_zorder(-1)
for j, gt in enumerate(gts):
# matching dt_box and gt_box
bb = gt['bbox']
x0 = bb[0] - bb[2];
x1 = bb[0] + bb[2] * 2
y0 = bb[1] - bb[3];
y1 = bb[1] + bb[3] * 2
# create bounds for ignore regions(double the gt bbox)
g = np.array(gt['keypoints'])
# xg = g[0::3]; yg = g[1::3];
vg = g[2::3]
for i, dt in enumerate(dts):
# Calculate Bbox IoU
dt_bb = dt['bbox']
dt_x0 = dt_bb[0] - dt_bb[2];
dt_x1 = dt_bb[0] + dt_bb[2] * 2
dt_y0 = dt_bb[1] - dt_bb[3];
dt_y1 = dt_bb[1] + dt_bb[3] * 2
ol_x = min(x1, dt_x1) - max(x0, dt_x0)
ol_y = min(y1, dt_y1) - max(y0, dt_y0)
ol_area = ol_x * ol_y
s_x = max(x1, dt_x1) - min(x0, dt_x0)
s_y = max(y1, dt_y1) - min(y0, dt_y0)
sum_area = s_x * s_y
iou = np.round(ol_area / (sum_area + np.spacing(1)), 2)
score = np.round(dt['score'], 2)
print(f"score: {dt['score']}")
if iou < 0.1 or score < threshold:
continue
else:
print(f'iou: {iou}')
dt_w = dt_x1 - dt_x0
dt_h = dt_y1 - dt_y0
ref = min(dt_w, dt_h)
num_box += 1
sum_score += dt['score']
dt_joints = np.array(dt['keypoints']).reshape(20, -1)
joints_dict = map_joint_dict(dt_joints)
# print(joints_dict)
# print(link_pairs)
# print(dt_joints)
# stick
for k, link_pair in enumerate(link_pairs):
if link_pair[0] in joints_dict \
and link_pair[1] in joints_dict:
# print(link_pair[0])
# print(vg)
if dt_joints[link_pair[0] - 1, 2] < joint_thres \
or dt_joints[link_pair[1] - 1, 2] < joint_thres \
or vg[link_pair[0] - 1] == 0 \
or vg[link_pair[1] - 1] == 0:
continue
# if k in range(6, 11):
# lw = 1
# else:
lw = ref / 100.
line = mlines.Line2D(
np.array([joints_dict[link_pair[0]][0],
joints_dict[link_pair[1]][0]]),
np.array([joints_dict[link_pair[0]][1],
joints_dict[link_pair[1]][1]]),
ls='-', lw=lw, alpha=1, color=link_pair[2], )
line.set_zorder(0)
ax.add_line(line)
# black ring
for k in range(dt_joints.shape[0]):
if dt_joints[k, 2] < joint_thres \
or vg[link_pair[0]] == 0 \
or vg[link_pair[1]] == 0:
continue
if dt_joints[k, 0] > w or dt_joints[k, 1] > h:
continue
# if k in range(5):
# radius = 1
# else:
radius = ref / 100
circle = mpatches.Circle(tuple(dt_joints[k, :2]),
radius=radius,
ec='black',
fc=ring_color[k],
alpha=1,
linewidth=1)
circle.set_zorder(1)
ax.add_patch(circle)
avg_score = (sum_score / (num_box + np.spacing(1))) * 1000
plt.gca().xaxis.set_major_locator(plt.NullLocator())
plt.gca().yaxis.set_major_locator(plt.NullLocator())
plt.axis('off')
plt.subplots_adjust(top=1, bottom=0, left=0, right=1, hspace=0, wspace=0)
plt.margins(0, 0)
if save:
plt.savefig(save_path + \
'score_' + str(np.int(avg_score)) + "_" +
imgId["file_name"].split(".")[0] + '.png',
format='png', bbox_inckes='tight', dpi=100)
# plt.savefig(save_path + 'id_' + str(imgId) + '.pdf', format='pdf',
# bbox_inckes='tight', dpi=100)
# plt.show()
plt.close()
print(f"total mean rmse: {np.mean(mean_rmse_list)}")
print(f"total mean rmse mask: {np.mean(mean_rmse_mask_list)}")
class GOLFDataset(HumanPoseEstimationDataset):
"""
COCODataset class.
"""
def __init__(
self,
root_path="/mldisk/nfs_shared_/dh/golfKeypointDB/data/golfKeypointDB",
data_version="train",
is_train=True,
use_gt_bboxes=True,
bbox_path="",
image_width=288,
image_height=384,
color_rgb=True,
scale=True,
scale_factor=0.35,
flip_prob=0.5,
rotate_prob=0.5,
rotation_factor=45.,
half_body_prob=0.3,
use_different_joints_weight=False,
heatmap_sigma=3,
soft_nms=False,
):
"""
Initializes a new COCODataset object.
Image and annotation indexes are loaded and stored in memory.
Annotations are preprocessed to have a simple list of annotations to iterate over.
Bounding boxes can be loaded from the ground truth or from a pickle file (in this case, no annotations are
provided).
Args:
root_path (str): dataset root path.
Default: "./datasets/COCO"
data_version (str): desired version/folder of COCO. Possible options are "train2017", "val2017".
Default: "train2017"
is_train (bool): train or eval mode. If true, train mode is used.
Default: True
use_gt_bboxes (bool): use ground truth bounding boxes. If False, bbox_path is required.
Default: True
bbox_path (str): bounding boxes pickle file path.
Default: ""
image_width (int): image width.
Default: 288
image_height (int): image height.
Default: ``384``
color_rgb (bool): rgb or bgr color mode. If True, rgb color mode is used.
Default: True
scale (bool): scale mode.
Default: True
scale_factor (float): scale factor.
Default: 0.35
flip_prob (float): flip probability.
Default: 0.5
rotate_prob (float): rotate probability.
Default: 0.5
rotation_factor (float): rotation factor.
Default: 45.
half_body_prob (float): half body probability.
Default: 0.3
use_different_joints_weight (bool): use different joints weights.
If true, the following joints weights will be used:
[1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2, 1.5, 1.5]
Default: False
heatmap_sigma (float): sigma of the gaussian used to create the heatmap.
Default: 3
soft_nms (bool): enable soft non-maximum suppression.
Default: False
"""
super(GOLFDataset, self).__init__()
self.root_path = root_path
self.data_version = data_version
self.is_train = is_train
self.use_gt_bboxes = use_gt_bboxes
self.bbox_path = bbox_path
self.image_width = image_width
self.image_height = image_height
self.color_rgb = color_rgb
self.scale = scale # ToDo Check
self.scale_factor = scale_factor
self.flip_prob = flip_prob
self.rotate_prob = rotate_prob
self.rotation_factor = rotation_factor
self.half_body_prob = half_body_prob
self.use_different_joints_weight = use_different_joints_weight # ToDo Check
self.heatmap_sigma = heatmap_sigma
self.soft_nms = soft_nms
self.annotation_dir = os.path.join(self.root_path, 'annotations')
self.data_path = os.path.join(self.root_path, self.data_version)
if self.data_version == 'train':
self.annotation_path = os.path.join(
self.annotation_dir, 'golfDB_18pts_train_200_2_conf50.json')
elif self.data_version == 'val':
self.annotation_path = os.path.join(self.annotation_dir,
'golfDB_18pts_val_20.json')
self.image_size = (self.image_width, self.image_height)
self.aspect_ratio = self.image_width * 1.0 / self.image_height
self.heatmap_size = (int(self.image_width / 4),
int(self.image_height / 4))
self.heatmap_type = 'gaussian'
self.pixel_std = 200 # I don't understand the meaning of pixel_std (=200) in the original implementation
self.nof_joints = 18
self.nof_joints_half_body = 8
self.flip_pairs = [[1, 2], [3, 4], [5, 6], [7, 8], [9, 10], [11, 12],
[13, 14], [15, 16]]
self.upper_body_ids = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.lower_body_ids = [11, 12, 13, 14, 15, 16]
self.joints_weight = np.asarray([
1., 1., 1., 1., 1., 1., 1., 1.2, 1.2, 1.5, 1.5, 1., 1., 1.2, 1.2,
1.5, 1.5, 1.8
],
dtype=np.float32).reshape(
(self.nof_joints, 1))
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
# Load COCO dataset - Create COCO object then load images and annotations
self.coco = COCO(self.annotation_path)
self.imgIds = self.coco.getImgIds()
# Create a list of annotations and the corresponding image (each image can contain more than one detection)
# Load bboxes and joints
# if self.use_gt_bboxes -> Load GT bboxes and joints
# else -> Load pre-predicted bboxes by a detector (as YOLOv3) and null joints
if not self.use_gt_bboxes:
# bboxes must be saved as the original COCO annotations
# i.e. the format must be:
# bboxes = {
# '<imgId>': [
# {
# 'id': <annId>, # progressive id for debugging
# 'clean_bbox': np.array([<x>, <y>, <w>, <h>])}
# },
# ...
# ],
# ...
# }
with open(self.bbox_path, 'rb') as fd:
bboxes = pickle.load(fd)
self.data = []
# load annotations for each image of COCO
# import pdb;pdb.set_trace()
for imgId in tqdm(self.imgIds):
ann_ids = self.coco.getAnnIds(imgIds=imgId, iscrowd=False)
img = self.coco.loadImgs(imgId)[0]
if self.use_gt_bboxes:
objs = self.coco.loadAnns(ann_ids)
# sanitize bboxes
valid_objs = []
for obj in objs:
# Skip non-person objects (it should never happen)
if obj['category_id'] != 1:
continue
# ignore objs without keypoints annotation
if max(obj['keypoints']) == 0:
continue
x, y, w, h = obj['bbox']
x1 = np.max((0, x))
y1 = np.max((0, y))
x2 = np.min((img['width'] - 1, x1 + np.max((0, w - 1))))
y2 = np.min((img['height'] - 1, y1 + np.max((0, h - 1))))
# Use only valid bounding boxes
if obj['area'] > 0 and x2 >= x1 and y2 >= y1:
obj['clean_bbox'] = [x1, y1, x2 - x1, y2 - y1]
valid_objs.append(obj)
objs = valid_objs
else:
objs = bboxes[imgId]
# for each annotation of this image, add the formatted annotation to self.data
for obj in objs:
joints = np.zeros((self.nof_joints, 2), dtype=np.float)
joints_visibility = np.ones((self.nof_joints, 2),
dtype=np.float)
if self.use_gt_bboxes:
# COCO pre-processing
# # Moved above
# # Skip non-person objects (it should never happen)
# if obj['category_id'] != 1:
# continue
#
# # ignore objs without keypoints annotation
# if max(obj['keypoints']) == 0:
# continue
for pt in range(self.nof_joints):
joints[pt, 0] = obj['keypoints'][pt * 3 + 0]
joints[pt, 1] = obj['keypoints'][pt * 3 + 1]
t_vis = int(np.clip(obj['keypoints'][pt * 3 + 2], 0,
1)) # ToDo check correctness
# COCO:
# if visibility == 0 -> keypoint is not in the image.
# if visibility == 1 -> keypoint is in the image BUT not visible (e.g. behind an object).
# if visibility == 2 -> keypoint looks clearly (i.e. it is not hidden).
joints_visibility[pt, 0] = t_vis
joints_visibility[pt, 1] = t_vis
center, scale = self._box2cs(obj['clean_bbox'][:4])
self.data.append({
'imgId':
imgId,
'annId':
obj['id'],
'imgPath':
os.path.join(self.root_path, self.data_version,
'%06d.jpg' % imgId),
'center':
center,
'scale':
scale,
'joints':
joints,
'joints_visibility':
joints_visibility,
})
# Done check if we need prepare_data -> We should not
# print('\nCOCO dataset loaded!')
# Default values
self.bbox_thre = 1.0
self.image_thre = 0.0
self.in_vis_thre = 0.2
self.nms_thre = 1.0
self.oks_thre = 0.9
def __len__(self):
return len(self.data)
def __getitem__(self, index):
joints_data = self.data[index].copy()
# Read the image from disk
# print(joints_data['imgPath'])
image = cv2.imread(joints_data['imgPath'],
cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)
if self.color_rgb:
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if image is None:
raise ValueError('Fail to read %s' % image)
joints = joints_data['joints']
joints_vis = joints_data['joints_visibility']
c = joints_data['center']
s = joints_data['scale']
score = joints_data['score'] if 'score' in joints_data else 1
r = 0
# Apply data augmentation
if self.is_train:
if self.half_body_prob and \
random.random() < self.half_body_prob and \
np.sum(joints_vis[:, 0]) > self.nof_joints_half_body:
c_half_body, s_half_body = self._half_body_transform(
joints, joints_vis)
if c_half_body is not None and s_half_body is not None:
c, s = c_half_body, s_half_body
sf = self.scale_factor
rf = self.rotation_factor
if self.scale:
s = s * np.clip(
random.random() * sf + 1, 1 - sf,
1 + sf) # A random scale factor in [1 - sf, 1 + sf]
if self.rotate_prob and random.random() < self.rotate_prob:
r = np.clip(random.random() * rf, -rf * 2, rf *
2) # A random rotation factor in [-2 * rf, 2 * rf]
else:
r = 0
if self.flip_prob and random.random() < self.flip_prob:
image = image[:, ::-1, :]
joints, joints_vis = fliplr_joints(joints, joints_vis,
image.shape[1],
self.flip_pairs)
c[0] = image.shape[1] - c[0] - 1
# Apply affine transform on joints and image
trans = get_affine_transform(c, s, self.pixel_std, r, self.image_size)
#cv2.imwrite( '/home/mmlab/CCTV_Server/1.jpg',image)
image = cv2.warpAffine(
image,
trans, (int(self.image_size[0]), int(self.image_size[1])),
flags=cv2.INTER_LINEAR)
#cv2.imwrite('/home/mmlab/CCTV_Server/1_affined.jpg',image)
#import pdb;pdb.set_trace()
for i in range(self.nof_joints):
if joints_vis[i, 0] > 0.:
joints[i, 0:2] = affine_transform(joints[i, 0:2], trans)
# Convert image to tensor and normalize
if self.transform is not None: # I could remove this check
image = self.transform(image)
target, target_weight = self._generate_target(joints, joints_vis)
# Update metadata
joints_data['joints'] = joints
joints_data['joints_visibility'] = joints_vis
joints_data['center'] = c
joints_data['scale'] = s
joints_data['rotation'] = r
joints_data['score'] = score
return image, target.astype(np.float32), target_weight.astype(
np.float32), joints_data
def evaluate_accuracy(self, output, target, params=None):
if params is not None:
hm_type = params['hm_type']
thr = params['thr']
accs, avg_acc, cnt, joints_preds, joints_target = evaluate_pck_accuracy(
output, target, hm_type, thr)
else:
accs, avg_acc, cnt, joints_preds, joints_target = evaluate_pck_accuracy(
output, target)
return accs, avg_acc, cnt, joints_preds, joints_target
def evaluate_overall_accuracy(self,
predictions,
bounding_boxes,
image_paths,
output_dir,
rank=0.):
res_folder = os.path.join(output_dir, 'results')
if not os.path.exists(res_folder):
os.makedirs(res_folder)
res_file = os.path.join(
res_folder,
f'golf_keypoints_{self.data_version}_results_{int(rank*100)}.json')
# person x (keypoints)
_kpts = []
for idx, kpt in enumerate(predictions):
# print(image_paths[idx])
_kpts.append({
'keypoints': kpt,
'center': bounding_boxes[idx][0:2],
'scale': bounding_boxes[idx][2:4],
'area': bounding_boxes[idx][4],
'score': bounding_boxes[idx][5],
'image': int(image_paths[idx][-10:-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.nof_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)
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.data_version:
info_str = self._do_python_keypoint_eval(res_file)
name_value = OrderedDict(info_str)
return name_value, name_value['AP']
else:
return {'Null': 0}, 0
# Private methods
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 _half_body_transform(self, joints, joints_vis):
upper_joints = []
lower_joints = []
for joint_id in range(self.nof_joints):
if joints_vis[joint_id][0] > 0:
if joint_id in self.upper_body_ids:
upper_joints.append(joints[joint_id])
else:
lower_joints.append(joints[joint_id])
if random.random() < 0.5 and len(upper_joints) > 2:
selected_joints = upper_joints
else:
selected_joints = lower_joints \
if len(lower_joints) > 2 else upper_joints
if len(selected_joints) < 2:
return None, None
selected_joints = np.array(selected_joints, dtype=np.float32)
center = selected_joints.mean(axis=0)[:2]
left_top = np.amin(selected_joints, axis=0)
right_bottom = np.amax(selected_joints, axis=0)
w = right_bottom[0] - left_top[0]
h = right_bottom[1] - left_top[1]
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)
scale = scale * 1.5
return center, scale
def _generate_target(self, joints, joints_vis):
"""
:param joints: [nof_joints, 3]
:param joints_vis: [nof_joints, 3]
:return: target, target_weight(1: visible, 0: invisible)
"""
target_weight = np.ones((self.nof_joints, 1), dtype=np.float32)
target_weight[:, 0] = joints_vis[:, 0]
if self.heatmap_type == 'gaussian':
target = np.zeros(
(self.nof_joints, self.heatmap_size[1], self.heatmap_size[0]),
dtype=np.float32)
tmp_size = self.heatmap_sigma * 3
for joint_id in range(self.nof_joints):
feat_stride = np.asarray(self.image_size) / np.asarray(
self.heatmap_size)
mu_x = int(joints[joint_id][0] / feat_stride[0] + 0.5)
mu_y = int(joints[joint_id][1] / feat_stride[1] + 0.5)
# Check that any part of the gaussian is in-bounds
ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)]
br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)]
if ul[0] >= self.heatmap_size[0] or ul[1] >= self.heatmap_size[1] \
or br[0] < 0 or br[1] < 0:
# If not, just return the image as is
target_weight[joint_id] = 0
continue
# # Generate gaussian
size = 2 * tmp_size + 1
x = np.arange(0, size, 1, np.float32)
y = x[:, np.newaxis]
x0 = y0 = size // 2
# The gaussian is not normalized, we want the center value to equal 1
g = np.exp(-((x - x0)**2 + (y - y0)**2) /
(2 * self.heatmap_sigma**2))
# Usable gaussian range
g_x = max(0, -ul[0]), min(br[0], self.heatmap_size[0]) - ul[0]
g_y = max(0, -ul[1]), min(br[1], self.heatmap_size[1]) - ul[1]
# Image range
img_x = max(0, ul[0]), min(br[0], self.heatmap_size[0])
img_y = max(0, ul[1]), min(br[1], self.heatmap_size[1])
v = target_weight[joint_id]
if v > 0.5:
target[joint_id][img_y[0]:img_y[1], img_x[0]:img_x[1]] = \
g[g_y[0]:g_y[1], g_x[0]:g_x[1]]
else:
raise NotImplementedError
if self.use_different_joints_weight:
target_weight = np.multiply(target_weight, self.joints_weight)
return target, target_weight
def _write_coco_keypoint_results(self, keypoints, res_file):
data_pack = [{
'cat_id': 1, # 1 == 'person'
'cls': 'person',
'ann_type': 'keypoints',
'keypoints': keypoints
}]
results = self._coco_keypoint_results_one_category_kernel(data_pack[0])
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))],
dtype=np.float32)
key_points = np.zeros((_key_points.shape[0], self.nof_joints * 3),
dtype=np.float32)
for ipt in range(self.nof_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'].astype(np.float32),
'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):
coco_dt = self.coco.loadRes(res_file)
coco_eval = COCOeval(self.coco, coco_dt, 'keypoints')
coco_eval.params.useSegm = None
coco_eval.params.imgIds = self.coco.getImgIds()
# import pdb;pdb.set_trace()
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
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>'
xml += img[
'file_name'] + '</filename>\n<source>\n<database>\nCOCO2014pascalformat\n</database>\n</source>\n<size>\n'
xml += '<width>\n' + str(
img['width']) + '\n</width>\n' + '<height>\n' + str(
img['height']) + '\n</height>\n'
xml += '<depth>\n3\n</depth>\n</size>\n<segmented>\n0\n</segmented>\n'
for i in range(len(anns)):
if (labelnames[int(anns[i]['category_id'])] in neededlabels):
bbox = anns[i]['bbox']
xml += '<object>\n<name>' + str(labelnames[int(
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']
