def encode(self, boxes, labels):
'''Encode target bounding boxes and class labels.
SSD coding rules:
tx = (x - anchor_x) / (variance[0]*anchor_w)
ty = (y - anchor_y) / (variance[0]*anchor_h)
tw = log(w / anchor_w)
th = log(h / anchor_h)
Args:
boxes: (tensor) bounding boxes of (xmin,ymin,xmax,ymax), sized [#obj,4].
labels: (tensor) object class labels, sized [#obj,].
Returns:
loc_targets: (tensor) encoded bounding boxes, sized [#anchors,4].
cls_targets: (tensor) encoded class labels, sized [#anchors,].
'''
anchor_boxes = self.anchor_boxes
ious = bbox_iou(anchor_boxes, boxes)
max_ious, max_ids = ious.max(1)
boxes = boxes[max_ids]
boxes = change_bbox_order(boxes, 'xyxy2xywh')
anchor_boxes = change_bbox_order(anchor_boxes, 'xyxy2xywh')
loc_xy = (boxes[:, :2] - anchor_boxes[:, :2]) / anchor_boxes[:, 2:]
loc_wh = torch.log(boxes[:, 2:] / anchor_boxes[:, 2:])
loc_targets = torch.cat([loc_xy, loc_wh], 1)
cls_targets = 1 + labels[max_ids]
# cls_targets[max_ious<0.5] = 0
# ignore = (max_ious>0.4) & (max_ious<0.5) # ignore ious between [0.4,0.5]
# cls_targets[ignore] = -1 # mark ignored to -1
return loc_targets, cls_targets
def tracker2object(self, boxes: [OBSTACLE], th=0.5):
n_b = len(boxes)
n_o = len(self.obstacles)
iou_mat = np.zeros((n_o, n_b))
for i in range(n_o):
for j in range(n_b):
iou_mat[i, j] = bbox_iou(self.obstacles[i], boxes[j])
count = min(n_b, n_o)
used = []
idmax = 0
obstacles = []
while count > 0:
r, k = np.unravel_index(np.argmax(iou_mat, axis=None),
iou_mat.shape)
if iou_mat[r, k] > th:
used.append(k)
obstacle = self.obstacles[r]
box = boxes[k]
if idmax < obstacle._id:
idmax = obstacle._id
obstacle.update_box(box)
obstacles.append(obstacle)
iou_mat[r, :] = -99
iou_mat[:, k] = -99
count = count - 1
idx = range(n_b)
idx = [elem for elem in idx if elem not in used]
self.obstacles = obstacles
for i, c in enumerate(idx):
# dst = self.calculate_position(boxes[c])
obstacle = OBSTACLE(boxes[c], i + idmax + 1)
self.obstacles.append(obstacle)
return
def encode(self, boxes, labels):
'''Encode target bounding boxes and class labels.
SSD coding rules:
tx = (x - anchor_x) / (variance[0]*anchor_w)
ty = (y - anchor_y) / (variance[0]*anchor_h)
tw = log(w / anchor_w)
th = log(h / anchor_h)
Args:
boxes: (tensor) bounding boxes of (xmin,ymin,xmax,ymax), sized [#obj,4].
labels: (tensor) object class labels, sized [#obj,].
Returns:
loc_targets: (tensor) encoded bounding boxes, sized [#anchors,4].
cls_targets: (tensor) encoded class labels, sized [#anchors,].
'''
def argmax(x):
v, i = x.max(0)
j = v.max(0)[1].item()
return (i[j], j)
default_boxes = self.default_boxes
default_boxes = change_bbox_order(default_boxes, 'xywh2xyxy')
ious = bbox_iou(default_boxes, boxes)
index = torch.empty(len(default_boxes), dtype=torch.long).fill_(-1)
masked_ious = ious.clone()
while True:
i, j = argmax(masked_ious)
if masked_ious[i, j] < 1e-6:
break
index[i] = j
masked_ious[i, :] = 0
masked_ious[:, j] = 0
mask = (index < 0) & (ious.max(1)[0] > 0.5)
if mask.any():
index[mask] = ious[mask].max(1)[1]
boxes = boxes[index.clamp(min=0)]
boxes = change_bbox_order(boxes, 'xyxy2xywh')
default_boxes = change_bbox_order(default_boxes, 'xyxy2xywh')
loc_xy = (boxes[:, :2] - default_boxes[:, :2]
) / default_boxes[:, 2:] / self.variances[0]
loc_wh = torch.log(
boxes[:, 2:] / default_boxes[:, 2:]) / self.variances[1]
loc_targets = torch.cat([loc_xy, loc_wh], dim=1)
cls_targets = 1 + labels[index.clamp(min=0)]
cls_targets[index < 0] = 0
return loc_targets, cls_targets
def compare(self, data1, data2, thresh_iou):
if data2['xmin'] <= data1['xmin'] <= data1['xmax'] <= data2['xmax'] \
and data2['ymin'] <= data1['ymin'] <= data1['ymax'] <= data2['ymax']:
return True
if data1['xmin'] <= data2['xmin'] <= data2['xmax'] <= data1['xmax'] \
and data1['ymin'] <= data2['ymin'] <= data2['ymax'] <= data1['ymax']:
return True
box1 = BoundBox(data1['xmin'], data1['ymin'], data1['xmax'], data1['ymax'])
box2 = BoundBox(data2['xmin'], data2['ymin'], data2['xmax'], data2['ymax'])
iou = bbox_iou(box1, box2)
if iou > thresh_iou:
return True
else:
return False
def random_crop(img, boxes, labels, min_scale=0.3, max_aspect_ratio=2.):
'''
:param img: (PIL.Image)
:param boxes: (tensor) [N,4]
:param labels: (tensor) [N,]
:param min_scale: (float) minimal image width/height scale
:param max_aspect_ratio: (float) maximum width/height aspect ratio
:return:
img, boxes, labels
'''
imw, imh = img.size
params = [(0, 0, imw, imh)] # crop roi (x,y,w,h) out
## ????
for min_iou in (0, 0.1, 0.3, 0.5, 0.7, 0.9):
for _ in range(100):
scale = random.uniform(min_scale, 1)
aspect_ratio = random.uniform(
max(1 / max_aspect_ratio, scale * scale),
min(max_aspect_ratio, 1 / (scale * scale)))
w = int(imw * scale * math.sqrt(aspect_ratio))
h = int(imh * scale / math.sqrt(aspect_ratio))
x = random.randrange(imw - w)
y = random.randrange(imh - h)
roi = torch.FloatTensor([[x, y, x + w, y + h]])
ious = bbox_iou(boxes, roi)
if ious.min() >= min_iou:
params.append((x, y, w, h))
break
x, y, w, h = random.choice(params)
img = img.crop((x, y, x + w, y + h))
center = (boxes[:, :2] + boxes[:, 2:]) / 2
mask = (center[:, 0] >= x) & (center[:, 0] <= x + w) \
& (center[:, 1] >= y) & (center[:, 1] <= y + h)
if mask.any():
boxes = boxes[mask] - torch.FloatTensor([x, y, x, y])
boxes = bbox_clamp(boxes, 0, 0, w, h)
labels = labels[mask]
else:
boxes = torch.FloatTensor([[0, 0, 0, 0]])
labels = torch.LongTensor([0])
return img, boxes, labels
def do_nms(boxes, nms_thresh):
if len(boxes) > 0:
nb_class = len(boxes[0].classes)
else:
return
for c in range(nb_class):
sorted_indices = np.argsort([-box.classes[c] for box in boxes])
for i in range(len(sorted_indices)):
index_i = sorted_indices[i]
if boxes[index_i].classes[c] == 0: continue
for j in range(i + 1, len(sorted_indices)):
index_j = sorted_indices[j]
if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh:
boxes[index_j].classes[c] = 0
def update(self,
pred_bboxes,
pred_labels,
pred_scores,
gt_bboxes,
gt_labels,
gt_difficults=None):
"""Update internal buffer with latest prediction and gt pairs.
Parameters
----------
pred_bboxes : list of mxnet.NDArray or numpy.ndarray
Prediction bounding boxes with shape `B, N, 4`.
Where B is the size of mini-batch, N is the number of bboxes.
pred_labels : list of mxnet.NDArray or numpy.ndarray
Prediction bounding boxes labels with shape `B, N`.
pred_scores : list of mxnet.NDArray or numpy.ndarray
Prediction bounding boxes scores with shape `B, N`.
gt_bboxes : list of mxnet.NDArray or numpy.ndarray
Ground-truth bounding boxes with shape `B, M, 4`.
Where B is the size of mini-batch, M is the number of ground-truths.
gt_labels : list of mxnet.NDArray or numpy.ndarray
Ground-truth bounding boxes labels with shape `B, M`.
gt_difficults : list of mxnet.NDArray or numpy.ndarray, optional, default is None
Ground-truth bounding boxes difficulty labels with shape `B, M`.
"""
def as_numpy(a):
"""Convert a (list of) mx.NDArray into numpy.ndarray"""
if isinstance(a, (list, tuple)):
out = [
x.asnumpy() if isinstance(x, mx.nd.NDArray) else x
for x in a
]
try:
out = np.concatenate(out, axis=0)
except ValueError:
out = np.array(out)
return out
elif isinstance(a, mx.nd.NDArray):
a = a.asnumpy()
return a
if gt_difficults is None:
gt_difficults = [None for _ in as_numpy(gt_labels)]
if isinstance(gt_labels, list):
if len(gt_difficults) != len(gt_labels) * gt_labels[0].shape[0]:
gt_difficults = [None] * len(gt_labels) * gt_labels[0].shape[0]
for pred_bbox, pred_label, pred_score, gt_bbox, gt_label, gt_difficult in zip(
*[
as_numpy(x) for x in [
pred_bboxes, pred_labels, pred_scores, gt_bboxes,
gt_labels, gt_difficults
]
]):
# strip padding -1 for pred and gt
valid_pred = np.where(pred_label.flat >= 0)[0]
pred_bbox = pred_bbox[valid_pred, :]
pred_label = pred_label.flat[valid_pred].astype(int)
pred_score = pred_score.flat[valid_pred]
valid_gt = np.where(gt_label.flat >= 0)[0]
gt_bbox = gt_bbox[valid_gt, :]
gt_label = gt_label.flat[valid_gt].astype(int)
if gt_difficult is None:
gt_difficult = np.zeros(gt_bbox.shape[0])
else:
gt_difficult = gt_difficult.flat[valid_gt]
for l in np.unique(
np.concatenate((pred_label, gt_label)).astype(int)):
pred_mask_l = pred_label == l
pred_bbox_l = pred_bbox[pred_mask_l]
pred_score_l = pred_score[pred_mask_l]
# sort by score
order = pred_score_l.argsort()[::-1]
pred_bbox_l = pred_bbox_l[order]
pred_score_l = pred_score_l[order]
# add by smher, 2019.11.19
valid_pred_score = np.where(pred_score_l > self._score_thresh)
pred_bbox_l = pred_bbox_l[valid_pred_score]
pred_score_l = pred_score_l[valid_pred_score]
gt_mask_l = gt_label == l
gt_bbox_l = gt_bbox[gt_mask_l]
gt_difficult_l = gt_difficult[gt_mask_l]
self._n_pos[l] += np.logical_not(gt_difficult_l).sum()
self._score[l].extend(pred_score_l)
if len(pred_bbox_l) == 0:
continue
if len(gt_bbox_l) == 0:
self._match[l].extend((0, ) * pred_bbox_l.shape[0])
continue
# VOC evaluation follows integer typed bounding boxes.
pred_bbox_l = pred_bbox_l.copy()
pred_bbox_l[:, 2:] += 1
gt_bbox_l = gt_bbox_l.copy()
gt_bbox_l[:, 2:] += 1
iou = bbox_iou(pred_bbox_l, gt_bbox_l)
gt_index = iou.argmax(axis=1)
# set -1 if there is no matching ground truth
gt_index[iou.max(axis=1) < self.iou_thresh] = -1
del iou
selec = np.zeros(gt_bbox_l.shape[0], dtype=bool)
for gt_idx in gt_index:
if gt_idx >= 0:
if gt_difficult_l[gt_idx]:
self._match[l].append(-1)
else:
if not selec[gt_idx]:
self._match[l].append(1)
else:
self._match[l].append(0)
selec[gt_idx] = True
else:
self._match[l].append(0)
def __getitem__(self, idx):
# get image input size, change every 10 batches
net_h, net_w = self._get_net_size(idx)
base_grid_h, base_grid_w = net_h//self.downsample, net_w//self.downsample
# determine the first and the last indices of the batch
l_bound = idx*self.batch_size
r_bound = (idx+1)*self.batch_size
if r_bound > len(self.instances):
r_bound = len(self.instances)
l_bound = r_bound - self.batch_size
x_batch = np.zeros((r_bound - l_bound, net_h, net_w, 3)) # input images
t_batch = np.zeros((r_bound - l_bound, 1, 1, 1, self.max_box_per_image, 4)) # list of groundtruth boxes
# initialize the inputs and the outputs
yolo_1 = np.zeros((r_bound - l_bound, 1*base_grid_h, 1*base_grid_w, 3, 4+1+self.objects)) # desired network output 1
yolo_2 = np.zeros((r_bound - l_bound, 2*base_grid_h, 2*base_grid_w, 3, 4+1+self.objects)) # desired network output 2
yolo_3 = np.zeros((r_bound - l_bound, 4*base_grid_h, 4*base_grid_w, 3, 4+1+self.objects)) # desired network output 3
yolos = [yolo_1, yolo_2, yolo_3]
instance_count = 0
true_box_index = 0
# do the logic to fill in the inputs and the output
for train_instance in self.instances[l_bound:r_bound]:
# augment input image and fix object's position and size
img, all_objs = self._aug_image(train_instance, net_h, net_w)
for obj in all_objs:
# find the best anchor box for this object
max_anchor = None
max_index = -1
max_iou = -1
shifted_box = BoundBox(0, 0, obj['xmax']-obj['xmin'], obj['ymax']-obj['ymin'])
for i in range(len(ANC_VALS)):
anchor =BoundBox(0, 0, ANC_VALS[i][0],ANC_VALS[i][1])
iou = bbox_iou(shifted_box, anchor)
if max_iou < iou:
max_anchor = anchor
max_index = i
max_iou = iou
# determine the yolo to be responsible for this bounding box
yolo = yolos[max_index//3]
grid_h, grid_w = yolo.shape[1:3]
# determine the position of the bounding box on the grid
center_x = .5*(obj['xmin'] + obj['xmax'])
g_center_x = center_x / float(net_w) * grid_w # sigma(t_x) + c_x
center_y = .5*(obj['ymin'] + obj['ymax'])
g_center_y = center_y / float(net_h) * grid_h # sigma(t_y) + c_y
# determine the sizes of the bounding box
w = obj['xmax'] - obj['xmin']
h = obj['ymax'] - obj['ymin']
box = [center_x, center_y, w, h]
# determine the index of the label
obj_indx = self.labels.index(obj['name'])
# determine the location of the cell responsible for this object
grid_x = int(np.floor(g_center_x))
grid_y = int(np.floor(g_center_y))
# assign ground truth x, y, w, h, confidence and class probs to y_batch
# yolo[instance_count, grid_y, grid_x, ] = 0
yolo[instance_count, grid_y, grid_x, max_index%3, 0:4] = box
yolo[instance_count, grid_y, grid_x, max_index%3, 4 ] = 1.
yolo[instance_count, grid_y, grid_x, max_index%3, 5+obj_indx] = 1
# assign input image to x_batch
x_batch[instance_count] = img/255.
# increase instance counter in the current batch
instance_count += 1
# yolo_1 = yolo_1.reshape((yolo_1.shape[0],yolo_1.shape[1],yolo_1.shape[2],3*(self.objects+5)))
# print(yolo_1.shape)
# return x_batch, yolo_3# [dummy_yolo_1]
return x_batch, [yolo_1, yolo_2, yolo_3]# [dummy_yolo_1]
return [x_batch, t_batch, yolo_1], [dummy_yolo_1]
def __getitem__(self, idx):
net_h, net_w = self._get_net_size(idx)
base_grid_h, base_grid_w = net_h // self.downsample, net_w // self.downsample
l_bound = idx * self.batch_size
r_bound = (idx + 1) * self.batch_size
if r_bound > len(self.train_list):
r_bound = len(self.train_list)
l_bound = r_bound - self.batch_size
x_batch = np.zeros((self.batch_size, net_h, net_w, 3))
t_batch = np.zeros(
(self.batch_size, 1, 1, 1, self.max_box_per_image, 4))
yolo_1 = np.zeros(
(self.batch_size, 1 * base_grid_h, 1 * base_grid_w,
len(self.anchors) // 3, 4 + 1 + len(self.label_list)))
yolo_2 = np.zeros(
(self.batch_size, 2 * base_grid_h, 2 * base_grid_w,
len(self.anchors) // 3, 4 + 1 + len(self.label_list)))
yolo_3 = np.zeros(
(self.batch_size, 4 * base_grid_h, 4 * base_grid_w,
len(self.anchors) // 3, 4 + 1 + len(self.label_list)))
yolos = [yolo_3, yolo_2, yolo_1]
dummy_yolo_1 = np.zeros((self.batch_size, 1))
dummy_yolo_2 = np.zeros((self.batch_size, 1))
dummy_yolo_3 = np.zeros((self.batch_size, 1))
true_box_index = 0
for instance_count, train_instace in enumerate(
self.train_list[l_bound:r_bound]):
aug_img, aug_objs = self.augmentation(train_instace, net_h, net_w)
for obj in aug_objs:
max_anchor = None
max_index = -1
max_iou = -1
shifted_box = BoundBox(0, 0, obj['xmax'] - obj['xmin'],
obj['ymax'] - obj['ymin'])
for i in range(len(self.anchors)):
anchor = self.anchors[i]
iou = bbox_iou(shifted_box, anchor)
if max_iou < iou:
max_anchor = anchor
max_index = i
max_iou = iou
yolo = yolos[max_index // 3]
grid_h, grid_w = yolo.shape[1:3]
center_x = .5 * (obj['xmin'] + obj['xmax'])
center_x = center_x / float(net_w) * grid_w
center_y = .5 * (obj['ymin'] + obj['ymax'])
center_y = center_y / float(net_h) * grid_h
w = np.log(
(obj['xmax'] - obj['xmin']) / float(max_anchor.xmax))
h = np.log(
(obj['ymax'] - obj['ymin']) / float(max_anchor.ymax))
box = [center_x, center_y, w, h]
obj_indx = self.label_list.index(obj['name'])
grid_x = int(np.floor(center_x))
grid_y = int(np.floor(center_y))
yolo[instance_count, grid_y, grid_x, max_index % 3] = 0
yolo[instance_count, grid_y, grid_x, max_index % 3, 0:4] = box
yolo[instance_count, grid_y, grid_x, max_index % 3, 4] = 1.
yolo[instance_count, grid_y, grid_x, max_index % 3,
5 + obj_indx] = 1
true_box = [
center_x, center_y, obj['xmax'] - obj['xmin'],
obj['ymax'] - obj['ymin']
]
t_batch[instance_count, 0, 0, 0, true_box_index] = true_box
true_box_index += 1
true_box_index = true_box_index % self.max_box_per_image
x_batch[instance_count] = normalize(aug_img)
return [x_batch, t_batch, yolo_1, yolo_2,
yolo_3], [dummy_yolo_1, dummy_yolo_2, dummy_yolo_3]
def random_crop_with_constraints(bbox,
size,
min_scale=0.3,
max_scale=1,
max_aspect_ratio=2,
constraints=None,
max_trial=50):
"""Crop an image randomly with bounding box constraints.
This data augmentation is used in training of
Single Shot Multibox Detector [#]_. More details can be found in
data augmentation section of the original paper.
.. [#] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy,
Scott Reed, Cheng-Yang Fu, Alexander C. Berg.
SSD: Single Shot MultiBox Detector. ECCV 2016.
Parameters
----------
bbox : numpy.ndarray
Numpy.ndarray with shape (N, 4+) where N is the number of bounding boxes.
The second axis represents attributes of the bounding box.
Specifically, these are :math:`(x_{min}, y_{min}, x_{max}, y_{max})`,
we allow additional attributes other than coordinates, which stay intact
during bounding box transformations.
size : tuple
Tuple of length 2 of image shape as (width, height).
min_scale : float
The minimum ratio between a cropped region and the original image.
The default value is :obj:`0.3`.
max_scale : float
The maximum ratio between a cropped region and the original image.
The default value is :obj:`1`.
max_aspect_ratio : float
The maximum aspect ratio of cropped region.
The default value is :obj:`2`.
constraints : iterable of tuples
An iterable of constraints.
Each constraint should be :obj:`(min_iou, max_iou)` format.
If means no constraint if set :obj:`min_iou` or :obj:`max_iou` to :obj:`None`.
If this argument defaults to :obj:`None`, :obj:`((0.1, None), (0.3, None),
(0.5, None), (0.7, None), (0.9, None), (None, 1))` will be used.
max_trial : int
Maximum number of trials for each constraint before exit no matter what.
Returns
-------
numpy.ndarray
Cropped bounding boxes with shape :obj:`(M, 4+)` where M <= N.
tuple
Tuple of length 4 as (x_offset, y_offset, new_width, new_height).
"""
# default params in paper
if constraints is None:
constraints = (
(0.1, None),
(0.3, None),
(0.5, None),
(0.7, None),
(0.9, None),
(None, 1),
)
w, h = size
candidates = [(0, 0, w, h)]
for min_iou, max_iou in constraints:
min_iou = -np.inf if min_iou is None else min_iou
max_iou = np.inf if max_iou is None else max_iou
for _ in range(max_trial):
scale = random.uniform(min_scale, max_scale)
aspect_ratio = random.uniform(
max(1 / max_aspect_ratio, scale * scale),
min(max_aspect_ratio, 1 / (scale * scale)))
crop_h = int(h * scale / np.sqrt(aspect_ratio))
crop_w = int(w * scale * np.sqrt(aspect_ratio))
crop_t = random.randrange(h - crop_h)
crop_l = random.randrange(w - crop_w)
crop_bb = np.array(
(crop_l, crop_t, crop_l + crop_w, crop_t + crop_h))
if len(bbox) == 0:
top, bottom = crop_t, crop_t + crop_h
left, right = crop_l, crop_l + crop_w
return bbox, (left, top, right - left, bottom - top)
iou = bbox_iou(bbox, crop_bb[np.newaxis])
if min_iou <= iou.min() and iou.max() <= max_iou:
top, bottom = crop_t, crop_t + crop_h
left, right = crop_l, crop_l + crop_w
candidates.append((left, top, right - left, bottom - top))
break
# random select one
while candidates:
crop = candidates.pop(np.random.randint(0, len(candidates)))
new_bbox = bbox_crop(bbox, crop, allow_outside_center=False)
if new_bbox.size < 1:
continue
new_crop = (crop[0], crop[1], crop[2], crop[3])
return new_bbox, new_crop
return bbox, (0, 0, w, h)
def __getitem__(self, idx):
# get image input size, change every 10 batches
base_grid_h, base_grid_w = self.net_h//self.downsample, self.net_w//self.downsample
# determine the first and the last indices of the batch
l_bound = idx*self.batch_size
r_bound = (idx+1)*self.batch_size
if r_bound > len(self.instances):
r_bound = len(self.instances)
l_bound = r_bound - self.batch_size
x_batch = np.zeros((r_bound - l_bound, self.net_h, self.net_w, 3)) # input images
# initialize the inputs and the outputs
yolo_1 = np.zeros((r_bound - l_bound, 1*base_grid_h, 1*base_grid_w, 3, 4+1+self.objects)) # desired network output 1
yolo_2 = np.zeros((r_bound - l_bound, 2*base_grid_h, 2*base_grid_w, 3, 4+1+self.objects)) # desired network output 2
yolo_3 = np.zeros((r_bound - l_bound, 4*base_grid_h, 4*base_grid_w, 3, 4+1+self.objects)) # desired network output 3
yolos = [yolo_1, yolo_2, yolo_3]
instance_count = 0
true_box_index = 0
# do the logic to fill in the inputs and the output
for train_instance in self.instances[l_bound:r_bound]:
# augment input image and fix object's position and size
img, all_objs = self._aug_image(train_instance, self.net_h, self.net_w)
for obj in all_objs:
# find the best anchor box for this object
max_anchor = None
max_index = -1
max_iou = -1
shifted_box = BoundBox(0, 0, obj['xmax']-obj['xmin'], obj['ymax']-obj['ymin'])
for i in range(len(ANC_VALS)):
anchor =BoundBox(0, 0, ANC_VALS[i][0],ANC_VALS[i][1])
iou = bbox_iou(shifted_box, anchor)
if max_iou < iou:
max_anchor = anchor
max_index = i
max_iou = iou
# determine the yolo to be responsible for this bounding box
yolo = yolos[max_index//3]
grid_h, grid_w = yolo.shape[1:3]
# determine the position of the bounding box on the grid
center_x = .5*(obj['xmin'] + obj['xmax'])
g_center_x = center_x / float(self.net_w) * grid_w # sigma(t_x) + c_x
center_y = .5*(obj['ymin'] + obj['ymax'])
g_center_y = center_y / float(self.net_h) * grid_h # sigma(t_y) + c_y
# determine the sizes of the bounding box
w = obj['xmax'] - obj['xmin']
h = obj['ymax'] - obj['ymin']
box = [center_x, center_y, w, h]
# determine the index of the label
obj_indx = self.labels.index(obj['name'])
# determine the location of the cell responsible for this object
grid_x = int(np.floor(g_center_x))
grid_y = int(np.floor(g_center_y))
# assign ground truth x, y, w, h, confidence and class probs to y_batch
yolo[instance_count, grid_y, grid_x, max_index%3, 0:4] = box
yolo[instance_count, grid_y, grid_x, max_index%3, 4 ] = 1.
yolo[instance_count, grid_y, grid_x, max_index%3, 5+obj_indx] = 1
# assign input image to x_batch
x_batch[instance_count] = img/255.
# increase instance counter in the current batch
instance_count += 1
return x_batch, [yolo_1, yolo_2, yolo_3]# [dummy_yolo_1]
def encode(self, boxes, labels, iou_threshold=0.5):
target_list = list()
objmask_list = list()
noobjmask_list = list()
for i, ori_anchors in enumerate(self.anchors):
in_h = in_w = int(self.fm_size[i])
# self.input_size[0] / in_w, self.input_size[1] / in_h
w_fm_stride, h_fm_stride = self.input_size / in_w, self.input_size / in_h
anchors = [(a_w / w_fm_stride, a_h / h_fm_stride)
for a_w, a_h in ori_anchors]
num_anchors = len(anchors)
obj_mask = torch.zeros(num_anchors, in_h, in_w)
noobj_mask = torch.ones(num_anchors, in_h, in_w)
tx = torch.zeros(num_anchors, in_h, in_w)
ty = torch.zeros(num_anchors, in_h, in_w)
tw = torch.zeros(num_anchors, in_h, in_w)
th = torch.zeros(num_anchors, in_h, in_w)
tconf = torch.zeros(num_anchors, in_h, in_w)
tcls = torch.zeros(num_anchors, in_h, in_w, self.num_classes)
for t in range(boxes.size(0)):
# Convert to position relative to box
gx = (boxes[t, 0].item() + boxes[t, 2].item()) / (
2.0 * self.input_size) * in_w # [0]
gy = (boxes[t, 1].item() + boxes[t, 3].item()) / (
2.0 * self.input_size) * in_h # [1]
gw = (boxes[t, 2].item() -
boxes[t, 0].item()) / self.input_size * in_w # [0]
gh = (boxes[t, 3].item() -
boxes[t, 1].item()) / self.input_size * in_h # [1]
if gw * gh == 0 or gx >= in_w or gy >= in_h:
continue
# Get grid box indices
gi = int(gx)
gj = int(gy)
# Get shape of gt box
gt_box = torch.FloatTensor([0, 0, gw, gh]).unsqueeze(0)
# Get shape of anchor box
anchor_shapes = torch.FloatTensor(
np.concatenate((np.zeros(
(num_anchors, 2)), np.array(anchors)), 1))
# Calculate iou between gt and anchor shapes
anch_ious = bbox_iou(gt_box, anchor_shapes)
# Where the overlap is larger than threshold set mask to zero (ignore)
noobj_mask[anch_ious[0] > iou_threshold] = 0
# Find the best matching anchor box
best_n = np.argmax(anch_ious, axis=1)
# Masks
obj_mask[best_n, gj, gi] = 1
# Coordinates
tx[best_n, gj, gi] = gx - gi
ty[best_n, gj, gi] = gy - gj
# Width and height
tw[best_n, gj, gi] = math.log(gw / anchors[best_n][0] + 1e-16)
th[best_n, gj, gi] = math.log(gh / anchors[best_n][1] + 1e-16)
# object
tconf[best_n, gj, gi] = 1
# One-hot encoding of label
tcls[best_n, gj, gi, int(labels[t])] = 1
obj_mask = obj_mask.view(-1, 1)
noobj_mask = noobj_mask.view(-1, 1)
tx = tx.view(-1, 1)
ty = ty.view(-1, 1)
tw = tw.view(-1, 1)
th = th.view(-1, 1)
tconf = tconf.view(-1, 1)
tcls = tcls.view(-1, self.num_classes)
target = torch.cat((tx, ty, tw, th, tconf, tcls), -1)
target_list.append(target)
objmask_list.append(obj_mask)
noobjmask_list.append(noobj_mask)
target = torch.cat(target_list, 0)
obj_mask = torch.cat(objmask_list, 0)
noobj_mask = torch.cat(noobjmask_list, 0)
return target, torch.cat([obj_mask, noobj_mask], dim=1)
def __getitem__(self, idx):
# get image input size, change every 10 batches
# net_h, net_w 是输入图像的高宽，每10个batch随机变换一次
net_h, net_w = self._get_net_size(idx)
# 32倍下采样的特征图的高宽
base_grid_h, base_grid_w = net_h//self.downsample, net_w//self.downsample
# determine the first and the last indices of the batch
l_bound = idx*self.batch_size
r_bound = (idx+1)*self.batch_size
# 这个感觉不是很合理
if r_bound > len(self.instances):
r_bound = len(self.instances)
l_bound = r_bound - self.batch_size
# 准备样本，一个batch的输入图像
x_batch = np.zeros((r_bound - l_bound, net_h, net_w, 3)) # input images
# 每个图像中的所有对象边框，shape=(batch,1,1,1,一个图像中最多几个对象,4个坐标)
t_batch = np.zeros((r_bound - l_bound, 1, 1, 1, self.max_box_per_image, 4)) # list of groundtruth boxes
# initialize the inputs and the outputs，分别对应32、16、8倍下采样的输出特征图
# [batch_size，特征图高，特征图宽，anchor数量3，边框坐标4+置信度1+预测对象类别数]
yolo_1 = np.zeros((r_bound - l_bound, 1*base_grid_h, 1*base_grid_w, len(self.anchors)//3, 4+1+len(self.labels))) # desired network output 1
yolo_2 = np.zeros((r_bound - l_bound, 2*base_grid_h, 2*base_grid_w, len(self.anchors)//3, 4+1+len(self.labels))) # desired network output 2
yolo_3 = np.zeros((r_bound - l_bound, 4*base_grid_h, 4*base_grid_w, len(self.anchors)//3, 4+1+len(self.labels))) # desired network output 3
# 8、16、32倍下采样对应到先验框 [55,69, 75,234, 133,240, 136,129, 142,363, 203,290, 228,184, 285,359, 341,260]
yolos = [yolo_3, yolo_2, yolo_1]
dummy_yolo_1 = np.zeros((r_bound - l_bound, 1))
dummy_yolo_2 = np.zeros((r_bound - l_bound, 1))
dummy_yolo_3 = np.zeros((r_bound - l_bound, 1))
instance_count = 0 # batch中的第几张图像
true_box_index = 0 # 图像中的第几个对象
# do the logic to fill in the inputs and the output
for train_instance in self.instances[l_bound:r_bound]:
# augment input image and fix object's position and size
img, all_objs = self._aug_image(train_instance, net_h, net_w)
for obj in all_objs:
# find the best anchor box for this object
max_anchor = None # IOU最大的那个anchor
max_index = -1 # IOU最大的那个anchor 的index
max_iou = -1
shifted_box = BoundBox(0,
0,
obj['xmax']-obj['xmin'],
obj['ymax']-obj['ymin'])
for i in range(len(self.anchors)):
anchor = self.anchors[i]
iou = bbox_iou(shifted_box, anchor)
if max_iou < iou:
max_anchor = anchor
max_index = i
max_iou = iou
# determine the yolo to be responsible for this bounding box
# 3种尺度的特征图，与当前对象最匹配的那种anchor，所属的那个特征图的tensor，就是这里的yolo
yolo = yolos[max_index//3]
grid_h, grid_w = yolo.shape[1:3]
# determine the position of the bounding box on the grid
# 对象的边框中心坐标 被转换到 特征图网格上，其值相当于 期望预测的坐标 sigma(t_x) + c_x，sigma(t_y) + c_y
center_x = .5*(obj['xmin'] + obj['xmax'])
center_x = center_x / float(net_w) * grid_w # 期望预测的坐标 sigma(t_x) + c_x = center_x
center_y = .5*(obj['ymin'] + obj['ymax'])
center_y = center_y / float(net_h) * grid_h # 期望预测的坐标 sigma(t_y) + c_y = center_y
# determine the sizes of the bounding box
w = np.log((obj['xmax'] - obj['xmin']) / float(max_anchor.xmax)) # t_w，注：truth_w = anchor_w * exp(t_w)
h = np.log((obj['ymax'] - obj['ymin']) / float(max_anchor.ymax)) # t_h，注：truth_h = anchor_h * exp(t_h)
box = [center_x, center_y, w, h]
# determine the index of the label
obj_indx = self.labels.index(obj['name'])
# determine the location of the cell responsible for this object
grid_x = int(np.floor(center_x))
grid_y = int(np.floor(center_y))
# assign ground truth x, y, w, h, confidence and class probs to y_batch
# max_index%3 对应到最佳匹配的anchor，一个对象仅有一个anchor负责检测
yolo[instance_count, grid_y, grid_x, max_index%3] = 0
yolo[instance_count, grid_y, grid_x, max_index%3, 0:4] = box # 边框坐标
yolo[instance_count, grid_y, grid_x, max_index%3, 4 ] = 1. # 边框置信度
yolo[instance_count, grid_y, grid_x, max_index%3, 5+obj_indx] = 1 # 对象分类
# assign the true box to t_batch. true_box的x、y是特征图上的坐标（比如13*13特征图），宽和高是原始图像上对象的宽和高
true_box = [center_x, center_y, obj['xmax'] - obj['xmin'], obj['ymax'] - obj['ymin']]
t_batch[instance_count, 0, 0, 0, true_box_index] = true_box
# 因为有 instance_count 区分不同的图像，true_box_index 应该只需在每次图像切换时 true_box_index=0 即可。这里在整个batch累加true_box_index，暂不确定是否有特别的用意。
true_box_index += 1
true_box_index = true_box_index % self.max_box_per_image
# assign input image to x_batch
if self.norm != None:
x_batch[instance_count] = self.norm(img)
else:
# plot image and bounding boxes for sanity check
for obj in all_objs:
cv2.rectangle(img, (obj['xmin'],obj['ymin']), (obj['xmax'],obj['ymax']), (255,0,0), 3)
cv2.putText(img, obj['name'],
(obj['xmin']+2, obj['ymin']+12),
0, 1.2e-3 * img.shape[0],
(0,255,0), 2)
x_batch[instance_count] = img
# increase instance counter in the current batch
instance_count += 1
return [x_batch, t_batch, yolo_1, yolo_2, yolo_3], [dummy_yolo_1, dummy_yolo_2, dummy_yolo_3]
def write_results_half(prediction,
confidence,
num_classes,
nms=True,
nms_conf=0.4):
conf_mask = (prediction[:, :, 4] > confidence).half().unsqueeze(2)
prediction = prediction * conf_mask
try:
ind_nz = torch.nonzero(prediction[:, :, 4]).transpose(0,
1).contiguous()
except:
return 0
box_a = prediction.new(prediction.shape)
box_a[:, :, 0] = (prediction[:, :, 0] - prediction[:, :, 2] / 2)
box_a[:, :, 1] = (prediction[:, :, 1] - prediction[:, :, 3] / 2)
box_a[:, :, 2] = (prediction[:, :, 0] + prediction[:, :, 2] / 2)
box_a[:, :, 3] = (prediction[:, :, 1] + prediction[:, :, 3] / 2)
prediction[:, :, :4] = box_a[:, :, :4]
batch_size = prediction.size(0)
output = prediction.new(1, prediction.size(2) + 1)
write = False
for ind in range(batch_size):
#select the image from the batch
image_pred = prediction[ind]
#Get the class having maximum score, and the index of that class
#Get rid of num_classes softmax scores
#Add the class index and the class score of class having maximum score
max_conf, max_conf_score = torch.max(image_pred[:, 5:5 + num_classes],
1)
max_conf = max_conf.half().unsqueeze(1)
max_conf_score = max_conf_score.half().unsqueeze(1)
seq = (image_pred[:, :5], max_conf, max_conf_score)
image_pred = torch.cat(seq, 1)
#Get rid of the zero entries
non_zero_ind = (torch.nonzero(image_pred[:, 4]))
try:
image_pred_ = image_pred[non_zero_ind.squeeze(), :]
except:
continue
#Get the various classes detected in the image
img_classes = unique(image_pred_[:, -1].long()).half()
#WE will do NMS classwise
for cls in img_classes:
#get the detections with one particular class
cls_mask = image_pred_ * (image_pred_[:, -1]
== cls).half().unsqueeze(1)
class_mask_ind = torch.nonzero(cls_mask[:, -2]).squeeze()
image_pred_class = image_pred_[class_mask_ind]
#sort the detections such that the entry with the maximum objectness
#confidence is at the top
conf_sort_index = torch.sort(image_pred_class[:, 4],
descending=True)[1]
image_pred_class = image_pred_class[conf_sort_index]
idx = image_pred_class.size(0)
#if nms has to be done
if nms:
#For each detection
for i in range(idx):
#Get the IOUs of all boxes that come after the one we are looking at
#in the loop
try:
ious = bbox_iou(image_pred_class[i].unsqueeze(0),
image_pred_class[i + 1:])
except ValueError:
break
except IndexError:
break
#Zero out all the detections that have IoU > treshhold
iou_mask = (ious < nms_conf).half().unsqueeze(1)
image_pred_class[i + 1:] *= iou_mask
#Remove the non-zero entries
non_zero_ind = torch.nonzero(
image_pred_class[:, 4]).squeeze()
image_pred_class = image_pred_class[non_zero_ind]
#Concatenate the batch_id of the image to the detection
#this helps us identify which image does the detection correspond to
#We use a linear straucture to hold ALL the detections from the batch
#the batch_dim is flattened
#batch is identified by extra batch column
batch_ind = image_pred_class.new(image_pred_class.size(0),
1).fill_(ind)
seq = batch_ind, image_pred_class
if not write:
output = torch.cat(seq, 1)
write = True
else:
out = torch.cat(seq, 1)
output = torch.cat((output, out))
return output
def update(self,
pred_bboxes,
pred_labels,
pred_scores,
gt_bboxes,
gt_labels,
gt_difficults=None):
def as_numpy(a):
"""Convert a (list of) mx.NDArray into numpy.ndarray"""
if isinstance(a, (list, tuple)):
out = [
x.asnumpy() if isinstance(x, mx.nd.NDArray) else x
for x in a
]
try:
out = np.concatenate(out, axis=0)
except ValueError:
out = np.array(out)
return out
elif isinstance(a, mx.nd.NDArray):
a = a.asnumpy()
return a
if gt_difficults is None:
gt_difficults = [None for _ in as_numpy(gt_labels)]
'''
if isinstance(gt_labels, list):
if len(gt_difficults) != len(gt_labels) * gt_labels[0].shape[0]:
gt_difficults = [None] * len(gt_labels) * gt_labels[0].shape[0]
'''
# calculate the FD, suppose the predicted label is all correct
# obj_nums = gt_bboxes[0].shape[1]
# if obj_nums < 101:
# gt_labels = [X[:, 0:obj_nums, :] for X in pred_labels]
# print('len of gt_labels: ', len(gt_labels))
# print('len of pred_labels: ', len(pred_labels))
# print('shape of gt_labels ele: ', gt_labels[0].shape)
# print('shape of pred_labels ele: ', pred_labels[0].shape)
for pred_bbox, pred_label, pred_score, gt_bbox, gt_label, gt_difficult in zip(
*[
as_numpy(x) for x in [
pred_bboxes, pred_labels, pred_scores, gt_bboxes,
gt_labels, gt_difficults
]
]):
# strip padding -1 for pred and gt
valid_pred = np.where(pred_label.flat >= 0)[0]
pred_bbox = pred_bbox[valid_pred, :]
pred_label = pred_label.flat[valid_pred].astype(int)
pred_score = pred_score.flat[valid_pred]
valid_gt = np.where(gt_label.flat >= 0)[0]
gt_bbox = gt_bbox[valid_gt, :]
gt_label = gt_label.flat[valid_gt].astype(int)
if gt_difficult is None:
gt_difficult = np.zeros(gt_bbox.shape[0])
else:
gt_difficult = gt_difficult.flat[valid_gt]
for l in np.unique(
np.concatenate((pred_label, gt_label)).astype(int)):
pred_mask_l = pred_label == l
pred_bbox_l = pred_bbox[pred_mask_l]
pred_score_l = pred_score[pred_mask_l]
# sort by score
order = pred_score_l.argsort()[::-1]
pred_bbox_l = pred_bbox_l[order]
pred_score_l = pred_score_l[order]
# get the predicts, which socre > scores thresh
pred_score_l_idx = np.where(
pred_score_l >= self.score_thresh)[0]
pred_bbox_l = pred_bbox_l[pred_score_l_idx]
pred_score_l = pred_score_l[pred_score_l_idx]
gt_mask_l = gt_label == l
gt_bbox_l = gt_bbox[gt_mask_l]
gt_difficult_l = gt_difficult[gt_mask_l]
self._n_pos[l] += np.logical_not(gt_difficult_l).sum()
self._score[l].extend(pred_score_l)
if len(pred_bbox_l) == 0:
continue
pred_bbox_l = pred_bbox_l.copy()
pred_bbox_l[:, 2:] += 1
gt_bbox_l = gt_bbox_l.copy()
gt_bbox_l[:, 2:] += 1
iou = bbox_iou(pred_bbox_l, gt_bbox_l)
for iou_thresh in self.ovp_thresh:
iou_thresh_key = str(iou_thresh)
if len(gt_bbox_l) == 0:
self._match[iou_thresh_key][l].extend(
(0, ) * pred_bbox_l.shape[0])
continue
# VOC evaluation follows integer typed bounding boxes.
gt_index = iou.argmax(axis=1)
# set -1 if there is no matching ground truth
gt_index[iou.max(axis=1) < iou_thresh] = -1
selec = np.zeros(gt_bbox_l.shape[0], dtype=bool)
for gt_idx in gt_index:
if gt_idx >= 0:
if gt_difficult_l[gt_idx]:
self._match[iou_thresh_key][l].append(-1)
else:
if not selec[gt_idx]:
self._match[iou_thresh_key][l].append(1)
else:
self._match[iou_thresh_key][l].append(0)
selec[gt_idx] = True
else:
self._match[iou_thresh_key][l].append(0)
def __getitem__(self, idx):
# get image input size, change every 10 batches
net_h, net_w = self._get_net_size(idx)
base_grid_h, base_grid_w = net_h//self.downsample, net_w//self.downsample
# determine the first and the last indices of the batch
l_bound = idx*self.batch_size
r_bound = (idx+1)*self.batch_size
if r_bound > len(self.instances):
r_bound = len(self.instances)
l_bound = r_bound - self.batch_size
x_batch = np.zeros((r_bound - l_bound, net_h, net_w, 3)) # input images
t_batch = np.zeros((r_bound - l_bound, 1, 1, 1, self.max_box_per_image, 4)) # list of groundtruth boxes
# initialize the inputs and the outputs
yolo_1 = np.zeros((r_bound - l_bound, 1*base_grid_h, 1*base_grid_w, len(self.anchors)//3, 4+1+len(self.labels))) # desired network output 1
yolo_2 = np.zeros((r_bound - l_bound, 2*base_grid_h, 2*base_grid_w, len(self.anchors)//3, 4+1+len(self.labels))) # desired network output 2
yolo_3 = np.zeros((r_bound - l_bound, 4*base_grid_h, 4*base_grid_w, len(self.anchors)//3, 4+1+len(self.labels))) # desired network output 3
yolos = [yolo_3, yolo_2, yolo_1]
dummy_yolo_1 = np.zeros((r_bound - l_bound, 1))
dummy_yolo_2 = np.zeros((r_bound - l_bound, 1))
dummy_yolo_3 = np.zeros((r_bound - l_bound, 1))
instance_count = 0
true_box_index = 0
# do the logic to fill in the inputs and the output
for train_instance in self.instances[l_bound:r_bound]:
# augment input image and fix object's position and size
img, all_objs = self._aug_image(train_instance, net_h, net_w)
for obj in all_objs:
# find the best anchor box for this object
max_anchor = None
max_index = -1
max_iou = -1
shifted_box = BoundBox(0,
0,
obj['xmax']-obj['xmin'],
obj['ymax']-obj['ymin'])
for i in range(len(self.anchors)):
anchor = self.anchors[i]
iou = bbox_iou(shifted_box, anchor)
if max_iou < iou:
max_anchor = anchor
max_index = i
max_iou = iou
# determine the yolo to be responsible for this bounding box
yolo = yolos[max_index//3]
grid_h, grid_w = yolo.shape[1:3]
# determine the position of the bounding box on the grid
center_x = .5*(obj['xmin'] + obj['xmax'])
center_x = center_x / float(net_w) * grid_w # sigma(t_x) + c_x
center_y = .5*(obj['ymin'] + obj['ymax'])
center_y = center_y / float(net_h) * grid_h # sigma(t_y) + c_y
# determine the sizes of the bounding box
w = np.log((obj['xmax'] - obj['xmin']) / float(max_anchor.xmax)) # t_w
h = np.log((obj['ymax'] - obj['ymin']) / float(max_anchor.ymax)) # t_h
box = [center_x, center_y, w, h]
# determine the index of the label
obj_indx = self.labels.index(obj['name'])
# determine the location of the cell responsible for this object
grid_x = int(np.floor(center_x))
grid_y = int(np.floor(center_y))
# assign ground truth x, y, w, h, confidence and class probs to y_batch
yolo[instance_count, grid_y, grid_x, max_index%3] = 0
yolo[instance_count, grid_y, grid_x, max_index%3, 0:4] = box
yolo[instance_count, grid_y, grid_x, max_index%3, 4 ] = 1.
yolo[instance_count, grid_y, grid_x, max_index%3, 5+obj_indx] = 1
# assign the true box to t_batch
true_box = [center_x, center_y, obj['xmax'] - obj['xmin'], obj['ymax'] - obj['ymin']]
t_batch[instance_count, 0, 0, 0, true_box_index] = true_box
true_box_index += 1
true_box_index = true_box_index % self.max_box_per_image
# assign input image to x_batch
if self.norm != None:
x_batch[instance_count] = self.norm(img)
else:
# plot image and bounding boxes for sanity check
for obj in all_objs:
cv2.rectangle(img, (obj['xmin'],obj['ymin']), (obj['xmax'],obj['ymax']), (255,0,0), 3)
cv2.putText(img, obj['name'],
(obj['xmin']+2, obj['ymin']+12),
0, 1.2e-3 * img.shape[0],
(0,255,0), 2)
x_batch[instance_count] = img
# increase instance counter in the current batch
instance_count += 1
return [x_batch, t_batch, yolo_1, yolo_2, yolo_3], [dummy_yolo_1, dummy_yolo_2, dummy_yolo_3]
def __getitem__(self, idx):
# get image input size, change every 10 batches
net_h, net_w = self._get_net_size(idx)
base_grid_h, base_grid_w = net_h//self.downsample, net_w//self.downsample
# determine the first and the last indices of the batch
l_bound = idx*self.batch_size
r_bound = (idx+1)*self.batch_size
if r_bound > len(self.instances):
r_bound = len(self.instances)
l_bound = r_bound - self.batch_size
x_batch = np.zeros((r_bound - l_bound, net_h, net_w, 3)) # input images
t_batch = np.zeros((r_bound - l_bound, 1, 1, 1, self.max_box_per_image, 4)) # list of groundtruth boxes
# initialize the inputs and the outputs
yolo_1 = np.zeros((r_bound - l_bound, 1*base_grid_h, 1*base_grid_w, len(self.anchors)//3, 4+1+len(self.labels))) # desired network output 1
yolo_2 = np.zeros((r_bound - l_bound, 2*base_grid_h, 2*base_grid_w, len(self.anchors)//3, 4+1+len(self.labels))) # desired network output 2
yolo_3 = np.zeros((r_bound - l_bound, 4*base_grid_h, 4*base_grid_w, len(self.anchors)//3, 4+1+len(self.labels))) # desired network output 3
yolos = [yolo_3, yolo_2, yolo_1]
dummy_yolo_1 = np.zeros((r_bound - l_bound, 1))
dummy_yolo_2 = np.zeros((r_bound - l_bound, 1))
dummy_yolo_3 = np.zeros((r_bound - l_bound, 1))
instance_count = 0
true_box_index = 0
# do the logic to fill in the inputs and the output
for train_instance in self.instances[l_bound:r_bound]:
# augment input image and fix object's position and size
img, all_objs = self._aug_image(train_instance, net_h, net_w)
for obj in all_objs:
# find the best anchor box for this object
max_anchor = None
max_index = -1
max_iou = -1
shifted_box = BoundBox(0,
0,
obj['xmax']-obj['xmin'],
obj['ymax']-obj['ymin'])
for i in range(len(self.anchors)):
anchor = self.anchors[i]
iou = bbox_iou(shifted_box, anchor)
if max_iou < iou:
max_anchor = anchor
max_index = i
max_iou = iou
# determine the yolo to be responsible for this bounding box
yolo = yolos[max_index//3]
grid_h, grid_w = yolo.shape[1:3]
# determine the position of the bounding box on the grid
center_x = .5*(obj['xmin'] + obj['xmax'])
center_x = center_x / float(net_w) * grid_w # sigma(t_x) + c_x
center_y = .5*(obj['ymin'] + obj['ymax'])
center_y = center_y / float(net_h) * grid_h # sigma(t_y) + c_y
# determine the sizes of the bounding box
w = np.log((obj['xmax'] - obj['xmin']) / float(max_anchor.xmax)) # t_w
h = np.log((obj['ymax'] - obj['ymin']) / float(max_anchor.ymax)) # t_h
box = [center_x, center_y, w, h]
# determine the index of the label
obj_indx = self.labels.index(obj['name'])
# determine the location of the cell responsible for this object
grid_x = int(np.floor(center_x))
grid_y = int(np.floor(center_y))
# assign ground truth x, y, w, h, confidence and class probs to y_batch
yolo[instance_count, grid_y, grid_x, max_index%3] = 0
yolo[instance_count, grid_y, grid_x, max_index%3, 0:4] = box
yolo[instance_count, grid_y, grid_x, max_index%3, 4 ] = 1.
yolo[instance_count, grid_y, grid_x, max_index%3, 5+obj_indx] = 1
# assign the true box to t_batch
true_box = [center_x, center_y, obj['xmax'] - obj['xmin'], obj['ymax'] - obj['ymin']]
t_batch[instance_count, 0, 0, 0, true_box_index] = true_box
true_box_index += 1
true_box_index = true_box_index % self.max_box_per_image
# assign input image to x_batch
if self.norm != None:
x_batch[instance_count] = self.norm(img)
else:
# plot image and bounding boxes for sanity check
for obj in all_objs:
cv2.rectangle(img, (obj['xmin'],obj['ymin']), (obj['xmax'],obj['ymax']), (255,0,0), 3)
cv2.putText(img, obj['name'],
(obj['xmin']+2, obj['ymin']+12),
0, 1.2e-3 * img.shape[0],
(0,255,0), 2)
x_batch[instance_count] = img
# increase instance counter in the current batch
instance_count += 1
return [x_batch, t_batch, yolo_1, yolo_2, yolo_3], [dummy_yolo_1, dummy_yolo_2, dummy_yolo_3]
