def find_head_edge(self, box, head):
head_dict = {0: '11', 1: '10', 2: '00', 3: '01'}
flag = head_dict[int(head)]
box[4] += random.random() * 0.1
box_eight = forward_convert(np.array([box]), False)[0]
box_eight = np.reshape(box_eight, [4, 2])
four_edges = [[box_eight[0],
box_eight[1]], [box_eight[1], box_eight[2]],
[box_eight[2], box_eight[3]],
[box_eight[3], box_eight[0]]]
for i in range(4):
center_x = (four_edges[i][0][0] + four_edges[i][1][0]) / 2.
center_y = (four_edges[i][0][1] + four_edges[i][1][1]) / 2.
if (center_x - box[0]) >= 0 and (center_y - box[1]) >= 0:
res = '11'
if res == flag:
return four_edges[i]
elif (center_x - box[0]) >= 0 and (center_y - box[1]) <= 0:
res = '10'
if res == flag:
return four_edges[i]
elif (center_x - box[0]) <= 0 and (center_y - box[1]) <= 0:
res = '00'
if res == flag:
return four_edges[i]
else:
res = '01'
if res == flag:
return four_edges[i]
def get_mask(img, boxes):
boxes = forward_convert(boxes)
h, w, _ = img.shape
mask = np.zeros([h, w])
for b in boxes:
b = np.reshape(b[0:-1], [4, 2])
rect = np.array(b, np.int32)
cv2.fillConvexPoly(mask, rect, 1)
# mask = cv2.resize(mask, dsize=(h // 16, w // 16))
mask = np.expand_dims(mask, axis=-1)
return np.array(mask, np.float32)
def worker(self, gpu_id, images, det_net, result_queue):
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None,
3]) # is RGB. not BGR
img_batch = tf.cast(img_plac, tf.float32)
if self.cfgs.NET_NAME in [
'resnet152_v1d', 'resnet101_v1d', 'resnet50_v1d'
]:
img_batch = (img_batch / 255 - tf.constant(
self.cfgs.PIXEL_MEAN_)) / tf.constant(self.cfgs.PIXEL_STD)
else:
img_batch = img_batch - tf.constant(self.cfgs.PIXEL_MEAN)
img_batch = tf.expand_dims(img_batch, axis=0)
detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network(
input_img_batch=img_batch)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
restorer, restore_ckpt = det_net.get_restorer()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
if not restorer is None:
restorer.restore(sess, restore_ckpt)
print('restore model %d ...' % gpu_id)
for img_path in images:
# if 'P0006' not in img_path:
# continue
img = cv2.imread(img_path)
box_res_rotate = []
label_res_rotate = []
score_res_rotate = []
imgH = img.shape[0]
imgW = img.shape[1]
img_short_side_len_list = self.cfgs.IMG_SHORT_SIDE_LEN if isinstance(
self.cfgs.IMG_SHORT_SIDE_LEN,
list) else [self.cfgs.IMG_SHORT_SIDE_LEN]
img_short_side_len_list = [
img_short_side_len_list[0]
] if not self.args.multi_scale else img_short_side_len_list
if imgH < self.args.h_len:
temp = np.zeros([self.args.h_len, imgW, 3], np.float32)
temp[0:imgH, :, :] = img
img = temp
imgH = self.args.h_len
if imgW < self.args.w_len:
temp = np.zeros([imgH, self.args.w_len, 3], np.float32)
temp[:, 0:imgW, :] = img
img = temp
imgW = self.args.w_len
for hh in range(0, imgH,
self.args.h_len - self.args.h_overlap):
if imgH - hh - 1 < self.args.h_len:
hh_ = imgH - self.args.h_len
else:
hh_ = hh
for ww in range(0, imgW,
self.args.w_len - self.args.w_overlap):
if imgW - ww - 1 < self.args.w_len:
ww_ = imgW - self.args.w_len
else:
ww_ = ww
src_img = img[hh_:(hh_ + self.args.h_len),
ww_:(ww_ + self.args.w_len), :]
for short_size in img_short_side_len_list:
max_len = self.cfgs.IMG_MAX_LENGTH
if self.args.h_len < self.args.w_len:
new_h, new_w = short_size, min(
int(short_size * float(self.args.w_len) /
self.args.h_len), max_len)
else:
new_h, new_w = min(
int(short_size * float(self.args.h_len) /
self.args.w_len), max_len), short_size
img_resize = cv2.resize(src_img, (new_w, new_h))
resized_img, det_boxes_r_, det_scores_r_, det_category_r_ = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: img_resize[:, :, ::-1]}
)
resized_h, resized_w = resized_img.shape[
1], resized_img.shape[2]
src_h, src_w = src_img.shape[0], src_img.shape[1]
if len(det_boxes_r_) > 0:
det_boxes_r_ = forward_convert(
det_boxes_r_, False)
det_boxes_r_[:, 0::2] *= (src_w / resized_w)
det_boxes_r_[:, 1::2] *= (src_h / resized_h)
for ii in range(len(det_boxes_r_)):
box_rotate = det_boxes_r_[ii]
box_rotate[0::2] = box_rotate[0::2] + ww_
box_rotate[1::2] = box_rotate[1::2] + hh_
box_res_rotate.append(box_rotate)
label_res_rotate.append(
det_category_r_[ii])
score_res_rotate.append(det_scores_r_[ii])
if self.args.flip_img:
det_boxes_r_flip, det_scores_r_flip, det_category_r_flip = \
sess.run(
[detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: cv2.flip(img_resize, flipCode=1)[:, :, ::-1]}
)
if len(det_boxes_r_flip) > 0:
det_boxes_r_flip = forward_convert(
det_boxes_r_flip, False)
det_boxes_r_flip[:, 0::2] *= (src_w /
resized_w)
det_boxes_r_flip[:, 1::2] *= (src_h /
resized_h)
for ii in range(len(det_boxes_r_flip)):
box_rotate = det_boxes_r_flip[ii]
box_rotate[0::2] = (
src_w - box_rotate[0::2]) + ww_
box_rotate[
1::2] = box_rotate[1::2] + hh_
box_res_rotate.append(box_rotate)
label_res_rotate.append(
det_category_r_flip[ii])
score_res_rotate.append(
det_scores_r_flip[ii])
det_boxes_r_flip, det_scores_r_flip, det_category_r_flip = \
sess.run(
[detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: cv2.flip(img_resize, flipCode=0)[:, :, ::-1]}
)
if len(det_boxes_r_flip) > 0:
det_boxes_r_flip = forward_convert(
det_boxes_r_flip, False)
det_boxes_r_flip[:, 0::2] *= (src_w /
resized_w)
det_boxes_r_flip[:, 1::2] *= (src_h /
resized_h)
for ii in range(len(det_boxes_r_flip)):
box_rotate = det_boxes_r_flip[ii]
box_rotate[
0::2] = box_rotate[0::2] + ww_
box_rotate[1::2] = (
src_h - box_rotate[1::2]) + hh_
box_res_rotate.append(box_rotate)
label_res_rotate.append(
det_category_r_flip[ii])
score_res_rotate.append(
det_scores_r_flip[ii])
box_res_rotate = np.array(box_res_rotate)
label_res_rotate = np.array(label_res_rotate)
score_res_rotate = np.array(score_res_rotate)
box_res_rotate_ = []
label_res_rotate_ = []
score_res_rotate_ = []
threshold = {
'roundabout': 0.1,
'tennis-court': 0.3,
'swimming-pool': 0.1,
'storage-tank': 0.2,
'soccer-ball-field': 0.3,
'small-vehicle': 0.2,
'ship': 0.2,
'plane': 0.3,
'large-vehicle': 0.1,
'helicopter': 0.2,
'harbor': 0.0001,
'ground-track-field': 0.3,
'bridge': 0.0001,
'basketball-court': 0.3,
'baseball-diamond': 0.3
}
for sub_class in range(1, self.cfgs.CLASS_NUM + 1):
index = np.where(label_res_rotate == sub_class)[0]
if len(index) == 0:
continue
tmp_boxes_r = box_res_rotate[index]
tmp_label_r = label_res_rotate[index]
tmp_score_r = score_res_rotate[index]
tmp_boxes_r_ = backward_convert(tmp_boxes_r, False)
try:
inx = nms_rotate.nms_rotate_cpu(
boxes=np.array(tmp_boxes_r_),
scores=np.array(tmp_score_r),
iou_threshold=threshold[
self.label_name_map[sub_class]],
max_output_size=5000)
except:
tmp_boxes_r_ = np.array(tmp_boxes_r_)
tmp = np.zeros(
[tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1])
tmp[:, 0:-1] = tmp_boxes_r_
tmp[:, -1] = np.array(tmp_score_r)
# Note: the IoU of two same rectangles is 0, which is calculated by rotate_gpu_nms
jitter = np.zeros(
[tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1])
jitter[:, 0] += np.random.rand(
tmp_boxes_r_.shape[0], ) / 1000
inx = rotate_gpu_nms(
np.array(tmp, np.float32) +
np.array(jitter, np.float32),
float(threshold[self.label_name_map[sub_class]]),
0)
box_res_rotate_.extend(np.array(tmp_boxes_r)[inx])
score_res_rotate_.extend(np.array(tmp_score_r)[inx])
label_res_rotate_.extend(np.array(tmp_label_r)[inx])
result_dict = {
'boxes': np.array(box_res_rotate_),
'scores': np.array(score_res_rotate_),
'labels': np.array(label_res_rotate_),
'image_id': img_path
}
result_queue.put_nowait(result_dict)
def eval_with_plac(self, img_dir, det_net, image_ext):
os.environ["CUDA_VISIBLE_DEVICES"] = self.args.gpu
# 1. preprocess img
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None,
3]) # is RGB. not BGR
img_batch = tf.cast(img_plac, tf.float32)
pretrain_zoo = PretrainModelZoo()
if self.cfgs.NET_NAME in pretrain_zoo.pth_zoo or self.cfgs.NET_NAME in pretrain_zoo.mxnet_zoo:
img_batch = (img_batch / 255 - tf.constant(
self.cfgs.PIXEL_MEAN_)) / tf.constant(self.cfgs.PIXEL_STD)
else:
img_batch = img_batch - tf.constant(self.cfgs.PIXEL_MEAN)
img_batch = tf.expand_dims(img_batch, axis=0)
detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network(
input_img_batch=img_batch)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
restorer, restore_ckpt = det_net.get_restorer()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
if not restorer is None:
restorer.restore(sess, restore_ckpt)
print('restore model')
all_boxes_r = []
imgs = os.listdir(img_dir)
pbar = tqdm(imgs)
for a_img_name in pbar:
a_img_name = a_img_name.split(image_ext)[0]
raw_img = cv2.imread(
os.path.join(img_dir, a_img_name + image_ext))
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
det_boxes_r_all, det_scores_r_all, det_category_r_all = [], [], []
img_short_side_len_list = self.cfgs.IMG_SHORT_SIDE_LEN if isinstance(
self.cfgs.IMG_SHORT_SIDE_LEN,
list) else [self.cfgs.IMG_SHORT_SIDE_LEN]
img_short_side_len_list = [
img_short_side_len_list[0]
] if not self.args.multi_scale else img_short_side_len_list
for short_size in img_short_side_len_list:
max_len = self.cfgs.IMG_MAX_LENGTH
if raw_h < raw_w:
new_h, new_w = short_size, min(
int(short_size * float(raw_w) / raw_h), max_len)
else:
new_h, new_w = min(
int(short_size * float(raw_h) / raw_w),
max_len), short_size
img_resize = cv2.resize(raw_img, (new_w, new_h))
resized_img, detected_boxes, detected_scores, detected_categories = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: img_resize[:, :, ::-1]}
)
if detected_boxes.shape[0] == 0:
continue
resized_h, resized_w = resized_img.shape[
1], resized_img.shape[2]
detected_boxes = forward_convert(detected_boxes, False)
detected_boxes[:, 0::2] *= (raw_w / resized_w)
detected_boxes[:, 1::2] *= (raw_h / resized_h)
det_boxes_r_all.extend(detected_boxes)
det_scores_r_all.extend(detected_scores)
det_category_r_all.extend(detected_categories)
det_boxes_r_all = np.array(det_boxes_r_all)
det_scores_r_all = np.array(det_scores_r_all)
det_category_r_all = np.array(det_category_r_all)
box_res_rotate_ = []
label_res_rotate_ = []
score_res_rotate_ = []
if det_scores_r_all.shape[0] != 0:
for sub_class in range(1, self.cfgs.CLASS_NUM + 1):
index = np.where(det_category_r_all == sub_class)[0]
if len(index) == 0:
continue
tmp_boxes_r = det_boxes_r_all[index]
tmp_label_r = det_category_r_all[index]
tmp_score_r = det_scores_r_all[index]
if self.args.multi_scale:
tmp_boxes_r_ = backward_convert(tmp_boxes_r, False)
# try:
# inx = nms_rotate.nms_rotate_cpu(boxes=np.array(tmp_boxes_r_),
# scores=np.array(tmp_score_r),
# iou_threshold=self.cfgs.NMS_IOU_THRESHOLD,
# max_output_size=5000)
# except:
tmp_boxes_r_ = np.array(tmp_boxes_r_)
tmp = np.zeros([
tmp_boxes_r_.shape[0],
tmp_boxes_r_.shape[1] + 1
])
tmp[:, 0:-1] = tmp_boxes_r_
tmp[:, -1] = np.array(tmp_score_r)
# Note: the IoU of two same rectangles is 0, which is calculated by rotate_gpu_nms
jitter = np.zeros([
tmp_boxes_r_.shape[0],
tmp_boxes_r_.shape[1] + 1
])
jitter[:, 0] += np.random.rand(
tmp_boxes_r_.shape[0], ) / 1000
inx = rotate_gpu_nms(
np.array(tmp, np.float32) +
np.array(jitter, np.float32),
float(self.cfgs.NMS_IOU_THRESHOLD), 0)
else:
inx = np.arange(0, tmp_score_r.shape[0])
box_res_rotate_.extend(np.array(tmp_boxes_r)[inx])
score_res_rotate_.extend(np.array(tmp_score_r)[inx])
label_res_rotate_.extend(np.array(tmp_label_r)[inx])
if len(box_res_rotate_) == 0:
all_boxes_r.append(np.array([]))
continue
det_boxes_r_ = np.array(box_res_rotate_)
det_scores_r_ = np.array(score_res_rotate_)
det_category_r_ = np.array(label_res_rotate_)
if self.args.draw_imgs:
detected_indices = det_scores_r_ >= self.cfgs.VIS_SCORE
detected_scores = det_scores_r_[detected_indices]
detected_boxes = det_boxes_r_[detected_indices]
detected_categories = det_category_r_[detected_indices]
detected_boxes = backward_convert(detected_boxes, False)
drawer = DrawBox(self.cfgs)
det_detections_r = drawer.draw_boxes_with_label_and_scores(
raw_img[:, :, ::-1],
boxes=detected_boxes,
labels=detected_categories,
scores=detected_scores,
method=1,
in_graph=True)
save_dir = os.path.join('test_hrsc', self.cfgs.VERSION,
'hrsc2016_img_vis')
tools.makedirs(save_dir)
cv2.imwrite(save_dir + '/{}.jpg'.format(a_img_name),
det_detections_r[:, :, ::-1])
det_boxes_r_ = backward_convert(det_boxes_r_, False)
x_c, y_c, w, h, theta = det_boxes_r_[:, 0], det_boxes_r_[:, 1], det_boxes_r_[:, 2], \
det_boxes_r_[:, 3], det_boxes_r_[:, 4]
boxes_r = np.transpose(np.stack([x_c, y_c, w, h, theta]))
dets_r = np.hstack((det_category_r_.reshape(-1, 1),
det_scores_r_.reshape(-1, 1), boxes_r))
all_boxes_r.append(dets_r)
pbar.set_description("Eval image %s" % a_img_name)
# fw1 = open(cfgs.VERSION + '_detections_r.pkl', 'wb')
# pickle.dump(all_boxes_r, fw1)
return all_boxes_r
def worker(self, gpu_id, images, det_net, result_queue):
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
# 1. preprocess img
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not BGR
img_batch = tf.cast(img_plac, tf.float32)
if self.cfgs.NET_NAME in ['resnet152_v1d', 'resnet101_v1d', 'resnet50_v1d',
'resnet152_v1b', 'resnet101_v1b', 'resnet50_v1b', 'resnet34_v1b', 'resnet18_v1b']:
img_batch = (img_batch / 255 - tf.constant(self.cfgs.PIXEL_MEAN_)) / tf.constant(self.cfgs.PIXEL_STD)
else:
img_batch = img_batch - tf.constant(self.cfgs.PIXEL_MEAN)
img_batch = tf.expand_dims(img_batch, axis=0)
detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network(
input_img_batch=img_batch,
gtboxes_batch_h=None,
gtboxes_batch_r=None,
gpu_id=0)
init_op = tf.group(
tf.global_variables_initializer(),
tf.local_variables_initializer()
)
restorer, restore_ckpt = det_net.get_restorer()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
if not restorer is None:
restorer.restore(sess, restore_ckpt)
print('restore model %d ...' % gpu_id)
for a_img in images:
raw_img = cv2.imread(a_img)
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
det_boxes_r_all, det_scores_r_all, det_category_r_all = [], [], []
img_short_side_len_list = self.cfgs.IMG_SHORT_SIDE_LEN if isinstance(self.cfgs.IMG_SHORT_SIDE_LEN, list) else [
self.cfgs.IMG_SHORT_SIDE_LEN]
img_short_side_len_list = [img_short_side_len_list[0]] if not self.args.multi_scale else img_short_side_len_list
for short_size in img_short_side_len_list:
max_len = self.cfgs.IMG_MAX_LENGTH
if raw_h < raw_w:
new_h, new_w = short_size, min(int(short_size * float(raw_w) / raw_h), max_len)
else:
new_h, new_w = min(int(short_size * float(raw_h) / raw_w), max_len), short_size
img_resize = cv2.resize(raw_img, (new_w, new_h))
resized_img, detected_boxes, detected_scores, detected_categories = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: img_resize[:, :, ::-1]}
)
detected_indices = detected_scores >= self.cfgs.VIS_SCORE
detected_scores = detected_scores[detected_indices]
detected_boxes = detected_boxes[detected_indices]
detected_categories = detected_categories[detected_indices]
if detected_boxes.shape[0] == 0:
continue
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
detected_boxes = forward_convert(detected_boxes, False)
detected_boxes[:, 0::2] *= (raw_w / resized_w)
detected_boxes[:, 1::2] *= (raw_h / resized_h)
det_boxes_r_all.extend(detected_boxes)
det_scores_r_all.extend(detected_scores)
det_category_r_all.extend(detected_categories)
if self.args.flip_img:
detected_boxes, detected_scores, detected_categories = \
sess.run(
[detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: cv2.flip(img_resize, flipCode=1)[:, :, ::-1]}
)
detected_indices = detected_scores >= self.cfgs.VIS_SCORE
detected_scores = detected_scores[detected_indices]
detected_boxes = detected_boxes[detected_indices]
detected_categories = detected_categories[detected_indices]
if detected_boxes.shape[0] == 0:
continue
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
detected_boxes = forward_convert(detected_boxes, False)
detected_boxes[:, 0::2] *= (raw_w / resized_w)
detected_boxes[:, 0::2] = (raw_w - detected_boxes[:, 0::2])
detected_boxes[:, 1::2] *= (raw_h / resized_h)
det_boxes_r_all.extend(sort_corners(detected_boxes))
det_scores_r_all.extend(detected_scores)
det_category_r_all.extend(detected_categories)
detected_boxes, detected_scores, detected_categories = \
sess.run(
[detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: cv2.flip(img_resize, flipCode=0)[:, :, ::-1]}
)
detected_indices = detected_scores >= self.cfgs.VIS_SCORE
detected_scores = detected_scores[detected_indices]
detected_boxes = detected_boxes[detected_indices]
detected_categories = detected_categories[detected_indices]
if detected_boxes.shape[0] == 0:
continue
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
detected_boxes = forward_convert(detected_boxes, False)
detected_boxes[:, 0::2] *= (raw_w / resized_w)
detected_boxes[:, 1::2] *= (raw_h / resized_h)
detected_boxes[:, 1::2] = (raw_h - detected_boxes[:, 1::2])
det_boxes_r_all.extend(sort_corners(detected_boxes))
det_scores_r_all.extend(detected_scores)
det_category_r_all.extend(detected_categories)
det_boxes_r_all = np.array(det_boxes_r_all)
det_scores_r_all = np.array(det_scores_r_all)
det_category_r_all = np.array(det_category_r_all)
box_res_rotate_ = []
label_res_rotate_ = []
score_res_rotate_ = []
if det_scores_r_all.shape[0] != 0:
for sub_class in range(1, self.cfgs.CLASS_NUM + 1):
index = np.where(det_category_r_all == sub_class)[0]
if len(index) == 0:
continue
tmp_boxes_r = det_boxes_r_all[index]
tmp_label_r = det_category_r_all[index]
tmp_score_r = det_scores_r_all[index]
if self.args.multi_scale:
tmp_boxes_r_ = backward_convert(tmp_boxes_r, False)
# try:
# inx = nms_rotate.nms_rotate_cpu(boxes=np.array(tmp_boxes_r_),
# scores=np.array(tmp_score_r),
# iou_threshold=self.cfgs.NMS_IOU_THRESHOLD,
# max_output_size=5000)
# except:
tmp_boxes_r_ = np.array(tmp_boxes_r_)
tmp = np.zeros([tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1])
tmp[:, 0:-1] = tmp_boxes_r_
tmp[:, -1] = np.array(tmp_score_r)
# Note: the IoU of two same rectangles is 0, which is calculated by rotate_gpu_nms
jitter = np.zeros([tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1])
jitter[:, 0] += np.random.rand(tmp_boxes_r_.shape[0], ) / 1000
inx = rotate_gpu_nms(np.array(tmp, np.float32) + np.array(jitter, np.float32),
float(self.cfgs.NMS_IOU_THRESHOLD), 0)
else:
inx = np.arange(0, tmp_score_r.shape[0])
box_res_rotate_.extend(np.array(tmp_boxes_r)[inx])
score_res_rotate_.extend(np.array(tmp_score_r)[inx])
label_res_rotate_.extend(np.array(tmp_label_r)[inx])
box_res_rotate_ = np.array(box_res_rotate_)
score_res_rotate_ = np.array(score_res_rotate_)
label_res_rotate_ = np.array(label_res_rotate_)
result_dict = {'scales': [1, 1], 'boxes': box_res_rotate_,
'scores': score_res_rotate_, 'labels': label_res_rotate_,
'image_id': a_img}
result_queue.put_nowait(result_dict)
temp_ious.append(0.0)
ious.append(temp_ious)
return np.array(ious, dtype=np.float32)
if __name__ == '__main__':
boxes1 = np.array([[50, 50, 70, 10, -45]], np.float32)
boxes2 = np.array([[50, 25, 70, 10, -45]], np.float32)
iou_r = riou(boxes1, boxes2)
print(iou_r)
from libs.utils.coordinate_convert import forward_convert
boxes1 = forward_convert(boxes1, False)
boxes2 = forward_convert(boxes2, False)
x1_min = np.min(boxes1[:, ::2], axis=-1)
y1_min = np.min(boxes1[:, 1::2], axis=-1)
x1_max = np.max(boxes1[:, ::2], axis=-1)
y1_max = np.max(boxes1[:, 1::2], axis=-1)
boxes1 = np.transpose(np.stack([x1_min, y1_min, x1_max, y1_max]))
x2_min = np.min(boxes2[:, ::2], axis=-1)
y2_min = np.min(boxes2[:, 1::2], axis=-1)
x2_max = np.max(boxes2[:, ::2], axis=-1)
y2_max = np.max(boxes2[:, 1::2], axis=-1)
boxes2 = np.transpose(np.stack([x2_min, y2_min, x2_max, y2_max]))
iou_h = hiou(boxes1, boxes2)
