def test(img_num):
with tf.Graph().as_default():
img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch = \
next_batch(dataset_name=cfgs.DATASET_NAME,
batch_size=cfgs.BATCH_SIZE,
shortside_len=cfgs.SHORT_SIDE_LEN,
is_training=False)
gtboxes_and_label, head = get_head(
tf.squeeze(gtboxes_and_label_batch, 0))
gtboxes_and_label = tf.py_func(back_forward_convert,
inp=[gtboxes_and_label],
Tout=tf.float32)
gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 6])
head_quadrant = tf.py_func(get_head_quadrant,
inp=[head, gtboxes_and_label],
Tout=tf.float32)
head_quadrant = tf.reshape(head_quadrant, [-1, 1])
gtboxes_and_label_minAreaRectangle = get_horizen_minAreaRectangle(
gtboxes_and_label)
gtboxes_and_label_minAreaRectangle = tf.reshape(
gtboxes_and_label_minAreaRectangle, [-1, 5])
# ***********************************************************************************************
# * share net *
# ***********************************************************************************************
_, share_net = get_network_byname(net_name=cfgs.NET_NAME,
inputs=img_batch,
num_classes=None,
is_training=True,
output_stride=None,
global_pool=False,
spatial_squeeze=False)
# ***********************************************************************************************
# * RPN *
# ***********************************************************************************************
rpn = build_rpn.RPN(
net_name=cfgs.NET_NAME,
inputs=img_batch,
gtboxes_and_label=None,
is_training=False,
share_head=cfgs.SHARE_HEAD,
share_net=share_net,
stride=cfgs.STRIDE,
anchor_ratios=cfgs.ANCHOR_RATIOS,
anchor_scales=cfgs.ANCHOR_SCALES,
scale_factors=cfgs.SCALE_FACTORS,
base_anchor_size_list=cfgs.
BASE_ANCHOR_SIZE_LIST, # P2, P3, P4, P5, P6
level=cfgs.LEVEL,
top_k_nms=cfgs.RPN_TOP_K_NMS,
rpn_nms_iou_threshold=cfgs.RPN_NMS_IOU_THRESHOLD,
max_proposals_num=cfgs.MAX_PROPOSAL_NUM,
rpn_iou_positive_threshold=cfgs.RPN_IOU_POSITIVE_THRESHOLD,
rpn_iou_negative_threshold=cfgs.RPN_IOU_NEGATIVE_THRESHOLD,
rpn_mini_batch_size=cfgs.RPN_MINIBATCH_SIZE,
rpn_positives_ratio=cfgs.RPN_POSITIVE_RATE,
remove_outside_anchors=False, # whether remove anchors outside
rpn_weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME])
# rpn predict proposals
rpn_proposals_boxes, rpn_proposals_scores = rpn.rpn_proposals(
) # rpn_score shape: [300, ]
# ***********************************************************************************************
# * Fast RCNN *
# ***********************************************************************************************
fast_rcnn = build_fast_rcnn.FastRCNN(
feature_pyramid=rpn.feature_pyramid,
rpn_proposals_boxes=rpn_proposals_boxes,
rpn_proposals_scores=rpn_proposals_scores,
img_shape=tf.shape(img_batch),
img_batch=img_batch,
roi_size=cfgs.ROI_SIZE,
roi_pool_kernel_size=cfgs.ROI_POOL_KERNEL_SIZE,
scale_factors=cfgs.SCALE_FACTORS,
gtboxes_and_label=None,
gtboxes_and_label_minAreaRectangle=
gtboxes_and_label_minAreaRectangle,
fast_rcnn_nms_iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD,
fast_rcnn_maximum_boxes_per_img=100,
fast_rcnn_nms_max_boxes_per_class=cfgs.
FAST_RCNN_NMS_MAX_BOXES_PER_CLASS,
show_detections_score_threshold=cfgs.FINAL_SCORE_THRESHOLD,
# show detections which score >= 0.6
num_classes=cfgs.CLASS_NUM,
fast_rcnn_minibatch_size=cfgs.FAST_RCNN_MINIBATCH_SIZE,
fast_rcnn_positives_ratio=cfgs.FAST_RCNN_POSITIVE_RATE,
fast_rcnn_positives_iou_threshold=cfgs.
FAST_RCNN_IOU_POSITIVE_THRESHOLD,
# iou>0.5 is positive, iou<0.5 is negative
use_dropout=cfgs.USE_DROPOUT,
weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME],
is_training=False,
level=cfgs.LEVEL,
head_quadrant=head_quadrant)
fast_rcnn_decode_boxes, fast_rcnn_score, num_of_objects, detection_category, \
fast_rcnn_decode_boxes_rotate, fast_rcnn_score_rotate, fast_rcnn_head_quadrant, \
num_of_objects_rotate, detection_category_rotate = fast_rcnn.fast_rcnn_predict()
# train
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
restorer, restore_ckpt = restore_model.get_restorer()
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.5
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')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess, coord)
for i in range(img_num):
start = time.time()
_img_name_batch, _img_batch, _gtboxes_and_label, _gtboxes_and_label_minAreaRectangle, _head_quadrant, \
_fast_rcnn_decode_boxes, _fast_rcnn_score, _detection_category, _fast_rcnn_decode_boxes_rotate, \
_fast_rcnn_score_rotate, _fast_rcnn_head_quadrant, _detection_category_rotate \
= sess.run([img_name_batch, img_batch, gtboxes_and_label, gtboxes_and_label_minAreaRectangle, head_quadrant,
fast_rcnn_decode_boxes, fast_rcnn_score, detection_category, fast_rcnn_decode_boxes_rotate,
fast_rcnn_score_rotate, fast_rcnn_head_quadrant, detection_category_rotate])
end = time.time()
_img_batch = np.squeeze(_img_batch, axis=0)
_img_batch_fpn_horizonal = help_utils.draw_box_cv(
_img_batch,
boxes=_fast_rcnn_decode_boxes,
labels=_detection_category,
scores=_fast_rcnn_score)
_img_batch_fpn_rotate = help_utils.draw_rotate_box_cv(
_img_batch,
boxes=_fast_rcnn_decode_boxes_rotate,
labels=_detection_category_rotate,
scores=_fast_rcnn_score_rotate,
head=np.argmax(_fast_rcnn_head_quadrant, axis=1))
mkdir(cfgs.TEST_SAVE_PATH)
cv2.imwrite(
cfgs.TEST_SAVE_PATH +
'/{}_horizontal_fpn.jpg'.format(str(_img_name_batch[0])),
_img_batch_fpn_horizonal)
cv2.imwrite(
cfgs.TEST_SAVE_PATH +
'/{}_rotate_fpn.jpg'.format(str(_img_name_batch[0])),
_img_batch_fpn_rotate)
temp_label_horizontal = np.reshape(_gtboxes_and_label[:, -1:],
[
-1,
]).astype(np.int64)
temp_label_rotate = np.reshape(_gtboxes_and_label[:, -1:], [
-1,
]).astype(np.int64)
_img_batch_gt_horizontal = help_utils.draw_box_cv(
_img_batch,
boxes=_gtboxes_and_label_minAreaRectangle[:, :-1],
labels=temp_label_horizontal,
scores=None)
_img_batch_gt_rotate = help_utils.draw_rotate_box_cv(
_img_batch,
boxes=_gtboxes_and_label[:, :-1],
labels=temp_label_rotate,
scores=None,
head=np.reshape(_head_quadrant, [
-1,
]))
cv2.imwrite(
cfgs.TEST_SAVE_PATH +
'/{}_horizontal_gt.jpg'.format(str(_img_name_batch[0])),
_img_batch_gt_horizontal)
cv2.imwrite(
cfgs.TEST_SAVE_PATH +
'/{}_rotate_gt.jpg'.format(str(_img_name_batch[0])),
_img_batch_gt_rotate)
view_bar(
'{} image cost {}s'.format(str(_img_name_batch[0]),
(end - start)), i + 1, img_num)
coord.request_stop()
coord.join(threads)
def test(img_num):
with tf.Graph().as_default():
img_name_batch, img_batch, gtboxes_and_label_batch, num_objects_batch = \
next_batch(dataset_name=cfgs.DATASET_NAME,
batch_size=cfgs.BATCH_SIZE,
shortside_len=cfgs.SHORT_SIDE_LEN,
is_training=False)
gtboxes_and_label = tf.py_func(
back_forward_convert,
inp=[tf.squeeze(gtboxes_and_label_batch, 0)],
Tout=tf.float32)
gtboxes_and_label = tf.reshape(gtboxes_and_label, [-1, 6])
gtboxes_and_label_minAreaRectangle = get_horizen_minAreaRectangle(
gtboxes_and_label)
gtboxes_and_label_minAreaRectangle = tf.reshape(
gtboxes_and_label_minAreaRectangle, [-1, 5])
# ***********************************************************************************************
# * share net *
# ***********************************************************************************************
_, share_net = get_network_byname(net_name=cfgs.NET_NAME,
inputs=img_batch,
num_classes=None,
is_training=True,
output_stride=None,
global_pool=False,
spatial_squeeze=False)
# ***********************************************************************************************
# * RPN *
# ***********************************************************************************************
rpn = build_rpn.RPN(
net_name=cfgs.NET_NAME,
inputs=img_batch,
gtboxes_and_label=None,
is_training=False,
share_head=cfgs.SHARE_HEAD,
share_net=share_net,
stride=cfgs.STRIDE,
anchor_ratios=cfgs.ANCHOR_RATIOS,
anchor_scales=cfgs.ANCHOR_SCALES,
scale_factors=cfgs.SCALE_FACTORS,
base_anchor_size_list=cfgs.
BASE_ANCHOR_SIZE_LIST, # P2, P3, P4, P5, P6
level=cfgs.LEVEL,
top_k_nms=cfgs.RPN_TOP_K_NMS,
rpn_nms_iou_threshold=cfgs.RPN_NMS_IOU_THRESHOLD,
max_proposals_num=cfgs.MAX_PROPOSAL_NUM,
rpn_iou_positive_threshold=cfgs.RPN_IOU_POSITIVE_THRESHOLD,
rpn_iou_negative_threshold=cfgs.RPN_IOU_NEGATIVE_THRESHOLD,
rpn_mini_batch_size=cfgs.RPN_MINIBATCH_SIZE,
rpn_positives_ratio=cfgs.RPN_POSITIVE_RATE,
remove_outside_anchors=False, # whether remove anchors outside
rpn_weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME])
# rpn predict proposals
rpn_proposals_boxes, rpn_proposals_scores = rpn.rpn_proposals(
) # rpn_score shape: [300, ]
# ***********************************************************************************************
# * Fast RCNN *
# ***********************************************************************************************
fast_rcnn = build_fast_rcnn1.FastRCNN(
feature_pyramid=rpn.feature_pyramid,
rpn_proposals_boxes=rpn_proposals_boxes,
rpn_proposals_scores=rpn_proposals_scores,
img_shape=tf.shape(img_batch),
roi_size=cfgs.ROI_SIZE,
roi_pool_kernel_size=cfgs.ROI_POOL_KERNEL_SIZE,
scale_factors=cfgs.SCALE_FACTORS,
gtboxes_and_label=None,
gtboxes_and_label_minAreaRectangle=
gtboxes_and_label_minAreaRectangle,
fast_rcnn_nms_iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD,
fast_rcnn_maximum_boxes_per_img=100,
fast_rcnn_nms_max_boxes_per_class=cfgs.
FAST_RCNN_NMS_MAX_BOXES_PER_CLASS,
show_detections_score_threshold=cfgs.FINAL_SCORE_THRESHOLD,
# show detections which score >= 0.6
num_classes=cfgs.CLASS_NUM,
fast_rcnn_minibatch_size=cfgs.FAST_RCNN_MINIBATCH_SIZE,
fast_rcnn_positives_ratio=cfgs.FAST_RCNN_POSITIVE_RATE,
fast_rcnn_positives_iou_threshold=cfgs.
FAST_RCNN_IOU_POSITIVE_THRESHOLD,
# iou>0.5 is positive, iou<0.5 is negative
use_dropout=cfgs.USE_DROPOUT,
weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME],
is_training=False,
level=cfgs.LEVEL)
fast_rcnn_decode_boxes, fast_rcnn_score, num_of_objects, detection_category, \
fast_rcnn_decode_boxes_rotate, fast_rcnn_score_rotate, num_of_objects_rotate, detection_category_rotate,_, _, _, _ = \
fast_rcnn.fast_rcnn_predict()
# train
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
restorer, restore_ckpt = restore_model.get_restorer()
config = tf.ConfigProto()
# config.gpu_options.per_process_gpu_memory_fraction = 0.5
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')
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess, coord)
TrueDetection = 0
FalseAlarm = 0
TotalObjects = 0
TotalDetections = 0
for i in range(img_num):
start = time.time()
_img_name_batch, _img_batch, _gtboxes_and_label, _gtboxes_and_label_minAreaRectangle, \
_fast_rcnn_decode_boxes, _fast_rcnn_score, _detection_category, _fast_rcnn_decode_boxes_rotate, \
_fast_rcnn_score_rotate, _detection_category_rotate \
= sess.run([img_name_batch, img_batch, gtboxes_and_label, gtboxes_and_label_minAreaRectangle,
fast_rcnn_decode_boxes, fast_rcnn_score, detection_category, fast_rcnn_decode_boxes_rotate,
fast_rcnn_score_rotate, detection_category_rotate])
end = time.time()
_img_batch = np.squeeze(_img_batch, axis=0)
_img_batch_fpn_horizonal = help_utils.draw_box_cv(
_img_batch,
boxes=_fast_rcnn_decode_boxes,
labels=_detection_category,
scores=_fast_rcnn_score)
_img_batch_fpn_rotate = help_utils.draw_rotate_box_cv(
_img_batch,
boxes=_fast_rcnn_decode_boxes_rotate,
labels=_detection_category_rotate,
scores=_fast_rcnn_score_rotate)
mkdir(cfgs.TEST_SAVE_PATH)
cv2.imwrite(
cfgs.TEST_SAVE_PATH + str(_img_name_batch[0]) +
'_horizontal_fpn.jpg', _img_batch_fpn_horizonal)
cv2.imwrite(
cfgs.TEST_SAVE_PATH + str(_img_name_batch[0]) +
'_rotate_fpn.jpg', _img_batch_fpn_rotate)
temp_label_horizontal = np.reshape(_gtboxes_and_label[:, -1:],
[
-1,
]).astype(np.int64)
temp_label_rotate = np.reshape(_gtboxes_and_label[:, -1:], [
-1,
]).astype(np.int64)
_img_batch_gt_horizontal = help_utils.draw_box_cv(
_img_batch,
boxes=_gtboxes_and_label_minAreaRectangle[:, :-1],
labels=temp_label_horizontal,
scores=None)
_img_batch_gt_rotate = help_utils.draw_rotate_box_cv(
_img_batch,
boxes=_gtboxes_and_label[:, :-1],
labels=temp_label_rotate,
scores=None)
cv2.imwrite(
cfgs.TEST_SAVE_PATH + str(_img_name_batch[0]) +
'_horizontal_gt.jpg', _img_batch_gt_horizontal)
cv2.imwrite(
cfgs.TEST_SAVE_PATH + str(_img_name_batch[0]) +
'_rotate_gt.jpg', _img_batch_gt_rotate)
Current_TrueDetection = 0
lock_list = []
for m in range(len(_gtboxes_and_label)):
for n, box in enumerate(_fast_rcnn_decode_boxes_rotate):
if n in lock_list:
continue
y_c, x_c, h, w, theta = box[0], box[1], box[2], box[
3], box[4]
r1 = ((x_c, y_c), (w, h), theta)
gt_box = _gtboxes_and_label[m]
gt_yc, gt_xc, gt_h, gt_w, gt_theta = gt_box[0], gt_box[
1], gt_box[2], gt_box[3], gt_box[4]
r2 = ((gt_xc, gt_yc), (gt_w, gt_h), gt_theta)
int_pts = cv2.rotatedRectangleIntersection(r1, r2)[0]
if int_pts > 0.5:
Current_TrueDetection += 1
lock_list.append(n)
TrueDetection += Current_TrueDetection
if (len(_fast_rcnn_decode_boxes_rotate) > 0):
FalseAlarm += len(
_fast_rcnn_score_rotate) - Current_TrueDetection
TotalDetections += len(_fast_rcnn_score_rotate)
TotalObjects += len(_gtboxes_and_label)
view_bar(
'{} image cost {}s'.format(str(_img_name_batch[0]),
(end - start)), i + 1, img_num)
Pd = float(TrueDetection) / float(TotalDetections)
Pf = float(FalseAlarm) / float(TotalDetections)
F1 = 2 * float(Pd * (1 - Pf)) / float(Pd + (1 - Pf))
Op = float(TrueDetection) / float(TotalObjects)
print("\nACC: " + str(Pd))
print("FalseAlarm: " + str(Pf))
print("F1: " + str(F1))
print("Op: " + str(TrueDetection) + "/" + str(TotalObjects) +
"=" + str(Op))
coord.request_stop()
coord.join(threads)