def eval_ship(img_num, mode):
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=False,
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()
if mode == 0:
fast_rcnn_decode_boxes_rotate = get_horizen_minAreaRectangle(
fast_rcnn_decode_boxes_rotate, False)
# train
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
restorer, restore_ckpt = restore_model.get_restorer()
with tf.Session() 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)
gtboxes_horizontal_dict = {}
predict_horizontal_dict = {}
gtboxes_rotate_dict = {}
predict_rotate_dict = {}
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()
# gtboxes convert dict
gtboxes_horizontal_dict[str(_img_name_batch[0])] = []
predict_horizontal_dict[str(_img_name_batch[0])] = []
gtboxes_rotate_dict[str(_img_name_batch[0])] = []
predict_rotate_dict[str(_img_name_batch[0])] = []
gtbox_horizontal_list, predict_horizontal_list = \
make_dict_packle(_gtboxes_and_label_minAreaRectangle, _fast_rcnn_decode_boxes,
_fast_rcnn_score, _detection_category)
if mode == 0:
gtbox_rotate_list, predict_rotate_list = \
make_dict_packle(_gtboxes_and_label_minAreaRectangle, _fast_rcnn_decode_boxes_rotate,
_fast_rcnn_score_rotate, _detection_category_rotate)
else:
gtbox_rotate_list, predict_rotate_list = \
make_dict_packle(_gtboxes_and_label, _fast_rcnn_decode_boxes_rotate,
_fast_rcnn_score_rotate, _detection_category_rotate)
gtboxes_horizontal_dict[str(
_img_name_batch[0])].extend(gtbox_horizontal_list)
predict_horizontal_dict[str(
_img_name_batch[0])].extend(predict_horizontal_list)
gtboxes_rotate_dict[str(
_img_name_batch[0])].extend(gtbox_rotate_list)
predict_rotate_dict[str(
_img_name_batch[0])].extend(predict_rotate_list)
view_bar(
'{} image cost {}s'.format(str(_img_name_batch[0]),
(end - start)), i + 1, img_num)
fw1 = open('gtboxes_horizontal_dict.pkl', 'w')
fw2 = open('predict_horizontal_dict.pkl', 'w')
fw3 = open('gtboxes_rotate_dict.pkl', 'w')
fw4 = open('predict_rotate_dict.pkl', 'w')
pickle.dump(gtboxes_horizontal_dict, fw1)
pickle.dump(predict_horizontal_dict, fw2)
pickle.dump(gtboxes_rotate_dict, fw3)
pickle.dump(predict_rotate_dict, fw4)
fw1.close()
fw2.close()
fw3.close()
fw4.close()
coord.request_stop()
coord.join(threads)
def detect_img(file_paths, des_folder, det_th, h_len, w_len, h_overlap, w_overlap, show_res=False):
with tf.Graph().as_default():
img_plac = tf.placeholder(shape=[None, None, 3], dtype=tf.uint8)
img_tensor = tf.cast(img_plac, tf.float32) - tf.constant([103.939, 116.779, 123.68])
img_batch = image_preprocess.short_side_resize_for_inference_data(img_tensor,
target_shortside_len=cfgs.SHORT_SIDE_LEN,
is_resize=False)
# ***********************************************************************************************
# * 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=None,
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=det_th,
# 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()
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 img_path in file_paths:
start = timer()
img = cv2.imread(img_path)
box_res = []
label_res = []
score_res = []
box_res_rotate = []
label_res_rotate = []
score_res_rotate = []
imgH = img.shape[0]
imgW = img.shape[1]
for hh in range(0, imgH, h_len - h_overlap):
h_size = min(h_len, imgH - hh)
if h_size < 10:
break
for ww in range(0, imgW, w_len - w_overlap):
w_size = min(w_len, imgW - ww)
if w_size < 10:
break
src_img = img[hh:(hh + h_size), ww:(ww + w_size), :]
boxes, labels, scores = sess.run([fast_rcnn_decode_boxes, detection_category, fast_rcnn_score],
feed_dict={img_plac: src_img})
boxes_rotate, labels_rotate, scores_rotate = sess.run([fast_rcnn_decode_boxes_rotate,
detection_category_rotate,
fast_rcnn_score_rotate],
feed_dict={img_plac: src_img})
if len(boxes) > 0:
for ii in range(len(boxes)):
box = boxes[ii]
box[0] = box[0] + hh
box[1] = box[1] + ww
box[2] = box[2] + hh
box[3] = box[3] + ww
box_res.append(box)
label_res.append(labels[ii])
score_res.append(scores[ii])
if len(boxes_rotate) > 0:
for ii in range(len(boxes_rotate)):
box_rotate = boxes_rotate[ii]
box_rotate[0] = box_rotate[0] + hh
box_rotate[1] = box_rotate[1] + ww
box_res_rotate.append(box_rotate)
label_res_rotate.append(labels_rotate[ii])
score_res_rotate.append(scores_rotate[ii])
# inx = nms_rotate.nms_rotate_cpu(boxes=np.array(box_res_rotate), scores=np.array(score_res_rotate),
# iou_threshold=0.5, max_output_size=100)
# box_res_rotate = np.array(box_res_rotate)[inx]
# score_res_rotate = np.array(score_res_rotate)[inx]
# label_res_rotate = np.array(label_res_rotate)[inx]
time_elapsed = timer() - start
print("{} detection time : {:.4f} sec".format(img_path.split('/')[-1].split('.')[0], time_elapsed))
mkdir(des_folder)
img_np = draw_box_cv(np.array(img, np.float32) - np.array([103.939, 116.779, 123.68]),
boxes=np.array(box_res),
labels=np.array(label_res),
scores=np.array(score_res))
img_np_rotate = draw_rotate_box_cv(np.array(img, np.float32) - np.array([103.939, 116.779, 123.68]),
boxes=np.array(box_res_rotate),
labels=np.array(label_res_rotate),
scores=np.array(score_res_rotate))
cv2.imwrite(des_folder + '/{}_horizontal_fpn.jpg'.format(img_path.split('/')[-1].split('.')[0]), img_np)
cv2.imwrite(des_folder + '/{}_rotate_fpn.jpg'.format(img_path.split('/')[-1].split('.')[0]), img_np_rotate)
coord.request_stop()
coord.join(threads)
def inference():
with tf.Graph().as_default():
img_plac = tf.placeholder(shape=[None, None, 3], dtype=tf.uint8)
img_tensor = tf.cast(img_plac, tf.float32) - tf.constant([103.939, 116.779, 123.68])
img_batch = image_preprocess.short_side_resize_for_inference_data(img_tensor,
target_shortside_len=cfgs.SHORT_SIDE_LEN)
# ***********************************************************************************************
# * 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=None,
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()
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)
imgs, img_names = get_imgs()
for i, img in enumerate(imgs):
start = time.time()
_img_batch, _fast_rcnn_decode_boxes, _fast_rcnn_score, _detection_category, \
_fast_rcnn_decode_boxes_rotate, _fast_rcnn_score_rotate, _detection_category_rotate = \
sess.run([img_batch, fast_rcnn_decode_boxes, fast_rcnn_score, detection_category,
fast_rcnn_decode_boxes_rotate, fast_rcnn_score_rotate, detection_category_rotate],
feed_dict={img_plac: img})
end = time.time()
img_np = np.squeeze(_img_batch, axis=0)
img_horizontal_np = draw_box_cv(img_np,
boxes=_fast_rcnn_decode_boxes,
labels=_detection_category,
scores=_fast_rcnn_score)
img_rotate_np = draw_rotate_box_cv(img_np,
boxes=_fast_rcnn_decode_boxes_rotate,
labels=_detection_category_rotate,
scores=_fast_rcnn_score_rotate)
mkdir(cfgs.INFERENCE_SAVE_PATH)
cv2.imwrite(cfgs.INFERENCE_SAVE_PATH + '/{}_horizontal_fpn.jpg'.format(img_names[i]), img_horizontal_np)
cv2.imwrite(cfgs.INFERENCE_SAVE_PATH + '/{}_rotate_fpn.jpg'.format(img_names[i]), img_rotate_np)
view_bar('{} cost {}s'.format(img_names[i], (end - start)), i + 1, len(imgs))
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)
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 + '/{}_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)
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)
print('{} image cost {}s'.format(str(_img_name_batch[0]), (end - start)) + str(i + 1)+'/'+str(img_num))
coord.request_stop()
coord.join(threads)
def train():
with tf.Graph().as_default():
with tf.name_scope('get_batch'):
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=True)
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])
with tf.name_scope('draw_gtboxes'):
gtboxes_in_img = draw_box_with_color(img_batch, tf.reshape(gtboxes_and_label_minAreaRectangle, [-1, 5])[:, :-1],
text=tf.shape(gtboxes_and_label_minAreaRectangle)[0])
gtboxes_rotate_in_img = draw_box_with_color_rotate(img_batch, tf.reshape(gtboxes_and_label, [-1, 6])[:, :-1],
text=tf.shape(gtboxes_and_label)[0])
# ***********************************************************************************************
# * 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=gtboxes_and_label_minAreaRectangle,
is_training=True,
share_head=False,
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, # iou>=0.7 is positive box, iou< 0.3 is negative
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_proposals_boxes, rpn_proposals_scores = rpn.rpn_proposals() # rpn_score shape: [300, ]
rpn_location_loss, rpn_classification_loss = rpn.rpn_losses()
rpn_total_loss = rpn_classification_loss + rpn_location_loss
with tf.name_scope('draw_proposals'):
# score > 0.5 is object
rpn_object_boxes_indices = tf.reshape(tf.where(tf.greater(rpn_proposals_scores, 0.5)), [-1])
rpn_object_boxes = tf.gather(rpn_proposals_boxes, rpn_object_boxes_indices)
rpn_proposals_objcet_boxes_in_img = draw_box_with_color(img_batch, rpn_object_boxes,
text=tf.shape(rpn_object_boxes)[0])
rpn_proposals_boxes_in_img = draw_box_with_color(img_batch, rpn_proposals_boxes,
text=tf.shape(rpn_proposals_boxes)[0])
# ***********************************************************************************************
# * 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=gtboxes_and_label,
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=True,
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()
fast_rcnn_location_loss, fast_rcnn_classification_loss, \
fast_rcnn_location_rotate_loss, fast_rcnn_classification_rotate_loss = fast_rcnn.fast_rcnn_loss()
fast_rcnn_total_loss = fast_rcnn_location_loss + fast_rcnn_classification_loss + \
fast_rcnn_location_rotate_loss + fast_rcnn_classification_rotate_loss
with tf.name_scope('draw_boxes_with_categories'):
fast_rcnn_predict_boxes_in_imgs = draw_boxes_with_categories(img_batch=img_batch,
boxes=fast_rcnn_decode_boxes,
labels=detection_category,
scores=fast_rcnn_score)
fast_rcnn_predict_rotate_boxes_in_imgs = draw_boxes_with_categories_rotate(img_batch=img_batch,
boxes=fast_rcnn_decode_boxes_rotate,
labels=detection_category_rotate,
scores=fast_rcnn_score_rotate)
# train
total_loss = slim.losses.get_total_loss()
global_step = slim.get_or_create_global_step()
lr = tf.train.piecewise_constant(global_step,
boundaries=[np.int64(20000), np.int64(40000)],
values=[0.001, 0.0001, 0.00001])
tf.summary.scalar('lr', lr)
optimizer = tf.train.MomentumOptimizer(lr, momentum=cfgs.MOMENTUM)
train_op = slim.learning.create_train_op(total_loss, optimizer, global_step) # rpn_total_loss,
# train_op = optimizer.minimize(second_classification_loss, global_step)
# ***********************************************************************************************
# * Summary *
# ***********************************************************************************************
# ground truth and predict
tf.summary.image('img/gtboxes', gtboxes_in_img)
tf.summary.image('img/gtboxes_rotate', gtboxes_rotate_in_img)
tf.summary.image('img/faster_rcnn_predict', fast_rcnn_predict_boxes_in_imgs)
tf.summary.image('img/faster_rcnn_predict_rotate', fast_rcnn_predict_rotate_boxes_in_imgs)
# rpn loss and image
tf.summary.scalar('rpn/rpn_location_loss', rpn_location_loss)
tf.summary.scalar('rpn/rpn_classification_loss', rpn_classification_loss)
tf.summary.scalar('rpn/rpn_total_loss', rpn_total_loss)
tf.summary.scalar('fast_rcnn/fast_rcnn_location_loss', fast_rcnn_location_loss)
tf.summary.scalar('fast_rcnn/fast_rcnn_classification_loss', fast_rcnn_classification_loss)
tf.summary.scalar('fast_rcnn/fast_rcnn_location_rotate_loss', fast_rcnn_location_rotate_loss)
tf.summary.scalar('fast_rcnn/fast_rcnn_classification_rotate_loss', fast_rcnn_classification_rotate_loss)
tf.summary.scalar('fast_rcnn/fast_rcnn_total_loss', fast_rcnn_total_loss)
tf.summary.scalar('loss/total_loss', total_loss)
tf.summary.image('rpn/rpn_all_boxes', rpn_proposals_boxes_in_img)
tf.summary.image('rpn/rpn_object_boxes', rpn_proposals_objcet_boxes_in_img)
# learning_rate
tf.summary.scalar('learning_rate', lr)
summary_op = tf.summary.merge_all()
init_op = tf.group(
tf.global_variables_initializer(),
tf.local_variables_initializer()
)
restorer, restore_ckpt = restore_model.get_restorer()
saver = tf.train.Saver(max_to_keep=10)
with tf.Session() 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)
summary_path = os.path.join(cfgs.SUMMARY_PATH, cfgs.VERSION)
tools.mkdir(summary_path)
summary_writer = tf.summary.FileWriter(summary_path, graph=sess.graph)
for step in range(cfgs.MAX_ITERATION):
training_time = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(time.time()))
start = time.time()
_global_step, _img_name_batch, _rpn_location_loss, _rpn_classification_loss, \
_rpn_total_loss, _fast_rcnn_location_loss, _fast_rcnn_classification_loss, \
_fast_rcnn_location_rotate_loss, _fast_rcnn_classification_rotate_loss, \
_fast_rcnn_total_loss, _total_loss, _ = \
sess.run([global_step, img_name_batch, rpn_location_loss, rpn_classification_loss,
rpn_total_loss, fast_rcnn_location_loss, fast_rcnn_classification_loss,
fast_rcnn_location_rotate_loss, fast_rcnn_classification_rotate_loss,
fast_rcnn_total_loss, total_loss, train_op])
end = time.time()
if step % 10 == 0:
print(""" {}: step{} image_name:{} |\t
rpn_loc_loss:{} |\t rpn_cla_loss:{} |\t
rpn_total_loss:{} |
fast_rcnn_loc_loss:{} |\t fast_rcnn_cla_loss:{} |\t
fast_rcnn_loc_rotate_loss:{} |\t fast_rcnn_cla_rotate_loss:{} |\t
fast_rcnn_total_loss:{} |\t
total_loss:{} |\t pre_cost_time:{}s""" \
.format(training_time, _global_step, str(_img_name_batch[0]), _rpn_location_loss,
_rpn_classification_loss, _rpn_total_loss, _fast_rcnn_location_loss,
_fast_rcnn_classification_loss, _fast_rcnn_location_rotate_loss,
_fast_rcnn_classification_rotate_loss, _fast_rcnn_total_loss, _total_loss,
(end - start)))
if step % 50 == 0:
summary_str = sess.run(summary_op)
summary_writer.add_summary(summary_str, _global_step)
summary_writer.flush()
if (step > 0 and step % 1000 == 0) or (step == cfgs.MAX_ITERATION - 1):
save_dir = os.path.join(FLAGS.trained_checkpoint, cfgs.VERSION)
if not os.path.exists(save_dir):
os.mkdir(save_dir)
save_ckpt = os.path.join(save_dir, 'voc_'+str(_global_step)+'model.ckpt')
saver.save(sess, save_ckpt)
print(' weights had been saved')
coord.request_stop()
coord.join(threads)
