def detect(det_net, inference_save_path, real_test_imgname_list):
# 1. preprocess img
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None,
3]) # is RGB. not GBR
img_batch = tf.cast(img_plac, tf.float32)
img_batch = short_side_resize_for_inference_data(
img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH)
img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
img_batch = tf.expand_dims(img_batch, axis=0) # [1, None, None, 3]
detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network(
input_img_batch=img_batch, gtboxes_batch=None)
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')
for i, a_img_name in enumerate(real_test_imgname_list):
raw_img = cv2.imread(a_img_name)
start = time.time()
resized_img, detected_boxes, detected_scores, detected_categories = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB
)
end = time.time()
# print("{} cost time : {} ".format(img_name, (end - start)))
show_indices = detected_scores >= 0.7
show_scores = detected_scores[show_indices]
show_boxes = detected_boxes[show_indices]
show_categories = detected_categories[show_indices]
final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(
np.squeeze(resized_img, 0),
boxes=show_boxes,
labels=show_categories,
scores=show_scores)
nake_name = a_img_name.split('/')[-1]
# print (inference_save_path + '/' + nake_name)
cv2.imwrite(inference_save_path + '/' + nake_name,
final_detections[:, :, ::-1])
tools.view_bar(
'{} image cost {}s'.format(a_img_name, (end - start)), i + 1,
len(real_test_imgname_list))
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 eval_with_plac(img_dir, det_net, image_ext, draw_imgs=False):
# 1. preprocess img
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None,
3]) # is RGB. not GBR
img_batch = tf.cast(img_plac, tf.float32)
img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
img_batch = short_side_resize_for_inference_data(
img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
is_resize=False)
det_boxes_r, det_scores_r, det_category_r = det_net.build_whole_detection_network(
input_img_batch=img_batch, gtboxes_h_batch=None, gtboxes_r_batch=None)
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_h = []
all_boxes_r = []
imgs = os.listdir(img_dir)
for i, a_img_name in enumerate(imgs):
a_img_name = a_img_name.split(image_ext)[0]
recs = {}
recs[a_img_name] = parse_rec(
os.path.join(test_annotation_path, a_img_name + '.xml'))
#R = [obj for obj in recs[a_img_name]]
bbox = np.squeeze(np.array([x['bbox'] for x in recs[a_img_name]]))
#labels = bbox[:, -1]
if len(bbox.shape) == 1:
bbox = np.expand_dims(bbox, axis=0)
labels = bbox[:, -1]
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]
start = time.time()
resized_img, \
det_boxes_r_, det_scores_r_, det_category_r_ = \
sess.run(
[img_batch,
det_boxes_r, det_scores_r, det_category_r],
feed_dict={img_plac: raw_img}
)
end = time.time()
det_boxes_r_ = det_boxes_r_[det_scores_r_ >= 0.4]
det_category_r_ = det_category_r_[det_scores_r_ >= 0.4]
det_scores_r_ = det_scores_r_[det_scores_r_ >= 0.4]
keep = nms_rotate.nms_rotate_cpu(det_boxes_r_, det_scores_r_, 0.3,
20)
det_boxes_r_ = det_boxes_r_[keep]
det_scores_r_ = det_scores_r_[keep]
det_category_r_ = det_category_r_[keep]
##### box_ratio > 2 or < 1/2
index = (det_boxes_r_[:, 2] / det_boxes_r_[:, 3] >
2) | (det_boxes_r_[:, 2] / det_boxes_r_[:, 3] <= 0.5)
det_boxes_r_ = det_boxes_r_[index]
det_scores_r_ = det_scores_r_[index]
det_category_r_ = det_category_r_[index]
# print("{} cost time : {} ".format(img_name, (end - start)))
if draw_imgs:
det_detections_h = draw_box_in_img.draw_rotate_box_cv1(
np.squeeze(resized_img, 0),
boxes=bbox,
labels=labels,
scores=np.ones(bbox.shape[0]))
det_detections_r = draw_box_in_img.draw_rotate_box_cv(
np.squeeze(resized_img, 0),
boxes=det_boxes_r_,
labels=det_category_r_,
scores=det_scores_r_)
save_dir = os.path.join(cfgs.TEST_SAVE_PATH, cfgs.VERSION)
tools.mkdir(save_dir)
cv2.imwrite(save_dir + '/' + a_img_name + '_h.jpg',
det_detections_h[:, :, ::-1])
cv2.imwrite(save_dir + '/' + a_img_name + '_r.jpg',
det_detections_r[:, :, ::-1])
# xmin, ymin, xmax, ymax = det_boxes_h_[:, 0], det_boxes_h_[:, 1], \
# det_boxes_h_[:, 2], det_boxes_h_[:, 3]
if det_boxes_r_.shape[0] != 0:
resized_h, resized_w = resized_img.shape[1], resized_img.shape[
2]
det_boxes_r_ = forward_convert(det_boxes_r_, False)
det_boxes_r_[:, 0::2] *= (raw_w / resized_w)
det_boxes_r_[:, 1::2] *= (raw_h / resized_h)
det_boxes_r_ = back_forward_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]
# xmin = xmin * raw_w / resized_w
# xmax = xmax * raw_w / resized_w
# ymin = ymin * raw_h / resized_h
# ymax = ymax * raw_h / resized_h
# boxes_h = np.transpose(np.stack([xmin, ymin, xmax, ymax]))
boxes_r = np.transpose(np.stack([x_c, y_c, w, h, theta]))
# dets_h = np.hstack((det_category_h_.reshape(-1, 1),
# det_scores_h_.reshape(-1, 1),
# boxes_h))
dets_r = np.hstack((det_category_r_.reshape(-1, 1),
det_scores_r_.reshape(-1, 1), boxes_r))
# all_boxes_h.append(dets_h)
all_boxes_r.append(dets_r)
tools.view_bar(
'{} image cost {}s'.format(a_img_name, (end - start)), i + 1,
len(imgs))
fw1 = open(cfgs.VERSION + '_detections_h.pkl', 'w')
fw2 = open(cfgs.VERSION + '_detections_r.pkl', 'w')
pickle.dump(all_boxes_h, fw1)
pickle.dump(all_boxes_r, fw2)
def eval_with_plac(det_net, real_test_imgname_list, img_root, draw_imgs=False):
# 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)
img_batch = short_side_resize_for_inference_data(
img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH)
img_batch = img_batch - tf.constant(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=None)
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 = []
for i, a_img_name in enumerate(real_test_imgname_list):
raw_img = cv2.imread(os.path.join(img_root, a_img_name))
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
start = time.time()
resized_img, detected_boxes, detected_scores, detected_categories = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB
)
end = time.time()
# print("{} cost time : {} ".format(img_name, (end - start)))
if draw_imgs:
show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD
show_scores = detected_scores[show_indices]
show_boxes = detected_boxes[show_indices]
show_categories = detected_categories[show_indices]
final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(
np.squeeze(resized_img, 0),
boxes=show_boxes,
labels=show_categories,
scores=show_scores)
if not os.path.exists(cfgs.TEST_SAVE_PATH):
os.makedirs(cfgs.TEST_SAVE_PATH)
nake_name = os.path.split(a_img_name)[1]
cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + nake_name,
final_detections[:, :, ::-1])
# cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + a_img_name + '.jpg',
# final_detections[:, :, ::-1])
xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \
detected_boxes[:, 2], detected_boxes[:, 3]
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
xmin = xmin * raw_w / resized_w
xmax = xmax * raw_w / resized_w
ymin = ymin * raw_h / resized_h
ymax = ymax * raw_h / resized_h
boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax]))
dets = np.hstack((detected_categories.reshape(-1, 1),
detected_scores.reshape(-1, 1), boxes))
all_boxes.append(dets)
tools.view_bar(
'{} image cost {}s'.format(a_img_name, (end - start)), i + 1,
len(real_test_imgname_list))
save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.VERSION)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
fw1 = open(os.path.join(save_dir, 'detections.pkl'), 'wb')
pickle.dump(all_boxes, fw1)
def test(img_num):
with tf.Graph().as_default():
# img = tf.placeholder(shape=[None, None, 3], dtype=tf.uint8)
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])
# ***********************************************************************************************
# * 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,
is_training=False,
share_head=False,
share_net=share_net,
anchor_ratios=cfgs.ANCHOR_RATIOS,
anchor_scales=cfgs.ANCHOR_SCALES,
anchor_angles=cfgs.ANCHOR_ANGLES,
scale_factors=cfgs.SCALE_FACTORS,
base_anchor_size_list=cfgs.BASE_ANCHOR_SIZE_LIST, # P2, P3, P4, P5, P6
level=cfgs.LEVEL,
anchor_stride=cfgs.ANCHOR_STRIDE,
top_k_nms=cfgs.RPN_TOP_K_NMS,
kernel_size=cfgs.KERNEL_SIZE,
use_angles_condition=True,
anchor_angle_threshold=cfgs.RPN_ANCHOR_ANGLES_THRESHOLD,
nms_angle_threshold=cfgs.RPN_NMS_ANGLES_THRESHOLD,
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],
scope='')
# rpn predict proposals
rpn_proposals_boxes, rpn_proposals_scores = rpn.rpn_proposals() # rpn_score shape: [300, ]
# _, _, rpn_top_k_boxes, rpn_top_k_scores = rpn.rpn_losses()
# ***********************************************************************************************
# * Fast RCNN *
# ***********************************************************************************************
fast_rcnn = build_fast_rcnn.FastRCNN(img_batch=img_batch,
feature_pyramid=rpn.feature_pyramid,
rpn_proposals_boxes=rpn_proposals_boxes,
rpn_proposals_scores=rpn_proposals_scores,
stop_gradient_for_proposals=False,
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,
fast_rcnn_nms_iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD,
top_k_nms=cfgs.FAST_RCNN_TOP_K_NMS,
nms_angle_threshold=cfgs.FAST_RCNN_NMS_ANGLES_THRESHOLD,
use_angle_condition=False,
level=cfgs.LEVEL,
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,
boxes_angle_threshold=cfgs.FAST_RCNN_BOXES_ANGLES_THRESHOLD,
use_dropout=cfgs.USE_DROPOUT,
weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME],
is_training=False)
fast_rcnn_decode_boxes, fast_rcnn_score, num_of_objects, detection_category = \
fast_rcnn.fast_rcnn_predict()
# test
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, _fast_rcnn_decode_boxes, \
_fast_rcnn_score, _detection_category \
= sess.run([img_name_batch, img_batch, gtboxes_and_label, fast_rcnn_decode_boxes,
fast_rcnn_score, detection_category])
end = time.time()
_img_batch = np.squeeze(_img_batch, axis=0)
_img_batch_fpn = help_utils.draw_box_cv(_img_batch,
boxes=_fast_rcnn_decode_boxes,
labels=_detection_category,
scores=_fast_rcnn_score)
cv2.imwrite(cfgs.TEST_SAVE_PATH + '/{}_fpn.jpg'.format(str(_img_name_batch[0]).split('.tif')[0]),
_img_batch_fpn)
temp_label = np.reshape(_gtboxes_and_label[:, -1:], [-1, ]).astype(np.int64)
_img_batch_gt = help_utils.draw_box_cv(_img_batch,
boxes=_gtboxes_and_label[:, :-1],
labels=temp_label,
scores=None)
cv2.imwrite(cfgs.TEST_SAVE_PATH + '/{}_gt.jpg'.format(str(_img_name_batch[0]).split('.tif')[0]),
_img_batch_gt)
view_bar('{} image cost {}s'.format(str(_img_name_batch[0]), (end - start)), i+1, img_num)
coord.request_stop()
coord.join(threads)
def eval_with_plac(det_net, real_test_imgname_list, draw_imgs=False):
# 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)
img_batch = short_side_resize_for_inference_data(
img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH)
img_batch = img_batch - tf.constant(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=None)
restorer, restore_ckpt = det_net.get_restorer()
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
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 = []
for i, a_img_name in enumerate(real_test_imgname_list):
raw_img = cv2.imread(a_img_name)
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
start = time.time()
resized_img, detected_boxes, detected_scores, detected_categories = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB
)
end = time.time()
if cfgs.SOFT_NMS:
boxes_soft_nms = []
scores_soft_nms = []
category_soft_nms = []
tmp_boxes = np.reshape(detected_boxes, (cfgs.CLASS_NUM, -1, 4),
order='C')
# print("tmp_boxes is ", tmp_boxes.shape, 'type is ', type(tmp_boxes))
tmp_scores = np.reshape(detected_scores, (cfgs.CLASS_NUM, -1),
order='C')
tmp_category = np.reshape(detected_categories,
(cfgs.CLASS_NUM, -1),
order='C')
for ind in range(cfgs.CLASS_NUM):
tmp_class_boxes = tmp_boxes[ind, :, :]
tmp_class_scores = tmp_scores[ind, :]
tmp_class_category = tmp_category[ind, :]
# print("tmp_boxes is ",tmp_class_boxes.shape,'type is ',type(tmp_class_boxes))
# print('tmp score is ',tmp_class_scores.shape)
dets = np.hstack(
(tmp_class_boxes,
tmp_class_scores[:, np.newaxis])).astype(np.float32,
copy=False)
# keep=py_cpu_softnms(tmp_class_boxes,tmp_class_scores,method=4)
keep = py_soft_nms(dets, 'greedy')
# keep=gpu_nms(dets,cfgs.FAST_RCNN_NMS_IOU_THRESHOLD,0)
class_boxes = tmp_class_boxes[keep]
class_scores = tmp_class_scores[keep]
class_category = tmp_class_category[keep]
boxes_soft_nms.append(class_boxes)
scores_soft_nms.append(class_scores)
category_soft_nms.append(class_category)
detected_boxes = np.concatenate(boxes_soft_nms, axis=0)
detected_scores = np.concatenate(scores_soft_nms, axis=0)
detected_categories = np.concatenate(category_soft_nms, axis=0)
# print("{} cost time : {} ".format(img_name, (end - start)))
if draw_imgs:
show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD
show_scores = detected_scores[show_indices]
show_boxes = detected_boxes[show_indices]
show_categories = detected_categories[show_indices]
final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(
np.squeeze(resized_img, 0),
boxes=show_boxes,
labels=show_categories,
scores=show_scores)
if not os.path.exists(cfgs.TEST_SAVE_PATH):
os.makedirs(cfgs.TEST_SAVE_PATH)
cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + a_img_name + '.jpg',
final_detections[:, :, ::-1])
xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \
detected_boxes[:, 2], detected_boxes[:, 3]
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
xmin = xmin * raw_w / resized_w
xmax = xmax * raw_w / resized_w
ymin = ymin * raw_h / resized_h
ymax = ymax * raw_h / resized_h
boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax]))
dets = np.hstack((detected_categories.reshape(-1, 1),
detected_scores.reshape(-1, 1), boxes))
all_boxes.append(dets)
tools.view_bar(
'{} image cost {}s'.format(os.path.basename(a_img_name),
(end - start)), i + 1,
len(real_test_imgname_list))
save_dir = os.path.join(cfgs.EVALUATE_DIR, cfgs.DATASET_NAME,
cfgs.VERSION)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
fw1 = open(os.path.join(save_dir, 'detections.pkl'), 'wb')
pickle.dump(all_boxes, fw1)
return all_boxes
def detect(det_net, src_dir, res_dir, draw_imgs):
# 1. preprocess img
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None,
3]) # is RGB. not GBR
img_batch = tf.cast(img_plac, tf.float32)
img_batch = short_side_resize_for_inference_data(
img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH)
img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
img_batch = tf.expand_dims(img_batch, axis=0) # [1, None, None, 3]
result_dict = det_net.build_whole_detection_network(
input_img_batch=img_batch, gtboxes_batch=None)
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')
sub_folders = os.listdir(src_dir)
for sub_folder in sub_folders:
folder_dir = os.path.join(src_dir, sub_folder)
real_test_imgname_list = [
os.path.join(folder_dir, img_name)
for img_name in os.listdir(folder_dir)
]
tools.mkdir(os.path.join(res_dir, sub_folder))
for i, a_img_name in enumerate(real_test_imgname_list):
raw_img = cv2.imread(a_img_name)
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
start = time.time()
resized_img, result_dict_ = \
sess.run(
[img_batch, result_dict],
feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB
)
end = time.time()
detected_boxes, detected_scores, detected_categories = merge_result(
result_dict_)
nake_name = a_img_name.split('/')[-1]
xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \
detected_boxes[:, 2], detected_boxes[:, 3]
resized_h, resized_w = resized_img.shape[1], resized_img.shape[
2]
xmin = xmin * raw_w / resized_w
xmax = xmax * raw_w / resized_w
ymin = ymin * raw_h / resized_h
ymax = ymax * raw_h / resized_h
boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax]))
dets = np.hstack((detected_categories.reshape(-1, 1),
detected_scores.reshape(-1, 1), boxes))
show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD
show_scores = detected_scores[show_indices]
show_boxes = boxes[show_indices]
show_categories = detected_categories[show_indices]
f = open(
os.path.join(res_dir, sub_folder) + '/' +
nake_name.split('.')[0] + '.txt', 'w')
f.write('{}\n'.format(nake_name.split('.')[0]))
# f.write('{}\n'.format(dets.shape[0]))
# for inx in range(dets.shape[0]):
#
# f.write('%d %d %d %d %.3f\n' % (int(dets[inx][2]),
# int(dets[inx][3]),
# int(dets[inx][4]) - int(dets[inx][2]),
# int(dets[inx][5]) - int(dets[inx][3]),
# dets[inx][1]))
f.write('{}\n'.format(show_boxes.shape[0]))
for inx in range(show_boxes.shape[0]):
f.write('%d %d %d %d %.3f\n' %
(int(show_boxes[inx][0]), int(show_boxes[inx][1]),
int(show_boxes[inx][2]) - int(show_boxes[inx][0]),
int(show_boxes[inx][3]) - int(show_boxes[inx][1]),
show_scores[inx]))
if draw_imgs:
final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(
raw_img - np.array(cfgs.PIXEL_MEAN),
boxes=show_boxes,
labels=show_categories,
scores=show_scores)
tools.mkdir(cfgs.TEST_SAVE_PATH)
cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + nake_name,
final_detections)
tools.view_bar(
'{} image cost {}s'.format(a_img_name, (end - start)),
i + 1, len(real_test_imgname_list))
def eval_coco(det_net, real_test_img_list, draw_imgs=False):
# 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)
img_batch = short_side_resize_for_inference_data(img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH,
is_resize=False)
if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']:
img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD)
else:
img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
# img_batch = (img_batch - tf.constant(cfgs.PIXEL_MEAN)) / (tf.constant(cfgs.PIXEL_STD)*255)
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=None)
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')
save_path = os.path.join('./eval_coco', cfgs.VERSION)
tools.mkdir(save_path)
fw_json_dt = open(os.path.join(save_path, 'coco_minival_ms.json'), 'w')
coco_det = []
for i, a_img in enumerate(real_test_img_list):
record = json.loads(a_img)
raw_img = cv2.imread(record['fpath'])
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
start = time.time()
detected_scores_, detected_boxes_, detected_categories_ = [], [], []
for ss in [600, 800, 1000, 1200]: # cfgs.IMG_SHORT_SIDE_LEN:
img_resize = cv2.resize(raw_img, (ss, ss))
resized_img, tmp_detected_boxes, tmp_detected_scores, tmp_detected_categories = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: img_resize[:, :, ::-1]} # cv is BGR. But need RGB
)
eval_indices = tmp_detected_scores >= 0.01
tmp_detected_scores = tmp_detected_scores[eval_indices]
tmp_detected_boxes = tmp_detected_boxes[eval_indices]
tmp_detected_categories = tmp_detected_categories[eval_indices]
xmin, ymin, xmax, ymax = tmp_detected_boxes[:, 0], tmp_detected_boxes[:, 1], \
tmp_detected_boxes[:, 2], tmp_detected_boxes[:, 3]
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
xmin = xmin * raw_w / resized_w
xmax = xmax * raw_w / resized_w
ymin = ymin * raw_h / resized_h
ymax = ymax * raw_h / resized_h
resize_boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax]))
detected_scores_.append(tmp_detected_scores)
detected_boxes_.append(resize_boxes)
detected_categories_.append(tmp_detected_categories)
detected_scores_ = np.concatenate(detected_scores_)
detected_boxes_ = np.concatenate(detected_boxes_)
detected_categories_ = np.concatenate(detected_categories_)
detected_scores, detected_boxes, detected_categories = [], [], []
for sub_class in range(1, cfgs.CLASS_NUM + 1):
index = np.where(detected_categories_ == sub_class)[0]
if len(index) == 0:
continue
tmp_boxes_h = detected_boxes_[index]
tmp_label_h = detected_categories_[index]
tmp_score_h = detected_scores_[index]
tmp_boxes_h = np.array(tmp_boxes_h)
tmp = np.zeros([tmp_boxes_h.shape[0], tmp_boxes_h.shape[1] + 1])
tmp[:, 0:-1] = tmp_boxes_h
tmp[:, -1] = np.array(tmp_score_h)
inx = nms.py_cpu_nms(dets=np.array(tmp, np.float32),
thresh=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD,
max_output_size=500)
detected_boxes.extend(np.array(tmp_boxes_h)[inx])
detected_scores.extend(np.array(tmp_score_h)[inx])
detected_categories.extend(np.array(tmp_label_h)[inx])
detected_scores = np.array(detected_scores)
detected_boxes = np.array(detected_boxes)
detected_categories = np.array(detected_categories)
# print("{} cost time : {} ".format(img_name, (end - start)))
if draw_imgs:
show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD
show_scores = detected_scores[show_indices]
show_boxes = detected_boxes[show_indices]
show_categories = detected_categories[show_indices]
# if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']:
# draw_img = (raw_img * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255
# else:
# draw_img = raw_img + np.array(cfgs.PIXEL_MEAN)
# draw_img = draw_img * (np.array(cfgs.PIXEL_STD)*255) + np.array(cfgs.PIXEL_MEAN)
raw_img = np.array(raw_img, np.float32)
final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(raw_img,
boxes=show_boxes,
labels=show_categories,
scores=show_scores,
in_graph=False)
if not os.path.exists(cfgs.TEST_SAVE_PATH):
os.makedirs(cfgs.TEST_SAVE_PATH)
cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + record['ID'],
final_detections)
# cost much time
for j, box in enumerate(detected_boxes):
coco_det.append({'bbox': [float(box[0]), float(box[1]), float(box[2]-box[0]), float(box[3]-box[1])],
'score': float(detected_scores[j]), 'image_id': int(record['ID'].split('.jpg')[0].split('_000000')[-1]),
'category_id': int(classes_originID[LABEl_NAME_MAP[detected_categories[j]]])})
end = time.time()
tools.view_bar('%s image cost %.3fs' % (record['ID'], (end - start)), i + 1, len(real_test_img_list))
json.dump(coco_det, fw_json_dt)
fw_json_dt.close()
return os.path.join(save_path, 'coco_minival_ms.json')
def detect(det_net, inference_save_path, real_test_imgname_list):
# 1. preprocess img
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None,
3]) # is RGB. not GBR
img_batch = tf.cast(img_plac, tf.float32)
img_batch = short_side_resize_for_inference_data(
img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH)
if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']:
img_batch = (img_batch / 255 - tf.constant(
cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD)
else:
img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
img_batch = tf.expand_dims(img_batch, axis=0) # [1, None, None, 3]
detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network(
input_img_batch=img_batch, gtboxes_batch=None)
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')
for i, a_img_name in enumerate(real_test_imgname_list):
raw_img = cv2.imread(a_img_name)
start = time.time()
resized_img, detected_boxes, detected_scores, detected_categories = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: raw_img[:, :, ::-1]}
)
end = time.time()
# print("{} cost time : {} ".format(img_name, (end - start)))
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \
detected_boxes[:, 2], detected_boxes[:, 3]
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
xmin = xmin * raw_w / resized_w
xmax = xmax * raw_w / resized_w
ymin = ymin * raw_h / resized_h
ymax = ymax * raw_h / resized_h
detected_boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax]))
show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD
show_scores = detected_scores[show_indices]
show_boxes = detected_boxes[show_indices]
show_categories = detected_categories[show_indices]
# if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']:
# raw_img = (raw_img / 255 - np.array(cfgs.PIXEL_MEAN_)) /np.array(cfgs.PIXEL_STD)
# else:
# raw_img = raw_img - np.array(cfgs.PIXEL_MEAN)
final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(
raw_img,
boxes=show_boxes,
labels=show_categories,
scores=show_scores,
in_graph=False)
nake_name = a_img_name.split('/')[-1]
# print (inference_save_path + '/' + nake_name)
cv2.imwrite(inference_save_path + '/' + nake_name,
final_detections[:, :, ::-1])
tools.view_bar(
'{} image cost {}s'.format(a_img_name, (end - start)), i + 1,
len(real_test_imgname_list))
def eval_with_plac(det_net, real_test_imgname_list, img_root, draw_imgs=False):
# 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)
img_batch = short_side_resize_for_inference_data(img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH)
if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']:
img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD)
else:
img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
# img_batch = (img_batch - tf.constant(cfgs.PIXEL_MEAN)) / (tf.constant(cfgs.PIXEL_STD)*255)
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=None)
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')
for i, a_img_name in enumerate(real_test_imgname_list):
raw_img = cv2.imread(os.path.join(img_root, a_img_name))
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
start = time.time()
resized_img, detected_boxes, detected_scores, detected_categories = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB
)
end = time.time()
# print("{} cost time : {} ".format(img_name, (end - start)))
if draw_imgs:
show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD
show_scores = detected_scores[show_indices]
show_boxes = detected_boxes[show_indices]
show_categories = detected_categories[show_indices]
draw_img = np.squeeze(resized_img, 0)
if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']:
draw_img = (draw_img * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255
else:
draw_img = draw_img + np.array(cfgs.PIXEL_MEAN)
# draw_img = draw_img * (np.array(cfgs.PIXEL_STD)*255) + np.array(cfgs.PIXEL_MEAN)
final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(draw_img,
boxes=show_boxes,
labels=show_categories,
scores=show_scores,
in_graph=False)
if not os.path.exists(cfgs.TEST_SAVE_PATH):
os.makedirs(cfgs.TEST_SAVE_PATH)
cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + a_img_name,
final_detections[:, :, ::-1])
xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \
detected_boxes[:, 2], detected_boxes[:, 3]
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
xmin = xmin * raw_w / resized_w
xmax = xmax * raw_w / resized_w
ymin = ymin * raw_h / resized_h
ymax = ymax * raw_h / resized_h
boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax]))
dets = np.hstack((detected_categories.reshape(-1, 1),
detected_scores.reshape(-1, 1),
boxes))
# all_boxes.append(dets)
# eval txt
CLASS_VOC = NAME_LABEL_MAP.keys()
write_handle = {}
txt_dir = os.path.join('voc2012_eval', cfgs.VERSION, 'results', 'VOC2012', 'Main')
tools.mkdir(txt_dir)
for sub_class in CLASS_VOC:
if sub_class == 'back_ground':
continue
write_handle[sub_class] = open(os.path.join(txt_dir, 'comp3_det_test_%s.txt' % sub_class), 'a+')
for det in dets:
command = '%s %.6f %.6f %.6f %.6f %.6f\n' % (a_img_name.split('/')[-1].split('.')[0],
det[1],
det[2], det[3], det[4], det[5])
write_handle[LABEl_NAME_MAP[det[0]]].write(command)
for sub_class in CLASS_VOC:
if sub_class == 'back_ground':
continue
write_handle[sub_class].close()
tools.view_bar('%s image cost %.3fs' % (a_img_name, (end - start)), i + 1, len(real_test_imgname_list))
def eval_with_plac(det_net, imgId_list, coco, out_json_root, draw_imgs=False):
# 1. preprocess img
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None,
3]) # is RGB. not GBR
img_batch = tf.cast(img_plac, tf.float32)
img_batch = short_side_resize_for_inference_data(
img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH)
if cfgs.NET_NAME in ['resnet152_v1d', 'resnet101_v1d', 'resnet50_v1d']:
img_batch = (img_batch / 255 - tf.constant(
cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD)
else:
img_batch = img_batch - tf.constant(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=None)
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
# coco_test_results = []
with tf.Session(config=config) as sess:
sess.run(init_op)
if not restorer is None:
restorer.restore(sess, restore_ckpt)
print('restore model')
for i, imgid in enumerate(imgId_list):
imgname = coco.loadImgs(ids=[imgid])[0]['file_name']
raw_img = cv2.imread(
os.path.join("/home/yjr/DataSet/COCO/2017/test2017", imgname))
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
start = time.time()
resized_img, detected_boxes, detected_scores, detected_categories = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB
)
end = time.time()
if draw_imgs:
show_indices = detected_scores >= cfgs.VIS_SCORE
show_scores = detected_scores[show_indices]
show_boxes = detected_boxes[show_indices]
show_categories = detected_categories[show_indices]
draw_img = np.squeeze(resized_img, 0)
if cfgs.NET_NAME in [
'resnet152_v1d', 'resnet101_v1d', 'resnet50_v1d'
]:
draw_img = (draw_img * np.array(cfgs.PIXEL_STD) +
np.array(cfgs.PIXEL_MEAN_)) * 255
else:
draw_img = draw_img + np.array(cfgs.PIXEL_MEAN)
final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(
draw_img,
boxes=show_boxes,
labels=show_categories,
scores=show_scores,
in_graph=False)
cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + str(imgid) + '.jpg',
final_detections[:, :, ::-1])
xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \
detected_boxes[:, 2], detected_boxes[:, 3]
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
xmin = xmin * raw_w / resized_w
xmax = xmax * raw_w / resized_w
ymin = ymin * raw_h / resized_h
ymax = ymax * raw_h / resized_h
boxes = np.transpose(
np.stack([xmin, ymin, xmax - xmin, ymax - ymin]))
dets = np.hstack((detected_categories.reshape(-1, 1),
detected_scores.reshape(-1, 1), boxes))
a_img_detect_result = []
for a_det in dets:
label, score, bbox = a_det[0], a_det[1], a_det[2:]
cat_id = classes_originID[LABEL_NAME_MAP[label]]
if score < 0.00001:
continue
det_object = {
"image_id": imgid,
"category_id": cat_id,
"bbox": bbox.tolist(),
"score": float(score)
}
# print (det_object)
a_img_detect_result.append(det_object)
f = open(
os.path.join(out_json_root, 'each_img',
str(imgid) + '.json'), 'w')
json.dump(a_img_detect_result, f) # , indent=4
f.close()
del a_img_detect_result
del dets
del boxes
del resized_img
del raw_img
tools.view_bar('{} image cost {}s'.format(imgid, (end - start)),
i + 1, len(imgId_list))
def detect(det_net, inference_save_path, rgb_real_test_imgname_list, ir_real_test_imgname_list):
# 1. preprocess img
rgb_img_plac = tf.placeholder(tf.uint8, [None, None, 3], 'rgb') # is RGB. not GBR
rgb_img_batch = tf.cast(rgb_img_plac, tf.float32)
rgb_img_batch = short_side_resize_for_inference_data(img_tensor=rgb_img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH)
ir_img_plac = tf.placeholder(tf.uint8, [None, None, 3], 'ir') # is RGB. not GBR
ir_img_batch = tf.cast(ir_img_plac, tf.float32)
ir_img_batch = short_side_resize_for_inference_data(img_tensor=ir_img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH)
rgb_img_batch = rgb_img_batch - tf.constant(cfgs.RGB_PIXEL_MEAN)
ir_img_batch = ir_img_batch - tf.constant(cfgs.IR_PIXEL_MEAN)
#img_batch = (img_batch - tf.constant(cfgs.PIXEL_MEAN)) / (tf.constant(cfgs.PIXEL_STD)*255)
rgb_img_batch = tf.expand_dims(rgb_img_batch, axis=0) # [1, None, None, 3]
ir_img_batch = tf.expand_dims(ir_img_batch, axis=0) # [1, None, None, 3]
detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network(
rgb_input_img_batch=rgb_img_batch, ir_input_img_batch=ir_img_batch, seg_mask_batch = None,
gtboxes_batch=None)
init_op = tf.group(
tf.global_variables_initializer(),
tf.local_variables_initializer()
)
restorer, restore_ckpt, model_variables = det_net.get_restorer_test()
print(restore_ckpt)
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')
for i, rgb_img_name in enumerate(rgb_real_test_imgname_list):
rgb_raw_img = cv2.imread(rgb_img_name)[:, :, ::-1]
ir_raw_img = cv2.imread(ir_real_test_imgname_list[i])[:, :, ::-1]
start = time.time()
rgb_resized_img, ir_resized_img, detected_boxes, detected_scores, detected_categories = \
sess.run(
[rgb_img_batch, ir_img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={rgb_img_plac: rgb_raw_img, ir_img_plac: ir_raw_img}
)
end = time.time()
# print("{} cost time : {} ".format(img_name, (end - start)))
raw_h, raw_w = ir_raw_img.shape[0], rgb_raw_img.shape[1]
xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \
detected_boxes[:, 2], detected_boxes[:, 3]
resized_h, resized_w = rgb_resized_img.shape[1], ir_resized_img.shape[2]
xmin = xmin * raw_w / resized_w
xmax = xmax * raw_w / resized_w
ymin = ymin * raw_h / resized_h
ymax = ymax * raw_h / resized_h
detected_boxes = np.transpose(np.stack([xmin, ymin, xmax, ymax]))
show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD
show_scores = detected_scores[show_indices]
show_boxes = detected_boxes[show_indices]
show_categories = detected_categories[show_indices]
nake_name = rgb_img_name.split('/')[-1]
f1 = open(inference_save_path + '/txt/' + nake_name.split('.')[0]+'.txt', 'w')
final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(rgb_raw_img - np.array(cfgs.RGB_PIXEL_MEAN),
boxes=show_boxes,
labels=show_categories,
scores=show_scores, txt_file=f1,
img_name=nake_name.split('.')[0])
# print (inference_save_path + '/' + nake_name)
cv2.imwrite(inference_save_path + '/img/' + nake_name,
final_detections[:, :, ::-1])
tools.view_bar('{} image cost {}s'.format(rgb_img_name, (end - start)), i + 1, len(ir_real_test_imgname_list))
def eval_dict_convert(img_num, mode):
with tf.Graph().as_default():
# img = tf.placeholder(shape=[None, None, 3], dtype=tf.uint8)
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_horizon_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=gtboxes_and_label,
is_training=False,
share_head=False,
share_net=share_net,
anchor_ratios=cfgs.ANCHOR_RATIOS,
anchor_scales=cfgs.ANCHOR_SCALES,
anchor_angles=cfgs.ANCHOR_ANGLES,
scale_factors=cfgs.SCALE_FACTORS,
base_anchor_size_list=cfgs.
BASE_ANCHOR_SIZE_LIST, # P2, P3, P4, P5, P6
level=cfgs.LEVEL,
anchor_stride=cfgs.ANCHOR_STRIDE,
top_k_nms=cfgs.RPN_TOP_K_NMS,
kernel_size=cfgs.KERNEL_SIZE,
use_angles_condition=False,
anchor_angle_threshold=cfgs.RPN_ANCHOR_ANGLES_THRESHOLD,
nms_angle_threshold=cfgs.RPN_NMS_ANGLES_THRESHOLD,
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=cfgs.
IS_FILTER_OUTSIDE_BOXES, # whether remove anchors outside
rpn_weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME],
scope='')
rpn_proposals_boxes, rpn_proposals_scores = rpn.rpn_proposals(
) # rpn_score shape: [300, ]
_, _, rpn_predict_boxes, rpn_predict_scores = rpn.rpn_losses()
# ***********************************************************************************************
# * Fast RCNN *
# ***********************************************************************************************
fast_rcnn = build_fast_rcnn.FastRCNN(
img_batch=img_batch,
feature_pyramid=rpn.feature_pyramid,
rpn_proposals_boxes=rpn_proposals_boxes,
rpn_proposals_scores=rpn_proposals_scores,
stop_gradient_for_proposals=False,
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,
fast_rcnn_nms_iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD,
top_k_nms=cfgs.FAST_RCNN_TOP_K_NMS,
nms_angle_threshold=cfgs.FAST_RCNN_NMS_ANGLES_THRESHOLD,
use_angle_condition=False,
level=cfgs.LEVEL,
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,
boxes_angle_threshold=cfgs.FAST_RCNN_BOXES_ANGLES_THRESHOLD,
use_dropout=cfgs.USE_DROPOUT,
weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME],
is_training=False)
fast_rcnn_decode_boxes, fast_rcnn_score, num_of_objects, detection_category = \
fast_rcnn.fast_rcnn_predict()
##############################################################################################
if cfgs.NEED_AUXILIARY:
predict_boxes = tf.concat(
[fast_rcnn_decode_boxes, rpn_predict_boxes], axis=0)
predict_scores = tf.concat(
[fast_rcnn_score, rpn_predict_scores - 0.2], axis=0)
rpn_predict_label = tf.ones([
tf.shape(rpn_predict_scores)[0],
], tf.int64)
labels = tf.concat([detection_category, rpn_predict_label], axis=0)
# valid_indices = nms_rotate.nms_rotate(decode_boxes=predict_boxes,
# scores=predict_scores,
# iou_threshold=0.15,
# max_output_size=30,
# use_angle_condition=False,
# angle_threshold=15,
# use_gpu=True)
valid_indices = tf.py_func(nms_rotate.nms_rotate_cpu,
inp=[
predict_boxes, predict_scores,
tf.constant(0.15, tf.float32),
tf.constant(30, tf.float32)
],
Tout=tf.int64)
fast_rcnn_decode_boxes = tf.gather(predict_boxes, valid_indices)
fast_rcnn_score = tf.gather(predict_scores, valid_indices)
detection_category = tf.gather(labels, valid_indices)
##############################################################################################
if mode == 0:
fast_rcnn_decode_boxes = get_horizon_minAreaRectangle(
fast_rcnn_decode_boxes, False)
# 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)
gtboxes_dict = {}
predict_dict = {}
for i in range(img_num):
start = time.time()
_img_name_batch, _img_batch, _gtboxes_and_label, _fast_rcnn_decode_boxes, \
_gtboxes_and_label_minAreaRectangle, _fast_rcnn_score, _detection_category \
= sess.run([img_name_batch, img_batch, gtboxes_and_label, fast_rcnn_decode_boxes,
gtboxes_and_label_minAreaRectangle, fast_rcnn_score, detection_category])
end = time.time()
# gtboxes convert dict
gtboxes_dict[str(_img_name_batch[0])] = []
predict_dict[str(_img_name_batch[0])] = []
# for j, box in enumerate(_gtboxes_and_label):
# bbox_dict = {}
# bbox_dict['bbox'] = np.array(_gtboxes_and_label[j, :-1], np.float64)
# bbox_dict['name'] = LABEl_NAME_MAP[int(_gtboxes_and_label[j, -1])]
# gtbox_dict[str(_img_name_batch[0])].append(bbox_dict)
#
# for label in NAME_LABEL_MAP.keys():
# if label == 'back_ground':
# continue
# else:
# temp_dict = {}
# temp_dict['name'] = label
#
# ind = np.where(_detection_category == NAME_LABEL_MAP[label])[0]
# temp_boxes = _fast_rcnn_decode_boxes[ind]
# temp_score = np.reshape(_fast_rcnn_score[ind], [-1, 1])
# temp_dict['bbox'] = np.array(np.concatenate([temp_boxes, temp_score], axis=1), np.float64)
# predict_dict[str(_img_name_batch[0])].append(temp_dict)
if mode == 0:
gtboxes_list, predict_list = \
make_dict_packle(_gtboxes_and_label_minAreaRectangle, _fast_rcnn_decode_boxes,
_fast_rcnn_score, _detection_category)
else:
gtboxes_list, predict_list = \
make_dict_packle(_gtboxes_and_label, _fast_rcnn_decode_boxes,
_fast_rcnn_score, _detection_category)
gtboxes_dict[str(_img_name_batch[0])].extend(gtboxes_list)
predict_dict[str(_img_name_batch[0])].extend(predict_list)
view_bar(
'{} image cost {}s'.format(str(_img_name_batch[0]),
(end - start)), i + 1, img_num)
fw1 = open('gtboxes_dict.pkl', 'w')
fw2 = open('predict_dict.pkl', 'w')
pickle.dump(gtboxes_dict, fw1)
pickle.dump(predict_dict, fw2)
fw1.close()
fw2.close()
coord.request_stop()
coord.join(threads)
def eval_with_plac(img_dir, det_net, num_imgs, image_ext, draw_imgs,
test_annotation_path):
# 1. preprocess img
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None,
3]) # is RGB. not GBR
img_batch = tf.cast(img_plac, tf.float32)
img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
img_batch = short_side_resize_for_inference_data(
img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
is_resize=False)
det_boxes_h, det_scores_h, det_category_h, \
det_boxes_r, det_scores_r, det_category_r = det_net.build_whole_detection_network(
input_img_batch=img_batch,
gtboxes_h_batch=None, gtboxes_r_batch=None)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
global_step_tensor = slim.get_or_create_global_step()
eval_result = []
last_checkpoint_name = None
while True:
restorer, restore_ckpt = det_net.get_restorer()
#saver = tf.train.Saver(max_to_keep=10)
start_time = time.time()
model_path = os.path.splitext(os.path.basename(restore_ckpt))[0]
if model_path == None:
print("Wait for available checkpoint")
elif last_checkpoint_name == model_path:
print(
"Already evaluated checkpoint {}, we will try evaluation in {} seconds"
.format(model_path, EVAL_INTERVAL))
#continue
else:
print('Last ckpt was {}, new ckpt is {}'.format(
last_checkpoint_name, model_path))
last_checkpoint_name = model_path
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
sess.run(global_step_tensor.initializer)
if not restorer is None:
restorer.restore(sess, restore_ckpt)
print('restore model', restore_ckpt)
global_stepnp = tf.train.global_step(sess, global_step_tensor)
print('#########################', global_stepnp)
all_boxes_h = []
all_boxes_r = []
imgs = os.listdir(img_dir)
imgs_len = len(imgs)
none_detected_image = []
for i, a_img_name in enumerate(imgs[:]):
a_img_name = a_img_name.split(image_ext)[0]
image_name = a_img_name + image_ext
print('\n', a_img_name)
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]
start = time.time()
resized_img, det_boxes_h_, det_scores_h_, det_category_h_, \
det_boxes_r_, det_scores_r_, det_category_r_ = \
sess.run(
[img_batch, det_boxes_h, det_scores_h, det_category_h,
det_boxes_r, det_scores_r, det_category_r],
feed_dict={img_plac: raw_img}
)
end = time.time()
print("det category H : ", det_category_h_)
print("det category R : ", det_category_r_)
# print("{} cost time : {} ".format(img_name, (end - start)))
if draw_imgs:
det_detections_h = draw_box_in_img.draw_box_cv(
np.squeeze(resized_img, 0),
boxes=det_boxes_h_,
labels=det_category_h_,
scores=det_scores_h_)
det_detections_r = draw_box_in_img.draw_rotate_box_cv(
np.squeeze(resized_img, 0),
boxes=det_boxes_r_,
labels=det_category_r_,
scores=det_scores_r_)
save_dir = os.path.join(cfgs.TEST_SAVE_PATH,
cfgs.VERSION)
tools.mkdir(save_dir)
cv2.imwrite(save_dir + '/' + a_img_name + '_h.jpg',
det_detections_h[:, :, ::-1])
cv2.imwrite(save_dir + '/' + a_img_name + '_r.jpg',
det_detections_r[:, :, ::-1])
xmin, ymin, xmax, ymax = det_boxes_h_[:, 0], det_boxes_h_[:, 1], \
det_boxes_h_[:, 2], det_boxes_h_[:, 3]
if det_boxes_r_.shape[0] != 0:
#print('### Has box ###')
resized_h, resized_w = resized_img.shape[
1], resized_img.shape[2]
det_boxes_r_ = forward_convert(det_boxes_r_, False)
det_boxes_r_[:, 0::2] *= (raw_w / resized_w)
det_boxes_r_[:, 1::2] *= (raw_h / resized_h)
det_boxes_r_ = back_forward_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]
xmin = xmin * raw_w / resized_w
xmax = xmax * raw_w / resized_w
ymin = ymin * raw_h / resized_h
ymax = ymax * raw_h / resized_h
boxes_h = np.transpose(
np.stack([xmin, ymin, xmax, ymax]))
boxes_r = np.transpose(
np.stack([x_c, y_c, w, h, theta]))
dets_h = np.hstack((det_category_h_.reshape(-1, 1),
det_scores_h_.reshape(-1,
1), boxes_h))
dets_r = np.hstack((det_category_r_.reshape(-1, 1),
det_scores_r_.reshape(-1,
1), boxes_r))
all_boxes_h.append(dets_h)
all_boxes_r.append(dets_r)
else:
imgs.remove(image_name)
none_detected_image.append(image_name)
print('No detected')
tools.view_bar(
'{} image cost {}s'.format(a_img_name, (end - start)),
i + 1, imgs_len)
fw1 = open(cfgs.VERSION + '_detections_h.pkl', 'wb')
fw2 = open(cfgs.VERSION + '_detections_r.pkl', 'wb')
pickle.dump(all_boxes_h, fw1)
pickle.dump(all_boxes_r, fw2)
# with open(cfgs.VERSION + '_detections_h.pkl', 'rb') as f1:
# all_boxes_h = pickle.load(f1, encoding='unicode')
# print(10 * "###")
# print(len(all_boxes_h))
#
# with open(cfgs.VERSION + '_detections_r.pkl', 'rb') as f2:
# all_boxes_r = pickle.load(f2, encoding='unicode')
#
# print(len(all_boxes_r))
# imgs = os.listdir(img_dir)
real_test_imgname_list = [i.split(image_ext)[0] for i in imgs]
print(10 * "**")
print('horizon eval:')
# print(len(all_boxes_h), len(all_boxes_r))
# print(len(real_test_imgname_list))
mAP_h, recall_h, precision_h, total_mAP_h, total_recall_h, total_precision_h = voc_eval.voc_evaluate_detections(
all_boxes=all_boxes_h,
test_imgid_list=real_test_imgname_list,
test_annotation_path=test_annotation_path)
print('mAP_h: ', mAP_h)
print('mRecall_h:', recall_h)
print('mPrecision_h:', precision_h)
print('total_mAP_h: ', total_mAP_h)
print('total_recall_h_list:', total_recall_h)
print('total_precision_h_list:', total_precision_h)
print(10 * "**")
print('rotation eval:')
mAP_r, recall_r, precision_r, total_mAP_r, total_recall_r, total_precision_r = voc_eval_r.voc_evaluate_detections(
all_boxes=all_boxes_r,
test_imgid_list=real_test_imgname_list,
test_annotation_path=test_annotation_path)
f1score_h_check = (1 + 1**2) * precision_h * recall_h / (
1**2 * precision_h + recall_h)
f1score_h = calc_fscore(precision_h, recall_h, 1)
f1score_r_check = (1 + 1**2) * precision_r * recall_r / (
1**2 * precision_r + recall_r)
f1score_r = calc_fscore(precision_r, recall_r, 1)
print(10 * '##')
print('mAP_r:', mAP_r)
print('mRecall_r:', recall_r)
print('mPrecision_r:', precision_r)
print('total_mAP_r_list: ', total_mAP_r)
print('total_recall_r_list:', total_recall_r)
print('total_precision_r_list:', total_precision_r)
print('f1score_r:', f1score_r)
summary_path = os.path.join(cfgs.SUMMARY_PATH,
cfgs.VERSION + '/eval_0')
tools.mkdir(summary_path)
summary_writer = tf.summary.FileWriter(summary_path,
graph=sess.graph)
mAP_h_summ = tf.Summary()
mAP_h_summ.value.add(tag='EVAL_Global/mAP_h',
simple_value=mAP_h)
summary_writer.add_summary(mAP_h_summ, global_stepnp)
mAP_r_summ = tf.Summary()
mAP_r_summ.value.add(tag='EVAL_Global/mAP_r',
simple_value=mAP_r)
summary_writer.add_summary(mAP_r_summ, global_stepnp)
mRecall_h_summ = tf.Summary()
mRecall_h_summ.value.add(tag='EVAL_Global/Recall_h',
simple_value=recall_h)
summary_writer.add_summary(mRecall_h_summ, global_stepnp)
mRecall_r_summ = tf.Summary()
mRecall_r_summ.value.add(tag='EVAL_Global/Recall_r',
simple_value=recall_r)
summary_writer.add_summary(mRecall_r_summ, global_stepnp)
mPrecision_h_summ = tf.Summary()
mPrecision_h_summ.value.add(tag='EVAL_Global/Precision_h',
simple_value=precision_h)
summary_writer.add_summary(mPrecision_h_summ, global_stepnp)
mPrecision_r_summ = tf.Summary()
mPrecision_r_summ.value.add(tag='EVAL_Global/Precision_r',
simple_value=precision_r)
summary_writer.add_summary(mPrecision_r_summ, global_stepnp)
mF1Score_h_summ = tf.Summary()
mF1Score_h_summ.value.add(tag='EVAL_Global/F1Score_h',
simple_value=f1score_h)
summary_writer.add_summary(mF1Score_h_summ, global_stepnp)
mF1Score_r_summ = tf.Summary()
mF1Score_r_summ.value.add(tag='EVAL_Global/F1Score_r',
simple_value=f1score_r)
summary_writer.add_summary(mF1Score_r_summ, global_stepnp)
mAP_h_class_dict = {}
mAP_r_class_dict = {}
recall_h_class_dict = {}
recall_r_class_dict = {}
precision_h_class_dict = {}
precision_r_class_dict = {}
f1score_h_class_dict = {}
f1score_r_class_dict = {}
label_list = list(NAME_LABEL_MAP.keys())
label_list.remove('back_ground')
for cls in label_list:
mAP_h_class_dict["cls_%s_mAP_h_summ" % cls] = tf.Summary()
mAP_r_class_dict["cls_%s_mAP_r_summ" % cls] = tf.Summary()
recall_h_class_dict["cls_%s_recall_h_summ" %
cls] = tf.Summary()
recall_r_class_dict["cls_%s_recall_r_summ" %
cls] = tf.Summary()
precision_h_class_dict["cls_%s_precision_h_summ" %
cls] = tf.Summary()
precision_r_class_dict["cls_%s_precision_r_summ" %
cls] = tf.Summary()
f1score_h_class_dict["cls_%s_f1score_h_summ" %
cls] = tf.Summary()
f1score_r_class_dict["cls_%s_f1score_r_summ" %
cls] = tf.Summary()
for cls in label_list:
mAP_h_class_dict["cls_%s_mAP_h_summ" % cls].value.add(
tag='EVAL_Class_mAP/{}_mAP_h'.format(cls),
simple_value=total_mAP_h[cls])
mAP_r_class_dict["cls_%s_mAP_r_summ" % cls].value.add(
tag='EVAL_Class_mAP/{}_mAP_r'.format(cls),
simple_value=total_mAP_r[cls])
recall_h_class_dict[
"cls_%s_recall_h_summ" % cls].value.add(
tag='EVAL_Class_recall/{}_recall_h'.format(cls),
simple_value=total_recall_h[cls])
recall_r_class_dict[
"cls_%s_recall_r_summ" % cls].value.add(
tag='EVAL_Class_recall/{}_recall_r'.format(cls),
simple_value=total_recall_r[cls])
precision_h_class_dict[
"cls_%s_precision_h_summ" % cls].value.add(
tag='EVAL_Class_precision/{}_precision_h'.format(
cls),
simple_value=total_precision_h[cls])
precision_r_class_dict[
"cls_%s_precision_r_summ" % cls].value.add(
tag='EVAL_Class_precision/{}_precision_r'.format(
cls),
simple_value=total_precision_r[cls])
f1score_h_cls = calc_fscore(total_precision_h[cls],
total_recall_h[cls], 1)
f1score_r_cls = calc_fscore(total_precision_r[cls],
total_recall_r[cls], 1)
f1score_h_class_dict[
"cls_%s_f1score_h_summ" % cls].value.add(
tag='EVAL_Class_f1score/{}_f1score_h'.format(cls),
simple_value=f1score_h_cls)
f1score_r_class_dict[
"cls_%s_f1score_r_summ" % cls].value.add(
tag='EVAL_Class_f1score/{}_f1score_r'.format(cls),
simple_value=f1score_r_cls)
for cls in label_list:
summary_writer.add_summary(
mAP_h_class_dict["cls_%s_mAP_h_summ" % cls],
global_stepnp)
summary_writer.add_summary(
mAP_r_class_dict["cls_%s_mAP_r_summ" % cls],
global_stepnp)
summary_writer.add_summary(
recall_h_class_dict["cls_%s_recall_h_summ" % cls],
global_stepnp)
summary_writer.add_summary(
recall_r_class_dict["cls_%s_recall_r_summ" % cls],
global_stepnp)
summary_writer.add_summary(
precision_h_class_dict["cls_%s_precision_h_summ" %
cls], global_stepnp)
summary_writer.add_summary(
precision_r_class_dict["cls_%s_precision_r_summ" %
cls], global_stepnp)
summary_writer.add_summary(
f1score_h_class_dict["cls_%s_f1score_h_summ" % cls],
global_stepnp)
summary_writer.add_summary(
f1score_r_class_dict["cls_%s_f1score_r_summ" % cls],
global_stepnp)
summary_writer.flush()
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_ckpt = os.path.join(save_dir,
'voc_' + str(global_stepnp) + 'model.ckpt')
#saver.save(sess, save_ckpt)
print(' weights had been saved')
time_to_next_eval = start_time + EVAL_INTERVAL - time.time()
if time_to_next_eval > 0:
time.sleep(time_to_next_eval)
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 eval_text(img_num, mode):
with tf.Graph().as_default():
faster_rcnn = build_whole_network.DetectionNetwork(
base_network_name=cfgs.NET_NAME, is_training=False)
# 通过readtfrecord来读取文件
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.IMG_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])
final_boxes_h, final_scores_h, final_category_h, \
final_boxes_r, final_scores_r, final_category_r= faster_rcnn.build_whole_detection_network(
input_img_batch=img_batch,
gtboxes_r_batch=gtboxes_and_label,
gtboxes_h_batch=gtboxes_and_label_minAreaRectangle)
if mode == 0:
final_boxes_r = get_horizen_minAreaRectangle(final_boxes_r, False)
# train
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
restorer, restore_ckpt = faster_rcnn.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)
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, \
_final_boxes_h, _final_scores_h, _final_category_h, _final_boxes_r, \
_final_scores_r, _final_category_r \
= sess.run([img_name_batch, img_batch, gtboxes_and_label, gtboxes_and_label_minAreaRectangle,
final_boxes_h, final_scores_h, final_category_h, final_boxes_r,
final_scores_r, final_category_r])
end = time.time()
# det_detections_h = draw_box_in_img.draw_box_cv(np.squeeze(_img_batch, 0),
# boxes=_final_boxes_h,
# labels=_final_category_h,
# scores=final_scores_h)
# det_detections_r = draw_box_in_img.draw_rotate_box_cv(np.squeeze(_img_batch, 0),
# boxes=_final_boxes_r,
# labels=_final_category_r,
# scores= _final_scores_r)
# save_dir = os.path.join('/home/sws/code/Tfrecord/', cfgs.VERSION)
# tools.mkdir(save_dir)
# cv2.imwrite(save_dir + '/' + _img_name_batch[0] + '_h.jpg',
# det_detections_h)
# cv2.imwrite(save_dir + '/' + _img_name_batch[0] + '_r.jpg',
# det_detections_r)
# 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, _final_boxes_h,
_final_scores_h, _final_category_h)
# 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)
if mode == 0:
gtbox_rotate_list, predict_rotate_list = \
make_dict_packle(_gtboxes_and_label_minAreaRectangle, _final_boxes_r,
_final_scores_r, _final_category_r)
else:
gtbox_rotate_list, predict_rotate_list = \
make_dict_packle(_gtboxes_and_label, _final_boxes_r,
_final_scores_r, _final_category_r)
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)
print('\n')
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 test_coco(det_net, real_test_img_list, eval_data, draw_imgs=False):
# 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)
img_batch = short_side_resize_for_inference_data(img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH)
if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']:
img_batch = (img_batch / 255 - tf.constant(cfgs.PIXEL_MEAN_)) / tf.constant(cfgs.PIXEL_STD)
else:
img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
# img_batch = (img_batch - tf.constant(cfgs.PIXEL_MEAN)) / (tf.constant(cfgs.PIXEL_STD)*255)
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=None)
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')
save_path = os.path.join('./eval_coco', cfgs.VERSION)
tools.mkdir(save_path)
fw_json_dt = open(os.path.join(save_path, 'coco_test-dev.json'), 'w')
coco_det = []
for i, a_img in enumerate(real_test_img_list):
raw_img = cv2.imread(os.path.join(eval_data, a_img['file_name']))
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
start = time.time()
resized_img, detected_boxes, detected_scores, detected_categories = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB
)
end = time.time()
eval_indices = detected_scores >= 0.01
detected_scores = detected_scores[eval_indices]
detected_boxes = detected_boxes[eval_indices]
detected_categories = detected_categories[eval_indices]
# print("{} cost time : {} ".format(img_name, (end - start)))
if draw_imgs:
show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD
show_scores = detected_scores[show_indices]
show_boxes = detected_boxes[show_indices]
show_categories = detected_categories[show_indices]
draw_img = np.squeeze(resized_img, 0)
if cfgs.NET_NAME in ['resnet101_v1d', 'resnet50_v1d']:
draw_img = (draw_img * np.array(cfgs.PIXEL_STD) + np.array(cfgs.PIXEL_MEAN_)) * 255
else:
draw_img = draw_img + np.array(cfgs.PIXEL_MEAN)
# draw_img = draw_img * (np.array(cfgs.PIXEL_STD)*255) + np.array(cfgs.PIXEL_MEAN)
final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(draw_img,
boxes=show_boxes,
labels=show_categories,
scores=show_scores,
in_graph=False)
if not os.path.exists(cfgs.TEST_SAVE_PATH):
os.makedirs(cfgs.TEST_SAVE_PATH)
cv2.imwrite(cfgs.TEST_SAVE_PATH + '/' + '{}.jpg'.format(a_img['id']),
final_detections[:, :, ::-1])
xmin, ymin, xmax, ymax = detected_boxes[:, 0], detected_boxes[:, 1], \
detected_boxes[:, 2], detected_boxes[:, 3]
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
xmin = xmin * raw_w / resized_w
xmax = xmax * raw_w / resized_w
ymin = ymin * raw_h / resized_h
ymax = ymax * raw_h / resized_h
boxes = np.transpose(np.stack([xmin, ymin, xmax-xmin, ymax-ymin]))
# cost much time
for j, box in enumerate(boxes):
coco_det.append({'bbox': [float(box[0]), float(box[1]), float(box[2]), float(box[3])],
'score': float(detected_scores[j]), 'image_id': a_img['id'],
'category_id': int(classes_originID[LABEl_NAME_MAP[detected_categories[j]]])})
tools.view_bar('%s image cost %.3fs' % (a_img['id'], (end - start)), i + 1, len(real_test_img_list))
json.dump(coco_det, fw_json_dt)
fw_json_dt.close()
def eval_with_plac(img_dir, det_net, num_imgs, image_ext, draw_imgs=False):
# 1. preprocess img
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None, 3]) # is RGB. not GBR
img_batch = tf.cast(img_plac, tf.float32)
img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
img_batch = short_side_resize_for_inference_data(img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
is_resize=False)
det_boxes_h, det_scores_h, det_category_h, \
det_boxes_r, det_scores_r, det_category_r = det_net.build_whole_detection_network(
input_img_batch=img_batch,
gtboxes_h_batch=None, gtboxes_r_batch=None)
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)
for i, a_img_name in enumerate(imgs):
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]
start = time.time()
resized_img, det_boxes_r_, det_scores_r_, det_category_r_ = \
sess.run(
[img_batch, det_boxes_h, det_scores_h, det_category_h,
det_boxes_r, det_scores_r, det_category_r],
feed_dict={img_plac: raw_img}
)
end = time.time()
# print("{} cost time : {} ".format(img_name, (end - start)))
if draw_imgs:
det_detections_r = draw_box_in_img.draw_rotate_box_cv(np.squeeze(resized_img, 0),
boxes=det_boxes_r_,
labels=det_category_r_,
scores=det_scores_r_)
save_dir = os.path.join(cfgs.TEST_SAVE_PATH, cfgs.VERSION)
tools.mkdir(save_dir)
cv2.imwrite(save_dir + '/' + a_img_name + '_r.jpg',
det_detections_r[:, :, ::-1])
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
det_boxes_r_ = forward_convert(det_boxes_r_, False)
det_boxes_r_[:, 0::2] *= (raw_w / resized_w)
det_boxes_r_[:, 1::2] *= (raw_h / resized_h)
det_boxes_r_ = back_forward_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)
tools.view_bar('{} image cost {}s'.format(a_img_name, (end - start)), i + 1, len(imgs))
fw2 = open(cfgs.VERSION + '_detections_r.pkl', 'w')
pickle.dump(all_boxes_r, fw2)
def detect(det_net, inference_save_path, real_test_imgname_list):
# 1. preprocess img
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None,
3]) # is RGB. not GBR
img_batch = tf.cast(img_plac, tf.float32)
img_batch = short_side_resize_for_inference_data(
img_tensor=img_batch,
target_shortside_len=cfgs.IMG_SHORT_SIDE_LEN,
length_limitation=cfgs.IMG_MAX_LENGTH)
img_batch = img_batch - tf.constant(cfgs.PIXEL_MEAN)
img_batch = tf.expand_dims(img_batch, axis=0) # [1, None, None, 3]
detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network(
input_img_batch=img_batch, gtboxes_batch=None)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
################################################################
###根据checkpoint恢复最新的
# restorer, restore_ckpt = det_net.get_restorer()
###恢复指定的
restore_ckpt = os.path.join(cfgs.TRAINED_CKPT,
cfgs.VERSION) + '/voc_16000model.ckpt'
restorer = tf.train.Saver()
print("model restore from :", restore_ckpt)
print(20 * "****")
################################################################
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_images_detect_result = []
for i, a_img_name in enumerate(real_test_imgname_list):
raw_img = cv2.imread(a_img_name)
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
start = time.time()
resized_img, detected_boxes, detected_scores, detected_categories = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: raw_img[:, :, ::-1]} # cv is BGR. But need RGB
)
end = time.time()
# print("{} cost time : {} ".format(img_name, (end - start)))
show_indices = detected_scores >= cfgs.SHOW_SCORE_THRSHOLD
show_scores = detected_scores[show_indices]
show_boxes = detected_boxes[show_indices]
show_categories = detected_categories[show_indices]
final_detections = draw_box_in_img.draw_boxes_with_label_and_scores(
np.squeeze(resized_img, 0),
boxes=show_boxes,
labels=show_categories,
scores=show_scores)
nake_name = os.path.split(a_img_name)[1]
print(inference_save_path + '/' + nake_name)
# cv2.imwrite(inference_save_path + '/' + nake_name,
# final_detections[:, :, ::-1])
xmin, ymin, xmax, ymax = show_boxes[:, 0], show_boxes[:, 1], \
show_boxes[:, 2], show_boxes[:, 3]
resized_h, resized_w = resized_img.shape[1], resized_img.shape[2]
xmin = xmin * raw_w / resized_w
xmax = xmax * raw_w / resized_w
ymin = ymin * raw_h / resized_h
ymax = ymax * raw_h / resized_h
a_img_detect_result = []
for idex in range(len(show_scores)):
# label, score, bbox = a_det[0], a_det[1], a_det[2:]
det_object = {
"xmin": int(xmin[idex]),
"xmax": int(xmax[idex]),
"ymin": int(ymin[idex]),
"ymax": int(ymax[idex]),
"label": int(show_categories[idex]),
"confidence": float(show_scores[idex])
}
# print (det_object)
a_img_detect_result.append(det_object)
image_result = {
"filename": nake_name,
"rects": a_img_detect_result
}
all_images_detect_result.append(image_result)
all_images_result_dict = {"results": all_images_detect_result}
f = open('result_jinlian20190314.json', 'w')
json.dump(all_images_result_dict, f) # , indent=4
f.close()
tools.view_bar(
'{} image cost {}s'.format(a_img_name, (end - start)), i + 1,
len(real_test_imgname_list))
def eval_dict_convert(img_num, mode):
with tf.Graph().as_default():
# img = tf.placeholder(shape=[None, None, 3], dtype=tf.uint8)
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_horizon_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=gtboxes_and_label,
is_training=False,
share_head=False,
share_net=share_net,
anchor_ratios=cfgs.ANCHOR_RATIOS,
anchor_scales=cfgs.ANCHOR_SCALES,
anchor_angles=cfgs.ANCHOR_ANGLES,
scale_factors=cfgs.SCALE_FACTORS,
base_anchor_size_list=cfgs.BASE_ANCHOR_SIZE_LIST, # P2, P3, P4, P5, P6
level=cfgs.LEVEL,
anchor_stride=cfgs.ANCHOR_STRIDE,
top_k_nms=cfgs.RPN_TOP_K_NMS,
kernel_size=cfgs.KERNEL_SIZE,
use_angles_condition=False,
anchor_angle_threshold=cfgs.RPN_ANCHOR_ANGLES_THRESHOLD,
nms_angle_threshold=cfgs.RPN_NMS_ANGLES_THRESHOLD,
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=cfgs.IS_FILTER_OUTSIDE_BOXES, # whether remove anchors outside
rpn_weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME],
scope='')
rpn_proposals_boxes, rpn_proposals_scores = rpn.rpn_proposals() # rpn_score shape: [300, ]
_, _, rpn_predict_boxes, rpn_predict_scores = rpn.rpn_losses()
# ***********************************************************************************************
# * Fast RCNN *
# ***********************************************************************************************
fast_rcnn = build_fast_rcnn.FastRCNN(img_batch=img_batch,
feature_pyramid=rpn.feature_pyramid,
rpn_proposals_boxes=rpn_proposals_boxes,
rpn_proposals_scores=rpn_proposals_scores,
stop_gradient_for_proposals=False,
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,
fast_rcnn_nms_iou_threshold=cfgs.FAST_RCNN_NMS_IOU_THRESHOLD,
top_k_nms=cfgs.FAST_RCNN_TOP_K_NMS,
nms_angle_threshold=cfgs.FAST_RCNN_NMS_ANGLES_THRESHOLD,
use_angle_condition=False,
level=cfgs.LEVEL,
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,
boxes_angle_threshold=cfgs.FAST_RCNN_BOXES_ANGLES_THRESHOLD,
use_dropout=cfgs.USE_DROPOUT,
weight_decay=cfgs.WEIGHT_DECAY[cfgs.NET_NAME],
is_training=False)
fast_rcnn_decode_boxes, fast_rcnn_score, num_of_objects, detection_category = \
fast_rcnn.fast_rcnn_predict()
##############################################################################################
if cfgs.NEED_AUXILIARY:
predict_boxes = tf.concat([fast_rcnn_decode_boxes, rpn_predict_boxes], axis=0)
predict_scores = tf.concat([fast_rcnn_score, rpn_predict_scores - 0.2], axis=0)
rpn_predict_label = tf.ones([tf.shape(rpn_predict_scores)[0], ], tf.int64)
labels = tf.concat([detection_category, rpn_predict_label], axis=0)
# valid_indices = nms_rotate.nms_rotate(decode_boxes=predict_boxes,
# scores=predict_scores,
# iou_threshold=0.15,
# max_output_size=30,
# use_angle_condition=False,
# angle_threshold=15,
# use_gpu=True)
valid_indices = tf.py_func(nms_rotate.nms_rotate_cpu,
inp=[predict_boxes, predict_scores,
tf.constant(0.15, tf.float32), tf.constant(30, tf.float32)],
Tout=tf.int64)
fast_rcnn_decode_boxes = tf.gather(predict_boxes, valid_indices)
fast_rcnn_score = tf.gather(predict_scores, valid_indices)
detection_category = tf.gather(labels, valid_indices)
##############################################################################################
if mode == 0:
fast_rcnn_decode_boxes = get_horizon_minAreaRectangle(fast_rcnn_decode_boxes, False)
# 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)
gtboxes_dict = {}
predict_dict = {}
for i in range(img_num):
start = time.time()
_img_name_batch, _img_batch, _gtboxes_and_label, _fast_rcnn_decode_boxes, \
_gtboxes_and_label_minAreaRectangle, _fast_rcnn_score, _detection_category \
= sess.run([img_name_batch, img_batch, gtboxes_and_label, fast_rcnn_decode_boxes,
gtboxes_and_label_minAreaRectangle, fast_rcnn_score, detection_category])
end = time.time()
# gtboxes convert dict
gtboxes_dict[str(_img_name_batch[0])] = []
predict_dict[str(_img_name_batch[0])] = []
# for j, box in enumerate(_gtboxes_and_label):
# bbox_dict = {}
# bbox_dict['bbox'] = np.array(_gtboxes_and_label[j, :-1], np.float64)
# bbox_dict['name'] = LABEl_NAME_MAP[int(_gtboxes_and_label[j, -1])]
# gtbox_dict[str(_img_name_batch[0])].append(bbox_dict)
#
# for label in NAME_LABEL_MAP.keys():
# if label == 'back_ground':
# continue
# else:
# temp_dict = {}
# temp_dict['name'] = label
#
# ind = np.where(_detection_category == NAME_LABEL_MAP[label])[0]
# temp_boxes = _fast_rcnn_decode_boxes[ind]
# temp_score = np.reshape(_fast_rcnn_score[ind], [-1, 1])
# temp_dict['bbox'] = np.array(np.concatenate([temp_boxes, temp_score], axis=1), np.float64)
# predict_dict[str(_img_name_batch[0])].append(temp_dict)
if mode == 0:
gtboxes_list, predict_list = \
make_dict_packle(_gtboxes_and_label_minAreaRectangle, _fast_rcnn_decode_boxes,
_fast_rcnn_score, _detection_category)
else:
gtboxes_list, predict_list = \
make_dict_packle(_gtboxes_and_label, _fast_rcnn_decode_boxes,
_fast_rcnn_score, _detection_category)
gtboxes_dict[str(_img_name_batch[0])].extend(gtboxes_list)
predict_dict[str(_img_name_batch[0])].extend(predict_list)
view_bar('{} image cost {}s'.format(str(_img_name_batch[0]), (end - start)), i + 1, img_num)
fw1 = open('gtboxes_dict.pkl', 'w')
fw2 = open('predict_dict.pkl', 'w')
pickle.dump(gtboxes_dict, fw1)
pickle.dump(predict_dict, fw2)
fw1.close()
fw2.close()
coord.request_stop()
coord.join(threads)
