def test_pb(self, frozen_graph_path, test_dir):
graph = self.load_graph(frozen_graph_path)
print("we are testing ====>>>>", frozen_graph_path)
img = graph.get_tensor_by_name("input_img:0")
dets = graph.get_tensor_by_name("DetResults:0")
with tf.Session(graph=graph) as sess:
for img_path in os.listdir(test_dir):
print(img_path)
a_img = cv2.imread(os.path.join(test_dir, img_path))[:, :, ::-1]
raw_h, raw_w = a_img.shape[0], a_img.shape[1]
short_size, max_len = self.cfgs.IMG_SHORT_SIDE_LEN, cfgs.IMG_MAX_LENGTH
if raw_h < raw_w:
new_h, new_w = short_size, min(int(short_size * float(raw_w) / raw_h), max_len)
else:
new_h, new_w = min(int(short_size * float(raw_h) / raw_w), max_len), short_size
img_resize = cv2.resize(a_img, (new_w, new_h))
dets_val = sess.run(dets, feed_dict={img: img_resize[:, :, ::-1]})
bbox_pred, cls_prob, proposal = dets_val[:, :5], dets_val[:, 5:(5+self.cfgs.CLASS_NUM)], \
dets_val[:, (5+self.cfgs.CLASS_NUM):]
detected_boxes, detected_scores, detected_categories = self.postprocess_detctions(bbox_pred, cls_prob, proposal)
if True:
# detected_indices = det_scores_r_ >= self.cfgs.VIS_SCORE
# detected_scores = det_scores_r_[detected_indices]
# detected_boxes = det_boxes_r_[detected_indices]
# detected_categories = det_category_r_[detected_indices]
drawer = DrawBox(self.cfgs)
det_detections_r = drawer.draw_boxes_with_label_and_scores(img_resize[:, :, ::-1],
boxes=detected_boxes,
labels=detected_categories,
scores=detected_scores,
method=1,
in_graph=True)
save_dir = os.path.join('test_pb', self.cfgs.VERSION, 'pb_img_vis')
tools.makedirs(save_dir)
cv2.imwrite(save_dir + '/{}'.format(img_path),
det_detections_r[:, :, ::-1])
def test_dota(self, det_net, real_test_img_list, txt_name):
save_path = os.path.join('./test_dota', self.cfgs.VERSION)
nr_records = len(real_test_img_list)
pbar = tqdm(total=nr_records)
gpu_num = len(self.args.gpus.strip().split(','))
nr_image = math.ceil(nr_records / gpu_num)
result_queue = Queue(500)
procs = []
for i, gpu_id in enumerate(self.args.gpus.strip().split(',')):
start = i * nr_image
end = min(start + nr_image, nr_records)
split_records = real_test_img_list[start:end]
proc = Process(target=self.worker,
args=(int(gpu_id), split_records, det_net,
result_queue))
print('process:%d, start:%d, end:%d' % (i, start, end))
proc.start()
procs.append(proc)
for i in range(nr_records):
res = result_queue.get()
if self.args.show_box:
nake_name = res['image_id'].split('/')[-1]
tools.makedirs(os.path.join(save_path, 'dota_img_vis'))
draw_path = os.path.join(save_path, 'dota_img_vis', nake_name)
draw_img = np.array(cv2.imread(res['image_id']), np.float32)
detected_boxes = backward_convert(res['boxes'],
with_label=False)
detected_indices = res['scores'] >= self.cfgs.VIS_SCORE
detected_scores = res['scores'][detected_indices]
detected_boxes = detected_boxes[detected_indices]
detected_categories = res['labels'][detected_indices]
drawer = DrawBox(self.cfgs)
final_detections = drawer.draw_boxes_with_label_and_scores(
draw_img,
boxes=detected_boxes,
labels=detected_categories,
scores=detected_scores,
method=1,
is_csl=True,
in_graph=False)
cv2.imwrite(draw_path, final_detections)
else:
CLASS_DOTA = self.name_label_map.keys()
write_handle = {}
tools.makedirs(os.path.join(save_path, 'dota_res'))
for sub_class in CLASS_DOTA:
if sub_class == 'back_ground':
continue
write_handle[sub_class] = open(
os.path.join(save_path, 'dota_res',
'Task1_%s.txt' % sub_class), 'a+')
for i, rbox in enumerate(res['boxes']):
command = '%s %.3f %.1f %.1f %.1f %.1f %.1f %.1f %.1f %.1f\n' % (
res['image_id'].split('/')[-1].split('.')[0],
res['scores'][i],
rbox[0],
rbox[1],
rbox[2],
rbox[3],
rbox[4],
rbox[5],
rbox[6],
rbox[7],
)
write_handle[self.label_name_map[res['labels'][i]]].write(
command)
for sub_class in CLASS_DOTA:
if sub_class == 'back_ground':
continue
write_handle[sub_class].close()
fw = open(txt_name, 'a+')
fw.write('{}\n'.format(res['image_id'].split('/')[-1]))
fw.close()
pbar.set_description("Test image %s" %
res['image_id'].split('/')[-1])
pbar.update(1)
for p in procs:
p.join()
def eval_with_plac(self, img_dir, det_net, image_ext):
os.environ["CUDA_VISIBLE_DEVICES"] = self.args.gpu
# 1. preprocess img
img_plac = tf.placeholder(dtype=tf.uint8, shape=[None, None,
3]) # is RGB. not BGR
img_batch = tf.cast(img_plac, tf.float32)
pretrain_zoo = PretrainModelZoo()
if self.cfgs.NET_NAME in pretrain_zoo.pth_zoo or self.cfgs.NET_NAME in pretrain_zoo.mxnet_zoo:
img_batch = (img_batch / 255 - tf.constant(
self.cfgs.PIXEL_MEAN_)) / tf.constant(self.cfgs.PIXEL_STD)
else:
img_batch = img_batch - tf.constant(self.cfgs.PIXEL_MEAN)
img_batch = tf.expand_dims(img_batch, axis=0)
detection_boxes, detection_scores, detection_category = det_net.build_whole_detection_network(
input_img_batch=img_batch)
init_op = tf.group(tf.global_variables_initializer(),
tf.local_variables_initializer())
restorer, restore_ckpt = det_net.get_restorer()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
sess.run(init_op)
if not restorer is None:
restorer.restore(sess, restore_ckpt)
print('restore model')
all_boxes_r = []
imgs = os.listdir(img_dir)
pbar = tqdm(imgs)
for a_img_name in pbar:
a_img_name = a_img_name.split(image_ext)[0]
raw_img = cv2.imread(
os.path.join(img_dir, a_img_name + image_ext))
raw_h, raw_w = raw_img.shape[0], raw_img.shape[1]
det_boxes_r_all, det_scores_r_all, det_category_r_all = [], [], []
img_short_side_len_list = self.cfgs.IMG_SHORT_SIDE_LEN if isinstance(
self.cfgs.IMG_SHORT_SIDE_LEN,
list) else [self.cfgs.IMG_SHORT_SIDE_LEN]
img_short_side_len_list = [
img_short_side_len_list[0]
] if not self.args.multi_scale else img_short_side_len_list
for short_size in img_short_side_len_list:
max_len = self.cfgs.IMG_MAX_LENGTH
if raw_h < raw_w:
new_h, new_w = short_size, min(
int(short_size * float(raw_w) / raw_h), max_len)
else:
new_h, new_w = min(
int(short_size * float(raw_h) / raw_w),
max_len), short_size
img_resize = cv2.resize(raw_img, (new_w, new_h))
resized_img, detected_boxes, detected_scores, detected_categories = \
sess.run(
[img_batch, detection_boxes, detection_scores, detection_category],
feed_dict={img_plac: img_resize[:, :, ::-1]}
)
if detected_boxes.shape[0] == 0:
continue
resized_h, resized_w = resized_img.shape[
1], resized_img.shape[2]
detected_boxes = forward_convert(detected_boxes, False)
detected_boxes[:, 0::2] *= (raw_w / resized_w)
detected_boxes[:, 1::2] *= (raw_h / resized_h)
det_boxes_r_all.extend(detected_boxes)
det_scores_r_all.extend(detected_scores)
det_category_r_all.extend(detected_categories)
det_boxes_r_all = np.array(det_boxes_r_all)
det_scores_r_all = np.array(det_scores_r_all)
det_category_r_all = np.array(det_category_r_all)
box_res_rotate_ = []
label_res_rotate_ = []
score_res_rotate_ = []
if det_scores_r_all.shape[0] != 0:
for sub_class in range(1, self.cfgs.CLASS_NUM + 1):
index = np.where(det_category_r_all == sub_class)[0]
if len(index) == 0:
continue
tmp_boxes_r = det_boxes_r_all[index]
tmp_label_r = det_category_r_all[index]
tmp_score_r = det_scores_r_all[index]
if self.args.multi_scale:
tmp_boxes_r_ = backward_convert(tmp_boxes_r, False)
# try:
# inx = nms_rotate.nms_rotate_cpu(boxes=np.array(tmp_boxes_r_),
# scores=np.array(tmp_score_r),
# iou_threshold=self.cfgs.NMS_IOU_THRESHOLD,
# max_output_size=5000)
# except:
tmp_boxes_r_ = np.array(tmp_boxes_r_)
tmp = np.zeros([
tmp_boxes_r_.shape[0],
tmp_boxes_r_.shape[1] + 1
])
tmp[:, 0:-1] = tmp_boxes_r_
tmp[:, -1] = np.array(tmp_score_r)
# Note: the IoU of two same rectangles is 0, which is calculated by rotate_gpu_nms
jitter = np.zeros([
tmp_boxes_r_.shape[0],
tmp_boxes_r_.shape[1] + 1
])
jitter[:, 0] += np.random.rand(
tmp_boxes_r_.shape[0], ) / 1000
inx = rotate_gpu_nms(
np.array(tmp, np.float32) +
np.array(jitter, np.float32),
float(self.cfgs.NMS_IOU_THRESHOLD), 0)
else:
inx = np.arange(0, tmp_score_r.shape[0])
box_res_rotate_.extend(np.array(tmp_boxes_r)[inx])
score_res_rotate_.extend(np.array(tmp_score_r)[inx])
label_res_rotate_.extend(np.array(tmp_label_r)[inx])
if len(box_res_rotate_) == 0:
all_boxes_r.append(np.array([]))
continue
det_boxes_r_ = np.array(box_res_rotate_)
det_scores_r_ = np.array(score_res_rotate_)
det_category_r_ = np.array(label_res_rotate_)
if self.args.draw_imgs:
detected_indices = det_scores_r_ >= self.cfgs.VIS_SCORE
detected_scores = det_scores_r_[detected_indices]
detected_boxes = det_boxes_r_[detected_indices]
detected_categories = det_category_r_[detected_indices]
detected_boxes = backward_convert(detected_boxes, False)
drawer = DrawBox(self.cfgs)
det_detections_r = drawer.draw_boxes_with_label_and_scores(
raw_img[:, :, ::-1],
boxes=detected_boxes,
labels=detected_categories,
scores=detected_scores,
method=1,
in_graph=True)
save_dir = os.path.join('test_hrsc', self.cfgs.VERSION,
'hrsc2016_img_vis')
tools.makedirs(save_dir)
cv2.imwrite(save_dir + '/{}.jpg'.format(a_img_name),
det_detections_r[:, :, ::-1])
det_boxes_r_ = backward_convert(det_boxes_r_, False)
x_c, y_c, w, h, theta = det_boxes_r_[:, 0], det_boxes_r_[:, 1], det_boxes_r_[:, 2], \
det_boxes_r_[:, 3], det_boxes_r_[:, 4]
boxes_r = np.transpose(np.stack([x_c, y_c, w, h, theta]))
dets_r = np.hstack((det_category_r_.reshape(-1, 1),
det_scores_r_.reshape(-1, 1), boxes_r))
all_boxes_r.append(dets_r)
pbar.set_description("Eval image %s" % a_img_name)
# fw1 = open(cfgs.VERSION + '_detections_r.pkl', 'wb')
# pickle.dump(all_boxes_r, fw1)
return all_boxes_r
def test_icdar2015(self, det_net, real_test_img_list, txt_name):
save_path = os.path.join('./test_icdar2015', self.cfgs.VERSION)
tools.makedirs(save_path)
nr_records = len(real_test_img_list)
pbar = tqdm(total=nr_records)
gpu_num = len(self.args.gpus.strip().split(','))
nr_image = math.ceil(nr_records / gpu_num)
result_queue = Queue(500)
procs = []
for i, gpu_id in enumerate(self.args.gpus.strip().split(',')):
start = i * nr_image
end = min(start + nr_image, nr_records)
split_records = real_test_img_list[start:end]
proc = Process(target=self.worker, args=(int(gpu_id), split_records, det_net, result_queue))
print('process:%d, start:%d, end:%d' % (i, start, end))
proc.start()
procs.append(proc)
for i in range(nr_records):
res = result_queue.get()
tools.makedirs(os.path.join(save_path, 'icdar2015_res'))
if res['boxes'].shape[0] == 0:
fw_txt_dt = open(os.path.join(save_path, 'icdar2015_res', 'res_{}.txt'.format(res['image_id'].split('/')[-1].split('.')[0])),
'w')
fw_txt_dt.close()
pbar.update(1)
fw = open(txt_name, 'a+')
fw.write('{}\n'.format(res['image_id'].split('/')[-1]))
fw.close()
continue
x1, y1, x2, y2, x3, y3, x4, y4 = res['boxes'][:, 0], res['boxes'][:, 1], res['boxes'][:, 2], res['boxes'][:, 3],\
res['boxes'][:, 4], res['boxes'][:, 5], res['boxes'][:, 6], res['boxes'][:, 7]
x1, y1 = x1 * res['scales'][0], y1 * res['scales'][1]
x2, y2 = x2 * res['scales'][0], y2 * res['scales'][1]
x3, y3 = x3 * res['scales'][0], y3 * res['scales'][1]
x4, y4 = x4 * res['scales'][0], y4 * res['scales'][1]
boxes = np.transpose(np.stack([x1, y1, x2, y2, x3, y3, x4, y4]))
if self.args.show_box:
boxes = backward_convert(boxes, False)
nake_name = res['image_id'].split('/')[-1]
tools.makedirs(os.path.join(save_path, 'icdar2015_img_vis'))
draw_path = os.path.join(save_path, 'icdar2015_img_vis', nake_name)
draw_img = np.array(cv2.imread(res['image_id']), np.float32)
drawer = DrawBox(self.cfgs)
final_detections = drawer.draw_boxes_with_label_and_scores(draw_img,
boxes=boxes,
labels=res['labels'],
scores=res['scores'],
method=1,
in_graph=False)
cv2.imwrite(draw_path, final_detections)
else:
fw_txt_dt = open(os.path.join(save_path, 'icdar2015_res', 'res_{}.txt'.format(res['image_id'].split('/')[-1].split('.')[0])), 'w')
for box in boxes:
line = '%d,%d,%d,%d,%d,%d,%d,%d\n' % (box[0], box[1], box[2], box[3],
box[4], box[5], box[6], box[7])
fw_txt_dt.write(line)
fw_txt_dt.close()
fw = open(txt_name, 'a+')
fw.write('{}\n'.format(res['image_id'].split('/')[-1]))
fw.close()
pbar.set_description("Test image %s" % res['image_id'].split('/')[-1])
pbar.update(1)
for p in procs:
p.join()
def __init__(self, cfgs):
self.cfgs = cfgs
self.drawer = DrawBox(cfgs)
def test_pb(self, frozen_graph_path, test_dir):
graph = self.load_graph(frozen_graph_path)
print("we are testing ====>>>>", frozen_graph_path)
img = graph.get_tensor_by_name("input_img:0")
dets = graph.get_tensor_by_name("DetResults:0")
with tf.Session(graph=graph) as sess:
for img_path in os.listdir(test_dir):
print(img_path)
a_img = cv2.imread(os.path.join(test_dir,
img_path))[:, :, ::-1]
raw_h, raw_w = a_img.shape[0], a_img.shape[1]
short_size, max_len = self.cfgs.IMG_SHORT_SIDE_LEN, cfgs.IMG_MAX_LENGTH
if raw_h < raw_w:
new_h, new_w = short_size, min(
int(short_size * float(raw_w) / raw_h), max_len)
else:
new_h, new_w = min(int(short_size * float(raw_h) / raw_w),
max_len), short_size
img_resize = cv2.resize(a_img, (new_w, new_h))
dets_val = sess.run(dets,
feed_dict={img: img_resize[:, :, ::-1]})
bbox_pred, cls_prob = dets_val[:, :5], dets_val[:, 5:(
5 + self.cfgs.CLASS_NUM)]
anchor = GenerateAnchors(self.cfgs, 'H')
h1, w1 = math.ceil(new_h / 2), math.ceil(new_w / 2)
h2, w2 = math.ceil(h1 / 2), math.ceil(w1 / 2)
h3, w3 = math.ceil(h2 / 2), math.ceil(w2 / 2)
h4, w4 = math.ceil(h3 / 2), math.ceil(w3 / 2)
h5, w5 = math.ceil(h4 / 2), math.ceil(w4 / 2)
h6, w6 = math.ceil(h5 / 2), math.ceil(w5 / 2)
h7, w7 = math.ceil(h6 / 2), math.ceil(w6 / 2)
h_dict = {'P3': h3, 'P4': h4, 'P5': h5, 'P6': h6, 'P7': h7}
w_dict = {'P3': w3, 'P4': w4, 'P5': w5, 'P6': w6, 'P7': w7}
anchors = anchor.generate_all_anchor_pb(h_dict, w_dict)
anchors = np.concatenate(anchors, axis=0)
x_c = (anchors[:, 2] + anchors[:, 0]) / 2
y_c = (anchors[:, 3] + anchors[:, 1]) / 2
h = anchors[:, 2] - anchors[:, 0] + 1
w = anchors[:, 3] - anchors[:, 1] + 1
theta = -90 * np.ones_like(x_c)
anchors = np.transpose(np.stack([x_c, y_c, w, h, theta]))
detected_boxes, detected_scores, detected_categories = self.postprocess_detctions(
bbox_pred, cls_prob, anchors)
if True:
# detected_indices = det_scores_r_ >= self.cfgs.VIS_SCORE
# detected_scores = det_scores_r_[detected_indices]
# detected_boxes = det_boxes_r_[detected_indices]
# detected_categories = det_category_r_[detected_indices]
drawer = DrawBox(self.cfgs)
det_detections_r = drawer.draw_boxes_with_label_and_scores(
img_resize[:, :, ::-1],
boxes=detected_boxes,
labels=detected_categories,
scores=detected_scores,
method=1,
in_graph=True)
save_dir = os.path.join('test_pb', self.cfgs.VERSION,
'pb_img_vis')
tools.makedirs(save_dir)
cv2.imwrite(save_dir + '/{}'.format(img_path),
det_detections_r[:, :, ::-1])
def test(self, frozen_graph_path, test_dir):
graph = self.load_graph(frozen_graph_path)
print("we are testing ====>>>>", frozen_graph_path)
img = graph.get_tensor_by_name("input_img:0")
dets = graph.get_tensor_by_name("DetResults:0")
with tf.Session(graph=graph) as sess:
for img_path in os.listdir(test_dir):
print(img_path)
a_img = cv2.imread(os.path.join(test_dir,
img_path))[:, :, ::-1]
raw_h, raw_w = a_img.shape[0], a_img.shape[1]
short_size, max_len = self.cfgs.IMG_SHORT_SIDE_LEN, cfgs.IMG_MAX_LENGTH
if raw_h < raw_w:
new_h, new_w = short_size, min(
int(short_size * float(raw_w) / raw_h), max_len)
else:
new_h, new_w = min(int(short_size * float(raw_h) / raw_w),
max_len), short_size
img_resize = cv2.resize(a_img, (new_w, new_h))
dets_val = sess.run(dets,
feed_dict={img: img_resize[:, :, ::-1]})
box_res_rotate_ = []
label_res_rotate_ = []
score_res_rotate_ = []
if dets_val.shape[0] != 0:
for sub_class in range(1, self.cfgs.CLASS_NUM + 1):
index = np.where(dets_val[:, 0] == sub_class)[0]
if len(index) == 0:
continue
tmp_boxes_r = dets_val[:, 2:][index]
tmp_label_r = dets_val[:, 0][index]
tmp_score_r = dets_val[:, 1][index]
# try:
inx = nms_rotate_cpu(
boxes=np.array(tmp_boxes_r),
scores=np.array(tmp_score_r),
iou_threshold=self.cfgs.NMS_IOU_THRESHOLD,
max_output_size=20)
# except:
# tmp_boxes_r_ = np.array(tmp_boxes_r)
# tmp = np.zeros([tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1])
# tmp[:, 0:-1] = tmp_boxes_r_
# tmp[:, -1] = np.array(tmp_score_r)
# # Note: the IoU of two same rectangles is 0, which is calculated by rotate_gpu_nms
# jitter = np.zeros([tmp_boxes_r_.shape[0], tmp_boxes_r_.shape[1] + 1])
# jitter[:, 0] += np.random.rand(tmp_boxes_r_.shape[0], ) / 1000
# inx = rotate_gpu_nms(np.array(tmp, np.float32) + np.array(jitter, np.float32),
# float(self.cfgs.NMS_IOU_THRESHOLD), 0)
box_res_rotate_.extend(np.array(tmp_boxes_r)[inx])
score_res_rotate_.extend(np.array(tmp_score_r)[inx])
label_res_rotate_.extend(np.array(tmp_label_r)[inx])
det_boxes_r_ = np.array(box_res_rotate_)
det_scores_r_ = np.array(score_res_rotate_)
det_category_r_ = np.array(label_res_rotate_)
if True:
detected_indices = det_scores_r_ >= self.cfgs.VIS_SCORE
detected_scores = det_scores_r_[detected_indices]
detected_boxes = det_boxes_r_[detected_indices]
detected_categories = det_category_r_[detected_indices]
drawer = DrawBox(self.cfgs)
det_detections_r = drawer.draw_boxes_with_label_and_scores(
img_resize[:, :, ::-1],
boxes=detected_boxes,
labels=detected_categories,
scores=detected_scores,
method=1,
in_graph=True)
save_dir = os.path.join('test_pb', self.cfgs.VERSION,
'pb_img_vis')
tools.makedirs(save_dir)
cv2.imwrite(save_dir + '/{}'.format(img_path),
det_detections_r[:, :, ::-1])
