results = read_cache_detections(loader)
maps = []
difficult_ids = []
if args.images == '':
images = loader.get_filenames()
else:
with open(args.images, 'r') as f:
images = f.read().split('\n')
results = {k: results[k] for k in results if k in images}
for i in range(len(results)):
img_id = images[i]
print(img_id)
gt_bboxes, _, gt_cats, w, h, difficulty = loader.read_annotations(img_id)
# print('==============================')
# print("GT: ", ' '.join(loader.ids_to_cats[j] for j in np.unique(gt_cats)))
img = loader.load_image(img_id)
gt = {cid: {} for cid in range(1, loader.num_classes)}
dets = {cid: [] for cid in range(1, loader.num_classes)}
for cid in np.unique(gt_cats):
mask = (gt_cats == cid)
bbox = gt_bboxes[mask]
diff = difficulty[mask]
det = np.zeros(len(diff), dtype=np.bool)
mscore = np.zeros(len(diff), dtype=np.float32)
gt[cid][int(img_id)] = {'bbox': bbox, 'difficult': diff, 'det': det, 'score': mscore}
for d in results[img_id]:
dets[d.cat].append((int(img_id), d.score, ) + d.bbox)
# if d.score > 0.6:
# print(loader.ids_to_cats[d.cat], d.score, d.bbox)
def main(argv=None): # pylint: disable=unused-argument
net = ResNet
depth = 50
loader = VOCLoader('07', 'test')
net = net(config=net_config, depth=depth, training=False)
num_classes = 21
batch_size = args.batch_size
img_size = args.image_size
image_ph = tf.placeholder(shape=[1, img_size, img_size, 3],
dtype=tf.float32, name='img_ph')
net.create_trunk(image_ph)
bboxer = PriorBoxGrid(net_config)
net.create_multibox_head(num_classes)
confidence = tf.nn.softmax(tf.squeeze(net.outputs['confidence']))
location = tf.squeeze(net.outputs['location'])
good_bboxes = decode_bboxes(location, bboxer.tiling)
detection_list = []
score_list = []
for i in range(1, num_classes):
class_mask = tf.greater(confidence[:, i], args.conf_thresh)
class_scores = tf.boolean_mask(confidence[:, i], class_mask)
class_bboxes = tf.boolean_mask(good_bboxes, class_mask)
K = tf.minimum(tf.size(class_scores), args.top_k_nms)
_, top_k_inds = tf.nn.top_k(class_scores, K)
top_class_scores = tf.gather(class_scores, top_k_inds)
top_class_bboxes = tf.gather(class_bboxes, top_k_inds)
final_inds = tf.image.non_max_suppression(top_class_bboxes,
top_class_scores,
max_output_size=50,
iou_threshold=args.nms_thresh)
final_class_bboxes = tf.gather(top_class_bboxes, final_inds)
final_scores = tf.gather(top_class_scores, final_inds)
detection_list.append(final_class_bboxes)
score_list.append(final_scores)
net.create_segmentation_head(num_classes)
segmentation = tf.cast(tf.argmax(tf.squeeze(net.outputs['segmentation']),
axis=-1), tf.int32)
times = []
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)) as sess:
sess.run(tf.global_variables_initializer())
ckpt_path = train_dir + '/model.ckpt-%i000' % args.ckpt
log.debug("Restoring checkpoint %s" % ckpt_path)
saver = tf.train.Saver(tf.global_variables())
saver.restore(sess, ckpt_path)
for i in range(200):
im = loader.load_image(loader.get_filenames()[i])
im = imresize(im, (img_size, img_size))
im = im.reshape((1, img_size, img_size, 3))
st = time.time()
sess.run([detection_list, score_list, segmentation], feed_dict={image_ph: im})
et = time.time()
if i > 10:
times.append(et-st)
m = np.mean(times)
s = np.std(times)
fps = 1/m
log.info("Mean={0:.2f}ms; Std={1:.2f}ms; FPS={2:.1f}".format(m*1000, s*1000, fps))
def main(argv=None): # pylint: disable=unused-argument
net = ResNet
depth = 50
loader = VOCLoader('07', 'test')
net = net(config=net_config, depth=depth, training=False)
num_classes = 21
batch_size = args.batch_size
img_size = args.image_size
image_ph = tf.placeholder(shape=[1, img_size, img_size, 3],
dtype=tf.float32,
name='img_ph')
net.create_trunk(image_ph)
bboxer = PriorBoxGrid(net_config)
net.create_multibox_head(num_classes)
confidence = tf.nn.softmax(tf.squeeze(net.outputs['confidence']))
location = tf.squeeze(net.outputs['location'])
good_bboxes = decode_bboxes(location, bboxer.tiling)
detection_list = []
score_list = []
for i in range(1, num_classes):
class_mask = tf.greater(confidence[:, i], args.conf_thresh)
class_scores = tf.boolean_mask(confidence[:, i], class_mask)
class_bboxes = tf.boolean_mask(good_bboxes, class_mask)
K = tf.minimum(tf.size(class_scores), args.top_k_nms)
_, top_k_inds = tf.nn.top_k(class_scores, K)
top_class_scores = tf.gather(class_scores, top_k_inds)
top_class_bboxes = tf.gather(class_bboxes, top_k_inds)
final_inds = tf.image.non_max_suppression(
top_class_bboxes,
top_class_scores,
max_output_size=50,
iou_threshold=args.nms_thresh)
final_class_bboxes = tf.gather(top_class_bboxes, final_inds)
final_scores = tf.gather(top_class_scores, final_inds)
detection_list.append(final_class_bboxes)
score_list.append(final_scores)
net.create_segmentation_head(num_classes)
segmentation = tf.cast(
tf.argmax(tf.squeeze(net.outputs['segmentation']), axis=-1), tf.int32)
times = []
# im = cv2.imread("/home/lear/kshmelko/testImage.jpg")
# im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)/255.0
# im = im.astype(np.float32)
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True,
log_device_placement=False)) as sess:
sess.run(tf.global_variables_initializer())
ckpt_path = train_dir + '/model.ckpt-%i000' % args.ckpt
log.debug("Restoring checkpoint %s" % ckpt_path)
saver = tf.train.Saver(tf.global_variables())
saver.restore(sess, ckpt_path)
for i in range(200):
im = loader.load_image(loader.get_filenames()[i])
im = cv2.resize(im, (img_size, img_size))
im = im.reshape((1, img_size, img_size, 3))
st = time.time()
sess.run([detection_list, score_list, segmentation],
feed_dict={image_ph: im})
et = time.time()
if i > 10:
times.append(et - st)
m = np.mean(times)
s = np.std(times)
fps = 1 / m
log.info("Mean={0:.2f}ms; Std={1:.2f}ms; FPS={2:.1f}".format(
m * 1000, s * 1000, fps))