def test():
net, saver, image = load_net_for_inference()
with tf.Session() as sess:
saver.restore(sess, util.tf.get_latest_ckpt(FLAGS.checkpoint_path))
files = util.io.ls(FLAGS.dataset_dir)
for image_name in files:
start = time.time()
file_path = util.io.join_path(FLAGS.dataset_dir, image_name)
image_data = util.img.imread(file_path)
image_data = cv2.resize(
image_data, (FLAGS.eval_image_width, FLAGS.eval_image_height),
interpolation=cv2.INTER_AREA)
link_scores, pixel_scores = predict(sess, net, image, image_data)
link_time = time.time()
#cProfile.runctx('post_process(image_data, pixel_scores, mask_vals)', globals(), locals())
post_process(image_data, link_scores, pixel_scores)
post_process_new(image_data, link_scores, pixel_scores)
others = time.time()
output_file = os.path.expanduser(util.get_temp_path(""))
cv2.imwrite(output_file, image_data)
sit_time = time.time()
print("Score %.3f, postprocess %.3f, sit %.3f" %
(link_time - start, others - link_time, sit_time - others))
def test():
file_path = util.io.join_path(FLAGS.dataset_dir, "img_20.jpg")
image_data = util.img.imread(file_path)
image_data = cv2.resize(image_data,
(FLAGS.eval_image_width, FLAGS.eval_image_height),
interpolation=cv2.INTER_AREA)
link_scores = np.load("link_scores.bin")
pixel_scores = np.load("pixel_scores.bin")
boxes_old = post_process(image_data, link_scores, pixel_scores)
boxes_new = post_process_new(image_data, link_scores, pixel_scores)
output_file = os.path.expanduser(util.get_temp_path(""))
cv2.imwrite(output_file, image_data)
compare_boxes(boxes_old, boxes_new)
def show_images(images,
titles=None,
shape=None,
share_axis=False,
bgr2rgb=False,
maximized=False,
show=True,
gray=False,
save=False,
color_bar=False,
path=None,
axis_off=False,
vertical=False,
subtitle=None):
plt.close('all')
if shape == None:
if vertical:
shape = (len(images), 1)
else:
shape = (1, len(images))
shape = list(shape)
if shape[0] < 0:
shape[0] = (len(images) + shape[1]) / shape[1]
elif shape[1] < 0:
shape[1] = (len(images + shape[0])) / shape[0]
ret_axes = []
ax0 = None
plt.figure()
for idx, img in enumerate(images):
if bgr2rgb:
img = util.img.bgr2rgb(img)
loc = (idx / shape[1], idx % shape[1])
if idx == 0:
ax = plt.subplot2grid(shape, loc)
ax0 = ax
else:
if share_axis:
ax = plt.subplot2grid(shape, loc, sharex=ax0, sharey=ax0)
else:
ax = plt.subplot2grid(shape, loc)
if len(np.shape(img)) == 2 and gray:
img_ax = ax.imshow(img, cmap='gray')
else:
img_ax = ax.imshow(img)
if len(np.shape(img)) == 2 and color_bar:
plt.colorbar(img_ax, ax=ax)
if titles != None:
ax.set_title(titles[idx])
if axis_off:
plt.axis('off')
# plt.xticks([]), plt.yticks([])
ret_axes.append(ax)
if subtitle is not None:
set_subtitle(subtitle)
if maximized:
maximize_figure()
if save:
if path is None:
path = util.get_temp_path()
# raise ValueError('path can not be None when save is True')
save_image(path)
if show:
plt.show()
return path