def postprocess_regular(image_info, np_pred_boxes, np_pred_confidences, H,
options):
pred_anno = al.Annotation()
pred_anno.imageName = image_info['path']
pred_anno.imagePath = os.path.abspath(image_info['path'])
_, rects = add_rectangles(H, [image_info['transformed']],
np_pred_confidences,
np_pred_boxes,
use_stitching=True,
rnn_len=H['rnn_len'],
min_conf=options['min_conf'],
tau=options['tau'],
show_suppressed=False)
h, w = image_info['original_shape']
if 'rotate90' in H['data'] and H['data']['rotate90']:
# original image height is a width for rotated one
rects = Rotate90.invert(h, rects)
rects = [
r for r in rects
if r.x1 < r.x2 and r.y1 < r.y2 and r.score > options['min_conf']
]
pred_anno.rects = rects
pred_anno = rescale_boxes((H['image_height'], H['image_width']), pred_anno,
h, w)
return pred_anno
def log_image(np_img, np_confidences, np_boxes, np_global_step, pred_or_true):
merged = train_utils.add_rectangles(self.H, np_img, np_confidences, np_boxes,
use_stitching=True,
rnn_len=self.H['rnn_len'], show_suppressed=True)[0]
num_images = 10
img_path = os.path.join(self.H['save_dir'], '%s_%s.jpg' % ((np_global_step / self.H['logging']['display_iter']) % num_images, pred_or_true))
misc.imsave(img_path, merged)
return merged
def log_image(np_img, np_confidences, np_boxes, np_global_step,
pred_or_true):
merged = train_utils.add_rectangles(np_img,
np_confidences,
np_boxes,
use_stitching=False,
rnn_len=FLAGS.rnn_len)[0]
num_images = 10
img_path = os.path.join(
save_dir,
'/%s_%s.jpg' % ((np_global_step / FLAGS.display_iter) %
num_images, pred_or_true))
print(img_path)
misc.imsave(img_path, merged)
return merged
def eval(self, weights, test_boxes, min_conf, tau, show_suppressed, expname):
self.H["grid_width"] = self.H["image_width"] / self.H["region_size"]
self.H["grid_height"] = self.H["image_height"] / self.H["region_size"]
x_in = tf.placeholder(tf.float32, name='x_in', shape=[self.H['image_height'], self.H['image_width'], 3])
if self.H['use_rezoom']:
pred_boxes, pred_logits, pred_confidences, pred_confs_deltas, pred_boxes_deltas = self.build_forward(tf.expand_dims(x_in, 0), 'test', reuse=None)
grid_area = self.H['grid_height'] * self.H['grid_width']
pred_confidences = tf.reshape(tf.nn.softmax(tf.reshape(pred_confs_deltas, [grid_area * self.H['rnn_len'], 2])),
[grid_area, self.H['rnn_len'], 2])
if self.H['reregress']:
pred_boxes = pred_boxes + pred_boxes_deltas
else:
pred_boxes, pred_logits, pred_confidences = self.build_forward(tf.expand_dims(x_in, 0), 'test', reuse=None)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
saver.restore(sess, weights)
pred_annolist = al.AnnoList()
true_annolist = al.parse(test_boxes)
data_dir = os.path.dirname(test_boxes)
image_dir = self.get_image_dir(weights, expname, test_boxes)
subprocess.call('mkdir -p %s' % image_dir, shell=True)
for i in range(len(true_annolist)):
true_anno = true_annolist[i]
orig_img = imread('%s/%s' % (data_dir, true_anno.imageName))[:,:,:3]
img = imresize(orig_img, (self.H["image_height"], self.H["image_width"]), interp='cubic')
feed = {x_in: img}
(np_pred_boxes, np_pred_confidences) = sess.run([pred_boxes, pred_confidences], feed_dict=feed)
pred_anno = al.Annotation()
pred_anno.imageName = true_anno.imageName
new_img, rects = add_rectangles(self.H, [img], np_pred_confidences, np_pred_boxes,
use_stitching=True, rnn_len=self.H['rnn_len'], min_conf=min_conf, tau=tau, show_suppressed=show_suppressed)
pred_anno.rects = rects
pred_anno.imagePath = os.path.abspath(data_dir)
pred_anno = rescale_boxes((self.H["image_height"], self.H["image_width"]), pred_anno, orig_img.shape[0], orig_img.shape[1])
pred_annolist.append(pred_anno)
imname = '%s/%s' % (image_dir, os.path.basename(true_anno.imageName))
misc.imsave(imname, new_img)
if i % 25 == 0:
print(i)
return pred_annolist, true_annolist