def run(self):
with tf.Graph().as_default():
self.get_voc_2007_test_data()
with tf.Session('') as sess:
init = tf.global_variables_initializer()
sess.run(init)
with slim.queues.QueueRunners(sess):
image, glabels, gbboxes, filename, jaccard = sess.run([
self.image, self.glabels, self.gbboxes, self.filename,
self.jaccard
])
print(filename)
print(glabels)
print(gbboxes)
print(jaccard)
#selet the first image in the batch
image_data = np_image_unwhitened(image)
self.__disp_image(image_data, glabels, gbboxes)
# found_matched = self.__disp_matched_anchors(image_data,target_labels_data, target_localizations_data, target_scores_data)
plt.show()
return
num_classes=9,
decode=True)
rbboxes = np_methods.bboxes_clip(rbbox_img, rbboxes)
rclasses, rscores, rbboxes = np_methods.bboxes_sort(rclasses,
rscores,
rbboxes,
top_k=400)
rclasses, rscores, rbboxes = np_methods.bboxes_nms(rclasses,
rscores,
rbboxes,
nms_threshold=0.3)
# In[31]:
# Draw bboxes
img_bboxes = np.copy(ssd_vgg_preprocessing.np_image_unwhitened(rimg))
bboxes_draw_on_img(img_bboxes,
rclasses,
rscores,
rbboxes,
colors_tableau,
thickness=1)
# bboxes_draw_on_img(img_bboxes, test_labels, test_scores, test_bboxes, colors_tableau, thickness=1)
print('Labels / scores:', list(zip(rclasses, rscores)))
print('Grountruth labels:', list(glabels))
print(gbboxes)
fig = plt.figure(figsize=(20, 20))
plt.imshow(img_bboxes)
def run(self):
with tf.Graph().as_default():
# batch_data= self.get_voc_2007_train_data(is_training_data=True)
# batch_data = self.get_voc_2007_test_data()
# batch_data = self.get_voc_2012_train_data()
# batch_data = self.get_voc_2007_2012_train_data(is_training_data = True)
batch_data = self.get_gtsdb_train_data(is_training_data=True)
return self.iterate_file_name(batch_data)
with tf.Session('') as sess:
init = tf.global_variables_initializer()
sess.run(init)
with slim.queues.QueueRunners(sess):
while True:
image, filename, glabels, gbboxes, gdifficults, gclasses, glocalisations, gscores = sess.run(
list(batch_data))
# print("min: {}, max: {}".format(gbboxes.min(), gbboxes.max()))
# return
# print(glabels)
# print("number of zero label patch {}".format((glabels.sum(axis=1) == 0).sum()))
# return
#
print(filename)
selected_file = b'000050'
picked_inds = None
#selet the first image in the batch
if selected_file is None:
picked_inds = 0
else:
picked_inds = (selected_file == filename).nonzero()
if len(picked_inds[0]) == 0:
picked_inds = None
else:
picked_inds = picked_inds[0][0]
if picked_inds is None:
continue
self.check_match_statistics(filename, gclasses,
gscores)
target_labels_data = [
item[picked_inds] for item in gclasses
]
target_localizations_data = [
item[picked_inds] for item in glocalisations
]
target_scores_data = [
item[picked_inds] for item in gscores
]
image_data = image[picked_inds]
print("picked file {}".format(filename[picked_inds]))
image_data = np_image_unwhitened(image_data)
self.__disp_image(image_data, glabels[picked_inds],
gbboxes[picked_inds])
found_matched = self.__disp_matched_anchors(
image_data, target_labels_data,
target_localizations_data, target_scores_data)
plt.show()
break
#exit the batch data testing right after a successful match have been found
# if found_matched:
#this could be a potential issue to be solved since sometime not all grouth truth bboxes are encoded.
return