def getIimg(image_filename, net, number_of_classes):
from tf_image_segmentation.models.fcn_32s import FCN_32s
im = Image.open(image_filename)
heatMap = np.empty((im.size[1], im.size[0], number_of_classes))
image_filename_placeholder = tf.placeholder(tf.string)
feed_dict_to_use = {image_filename_placeholder: image_filename}
image_tensor = tf.read_file(image_filename_placeholder)
image_tensor = tf.image.decode_jpeg(image_tensor, channels=3)
# Fake batch for image and annotation by adding leading empty axis.
image_batch_tensor = tf.expand_dims(image_tensor, axis=0)
# Be careful: after adaptation, network returns final labels and not logits
FCN_32s = adapt_network_for_any_size_input(FCN_32s, 32)
if imgNum == 0 and net_reuse == 0:
upsampled_logit, fcn_16s_variables_mapping_train = FCN_32s(
image_batch_tensor=image_batch_tensor,
number_of_classes=number_of_classes,
is_training=False,
reuse=None)
else:
upsampled_logit, fcn_16s_variables_mapping_train = FCN_32s(
image_batch_tensor=image_batch_tensor,
number_of_classes=number_of_classes,
is_training=False,
reuse=True)
pred = tf.argmax(upsampled_logit, dimension=3)
probabilities = tf.nn.softmax(upsampled_logit)
initializer = tf.local_variables_initializer()
saver = tf.train.Saver()
cmap = plt.get_cmap('bwr')
with tf.Session() as sess:
sess.run(initializer)
saver.restore(sess, net)
image_np, pred_np, probabilities_np = sess.run(
[image_tensor, pred, probabilities], feed_dict=feed_dict_to_use)
for i in range(0, number_of_classes):
heatMap[:, :, i] = probabilities_np.squeeze()[:, :, i]
iimg = heatMap.cumsum(axis=0).cumsum(axis=1)
return iimg
#
# img = cv2.imread(image_filename)
image_filename_placeholder = tf.placeholder(tf.string)
# feed_dict_to_use = {image_filename_placeholder: image_filename}
image_tensor = tf.read_file(image_filename_placeholder)
image_tensor = tf.image.decode_jpeg(image_tensor, channels=3)
# Fake batch for image and annotation by adding leading empty axis.
image_batch_tensor = tf.expand_dims(image_tensor, axis=0)
# Be careful: after adaptation, network returns final labels and not logits
FCN_32s = adapt_network_for_any_size_input(FCN_32s, 32)
# if imgNum == 0:
# reuse_var = False
# else:
# reuse_var = True
upsampled_logit, fcn_16s_variables_mapping_train = FCN_32s(
image_batch_tensor=image_batch_tensor,
number_of_classes=number_of_classes,
is_training=False,
reuse=False)
imgNum += 1
upsampled_logit_resized = tf.image.resize_images(upsampled_logit,