def process_image(img,
select_threshold=0.5,
nms_threshold=.45,
net_shape=(300, 300)):
# Run SSD network.
rimg, rpredictions, rlocalisations, rbbox_img = isess.run(
[image_4d, predictions, localisations, bbox_img],
feed_dict={img_input: img})
# Get classes and bboxes from the net outputs.
rclasses, rscores, rbboxes = np_methods.ssd_bboxes_select(
rpredictions,
rlocalisations,
ssd_anchors,
select_threshold=select_threshold,
img_shape=net_shape,
num_classes=21,
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=nms_threshold)
# Resize bboxes to original image shape. Note: useless for Resize.WARP!
rbboxes = np_methods.bboxes_resize(rbbox_img, rbboxes)
return rclasses, rscores, rbboxes
def get_bounding_box(self,
img,
select_threshold=0.5,
nms_threshold=.45,
net_shape=(300, 300)):
# Run SSD network.
rimg, rpredictions, rlocalisations, rbbox_img = self.isess.run(
[
self.image_4d, self.predictions, self.localisations,
self.bbox_img
],
feed_dict={self.img_input: img})
# Get classes and bboxes from the net outputs.
rclasses, rscores, rbboxes = np_methods.ssd_bboxes_select(
rpredictions,
rlocalisations,
self.ssd_anchors,
select_threshold=select_threshold,
img_shape=net_shape,
num_classes=21,
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=nms_threshold)
# Resize bboxes to original image shape. Note: useless for Resize.WARP!
rbboxes = np_methods.bboxes_resize(rbbox_img, rbboxes)
# Original SSD output is (ymin, xmin, ymax, xmax)
for i, rbbox in enumerate(rbboxes):
rbboxes[i] = (rbbox[1], rbbox[0], rbbox[3], rbbox[2])
return rclasses, rscores, rbboxes
def process_image(img,
select_threshold=0.5,
nms_threshold=.45,
net_shape=(300, 300)):
# In[5]:
# TensorFlow session: grow memory when needed. TF, DO NOT USE ALL MY GPU MEMORY!!!
gpu_options = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(log_device_placement=False,
gpu_options=gpu_options)
isess = tf.InteractiveSession(config=config)
# ## SSD 300 Model
#
# The SSD 300 network takes 300x300 image inputs. In order to feed any image, the latter is resize to this input shape (i.e.`Resize.WARP_RESIZE`). Note that even though it may change the ratio width / height, the SSD model performs well on resized images (and it is the default behaviour in the original Caffe implementation).
#
# SSD anchors correspond to the default bounding boxes encoded in the network. The SSD net output provides offset on the coordinates and dimensions of these anchors.
# In[6]:
# Input placeholder.
net_shape = (300, 300)
data_format = 'NHWC'
img_input = tf.placeholder(tf.uint8, shape=(None, None, 3))
# Evaluation pre-processing: resize to SSD net shape.
image_pre, labels_pre, bboxes_pre, bbox_img = ssd_vgg_preprocessing.preprocess_for_eval(
img_input,
None,
None,
net_shape,
data_format,
resize=ssd_vgg_preprocessing.Resize.WARP_RESIZE)
image_4d = tf.expand_dims(image_pre, 0)
# Define the SSD model.
reuse = True if 'ssd_net' in locals() else None
ssd_net = ssd_vgg_300.SSDNet()
with slim.arg_scope(ssd_net.arg_scope(data_format=data_format)):
predictions, localisations, _, _ = ssd_net.net(image_4d,
is_training=False,
reuse=reuse)
# Restore SSD model.
ROOT_DIR = os.path.dirname(os.path.realpath(__file__))
CKPT_DIR = os.path.join(ROOT_DIR, "checkpoints")
ckpt_filename = os.path.join(CKPT_DIR, "ssd_300_vgg.ckpt")
# ckpt_filename = '../checkpoints/VGG_VOC0712_SSD_300x300_ft_iter_120000.ckpt'
isess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(isess, ckpt_filename)
# SSD default anchor boxes.
ssd_anchors = ssd_net.anchors(net_shape)
# Run SSD network.
rimg, rpredictions, rlocalisations, rbbox_img = isess.run(
[image_4d, predictions, localisations, bbox_img],
feed_dict={img_input: img})
isess.close()
K.clear_session()
tf.reset_default_graph()
# Get classes and bboxes from the net outputs.
rclasses, rscores, rbboxes = np_methods.ssd_bboxes_select(
rpredictions,
rlocalisations,
ssd_anchors,
select_threshold=select_threshold,
img_shape=net_shape,
num_classes=21,
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=nms_threshold)
# Resize bboxes to original image shape. Note: useless for Resize.WARP!
rbboxes = np_methods.bboxes_resize(rbbox_img, rbboxes)
return rclasses, rscores, rbboxes