def Setup(self):
# self.ckpt = SSD_CKPT
self.thres = SSD_THRES
self.nms_thres = SSD_NMS
self.net_shape = SSD_NET_SHAPE
# gpu_options = tf.GPUOptions(allow_growth=True)
# config = tf.ConfigProto(log_device_placement=False, gpu_options=gpu_options)
# self.isess = tf.InteractiveSession(config=config)
# data_format = 'NHWC'
# self.img_input = tf.placeholder(tf.uint8, shape=(None, None, 3))
# image_pre, labels_pre, bboxes_pre, bbox_img = ssd_vgg_preprocessing.preprocess_for_eval(
# self.img_input, None, None, self.net_shape, data_format,
# resize=ssd_vgg_preprocessing.Resize.WARP_RESIZE)
# self.image_4d = tf.expand_dims(image_pre, 0)
# self.bbx = bbox_img
# reuse = True if 'ssd_net' in locals() else None
ssd_net = ssd_vgg_512.SSDNet()
# slim = tf.contrib.slim
# with slim.arg_scope(ssd_net.arg_scope(data_format=data_format)):
# predictions, localisations, _, _ = ssd_net.net(self.image_4d, is_training=False, reuse=reuse)
# self.isess.run(tf.global_variables_initializer())
# saver = tf.train.Saver()
# saver.restore(self.isess, self.ckpt)
# self.pred = predictions
# self.loc = localisations
self.ssd_anchors = ssd_net.anchors(self.net_shape)
self.total_classes = 21
def __init__(self):
# 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)
# Input placeholder.
self.net_shape = (512, 512)
data_format = 'NHWC'
self.img_input = tf.placeholder(tf.uint8, shape=(None, None, 3))
# Evaluation pre-processing: resize to SSD net shape.
image_pre, labels_pre, bboxes_pre, self.bbox_img = ssd_vgg_preprocessing.preprocess_for_eval(
self.img_input, None, None, self.net_shape, data_format, resize=ssd_vgg_preprocessing.Resize.WARP_RESIZE)
self.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_512.SSDNet()
with slim.arg_scope(ssd_net.arg_scope(data_format=data_format)):
self.predictions, self.localisations, _, _ = ssd_net.net(self.image_4d, is_training=False, reuse=reuse)
# Restore SSD model.
ckpt_filename = 'tl_ssd_detector/checkpoint/model.ckpt-226812'
self.isess = tf.Session(config=config)
self.isess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.isess, ckpt_filename)
# SSD default anchor boxes.
self.ssd_anchors = ssd_net.anchors(self.net_shape)
def Setup():
SSD.thres = SSD_THRES
SSD.nms_thres = SSD_NMS
SSD.net_shape = SSD_NET_SHAPE
ssd_net = ssd_vgg_512.SSDNet()
SSD.ssd_anchors = ssd_net.anchors(SSD.net_shape)
SSD.total_classes = 21
def __init__(self, params=None):
# TensorFlow session: grow memory when needed. TF, DO NOT USE ALL MY GPU MEMORY!!!
self.gpu_options = tf.GPUOptions(allow_growth=True)
self.config = tf.ConfigProto(log_device_placement=False,
gpu_options=self.gpu_options)
# Input placeholder.
self.net_shape = (512, 512) if MODEL == 512 else (300, 300)
self.data_format = 'NHWC'
self.img_input = tf.placeholder(tf.uint8, shape=(None, None, 3))
# Evaluation pre-processing: resize to SSD net shape.
self.image_pre, self.labels_pre, self.bboxes_pre, self.bbox_img = ssd_vgg_preprocessing.preprocess_for_eval(
self.img_input,
None,
None,
self.net_shape,
self.data_format,
resize=ssd_vgg_preprocessing.Resize.WARP_RESIZE)
self.image_4d = tf.expand_dims(self.image_pre, 0)
# Define the SSD model.
try:
tf.get_variable('ssd_512_vgg/conv1/conv1_1/weights'
) if MODEL == 512 else tf.get_variable(
'ssd_300_vgg/conv1/conv1_1/weights')
self.reuse = True # if tf.variable_scope('ssd_300_vgg/conv1/conv1_1/weights') else None
except ValueError:
print('model loading failed')
self.reuse = None
self.ssd_net = ssd_vgg_512.SSDNet(
) if MODEL == 512 else ssd_vgg_300.SSDNet()
with slim.arg_scope(
self.ssd_net.arg_scope(data_format=self.data_format)):
self.predictions, self.localisations, _, _ = self.ssd_net.net(
self.image_4d, is_training=False, reuse=self.reuse)
# Restore SSD model.
self.ckpt_filename = './checkpoints/VGG_VOC0712_SSD_512x512_ft_iter_120000.ckpt' if MODEL == 512 else './checkpoints/ssd_300_vgg.ckpt'
# SSD default anchor boxes.
self.ssd_anchors = self.ssd_net.anchors(self.net_shape)
self.isess = tf.InteractiveSession(config=self.config)
# Load Model
self.isess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver()
self.saver.restore(self.isess, self.ckpt_filename)
def load(self):
try:
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,
self.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
if self.net_to_use == 'ssd-300':
ssd_net = ssd_vgg_300.SSDNet()
else:
ssd_net = ssd_vgg_512.SSDNet()
with tf.contrib.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.
self.isess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
saver.restore(self.isess, self.ckpt_filename)
# SSD default anchor boxes.
ssd_anchors = ssd_net.anchors(self.net_shape)
cropper_model = SSD_Bundle(ssd_net, img_input, predictions,
localisations, bbox_img, image_4d,
ssd_anchors)
return cropper_model
except Exception as e:
message = "Could not load model."
print(message + str(e))
raise Exception(message)
gpu_options = tf.GPUOptions(allow_growth=True)
config = tf.ConfigProto(log_device_placement=False, gpu_options=gpu_options)
isess = tf.InteractiveSession(config=config)
# Input placeholder.
net_shape = (512, 512)
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_512.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.
ckpt_filename = './checkpoints/VGG_VOC0712_SSD_512x512_ft_iter_120000.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)
# Main image processing routine.
def process_image(img, select_threshold=0.55, nms_threshold=.45, net_shape=(512, 512)):
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
from nets import ssd_vgg_512
from nets import ssd_common
from preprocessing import ssd_vgg_preprocessing
ckpt_filename = '/mogu/liubang/mytf/SSD-Tensorflow/logs2/model.ckpt-122449'
NUM = 7
# SSD object.
reuse = True if 'ssd' in locals() else None
params = ssd_vgg_512.SSDNet.default_params
ssd_params = params._replace(num_classes=NUM)
ssd = ssd_vgg_512.SSDNet(ssd_params)
# Image pre-processimg
out_shape = ssd.params.img_shape
image_pre, labels_pre, bboxes_pre, bbox_img = \
ssd_vgg_preprocessing.preprocess_for_eval(image, labels, bboxes, out_shape,
resize=ssd_vgg_preprocessing.Resize.WARP_RESIZE)
image_4d = tf.expand_dims(image_pre, 0)
# SSD construction.
with slim.arg_scope(ssd.arg_scope(weight_decay=0.0005)):
predictions, localisations, logits, end_points = ssd.net(image_4d,
is_training=False,
reuse=reuse)
