cfg.TRAIN.USE_JPG_AUG=False
_, valroidb = combined_roidb(args.imdbval_name)
print('{:d} validation roidb entries'.format(len(valroidb)))
cfg.TRAIN.USE_FLIPPED = orgflip
cfg.TRAIN.USE_NOISE_AUG = orgnoise
cfg.TRAIN.USE_JPG_AUG=orgjpg
# load network
if args.net=='inception_v3':
net=[]
elif args.net == 'vgg16':
net = vgg16(batch_size=cfg.TRAIN.IMS_PER_BATCH)
elif args.net == 'vgg16_noise':
net = vgg16_noise(batch_size=cfg.TRAIN.IMS_PER_BATCH)
elif args.net == 'res50':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=50)
elif args.net == 'res50_noise':
net = resnet_noise(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=50)
elif args.net == 'res101':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res101_noise':
net = resnet_noise(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res101_noise_init':
net = resnet_noise_init(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res101_fusion':
net = resnet_fusion(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res101_fusion_2rpn':
net = resnet_fusion_2rpn(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res101_fusion_2rpn_sep':
net = resnet_fusion_2rpn_sep(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res101_fusion_late_fusion':
if not os.path.isfile(tfmodel + '.meta'):
raise IOError(
('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(tfmodel + '.meta'))
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
if demonet == 'vgg16':
net = vgg16(batch_size=1)
elif demonet == 'res101':
net = resnetv1(batch_size=1, num_layers=101)
elif demonet == 'res50':
net = resnetv1(batch_size=1, num_layers=50)
else:
raise NotImplementedError
net.create_architecture(sess,
"TEST",
20,
tag='default',
anchor_scales=[8, 16, 32])
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
CONF_THRESH = 0.0
cap = cv2.VideoCapture(int(args.demo_file))
else:
AssertionError('type is not correct')
prior_mask = pickle.load(open(cfg.DATA_DIR + '/' + 'prior_mask.pkl', "rb"),
encoding='iso-8859-1')
Action_dic = json.load(open(cfg.DATA_DIR + '/' + 'action_index.json'))
Action_dic_inv = {y: x for x, y in Action_dic.items()}
# load detection model
detection_model = 'output/res50_faster_rcnn_iter_1190000.pth'
if not os.path.isfile(detection_model):
raise IOError(
('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(detection_model))
detection_net = resnetv1(num_layers=50)
detection_net.create_architecture(81,
tag='default',
anchor_scales=[4, 8, 16, 32],
anchor_ratios=[0.5, 1, 2])
detection_net.load_state_dict(
torch.load(detection_model, map_location=lambda storage, loc: storage))
# load hoi_detection model
hoi_model = 'output/HOI_iter_250000.pth'
if not os.path.isfile(hoi_model):
raise IOError(
('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(hoi_model))
# load network
tag = tag if tag else 'default'
filename = tag + '/' + filename
imdb = get_imdb(args.imdb_name)
imdb.competition_mode(args.comp_mode)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
if args.net == 'vgg16':
net = vgg16()
elif args.net == 'res50':
net = resnetv1(num_layers=50)
elif args.net == 'res101':
net = resnetv1(num_layers=101)
elif args.net == 'res152':
net = resnetv1(num_layers=152)
elif args.net == 'mobile':
net = mobilenetv1()
else:
raise NotImplementedError
# load model
net.create_architecture("TEST",
imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
print('Output will be saved to `{:s}`'.format(output_dir))
# tensorboard directory where the summaries are saved during training
tb_dir = get_output_tb_dir(imdb, args.tag)
print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))
# also add the validation set, but with no flipping images
orgflip = cfg.TRAIN.USE_FLIPPED
cfg.TRAIN.USE_FLIPPED = False
_, valroidb = combined_roidb(args.imdbval_name)
print('{:d} validation roidb entries'.format(len(valroidb)))
cfg.TRAIN.USE_FLIPPED = orgflip
# load network
if args.net == 'vgg16':
net = vgg16(batch_size=cfg.TRAIN.IMS_PER_BATCH)
elif args.net == 'res50':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=50)
elif args.net == 'res101':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
elif args.net == 'res152':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=152)
elif args.net == 'mobile':
net = mobilenetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH)
else:
raise NotImplementedError
train_net(net, imdb, roidb, valroidb, output_dir, tb_dir,
pretrained_model=args.weight,
max_iters=args.max_iters)
def __init__(self):
## Object of predictNearCollision class will be created here
#self.predNet = predictNearCollision()
self.nstream = MultiStreamNearCollision().cuda()
self.nstream.eval()
#self.nstream.load_state_dict(torch.load('../../../data/model_files/4Image6s_004'))
self.nstream.load_state_dict(
torch.load('../../../data/model_files/6Image6s_027'))
self.bridge = CvBridge()
self.image_sub = rospy.Subscriber("/zed/zed_node/rgb/image_rect_color",
Image, self.callback)
cfg.TEST.HAS_PRN = True
args = self.parse_args()
# model path
demonet = args.demo_net
dataset = args.dataset
tfmodel = os.path.join('../../../output', demonet,
DATASETS[dataset][0], 'default',
NETS[demonet][0])
if not os.path.isfile(tfmodel + '.meta'):
raise IOError(
('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(tfmodel +
'.meta'))
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
self.sess = tf.Session(config=tfconfig)
# load network
if demonet == 'vgg16':
self.net = vgg16()
elif demonet == 'res101':
self.net = resnetv1(num_layers=101)
else:
raise NotImplementedError
self.net.create_architecture("TEST",
21,
tag='default',
anchor_scales=[8, 16, 32])
saver = tf.train.Saver()
saver.restore(self.sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
self.counter = 0 ## Intializing a counter, alternately I can initialize a queue
self.stack_imgs = deque(maxlen=6) ## 4 frames
## To check the frequency
#self.image_pub = rospy.Publisher("image_topic_2", Image)
self.time_pub = rospy.Publisher('near_collision_time',
String,
queue_size=10)
def __init__(self,
net_name,
model_path,
cfg_file,
num_classes=2,
max_object_per_image=15,
conf_thresh=0.3,
nms_thresh=0.5,
iou_thresh=0.5):
self.net_name = net_name
# self.sess = sess
self.model_path = model_path
self.cfg_file = cfg_file
self.num_images = 1
self.num_classes = num_classes
self.conf_thresh = conf_thresh
self.nms_thresh = nms_thresh
self.iou_thresh = iou_thresh
self.max_object_per_image = max_object_per_image
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# tfconfig=tf.ConfigProto(log_device_placement=False,allow_soft_placement=True)
# init session
self.sess = tf.Session(config=tfconfig)
if not os.path.isfile(self.model_path + '.meta'):
raise IOError((
'{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(self.model_path +
'.meta'))
# load network configuration
cfg_from_file(self.cfg_file)
# pprint.pprint(cfg)
# load network
if self.net_name == 'vgg16':
self.net = vgg16(batch_size=1)
elif self.net_name == 'res50':
self.net = resnetv1(batch_size=1, num_layers=50)
elif self.net_name == 'res101':
self.net = resnetv1(batch_size=1, num_layers=101)
elif self.net_name == 'res152':
self.net = resnetv1(batch_size=1, num_layers=152)
elif self.net_name == 'mobile':
self.net = mobilenetv1(batch_size=1)
else:
raise NotImplementedError
with self.sess.as_default():
self.net.create_architecture(self.sess,
"TEST",
self.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
saver = tf.train.Saver()
saver.restore(self.sess, self.model_path)
if not os.path.isfile(tfmodel + '.meta'):
raise IOError(('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(tfmodel + '.meta'))
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth=True
# init session
sess = tf.Session(config=tfconfig)
# load network
if demonet == 'vgg16':
net = vgg16(batch_size=1)
elif demonet == 'res101':
net = resnetv1(batch_size=1, num_layers=101)
else:
raise NotImplementedError
net.create_architecture(sess, "TEST", 21,
tag='default', anchor_scales=[8, 16, 32])
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
im_names = ['000456.jpg', '000542.jpg', '001150.jpg',
'001763.jpg', '004545.jpg']
for im_name in im_names:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('Demo for data/demo/{}'.format(im_name))
demo(sess, net, im_name)
def sgv_test(sess,
dataset,
demonet,
checkpoint_file,
tfmodel,
result_path,
config=None):
"""Test one sequence
Args:
dataset: Reference to a Dataset object instance
checkpoint_path: Path of the checkpoint to use for the evaluation
result_path: Path to save the output images
config: Reference to a Configuration object used in the creation of a Session
Returns:
"""
if config is None:
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
# config.log_device_placement = True
config.allow_soft_placement = True
tf.logging.set_verbosity(tf.logging.INFO)
# Input data
batch_size = 1
input_image = tf.placeholder(tf.float32, [batch_size, None, None, 3])
# Create the cnn
with slim.arg_scope(osvos.osvos_arg_scope()):
net, end_points = osvos.osvos(input_image)
probabilities = tf.nn.sigmoid(net)
# global_step = tf.Variable(0, name='global_step', trainable=False)
# Create a saver to load the network
saver = tf.train.Saver([
v for v in tf.global_variables()
if '-up' not in v.name and '-cr' not in v.name
])
# with g.as_default():
# with tf.device('/gpu:' + str(gpu_id)):
# with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
sess.run(osvos.interp_surgery(tf.global_variables()))
saver.restore(sess, checkpoint_file)
if not os.path.exists(result_path):
os.makedirs(result_path)
#run osvos on all the frames
for frame in range(0, dataset.get_test_size()):
img, curr_img = dataset.next_batch(batch_size, 'test')
curr_frame = curr_img[0].split('/')[-1].split('.')[0] + '.png'
#test - osvos
image = osvos.preprocess_img(img[0])
res = sess.run(probabilities, feed_dict={input_image: image})
res_np = res.astype(np.float32)[0, :, :, 0] > 162.0 / 255.0
scipy.misc.imsave(os.path.join(result_path, curr_frame),
res_np.astype(np.float32))
# mask = res_np
# fig, ax = plt.subplots(figsize=(12, 12))
# vis_masks(img[0], mask, ax )
# plt.imsave(os.path.join("output","mask_"+curr_frame),mask)
#run faster-rcnn on all the frames
if demonet == 'vgg16':
net_rcnn = vgg16(batch_size=1)
elif demonet == 'res101':
net_rcnn = resnetv1(batch_size=1, num_layers=101)
else:
raise NotImplementedError
net_rcnn.create_architecture(sess,
"TEST",
21,
tag='default',
anchor_scales=[8, 16, 32])
vlist = [
v for v in tf.global_variables()
if 'osvos' not in v.name.split('/')[0] and 'global_step' not in v.name
]
saver = tf.train.Saver(vlist)
saver.restore(sess, tfmodel)
# saver = tf.train.Saver()
# saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
dataset.reset_iter()
for frame in range(0, dataset.get_test_size()):
img, curr_img = dataset.next_batch(batch_size, 'test')
curr_frame = curr_img[0].split('/')[-1].split('.')[0] + '.png'
#load mask
mask = scipy.misc.imread(os.path.join(result_path, curr_frame))
# mask = plt.imread(os.path.join("output", "mask_"+curr_frame))
#convert image rgb --> bgr
image = img[0][..., (2, 1, 0)]
#test - faster rcnn
timer = Timer()
timer.tic()
scores, boxes = im_detect(sess, net_rcnn, image)
timer.toc()
print('Detection took {:.3f}s for {:d} object proposals'.format(
timer.total_time, boxes.shape[0]))
CONF_THRESH = 0.8
NMS_THRESH = 0.3
classify_fg_bk(sess, mask, boxes)
#save the mask + detections overlay
fig, ax = plt.subplots(figsize=(12, 12))
# vis_masks(img[0], mask, ax)
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4 * cls_ind:4 * (cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack(
(cls_boxes, cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
vis_detections_masks(image,
mask,
ax,
curr_frame,
cls,
dets,
thresh=CONF_THRESH)
# image = osvos.preprocess_img(img[0])
# res = sess.run(probabilities, feed_dict={input_image: image})
# res_np = res.astype(np.float32)[0, :, :, 0] > 162.0/255.0
# scipy.misc.imsave(os.path.join(result_path, curr_frame), res_np.astype(np.float32))
outputpath = 'output'
plt.savefig(os.path.join(outputpath, curr_frame))
print('Saving ' + os.path.join(result_path, curr_frame))
def main():
global img
net = resnetv1(num_layers=50)
net.create_architecture(41,
tag='default', anchor_scales=[8, 16, 32])
saved_model = '/home/zhbli/Project/fast-rcnn/output/res50/voc_2007_trainval/default/res50_faster_rcnn_iter_70000.pth'
net.load_state_dict(torch.load(saved_model))
net.eval()
net.cuda()
# v4.0
# hook the feature extractor
finalconv_name = 'resnet'
features_blobs = [-1] # shape shoule be [2048, 7, 7]
def hook_feature(module, input, output):
features_blobs[0] = output.data.cpu().numpy()
net._modules.get(finalconv_name)._modules.get('layer4').register_forward_hook(hook_feature)
# get the softmax weight
params = list(net.parameters())
weight_softmax = np.squeeze(params[-4].data.cpu().numpy()) # shape = [41, 2048]
def returnCAM(feature_conv, weight_softmax, class_idx):
# generate the class activation maps upsample to 256x256
size_upsample = (256, 256)
bz, nc, h, w = feature_conv.shape
output_cam = []
for idx in class_idx:
cam = weight_softmax[class_idx].dot(feature_conv.reshape((nc, h * w)))
cam = cam.reshape(h, w)
cam = cam - np.min(cam)
cam_img = cam / np.max(cam)
cam_img = np.uint8(255 * cam_img)
output_cam.append(cv2.resize(cam_img, size_upsample))
return output_cam
# v4.0
"""loop"""
while 1:
img = cv2.imread('/data/zhbli/VOCdevkit/VOC2007/JPEGImages/000698.jpg')
assert img is not None, "fail to load img"
cv2.namedWindow('image')
cv2.setMouseCallback('image', on_mouse)
cv2.imshow('image', img)
cv2.waitKey(5000)
print('got rectangle')
cv2.destroyAllWindows()
print('Loaded network {:s}'.format(saved_model))
scores, boxes = im_detect(net, img)
CLASSES = ('__background__',
'aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor',
'aeroplane_truncated', 'bicycle_truncated', 'bird_truncated', 'boat_truncated',
'bottle_truncated', 'bus_truncated', 'car_truncated', 'cat_truncated', 'chair_truncated',
'cow_truncated', 'diningtable_truncated', 'dog_truncated', 'horse_truncated',
'motorbike_truncated', 'person_truncated', 'pottedplant_truncated',
'sheep_truncated', 'sofa_truncated', 'train_truncated', 'tvmonitor_truncated'
)
idx = np.argmax(scores, 1).squeeze()
box = boxes[:, 4 * idx:4 * (idx + 1)][0]
cls = CLASSES[idx]
# v4.0
CAMs = returnCAM(features_blobs[0], weight_softmax, [idx])
heatmap = cv2.applyColorMap(cv2.resize(CAMs[0], (roi[2]-roi[0], roi[3]-roi[1])), cv2.COLORMAP_JET)
result = heatmap * 0.3 + img[roi[1]:roi[3], roi[0]:roi[2], :] * 0.5
cv2.imwrite('CAM.jpg', result)
# v4.0
im = img[:, :, (2, 1, 0)]
fig, ax = plt.subplots(figsize=(12, 12))
ax.imshow(im, aspect='equal')
bbox = box
score = np.max(scores)
ax.add_patch(
plt.Rectangle((bbox[0], bbox[1]),
bbox[2] - bbox[0],
bbox[3] - bbox[1], fill=False,
edgecolor='red', linewidth=3.5)
)
ax.text(bbox[0], bbox[1] - 2,
'{:s} {:.3f}'.format(cls, score),
bbox=dict(facecolor='blue', alpha=0.5),
fontsize=14, color='white')
plt.axis('off')
plt.tight_layout()
plt.draw()
plt.show()
print('Called with args:')
print(args)
if args.cfg_file is not None:
cfg_from_file(args.cfg_file)
if args.set_cfgs is not None:
cfg_from_list(args.set_cfgs)
print('Using config:')
pprint.pprint(cfg)
np.random.seed(cfg.RNG_SEED)
# test set
orgflip = cfg.TRAIN.USE_FLIPPED
cfg.TRAIN.USE_FLIPPED = False
imdb, roidb = combined_roidb(args.imdbtest_name)
print('{:d} test roidb entries'.format(len(roidb)))
cfg.TRAIN.USE_FLIPPED = orgflip
cfg.TRAIN.SNAPSHOT_PREFIX = ""
cfg.TRAIN.SNAPSHOT_LOAD_PREFIX = ""
net = resnetv1(imdb.nof_ent_classes, imdb.nof_rel_classes, num_layers=101)
train_net(net, imdb, [], roidb, "", "",
pretrained_model=args.model,
max_iters=1,
just_test=True)
def launch_train(self, conf):
'''
'''
args = {}
args['cfg_file'] = conf.frcnn_cfg
args['weight'] = conf.starting_weights
args['imdb_name'] = conf.train_set
args['imdbval_name'] = conf.valid_set
args['max_iters'] = conf.iters
args['tag'] = conf.frcnn_tag
args['net'] = conf.frcnn_net
args['set_cfgs'] = None
print('Called with args:')
print(args)
if args['cfg_file'] is not None:
cfg_from_file(args['cfg_file'])
if args['set_cfgs'] is not None:
cfg_from_list(args['set_cfgs'])
print('Using config:')
pprint.pprint(cfg)
np.random.seed(cfg.RNG_SEED)
# train set
imdb, roidb = combined_roidb(args['imdb_name'], conf)
print('{:d} roidb entries'.format(len(roidb)))
# output directory where the models are saved
output_dir = conf.backup_folder #get_output_dir(imdb, args.tag)
print('Output will be saved to `{:s}`'.format(output_dir))
# tensorboard directory where the summaries are saved during training
tb_dir = conf.backup_folder # get_output_tb_dir(imdb, args.tag)
print('TensorFlow summaries will be saved to `{:s}`'.format(tb_dir))
# also add the validation set, but with no flipping images
orgflip = cfg.TRAIN.USE_FLIPPED
cfg.TRAIN.USE_FLIPPED = False
_, valroidb = combined_roidb(args['imdbval_name'], conf)
print('{:d} validation roidb entries'.format(len(valroidb)))
cfg.TRAIN.USE_FLIPPED = orgflip
if args['net'] == 'vgg16':
net = vgg16(batch_size=cfg.TRAIN.IMS_PER_BATCH)
elif args['net'] == 'res50':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=50)
elif args['net'] == 'res101':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=101)
# load network
elif args['net'] == 'res152':
net = resnetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH, num_layers=152)
elif args['net'] == 'mobile':
net = mobilenetv1(batch_size=cfg.TRAIN.IMS_PER_BATCH)
else:
raise NotImplementedError
train_net(net,
imdb,
roidb,
valroidb,
output_dir,
tb_dir,
pretrained_model=args['weight'],
max_iters=args['max_iters'])
def launch_test(self, conf, hash_model):
'''
'''
args = {}
args['cfg_file'] = conf.frcnn_cfg
args['weight'] = conf.starting_weights
args['model'] = hash_model
args['imdb_name'] = conf.valid_set
args['comp_mode'] = False
args['tag'] = conf.frcnn_tag
args['net'] = conf.frcnn_net
args['set_cfgs'] = None
args['max_per_image'] = 5
print('Called with args:')
print(args)
if args['cfg_file'] is not None:
cfg_from_file(argsargs['cfg_file'])
if args['set_cfgs'] is not None:
cfg_from_list(args['set_cfgs'])
print('Using config:')
pprint.pprint(cfg)
# if has model, get the name from it
# if does not, then just use the inialization weights
if args['model']:
filename = os.path.splitext(os.path.basename(args['model']))[0]
else:
filename = os.path.splitext(os.path.basename(args['weight']))[0]
tag = args['tag']
tag = tag if tag else 'default'
filename = tag + '/' + filename
# TODO This is really bad but it works, I'm sincerely sorry
conf_copy = copy.deepcopy(conf)
conf_copy.train_set = conf_copy.valid_set
imdb = get_imdb(args['imdb_name'], conf_copy)
print(args['imdb_name'])
imdb.competition_mode(args['comp_mode'])
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
if args['net'] == 'vgg16':
net = vgg16(batch_size=1)
elif args['net'] == 'res50':
net = resnetv1(batch_size=1, num_layers=50)
elif args['net'] == 'res101':
net = resnetv1(batch_size=1, num_layers=101)
elif args['net'] == 'res152':
net = resnetv1(batch_size=1, num_layers=152)
elif args['net'] == 'mobile':
net = mobilenetv1(batch_size=1)
else:
raise NotImplementedError
# load model
net.create_architecture(sess,
"TEST",
imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if args['model']:
print(
('Loading model check point from {:s}').format(args['model']))
saver = tf.train.Saver()
saver.restore(sess, args['model'])
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(args['weight']))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess,
net,
imdb,
filename,
max_per_image=args['max_per_image'])
sess.close()
if not os.path.isfile(tfmodel + '.meta'):
raise IOError(
('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(tfmodel +
'.meta'))
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
if demonet == 'res101':
net = resnetv1(num_layers=101)
else:
raise NotImplementedError
net.create_architecture("TEST",
len(CLASSES),
tag='default',
anchor_scales=[4, 8, 16, 32])
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
if not os.path.exists(OUTPUT_DIR):
os.mkdir(OUTPUT_DIR)
def print_detection(im_file,im, class_name, dets, img_shape,thresh=0.5):
"""Draw detected bounding boxes."""
inds = np.where(dets[:, -1] >= thresh)[0]
if len(inds) == 0:
return
contents=[]
for i in inds:
bbox = dets[i, :4]
score = dets[i, -1]
cv2.rectangle(im,(bbox[0],bbox[1]),(bbox[2],bbox[3]),(0,0,255),2)
font = cv2.FONT_HERSHEY_SIMPLEX
score=("%.3f" % float(score))
cv2.putText(im,str(class_name)+":"+str(score),(int(bbox[0]),int(bbox[1]-2)),font,0.45,(255,0,0),1)
print(("{} detections with p({} | box) >= {:.1f}").format(class_name, class_name,thresh))
element=[]
element.append(int(bbox[0]))
element.append(int(bbox[1]))
element.append(int(bbox[2]))
element.append(int(bbox[3]))
element.append(score)
element.append(class_name)
contents.append(element)
im_file=im_file.split('/')[-1]
if WRITE_IMG:
cv2.imwrite(os.path.join(output_img_path,im_file),im)
if WRITE_XML:
xml_write(test_img_path, output_xml_path, im_file,img_shape,contents,with_score=False)
def demo(sess, net, image_name):
"""Detect object classes in an image using pre-computed object proposals."""
# Load the demo image
im_file = os.path.join(cfg.DATA_DIR, 'demo/test_imgs', image_name)
if not os.path.exists(im_file):
print("Please check where test images exist!!!!!\n")
im = cv2.imread(im_file)
shape=im.shape
# Detect all object classes and regress object bounds
timer = Timer()
timer.tic()
scores, boxes = im_detect(sess, net, im)
timer.toc()
print('Detection took {:.3f}s for {:d} object proposals'.format(timer.total_time, boxes.shape[0]))
# Visualize detections for each class
CONF_THRESH = 0.8
NMS_THRESH = 0.3
for cls_ind, cls in enumerate(CLASSES[1:]):
cls_ind += 1 # because we skipped background
cls_boxes = boxes[:, 4*cls_ind:4*(cls_ind + 1)]
cls_scores = scores[:, cls_ind]
dets = np.hstack((cls_boxes,
cls_scores[:, np.newaxis])).astype(np.float32)
keep = nms(dets, NMS_THRESH)
dets = dets[keep, :]
# vis_detections(im, cls, dets, thresh=CONF_THRESH)
print_detection(im_file,im,cls,dets,shape,thresh=CONF_THRESH)
def parse_args():
"""Parse input arguments."""
parser = argparse.ArgumentParser(description='Tensorflow Faster R-CNN demo')
parser.add_argument('--net', dest='demo_net', help='Network to use [vgg16 res101]',
choices=NETS.keys(), default='res101')
parser.add_argument('--dataset', dest='dataset', help='Trained dataset [pascal_voc pascal_voc_0712]',
choices=DATASETS.keys(), default='pascal_voc')
args = parser.parse_args()
return args
if __name__ == '__main__':
cfg.TEST.HAS_RPN = True # Use RPN for proposals
args = parse_args()
# model path
demonet = args.demo_net
dataset = args.dataset
tfmodel = os.path.join('output', demonet, DATASETS[dataset][0], 'default',
'res101_faster_rcnn_iter_50000.ckpt')
if not os.path.isfile(tfmodel + '.meta'):
raise IOError(('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(tfmodel + '.meta'))
# set config
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth=True
# init session
sess = tf.Session(config=tfconfig)
# load network
if demonet == 'vgg16':
net = vgg16()
elif demonet == 'res101':
net = resnetv1(num_layers=101)
else:
raise NotImplementedError
net.create_architecture("TEST", 6,
tag='default', anchor_scales=[8, 16, 32])
saver = tf.train.Saver()
saver.restore(sess, tfmodel)
print('Loaded network {:s}'.format(tfmodel))
start_time=cv2.getTickCount()
im_names=[]
for temp_file in os.listdir(test_img_path):
im_names.append(temp_file)
for im_name in im_names:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('Demo for {}{}'.format(test_img_path,im_name))
demo(sess, net, im_name)
total_time=(cv2.getTickCount()-start_time)/float(cv2.getTickFrequency())
num_test_img=len(im_names)
time_per_img=float(total_time)/num_test_img
print("per image costs %f s in average!" % time_per_img)
tag = tag if tag else 'default'
filename = tag + '/' + filename
imdb = get_imdb(args.imdb_name)
imdb.competition_mode(args.comp_mode)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth=True
# init session
sess = tf.Session(config=tfconfig)
# load network
if args.net == 'vgg16':
net = vgg16()
elif args.net == 'res50':
net = resnetv1(num_layers=50)
elif args.net == 'res101':
net = resnetv1(num_layers=101)
elif args.net == 'res152':
net = resnetv1(num_layers=152)
elif args.net == 'mobile':
net = mobilenetv1()
else:
raise NotImplementedError
# load model
net.create_architecture("TEST", imdb.num_classes, tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if args.model:
tag = tag if tag else 'default'
filename = tag + '/' + filename
imdb = get_imdb(args.imdb_name)
imdb.competition_mode(args.comp_mode)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
if args.net == 'res41':
net = resnetv1_41(num_layers=41)
elif args.net == 'res50':
net = resnetv1(num_layers=50)
elif args.net == 'mobile':
net = mobilenetv1()
elif args.net == 'mobileenh':
net = mobilenetv1enh()
else:
raise NotImplementedError
# load model
net.create_architecture("TEST",
imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if args.model:
# model path
demonet = args.demo_net
dataset = args.dataset
saved_model = os.path.join('output', demonet, DATASETS[dataset][0], 'default',
NETS[demonet][0] % (70000 if dataset == 'pascal_voc' else 110000))
if not os.path.isfile(saved_model):
raise IOError(('{:s} not found.\nDid you download the proper networks from '
'our server and place them properly?').format(saved_model))
# load network
if demonet == 'vgg16':
net = vgg16()
elif demonet == 'res101':
net = resnetv1(num_layers=101)
else:
raise NotImplementedError
net.create_architecture(21, tag='default', anchor_scales=[8, 16, 32])
net.load_state_dict(torch.load(saved_model))
net.eval()
net.cuda()
print('Loaded network {:s}'.format(saved_model))
im_names = [i for i in os.listdir('data/demo/') # Pull in all jpgs
if i.lower().endswith(".jpg")]
for im_name in im_names:
def testing(imdbval_name, classes, cfg_file, model, weights, tag, net,
max_per_image):
__sets = {}
for split in ['train', 'val', 'trainval', 'test']:
name = imdbval_name.split('_')[0] + '_{}'.format(split)
__sets[name] = (lambda split=split: dataset(split, classes,
name.split('_')[0]))
if cfg_file is not None:
cfg_from_file(cfg_file)
print('Using config:')
pprint.pprint(cfg)
# if has model, get the name from it
# if does not, then just use the inialization weights
if model:
filename = os.path.splitext(os.path.basename(model))[0]
else:
filename = os.path.splitext(os.path.basename(weights))[0]
tag = tag if tag else 'default'
filename = tag + '/' + filename
imdb = get_imdb(imdbval_name, __sets)
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
# init session
sess = tf.Session(config=tfconfig)
# load network
if net == 'vgg16':
net = vgg16(batch_size=1)
elif net == 'res50':
net = resnetv1(batch_size=1, num_layers=50)
elif net == 'res101':
net = resnetv1(batch_size=1, num_layers=101)
elif net == 'res152':
net = resnetv1(batch_size=1, num_layers=152)
else:
raise NotImplementedError
# load model
net.create_architecture(sess,
"TEST",
imdb.num_classes,
tag='default',
anchor_scales=cfg.ANCHOR_SCALES,
anchor_ratios=cfg.ANCHOR_RATIOS)
if model:
print(('Loading model check point from {:s}').format(model))
saver = tf.train.Saver()
saver.restore(sess, model)
print('Loaded.')
else:
print(('Loading initial weights from {:s}').format(weights))
sess.run(tf.global_variables_initializer())
print('Loaded.')
test_net(sess, net, imdb, filename, max_per_image=max_per_image)
sess.close()
