def __init__(self):
self.class_names = [name.strip() for name in open(label_path).readlines()]
self.num_classes = len(self.class_names)
self.net = create_mobilenetv2_ssd_lite(len(self.class_names), is_test=True)
self.net.load(model_path)
self.predictor = create_mobilenetv2_ssd_lite_predictor(self.net, candidate_size=200,
device=torch.device("cuda:0"))
def __init__(self, **kwargs):
self.__dict__.update(self._defaults) # set up default values
self.__dict__.update(kwargs) # and update with user overrides
self.class_names = self._get_class()
self.net = create_mobilenetv2_ssd_lite(len(self.class_names), is_test=True)
self.net.load(self.model_path)
self.net.eval()
self.net.cuda()
self.predictor = create_mobilenetv2_ssd_lite_predictor(self.net, candidate_size=200, device='cuda')
def __init__(self):
model = "v1"
self.prob_threshold = 0.85
self.cv_bridge = CvBridge()
self.labels = ['background' , # always index 0
'person','palm']
self.objects = []
if model == "v2_lite":
self.network = create_mobilenetv2_ssd_lite(len(self.labels), is_test=True)
elif model == "v1":
self.network = create_mobilenetv1_ssd(len(self.labels), is_test=True)
elif model == "v1_lite":
self.network = create_mobilenetv1_ssd_lite(len(self.labels), is_test=True)
model_path = '/home/arg_ws3/pytorch-ssd/models/argbot_person_palm_new_new/mb1-ssd-Epoch-749-Loss-1.8576.pth'
state_dict = torch.load(os.path.join(model_path))
self.network.load_state_dict(state_dict)
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.network.to(DEVICE)
if model == "v2_lite":
self.predictor = create_mobilenetv2_ssd_lite_predictor(self.network, candidate_size=200, device = DEVICE)
elif model == "v1_lite":
self.predictor = create_mobilenetv1_ssd_lite_predictor(self.network, candidate_size=200, device = DEVICE)
elif model == "v1":
self.predictor = create_mobilenetv1_ssd_predictor(self.network, candidate_size=200, device = DEVICE)
#### Publisher
self.origin = rospy.Publisher('/input', bb_input, queue_size=1)
self.image_pub = rospy.Publisher("/predict_img", Image, queue_size = 1)
self.pub_tracking = rospy.Publisher("/tracking_point", Marker, queue_size = 1)
self.pub_point_array = rospy.Publisher("/person_point_array", MarkerArray, queue_size = 1)
self.kf_x = KalmanFilter(dim_x=2, dim_z=1)
self.kf_y = KalmanFilter(dim_x=2, dim_z=1)
self.path = []
self.person_list = []
self.cv_depthimage = None
self.start_tracking = False
self.frame_id = 'camera_link'
self.t_old = None
self.t_now = None
info_msg = rospy.wait_for_message('/camera/color/camera_info', CameraInfo, timeout=None)
self.fx = info_msg.P[0]
self.fy = info_msg.P[5]
self.cx = info_msg.P[2]
self.cy = info_msg.P[6]
### msg filter
self.is_compressed = False
image_sub = message_filters.Subscriber('/camera/color/image_raw', Image)
depth_sub = message_filters.Subscriber('/camera/aligned_depth_to_color/image_raw', Image)
ts = message_filters.TimeSynchronizer([image_sub, depth_sub], 10)
ts.registerCallback(self.callback)
def __init__(self):
model = "v1"
self.prob_threshold = 0.85
self.cv_bridge = CvBridge()
self.num_points = 8000
self.labels = [
'background', # always index 0
'person',
'palm'
]
self.objects = []
if model == "v2_lite":
self.network = create_mobilenetv2_ssd_lite(len(self.labels),
is_test=True)
elif model == "v1":
self.network = create_mobilenetv1_ssd(len(self.labels),
is_test=True)
elif model == "v1_lite":
self.network = create_mobilenetv1_ssd_lite(len(self.labels),
is_test=True)
model_path = '/home/arg_ws3/pytorch-ssd/models/argbot_person_palm/mb1-ssd-Epoch-10-Loss-3.1767.pth'
state_dict = torch.load(os.path.join(model_path))
self.network.load_state_dict(state_dict)
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.network.to(DEVICE)
if model == "v2_lite":
self.predictor = create_mobilenetv2_ssd_lite_predictor(
self.network, candidate_size=200, device=DEVICE)
elif model == "v1_lite":
self.predictor = create_mobilenetv1_ssd_lite_predictor(
self.network, candidate_size=200, device=DEVICE)
elif model == "v1":
self.predictor = create_mobilenetv1_ssd_predictor(
self.network, candidate_size=200, device=DEVICE)
#### Publisher
self.origin = rospy.Publisher('/input', bb_input, queue_size=1)
self.image_pub = rospy.Publisher("/predict_img", Image, queue_size=1)
self.mask_pub = rospy.Publisher("/predict_mask", Image, queue_size=1)
### msg filter
self.is_compressed = False
video_mode = False
if video_mode:
image_sub = rospy.Subscriber('/camera/color/image_raw', Image,
self.video_callback)
else:
image_sub = message_filters.Subscriber('/camera/color/image_raw',
Image)
depth_sub = message_filters.Subscriber(
'/camera/aligned_depth_to_color/image_raw', Image)
ts = message_filters.TimeSynchronizer([image_sub, depth_sub], 10)
ts.registerCallback(self.callback)
def __init__(self):
model = "v1"
self.prob_threshold = 0.65
r = rospkg.RosPack()
self.path = r.get_path('ssd_mobile_lite')
self.cv_bridge = CvBridge()
self.num_points = 8000
self.labels = [
'background', # always index 0
'duckie_car',
'house',
'broken',
'duck'
]
self.objects = []
if model == "v2_lite":
self.network = create_mobilenetv2_ssd_lite(len(self.labels),
is_test=True)
model_dir = "/home/nvidia"
model_name = "model1.pth"
elif model == "v1":
self.network = create_mobilenetv1_ssd(len(self.labels),
is_test=True)
model_name = "/models/model.pth"
elif model == "v1_lite":
self.network = create_mobilenetv1_ssd_lite(len(self.labels),
is_test=True)
model_name = "/models/model.pth"
state_dict = torch.load(self.path + model_name)
self.network.load_state_dict(state_dict)
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.network.to(DEVICE)
if model == "v2_lite":
self.predictor = create_mobilenetv2_ssd_lite_predictor(
self.network, candidate_size=200, device=DEVICE)
elif model == "v1_lite":
self.predictor = create_mobilenetv1_ssd_lite_predictor(
self.network, candidate_size=200, device=DEVICE)
elif model == "v1":
self.predictor = create_mobilenetv1_ssd_predictor(
self.network, candidate_size=200, device=DEVICE)
print("finish load model")
#### Publisher
self.image_pub = rospy.Publisher("/predict_img", Image, queue_size=1)
### msg filter
self.is_compressed = True
image_sub = rospy.Subscriber('/camera/color/image_raw/compressed',
CompressedImage, self.callback)
def init_mobile_net(self):
print(sys.path)
self.label_path = '/home/keita/catkin_ws/src/preprocess/scripts/models/voc-model-labels.txt'
self.model_path = '/home/keita/catkin_ws/src/preprocess/scripts/models/mb2-ssd-lite-mp-0_686.pth'
self.class_names = [
name.strip() for name in open(self.label_path).readlines()
]
self.num_classes = len(self.class_names)
self.net = create_mobilenetv2_ssd_lite(len(self.class_names),
is_test=True)
self.net.load(self.model_path)
self.predictor = create_mobilenetv2_ssd_lite_predictor(
self.net, candidate_size=200)
def create_predictor(model, model_type):
if model_type == 'vgg16-ssd':
return create_vgg_ssd_predictor(model, candidate_size=200)
elif model_type == 'mb1-ssd':
return create_mobilenetv1_ssd_predictor(model, candidate_size=200)
elif model_type == 'mb1-ssd-lite':
return create_mobilenetv1_ssd_lite_predictor(model, candidate_size=200)
elif model_type == 'mb2-ssd-lite':
return create_mobilenetv2_ssd_lite_predictor(model, candidate_size=200)
elif model_type == 'sq-ssd-lite':
return create_squeezenet_ssd_lite_predictor(model, candidate_size=200)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
def Init_Net(self, net_type):
print("==========Init Net==========")
if net_type=="mb2-ssd":
model_path = "./models/mobilenet2-ssd.pth"
label_path = "./models/voc-model-labels_mb2.txt"
self.class_names = [name.strip() for name in open(label_path).readlines()]
net = create_mobilenetv2_ssd_lite(len(self.class_names), is_test=True)
net.load(model_path)
self.predictor = create_mobilenetv2_ssd_lite_predictor(net, candidate_size=200)
elif net_type=="mb3-large-ssd":
model_path = "./models/mobilenet3-large-ssd.pth"
label_path = "./models/voc-model-labels_mb3.txt"
self.class_names = [name.strip() for name in open(label_path).readlines()]
net = create_mobilenetv3_ssd_lite("Large", len(self.class_names), is_test=True)
net.load(model_path)
self.predictor = create_mobilenetv3_ssd_lite_predictor(net, candidate_size=200)
def _create_predictor(self, net_type):
if net_type == 'vgg16-ssd':
return create_vgg_ssd_predictor(self.net, candidate_size=200)
elif net_type == 'mb1-ssd':
return create_mobilenetv1_ssd_predictor(self.net,
candidate_size=200)
elif net_type == 'mb1-ssd-lite':
return create_mobilenetv1_ssd_lite_predictor(self.net,
candidate_size=200)
elif net_type == 'mb2-ssd-lite':
return create_mobilenetv2_ssd_lite_predictor(self.net,
candidate_size=200)
elif net_type == 'sq-ssd-lite':
return create_squeezenet_ssd_lite_predictor(self.net,
candidate_size=200)
else:
raise RuntimeError(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
def select_net(model_path, net_type, num_classes):
'''
选择模型
:param model_path: 模型路径
:param net_type: 模型类型
:param num_classes: label个数，label=0,是背景
:return:
'''
if net_type == 'vgg16-ssd':
net = create_vgg_ssd(num_classes, is_test=True)
elif net_type == 'mb1-ssd':
net = create_mobilenetv1_ssd(num_classes, is_test=True)
elif net_type == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(num_classes, is_test=True)
elif net_type == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(num_classes,
is_test=True,
device=device)
elif net_type == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(num_classes, is_test=True)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
net.load(model_path)
if net_type == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(net,
candidate_size=200)
elif net_type == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(net,
candidate_size=200,
device=device)
elif net_type == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(net,
candidate_size=200)
else:
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
return predictor
def __init__(self):
model = "v1"
self.prob_threshold = 0.65
self.cv_bridge = CvBridge()
self.num_points = 8000
self.labels = ['background' , # always index 0
'duckie_car','house','broken','duck']
self.objects = []
if model == "v2_lite":
self.network = create_mobilenetv2_ssd_lite(len(self.labels), is_test=True)
model_dir = "/home/nvidia"
model_name = "model1.pth"
elif model == "v1":
self.network = create_mobilenetv1_ssd(len(self.labels), is_test=True)
model_dir = "/home/nvidia"
model_name = "model.pth"
elif model == "v1_lite":
self.network = create_mobilenetv1_ssd_lite(len(self.labels), is_test=True)
model_dir = "/home/nvidia/"
model_name = "model.pth"
state_dict = torch.load(os.path.join(model_dir, model_name))
self.network.load_state_dict(state_dict)
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.network.to(DEVICE)
if model == "v2_lite":
self.predictor = create_mobilenetv2_ssd_lite_predictor(self.network, candidate_size=200, device = DEVICE)
elif model == "v1_lite":
self.predictor = create_mobilenetv1_ssd_lite_predictor(self.network, candidate_size=200, device = DEVICE)
elif model == "v1":
self.predictor = create_mobilenetv1_ssd_predictor(self.network, candidate_size=200, device = DEVICE)
#### Publisher
self.origin = rospy.Publisher('/input', bb_input, queue_size=1)
self.image_pub = rospy.Publisher("/predict_img", Image, queue_size = 1)
self.mask_pub = rospy.Publisher("/predict_mask", Image, queue_size = 1)
self.position = rospy.Publisher("/position", Point32, queue_size = 1)
### msg filter
self.is_compressed = False
image_sub = rospy.Subscriber('/camera/color/image_raw', Image, self.callback)
def __init__(self):
model = "v1"
r = rospkg.RosPack()
path = r.get_path('ssd_mobile_lite')
model_name = "Epoch-630-Loss-0.4744.pth"
self.prob_threshold = 0.5
self.cv_bridge = CvBridge()
self.labels = ['BACKGROUND', 'backpack']
if model == "v2_lite":
self.network = create_mobilenetv2_ssd_lite(len(self.labels), is_test=True)
elif model == "v1":
self.network = create_mobilenetv1_ssd(len(self.labels), is_test=True)
elif model == "v1_lite":
self.network = create_mobilenetv1_ssd_lite(len(self.labels), is_test=True)
state_dict = torch.load(os.path.join(path, "weights/", model_name))
self.network.load_state_dict(state_dict)
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.network.to(DEVICE)
if model == "v2_lite":
self.predictor = create_mobilenetv2_ssd_lite_predictor(self.network, candidate_size=200, device = DEVICE)
elif model == "v1_lite":
self.predictor = create_mobilenetv1_ssd_lite_predictor(self.network, candidate_size=200, device = DEVICE)
elif model == "v1":
self.predictor = create_mobilenetv1_ssd_predictor(self.network, candidate_size=200, device = DEVICE)
## Publisher
self.image_pub = rospy.Publisher("camera/predict_img/", Image, queue_size=1)
self.BoundingBoxes_pub = rospy.Publisher("BoundingBoxes/", BoundingBoxes, queue_size = 1)
## msg filter
self.depth_sub = message_filters.Subscriber(
"camera/aligned_depth_to_color/image_raw", Image)
self.image_sub = message_filters.Subscriber("camera/color/image_raw", Image)
self.ts = message_filters.ApproximateTimeSynchronizer(
[self.image_sub, self.depth_sub], 5, 5)
self.ts.registerCallback(self.img_cb)
print("Start Predicting image")
def prepare_predictor(net_type, model_path, label_path):
class_names = [name.strip() for name in open(label_path).readlines()]
num_classes = len(class_names)
if net_type == 'vgg16-ssd':
net = create_vgg_ssd(num_classes, is_test=True)
elif net_type == 'mb1-ssd':
net = create_mobilenetv1_ssd(num_classes, is_test=True)
elif net_type == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(num_classes, is_test=True)
elif net_type == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(num_classes, is_test=True)
elif net_type == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(num_classes, is_test=True)
else:
raise ValueError(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
net.load(model_path)
if net_type == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(net,
candidate_size=200)
elif net_type == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(net,
candidate_size=200)
elif net_type == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(net,
candidate_size=200)
else:
raise ValueError(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
return class_names, predictor
def main(args):
net_type = args.net_type
model_path = args.weights_path
label_path = args.label_path
class_names = [name.strip() for name in open(label_path).readlines()]
num_classes = len(class_names)
if args.live:
cap = cv2.VideoCapture(0)
cap.set(3, 640)
cap.set(4, 480)
else:
cap = cv2.VideoCapture(args.video_path)
Fourcc = cv2.VideoWriter_fourcc('M', 'P', '4', 'V')
writer = cv2.VideoWriter('result.mp4',
fourcc=Fourcc,
fps=15,
frameSize=(640, 480))
num_gpus = torch.cuda.device_count()
device = 'cuda' if num_gpus else 'cpu'
if net_type == 'vgg16-ssd':
net = create_vgg_ssd(len(class_names), is_test=True)
elif net_type == 'mb1-ssd':
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
elif net_type == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(len(class_names), is_test=True)
elif net_type == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(len(class_names), is_test=True)
elif net_type == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(len(class_names), is_test=True)
#elif net_type == 'mb3-ssd-lite':
# net = create_mobilenetv3_ssd_lite(len(class_names), is_test=True)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
net.load(model_path)
if net_type == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net,
candidate_size=20,
device=device)
elif net_type == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(net,
candidate_size=20,
device=device)
elif net_type == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(net,
candidate_size=20,
device=device)
elif net_type == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(net,
candidate_size=20,
device=device)
elif net_type == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(net,
candidate_size=20,
device=device)
#elif net_type == 'mb3-ssd-lite':
# predictor = create_mobilenetv3_ssd_lite_predictor(net, candidate_size=10)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
timer = Timer()
while True:
_, orig_image = cap.read()
if orig_image is None:
print('END')
break
image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)
timer.start()
boxes, labels, probs = predictor.predict(image, 10, 0.4)
interval = timer.end()
print('Time: {:.2f}s, Detect Objects: {:d}.'.format(
interval, labels.size(0)))
for i in range(boxes.size(0)):
box = boxes[i, :]
label = f"{class_names[labels[i]]}: {probs[i]:.2f}"
cv2.rectangle(orig_image, (box[0], box[1]), (box[2], box[3]),
(255, 255, 0), 4)
cv2.putText(
orig_image,
label,
(box[0] + 20, box[1] + 40),
cv2.FONT_HERSHEY_SIMPLEX,
1, # font scale
(255, 0, 255),
2) # line type
writer.write(orig_image)
cv2.imshow('annotated', orig_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
writer.release()
cv2.destroyAllWindows()
if args.out_video:
shutil.move('result.mp4', args.out_video)
else:
os.remove('result.mp4')
def main(args):
DEVICE = torch.device("cuda:0" if torch.cuda.is_available()
and args['flow_control']['use_cuda'] else "cpu")
# eval_path = pathlib.Path(args.eval_dir)
# eval_path.mkdir(exist_ok=True)
if not os.path.exists(args['flow_control']['eval_dir']):
os.mkdir(args['flow_control']['eval_dir'])
timer = Timer()
class_names = [
name.strip()
for name in open(args['flow_control']['label_file']).readlines()
]
_net = args['flow_control']['net']
_dataset_type = args['flow_control']['dataset_type']
if _dataset_type == "voc":
raise NotImplementedError("Not implement error")
dataset = VOCDataset(args['flow_control']['dataset'], is_test=True)
elif _dataset_type == 'open_images':
raise NotImplementedError("Not implement error")
dataset = OpenImagesDataset(args['flow_control']['dataset'],
dataset_type="test")
elif _dataset_type == "coco":
# dataset = CocoDetection("/home/wenyen4desh/datasets/coco/test2017","/home/wenyen4desh/datasets/annotations/image_info_test2017.json")
#dataset = CocoDetection("../../dataset/datasets/coco/val2017","../../dataset/datasets/coco/annotations/instances_val2017.json")
# dataset = CocoDetection("/home/wenyen4desh/datasets/coco/train2017","/home/wenyen4desh/datasets/coco/annotations/instances_train2017.json")
dataset = CocoDetection(args['Datasets']['coco']['val_image_path'],
args['Datasets']['coco']['val_anno_path'])
elif _dataset_type == "ecp":
dataset = EuroCity_Dataset(args['Datasets']['ecp']['val_image_path'],
args['Datasets']['ecp']['val_anno_path'])
true_case_stat, all_gb_boxes, all_difficult_cases = group_annotation_by_class(
dataset)
if _net == 'vgg16-ssd':
net = create_vgg_ssd(len(class_names), is_test=True)
elif _net == 'mb1-ssd':
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
elif _net == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(len(class_names), is_test=True)
elif _net == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(len(class_names), is_test=True)
elif _net == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(
len(class_names),
width_mult=args['flow_control']['mb2_width_mult'],
is_test=True)
else:
logging.fatal(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
parser.print_help(sys.stderr)
sys.exit(1)
#train_transform = MatchPrior(config.priors, config.center_variance,
# config.size_variance, 0.5)
#test_transform = TestTransform(config.image_size, config.image_mean, config.image_std)
import pdb
pdb.set_trace()
############################## automatically validation ############################################
timer.start("Load Model")
net.load(args['flow_control']['trained_model'])
net = net.to(DEVICE)
print('It took {} seconds to load the model.'.format(
timer.end("Load Model")))
_nms_method = args['flow_control']['nms_method']
if _net == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net,
nms_method=_nms_method,
device=DEVICE)
elif _net == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(net,
nms_method=_nms_method,
device=DEVICE)
elif _net == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(
net, nms_method=_nms_method, device=DEVICE)
elif _net == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(
net, nms_method=_nms_method, device=DEVICE)
elif _net == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(
net, nms_method=_nms_method, device=DEVICE)
else:
logging.fatal(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
parser.print_help(sys.stderr)
sys.exit(1)
results = []
# Predict Bounding Box
for i in range(len(dataset)):
print("process image {}", i)
timer.start("Load Image")
image = dataset.get_image(i)
print("Load Image: {:4f} seconds.".format(timer.end("Load Image")))
timer.start("Predict")
boxes, labels, probs = predictor.predict(image)
print("Prediction: {:4f} seconds.".format(timer.end("Predict")))
indexes = torch.ones(labels.size(0), 1, dtype=torch.float32) * i
results.append(
torch.cat(
[
indexes.reshape(-1, 1),
labels.reshape(-1, 1).float(),
probs.reshape(-1, 1),
boxes + 1.0 # matlab's indexes start from 1
],
dim=1))
results = torch.cat(results)
# Write the result to file
for class_index, class_name in enumerate(class_names):
if class_index == 0: continue # ignore background
file_name = "det_test_{}.txt".format(class_name)
prediction_path = os.path.join(args['flow_control']['eval_dir'],
file_name)
with open(prediction_path, "w") as f:
sub = results[results[:, 1] == class_index, :]
for i in range(sub.size(0)):
prob_box = sub[i, 2:].numpy()
image_id, _ = dataset.get_annotation(int(sub[i, 0]))
f.write(
str(image_id) + " " + " ".join([str(v)
for v in prob_box]) + "\n")
# image_id = dataset.ids[int(sub[i, 0])]
# print(str(image_id) + " " + " ".join([str(v) for v in prob_box]), file=f)
aps = []
prcs = []
recalls = []
print("\n\nAverage Precision Per-class:")
for class_index, class_name in enumerate(class_names):
if class_index == 0:
continue
file_name = "det_test_{}.txt".format(class_name)
prediction_path = os.path.join(args['flow_control']['eval_dir'],
file_name)
# [email protected] evaluation method
ap, precision, recall = compute_average_precision_per_class(
args, true_case_stat[class_index], all_gb_boxes[class_index],
all_difficult_cases[class_index], prediction_path,
args['flow_control']['iou_threshold'],
args['flow_control']['use_2007_metric'])
# # COCO eval
# ap, precision, recall = coco_ap_per_class(
# true_case_stat[class_index],
# all_gb_boxes[class_index],
# all_difficult_cases[class_index],
# prediction_path,
# args.use_2007_metric
# )
aps.append(ap)
prcs.append(precision)
recalls.append(recall)
print("{}: {}".format(class_name, ap))
print("\nAverage Precision Across All Classes:{}".format(
sum(aps[0:5]) / len(aps[0:5])))
print("\nAverage Precision :{}".format(sum(prcs[0:5]) / len(prcs[0:5])))
print("\nAverage Recall :{}".format(sum(recalls[0:5]) / len(recalls[0:5])))
def imwrite(dataset, net_type, epoch, model_path):
num_classes = len(dataset.class_names)
if net_type == 'vgg16-ssd':
net = create_vgg_ssd(num_classes, is_test=True)
elif net_type == 'mb1-ssd':
net = create_mobilenetv1_ssd(num_classes, is_test=True)
elif net_type == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(num_classes, is_test=True)
elif net_type == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(num_classes, is_test=True)
elif net_type == 'mb3-large-ssd-lite':
net = create_mobilenetv3_large_ssd_lite(num_classes, is_test=True)
elif net_type == 'mb3-small-ssd-lite':
net = create_mobilenetv3_small_ssd_lite(num_classes, is_test=True)
elif net_type == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(num_classes, is_test=True)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
net.load(model_path)
if net_type == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(net,
candidate_size=200)
elif net_type == 'mb2-ssd-lite' or net_type == "mb3-large-ssd-lite" or net_type == "mb3-small-ssd-lite":
predictor = create_mobilenetv2_ssd_lite_predictor(net,
candidate_size=200)
elif net_type == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(net,
candidate_size=200)
else:
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
for i in range(10):
image, orig_boxes, labels = dataset[i]
orig_image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
boxes, labels, probs = predictor.predict(image, 10, 0.4)
for j in range(boxes.size(0)): # predict
box = boxes[j, :]
box = [int(i) for i in box]
cv2.rectangle(orig_image, (box[0], box[1]), (box[2], box[3]),
(255, 255, 0), 1)
label = f"{probs[j]:.2f}"
cv2.putText(
orig_image,
label,
(box[0] + 3, box[1] + 5),
cv2.FONT_HERSHEY_SIMPLEX,
1, # font scale
(255, 255, 0),
1) # line type
for j in range(orig_boxes.shape[0]): # ground truth
box = orig_boxes[j, :]
cv2.rectangle(orig_image, (box[0], box[1]), (box[2], box[3]),
(255, 0, 255), 1)
path = f"out/{i:02}_{epoch:04}.jpg"
cv2.imwrite(path, orig_image)
def main(args):
net_type = args.net_type
img_folder = args.img_folder
model_path = args.weights_path
label_path = args.label_path
class_names = [name.strip() for name in open(label_path).readlines()]
out_path = args.out_path
if not os.path.exists(out_path):
os.mkdir(out_path)
num_gpus = torch.cuda.device_count()
device = 'cuda' if num_gpus else 'cpu'
if net_type == 'vgg16-ssd':
net = create_vgg_ssd(len(class_names), is_test=True)
elif net_type == 'mb1-ssd':
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
elif net_type == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(len(class_names), is_test=True)
elif net_type == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(len(class_names), is_test=True)
elif net_type == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(len(class_names), is_test=True)
#elif net_type == 'mb3-ssd-lite':
# net = create_mobilenetv3_ssd_lite(len(class_names), is_test=True)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
net.load(model_path)
if net_type == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net,
candidate_size=20,
device=device)
elif net_type == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(net,
candidate_size=20,
device=device)
elif net_type == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(net,
candidate_size=20,
device=device)
elif net_type == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(net,
candidate_size=20,
device=device)
elif net_type == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(net,
candidate_size=20,
device=device)
#elif net_type == 'mb3-ssd-lite':
# predictor = create_mobilenetv3_ssd_lite_predictor(net, candidate_size=10)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
timer = Timer()
img_names = glob.glob(img_folder + os.sep + '*.jpg')
#result_csv=os.path.join(out_path,'rest_result.csv')
if len(img_names) == 0:
print('No imagesfound in {}'.format(img_folder))
exit(-1)
for img_name in img_names:
image = cv2.imread(img_name)
timer.start()
boxes, labels, probs = predictor.predict(image, 10, 0.3)
interval = timer.end()
print('Time: {:.2f}s, Detect Objects: {:d}.'.format(
interval, labels.size(0)))
label_text = []
for i in range(boxes.size(0)):
box = boxes[i, :]
label = f"{class_names[labels[i]]}: {probs[i]:.2f}"
label_text.append(label)
cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]),
(255, 255, 0), 4)
cv2.putText(image, label, (box[0] + 20, box[1] + 40),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 255), 2)
if args.store_result:
new_name = '{}/{}'.format(out_path, img_name.split('/')[-1])
cv2.imwrite(new_name, image)
if not label_text:
result_label = 'empty'
else:
result_label = label_text[0]
with open(os.path.join(out_path, 'rest_result.csv'),
'a+') as result_writer:
result_writer.write(
img_name.split('/')[-1] + ',' + result_label + '\n')
cv2.imshow('result', image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
def PersonDetector(orig_image, net_type="mb1-ssd"):
class_names = [name.strip() for name in open(label_path).readlines()]
num_classes = len(class_names)
if net_type == 'vgg16-ssd':
net = create_vgg_ssd(len(class_names), is_test=True)
elif net_type == 'mb1-ssd':
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
elif net_type == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(len(class_names), is_test=True)
elif net_type == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(len(class_names), is_test=True)
elif net_type == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(len(class_names), is_test=True)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
net.load(model_path)
if net_type == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(net,
candidate_size=200)
elif net_type == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(net,
candidate_size=200)
elif net_type == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(net,
candidate_size=200)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
timer = Timer()
image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)
timer.start()
boxes, labels, probs = predictor.predict(image, 10, 0.4)
interval = timer.end()
print('Time: {:.2f}s, Detect Objects: {:d}.'.format(
interval, labels.size(0)))
max_width = -1
x, y, w, h = None, None, None, None
for i in range(boxes.size(0)):
box = boxes[i, :]
label = f"{class_names[labels[i]]}: {probs[i]:.2f}"
if (max_width < box[2] - box[0]):
x, y = box[0], box[1]
w, h = box[2] - box[0], box[3] - box[1]
max_width = w
if (x is not None and y is not None and w is not None and h is not None):
cv2.rectangle(orig_image, (x, y), (w + x, h + y), (255, 255, 0), 4)
return (x, y, w, h)
# img = cv2.imread("Image/img.jpg")
# PersonDetector(img)
import threading
import os
# --- https://github.com/yscylhy/pytorch-ssd.git
sys.path.append('../pytorch-ssd')
from vision.ssd.mobilenet_v2_ssd_lite import create_mobilenetv2_ssd_lite, create_mobilenetv2_ssd_lite_predictor
email_update_interval = 10 # sends an email only once in this time interval
video_camera = USBCamera(flip=False) # creates a camera object, flip vertically
cur_path = os.path.dirname(os.path.realpath(__file__))
label_path = './models/voc-model-labels.txt'
model_path = './models/mb2-ssd-lite-mp-0_686.pth'
class_names = [name.strip() for name in open(label_path).readlines()]
net = create_mobilenetv2_ssd_lite(len(class_names), is_test=True)
net.load(model_path)
object_classifier = create_mobilenetv2_ssd_lite_predictor(net, candidate_size=200, device='cuda')
# object_classifier = cv2.CascadeClassifier("models/facial_recognition_model.xml") # an opencv classifier
# --- change to your email --- #
with open('email_config.txt') as f:
fromEmail, fromEmailPassword, toEmail, username, password = f.read().splitlines()
# App Globals (do not edit)
app = Flask(__name__)
app.config['BASIC_AUTH_USERNAME'] = username
app.config['BASIC_AUTH_PASSWORD'] = password
app.config['BASIC_AUTH_FORCE'] = True
sys.exit(1)
net.load(model_path)
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if net_type == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(net,
candidate_size=200,
device=DEVICE)
elif net_type == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(net,
candidate_size=200,
device=DEVICE)
elif net_type == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(net,
candidate_size=200,
device=DEVICE)
elif net_type == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(net, candidate_size=200)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
timer = Timer()
while True:
ret, orig_image = cap.read()
if orig_image is None:
continue
image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2RGB)
net = create_squeezenet_ssd_lite(len(class_names), is_test=True)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
net.load(model_path)
if net_type == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(net, candidate_size=200)
elif net_type == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(net, candidate_size=200)
elif net_type == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(
net, candidate_size=200, device=torch.device('cuda'))
elif net_type == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(net, candidate_size=200)
else:
predictor = create_vgg_ssd_predictor(net, candidate_size=200)
orig_image = cv2.imread(image_path)
image = cv2.cvtColor(orig_image, cv2.COLOR_BGR2GRAY)
cv2.imwrite('teste.jpg', image)
#image = cv2.cvtColor(orig_image, cv2.COLOR_RGB2GRAY)
#image = cv2.cvtColor(orig_image, cv2.COLOR_GRAY2RGB)
image = cv2.merge([image, image, image])
print('[DEBUG IMG SHAPE')
print(image.shape)
boxes, labels, probs = predictor.predict(image, 10, 0.4)
def load_model(self,
net="mb1-ssd",
model_path=None,
label_path=None,
use_gpu=True):
net_type = net
self.system_dict["device"] = torch.device(
'cuda' if torch.cuda.is_available() and use_gpu else 'cpu')
self.system_dict["class_names"] = [
name.strip() for name in open(label_path).readlines()
]
self.system_dict["class_names"].insert(0, 'BACKGROUND')
if net_type == 'vgg16-ssd':
self.system_dict["net"] = create_vgg_ssd(
len(self.system_dict["class_names"]),
is_test=True,
device=self.system_dict["device"])
elif net_type == 'mb1-ssd':
self.system_dict["net"] = create_mobilenetv1_ssd(
len(self.system_dict["class_names"]),
is_test=True,
device=self.system_dict["device"])
elif net_type == 'mb1-ssd-lite':
self.system_dict["net"] = create_mobilenetv1_ssd_lite(
len(self.system_dict["class_names"]),
is_test=True,
device=self.system_dict["device"])
elif net_type == 'mb2-ssd-lite':
self.system_dict["net"] = create_mobilenetv2_ssd_lite(
len(self.system_dict["class_names"]),
is_test=True,
device=self.system_dict["device"])
elif net_type == 'sq-ssd-lite':
self.system_dict["net"] = create_squeezenet_ssd_lite(
len(self.system_dict["class_names"]),
is_test=True,
device=self.system_dict["device"])
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
self.system_dict["net"].load(model_path)
if net_type == 'vgg16-ssd':
self.system_dict["predictor"] = create_vgg_ssd_predictor(
self.system_dict["net"],
candidate_size=200,
device=self.system_dict["device"])
elif net_type == 'mb1-ssd':
self.system_dict["predictor"] = create_mobilenetv1_ssd_predictor(
self.system_dict["net"],
candidate_size=200,
device=self.system_dict["device"])
elif net_type == 'mb1-ssd-lite':
self.system_dict[
"predictor"] = create_mobilenetv1_ssd_lite_predictor(
self.system_dict["net"],
candidate_size=200,
device=self.system_dict["device"])
elif net_type == 'mb2-ssd-lite':
self.system_dict[
"predictor"] = create_mobilenetv2_ssd_lite_predictor(
self.system_dict["net"],
candidate_size=200,
device=self.system_dict["device"])
elif net_type == 'sq-ssd-lite':
self.system_dict[
"predictor"] = create_squeezenet_ssd_lite_predictor(
self.system_dict["net"],
candidate_size=200,
device=self.system_dict["device"])
else:
self.system_dict["predictor"] = create_vgg_ssd_predictor(
self.system_dict["net"],
candidate_size=200,
device=self.system_dict["device"])
def main():
timer = Timer()
DEVICE = torch.device("cuda:0" if args.use_cuda and torch.cuda.
is_available() and args.use_cuda else "cpu")
print("=== Use ", DEVICE)
classes = []
with open("./models/char_obj_EN.names", "r") as f:
lines = f.readlines()
for i in lines:
if "\n" in i:
classes.append(i.split("\n")[0])
else:
classes.append(i)
class_names = classes
print("label size is ", len(class_names))
net = create_mobilenetv2_ssd_lite(len(class_names),
width_mult=args.mb2_width_mult,
is_test=True)
timer.start("Load Model")
net.load(args.trained_model)
net = net.to(DEVICE)
print(f'{timer.end("Load Model")} sec to load the model.')
predictor = create_mobilenetv2_ssd_lite_predictor(
net, nms_method=args.nms_method, device=DEVICE)
test_list = [
x for x in os.listdir(args.test_folder) if x.split(".")[1] == "jpg"
]
total_time = 0.0
for i in test_list:
# Read Images
img_path = args.test_folder + "/" + i
image = cv2.imread(img_path)
cvt_image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Predict
timer.start("Predict")
boxes, labels, probs = predictor.predict(cvt_image)
end_time = timer.end("Predict")
print("Prediction: {:4f} sec.".format(end_time))
total_time += end_time
# to numpy
bb = boxes.numpy()
lb = labels.numpy()
pb = probs.numpy()
save_name = ""
# resize
s_factor = 4
rows, cols = image.shape[:2]
image = cv2.resize(image, (cols * s_factor, rows * s_factor))
# score > 0.5
for b in range(pb.size):
if pb[b] > args.prob_thresh:
cv2.rectangle(
image,
(int(bb[b][0] * s_factor), int(bb[b][1] * s_factor)),
(int(bb[b][2] * s_factor), int(bb[b][3] * s_factor)),
(0, 255, 0), 2)
cv2.putText(
image, class_names[int(lb[b]) + 1],
(int(bb[b][0] * s_factor), int(bb[b][1] * s_factor)),
cv2.FONT_HERSHEY_SIMPLEX, 1.5, (0, 0, 255), 2)
save_name += class_names[int(lb[b]) + 1]
cv2.imwrite("./output/Final_Testset/" + save_name + ".jpg", image)
print("Input Image : {} ====> Predict : {}".format(i, save_name))
print("Avg Time is {:4f} sec.".format(total_time / len(test_list)))
print(f'It took {timer.end("Load Model")} seconds to load the model.')
if args.net == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net,
nms_method=args.nms_method,
device=DEVICE)
elif args.net == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(
net, nms_method=args.nms_method, device=DEVICE)
elif args.net == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(
net, nms_method=args.nms_method, device=DEVICE)
elif args.net == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(
net, nms_method=args.nms_method, device=DEVICE)
elif args.net == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(
net, nms_method=args.nms_method, device=DEVICE)
else:
logging.fatal(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
parser.print_help(sys.stderr)
sys.exit(1)
results = []
for i in range(len(dataset)):
print("process image", i)
timer.start("Load Image")
image = dataset.get_image(i)
print("Load Image: {:4f} seconds.".format(timer.end("Load Image")))
timer.start("Predict")
boxes, labels, probs = predictor.predict(image)
def main(args):
DEVICE = torch.device(
"cuda:0" if torch.cuda.is_available() and args.use_cuda else "cpu")
if not os.path.exists(args.eval_dir):
os.mkdir(args.eval_dir)
timer = Timer()
class_names = [name.strip() for name in open(args.label_file).readlines()]
# dataset = Folder_image_set()
# true_case_stat, all_gb_boxes, all_difficult_cases = group_annotation_by_class(dataset)
if args.net == 'vgg16-ssd':
net = create_vgg_ssd(len(class_names), is_test=True)
elif args.net == 'mb1-ssd':
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
elif args.net == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(len(class_names), is_test=True)
elif args.net == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(len(class_names), is_test=True)
elif args.net == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(len(class_names),
width_mult=args.mb2_width_mult,
is_test=True)
else:
logging.fatal(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
parser.print_help(sys.stderr)
sys.exit(1)
#train_transform = MatchPrior(config.priors, config.center_variance,
# config.size_variance, 0.5)
#test_transform = TestTransform(config.image_size, config.image_mean, config.image_std)
# test_transform = TestTransform(config.image_size, config.image_mean, config.image_std)
# dataset = FolderDataset("/media/wy_disk/wy_file/Detection/dataset/datasets/ECP_Golden_pattern", transform = test_transform)
# dataset = FolderDataset("/media/wy_disk/wy_file/Detection/dataset/datasets/ECP_Golden_pattern")
dataset = FolderDataset("/media/wy_disk/ChenYen/VIRAT/dataset_orgnize/val")
timer.start("Load Model")
net.load(args.trained_model)
net = net.to(DEVICE)
print('It took {} seconds to load the model.'.format(
timer.end("Load Model")))
if args.net == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net,
nms_method=args.nms_method,
device=DEVICE)
elif args.net == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(
net, nms_method=args.nms_method, device=DEVICE)
elif args.net == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(
net, nms_method=args.nms_method, device=DEVICE)
elif args.net == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(
net, nms_method=args.nms_method, device=DEVICE)
elif args.net == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(
net, nms_method=args.nms_method, device=DEVICE)
else:
logging.fatal(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
parser.print_help(sys.stderr)
sys.exit(1)
results = []
eval_path = "eval_results"
# eval_whole_image(dataset,5, predictor)
eval_subblock_image(dataset, 5, predictor)
import pdb
pdb.set_trace()
for i in range(len(dataset)):
print("process image", i)
timer.start("Load Image")
import pdb
pdb.set_trace()
image = dataset.get_image(i)
print("Load Image: {:4f} seconds.".format(timer.end("Load Image")))
timer.start("Predict")
boxes, labels, probs = predictor.predict(image, 10, 0.5)
print("Prediction: {:4f} seconds.".format(timer.end("Predict")))
# indexes = torch.ones(labels.size(0), 1, dtype=torch.float32) * i
# print("index:p\t{}".format(sum(probs>0.5)))
# import pdb;pdb.set_trace()
boxes, labels, probs = boxes.data.numpy(), labels.data.numpy(
), probs.data.numpy()
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
for box, _label, _prob in zip(boxes, labels, probs):
if _prob < 0.7: continue
print(box)
box = box.astype(int)
# import pdb;pdb.set_trace()
print(box)
cv2.rectangle(image, (box[0], box[1]), (box[2], box[3]),
(255, 255, 0), 4)
# str(str.split(class_names[_label]," ")[1])
cv2.putText(
image,
dataset.class_names[_label],
(box[0] + 20, box[1] + 40),
cv2.FONT_HERSHEY_SIMPLEX,
1, # font scale
(255, 0, 255),
2) # line type
print(boxes.shape[0])
cv2.imshow('annotated', image)
# key = cv2.waitKey(0)
if cv2.waitKey(0) & 0xFF == ord('q'):
break
def main():
eval_path = pathlib.Path(args.eval_dir)
eval_path.mkdir(exist_ok=True)
timer = Timer()
class_names = {
'BACKGROUND', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus',
'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
'tvmonitor'
}
if args.dataset_type == "voc":
dataset = VOCDataset(args.dataset, is_test=True)
elif args.dataset_type == 'open_images':
dataset = OpenImagesDataset(args.dataset, dataset_type="test")
# true_case_stat, all_gb_boxes, all_difficult_cases = group_annotation_by_class(dataset)
if args.net == 'vgg16-ssd':
net = create_vgg_ssd(len(class_names), is_test=True)
elif args.net == 'mb1-ssd':
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
elif args.net == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(len(class_names), is_test=True)
elif args.net == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(len(class_names), is_test=True)
elif args.net == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(len(class_names),
width_mult=args.mb2_width_mult,
is_test=True)
else:
logging.fatal(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
parser.print_help(sys.stderr)
sys.exit(1)
timer.start("Load Model")
net.load(args.trained_model)
net = net.to(DEVICE)
print('It took {timer.end("Load Model")} seconds to load the model.')
if args.net == 'vgg16-ssd':
predictor = create_vgg_ssd_predictor(net,
nms_method=args.nms_method,
device=DEVICE)
elif args.net == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(
net, nms_method=args.nms_method, device=DEVICE)
elif args.net == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(
net, nms_method=args.nms_method, device=DEVICE)
elif args.net == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(
net, nms_method=args.nms_method, device=DEVICE)
elif args.net == 'mb2-ssd-lite':
predictor = create_mobilenetv2_ssd_lite_predictor(
net, nms_method=args.nms_method, device=DEVICE)
else:
logging.fatal(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
parser.print_help(sys.stderr)
sys.exit(1)
print(
ssd_eval(predictor=predictor,
dataset=dataset,
data_path=eval_path,
class_names=class_names))
def get_map(trained_model, label_file):
eval_dir = "eval_results"
eval_path = pathlib.Path(eval_dir)
eval_path.mkdir(exist_ok=True)
timer = Timer()
class_names = [name.strip() for name in open(label_file).readlines()]
dataset_path = "/home/qindanfeng//work/YOLOv3/datasets/VOC/VOCtest_06-Nov-2007/VOCdevkit/VOC2007"
dataset = VOCDataset(dataset_path, is_test=True)
true_case_stat, all_gb_boxes, all_difficult_cases = group_annotation_by_class(
dataset)
net = create_mobilenetv2_ssd_lite(len(class_names),
width_mult=1.0,
is_test=True)
timer.start("Load Model")
net.load(trained_model)
net = net.to(DEVICE)
predictor = create_mobilenetv2_ssd_lite_predictor(net,
nms_method="hard",
device=DEVICE)
results = []
for i in range(len(dataset)):
print("process image", i)
timer.start("Load Image")
image = dataset.get_image(i)
print("Load Image: {:4f} seconds.".format(timer.end("Load Image")))
timer.start("Predict")
boxes, labels, probs = predictor.predict(image)
print("Prediction: {:4f} seconds.".format(timer.end("Predict")))
indexes = torch.ones(labels.size(0), 1, dtype=torch.float32) * i
results.append(
torch.cat(
[
indexes.reshape(-1, 1),
labels.reshape(-1, 1).float(),
probs.reshape(-1, 1),
boxes + 1.0 # matlab's indexes start from 1
],
dim=1))
results = torch.cat(results)
for class_index, class_name in enumerate(class_names):
if class_index == 0: continue # ignore background
prediction_path = eval_path / f"det_test_{class_name}.txt"
with open(prediction_path, "w") as f:
sub = results[results[:, 1] == class_index, :]
for i in range(sub.size(0)):
prob_box = sub[i, 2:].numpy()
image_id = dataset.ids[int(sub[i, 0])]
print(image_id + " " + " ".join([str(v) for v in prob_box]),
file=f)
aps = []
print("\n\nAverage Precision Per-class:")
for class_index, class_name in enumerate(class_names):
if class_index == 0:
continue
prediction_path = eval_path / f"det_test_{class_name}.txt"
ap = compute_average_precision_per_class(
true_case_stat[class_index], all_gb_boxes[class_index],
all_difficult_cases[class_index], prediction_path, 0.5, True)
aps.append(ap)
print(f"{class_name}: {ap}")
print(f"\nAverage Precision Across All Classes:{sum(aps) / len(aps)}")
return sum(aps) / len(aps)
elif args.net == 'vgg16-ssd2':
predictor = create_vgg_ssd_predictor2(net,
nms_method=args.nms_method,
iou_threshold=0.2,
device=DEVICE)
elif args.net == 'mb1-ssd':
predictor = create_mobilenetv1_ssd_predictor(
net, nms_method=args.nms_method, iou_threshold=0.5, device=DEVICE)
elif args.net == 'mb1-ssd-lite':
predictor = create_mobilenetv1_ssd_lite_predictor(
net, nms_method=args.nms_method, device=DEVICE)
elif args.net == 'sq-ssd-lite':
predictor = create_squeezenet_ssd_lite_predictor(
net, nms_method=args.nms_method, device=DEVICE)
elif args.net == 'mb2-ssd-lite' or args.net == "mb3-large-ssd-lite" or args.net == "mb3-small-ssd-lite":
predictor = create_mobilenetv2_ssd_lite_predictor(
net, nms_method=args.nms_method, iou_threshold=0.5, device=DEVICE)
else:
logging.fatal(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
parser.print_help(sys.stderr)
sys.exit(1)
results = []
for i in range(len(dataset)):
print("process image", i)
timer.start("Load Image")
image = dataset.get_image(i)
print("Load Image: {:4f} seconds.".format(timer.end("Load Image")))
timer.start("Predict")
boxes, labels, probs = predictor.predict(image, prob_threshold=0.3)
from src.WorkWithImage import WorkWithImage
from vision.ssd.mobilenet_v2_ssd_lite import create_mobilenetv2_ssd_lite, create_mobilenetv2_ssd_lite_predictor
app = Flask(__name__)
app.config.from_object(Configuration)
# to disable caching files
app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0
label_path = 'voc-model-labels.txt'
model_path = 'mb2-ssd-lite-mp-0_686.pth'
class_names = [name.strip() for name in open(label_path).readlines()]
net = create_mobilenetv2_ssd_lite(len(class_names), is_test=True)
net.load(model_path)
predictor = create_mobilenetv2_ssd_lite_predictor(net, candidate_size=200)
@app.route('/', methods=['GET'])
def index():
return render_template('index.html')
@app.route('/upload', methods=['POST'])
def detect_image():
img = img_url = None
try:
img = request.files['image']
except KeyError:
try:
img_url = request.form['image_url']
def __init__(self):
model = "v1_lite"
self.prob_threshold = 0.85
self.cv_bridge = CvBridge()
self.num_points = 8000
self.labels = [
'background', # always index 0
'bb_extinguisher',
'bb_drill',
'bb_backpack'
]
self.objects = []
if model == "v2_lite":
self.network = create_mobilenetv2_ssd_lite(len(self.labels),
is_test=True)
model_dir = "/home/andyser/code/exercise_ML/pytorch-ssd-mobile/models/subt_v2_lite"
model_name = "model.pth"
elif model == "v1":
self.network = create_mobilenetv1_ssd(len(self.labels),
is_test=True)
model_dir = "/home/andyser/code/exercise_ML/pytorch-ssd-mobile/models/subt_v1"
model_name = "model.pth"
elif model == "v1_lite":
self.network = create_mobilenetv1_ssd_lite(len(self.labels),
is_test=True)
model_dir = "/home/andyser/code/exercise_ML/pytorch-ssd-mobile/models/subt_v1_lite"
model_name = "model.pth"
state_dict = torch.load(os.path.join(model_dir, model_name))
self.network.load_state_dict(state_dict)
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.network.to(DEVICE)
if model == "v2_lite":
self.predictor = create_mobilenetv2_ssd_lite_predictor(
self.network, candidate_size=200, device=DEVICE)
elif model == "v1_lite":
self.predictor = create_mobilenetv1_ssd_lite_predictor(
self.network, candidate_size=200, device=DEVICE)
elif model == "v1":
self.predictor = create_mobilenetv1_ssd_predictor(
self.network, candidate_size=200, device=DEVICE)
#### Publisher
self.origin = rospy.Publisher('/input', bb_input, queue_size=1)
self.image_pub = rospy.Publisher("/predict_img", Image, queue_size=1)
self.mask_pub = rospy.Publisher("/predict_mask", Image, queue_size=1)
### msg filter
self.is_compressed = False
video_mode = False
if video_mode:
image_sub = rospy.Subscriber('/camera/color/image_raw', Image,
self.video_callback)
else:
image_sub = message_filters.Subscriber('/camera/color/image_raw',
Image)
depth_sub = message_filters.Subscriber(
'/camera/aligned_depth_to_color/image_raw', Image)
ts = message_filters.TimeSynchronizer([image_sub, depth_sub], 10)
ts.registerCallback(self.callback)
