def __init__(self, model_path):
net_type = 'mb1-ssd'
label_path = os.path.join(os.path.dirname(model_path), 'voc-model-labels.txt')
self.class_names = [name.strip() for name in open(label_path).readlines()]
class_length = len(self.class_names)
if net_type == 'vgg16-ssd':
net = create_vgg_ssd(class_length, is_test=True)
elif net_type == 'mb1-ssd':
net = create_mobilenetv1_ssd(class_length, is_test=True)
elif net_type == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(class_length, 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)
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)
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)
self.predictor = predictor
def net_initilize(self, net_type, model_path, 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)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
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)
else:
print(
"The net type is wrong. It should be one of vgg16-ssd, mb1-ssd and mb1-ssd-lite."
)
sys.exit(1)
self.net = net
self.net.load(model_path)
self.pdc = predictor
def __init__(self):
model_path = './models/mb1-ssd-Epoch-1290-Loss-2.35230020682017.pth'
label_path = './models/voc-model-labels.txt'
self.class_names = [name.strip() for name in open(label_path).readlines()]
net = create_mobilenetv1_ssd(len(self.class_names), is_test=True)
net.load(model_path)
self.predictor = create_mobilenetv1_ssd_predictor(net, candidate_size=200)
self.alert_sum = 0
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 get_model(self):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
net_type = 'mb1-ssd' # sys.argv[1]
model_path = './models/mobilenet-v1-ssd-mp-0_675.pth' # sys.argv[2]
label_path = './models/voc-model-labels.txt' # sys.argv[3]
class_names = [name.strip() for name in open(label_path).readlines()]
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
net.load(model_path)
predictor = create_mobilenetv1_ssd_predictor(net,
candidate_size=200,
device=device)
return predictor
def __init__(self, mode):
self.config = patch_config.patch_configs[mode](
) # select the mode for the patch
# load cfg file (.yaml) and override default cfg options in lib_ssd.utils.config_parse
# cfg_from_file(self.config.cfgfile_ssds)
# self.cfgfile_ssds = cfg
self.device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
print(torch.cuda.device_count())
#self.darknet_model = Darknet(self.config.cfgfile)
#self.darknet_model.load_weights(self.config.weightfile)
#self.mbntv2_ssdlite_model, self.priorbox = create_model(self.cfgfile_ssds.MODEL) # COCO
#self.priors = Variable(self.priorbox.forward(), volatile=True) # num_priors = grid x grid x num_anchors
self.mbntv1_ssd_model = create_mobilenetv1_ssd(21,
is_test=True) # VOC
self.mbntv1_ssd_model.load(self.config.ssdmbntv1_model_path)
if use_cuda:
#self.darknet_model = self.darknet_model.eval().to(self.device) # Why eval? test!
self.mbntv1_ssd_model = self.mbntv1_ssd_model.eval().to(
self.device)
self.patch_applier = PatchApplier().to(self.device)
self.patch_transformer = PatchTransformer().to(self.device)
#self.prob_extractor = MaxProbExtractor(0, 80, self.config).to(self.device)
self.score_extractor_ssd = ssd_feature_output_manage(
15, 21, self.config).to(
self.device
) # 15 is person class in VOC (with 21 elements)
self.nps_calculator = NPSCalculator(self.config.printfile,
self.config.patch_size).to(
self.device)
self.total_variation = TotalVariation().to(self.device)
else:
#self.darknet_model = self.darknet_model.eval() # Why eval? test!
self.mbntv1_ssd_model = self.mbntv1_ssd_model.eval()
self.patch_applier = PatchApplier()
self.patch_transformer = PatchTransformer()
#self.prob_extractor = MaxProbExtractor(0, 80, self.config)
self.score_extractor_ssd = ssd_feature_output_manage(
15, 21, self.config).to(self.device)
self.nps_calculator = NPSCalculator(self.config.printfile,
self.config.patch_size)
self.total_variation = TotalVariation()
def net_select(model_type, class_names):
if model_type == 'vgg16-ssd':
return create_vgg_ssd(len(class_names), is_test=True)
elif model_type == 'mb1-ssd':
return create_mobilenetv1_ssd(len(class_names), is_test=True)
elif model_type == 'mb1-ssd-lite':
return create_mobilenetv1_ssd_lite(len(class_names), is_test=True)
elif model_type == 'mb2-ssd-lite':
return create_mobilenetv2_ssd_lite(len(class_names), is_test=True)
elif model_type == 'sq-ssd-lite':
return 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)
def _create_network(self, net_type):
if net_type == 'vgg16-ssd':
return create_vgg_ssd(len(self.class_names), is_test=True)
elif net_type == 'mb1-ssd':
return create_mobilenetv1_ssd(len(self.class_names), is_test=True)
elif net_type == 'mb1-ssd-lite':
return create_mobilenetv1_ssd_lite(len(self.class_names),
is_test=True)
elif net_type == 'mb2-ssd-lite':
return create_mobilenetv2_ssd_lite(len(self.class_names),
is_test=True)
elif net_type == 'sq-ssd-lite':
return create_squeezenet_ssd_lite(len(self.class_names),
is_test=True)
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"
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 __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 load_model(args):
device = torch.device(args.device)
print("Loading model")
if (args.model == 'yolo'):
# Initialize model
model = Darknet(args.cfg, img_size=416)
# Load weights
attempt_download(args.weights)
if args.weights.endswith('.pt'): # pytorch format
model.load_state_dict(
torch.load(args.weights, map_location=device)['model'])
else: # darknet format
load_darknet_weights(model, args.weights)
elif (args.model == 'mobilenetv1_ssd'):
model = create_mobilenetv1_ssd(len(voc_class_names), is_test=True)
model.load('./saved_models/mobilenet-v1-ssd-mp-0_675.pth')
else:
model = detection.__dict__[args.model](num_classes=91,
pretrained=args.pretrained)
return model
def main():
print('cuda device count: ', torch.cuda.device_count())
DEVICE = 'cuda:0'
class_names = [
name.strip()
for name in open('models/voc-model-labels.txt').readlines()
]
image = torch.ones(1, 3, 300, 300).to(DEVICE)
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
net.load('models/mobilenet-v1-ssd-mp-0_675.pth')
net = net.to(DEVICE)
net = net.eval()
scores, boxes = net(image)
print(net(image))
print("Input shape ", image.shape)
print("Scores shape ", scores.shape)
print("Boxes shape ", boxes.shape)
export_as_weights(net)
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
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 __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)
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')
import sys
if len(sys.argv) < 5:
print(
'Usage: python run_ssd_example.py <net type> <model path> <image path>'
)
sys.exit(0)
net_type = sys.argv[1]
model_path = sys.argv[2]
label_path = sys.argv[3]
image_path = sys.argv[4]
class_names = [name.strip() for name in open(label_path).readlines()]
num_classes = len(class_names)
if net_type == "mobilenet-v1-ssd":
net = create_mobilenetv1_ssd(num_classes, is_test=True)
else:
net = create_vgg_ssd(num_classes, is_test=True)
net.load(model_path)
if net_type == "mobilenet-v1-ssd":
predictor = create_mobilenetv1_ssd_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_BGR2RGB)
boxes, labels, probs = predictor.predict(image, 10, 0.4)
for i in range(boxes.size(0)):
box = boxes[i, :]
cv2.rectangle(orig_image, (box[0], box[1]), (box[2], box[3]),
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
from vision.ssd.mobilenetv1_ssd import create_mobilenetv1_ssd, create_mobilenetv1_ssd_predictor from torchsummary import summary import sys path = './models/mb1-ssd-Epoch-99-Loss-2.7295520901679993.pth' net = create_mobilenetv1_ssd(2) # net.load_state_dict() # summary(net, (300, 300, 3)) print(net)
for n in range(N):
for k in range(K):
v_mat = np.resize(vh[k, N * d:(N + 1) * d], (1, d))
H[n, k, :, :] = np.sqrt(s[k]) * torch.from_numpy(v_mat)
return V, H
timer = Timer()
#pretrained Model
model_path = 'models/BaseModel.pth'
k = 4 #Rank
#Create the network using the architectures.
net = create_mobilenetv1_ssd(2, is_test=True)
net_copy = create_shrinked_mobilenetv1_ssd(2, 4, is_test=True)
#Load the original model
net.load(model_path)
#Extract the parameters of the original model
params = net.state_dict()
print(params.keys())
for layer in net_copy.state_dict().keys():
print(layer, net_copy.state_dict()[layer].shape)
#Select the layer in the original model for computing the Low-Rank Factorization:
layer = 'extras.0.2.weight'
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)
#Dimension of the rectangle for the bounding box for multi-components sign
x = 300
y = 100
h = 300
w = 300
elif (args.bbox == 'small'):
#Dimension of the rectangle for the bounding box for handshape
x = 100
y = 150
h = 200
w = 200
#Detect boundaries of hand using a detection model
else:
#Loading detection model
detect_model = create_mobilenetv1_ssd(2, is_test=True)
detect_model.load(detection_path)
# Device configuration
device = torch.device(
'cuda:0' if torch.cuda.is_available() else 'cpu')
if device == 'cpu':
print("Running on CPU.")
elif device == 'cuda:0':
print("Running on GPU.")
predictor = create_mobilenetv1_ssd_predictor(detect_model,
candidate_size=200,
device=device)
print("Detection Network successfully loaded...")
parser.add_argument('--mb2_width_mult', default=1.0, type=float,
help='Width Multiplifier for MobilenetV2')
args = parser.parse_args()
DEVICE = torch.device("cuda:0" if torch.cuda.is_available() and args.use_cuda else "cpu")
if __name__ == '__main__':
eval_path = pathlib.Path(args.eval_dir)
eval_path.mkdir(exist_ok=True)
timer = Timer()
class_names = [name.strip() for name in open(args.label_file).readlines()]
if args.net == 'vgg16-ssd':
net = create_vgg_ssd(len(class_names), is_test=True, convert_to_boxes=False)
config = vgg_ssd_config
elif args.net == 'mb1-ssd':
net = create_mobilenetv1_ssd(len(class_names), is_test=True, convert_to_boxes=False)
config = mobilenetv1_ssd_config
elif args.net == 'mb1-ssd-lite':
net = create_mobilenetv1_ssd_lite(len(class_names), is_test=True, convert_to_boxes=False)
print("Not defined config for mb1 lite")
exit(-1)
elif args.net == 'sq-ssd-lite':
net = create_squeezenet_ssd_lite(len(class_names), is_test=True, convert_to_boxes=False)
config = squeezenet_ssd_config
elif args.net == 'mb2-ssd-lite':
net = create_mobilenetv2_ssd_lite(len(class_names), width_mult=args.mb2_width_mult, is_test=True, convert_to_boxes=False)
print("Not defined config for mb2 lite")
exit(-1)
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)
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])))
running_loss += loss.item()
running_regression_loss += regression_loss.item()
running_classification_loss += classification_loss.item()
return running_loss / num, running_regression_loss / num, running_classification_loss / num
if __name__ == '__main__':
timer = Timer()
logging.info(args)
if args.net == 'vgg16-ssd':
create_net = create_vgg_ssd
config = vgg_ssd_config
elif args.net == 'mb1-ssd':
create_net = lambda num: create_mobilenetv1_ssd(num, alpha=args.mb1_width_mult)
# create_net = create_mobilenetv1_ssd
config = mobilenetv1_ssd_config
config = mobilenetv1_ssd_config
elif args.net == 'mb1-ssd-lite':
create_net = create_mobilenetv1_ssd_lite
config = mobilenetv1_ssd_config
elif args.net == 'sq-ssd-lite':
create_net = create_squeezenet_ssd_lite
config = squeezenet_ssd_config
elif args.net == 'mb2-ssd-lite':
create_net = lambda num: create_mobilenetv2_ssd_lite(num, width_mult=args.mb2_width_mult)
config = mobilenetv1_ssd_config
else:
logging.fatal("The net type is wrong.")
parser.print_help(sys.stderr)
from vision.ssd.mobilenet_v2_ssd_lite import create_mobilenetv2_ssd_lite, create_mobilenetv2_ssd_lite_predictor
from vision.utils.misc import Timer
import PySimpleGUI as sg
import cv2
import numpy as np
import sys
import time
net_type = 'mb1-ssd'
model_path = './models/mb1-ssd-Epoch-1290-Loss-2.35230020682017.pth'
label_path = './models/voc-model-labels.txt'
class_names = [name.strip() for name in open(label_path).readlines()]
num_classes = len(class_names)
net = create_mobilenetv1_ssd(len(class_names), is_test=True)
net.load(model_path)
predictor = create_mobilenetv1_ssd_predictor(net, candidate_size=200)
cap = cv2.VideoCapture(0, cv2.CAP_V4L2) # capture from camera
#cap = cv2.VideoCapture(0)
#cap.set(3, 1080)
#cap.set(4, 940)
sg.theme('DarkAmber')
# define the window layout
layout = [[sg.Image(filename='', key='image')],
[
sg.Checkbox('ヘルメット', size=(8, 1), font='Helvetica 14'),
sg.Checkbox('溶接マスク', size=(8, 1), font='Helvetica 14'),
sg.Checkbox('作業着', size=(8, 1), font='Helvetica 14'),
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)
