def show_prediction(data_index):
with open("../data/data_detect_local_evaluate_100.json") as json_file:
data = json.load(json_file)
if (data_index not in data):
print("Index {} out of range".format(data_index))
return
yolo = Yolo()
yolo.load_weights("./weights/yolo")
img_path = "../pictures/pictures_detect_local_evaluate_100/{}.png".format(
data_index)
img = get_img(img_path)
preds = yolo(img)
boxes = get_boxes(preds)
show(data_index, img_path, boxes)
def train():
yolo = Yolo()
yolo.load_weights("./weights/yolo")
opt = Adam(learning_rate=5e-5)
with open("../data/data_detect_local_train.json") as json_file:
data = json.load(json_file)
data_index = 0
while str(data_index) in data:
img = get_img("../pictures/pictures_detect_local_train/{}.png".format(
data_index))
true_labels, true_boxes, true_preds = get_localization_data(
data[str(data_index)])
def get_loss():
preds = yolo(img)
return calculate_loss(preds, true_labels, true_boxes, true_preds)
opt.minimize(get_loss, [yolo.trainable_weights])
if (data_index % 100 == 99):
yolo.save_weights("./weights/yolo")
data_index += 1
images = args.images
batch_size = args.batch_size
nms_thresh = args.nms_thresh
confidence = args.confidence
CUDA = torch.cuda.is_available()
start = 0
num_classes = 80
# Load the classes
classes = load_classes("data/coco.names")
# Set up the neural network and load weights
print("Loading network.....")
model = Yolo(args.cfgfile)
model.load_weights(args.weightsfile)
print("Network successfully loaded")
# get the input dimension (we assume square images)
inp_dim = int(model.net_info["height"])
assert inp_dim % 32 == 0
assert inp_dim > 32
#If there's a GPU availible, put the model on GPU
if CUDA:
model.cuda()
#Set the model in evaluation mode
model.eval()
read_dir = time.time()
