def __init__(self, config):
self.calib_file = config["dir"]["calib_file"]
self.box2d_dir = config["dir"]["box2d_dir"]
self.model = bbox_3D_net((224, 224, 3))
self.model.load_weights(config["model_dir"])
self.classes = config["class"]
self.cls_to_ind = {cls: i for i, cls in enumerate(self.classes)}
self.dims_avg_dir = dir["dims_avg"]
self.dims_avg = np.loadtxt(self.dims_avg_dir, delimiter=',')
self.cam_to_img = get_cam_data(self.calib_file)
self.fx = self.cam_to_img[0][0]
self.u0 = self.cam_to_img[0][2]
self.v0 = self.cam_to_img[1][2]
import cv2
import numpy as np
import os
from util.post_processing import gen_3D_box, draw_3D_box, draw_2D_box
from net.bbox_3D_net import bbox_3D_net
from util.process_data import get_cam_data, get_dect2D_data
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
# Construct the network
model = bbox_3D_net((224, 224, 3))
model.load_weights('./model_saved/weights.h5')
image_dir = '/home/kid/workspace/3D_detection/dataset/test/image/'
calib_file = '/home/kid/workspace/3D_detection/dataset/test/calib.txt'
box2d_dir = '/home/kid/workspace/3D_detection/dataset/test/label/'
classes = [
'Car', 'Van', 'Truck', 'Pedestrian', 'Person_sitting', 'Cyclist', 'Tram'
]
cls_to_ind = {cls: i for i, cls in enumerate(classes)}
dims_avg = np.loadtxt(r'dataset/voc_dims.txt', delimiter=',')
all_image = sorted(os.listdir(image_dir))
# np.random.shuffle(all_image)
cam_to_img = get_cam_data(calib_file)
fx = cam_to_img[0][0]
import numpy as np
import os
from keras.callbacks import TensorBoard, EarlyStopping, ModelCheckpoint
from keras.optimizers import Adam
from layer.loss_func import orientation_loss
from util.process_data import load_and_process_annotation_data,train_data_gen
from net.bbox_3D_net import bbox_3D_net
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="0"
# Construct the network
model = bbox_3D_net((224,224,3),bin_num=6,vgg_weights='imagenet')
minimizer = Adam(lr=1e-5)
early_stop = EarlyStopping(monitor='val_loss', min_delta=0.001, patience=10, mode='min', verbose=1)
checkpoint = ModelCheckpoint('weights.hdf5', monitor='val_loss', verbose=1, save_best_only=True, mode='min', period=1)
tensorboard = TensorBoard(log_dir='./logs/', histogram_freq=0, write_graph=True, write_images=False)
model.compile(optimizer=minimizer,#minimizer,
loss={'dimension': 'mean_squared_error', 'orientation': orientation_loss, 'confidence': 'categorical_crossentropy'},
loss_weights={'dimension': 2., 'orientation': 1., 'confidence': 4.})
image_dir = '/home/kid/workspace/3D_detection/dataset/image_2/'
label_dir = '/home/kid/workspace/3D_detection/dataset/label_2/'
classes = [line.strip() for line in open(r'dataset/voc_labels.txt').readlines()]
cls_to_ind = {cls:i for i,cls in enumerate(classes)}