def __init__(self):
self.input_size = cfg.input_image_h
self.classes = utils.read_class_names(cfg.classes_file)
self.num_classes = len(self.classes)
self.score_threshold = cfg.score_threshold
self.iou_threshold = cfg.nms_thresh
self.moving_ave_decay = cfg.moving_ave_decay
self.annotation_path = cfg.test_data_file
self.weight_file = cfg.weight_file
self.write_image = cfg.write_image
self.write_image_path = cfg.write_image_path
self.show_label = cfg.show_label
self.input_data = tf.placeholder(
shape=[1, cfg.input_image_h, cfg.input_image_w, 3],
dtype=tf.float32)
self.sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
self.sess.run(tf.global_variables_initializer())
model = CenterNet(self.input_data, False)
saver = tf.train.Saver()
saver.restore(
self.sess,
'./checkpoint/2021_02_24-centernet_test_loss=0.5797.ckpt-80')
self.hm = model.pred_hm
self.wh = model.pred_wh
self.reg = model.pred_reg
self.det = decode(self.hm, self.wh, self.reg, K=cfg.max_objs)
def __init__(self):
K.clear_session()
K.set_learning_phase(0)
self.classes = utils.read_class_names(cfg.RDT_Reader.CLASSES)
self.num_classes = len(self.classes)
self.time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
self.moving_ave_decay = cfg.TRAIN.MOVING_AVE_DECAY
self.train_logdir = "./dataset/log/train"
# self.testset = data_loader.loadDataObjSSD('test')
self.checkpoint_name = cfg.TEST.EVAL_MODEL_PATH + "/eval.ckpt"
self.model_path = cfg.TEST.EVAL_MODEL_PATH + "/model/"
self.eval_tflite = cfg.TEST.EVAL_MODEL_PATH + "/OD_180x320_newarch_resnet_data_qnt.lite"
self.initial_weight = cfg.TEST.WEIGHT_FILE
self.output_node_names = ["define_loss/reshapedOutput"]
self.learn_rate_init = cfg.TRAIN.LEARN_RATE_INIT
self.learn_rate_end = cfg.TRAIN.LEARN_RATE_END
self.first_stage_epochs = cfg.TRAIN.FISRT_STAGE_EPOCHS
self.second_stage_epochs = cfg.TRAIN.SECOND_STAGE_EPOCHS
self.warmup_periods = cfg.TRAIN.WARMUP_EPOCHS
# self.trainset = data_loader.loadData('train')
self.testset = data_loader.loadDataObjSSDFromYoloFormat('test')
# self.trainset = data_loader.loadDataObjSSDFromYoloFormat('train')
# self.steps_per_period = len(self.trainset)
self.quant_delay = cfg.TRAIN.QUANT_DELAY
self.quantizedPb = cfg.TEST.QUANTIZED_WEIGHT_FILE
self.resize_dim = tuple(cfg.TEST.INPUT_SIZE)
self.number_blocks = cfg.TRAIN.NUMBER_BLOCKS
self.model = ObjectDetection(True, self.initial_weight).model
self.anch = cfg.TRAIN.ANCHOR_ASPECTRATIO
def __init__(self, loadWeights, weightsFile):
self.classes = utils.read_class_names(cfg.RDT_Reader.CLASSES)
self.num_class = cfg.TRAIN.NUMBER_CLASSES
self.loadWeights = loadWeights
self.weightsFile = weightsFile
self.resize_dim = tuple(cfg.TEST.INPUT_SIZE)
self.number_anchors = len(cfg.TRAIN.ANCHOR_ASPECTRATIO[0])
self.model = self.__build_network__imgClass()
def __init__(self):
self.classes = utils.read_class_names(cfg.RDT_Reader.CLASSES)
self.num_classes = len(self.classes)
self.learn_rate_init = cfg.TRAIN.LEARN_RATE_INIT
self.learn_rate_end = cfg.TRAIN.LEARN_RATE_END
self.initial_weight = cfg.TRAIN.INITIAL_WEIGHT
self.saveModelpath = cfg.TEST.WEIGHT_FILE
self.time = time.strftime('%Y-%m-%d-%H-%M-%S',
time.localtime(time.time()))
self.train_logdir = "./dataset/log/"
self.trainset = data_loader.loadDataObjSSDFromYoloFormat('train')
self.testset = data_loader.loadDataObjSSDFromYoloFormat('test')
self.model = ObjectDetection(False, self.initial_weight).model
self.number_blocks = cfg.TRAIN.NUMBER_BLOCKS
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
ckpt_path = './checkpoint/'
sess = tf.Session()
inputs = tf.placeholder(shape=[None, None, None, 3], dtype=tf.float32)
model = CenterNet(inputs, False, net_name=cfg.net)
saver = tf.train.Saver()
saver.restore(sess, tf.train.latest_checkpoint(ckpt_path))
hm = model.pred_hm
wh = model.pred_wh
reg = model.pred_reg
det = decode(hm, wh, reg, K=cfg.max_objs)
class_names = read_class_names(cfg.classes_file)
img_names = os.listdir(
'/home/pcl/tf_work/TF_CenterNet/VOC/test/VOCdevkit/VOC2007/JPEGImages')
for img_name in img_names:
img_path = '/home/pcl/tf_work/TF_CenterNet/VOC/test/VOCdevkit/VOC2007/JPEGImages/' + img_name
print(img_path)
original_image = cv2.imread(img_path)
original_image_size = original_image.shape[:2]
image_data = image_preporcess(np.copy(original_image),
[cfg.input_image_h, cfg.input_image_w])
image_data = image_data[np.newaxis, ...]
t0 = time.time()
detections = sess.run(det, feed_dict={inputs: image_data})
detections = post_process(detections, original_image_size,
[cfg.input_image_h, cfg.input_image_w],
# -*- coding:utf-8 -*- from utils.utils import parse_anchors, read_class_names, get_color_table """ 检测配置 """ detect_object = 'img' # 默认检测对象 input_image = './data/test_img/test8.jpg' # 默认图片路径 input_video = './data/test_video/video_demo.mp4' # 默认视频路径 output_image = './data/test_img/result/result8.jpg' # 保存图片路径 output_video = './data/test_video/result/result.mp4' # 保存视频路径 anchor_path = './data/yolo_anchors.txt' # anchor 文件路径 anchors = parse_anchors(anchor_path) # anchor内容 weight_path = './data/weights_yolo/yolo_face' # weights路径 class_name_path = './data/face.names' # 类别文件路径 classes = read_class_names(class_name_path) # 类别文件list num_class = len(classes) # 类别数量 new_size = [416, 416] # 图片改变后的大小 use_letterbox_resize = True # 是否使用letterbox