示例#1
0
文件: test.py 项目: 530824679/YOLOv2
def predict_image():
    image_path = "/home/chenwei/HDD/Project/datasets/object_detection/FDDB2016/convert/images/2002_07_19_big_img_130.jpg"

    image = cv2.imread(image_path)
    image_size = image.shape[:2]
    input_shape = [model_params['input_height'], model_params['input_width']]
    image_data = pre_process(image, input_shape)
    image_data = image_data[np.newaxis, ...]

    input = tf.placeholder(shape=[1, None, None, 3], dtype=tf.float32)

    network = Network(is_train=False)
    logits = network.build_network(input)
    output = network.reorg_layer(logits, model_params['anchors'])

    checkpoints = "./checkpoints/model.ckpt-128"
    saver = tf.train.Saver()
    with tf.Session() as sess:
        saver.restore(sess, checkpoints)
        bboxes, obj_probs, class_probs = sess.run(
            output, feed_dict={input: image_data})

    bboxes, scores, class_id = postprocess(bboxes,
                                           obj_probs,
                                           class_probs,
                                           image_shape=image_size,
                                           input_shape=input_shape)

    img_detection = visualization(image, bboxes, scores, class_id,
                                  model_params["classes"])
    cv2.imshow("result", img_detection)
    cv2.waitKey(0)
示例#2
0
文件: test.py 项目: 530824679/YOLOv2
def predict_video():
    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True

    capture = cv2.VideoCapture(0)

    input = tf.placeholder(shape=[1, None, None, 3], dtype=tf.float32)

    network = Network(is_train=False)
    logits = network.build_network(input)
    output = network.reorg_layer(logits, model_params['anchors'])

    checkpoints = "./checkpoints/model.ckpt-128"
    saver = tf.train.Saver()

    with tf.Session(config=config) as sess:
        saver.restore(sess, checkpoints)

        while (True):
            ref, image = capture.read()

            image_size = image.shape[:2]
            input_shape = [
                model_params['input_height'], model_params['input_width']
            ]
            image_data = pre_process(image, input_shape)
            image_data = image_data[np.newaxis, ...]

            bboxes, obj_probs, class_probs = sess.run(
                output, feed_dict={input: image_data})

            bboxes, scores, class_id = postprocess(bboxes,
                                                   obj_probs,
                                                   class_probs,
                                                   image_shape=image_size,
                                                   input_shape=input_shape)

            img_detection = visualization(image, bboxes, scores, class_id,
                                          model_params["classes"])
            cv2.imshow("result", img_detection)
            cv2.waitKey(1)

    cv2.destroyAllWindows()