def predict_image():
image_path = "/home/chenwei/HDD/Project/datasets/object_detection/VOCdevkit/VOC2007/JPEGImages/000066.jpg"
image = cv2.imread(image_path)
image_size = image.shape[:2]
input_shape = [model_params['input_height'], model_params['input_width']]
image_data = preporcess(image, input_shape)
image_data = image_data[np.newaxis, ...]
input = tf.placeholder(shape=[1, input_shape[0], input_shape[1], 3],
dtype=tf.float32)
model = Network(len(model_params['classes']),
model_params['anchors'],
is_train=False)
with tf.variable_scope('yolov3'):
logits = model.build_network(input)
output = model.inference(logits)
checkpoints = "/home/chenwei/HDD/Project/YOLOv3/weights/yolov3.ckpt"
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_max_index = postprocess(bboxes,
obj_probs,
class_probs,
image_shape=image_size,
input_shape=input_shape)
resize_ratio = min(input_shape[1] / image_size[1],
input_shape[0] / image_size[0])
dw = (input_shape[1] - resize_ratio * image_size[1]) / 2
dh = (input_shape[0] - resize_ratio * image_size[0]) / 2
bboxes[:, [0, 2]] = (bboxes[:, [0, 2]] - dw) / resize_ratio
bboxes[:, [1, 3]] = (bboxes[:, [1, 3]] - dh) / resize_ratio
img_detection = visualization(image, bboxes, scores, class_max_index,
model_params["classes"])
cv2.imshow("result", img_detection)
cv2.waitKey(0)
ToTensor()
]),
),
batch_size=1,
shuffle=False,
num_workers=0)
useful_list = []
repeat_list = []
with torch.no_grad():
for i_batch, sample_batched in enumerate(data_loader, 1):
im1_data, im1_info, homo12, im2_data, im2_info, homo21, im1_raw, im2_raw = parse_batch(
sample_batched, device)
# (angle, class_id, octave, pt, response, size)
scale1, kp1, des1, _, _, _ = model.inference(
im1_data, im1_info, im1_raw)
scale2, kp2, des2, _, _, _ = model.inference(
im2_data, im2_info, im2_raw)
kp1c = np.array([[kp[2].cpu().numpy(), kp[1].cpu().numpy()]
for kp in kp1])
kp2c = np.array([[kp[2].cpu().numpy(), kp[1].cpu().numpy()]
for kp in kp2])
# import pdb;pdb.set_trace()
im1_data, im1_info, homo12, im2_data, im2_info, homo21, im1_raw, im2_raw = parse_batch_np(
sample_batched, mean, std)
repeatable, useful = caluseful(kp1c, kp2c, homo12, im2_data)
useful_list.append(useful), repeat_list.append(repeatable)
usefuls = np.array(useful_list)
repeats = np.array(repeat_list)
