def predict(model, epoch):
model.eval()
img_path_list = preprossing.get_images(cfg.test_img_path)
for index in range(len(img_path_list)):
im_fn = img_path_list[index]
im = cv2.imread(im_fn)[:, :, ::-1]
if im is None:
print("can not find image of %s" % (im_fn))
continue
print('EAST <==> TEST <==> epoch:{}, idx:{} <==> Begin'.format(
epoch, index))
# 图像进行放缩
im_resized, (ratio_h, ratio_w) = utils.resize_image(im)
im_resized = im_resized.astype(np.float32)
# 图像转换成tensor格式
im_resized = im_resized.transpose(2, 0, 1)
im_tensor = torch.from_numpy(im_resized)
im_tensor = im_tensor.cuda()
# 图像数据增加一维
im_tensor = im_tensor.unsqueeze(0)
timer = {'net': 0, 'restore': 0, 'nms': 0}
start = time.time()
# 输入网络进行推断
score, geometry = model(im_tensor)
timer['net'] = time.time() - start
# score与geometry转换成numpy格式
score = score.permute(0, 2, 3, 1)
geometry = geometry.permute(0, 2, 3, 1)
score = score.data.cpu().numpy()
geometry = geometry.data.cpu().numpy()
# 文本框检测
boxes, timer = utils.detect(score_map=score,
geo_map=geometry,
timer=timer,
score_map_thresh=cfg.score_map_thresh,
box_thresh=cfg.box_thresh,
nms_thres=cfg.box_thresh)
print(
'EAST <==> TEST <==> idx:{} <==> model:{:.2f}ms, restore:{:.2f}ms, nms:{:.2f}ms'
.format(index, timer['net'] * 1000, timer['restore'] * 1000,
timer['nms'] * 1000))
if boxes is not None:
boxes = boxes[:, :8].reshape((-1, 4, 2))
boxes[:, :, 0] /= ratio_w
boxes[:, :, 1] /= ratio_h
# save to txt file
if boxes is not None:
res_file = os.path.join(
cfg.res_img_path,
'res_{}.txt'.format(os.path.basename(im_fn).split('.')[0]))
with open(res_file, 'w') as f:
for box in boxes:
# to avoid submitting errors
box = utils.sort_poly(box.astype(np.int32))
if np.linalg.norm(box[0] - box[1]) < 5 or np.linalg.norm(
box[3] - box[0]) < 5:
continue
f.write('{},{},{},{},{},{},{},{}\r\n'.format(
box[0, 0],
box[0, 1],
box[1, 0],
box[1, 1],
box[2, 0],
box[2, 1],
box[3, 0],
box[3, 1],
))
cv2.polylines(im[:, :, ::-1],
[box.astype(np.int32).reshape((-1, 1, 2))],
True,
color=(255, 255, 0),
thickness=1)
print('EAST <==> TEST <==> Save txt at:{} <==> Done'.format(
res_file))
# 图片输出
if cfg.write_images:
img_path = os.path.join(cfg.res_img_path, os.path.basename(im_fn))
cv2.imwrite(img_path, im[:, :, ::-1])
print('EAST <==> TEST <==> Save image at:{} <==> Done'.format(
img_path))
print(
'EAST <==> TEST <==> Record and Save <==> epoch:{}, ids:{} <==> Done'
.format(epoch, index))
def __init__(self, transform=None):
# 获取文件路径
self.img_path_list = preprossing.get_images(cfg.train_data_path)
self.transform = transform
result_root = os.path.abspath(cfg.res_img_path)
if not os.path.exists(result_root):
os.mkdir(result_root)
print('EAST <==> Prepare <==> Network <==> Begin')
model = East()
model = torch.nn.DataParallel(model, device_ids=cfg.gpu_ids)
model.cuda()
# 载入模型
if os.path.isfile(cfg.checkpoint):
print(
"EAST <==> Prepare <==> Loading checkpoint '{}' <==> Begin".format(
cfg.checkpoint))
checkpoint = torch.load(cfg.checkpoint)
epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
print(
"EAST <==> Prepare <==> Loading checkpoint '{}' <==> Done".format(
cfg.checkpoint))
else:
print('Can not find checkpoint !!!')
exit(1)
predict(model, epoch)
if __name__ == "__main__":
main()
img_path_list = preprossing.get_images(cfg.test_img_path)