def process_data_item(data_item, dim, model_stride):
# data_item[0] dữ liệu ảnh opencv
# data_item[1].pts thông tin tọa độ đã scale về 0-1
# dim là 208
XX, llp, pts = augment_sample(data_item[0], data_item[1].pts, dim)
YY = labels2output_map(llp, pts, dim, model_stride)
return XX, YY
def process_data_item(data_item, dim_w, dim_h, model_stride):
image_abs_path = data_item[0]
image_ndarray = cv2.imread(image_abs_path)
# image_ndarray = data_item[0]
XX, llp, pts_transformed = augment_sample(image_ndarray, data_item[1].pts,
dim_w, dim_h, data_item[1].cls)
YY, is_there_plate = labels2output_map(llp, pts_transformed, dim_w, dim_h,
model_stride, data_item[1].cls)
# salvar_imagem_bbox(XX, pts_transformed, image_abs_path, is_there_plate, data_item[1].cls)
if not is_there_plate:
# salvar_imagem(XX, pts_transformed, image_abs_path)
return None, None, None
return XX, YY, pts_transformed
def process_data_item(data_item, dim, model_stride):
XX, llp, pts = augment_sample(data_item[0], data_item[1].pts, dim)
YY = labels2output_map(llp, pts, dim, model_stride)
return XX, YY
print '%d images+labels found' % len(Data)
Xtrain = np.empty((batch_size, dim, dim, 3), dtype='single')
Ytrain = np.empty(
(batch_size, dim / model_stride, dim / model_stride, 2 * 4 + 1))
model_path_backup = '%s/%s_backup' % (outdir, netname)
model_path_final = '%s/%s_final' % (outdir, netname)
for it in range(iterations):
print 'Iter. %d (of %d)' % (it + 1, iterations)
for k in range(batch_size):
data = choice(Data)
XX, llp, pts = augment_sample(data[0], data[1].pts, dim)
YY = labels2output_map(llp, pts, dim, model_stride)
Xtrain[k] = XX
Ytrain[k] = YY
train_loss = model.train_on_batch(Xtrain, Ytrain)
print '\tLoss: %f' % train_loss
# Save model every 1000 iterations
if (it + 1) % 1000 == 0:
print 'Saving model (%s)' % model_path_backup
save_model(model, model_path_backup)
print 'Saving model (%s)' % model_path_final
def process_data_item(data_item,dim_w, dim_h,model_stride): image_ndarray = cv2.imread(data_item[0]) XX,llp,pts_transformed = augment_sample(image_ndarray,data_item[1].pts,dim_w, dim_h, data_item[1].cls) YY = labels2output_map(llp,pts_transformed, dim_w, dim_h,model_stride, data_item[1].cls) return XX, YY, pts_transformed
def _process_data_item(self,data_item):
XX,llp,pts = augment_sample(data_item[0],data_item[1].pts,self._dim)
YY = labels2output_map(llp,pts,self._dim,self._model_stride)
return XX,YY
