with open(ann_files[0]) as json_file:
data = json.load(json_file)
tags = data['tags']
objects = data['objects']
img_path = ann_files[0].replace('/ann/', '/img/').split('.json')[0]
label_path = ann_files[0].replace('/ann/', '/masks_machine/').split('.json')[0]
print("Images dtype: {}".format(get_image(img_path).dtype))
print("Labels dtype: {}\n".format(get_image(label_path, label = True).dtype))
print("Images shape: {}".format(get_image(img_path, resize = True if resize else False).shape))
print("Labels shape: {}\n".format(get_image(label_path, label = True, resize = True if resize else False).shape))
# In[]:
model = sm.Linknet_bottleneck_crop(backbone_name=backbone, input_shape=input_shape, classification_classes=classification_classes, segmentation_classes = segmentation_classes, classification_activation = 'sigmoid', segmentation_activation='sigmoid')
model.load_weights('weights/' + weights + '.hdf5')
# In[]:
from keras.utils import plot_model
plot_model(model, to_file='keras_model.png')
# In[]:
onnx_model = keras2onnx.convert_keras(model, model.name)
# In[]:
import onnx
onnx.save_model(onnx_model, 'model.onnx')
# In[]:
preprocessing_fn = sm.get_preprocessing(backbone)
input_shape = (256, 640, 3) if resize else (512, 1280, 3)
classification_classes = 1
segmentation_classes = 1
classification_activation = 'sigmoid' if classification_classes == 1 else 'softmax'
segmentation_activation = 'sigmoid' if segmentation_classes == 1 else 'softmax'
backbone = 'resnet18'
weights = "2019-09-30 17-32-13"
model = sm.Linknet_bottleneck_crop(
backbone_name=backbone,
input_shape=(256, 640, 3),
classification_classes=classification_classes,
segmentation_classes=segmentation_classes,
classification_activation=classification_activation,
segmentation_activation=segmentation_activation)
model.load_weights('weights/' + weights + '.hdf5')
preprocessing_fn = sm.get_preprocessing(backbone)
model._make_predict_function()
print("START ZERO PREDICT")
model.predict(np.zeros(((1, ) + input_shape), dtype=np.uint8))
print("END ZERO PREDICT")
font = cv2.FONT_HERSHEY_SIMPLEX
fontScale = 1