def test_image():
resized_height = 224
resized_width = 224
#init local model
global model
model = azure_chestxray_keras_utils.build_model(
keras_contrib.applications.densenet.DenseNetImageNet121)
model.load_weights(
'azure_chest_xray_14_weights_712split_epoch_250_val_loss_179-4776.hdf5'
)
#script for later use
import cv2
image_dir = "./../../../data/chestxray/ChestX-ray8/ChestXray-NIHCC/images"
image_name = "00000003_000.png"
full_path = os.path.join(image_dir, image_name)
cv2_image = cv2.resize(cv2.imread(full_path),
(resized_height, resized_width))
# ans = model.predict(np.expand_dims(cv2_image,0))
test_images = json.dumps({"data": cv2_image.tolist()})
test_images = bytes(test_images, encoding="utf8")
ans = run(test_images, True)
return ans
data_base_output_dir,
os.path.join(*(prj_consts.FULLY_PRETRAINED_MODEL_DIR_list)))
test_images_dir = os.path.join(data_base_input_dir,
os.path.join(*(['test_images'])))
test_images = azure_chestxray_utils.get_files_in_dir(test_images_dir)
nih_chest_xray_data_dir = os.path.join(
data_base_input_dir, os.path.join(*(prj_consts.ChestXray_IMAGES_DIR_list)))
chestXray_images = azure_chestxray_utils.get_files_in_dir(
nih_chest_xray_data_dir)
import keras_contrib
model = azure_chestxray_keras_utils.build_model(
keras_contrib.applications.densenet.DenseNetImageNet121)
model_file_name = prj_consts.PRETRAINED_DENSENET201_IMAGENET_CHESTXRAY_MODEL_FILE_NAME
model_file_name = 'weights_only_azure_chest_xray_14_weights_712split_epoch_054_val_loss_191.2588.hdf5'
model.load_weights(os.path.join(fully_trained_weights_dir, model_file_name))
import cv2
cv2_image = cv2.imread(os.path.join(test_images_dir, test_images[3]))
from keras.models import Model
predictions = model.predict(cv2_image[None, :, :, :])
print(predictions)
conv_map_model = Model(inputs=model.input,
outputs=model.get_layer(index=-3).output)
conv_features = conv_map_model.predict(cv2_image[None, :, :, :])
def init():
global model
model_path = Model.get_model_path("chest_xray_model_179.hdf5")
model = azure_chestxray_keras_utils.build_model(
keras_contrib.applications.densenet.DenseNetImageNet121)
model.load_weights(model_path)