def test_with_model(model):
print(" - Loading test data")
testing_files_path = "./dataset/test/raw"
testing_num_files = count_files_in_folder(testing_files_path)
TESTING_BATCH_SIZE = testing_num_files
raw_dataset = load_data_set()
test_raw_dataset = raw_dataset['test']
test = test_raw_dataset.map(load_image_test)
test_dataset = test.batch(TESTING_BATCH_SIZE)
for images, true_masks in test_dataset:
pass # Hack needed to be able to extrac images and true masks from map datasets
images = images.numpy()
true_masks = true_masks.numpy()
print(f" - Test data loaded for {testing_num_files} images")
print(" - Prediction started")
predictions = model.predict(test_dataset)
predicted_images_number = len(predictions)
print(f" - Prediction finished for {predicted_images_number} images")
print(f" - Let's transform predictions into labeled values.")
labeled_predictions = []
for image_index in range(predicted_images_number):
prediction = predictions[image_index]
predicted_mask = map_prediction_to_mask(prediction)
labeled_predictions.append(predicted_mask)
print(f" - Saving labeled images into ./output folder")
predicted_index = 0
output_path = "./output/"
if not os.path.exists(output_path):
os.makedirs(output_path)
for predicted_result in labeled_predictions:
prediction_as_image = labeled_prediction_to_image(predicted_result)
prediction_as_image.save(f"{output_path}{predicted_index:03d}.jpg")
prediction_as_image.close()
print(f" - Image with index {predicted_index} saved.")
predicted_index += 1
print(f" - Generating sample output page")
generate_output_template()
print(f" - Images saved. Time to check accuracy metrics per label:")
background_acc, border_acc, content_acc = compare_accuracy(true_masks, labeled_predictions)
print(" - Accuracy measures per label:")
print(f" - Border label = {border_acc}")
print(f" - Content label = {content_acc}")
print(f" - Background label = {background_acc}")
print(f" - Saving predicted masks as images")
if __name__ == "__main__":
print(" - Loading saved model")
tf.random.set_seed(11)
custom_objects = {
"border_acc": border_acc,
"background_acc": background_acc,
"content_acc": content_acc,
"iou_coef": iou_coef,
"dice_coef": dice_coef
}
model = keras.models.load_model("./model", custom_objects=custom_objects)
print(" - Loading test data")
testing_files_path = "./dataset/test/raw"
testing_num_files = count_files_in_folder(testing_files_path)
TESTING_BATCH_SIZE = testing_num_files
raw_dataset = load_data_set()
test_raw_dataset = raw_dataset['test']
test = test_raw_dataset.map(load_image_test)
test_dataset = test.batch(TESTING_BATCH_SIZE)
for images, true_masks in test_dataset:
pass # Hack needed to be able to extrac images and true masks from map datasets
images = images.numpy()
true_masks = true_masks.numpy()
print(f" - Test data loaded for {testing_num_files} images")
print(" - Prediction started")
predictions = model.predict(test_dataset)
predicted_images_number = len(predictions)
print(f" - Prediction finished for {predicted_images_number} images")
def train_samples_number(self):
return count_files_in_folder(self.train_directory_path)
def validation_samples_number(self):
return count_files_in_folder(self.validation_directory_path)
def load_tflite():
# Load the TFLite model and allocate tensors.
interpreter = tf.lite.Interpreter(model_path="pretrained.model.tflite")
interpreter.allocate_tensors()
# Get input and output tensors.
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
# Test the model on random input data.
input_shape = input_details[0]['shape']
print(" - Loading test data")
testing_files_path = "./dataset/test/raw"
testing_num_files = count_files_in_folder(testing_files_path)
TESTING_BATCH_SIZE = testing_num_files
raw_dataset = load_data_set()
test_raw_dataset = raw_dataset['test']
test = test_raw_dataset.map(load_image_test)
test_dataset = test.batch(TESTING_BATCH_SIZE)
for images, true_masks in test_dataset:
pass # Hack needed to be able to extrac images and true masks from map datasets
images = images.numpy()
true_masks = true_masks.numpy()
# We need to manipulate here
random_input = np.array(np.random.random_sample(input_shape),
dtype=np.float32)
input_data = images
num_images = images.shape[0]
predictions = []
for image_idx in range(0, num_images):
img = np.expand_dims(input_data[image_idx, :, :, :], axis=0)
interpreter.set_tensor(input_details[0]['index'], img)
interpreter.invoke()
# The function `get_tensor()` returns a copy of the tensor data.
# Use `tensor()` in order to get a pointer to the tensor.
output_data = interpreter.get_tensor(output_details[0]['index'])
predictions.append(output_data)
print(f" - Let's transform predictions into labeled values.")
labeled_predictions = []
for image_index in range(len(predictions)):
prediction = predictions[image_index]
prediction = np.squeeze(prediction)
predicted_mask = map_prediction_to_mask(prediction)
labeled_predictions.append(predicted_mask)
print(f" - Saving labeled images into ./output folder")
predicted_index = 0
output_path = "./output/"
if not os.path.exists(output_path):
os.makedirs(output_path)
for predicted_result in labeled_predictions:
prediction_as_image = labeled_prediction_to_image(predicted_result)
prediction_as_image.save(f"{output_path}{predicted_index:03d}.jpg")
prediction_as_image.close()
print(f" - Image with index {predicted_index} saved.")
predicted_index += 1
print(f" - Generating sample output page")
generate_output_template()