mask_rcnn_generate_predictions.py

class InferenceConfig(config.__class__):
        # Run detection on one image at a time
        GPU_COUNT = 1
        IMAGES_PER_GPU = 1
        DETECTION_MIN_CONFIDENCE = 0.95
        DETECTION_NMS_THRESHOLD = 0.0
        IMAGE_MIN_DIM = 768
        IMAGE_MAX_DIM = 768
        RPN_ANCHOR_SCALES = (64, 96, 128, 256, 512)
        DETECTION_MAX_INSTANCES = 20

# Create model object in inference mode.
config = InferenceConfig()
model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)

# Instantiate dataset
dataset = ship.ShipDataset()

# Load weights
model.load_weights(os.path.join(ROOT_DIR, SHIP_WEIGHTS_PATH), by_name=True)

class_names = ['BG', 'ship']

# Run detection
# Load image ids (filenames) and run length encoded pixels
images_path = "datasets/test"
sample_sub_csv = "sample_submission.csv"
# images_path = "datasets/val"
# sample_sub_csv = "val_ship_segmentations.csv"
sample_submission_df = pd.read_csv(os.path.join(images_path,sample_sub_csv))
unique_image_ids = sample_submission_df.ImageId.unique()

out_pred_rows = []
count = 0
for image_id in unique_image_ids:
image_path = os.path.join(images_path, image_id)
if os.path.isfile(image_path):
    count += 1
    print("Step: ", count)

    # Start counting prediction time
    tic = time.clock()

    image = skimage.io.imread(image_path)
    results = model.detect([image], verbose=1)
    r = results[0]

    # First Image
    re_encoded_to_rle_list = []
    for i in np.arange(np.array(r['masks']).shape[-1]):
        boolean_mask = r['masks'][:,:,i]
        re_encoded_to_rle = dataset.rle_encode(boolean_mask)
        re_encoded_to_rle_list.append(re_encoded_to_rle)

    if len(re_encoded_to_rle_list) == 0:
        out_pred_rows += [{'ImageId': image_id, 'EncodedPixels': None}]
        print("Found Ship: ", "NO")
    else:
        for rle_mask in re_encoded_to_rle_list:
            out_pred_rows += [{'ImageId': image_id, 'EncodedPixels': rle_mask}]
            print("Found Ship: ", rle_mask)

    toc = time.clock()
    print("Prediction time: ",toc-tic)


submission_df = pd.DataFrame(out_pred_rows)[['ImageId', 'EncodedPixels']]

filename = "{}{:%Y%m%dT%H%M}.csv".format("./submissions/submission_", datetime.datetime.now())
submission_df.to_csv(filename, index=False)

Hi ,I am getting error in rle_encode

rle_encode() takes 1 positional argument but 2 were given.

could you please please provide the code for rle_encode()