# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""
Testing the random resize with bounding boxes op in DE
"""
import numpy as np
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as c_vision

from mindspore import log as logger
from util import visualize_with_bounding_boxes, InvalidBBoxType, check_bad_bbox, \
    config_get_set_seed, config_get_set_num_parallel_workers, save_and_check_md5

GENERATE_GOLDEN = False

DATA_DIR = "../data/dataset/testVOC2012_2"
DATA_DIR_2 = ["../data/dataset/testCOCO/train/",
              "../data/dataset/testCOCO/annotations/train.json"]  # DATA_DIR, ANNOTATION_DIR


def test_random_resize_with_bbox_op_voc_c(plot_vis=False):
    """
    Prints images and bboxes side by side with and without RandomResizeWithBBox Op applied
    testing with VOC dataset
    """
    logger.info("test_random_resize_with_bbox_op_voc_c")
    original_seed = config_get_set_seed(123)
    original_num_parallel_workers = config_get_set_num_parallel_workers(1)
    # Load dataset
    dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True)

    dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True)

    test_op = c_vision.RandomResizeWithBBox(100)

    # map to apply ops
    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
                            output_columns=["image", "bbox"],
                            column_order=["image", "bbox"],
                            operations=[test_op])

    filename = "random_resize_with_bbox_op_01_c_voc_result.npz"
    save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)

    unaugSamp, augSamp = [], []

    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
        unaugSamp.append(unAug)
        augSamp.append(Aug)

    if plot_vis:
        visualize_with_bounding_boxes(unaugSamp, augSamp)

    # Restore config setting
    ds.config.set_seed(original_seed)
    ds.config.set_num_parallel_workers(original_num_parallel_workers)


def test_random_resize_with_bbox_op_rand_coco_c(plot_vis=False):
    """
    Prints images and bboxes side by side with and without RandomResizeWithBBox Op applied,
    tests with MD5 check, expected to pass
    testing with COCO dataset
    """
    logger.info("test_random_resize_with_bbox_op_rand_coco_c")
    original_seed = config_get_set_seed(231)
    original_num_parallel_workers = config_get_set_num_parallel_workers(1)

    # Load dataset
    dataCoco1 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection",
                               decode=True, shuffle=False)

    dataCoco2 = ds.CocoDataset(DATA_DIR_2[0], annotation_file=DATA_DIR_2[1], task="Detection",
                               decode=True, shuffle=False)

    test_op = c_vision.RandomResizeWithBBox(200)

    # map to apply ops

    dataCoco2 = dataCoco2.map(input_columns=["image", "bbox"],
                              output_columns=["image", "bbox"],
                              column_order=["image", "bbox"],
                              operations=[test_op])

    filename = "random_resize_with_bbox_op_01_c_coco_result.npz"
    save_and_check_md5(dataCoco2, filename, generate_golden=GENERATE_GOLDEN)

    unaugSamp, augSamp = [], []

    for unAug, Aug in zip(dataCoco1.create_dict_iterator(num_epochs=1), dataCoco2.create_dict_iterator(num_epochs=1)):
        unaugSamp.append(unAug)
        augSamp.append(Aug)

    if plot_vis:
        visualize_with_bounding_boxes(unaugSamp, augSamp, annot_name="bbox")

    # Restore config setting
    ds.config.set_seed(original_seed)
    ds.config.set_num_parallel_workers(original_num_parallel_workers)


def test_random_resize_with_bbox_op_edge_c(plot_vis=False):
    """
    Prints images and bboxes side by side with and without RandomresizeWithBBox Op applied,
    applied on dynamically generated edge case, expected to pass. edge case is when bounding
    box has dimensions as the image itself.
    """
    logger.info("test_random_resize_with_bbox_op_edge_c")
    dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True)

    dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True)

    test_op = c_vision.RandomResizeWithBBox(500)

    # maps to convert data into valid edge case data
    dataVoc1 = dataVoc1.map(input_columns=["image", "bbox"],
                            output_columns=["image", "bbox"],
                            column_order=["image", "bbox"],
                            operations=[lambda img, bboxes: (
                                img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype))])

    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
                            output_columns=["image", "bbox"],
                            column_order=["image", "bbox"],
                            operations=[lambda img, bboxes: (
                                img, np.array([[0, 0, img.shape[1], img.shape[0]]]).astype(bboxes.dtype)), test_op])

    unaugSamp, augSamp = [], []

    for unAug, Aug in zip(dataVoc1.create_dict_iterator(num_epochs=1), dataVoc2.create_dict_iterator(num_epochs=1)):
        unaugSamp.append(unAug)
        augSamp.append(Aug)

    if plot_vis:
        visualize_with_bounding_boxes(unaugSamp, augSamp)


def test_random_resize_with_bbox_op_invalid_c():
    """
    Test RandomResizeWithBBox Op on invalid constructor parameters, expected to raise ValueError
    """
    logger.info("test_random_resize_with_bbox_op_invalid_c")

    try:
        # zero value for resize
        c_vision.RandomResizeWithBBox(0)

    except ValueError as err:
        logger.info("Got an exception in DE: {}".format(str(err)))
        assert "Input is not within the required interval of (1 to 16777216)." in str(err)

    try:
        # one of the size values is zero
        c_vision.RandomResizeWithBBox((0, 100))

    except ValueError as err:
        logger.info("Got an exception in DE: {}".format(str(err)))
        assert "Input size at dim 0 is not within the required interval of (1 to 2147483647)." in str(err)

    try:
        # negative value for resize
        c_vision.RandomResizeWithBBox(-10)

    except ValueError as err:
        logger.info("Got an exception in DE: {}".format(str(err)))
        assert "Input is not within the required interval of (1 to 16777216)." in str(err)

    try:
        # invalid input shape
        c_vision.RandomResizeWithBBox((100, 100, 100))

    except TypeError as err:
        logger.info("Got an exception in DE: {}".format(str(err)))
        assert "Size should be" in str(err)


def test_random_resize_with_bbox_op_bad_c():
    """
    Tests RandomResizeWithBBox Op with invalid bounding boxes, expected to catch multiple errors
    """
    logger.info("test_random_resize_with_bbox_op_bad_c")
    test_op = c_vision.RandomResizeWithBBox((400, 300))

    data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True)
    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image")
    data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True)
    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image")
    data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True)
    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.NegativeXY, "min_x")
    data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True)
    check_bad_bbox(data_voc2, test_op, InvalidBBoxType.WrongShape, "4 features")


if __name__ == "__main__":
    test_random_resize_with_bbox_op_voc_c(plot_vis=False)
    test_random_resize_with_bbox_op_rand_coco_c(plot_vis=False)
    test_random_resize_with_bbox_op_edge_c(plot_vis=False)
    test_random_resize_with_bbox_op_invalid_c()
    test_random_resize_with_bbox_op_bad_c()