# 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 horizontal flip with bounding boxes op in DE
"""
import numpy as np
import mindspore.log as logger
import mindspore.dataset as ds
import mindspore.dataset.vision.c_transforms as c_vision
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

# updated VOC dataset with correct annotations
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_horizontal_flip_with_bbox_op_c(plot_vis=False):
    """
    Prints images and bboxes side by side with and without RandomHorizontalFlipWithBBox Op applied
    """
    logger.info("test_random_horizontal_flip_with_bbox_op_c")

    # 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.RandomHorizontalFlipWithBBox(1)

    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
                            output_columns=["image", "bbox"],
                            column_order=["image", "bbox"],
                            operations=[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_horizontal_flip_with_bbox_op_coco_c(plot_vis=False):
    """
    Prints images and bboxes side by side with and without RandomHorizontalFlipWithBBox Op applied,
    Testing with COCO dataset
    """
    logger.info("test_random_horizontal_flip_with_bbox_op_coco_c")

    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.RandomHorizontalFlipWithBBox(1)

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

    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, "bbox")


def test_random_horizontal_flip_with_bbox_valid_rand_c(plot_vis=False):
    """
    Uses a valid non-default input, expect to pass
    Prints images side by side with and without Aug applied + bboxes to
    compare and test
    """
    logger.info("test_random_horizontal_bbox_valid_rand_c")

    original_seed = config_get_set_seed(1)
    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.RandomHorizontalFlipWithBBox(0.6)

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

    filename = "random_horizontal_flip_with_bbox_01_c_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_horizontal_flip_with_bbox_valid_edge_c(plot_vis=False):
    """
    Test RandomHorizontalFlipWithBBox op (testing with valid edge case, box covering full image).
    Prints images side by side with and without Aug applied + bboxes to compare and test
    """
    logger.info("test_horizontal_flip_with_bbox_valid_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.RandomHorizontalFlipWithBBox(1)

    # map to apply ops
    # Add column for "bbox"
    dataVoc1 = dataVoc1.map(input_columns=["image", "bbox"],
                            output_columns=["image", "bbox"],
                            column_order=["image", "bbox"],
                            operations=lambda img, bbox:
                            (img, np.array([[0, 0, img.shape[1], img.shape[0], 0, 0, 0]]).astype(np.float32)))
    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
                            output_columns=["image", "bbox"],
                            column_order=["image", "bbox"],
                            operations=lambda img, bbox:
                            (img, np.array([[0, 0, img.shape[1], img.shape[0], 0, 0, 0]]).astype(np.float32)))
    dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
                            output_columns=["image", "bbox"],
                            column_order=["image", "bbox"],
                            operations=[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_horizontal_flip_with_bbox_invalid_prob_c():
    """
    Test RandomHorizontalFlipWithBBox op with invalid input probability
    """
    logger.info("test_random_horizontal_bbox_invalid_prob_c")

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

    try:
        # Note: Valid range of prob should be [0.0, 1.0]
        test_op = c_vision.RandomHorizontalFlipWithBBox(1.5)
        # map to apply ops
        dataVoc2 = dataVoc2.map(input_columns=["image", "bbox"],
                                output_columns=["image", "bbox"],
                                column_order=["image", "bbox"],
                                operations=[test_op])  # Add column for "bbox"
    except ValueError as error:
        logger.info("Got an exception in DE: {}".format(str(error)))
        assert "Input prob is not within the required interval of (0.0 to 1.0)." in str(error)


def test_random_horizontal_flip_with_bbox_invalid_bounds_c():
    """
    Test RandomHorizontalFlipWithBBox op with invalid bounding boxes
    """
    logger.info("test_random_horizontal_bbox_invalid_bounds_c")

    test_op = c_vision.RandomHorizontalFlipWithBBox(1)

    dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True)
    check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image")
    dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True)
    check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image")
    dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True)
    check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.NegativeXY, "min_x")
    dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", usage="train", shuffle=False, decode=True)
    check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.WrongShape, "4 features")


if __name__ == "__main__":
    # set to false to not show plots
    test_random_horizontal_flip_with_bbox_op_c(plot_vis=False)
    test_random_horizontal_flip_with_bbox_op_coco_c(plot_vis=False)
    test_random_horizontal_flip_with_bbox_valid_rand_c(plot_vis=False)
    test_random_horizontal_flip_with_bbox_valid_edge_c(plot_vis=False)
    test_random_horizontal_flip_with_bbox_invalid_prob_c()
    test_random_horizontal_flip_with_bbox_invalid_bounds_c()