# Copyright 2020-2022 Huawei Technologies Co., Ltd
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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# http://www.apache.org/licenses/LICENSE-2.0
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# ==============================================================================
"""
Testing RandomCropAndResizeWithBBox op in DE
"""
import mindspore.dataset as ds
import mindspore.dataset.vision as vision

from mindspore import log as logger
from util import config_get_set_seed, config_get_set_num_parallel_workers, save_and_check_md5, \
    helper_perform_ops_bbox, helper_test_visual_bbox, helper_invalid_bounding_box_test

GENERATE_GOLDEN = False

# Updated VOC dataset with correct annotations - DATA_DIR
DATA_DIR_VOC = "../data/dataset/testVOC2012_2"
# COCO dataset - DATA_DIR, ANNOTATION_DIR
DATA_DIR_COCO = ["../data/dataset/testCOCO/train/",
                 "../data/dataset/testCOCO/annotations/train.json"]


def test_random_resized_crop_with_bbox_op_c(plot_vis=False):
    """
    Feature: RandomResizedCropWithBBox op
    Description: Prints images and bboxes side by side with and without RandomResizedCropWithBBox Op applied
    Expectation: Passes the MD5 check test
    """
    logger.info("test_random_resized_crop_with_bbox_op_c")

    original_seed = config_get_set_seed(23415)
    original_num_parallel_workers = config_get_set_num_parallel_workers(1)

    # Load dataset
    data_voc1 = ds.VOCDataset(
        DATA_DIR_VOC, task="Detection", usage="train", shuffle=False, decode=True)
    data_voc2 = ds.VOCDataset(
        DATA_DIR_VOC, task="Detection", usage="train", shuffle=False, decode=True)

    test_op = vision.RandomResizedCropWithBBox(
        (256, 512), (0.5, 0.5), (0.5, 0.5))

    # map to apply ops
    data_voc2 = helper_perform_ops_bbox(data_voc2, test_op)
    data_voc2 = data_voc2.project(["image", "bbox"])

    filename = "random_resized_crop_with_bbox_01_c_result.npz"
    save_and_check_md5(data_voc2, filename, generate_golden=GENERATE_GOLDEN)

    helper_test_visual_bbox(plot_vis, data_voc1, data_voc2)

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


def METHOD_NAME(plot_vis=False):
    """
    Feature: RandomResizedCropWithBBox op
    Description: Prints images and bboxes side by side with and without the Op applied with CocoDataset
    Expectation: Prints images and bboxes side by side as expected
    """
    logger.info("test_random_resized_crop_with_bbox_op_coco_c")
    # load dataset
    data_coco1 = ds.CocoDataset(DATA_DIR_COCO[0], annotation_file=DATA_DIR_COCO[1], task="Detection",
                                decode=True, shuffle=False)

    data_coco2 = ds.CocoDataset(DATA_DIR_COCO[0], annotation_file=DATA_DIR_COCO[1], task="Detection",
                                decode=True, shuffle=False)

    test_op = vision.RandomResizedCropWithBBox((512, 512), (0.5, 1), (0.5, 1))

    data_coco2 = helper_perform_ops_bbox(data_coco2, test_op)

    helper_test_visual_bbox(plot_vis, data_coco1, data_coco2)


def test_random_resized_crop_with_bbox_op_edge_c(plot_vis=False):
    """
    Feature: RandomResizedCropWithBBox op
    Description: Prints images and bboxes side by side with and without the Op applied on edge case
    Expectation: Passes the dynamically generated edge case
    """
    logger.info("test_random_resized_crop_with_bbox_op_edge_c")

    # Load dataset
    data_voc1 = ds.VOCDataset(
        DATA_DIR_VOC, task="Detection", usage="train", shuffle=False, decode=True)
    data_voc2 = ds.VOCDataset(
        DATA_DIR_VOC, task="Detection", usage="train", shuffle=False, decode=True)

    test_op = vision.RandomResizedCropWithBBox(
        (256, 512), (0.5, 0.5), (0.5, 0.5))

    # maps to convert data into valid edge case data
    data_voc1 = helper_perform_ops_bbox(data_voc1, None, True)

    # Test Op added to list of Operations here
    data_voc2 = helper_perform_ops_bbox(data_voc2, test_op, True)

    helper_test_visual_bbox(plot_vis, data_voc1, data_voc2)


def test_random_resized_crop_with_bbox_op_invalid_c():
    """
    Feature: RandomResizedCropWithBBox op
    Description: Test RandomResizedCropWithBBox on invalid constructor parameters (range of scale)
    Expectation: Error is raised as expected
    """
    logger.info("test_random_resized_crop_with_bbox_op_invalid_c")

    # Load dataset, only Augmented Dataset as test will raise ValueError
    data_voc2 = ds.VOCDataset(
        DATA_DIR_VOC, task="Detection", usage="train", shuffle=False, decode=True)

    try:
        # If input range of scale is not in the order of (min, max), ValueError will be raised.
        test_op = vision.RandomResizedCropWithBBox(
            (256, 512), (1, 0.5), (0.5, 0.5))

        # map to apply ops
        data_voc2 = helper_perform_ops_bbox(data_voc2, test_op)

        for _ in data_voc2.create_dict_iterator(num_epochs=1):
            break

    except ValueError as err:
        logger.info("Got an exception in DE: {}".format(str(err)))
        assert "scale should be in (min,max) format. Got (max,min)." in str(
            err)


def test_random_resized_crop_with_bbox_op_invalid2_c():
    """
    Feature: RandomResizedCropWithBBox op
    Description: Test RandomResizedCropWithBBox on invalid constructor parameters (range of ratio)
    Expectation: Error is raised as expected
    """
    logger.info("test_random_resized_crop_with_bbox_op_invalid2_c")
    # Load dataset # only loading the to AugDataset as test will fail on this
    data_voc2 = ds.VOCDataset(
        DATA_DIR_VOC, task="Detection", usage="train", shuffle=False, decode=True)

    try:
        # If input range of ratio is not in the order of (min, max), ValueError will be raised.
        test_op = vision.RandomResizedCropWithBBox(
            (256, 512), (1, 1), (1, 0.5))

        # map to apply ops
        data_voc2 = helper_perform_ops_bbox(data_voc2, test_op)

        for _ in data_voc2.create_dict_iterator(num_epochs=1):
            break

    except ValueError as err:
        logger.info("Got an exception in DE: {}".format(str(err)))
        assert "ratio should be in (min,max) format. Got (max,min)." in str(
            err)


def test_random_resized_crop_with_bbox_op_bad_c():
    """
    Feature: RandomResizedCropWithBBox op
    Description: Test RandomResizedCropWithBBox op with invalid bounding boxes
    Expectation: Multiple correct errors are caught as expected
    """
    logger.info("test_random_resized_crop_with_bbox_op_bad_c")
    test_op = vision.RandomResizedCropWithBBox(
        (256, 512), (0.5, 0.5), (0.5, 0.5))

    helper_invalid_bounding_box_test(DATA_DIR_VOC, test_op)


if __name__ == "__main__":
    test_random_resized_crop_with_bbox_op_c(plot_vis=False)
    METHOD_NAME(plot_vis=False)
    test_random_resized_crop_with_bbox_op_edge_c(plot_vis=False)
    test_random_resized_crop_with_bbox_op_invalid_c()
    test_random_resized_crop_with_bbox_op_invalid2_c()
    test_random_resized_crop_with_bbox_op_bad_c()