forked from mindspore-Ecosystem/mindspore
295 lines
12 KiB
Python
295 lines
12 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""
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Testing the bounding box augment op in DE
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"""
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from enum import Enum
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import mindspore.log as logger
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import mindspore.dataset as ds
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import mindspore.dataset.transforms.vision.c_transforms as c_vision
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import matplotlib.pyplot as plt
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import matplotlib.patches as patches
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import numpy as np
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GENERATE_GOLDEN = False
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DATA_DIR = "../data/dataset/testVOC2012_2"
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class BoxType(Enum):
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"""
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Defines box types for test cases
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"""
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WidthOverflow = 1
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HeightOverflow = 2
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NegativeXY = 3
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OnEdge = 4
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WrongShape = 5
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def add_bad_annotation(img, bboxes, box_type):
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"""
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Used to generate erroneous bounding box examples on given img.
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:param img: image where the bounding boxes are.
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:param bboxes: in [x_min, y_min, w, h, label, truncate, difficult] format
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:param box_type: type of bad box
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:return: bboxes with bad examples added
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"""
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height = img.shape[0]
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width = img.shape[1]
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if box_type == BoxType.WidthOverflow:
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# use box that overflows on width
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return img, np.array([[0, 0, width + 1, height, 0, 0, 0]]).astype(np.uint32)
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if box_type == BoxType.HeightOverflow:
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# use box that overflows on height
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return img, np.array([[0, 0, width, height + 1, 0, 0, 0]]).astype(np.uint32)
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if box_type == BoxType.NegativeXY:
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# use box with negative xy
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return img, np.array([[-10, -10, width, height, 0, 0, 0]]).astype(np.uint32)
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if box_type == BoxType.OnEdge:
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# use box that covers the whole image
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return img, np.array([[0, 0, width, height, 0, 0, 0]]).astype(np.uint32)
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if box_type == BoxType.WrongShape:
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# use box that covers the whole image
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return img, np.array([[0, 0, width - 1]]).astype(np.uint32)
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return img, bboxes
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def check_bad_box(data, box_type, expected_error):
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"""
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:param data: de object detection pipeline
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:param box_type: type of bad box
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:param expected_error: error expected to get due to bad box
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:return: None
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"""
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try:
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test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1),
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1) # DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM)
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data = data.map(input_columns=["annotation"],
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output_columns=["annotation"],
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operations=fix_annotate)
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# map to use width overflow
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data = data.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=lambda img, bboxes: add_bad_annotation(img, bboxes, box_type))
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# map to apply ops
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data = data.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[test_op]) # Add column for "annotation"
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for _, _ in enumerate(data.create_dict_iterator()):
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break
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except RuntimeError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert expected_error in str(error)
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def fix_annotate(bboxes):
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"""
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Fix annotations to format followed by mindspore.
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:param bboxes: in [label, x_min, y_min, w, h, truncate, difficult] format
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:return: annotation in [x_min, y_min, w, h, label, truncate, difficult] format
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"""
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for bbox in bboxes:
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tmp = bbox[0]
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bbox[0] = bbox[1]
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bbox[1] = bbox[2]
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bbox[2] = bbox[3]
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bbox[3] = bbox[4]
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bbox[4] = tmp
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return bboxes
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def add_bounding_boxes(axis, bboxes):
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"""
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:param axis: axis to modify
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:param bboxes: bounding boxes to draw on the axis
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:return: None
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"""
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for bbox in bboxes:
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rect = patches.Rectangle((bbox[0], bbox[1]),
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bbox[2], bbox[3],
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linewidth=1, edgecolor='r', facecolor='none')
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# Add the patch to the Axes
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axis.add_patch(rect)
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def visualize(unaugmented_data, augment_data):
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"""
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:param unaugmented_data: original data
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:param augment_data: data after augmentations
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:return: None
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"""
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for idx, (un_aug_item, aug_item) in \
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enumerate(zip(unaugmented_data.create_dict_iterator(),
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augment_data.create_dict_iterator())):
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axis = plt.subplot(141)
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plt.imshow(un_aug_item["image"])
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add_bounding_boxes(axis, un_aug_item["annotation"]) # add Orig BBoxes
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plt.title("Original" + str(idx + 1))
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logger.info("Original ", str(idx + 1), " :", un_aug_item["annotation"])
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axis = plt.subplot(142)
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plt.imshow(aug_item["image"])
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add_bounding_boxes(axis, aug_item["annotation"]) # add AugBBoxes
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plt.title("Augmented" + str(idx + 1))
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logger.info("Augmented ", str(idx + 1), " ", aug_item["annotation"], "\n")
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plt.show()
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def test_bounding_box_augment_with_rotation_op(plot=False):
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"""
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Test BoundingBoxAugment op
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Prints images side by side with and without Aug applied + bboxes to compare and test
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"""
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logger.info("test_bounding_box_augment_with_rotation_op")
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data_voc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
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data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
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test_op = c_vision.BoundingBoxAugment(c_vision.RandomRotation(90), 1)
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# DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM)
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# maps to fix annotations to minddata standard
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data_voc1 = data_voc1.map(input_columns=["annotation"],
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output_columns=["annotation"],
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operations=fix_annotate)
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data_voc2 = data_voc2.map(input_columns=["annotation"],
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output_columns=["annotation"],
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operations=fix_annotate)
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# map to apply ops
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data_voc2 = data_voc2.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[test_op]) # Add column for "annotation"
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if plot:
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visualize(data_voc1, data_voc2)
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def test_bounding_box_augment_with_crop_op(plot=False):
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"""
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Test BoundingBoxAugment op
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Prints images side by side with and without Aug applied + bboxes to compare and test
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"""
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logger.info("test_bounding_box_augment_with_crop_op")
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data_voc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
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data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
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test_op = c_vision.BoundingBoxAugment(c_vision.RandomCrop(90), 1)
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# maps to fix annotations to minddata standard
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data_voc1 = data_voc1.map(input_columns=["annotation"],
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output_columns=["annotation"],
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operations=fix_annotate)
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data_voc2 = data_voc2.map(input_columns=["annotation"],
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output_columns=["annotation"],
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operations=fix_annotate)
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# map to apply ops
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data_voc2 = data_voc2.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[test_op]) # Add column for "annotation"
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if plot:
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visualize(data_voc1, data_voc2)
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def test_bounding_box_augment_valid_ratio_c(plot=False):
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"""
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Test RandomHorizontalFlipWithBBox op
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Prints images side by side with and without Aug applied + bboxes to compare and test
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"""
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logger.info("test_bounding_box_augment_valid_ratio_c")
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data_voc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
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data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
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test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 0.9)
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# DEFINE TEST OP HERE -- (PROB 1 IN CASE OF RANDOM)
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# maps to fix annotations to minddata standard
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data_voc1 = data_voc1.map(input_columns=["annotation"],
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output_columns=["annotation"],
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operations=fix_annotate)
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data_voc2 = data_voc2.map(input_columns=["annotation"],
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output_columns=["annotation"],
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operations=fix_annotate)
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# map to apply ops
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data_voc2 = data_voc2.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[test_op]) # Add column for "annotation"
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if plot:
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visualize(data_voc1, data_voc2)
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def test_bounding_box_augment_invalid_ratio_c():
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"""
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Test RandomHorizontalFlipWithBBox op with invalid input probability
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"""
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logger.info("test_bounding_box_augment_invalid_ratio_c")
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data_voc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
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data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
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try:
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# ratio range is from 0 - 1
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test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 1.5)
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# maps to fix annotations to minddata standard
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data_voc1 = data_voc1.map(input_columns=["annotation"],
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output_columns=["annotation"],
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operations=fix_annotate)
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data_voc2 = data_voc2.map(input_columns=["annotation"],
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output_columns=["annotation"],
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operations=fix_annotate)
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# map to apply ops
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data_voc2 = data_voc2.map(input_columns=["image", "annotation"],
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output_columns=["image", "annotation"],
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columns_order=["image", "annotation"],
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operations=[test_op]) # Add column for "annotation"
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except ValueError as error:
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logger.info("Got an exception in DE: {}".format(str(error)))
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assert "Input is not" in str(error)
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def test_bounding_box_augment_invalid_bounds_c():
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"""
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Test BoundingBoxAugment op with invalid bboxes.
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"""
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logger.info("test_bounding_box_augment_invalid_bounds_c")
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data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
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check_bad_box(data_voc2, BoxType.WidthOverflow, "bounding boxes is out of bounds of the image")
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data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
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check_bad_box(data_voc2, BoxType.HeightOverflow, "bounding boxes is out of bounds of the image")
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data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
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check_bad_box(data_voc2, BoxType.NegativeXY, "min_x")
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data_voc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
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check_bad_box(data_voc2, BoxType.WrongShape, "4 features")
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if __name__ == "__main__":
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# set to false to not show plots
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test_bounding_box_augment_with_rotation_op(False)
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test_bounding_box_augment_with_crop_op(False)
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test_bounding_box_augment_valid_ratio_c(False)
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test_bounding_box_augment_invalid_ratio_c()
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test_bounding_box_augment_invalid_bounds_c()
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