forked from mindspore-Ecosystem/mindspore
Update RandomHorizontalFlipWithBBox and BoundingBouxAugment C++ Ops to use floats
This commit is contained in:
islam_amin
parent
7f37bfbb16
commit
dfc3409f67
|
|
@ -60,7 +60,7 @@
|
|||
#include "dataset/kernels/image/random_crop_decode_resize_op.h"
|
||||
#include "dataset/kernels/image/random_crop_op.h"
|
||||
#include "dataset/kernels/image/random_crop_with_bbox_op.h"
|
||||
#include "dataset/kernels/image/random_horizontal_flip_bbox_op.h"
|
||||
#include "dataset/kernels/image/random_horizontal_flip_with_bbox_op.h"
|
||||
#include "dataset/kernels/image/random_horizontal_flip_op.h"
|
||||
#include "dataset/kernels/image/random_resize_op.h"
|
||||
#include "dataset/kernels/image/random_resize_with_bbox_op.h"
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ add_library(kernels-image OBJECT
|
|||
random_crop_op.cc
|
||||
random_crop_with_bbox_op.cc
|
||||
random_horizontal_flip_op.cc
|
||||
random_horizontal_flip_bbox_op.cc
|
||||
random_horizontal_flip_with_bbox_op.cc
|
||||
bounding_box_augment_op.cc
|
||||
random_resize_op.cc
|
||||
random_rotation_op.cc
|
||||
|
|
|
|||
|
|
@ -43,28 +43,29 @@ Status BoundingBoxAugmentOp::Compute(const TensorRow &input, TensorRow *output)
|
|||
std::shared_ptr<Tensor> crop_out;
|
||||
std::shared_ptr<Tensor> res_out;
|
||||
std::shared_ptr<CVTensor> input_restore = CVTensor::AsCVTensor(input[0]);
|
||||
|
||||
for (uint32_t i = 0; i < num_to_aug; i++) {
|
||||
uint32_t min_x = 0;
|
||||
uint32_t min_y = 0;
|
||||
uint32_t b_w = 0;
|
||||
uint32_t b_h = 0;
|
||||
float min_x = 0;
|
||||
float min_y = 0;
|
||||
float b_w = 0;
|
||||
float b_h = 0;
|
||||
// get the required items
|
||||
input[1]->GetItemAt<uint32_t>(&min_x, {selected_boxes[i], 0});
|
||||
input[1]->GetItemAt<uint32_t>(&min_y, {selected_boxes[i], 1});
|
||||
input[1]->GetItemAt<uint32_t>(&b_w, {selected_boxes[i], 2});
|
||||
input[1]->GetItemAt<uint32_t>(&b_h, {selected_boxes[i], 3});
|
||||
Crop(input_restore, &crop_out, min_x, min_y, b_w, b_h);
|
||||
RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&min_x, {selected_boxes[i], 0}));
|
||||
RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&min_y, {selected_boxes[i], 1}));
|
||||
RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&b_w, {selected_boxes[i], 2}));
|
||||
RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&b_h, {selected_boxes[i], 3}));
|
||||
RETURN_IF_NOT_OK(Crop(input_restore, &crop_out, static_cast<int>(min_x), static_cast<int>(min_y),
|
||||
static_cast<int>(b_w), static_cast<int>(b_h)));
|
||||
// transform the cropped bbox region
|
||||
transform_->Compute(crop_out, &res_out);
|
||||
RETURN_IF_NOT_OK(transform_->Compute(crop_out, &res_out));
|
||||
// place the transformed region back in the restored input
|
||||
std::shared_ptr<CVTensor> res_img = CVTensor::AsCVTensor(res_out);
|
||||
// check if transformed crop is out of bounds of the box
|
||||
if (res_img->mat().cols > b_w || res_img->mat().rows > b_h || res_img->mat().cols < b_w ||
|
||||
res_img->mat().rows < b_h) {
|
||||
// if so, resize to fit in the box
|
||||
std::shared_ptr<TensorOp> resize_op = std::make_shared<ResizeOp>(b_h, b_w);
|
||||
resize_op->Compute(std::static_pointer_cast<Tensor>(res_img), &res_out);
|
||||
std::shared_ptr<TensorOp> resize_op =
|
||||
std::make_shared<ResizeOp>(static_cast<int32_t>(b_h), static_cast<int32_t>(b_w));
|
||||
RETURN_IF_NOT_OK(resize_op->Compute(std::static_pointer_cast<Tensor>(res_img), &res_out));
|
||||
res_img = CVTensor::AsCVTensor(res_out);
|
||||
}
|
||||
res_img->mat().copyTo(input_restore->mat()(cv::Rect(min_x, min_y, res_img->mat().cols, res_img->mat().rows)));
|
||||
|
|
|
|||
|
|
@ -14,7 +14,7 @@
|
|||
* limitations under the License.
|
||||
*/
|
||||
#include <utility>
|
||||
#include "dataset/kernels/image/random_horizontal_flip_bbox_op.h"
|
||||
#include "dataset/kernels/image/random_horizontal_flip_with_bbox_op.h"
|
||||
#include "dataset/kernels/image/image_utils.h"
|
||||
#include "dataset/util/status.h"
|
||||
#include "dataset/core/cv_tensor.h"
|
||||
|
|
@ -31,21 +31,19 @@ Status RandomHorizontalFlipWithBBoxOp::Compute(const TensorRow &input, TensorRow
|
|||
// To test bounding boxes algorithm, create random bboxes from image dims
|
||||
size_t num_of_boxes = input[1]->shape()[0]; // set to give number of bboxes
|
||||
float img_center = (input[0]->shape()[1] / 2.); // get the center of the image
|
||||
|
||||
for (int i = 0; i < num_of_boxes; i++) {
|
||||
uint32_t b_w = 0; // bounding box width
|
||||
uint32_t min_x = 0;
|
||||
float b_w = 0; // bounding box width
|
||||
float min_x = 0;
|
||||
// get the required items
|
||||
input[1]->GetItemAt<uint32_t>(&min_x, {i, 0});
|
||||
input[1]->GetItemAt<uint32_t>(&b_w, {i, 2});
|
||||
RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&min_x, {i, 0}));
|
||||
RETURN_IF_NOT_OK(input[1]->GetItemAt<float>(&b_w, {i, 2}));
|
||||
// do the flip
|
||||
float diff = img_center - min_x; // get distance from min_x to center
|
||||
uint32_t refl_min_x = diff + img_center; // get reflection of min_x
|
||||
uint32_t new_min_x = refl_min_x - b_w; // subtract from the reflected min_x to get the new one
|
||||
input[1]->SetItemAt<uint32_t>({i, 0}, new_min_x);
|
||||
float diff = img_center - min_x; // get distance from min_x to center
|
||||
float refl_min_x = diff + img_center; // get reflection of min_x
|
||||
float new_min_x = refl_min_x - b_w; // subtract from the reflected min_x to get the new one
|
||||
RETURN_IF_NOT_OK(input[1]->SetItemAt<float>({i, 0}, new_min_x));
|
||||
}
|
||||
(*output).push_back(nullptr);
|
||||
(*output).push_back(nullptr);
|
||||
(*output).resize(2);
|
||||
// move input to output pointer of bounding boxes
|
||||
(*output)[1] = std::move(input[1]);
|
||||
// perform HorizontalFlip on the image
|
||||
|
|
@ -55,6 +53,5 @@ Status RandomHorizontalFlipWithBBoxOp::Compute(const TensorRow &input, TensorRow
|
|||
*output = input;
|
||||
return Status::OK();
|
||||
}
|
||||
|
||||
} // namespace dataset
|
||||
} // namespace mindspore
|
||||
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,274 @@
|
|||
# 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 bounding box augment op in DE
|
||||
"""
|
||||
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
|
||||
import numpy as np
|
||||
import mindspore.log as logger
|
||||
import mindspore.dataset as ds
|
||||
import mindspore.dataset.transforms.vision.c_transforms as c_vision
|
||||
|
||||
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_bounding_box_augment_with_rotation_op(plot_vis=False):
|
||||
"""
|
||||
Test BoundingBoxAugment op (passing rotation op as transform)
|
||||
Prints images side by side with and without Aug applied + bboxes to compare and test
|
||||
"""
|
||||
logger.info("test_bounding_box_augment_with_rotation_op")
|
||||
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
|
||||
# Ratio is set to 1 to apply rotation on all bounding boxes.
|
||||
test_op = c_vision.BoundingBoxAugment(c_vision.RandomRotation(90), 1)
|
||||
|
||||
# map to apply ops
|
||||
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[test_op])
|
||||
|
||||
filename = "bounding_box_augment_rotation_c_result.npz"
|
||||
save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
|
||||
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_bounding_box_augment_with_crop_op(plot_vis=False):
|
||||
"""
|
||||
Test BoundingBoxAugment op (passing crop op as transform)
|
||||
Prints images side by side with and without Aug applied + bboxes to compare and test
|
||||
"""
|
||||
logger.info("test_bounding_box_augment_with_crop_op")
|
||||
|
||||
original_seed = config_get_set_seed(0)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
|
||||
# Ratio is set to 1 to apply rotation on all bounding boxes.
|
||||
test_op = c_vision.BoundingBoxAugment(c_vision.RandomCrop(50), 0.5)
|
||||
|
||||
# map to apply ops
|
||||
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[test_op])
|
||||
|
||||
filename = "bounding_box_augment_crop_c_result.npz"
|
||||
save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
|
||||
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_bounding_box_augment_valid_ratio_c(plot_vis=False):
|
||||
"""
|
||||
Test BoundingBoxAugment op (testing with valid ratio, less than 1.
|
||||
Prints images side by side with and without Aug applied + bboxes to compare and test
|
||||
"""
|
||||
logger.info("test_bounding_box_augment_valid_ratio_c")
|
||||
|
||||
original_seed = config_get_set_seed(1)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
|
||||
test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 0.9)
|
||||
|
||||
# map to apply ops
|
||||
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[test_op]) # Add column for "annotation"
|
||||
filename = "bounding_box_augment_valid_ratio_c_result.npz"
|
||||
save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
|
||||
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_bounding_box_augment_op_coco_c(plot_vis=False):
|
||||
"""
|
||||
Prints images and bboxes side by side with and without BoundingBoxAugment Op applied,
|
||||
Testing with COCO dataset
|
||||
"""
|
||||
logger.info("test_bounding_box_augment_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.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 1)
|
||||
|
||||
dataCoco2 = dataCoco2.map(input_columns=["image", "bbox"],
|
||||
output_columns=["image", "bbox"],
|
||||
columns_order=["image", "bbox"],
|
||||
operations=[test_op])
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()):
|
||||
unaugSamp.append(unAug)
|
||||
augSamp.append(Aug)
|
||||
|
||||
if plot_vis:
|
||||
visualize_with_bounding_boxes(unaugSamp, augSamp, "bbox")
|
||||
|
||||
|
||||
def test_bounding_box_augment_valid_edge_c(plot_vis=False):
|
||||
"""
|
||||
Test BoundingBoxAugment 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_bounding_box_augment_valid_edge_c")
|
||||
|
||||
original_seed = config_get_set_seed(1)
|
||||
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
|
||||
|
||||
dataVoc1 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
|
||||
test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 1)
|
||||
|
||||
# map to apply ops
|
||||
# Add column for "annotation"
|
||||
dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
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", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
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", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[test_op])
|
||||
filename = "bounding_box_augment_valid_edge_c_result.npz"
|
||||
save_and_check_md5(dataVoc2, filename, generate_golden=GENERATE_GOLDEN)
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
|
||||
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_bounding_box_augment_invalid_ratio_c():
|
||||
"""
|
||||
Test BoundingBoxAugment op with invalid input ratio
|
||||
"""
|
||||
logger.info("test_bounding_box_augment_invalid_ratio_c")
|
||||
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
|
||||
try:
|
||||
# ratio range is from 0 - 1
|
||||
test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1), 1.5)
|
||||
# map to apply ops
|
||||
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[test_op]) # Add column for "annotation"
|
||||
except ValueError as error:
|
||||
logger.info("Got an exception in DE: {}".format(str(error)))
|
||||
assert "Input is not" in str(error)
|
||||
|
||||
|
||||
def test_bounding_box_augment_invalid_bounds_c():
|
||||
"""
|
||||
Test BoundingBoxAugment op with invalid bboxes.
|
||||
"""
|
||||
logger.info("test_bounding_box_augment_invalid_bounds_c")
|
||||
|
||||
test_op = c_vision.BoundingBoxAugment(c_vision.RandomHorizontalFlip(1),
|
||||
1)
|
||||
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image")
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image")
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.NegativeXY, "min_x")
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.WrongShape, "4 features")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# set to false to not show plots
|
||||
test_bounding_box_augment_with_rotation_op(plot_vis=False)
|
||||
test_bounding_box_augment_with_crop_op(plot_vis=False)
|
||||
test_bounding_box_augment_op_coco_c(plot_vis=False)
|
||||
test_bounding_box_augment_valid_ratio_c(plot_vis=False)
|
||||
test_bounding_box_augment_valid_edge_c(plot_vis=False)
|
||||
test_bounding_box_augment_invalid_ratio_c()
|
||||
test_bounding_box_augment_invalid_bounds_c()
|
||||
|
|
@ -0,0 +1,221 @@
|
|||
# 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.transforms.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", mode="train",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
test_op = c_vision.RandomHorizontalFlipWithBBox(1)
|
||||
|
||||
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[test_op])
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
|
||||
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"],
|
||||
columns_order=["image", "bbox"],
|
||||
operations=[test_op])
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataCoco1.create_dict_iterator(), dataCoco2.create_dict_iterator()):
|
||||
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", mode="train",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train",
|
||||
decode=True, shuffle=False)
|
||||
|
||||
test_op = c_vision.RandomHorizontalFlipWithBBox(0.6)
|
||||
|
||||
# map to apply ops
|
||||
dataVoc2 = dataVoc2.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
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(), dataVoc2.create_dict_iterator()):
|
||||
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", mode="train", decode=True, shuffle=False)
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
|
||||
test_op = c_vision.RandomHorizontalFlipWithBBox(1)
|
||||
|
||||
# map to apply ops
|
||||
# Add column for "annotation"
|
||||
dataVoc1 = dataVoc1.map(input_columns=["image", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
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", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
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", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[test_op])
|
||||
|
||||
unaugSamp, augSamp = [], []
|
||||
|
||||
for unAug, Aug in zip(dataVoc1.create_dict_iterator(), dataVoc2.create_dict_iterator()):
|
||||
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", mode="train", decode=True, shuffle=False)
|
||||
|
||||
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", "annotation"],
|
||||
output_columns=["image", "annotation"],
|
||||
columns_order=["image", "annotation"],
|
||||
operations=[test_op]) # Add column for "annotation"
|
||||
except ValueError as error:
|
||||
logger.info("Got an exception in DE: {}".format(str(error)))
|
||||
assert "Input is not" 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", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.WidthOverflow, "bounding boxes is out of bounds of the image")
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.HeightOverflow, "bounding boxes is out of bounds of the image")
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
check_bad_bbox(dataVoc2, test_op, InvalidBBoxType.NegativeXY, "min_x")
|
||||
dataVoc2 = ds.VOCDataset(DATA_DIR, task="Detection", mode="train", decode=True, shuffle=False)
|
||||
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()
|
||||
Loading…
Reference in New Issue