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ppocr/data/imaug/iaa_augment.py
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213
ppocr/data/imaug/iaa_augment.py
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# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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This code is refer from:
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https://github.com/WenmuZhou/DBNet.pytorch/blob/master/data_loader/modules/iaa_augment.py
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"""
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import os
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# Prevent automatic updates in Albumentations for stability in augmentation behavior
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os.environ["NO_ALBUMENTATIONS_UPDATE"] = "1"
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import numpy as np
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import albumentations as A
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from albumentations.core.transforms_interface import DualTransform
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from albumentations.augmentations.geometric import functional as fgeometric
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from packaging import version
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ALBU_VERSION = version.parse(A.__version__)
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IS_ALBU_NEW_VERSION = ALBU_VERSION >= version.parse("1.4.15")
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# Custom resize transformation mimicking Imgaug's behavior with scaling
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class ImgaugLikeResize(DualTransform):
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def __init__(self, scale_range=(0.5, 3.0), interpolation=1, p=1.0):
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super(ImgaugLikeResize, self).__init__(p)
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self.scale_range = scale_range
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self.interpolation = interpolation
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# Resize the image based on a randomly chosen scale within the scale range
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def apply(self, img, scale=1.0, **params):
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height, width = img.shape[:2]
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new_height = int(height * scale)
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new_width = int(width * scale)
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if IS_ALBU_NEW_VERSION:
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return fgeometric.resize(
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img, (new_height, new_width), interpolation=self.interpolation
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)
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return fgeometric.resize(
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img, new_height, new_width, interpolation=self.interpolation
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)
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# Apply the same scaling transformation to keypoints (e.g., polygon points)
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def apply_to_keypoints(self, keypoints, scale=1.0, **params):
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return np.array(
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[(x * scale, y * scale) + tuple(rest) for x, y, *rest in keypoints]
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)
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# Get random scale parameter within the specified range
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def get_params(self):
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scale = np.random.uniform(self.scale_range[0], self.scale_range[1])
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return {"scale": scale}
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# Builder class to translate custom augmenter arguments into Albumentations-compatible format
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class AugmenterBuilder(object):
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def __init__(self):
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# Map common Imgaug transformations to equivalent Albumentations transforms
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self.imgaug_to_albu = {
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"Fliplr": "HorizontalFlip",
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"Flipud": "VerticalFlip",
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"Affine": "Affine",
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# Additional mappings can be added here if needed
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}
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# Recursive method to construct augmentation pipeline based on provided arguments
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def build(self, args, root=True):
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if args is None or len(args) == 0:
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return None
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elif isinstance(args, list):
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# Build the full augmentation sequence if it's a root-level call
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if root:
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sequence = [self.build(value, root=False) for value in args]
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return A.Compose(
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sequence,
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keypoint_params=A.KeypointParams(
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format="xy", remove_invisible=False
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),
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)
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else:
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# Build individual augmenters for nested arguments
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augmenter_type = args[0]
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augmenter_args = args[1] if len(args) > 1 else {}
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augmenter_args_mapped = self.map_arguments(
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augmenter_type, augmenter_args
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)
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augmenter_type_mapped = self.imgaug_to_albu.get(
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augmenter_type, augmenter_type
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)
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if augmenter_type_mapped == "Resize":
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return ImgaugLikeResize(**augmenter_args_mapped)
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else:
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cls = getattr(A, augmenter_type_mapped)
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return cls(
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**{
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k: self.to_tuple_if_list(v)
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for k, v in augmenter_args_mapped.items()
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}
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)
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elif isinstance(args, dict):
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# Process individual transformation specified as dictionary
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augmenter_type = args["type"]
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augmenter_args = args.get("args", {})
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augmenter_args_mapped = self.map_arguments(augmenter_type, augmenter_args)
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augmenter_type_mapped = self.imgaug_to_albu.get(
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augmenter_type, augmenter_type
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)
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if augmenter_type_mapped == "Resize":
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return ImgaugLikeResize(**augmenter_args_mapped)
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else:
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cls = getattr(A, augmenter_type_mapped)
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return cls(
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**{
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k: self.to_tuple_if_list(v)
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for k, v in augmenter_args_mapped.items()
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}
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)
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else:
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raise RuntimeError("Unknown augmenter arg: " + str(args))
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# Map arguments to expected format for each augmenter type
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def map_arguments(self, augmenter_type, augmenter_args):
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augmenter_args = augmenter_args.copy() # Avoid modifying the original arguments
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if augmenter_type == "Resize":
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# Ensure size is a valid 2-element list or tuple
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size = augmenter_args.get("size")
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if size:
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if not isinstance(size, (list, tuple)) or len(size) != 2:
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raise ValueError(
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f"'size' must be a list or tuple of two numbers, but got {size}"
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)
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min_scale, max_scale = size
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return {
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"scale_range": (min_scale, max_scale),
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"interpolation": 1, # Linear interpolation
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"p": 1.0,
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}
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else:
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return {"scale_range": (1.0, 1.0), "interpolation": 1, "p": 1.0}
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elif augmenter_type == "Affine":
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# Map rotation to a tuple and ensure p=1.0 to apply transformation
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rotate = augmenter_args.get("rotate", 0)
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if isinstance(rotate, list):
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rotate = tuple(rotate)
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elif isinstance(rotate, (int, float)):
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rotate = (float(rotate), float(rotate))
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augmenter_args["rotate"] = rotate
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augmenter_args["p"] = 1.0
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return augmenter_args
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else:
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# For other augmenters, ensure 'p' probability is specified
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p = augmenter_args.get("p", 1.0)
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augmenter_args["p"] = p
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return augmenter_args
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# Convert lists to tuples for Albumentations compatibility
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def to_tuple_if_list(self, obj):
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if isinstance(obj, list):
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return tuple(obj)
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return obj
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# Wrapper class for image and polygon transformations using Imgaug-style augmentation
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class IaaAugment:
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def __init__(self, augmenter_args=None, **kwargs):
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if augmenter_args is None:
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# Default augmenters if none are specified
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augmenter_args = [
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{"type": "Fliplr", "args": {"p": 0.5}},
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{"type": "Affine", "args": {"rotate": [-10, 10]}},
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{"type": "Resize", "args": {"size": [0.5, 3]}},
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]
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self.augmenter = AugmenterBuilder().build(augmenter_args)
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# Apply the augmentations to image and polygon data
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def __call__(self, data):
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image = data["image"]
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if self.augmenter:
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# Flatten polygons to individual keypoints for transformation
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keypoints = []
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keypoints_lengths = []
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for poly in data["polys"]:
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keypoints.extend([tuple(point) for point in poly])
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keypoints_lengths.append(len(poly))
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# Apply the augmentation pipeline to image and keypoints
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transformed = self.augmenter(image=image, keypoints=keypoints)
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data["image"] = transformed["image"]
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# Extract transformed keypoints and reconstruct polygon structures
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transformed_keypoints = transformed["keypoints"]
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# Reassemble polygons from transformed keypoints
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new_polys = []
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idx = 0
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for length in keypoints_lengths:
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new_poly = transformed_keypoints[idx : idx + length]
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new_polys.append(np.array([kp[:2] for kp in new_poly]))
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idx += length
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data["polys"] = np.array(new_polys)
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return data
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