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【Python】目标检测图像增强操作

作者:笑红 / 发布于2018/3/26/ 1140

目标检测图像增强操作

# -*- coding: utf-8 -*-

import numpy as np
import cv2
import imgaug as ia
from imgaug import augmenters as iaa


class CDataAugment(object):
    def __init__(self):
        self._fixed_input_height = 144
        self._fixed_input_width = 160
        self._seq = iaa.Sequential([
            iaa.Scale({"height": self._fixed_input_height,
                       "width": self._fixed_input_width}),
            iaa.Fliplr(0.5)
        ])

    def _get_bounding_boxes(self, boxes):
        bbs = list()
        for box in boxes:
            x1 = int(box[0] * self._fixed_input_width)
            y1 = int(box[1] * self._fixed_input_height)
            x2 = int(box[2] * self._fixed_input_width)
            y2 = int(box[3] * self._fixed_input_height)
            bbs.append(ia.BoundingBox(x1=x1, y1=y1, x2=x2, y2=y2))
        return bbs

    @staticmethod
    def get_boxes(bbs_on_image):
        boxes = list()
        for bb in bbs_on_image.bounding_boxes:
            box = [int(bb.x1), int(bb.y1), int(bb.x2), int(bb.y2)]
            boxes.append(box)
        return boxes

    def __call__(self, image, boxes):
        seq_det = self._seq.to_deterministic()  # make sure each time has different random seed
        bbs = ia.BoundingBoxesOnImage(
            bounding_boxes=self._get_bounding_boxes(boxes),
            shape=(self._fixed_input_height, self._fixed_input_width))
        img_aug = seq_det.augment_image(image)
        boxes_aug = seq_det.augment_bounding_boxes([bbs])[0]
        boxes_aug = self.get_boxes(boxes_aug)
        return img_aug, boxes_aug

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