imgaug_support.py

import numpy as np

#def execute_imgaug(augmenter, im, bboxes=None, polygons=None, segment_map=None):
#    import imgaug.augmentables.polys as polys
#    import imgaug.augmentables.bbs as bbs
#
#    # 预处理，将bboxes, polygons转换成imgaug格式
#    aug_bboxes = None
#    if bboxes is not None:
#        aug_bboxes = list()
#        for i in range(len(bboxes)):
#            x1 = bboxes[i, 0]-1
#            y1 = bboxes[i, 1]
#            x2 = bboxes[i, 2]
#            y2 = bboxes[i, 3]
#            aug_bboxes.append(bbs.BoundingBox(x1, y1, x2, y2))
#
#    aug_polygons = None
#    lod_info = list()
#    if polygons is not None:
#        aug_polygons = list()
#        for i in range(len(polygons)):
#            num = len(polygons[i])
#            lod_info.append(num)
#            for j in range(num):
#                points = np.reshape(polygons[i][j], (-1, 2))
#                aug_polygons.append(polys.Polygon(points))
#
#    aug_segment_map = None
#    if segment_map is not None:
#        if len(segment_map.shape) == 2:
#            h, w = segment_map.shape
#            aug_segment_map = np.reshape(segment_map, (1, h, w, 1))
#        elif len(segment_map.shape) == 3:
#            h, w, c = segment_map.shape
#            aug_segment_map = np.reshape(segment_map, (1, h, w, c))
#        else:
#            raise Exception("Only support 2-dimensions for 3-dimensions for segment_map")
#
#    aug_im = im.astype('uint8')
#    aug_im, aug_bboxes, aug_polygons, aug_seg_map = augmenter.augment(image=aug_im, bounding_boxes=aug_bboxes,
#                                                                  polygons=aug_polygons,
#                                                                  segmentation_maps=aug_segment_map)
#
#    if aug_polygons is not None:
#        assert len(aug_bboxes) == len(lod_info), "length of bboxes should be equal to the length of polygons"
#
#    if aug_bboxes is not None:
#        # 裁剪掉在图像之外的bbox和polygon
#        for i in range(len(aug_bboxes)):
#            aug_bboxes[i] = aug_bboxes[i].clip_out_of_image(aug_im)
#        if aug_polygons is not None:
#            for i in range(len(aug_polygons)):
#                aug_polygons[i] = aug_polygons[i].clip_out_of_image(aug_im)
#
#        # 过滤掉无效的bbox和polygon，并转换为训练数据格式
#        converted_bboxes = list()
#        converted_polygons = list()
#        poly_index = 0
#        for i in range(len(aug_bboxes)):
#            # 过滤width或height不足1像素的框
#            if aug_bboxes[i].width < 1 or aug_bboxes[i].height < 1:
#                continue
#            if aug_polygons is None:
#                converted_bboxes.append([aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2, aug_bboxes[i].y2])
#                continue
#
#            # 如若有polygons，将会继续执行下面代码
#            polygons_this_box = list()
#            for ps in aug_polygons[poly_index:poly_index+lod_info[i]]:
#                if len(ps) == 0:
#                    continue
#                for p in ps:
#                    # 没有3个point的polygon被过滤
#                    if len(p.exterior) < 3:
#                        continue
#                    polygons_this_box.append(p.exterior.flatten().tolist())
#            poly_index += lod_info[i]
#
#            if len(polygons_this_box) == 0:
#                continue
#            converted_bboxes.append([aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2, aug_bboxes[i].y2])
#            converted_polygons.append(polygons_this_box)
#        if len(converted_bboxes) == 0:
#			# 若完成增强后，没有可用的目标，则仍然返回原图进行训练
#            aug_im = im
#            converted_bboxes = bboxes
#            converted_polygons = polygons
#
#    result = [aug_im.astype('float32')]
#    if bboxes is not None:
#        result.append(np.array(converted_bboxes))
#    if polygons is not None:
#        result.append(converted_polygons)
#    if segment_map is not None:
#        n, h, w, c = aug_seg_map.shape
#        if len(segment_map.shape) == 2:
#            aug_seg_map = np.reshape(aug_seg_map, (h, w))
#        elif len(segment_map.shape) == 3:
#            aug_seg_map = np.reshape(aug_seg_map, (h, w, c))
#        result.append(aug_seg_map)
#    return result

import numpy as np


def execute_imgaug(augmenter, im, bboxes=None, polygons=None,
                   segment_map=None):
    # 预处理，将bboxes, polygons转换成imgaug格式
    import imgaug.augmentables.polys as polys
    import imgaug.augmentables.bbs as bbs

    aug_im = im.astype('uint8')

    aug_bboxes = None
    if bboxes is not None:
        aug_bboxes = list()
        for i in range(len(bboxes)):
            x1 = bboxes[i, 0] - 1
            y1 = bboxes[i, 1]
            x2 = bboxes[i, 2]
            y2 = bboxes[i, 3]
            aug_bboxes.append(bbs.BoundingBox(x1, y1, x2, y2))

    aug_polygons = None
    lod_info = list()
    if polygons is not None:
        aug_polygons = list()
        for i in range(len(polygons)):
            num = len(polygons[i])
            lod_info.append(num)
            for j in range(num):
                points = np.reshape(polygons[i][j], (-1, 2))
                aug_polygons.append(polys.Polygon(points))

    aug_segment_map = None
    if segment_map is not None:
        if len(segment_map.shape) == 2:
            h, w = segment_map.shape
            aug_segment_map = np.reshape(segment_map, (1, h, w, 1))
        elif len(segment_map.shape) == 3:
            h, w, c = segment_map.shape
            aug_segment_map = np.reshape(segment_map, (1, h, w, c))
        else:
            raise Exception(
                "Only support 2-dimensions for 3-dimensions for segment_map")

    aug_im, aug_bboxes, aug_polygons, aug_seg_map = augmenter.augment(
        image=aug_im,
        bounding_boxes=aug_bboxes,
        polygons=aug_polygons,
        segmentation_maps=aug_segment_map)

    aug_im = aug_im.astype('float32')

    if aug_polygons is not None:
        assert len(aug_bboxes) == len(
            lod_info
        ), "Number of aug_bboxes should be equal to number of aug_polygons"

    if aug_bboxes is not None:
        # 裁剪掉在图像之外的bbox和polygon
        for i in range(len(aug_bboxes)):
            aug_bboxes[i] = aug_bboxes[i].clip_out_of_image(aug_im)
        if aug_polygons is not None:
            for i in range(len(aug_polygons)):
                aug_polygons[i] = aug_polygons[i].clip_out_of_image(aug_im)

        # 过滤掉无效的bbox和polygon，并转换为训练数据格式
        converted_bboxes = list()
        converted_polygons = list()
        poly_index = 0
        for i in range(len(aug_bboxes)):
            # 过滤width或height不足1像素的框
            if aug_bboxes[i].width < 1 or aug_bboxes[i].height < 1:
                continue
            if aug_polygons is None:
                converted_bboxes.append([
                    aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2,
                    aug_bboxes[i].y2
                ])
                continue

            # 如若有polygons，将会继续执行下面代码
            polygons_this_box = list()
            for ps in aug_polygons[poly_index:poly_index + lod_info[i]]:
                if len(ps) == 0:
                    continue
                for p in ps:
                    # 没有3个point的polygon被过滤
                    if len(p.exterior) < 3:
                        continue
                    polygons_this_box.append(p.exterior.flatten().tolist())
            poly_index += lod_info[i]

            if len(polygons_this_box) == 0:
                continue
            converted_bboxes.append([
                aug_bboxes[i].x1, aug_bboxes[i].y1, aug_bboxes[i].x2,
                aug_bboxes[i].y2
            ])
            converted_polygons.append(polygons_this_box)
        if len(converted_bboxes) == 0:
            aug_im = im
            converted_bboxes = bboxes
            converted_polygons = polygons

    result = [aug_im]
    if bboxes is not None:
        result.append(np.array(converted_bboxes))
    if polygons is not None:
        result.append(converted_polygons)
    if segment_map is not None:
        n, h, w, c = aug_seg_map.shape
        if len(segment_map.shape) == 2:
            aug_seg_map = np.reshape(aug_seg_map, (h, w))
        elif len(segment_map.shape) == 3:
            aug_seg_map = np.reshape(aug_seg_map, (h, w, c))
        result.append(aug_seg_map)
    return result