jeonghyo.k
3946ff6a25
- prediction/image/ FastAPI 서버 Docker 환경 구성 - Dockerfile: PyTorch 2.1 + CUDA 12.1 기반 GPU 이미지 - docker-compose.yml: GPU 할당 + 데이터 볼륨 마운트 - requirements.txt: 서버 의존성 목록 - .env.example: 환경변수 템플릿 - DOCKER_USAGE.md: 빌드/실행/API 사용법 문서 - Dockerfile에 .dockerignore 제외 폴더 mkdir -p 추가 - .gitignore: prediction/image 결과물 및 모델 가중치(.pth) 제외 추가 - dbInsert_csv.py, dbInsert_shp.py 삭제 (미사용 DB 로직) - api.py: dbInsert import 및 주석 처리된 DB 호출 코드 제거 - aerialRouter.ts: req.params 타입 오류 수정
246 lines
8.0 KiB
Python
246 lines
8.0 KiB
Python
# Copyright (c) OpenMMLab. All rights reserved.
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import argparse
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import glob
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import os
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import os.path as osp
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import shutil
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import tempfile
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import zipfile
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import mmcv
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import numpy as np
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from PIL import Image
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iSAID_palette = \
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{
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0: (0, 0, 0),
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1: (0, 0, 63),
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2: (0, 63, 63),
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3: (0, 63, 0),
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4: (0, 63, 127),
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5: (0, 63, 191),
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6: (0, 63, 255),
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7: (0, 127, 63),
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8: (0, 127, 127),
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9: (0, 0, 127),
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10: (0, 0, 191),
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11: (0, 0, 255),
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12: (0, 191, 127),
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13: (0, 127, 191),
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14: (0, 127, 255),
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15: (0, 100, 155)
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}
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iSAID_invert_palette = {v: k for k, v in iSAID_palette.items()}
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def iSAID_convert_from_color(arr_3d, palette=iSAID_invert_palette):
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"""RGB-color encoding to grayscale labels."""
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arr_2d = np.zeros((arr_3d.shape[0], arr_3d.shape[1]), dtype=np.uint8)
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for c, i in palette.items():
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m = np.all(arr_3d == np.array(c).reshape(1, 1, 3), axis=2)
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arr_2d[m] = i
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return arr_2d
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def slide_crop_image(src_path, out_dir, mode, patch_H, patch_W, overlap):
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img = np.asarray(Image.open(src_path).convert('RGB'))
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img_H, img_W, _ = img.shape
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if img_H < patch_H and img_W > patch_W:
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img = mmcv.impad(img, shape=(patch_H, img_W), pad_val=0)
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img_H, img_W, _ = img.shape
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elif img_H > patch_H and img_W < patch_W:
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img = mmcv.impad(img, shape=(img_H, patch_W), pad_val=0)
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img_H, img_W, _ = img.shape
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elif img_H < patch_H and img_W < patch_W:
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img = mmcv.impad(img, shape=(patch_H, patch_W), pad_val=0)
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img_H, img_W, _ = img.shape
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for x in range(0, img_W, patch_W - overlap):
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for y in range(0, img_H, patch_H - overlap):
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x_str = x
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x_end = x + patch_W
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if x_end > img_W:
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diff_x = x_end - img_W
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x_str -= diff_x
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x_end = img_W
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y_str = y
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y_end = y + patch_H
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if y_end > img_H:
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diff_y = y_end - img_H
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y_str -= diff_y
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y_end = img_H
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img_patch = img[y_str:y_end, x_str:x_end, :]
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img_patch = Image.fromarray(img_patch.astype(np.uint8))
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image = osp.basename(src_path).split('.')[0] + '_' + str(
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y_str) + '_' + str(y_end) + '_' + str(x_str) + '_' + str(
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x_end) + '.png'
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# print(image)
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save_path_image = osp.join(out_dir, 'img_dir', mode, str(image))
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img_patch.save(save_path_image)
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def slide_crop_label(src_path, out_dir, mode, patch_H, patch_W, overlap):
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label = mmcv.imread(src_path, channel_order='rgb')
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label = iSAID_convert_from_color(label)
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img_H, img_W = label.shape
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if img_H < patch_H and img_W > patch_W:
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label = mmcv.impad(label, shape=(patch_H, img_W), pad_val=255)
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img_H = patch_H
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elif img_H > patch_H and img_W < patch_W:
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label = mmcv.impad(label, shape=(img_H, patch_W), pad_val=255)
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img_W = patch_W
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elif img_H < patch_H and img_W < patch_W:
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label = mmcv.impad(label, shape=(patch_H, patch_W), pad_val=255)
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img_H = patch_H
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img_W = patch_W
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for x in range(0, img_W, patch_W - overlap):
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for y in range(0, img_H, patch_H - overlap):
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x_str = x
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x_end = x + patch_W
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if x_end > img_W:
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diff_x = x_end - img_W
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x_str -= diff_x
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x_end = img_W
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y_str = y
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y_end = y + patch_H
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if y_end > img_H:
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diff_y = y_end - img_H
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y_str -= diff_y
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y_end = img_H
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lab_patch = label[y_str:y_end, x_str:x_end]
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lab_patch = Image.fromarray(lab_patch.astype(np.uint8), mode='P')
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image = osp.basename(src_path).split('.')[0].split(
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'_')[0] + '_' + str(y_str) + '_' + str(y_end) + '_' + str(
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x_str) + '_' + str(x_end) + '_instance_color_RGB' + '.png'
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lab_patch.save(osp.join(out_dir, 'ann_dir', mode, str(image)))
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def parse_args():
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parser = argparse.ArgumentParser(
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description='Convert iSAID dataset to mmsegmentation format')
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parser.add_argument('dataset_path', help='iSAID folder path')
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parser.add_argument('--tmp_dir', help='path of the temporary directory')
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parser.add_argument('-o', '--out_dir', help='output path')
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parser.add_argument(
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'--patch_width',
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default=896,
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type=int,
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help='Width of the cropped image patch')
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parser.add_argument(
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'--patch_height',
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default=896,
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type=int,
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help='Height of the cropped image patch')
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parser.add_argument(
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'--overlap_area', default=384, type=int, help='Overlap area')
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args = parser.parse_args()
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return args
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def main():
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args = parse_args()
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dataset_path = args.dataset_path
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# image patch width and height
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patch_H, patch_W = args.patch_width, args.patch_height
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overlap = args.overlap_area # overlap area
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if args.out_dir is None:
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out_dir = osp.join('data', 'iSAID')
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else:
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out_dir = args.out_dir
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print('Making directories...')
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mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'train'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'val'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'img_dir', 'test'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'train'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'val'))
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mmcv.mkdir_or_exist(osp.join(out_dir, 'ann_dir', 'test'))
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assert os.path.exists(os.path.join(dataset_path, 'train')), \
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'train is not in {}'.format(dataset_path)
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assert os.path.exists(os.path.join(dataset_path, 'val')), \
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'val is not in {}'.format(dataset_path)
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assert os.path.exists(os.path.join(dataset_path, 'test')), \
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'test is not in {}'.format(dataset_path)
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with tempfile.TemporaryDirectory(dir=args.tmp_dir) as tmp_dir:
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for dataset_mode in ['train', 'val', 'test']:
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# for dataset_mode in [ 'test']:
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print('Extracting {}ing.zip...'.format(dataset_mode))
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img_zipp_list = glob.glob(
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os.path.join(dataset_path, dataset_mode, 'images', '*.zip'))
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print('Find the data', img_zipp_list)
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for img_zipp in img_zipp_list:
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zip_file = zipfile.ZipFile(img_zipp)
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zip_file.extractall(os.path.join(tmp_dir, dataset_mode, 'img'))
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src_path_list = glob.glob(
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os.path.join(tmp_dir, dataset_mode, 'img', 'images', '*.png'))
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src_prog_bar = mmcv.ProgressBar(len(src_path_list))
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for i, img_path in enumerate(src_path_list):
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if dataset_mode != 'test':
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slide_crop_image(img_path, out_dir, dataset_mode, patch_H,
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patch_W, overlap)
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else:
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shutil.move(img_path,
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os.path.join(out_dir, 'img_dir', dataset_mode))
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src_prog_bar.update()
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if dataset_mode != 'test':
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label_zipp_list = glob.glob(
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os.path.join(dataset_path, dataset_mode, 'Semantic_masks',
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'*.zip'))
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for label_zipp in label_zipp_list:
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zip_file = zipfile.ZipFile(label_zipp)
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zip_file.extractall(
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os.path.join(tmp_dir, dataset_mode, 'lab'))
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lab_path_list = glob.glob(
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os.path.join(tmp_dir, dataset_mode, 'lab', 'images',
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'*.png'))
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lab_prog_bar = mmcv.ProgressBar(len(lab_path_list))
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for i, lab_path in enumerate(lab_path_list):
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slide_crop_label(lab_path, out_dir, dataset_mode, patch_H,
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patch_W, overlap)
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lab_prog_bar.update()
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print('Removing the temporary files...')
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print('Done!')
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if __name__ == '__main__':
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main()
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