146 lines
5.6 KiB
Python
146 lines
5.6 KiB
Python
import os
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import shutil
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import yaml
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from PIL import Image
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from schemas.train_request import TrainDataInfo
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from schemas.predict_response import LabelData
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import urllib
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import json
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def get_dataset_root_path(project_id):
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"""데이터셋 루트 절대 경로 반환"""
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return os.path.join(os.getcwd(), 'resources', 'projects', str(project_id), "train")
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def make_dir(path:str, init: bool):
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"""
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path : 디렉토리 경로
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init : 폴더를 초기화 할지 여부
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"""
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if (os.path.exists(path) and init):
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shutil.rmtree(path)
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os.makedirs(path, exist_ok=True)
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def make_yml(path:str, model_categories):
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data = {
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"train": f"{path}/train",
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"val": f"{path}/val",
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"nc": len(model_categories),
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"names": model_categories
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}
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with open(os.path.join(path, "dataset.yaml"), 'w') as f:
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yaml.dump(data, f)
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def process_directories(dataset_root_path:str, model_categories:list[str]):
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"""학습을 위한 디렉토리 생성"""
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make_dir(dataset_root_path, init=False)
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make_dir(os.path.join(dataset_root_path, "train"), init=True)
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make_dir(os.path.join(dataset_root_path, "val"), init=True)
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if os.path.exists(os.path.join(dataset_root_path, "result")):
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shutil.rmtree(os.path.join(dataset_root_path, "result"))
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make_yml(dataset_root_path, model_categories)
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def process_image_and_label(data:TrainDataInfo, dataset_root_path:str, child_path:str):
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"""이미지 저장 및 레이블 파일 생성"""
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# 이미지 url로부터 파일명 분리
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img_name = data.image_url.split('/')[-1]
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img_path = os.path.join(dataset_root_path,child_path,img_name)
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# url로부터 이미지 다운로드
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urllib.request.urlretrieve(data.image_url, img_path)
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# 파일명에서 확장자를 제거하여 img_title을 얻는다
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img_title = os.path.splitext(os.path.basename(img_path))[0]
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# 레이블 파일 경로
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label_path = os.path.join(dataset_root_path, child_path, f"{img_title}.txt")
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# 레이블 객체 불러오기
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label = json.loads(urllib.request.urlopen(data.data_url).read())
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# 레이블 -> 학습용 레이블 데이터 파싱 후 생성
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if label['task_type'] == "det":
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create_detection_train_label(label, label_path)
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elif label["task_type"] == "seg":
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create_segmentation_train_label(label, label_path)
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def create_detection_train_label(label:dict, label_path:str):
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with open(label_path, "w") as train_label_txt:
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for shape in label["shapes"]:
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train_label = []
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x1 = shape["points"][0][0]
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y1 = shape["points"][0][1]
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x2 = shape["points"][1][0]
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y2 = shape["points"][1][1]
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train_label.append(str(shape["group_id"])) # label Id
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train_label.append(str((x1 + x2) / 2 / label["imageWidth"])) # 중심 x 좌표
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train_label.append(str((y1 + y2) / 2 / label["imageHeight"])) # 중심 y 좌표
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train_label.append(str((x2 - x1) / label["imageWidth"])) # 너비
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train_label.append(str((y2 - y1) / label["imageHeight"] )) # 높이
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train_label_txt.write(" ".join(train_label)+"\n")
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def create_segmentation_train_label(label:dict, label_path:str):
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with open(label_path, "w") as train_label_txt:
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for shape in label["shapes"]:
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train_label = []
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train_label.append(str(shape["group_id"])) # label Id
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for x, y in shape["points"]:
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train_label.append(str(x / label["imageWidth"]))
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train_label.append(str(y / label["imageHeight"]))
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train_label_txt.write(" ".join(train_label)+"\n")
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def join_path(path, *paths):
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"""os.path.join()과 같은 기능, os import 하기 싫어서 만듦"""
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return os.path.join(path, *paths)
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def get_model_keys(project_id:int):
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path = os.path.join("resources","projects",str(project_id), "models")
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if not os.path.exists(path):
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raise FileNotFoundError()
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files = os.listdir(path)
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return files
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def delete_file(path):
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if not os.path.exists(path):
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raise FileNotFoundError()
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os.remove(path)
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def save_file(path, file):
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# 경로에서 디렉토리 부분만 추출 (파일명을 제외한 경로)
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dir_path = os.path.dirname(path)
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os.makedirs(dir_path, exist_ok=True)
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with open(path, "wb") as buffer:
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shutil.copyfileobj(file.file, buffer)
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def get_file_name(path):
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if not os.path.exists(path):
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raise FileNotFoundError()
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return os.path.basename(path)
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def process_directories_in_cls(dataset_root_path:str, model_categories:dict[int,str]):
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"""classification 학습을 위한 디렉토리 생성"""
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make_dir(dataset_root_path, init=False)
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for category in model_categories.values():
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make_dir(os.path.join(dataset_root_path, "train", category), init=True)
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make_dir(os.path.join(dataset_root_path, "test", category), init=True)
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if os.path.exists(os.path.join(dataset_root_path, "result")):
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shutil.rmtree(os.path.join(dataset_root_path, "result"))
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def process_image_and_label_in_cls(data:TrainDataInfo, dataset_root_path:str, child_path:str):
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"""이미지 저장 및 레이블 파일 생성"""
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# 이미지 url로부터 파일명 분리
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img_name = data.image_url.split('/')[-1]
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# 레이블 객체 불러오기
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label = json.loads(urllib.request.urlopen(data.data_url).read())
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label_name = label["shapes"][0]["label"]
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label_path = os.path.join(dataset_root_path,child_path,label_name)
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# url로부터 이미지 다운로드
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urllib.request.urlretrieve(data.image_url, os.path.join(label_path, img_name))
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def download_image(url, path):
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urllib.request.urlretrieve(url, path) |