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Refactor: create_detection_train_label 레이블데이터의 이미지 바깥의 점에 대해 예외 처리

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김진현 2024-09-30 12:12:44 +09:00
parent 798f313e81
commit 1b7fea1415
1 changed files with 13 additions and 7 deletions
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20
ai/app/utils/file_utils.py
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@ -67,17 +67,23 @@ def process_image_and_label(data:TrainDataInfo, dataset_root_path:str, child_pat
def create_detection_train_label(label:dict, label_path:str, label_converter:dict[int, int]): def create_detection_train_label(label:dict, label_path:str, label_converter:dict[int, int]):
with open(label_path, "w") as train_label_txt: with open(label_path, "w") as train_label_txt:
for shape in label["shapes"]: for shape in label["shapes"]:
train_label = []
x1 = shape["points"][0][0] x1 = shape["points"][0][0]
y1 = shape["points"][0][1] y1 = shape["points"][0][1]
x2 = shape["points"][1][0] x2 = shape["points"][1][0]
y2 = shape["points"][1][1] y2 = shape["points"][1][1]
train_label.append(str(label_converter[shape["group_id"]])) # label Id train_label = {
train_label.append(str((x1 + x2) / 2 / label["imageWidth"])) # 중심 x 좌표 'label_id': label_converter[shape["group_id"]], # 모델의 id (converter : pjt category pk -> model category id)
train_label.append(str((y1 + y2) / 2 / label["imageHeight"])) # 중심 y 좌표 'center_x': (x1 + x2) / 2 / label["imageWidth"], # 중심 x 좌표
train_label.append(str((x2 - x1) / label["imageWidth"])) # 너비 'center_y': (y1 + y2) / 2 / label["imageHeight"], # 중심 y 좌표
train_label.append(str((y2 - y1) / label["imageHeight"] )) # 높이 'width': (x2 - x1) / label["imageWidth"], # 너비
train_label_txt.write(" ".join(train_label)+"\n") 'height': (y2 - y1) / label["imageHeight"] # 높이
}
for key, value in train_label[1:].items(): # label_id를 제외한 다른 key에 대해
if value<0 or value >1: # 0과 1사이가 아니라면 에러
raise ValueError(f"Improper value in {label_path}: {key} = {value}")
train_label_txt.write(" ".join(map(str, train_label.values()))+"\n") # str변환 후 txt에 쓰기
def create_segmentation_train_label(label:dict, label_path:str, label_converter:dict[int, int]): def create_segmentation_train_label(label:dict, label_path:str, label_converter:dict[int, int]):
with open(label_path, "w") as train_label_txt: with open(label_path, "w") as train_label_txt: