AiDA_Python/app/service/design/items/pipelines/segmentation.py

import logging
import os

import cv2
import numpy as np

from app.core.config import SEG_CACHE_PATH
from app.service.utils.oss_client import oss_get_image
from ..builder import PIPELINES
from ...utils.design_ensemble import get_seg_result

logger = logging.getLogger()


@PIPELINES.register_module()
class Segmentation(object):

    # @ClassCallRunTime
    def __call__(self, result):
        if "seg_mask_url" in result.keys() and result['seg_mask_url'] != "":
            seg_mask = oss_get_image(bucket=result['seg_mask_url'].split('/')[0], object_name=result['seg_mask_url'][result['seg_mask_url'].find('/') + 1:], data_type="cv2")
            seg_mask = cv2.resize(seg_mask, (result['img_shape'][1], result['img_shape'][0]))
            # 转换颜色空间为 RGB（OpenCV 默认是 BGR）
            image_rgb = cv2.cvtColor(seg_mask, cv2.COLOR_BGR2RGB)

            # 定义红色和绿色的颜色范围
            # 红色范围: 下界 [R-10, G-10, B-10], 上界 [R+10, G+10, B+10]
            red_lower = np.array([50, 0, 0], dtype=np.uint8)
            red_upper = np.array([255, 50, 50], dtype=np.uint8)

            # 绿色范围: 下界 [R-10, G-10, B-10], 上界 [R+10, G+10, B+10]
            green_lower = np.array([0, 50, 0], dtype=np.uint8)
            green_upper = np.array([50, 255, 50], dtype=np.uint8)

            # 创建红色和绿色掩码
            result['front_mask'] = cv2.inRange(image_rgb, red_lower, red_upper)
            result['back_mask'] = cv2.inRange(image_rgb, green_lower, green_upper)
            result['mask'] = result['front_mask'] + result['back_mask']
        else:
            # 本地查询seg 缓存是否存在
            _, seg_result = self.load_seg_result(result["image_id"])
            result['seg_result'] = seg_result
            if not _:
                # 推理获得seg 结果
                seg_result = get_seg_result(result["image_id"], result['image'])[0]
                self.save_seg_result(seg_result, result['image_id'])
            # 处理前片后片
            temp_front = seg_result == 1.0
            result['front_mask'] = (255 * (temp_front + 0).astype(np.uint8))
            temp_back = seg_result == 2.0
            result['back_mask'] = (255 * (temp_back + 0).astype(np.uint8))
            result['mask'] = result['front_mask'] + result['back_mask']
        return result

    @staticmethod
    def save_seg_result(seg_result, image_id):
        file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
        try:
            np.save(file_path, seg_result)
            print("保存成功", os.path.abspath(file_path))
        except Exception as e:
            print(f"保存失败: {e}")

    @staticmethod
    def load_seg_result(image_id):
        file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
        logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy")
        try:
            seg_result = np.load(file_path)
            return True, seg_result
        except FileNotFoundError:
            print("文件不存在")
            return False, None
        except Exception as e:
            print(f"加载失败: {e}")
            return False, None