86 lines
3.7 KiB
Python
86 lines
3.7 KiB
Python
import logging
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import os
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import cv2
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import numpy as np
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from app.core.config import SEG_CACHE_PATH
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from app.service.design_fast.utils.design_ensemble import get_seg_result
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from app.service.utils.decorator import ClassCallRunTime
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from app.service.utils.new_oss_client import oss_get_image
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logger = logging.getLogger()
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class Segmentation:
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def __init__(self, minio_client):
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self.minio_client = minio_client
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@ClassCallRunTime
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def __call__(self, result):
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if "seg_mask_url" in result.keys() and result['seg_mask_url'] != "":
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seg_mask = oss_get_image(oss_client=self.minio_client, bucket=result['seg_mask_url'].split('/')[0], object_name=result['seg_mask_url'][result['seg_mask_url'].find('/') + 1:], data_type="cv2")
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seg_mask = cv2.resize(seg_mask, (result['img_shape'][1], result['img_shape'][0]), interpolation=cv2.INTER_NEAREST)
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# 转换颜色空间为 RGB（OpenCV 默认是 BGR）
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image_rgb = cv2.cvtColor(seg_mask, cv2.COLOR_BGR2RGB)
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r, g, b = cv2.split(image_rgb)
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red_mask = r > g
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green_mask = g > r
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# 创建红色和绿色掩码
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result['front_mask'] = np.array(red_mask, dtype=np.uint8) * 255
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result['back_mask'] = np.array(green_mask, dtype=np.uint8) * 255
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result['mask'] = result['front_mask'] + result['back_mask']
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else:
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# preview 过模型 不缓存
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if "preview_submit" in result.keys() and result['preview_submit'] == "preview":
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# 推理获得seg 结果
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seg_result = get_seg_result(result["image_id"], result['image'])
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# submit 过模型 缓存
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elif "preview_submit" in result.keys() and result['preview_submit'] == "submit":
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# 推理获得seg 结果
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seg_result = get_seg_result(result["image_id"], result['image'])
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self.save_seg_result(seg_result, result['image_id'])
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# null 正常流程 加载本地缓存 无缓存则过模型
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else:
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# 本地查询seg 缓存是否存在
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_, seg_result = self.load_seg_result(result["image_id"])
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# 判断缓存和实际图片size是否相同
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if not _ or result["image"].shape[:2] != seg_result.shape:
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# 推理获得seg 结果
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seg_result = get_seg_result(result["image_id"], result['image'])
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self.save_seg_result(seg_result, result['image_id'])
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result['seg_result'] = seg_result
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# 处理前片后片
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temp_front = seg_result == 1
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result['front_mask'] = (255 * (temp_front + 0).astype(np.uint8))
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temp_back = seg_result == 2
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result['back_mask'] = (255 * (temp_back + 0).astype(np.uint8))
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result['mask'] = result['front_mask'] + result['back_mask']
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return result
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@staticmethod
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def save_seg_result(seg_result, image_id):
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file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
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try:
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np.save(file_path, seg_result)
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logger.info(f"保存成功 ：{os.path.abspath(file_path)}")
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except Exception as e:
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logger.error(f"保存失败: {e}")
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@staticmethod
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def load_seg_result(image_id):
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file_path = f"{SEG_CACHE_PATH}{image_id}.npy"
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logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy")
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try:
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seg_result = np.load(file_path)
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return True, seg_result
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except FileNotFoundError:
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logger.warning("文件不存在")
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return False, None
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except Exception as e:
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logger.error(f"加载失败: {e}")
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return False, None
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