aidlab/AiDA_Python
6
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

feat generate slogan | to product image | slogan 接口部署

Browse Source
This commit is contained in:
zhouchengrong
2024-05-30 15:01:39 +08:00
parent 1f47d94431
commit 401b76bd95
11 changed files with 280 additions and 174 deletions
Download Patch File Download Diff File Expand all files Collapse all files

60
app/service/design/items/pipelines/split.py
View File

@@ -2,6 +2,7 @@ import logging
import cv2
import numpy as np
from cv2 import cvtColor, COLOR_BGR2RGBA
from app.service.utils.generate_uuid import generate_uuid
from ..builder import PIPELINES
from PIL import Image
@@ -45,8 +46,11 @@ class Split(object):
result_front_image[front_mask != 0] = rgba_image[front_mask != 0]
result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA))
front_new_size = (int(result_front_image_pil.width * result["scale"] * result["resize_scale"]), int(result_front_image_pil.height * result["scale"] * result["resize_scale"]))
front_new_size = (int(result_front_image_pil.width * result["scale"] * result["resize_scale"][0]), int(result_front_image_pil.height * result["scale"] * result["resize_scale"][1]))
result_front_image_pil = result_front_image_pil.resize(front_new_size, Image.LANCZOS)
# TODO 多线程外部上传图片到minio
# result['front_mask_image'] = cv2.resize(front_mask, front_new_size)
# result['front_image'] = result_front_image_pil
front_mask = cv2.resize(front_mask, front_new_size)
result['front_image'], result["front_image_url"], result["front_mask_url"] = upload_png_mask(result_front_image_pil, f'{generate_uuid()}', mask=front_mask)
@@ -55,61 +59,19 @@ class Split(object):
result_back_image[back_mask != 0] = rgba_image[back_mask != 0]
result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
back_new_size = (int(result_back_image_pil.width * result["scale"] * result["resize_scale"]), int(result_back_image_pil.height * result["scale"] * result["resize_scale"]))
back_new_size = (int(result_back_image_pil.width * result["scale"] * result["resize_scale"][0]), int(result_back_image_pil.height * result["scale"] * result["resize_scale"][1]))
result_back_image_pil = result_back_image_pil.resize(back_new_size, Image.LANCZOS)
# TODO 多线程外部上传图片到minio
# result['back_mask_image'] = cv2.resize(back_mask, back_new_size)
# result['back_image'] = result_back_image_pil
back_mask = cv2.resize(back_mask, back_new_size)
result['back_image'], result["back_image_url"], result["back_mask_url"] = upload_png_mask(result_back_image_pil, f'{generate_uuid()}', mask=back_mask)
else:
result['back_image'] = None
result["back_image_url"] = None
result["back_mask_url"] = None
result['back_mask_image'] = None
return result
except Exception as e:
logging.warning(f"split runtime exception : {e} image_id : {result['image_id']}")
# @ RunTime
# def __call__(self, result):
# try:
# if 'mask' not in result.keys():
# raise KeyError(f'Cannot find mask in result dict, please check ContourDetection is included in process pipelines.')
# if 'seg_result' not in result.keys(): # 没过seg模型
# result['front_mask'] = result['mask'].copy()
# result['back_mask'] = np.zeros_like(result['mask'])
# else:
# temp_front = result['seg_result'] == 1
# result['front_mask'] = (result['mask'] * (temp_front + 0).astype(np.uint8))
# temp_back = result['seg_result'] == 2
# result['back_mask'] = (result['mask'] * (temp_back + 0).astype(np.uint8))
#
# if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms'):
# if len(result['front_mask'].shape) > 2:
# front_mask = result['front_mask'][0]
# else:
# front_mask = result['front_mask']
#
# rgba_image = rgb_to_rgba((result['final_image'].shape[0], result['final_image'].shape[1]), result['final_image'], result['mask'])
# result_front_image = np.zeros_like(rgba_image)
# result_front_image[front_mask != 0] = rgba_image[front_mask != 0]
#
# result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA))
# front_new_size = (int(result_front_image_pil.width * result["scale"] * result["resize_scale"]), int(result_front_image_pil.height * result["scale"] * result["resize_scale"]))
# result_front_image_pil = result_front_image_pil.resize(front_new_size, Image.LANCZOS)
# front_mask = cv2.resize(front_mask, front_new_size)
# result['front_image'], result["front_image_url"], result["front_mask_url"] = upload_png_mask(result_front_image_pil, f'{generate_uuid()}', mask=front_mask)
#
# if result["name"] in ('blouse', 'dress', 'outwear', 'tops'):
# result_back_image = np.zeros_like(rgba_image)
# result_back_image[result['back_mask'] != 0] = rgba_image[result['back_mask'] != 0]
#
# result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
# back_new_size = (int(result_back_image_pil.width * result["scale"] * result["resize_scale"]), int(result_back_image_pil.height * result["scale"] * result["resize_scale"]))
# result_back_image_pil = result_back_image_pil.resize(back_new_size, Image.LANCZOS)
# back_mask = cv2.resize(result['back_mask'], back_new_size)
# result['back_image'], result["back_image_url"], result["back_mask_url"] = upload_png_mask(result_back_image_pil, f'{generate_uuid()}', mask=back_mask)
# else:
# result['back_image'] = None
# result["back_image_url"] = None
# result["back_mask_url"] = None
# return result
# except Exception as e:
# logging.warning(f"split runtime exception : {e} image_id : {result['image_id']}")