133 lines
7.8 KiB
Python
133 lines
7.8 KiB
Python
import io
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import logging
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import cv2
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import numpy as np
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from PIL import Image
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from cv2 import cvtColor, COLOR_BGR2RGBA
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from app.core.config import AIDA_CLOTHING
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from app.service.design_fast.utils.conversion_image import rgb_to_rgba
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from app.service.design_fast.utils.transparent import sketch_to_transparent
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from app.service.design_fast.utils.upload_image import upload_png_mask
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from app.service.utils.generate_uuid import generate_uuid
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from app.service.utils.new_oss_client import oss_upload_image, oss_get_image
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class Split(object):
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def __init__(self, minio_client):
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self.minio_client = minio_client
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def __call__(self, result):
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try:
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if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms', 'accessories'):
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ori_front_mask = result['front_mask'].copy()
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ori_back_mask = result['back_mask'].copy()
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if result['resize_scale'][0] == 1.0 and result['resize_scale'][1] == 1.0:
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front_mask = result['front_mask']
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back_mask = result['back_mask']
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else:
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height, width = result['front_mask'].shape[:2]
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new_width = int(width * result['resize_scale'][0])
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new_height = int(height * result['resize_scale'][1])
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front_mask = cv2.resize(result['front_mask'], (new_width, new_height))
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back_mask = cv2.resize(result['back_mask'], (new_width, new_height))
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rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask)
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new_size = (int(rgba_image.shape[1] * result["scale"]), int(rgba_image.shape[0] * result["scale"]))
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rgba_image = cv2.resize(rgba_image, new_size)
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result_front_image = np.zeros_like(rgba_image)
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front_mask = cv2.resize(front_mask, new_size)
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result_front_image[front_mask != 0] = rgba_image[front_mask != 0]
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result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA))
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if 'transparent' in result.keys():
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# 用户自选区域transparent
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transparent = result['transparent']
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if transparent['mask_url'] is not None and transparent['mask_url'] != "":
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# 预处理用户自选区mask
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seg_mask = oss_get_image(oss_client=self.minio_client, bucket=transparent['mask_url'].split('/')[0], object_name=transparent['mask_url'][transparent['mask_url'].find('/') + 1:], data_type="cv2")
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seg_mask = cv2.resize(seg_mask, new_size, 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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blue_mask = b > r
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# 创建红色和绿色掩码
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transparent_mask = np.array(blue_mask, dtype=np.uint8) * 255
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result_front_image_pil = sketch_to_transparent(result_front_image_pil, transparent_mask, transparent["scale"])
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else:
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result_front_image_pil = sketch_to_transparent(result_front_image_pil, front_mask, transparent["scale"])
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result['front_image'], result["front_image_url"], _ = upload_png_mask(self.minio_client, result_front_image_pil, f'{generate_uuid()}', mask=None)
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# 前片部分 (红图部分)
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# height, width = front_mask.shape
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# mask_image = np.zeros((height, width, 3))
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# mask_image[front_mask != 0] = [0, 0, 255]
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# 切换为原始图片尺寸-------------------------------
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height, width = ori_front_mask.shape
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mask_image = np.zeros((height, width, 3))
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mask_image[ori_front_mask != 0] = [0, 0, 255]
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# -----------------------------------------------
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# if result["name"] in ('blouse', 'dress', 'outwear', 'tops'):
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# result_back_image = np.zeros_like(rgba_image)
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# back_mask = cv2.resize(back_mask, new_size)
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# result_back_image[back_mask != 0] = rgba_image[back_mask != 0]
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# result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
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# result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None)
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# mask_image[back_mask != 0] = [0, 255, 0]
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#
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# rbga_mask = rgb_to_rgba(mask_image, front_mask + back_mask)
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# mask_pil = Image.fromarray(cvtColor(rbga_mask.astype(np.uint8), COLOR_BGR2RGBA))
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# image_data = io.BytesIO()
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# mask_pil.save(image_data, format='PNG')
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# image_data.seek(0)
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# image_bytes = image_data.read()
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# req = oss_upload_image(oss_client=self.minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes)
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# result['mask_url'] = req.bucket_name + "/" + req.object_name
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# else:
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# rbga_mask = rgb_to_rgba(mask_image, front_mask)
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# mask_pil = Image.fromarray(cvtColor(rbga_mask.astype(np.uint8), COLOR_BGR2RGBA))
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# image_data = io.BytesIO()
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# mask_pil.save(image_data, format='PNG')
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# image_data.seek(0)
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# image_bytes = image_data.read()
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# req = oss_upload_image(oss_client=self.minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes)
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# result['mask_url'] = req.bucket_name + "/" + req.object_name
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# result['back_image'] = None
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# result["back_image_url"] = None
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# # result["back_mask_url"] = None
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# # result['back_mask_image'] = None
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result_back_image = np.zeros_like(rgba_image)
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back_mask = cv2.resize(back_mask, new_size)
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result_back_image[back_mask != 0] = rgba_image[back_mask != 0]
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result_back_image_pil = Image.fromarray(cvtColor(result_back_image, COLOR_BGR2RGBA))
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result['back_image'], result["back_image_url"], _ = upload_png_mask(self.minio_client, result_back_image_pil, f'{generate_uuid()}', mask=None)
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# mask_image[back_mask != 0] = [0, 255, 0]
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mask_image[ori_back_mask != 0] = [0, 255, 0]
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rbga_mask = rgb_to_rgba(mask_image, ori_front_mask + ori_back_mask)
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mask_pil = Image.fromarray(cvtColor(rbga_mask.astype(np.uint8), COLOR_BGR2RGBA))
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image_data = io.BytesIO()
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mask_pil.save(image_data, format='PNG')
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image_data.seek(0)
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image_bytes = image_data.read()
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req = oss_upload_image(oss_client=self.minio_client, bucket=AIDA_CLOTHING, object_name=f"mask/mask_{generate_uuid()}.png", image_bytes=image_bytes)
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result['mask_url'] = req.bucket_name + "/" + req.object_name
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# 创建中间图层
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result_pattern_image_rgba = rgb_to_rgba(result['pattern_image'], result['mask'])
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result_pattern_image_pil = Image.fromarray(cvtColor(result_pattern_image_rgba, COLOR_BGR2RGBA))
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result['pattern_image'], result['pattern_image_url'], _ = upload_png_mask(self.minio_client, result_pattern_image_pil, f'{generate_uuid()}')
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return result
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except Exception as e:
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logging.warning(f"split runtime exception : {e} image_id : {result['image_id']}")
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