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260 lines
12 KiB
Python
260 lines
12 KiB
Python
#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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"""
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@Project ：trinity_client
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@File ：synthesis_item.py
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@Author ：周成融
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@Date ：2023/8/26 14:13:04
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@detail ：
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"""
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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 minio import Minio
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from app.core.config import settings
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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
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minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
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def positioning(all_mask_shape, mask_shape, offset):
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all_start = 0
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all_end = 0
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mask_start = 0
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mask_end = 0
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if offset == 0:
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all_start = 0
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all_end = min(all_mask_shape, mask_shape)
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mask_start = 0
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mask_end = min(all_mask_shape, mask_shape)
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elif offset > 0:
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all_start = min(offset, all_mask_shape)
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all_end = min(offset + mask_shape, all_mask_shape)
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mask_start = 0
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mask_end = 0 if offset > all_mask_shape else min(all_mask_shape - offset, mask_shape)
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elif offset < 0:
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if abs(offset) > mask_shape:
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all_start = 0
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all_end = 0
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else:
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all_start = 0
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if mask_shape - abs(offset) > all_mask_shape:
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all_end = min(mask_shape - abs(offset), all_mask_shape)
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else:
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all_end = mask_shape - abs(offset)
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if abs(offset) > mask_shape:
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mask_start = mask_shape
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mask_end = mask_shape
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else:
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mask_start = abs(offset)
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if mask_shape - abs(offset) >= all_mask_shape:
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mask_end = all_mask_shape + abs(offset)
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else:
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mask_end = mask_shape
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return all_start, all_end, mask_start, mask_end
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# @RunTime
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def synthesis(data, size, basic_info):
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"""
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Args:
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data:
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size:
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basic_info:
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Returns:
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"""
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# out_of_bounds_control: 是否允许服装越界 True 允许 False 不允许 默认情况允许
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out_of_bounds_control = basic_info.get('out_of_bounds_control', True)
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# 创建底图
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base_image = Image.new('RGBA', size, (0, 0, 0, 0))
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try:
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all_mask_shape = (size[1], size[0])
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body_mask = None
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for d in data:
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if d['name'] == 'body' or d['name'] == 'mannequin':
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# 创建一个新的宽高透明图像， 把模特贴上去获取mask
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transparent_image = Image.new("RGBA", size, (0, 0, 0, 0))
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transparent_image.paste(d['image'], (d['adaptive_position'][1], d['adaptive_position'][0]), d['image']) # 此处可变数组会被paste篡改值，所以使用下标获取position
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body_mask = np.array(transparent_image.split()[3])
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# 根据新的坐标获取新的肩点
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left_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_left'], [d['adaptive_position'][1], d['adaptive_position'][0]])]
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right_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_right'], [d['adaptive_position'][1], d['adaptive_position'][0]])]
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body_mask[:min(left_shoulder[1], right_shoulder[1]), left_shoulder[0]:right_shoulder[0]] = 255
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_, binary_body_mask = cv2.threshold(body_mask, 127, 255, cv2.THRESH_BINARY)
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top_outer_mask = np.array(binary_body_mask)
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bottom_outer_mask = np.array(binary_body_mask)
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others_outer_mask = np.array(binary_body_mask)
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top = True
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bottom = True
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others = True
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i = len(data)
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while i:
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i -= 1
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if top and data[i]['name'] in ["blouse_front", "outwear_front", "dress_front", "tops_front"]:
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if out_of_bounds_control:
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top = True
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else:
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top = False
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mask_shape = data[i]['mask'].shape
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y_offset, x_offset = data[i]['adaptive_position']
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# 初始化叠加区域的起始和结束位置
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all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset)
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all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset)
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# 将叠加区域赋值为相应的像素值
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_, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY)
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background = np.zeros_like(top_outer_mask)
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background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end]
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top_outer_mask = background + top_outer_mask
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elif bottom and data[i]['name'] in ["trousers_front", "skirt_front", "bottoms_front", "dress_front"]:
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# bottom = False
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mask_shape = data[i]['mask'].shape
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y_offset, x_offset = data[i]['adaptive_position']
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# 初始化叠加区域的起始和结束位置
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all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset)
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all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset)
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# 将叠加区域赋值为相应的像素值
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_, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY)
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background = np.zeros_like(top_outer_mask)
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background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end]
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bottom_outer_mask = background + bottom_outer_mask
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elif others and data[i]['name'] in ['others_front']:
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mask_shape = data[i]['mask'].shape
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y_offset, x_offset = data[i]['adaptive_position']
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# 初始化叠加区域的起始和结束位置
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all_y_start, all_y_end, mask_y_start, mask_y_end = positioning(all_mask_shape=all_mask_shape[0], mask_shape=mask_shape[0], offset=y_offset)
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all_x_start, all_x_end, mask_x_start, mask_x_end = positioning(all_mask_shape=all_mask_shape[1], mask_shape=mask_shape[1], offset=x_offset)
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# 将叠加区域赋值为相应的像素值
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_, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY)
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background = np.zeros_like(top_outer_mask)
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background[all_y_start:all_y_end, all_x_start:all_x_end] = sketch_mask[mask_y_start:mask_y_end, mask_x_start:mask_x_end]
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others_outer_mask = background + others_outer_mask
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pass
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elif bottom is False and top is False:
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break
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all_mask = cv2.bitwise_or(top_outer_mask, bottom_outer_mask)
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all_mask = cv2.bitwise_or(all_mask, others_outer_mask)
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for layer in data:
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if layer['image'] is not None:
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if layer['name'] != "body":
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test_image = Image.new('RGBA', size, (0, 0, 0, 0))
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paste_img, position = transpose_rotate(layer, layer['image'])
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test_image.paste(paste_img, position, paste_img)
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mask_data = np.where(all_mask > 0, 255, 0).astype(np.uint8)
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mask_alpha = Image.fromarray(mask_data)
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mask_alpha.paste(paste_img.getchannel('A'), position, paste_img.getchannel('A'))
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cropped_image = Image.composite(test_image, Image.new("RGBA", test_image.size, (255, 255, 255, 0)), mask_alpha)
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base_image.paste(test_image, (0, 0), cropped_image) # test_image 已经按照坐标贴到最大宽值的图片上 坐着这里坐标为00
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else:
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base_image.paste(layer['image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['image'])
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result_image = base_image
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image_data = io.BytesIO()
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result_image.save(image_data, format='PNG')
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image_data.seek(0)
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# oss upload
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image_bytes = image_data.read()
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bucket_name = "aida-results"
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object_name = f'result_{generate_uuid()}.png'
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oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
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return f"{bucket_name}/{object_name}"
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# return f"aida-results/{minio_client.put_object('aida-results', f'result_{generate_uuid()}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
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# object_name = f'result_{generate_uuid()}.png'
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# response = s3.put_object(Bucket="aida-results", Key=object_name, Body=data, ContentType='image/png')
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# object_url = f"aida-results/{object_name}"
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# if response['ResponseMetadata']['HTTPStatusCode'] == 200:
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# return object_url
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# else:
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# return ""
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except Exception as e:
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logging.warning(f"synthesis runtime exception : {e}")
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def synthesis_single(front_image, back_image):
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result_image = None
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if front_image:
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result_image = front_image
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if back_image:
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result_image.paste(back_image, (0, 0), back_image)
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# with io.BytesIO() as output:
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# result_image.save(output, format='PNG')
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# data = output.getvalue()
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# object_name = f'result_{generate_uuid()}.png'
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# response = s3.put_object(Bucket="aida-results", Key=object_name, Body=data, ContentType='image/png')
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# object_url = f"aida-results/{object_name}"
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# if response['ResponseMetadata']['HTTPStatusCode'] == 200:
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# return object_url
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# else:
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# return ""
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image_data = io.BytesIO()
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result_image.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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# return f"aida-results/{minio_client.put_object('aida-results', f'result_{generate_uuid()}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
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# oss upload
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bucket_name = 'aida-results'
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object_name = f'result_{generate_uuid()}.png'
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oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
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return f"{bucket_name}/{object_name}"
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def update_base_size_priority(layers):
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# 计算透明背景图片的宽度
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min_x = min(info['position'][1] for info in layers)
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x_list = []
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new_height = 700
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for info in layers:
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if info['image'] is not None:
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x_list.append(info['position'][1] + info['image'].width)
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if info['name'] == 'mannequin':
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new_height = info['image'].height
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max_x = max(x_list)
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# x坐标中最小偏移量的绝对值 + 最大偏移量
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new_width = max_x + abs(min_x)
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# 更新坐标
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for info in layers:
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info['adaptive_position'] = (info['position'][0], info['position'][1] - min_x)
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return layers, (new_width, new_height)
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def transpose_rotate(layer, image):
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transpose = layer.get('transpose', False)
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rotate = layer.get('rotate', 0)
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paste_x, paste_y = layer['adaptive_position'][1], layer['adaptive_position'][0]
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if transpose:
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flip = 0 if transpose == "left_right" else 1
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image = image.transpose(flip)
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if rotate:
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image = image.rotate(rotate, expand=True)
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# 4. 计算粘贴位置以保持视觉中心一致
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# 原本 (15, 36) 是 288*288 的左上角，我们计算其中心点
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target_center_x = 15 + 288 // 2
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target_center_y = 36 + 288 // 2
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# 获取旋转后图像的新尺寸
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new_w, new_h = image.size
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# 计算新的左上角坐标，使得旋转后的图像中心依然在原定的中心位置
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paste_x = target_center_x - new_w // 2
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paste_y = target_center_y - new_h // 2
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return image, (paste_x, paste_y)
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