AiDA_Python/app/service/design_fast/utils/synthesis_item.py

#!/usr/bin/env python
# -*- coding: UTF-8 -*-
"""
@Project ：trinity_client
@File    ：synthesis_item.py
@Author  ：周成融
@Date    ：2023/8/26 14:13:04
@detail  ：
"""
import io
import logging

import cv2
import numpy as np
from PIL import Image
from minio import Minio
from app.core.config import settings
from app.service.utils.generate_uuid import generate_uuid
from app.service.utils.new_oss_client import oss_upload_image

minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)


def positioning(all_mask_shape, mask_shape, offset):
    all_start = 0
    all_end = 0
    mask_start = 0
    mask_end = 0
    if offset == 0:
        all_start = 0
        all_end = min(all_mask_shape, mask_shape)

        mask_start = 0
        mask_end = min(all_mask_shape, mask_shape)
    elif offset > 0:
        all_start = min(offset, all_mask_shape)
        all_end = min(offset + mask_shape, all_mask_shape)

        mask_start = 0
        mask_end = 0 if offset > all_mask_shape else min(all_mask_shape - offset, mask_shape)
    elif offset < 0:
        if abs(offset) > mask_shape:
            all_start = 0
            all_end = 0
        else:
            all_start = 0
            if mask_shape - abs(offset) > all_mask_shape:
                all_end = min(mask_shape - abs(offset), all_mask_shape)
            else:
                all_end = mask_shape - abs(offset)

        if abs(offset) > mask_shape:
            mask_start = mask_shape
            mask_end = mask_shape
        else:
            mask_start = abs(offset)
            if mask_shape - abs(offset) >= all_mask_shape:
                mask_end = all_mask_shape + abs(offset)
            else:
                mask_end = mask_shape
    return all_start, all_end, mask_start, mask_end


# @RunTime
def synthesis(data, size, basic_info):
    """

    Args:
        data:
        size:
        basic_info:

    Returns:

    """
    # 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
    out_of_bounds_control = basic_info.get('out_of_bounds_control', True)
    # 创建底图
    base_image = Image.new('RGBA', size, (0, 0, 0, 0))
    try:
        all_mask_shape = (size[1], size[0])
        body_mask = None
        for d in data:
            if d['name'] == 'body' or d['name'] == 'mannequin':
                # 创建一个新的宽高透明图像， 把模特贴上去获取mask
                transparent_image = Image.new("RGBA", size, (0, 0, 0, 0))
                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
                body_mask = np.array(transparent_image.split()[3])

                # 根据新的坐标获取新的肩点
                left_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_left'], [d['adaptive_position'][1], d['adaptive_position'][0]])]
                right_shoulder = [x + y for x, y in zip(basic_info['body_point_test']['shoulder_right'], [d['adaptive_position'][1], d['adaptive_position'][0]])]
                body_mask[:min(left_shoulder[1], right_shoulder[1]), left_shoulder[0]:right_shoulder[0]] = 255
        _, binary_body_mask = cv2.threshold(body_mask, 127, 255, cv2.THRESH_BINARY)
        top_outer_mask = np.array(binary_body_mask)
        bottom_outer_mask = np.array(binary_body_mask)
        others_outer_mask = np.array(binary_body_mask)

        top = True
        bottom = True
        others = True
        i = len(data)
        while i:
            i -= 1
            if top and data[i]['name'] in ["blouse_front", "outwear_front", "dress_front", "tops_front"]:
                if out_of_bounds_control:
                    top = True
                else:
                    top = False
                mask_shape = data[i]['mask'].shape
                y_offset, x_offset = data[i]['adaptive_position']
                # 初始化叠加区域的起始和结束位置
                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)
                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)
                # 将叠加区域赋值为相应的像素值
                _, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY)
                background = np.zeros_like(top_outer_mask)
                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]
                top_outer_mask = background + top_outer_mask
            elif bottom and data[i]['name'] in ["trousers_front", "skirt_front", "bottoms_front", "dress_front"]:
                # bottom = False
                mask_shape = data[i]['mask'].shape
                y_offset, x_offset = data[i]['adaptive_position']
                # 初始化叠加区域的起始和结束位置
                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)
                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)
                # 将叠加区域赋值为相应的像素值
                _, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY)
                background = np.zeros_like(top_outer_mask)
                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]
                bottom_outer_mask = background + bottom_outer_mask
            elif others and data[i]['name'] in ['others_front']:
                mask_shape = data[i]['mask'].shape
                y_offset, x_offset = data[i]['adaptive_position']
                # 初始化叠加区域的起始和结束位置
                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)
                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)
                # 将叠加区域赋值为相应的像素值
                _, sketch_mask = cv2.threshold(data[i]['mask'], 127, 255, cv2.THRESH_BINARY)
                background = np.zeros_like(top_outer_mask)
                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]
                others_outer_mask = background + others_outer_mask
                pass
            elif bottom is False and top is False:
                break

        all_mask = cv2.bitwise_or(top_outer_mask, bottom_outer_mask)
        all_mask = cv2.bitwise_or(all_mask, others_outer_mask)

        for layer in data:
            if layer['image'] is not None:
                if layer['name'] != "body":
                    test_image = Image.new('RGBA', size, (0, 0, 0, 0))
                    test_image.paste(layer['image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['image'])
                    mask_data = np.where(all_mask > 0, 255, 0).astype(np.uint8)
                    mask_alpha = Image.fromarray(mask_data)
                    cropped_image = Image.composite(test_image, Image.new("RGBA", test_image.size, (255, 255, 255, 0)), mask_alpha)
                    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
                else:
                    base_image.paste(layer['image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['image'])

        result_image = base_image

        image_data = io.BytesIO()
        result_image.save(image_data, format='PNG')
        image_data.seek(0)

        # oss upload
        image_bytes = image_data.read()
        bucket_name = "aida-results"
        object_name = f'result_{generate_uuid()}.png'
        oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
        return f"{bucket_name}/{object_name}"
        # 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}"

        # object_name = f'result_{generate_uuid()}.png'
        # response = s3.put_object(Bucket="aida-results", Key=object_name, Body=data, ContentType='image/png')
        # object_url = f"aida-results/{object_name}"
        # if response['ResponseMetadata']['HTTPStatusCode'] == 200:
        #     return object_url
        # else:
        #     return ""

    except Exception as e:
        logging.warning(f"synthesis runtime exception : {e}")


def synthesis_single(front_image, back_image):
    result_image = None
    if front_image:
        result_image = front_image
        if back_image:
            result_image.paste(back_image, (0, 0), back_image)

    # with io.BytesIO() as output:
    #     result_image.save(output, format='PNG')
    #     data = output.getvalue()
    # object_name = f'result_{generate_uuid()}.png'
    # response = s3.put_object(Bucket="aida-results", Key=object_name, Body=data, ContentType='image/png')
    # object_url = f"aida-results/{object_name}"
    # if response['ResponseMetadata']['HTTPStatusCode'] == 200:
    #     return object_url
    # else:
    #     return ""
    image_data = io.BytesIO()
    result_image.save(image_data, format='PNG')
    image_data.seek(0)
    image_bytes = image_data.read()
    # 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}"
    # oss upload
    bucket_name = 'aida-results'
    object_name = f'result_{generate_uuid()}.png'
    oss_upload_image(oss_client=minio_client, bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
    return f"{bucket_name}/{object_name}"


def update_base_size_priority(layers):
    # 计算透明背景图片的宽度
    min_x = min(info['position'][1] for info in layers)
    x_list = []
    new_height = 700
    for info in layers:
        if info['image'] is not None:
            x_list.append(info['position'][1] + info['image'].width)
        if info['name'] == 'mannequin':
            new_height = info['image'].height
    max_x = max(x_list)

    # x坐标中最小偏移量的绝对值 + 最大偏移量
    new_width = max_x + abs(min_x)
    # 更新坐标
    for info in layers:
        info['adaptive_position'] = (info['position'][0], info['position'][1] - min_x)
    return layers, (new_width, new_height)