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design batch 代码整理

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alab
2024-09-26 06:09:05 +00:00
parent a539923207
commit 04b15aa200
33 changed files with 585 additions and 61 deletions
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app/service/design_batch/utils/synthesis_item.py Normal file
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#!/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 app.service.utils.generate_uuid import generate_uuid
from app.service.utils.oss_client import oss_upload_image
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):
# 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)
top = True
bottom = True
i = len(data)
while i:
i -= 1
if top and data[i]['name'] in ["blouse_front", "outwear_front", "dress_front", "tops_front"]:
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 bottom is False and top is False:
break
all_mask = cv2.bitwise_or(top_outer_mask, bottom_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'
req = oss_upload_image(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'
req = oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes)
return f"{bucket_name}/{object_name}"
def update_base_size_priority(layers, size):
# 计算透明背景图片的宽度
min_x = min(info['position'][1] for info in layers)
x_list = []
for info in layers:
if info['image'] is not None:
x_list.append(info['position'][1] + info['image'].width)
max_x = max(x_list)
new_width = max_x - min_x
new_height = 700
# 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)