1744 lines
68 KiB
Python
1744 lines
68 KiB
Python
import io
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import json
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import logging
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import threading
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import time
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import uuid
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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 PRIORITY_DICT
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from app.service.design.utils.redis_utils import Redis
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from app.service.design_test.item import BodyItem, TopItem, BottomItem
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from app.service.utils.oss_client import oss_upload_image
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id_lock = threading.Lock()
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logger = logging.getLogger()
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# minio 配置
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MINIO_URL = "www.minio.aida.com.hk:12024"
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MINIO_ACCESS = 'vXKFLSJkYeEq2DrSZvkB'
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MINIO_SECRET = 'uKTZT3x7C43WvPN9QTc99DiRkwddWZrG9Uh3JVlR'
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MINIO_SECURE = True
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minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
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def process_item(item, basic):
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if item['type'] == "Body":
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body_server = BodyItem(data=item, basic=basic, minio_client=minio_client)
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item_data = body_server.process()
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elif item['type'].lower() in ['blouse', 'outwear', 'dress', 'tops']:
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top_server = TopItem(data=item, basic=basic, minio_client=minio_client)
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item_data = top_server.process()
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else:
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bottom_server = BottomItem(data=item, basic=basic, minio_client=minio_client)
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item_data = bottom_server.process()
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return item_data
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def process_layer(item, layers):
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if item['name'] == "mannequin":
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body_layer = organize_body(item)
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layers.append(body_layer)
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return item['body_image'].size
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else:
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front_layer, back_layer = organize_clothing(item)
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layers.append(front_layer)
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layers.append(back_layer)
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def organize_body(layer):
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body_layer = dict(priority=0,
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name=layer["name"].lower(),
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image=layer['body_image'],
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image_url=layer['body_path'],
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mask_image=None,
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mask_url=None,
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sacle=1,
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# mask=layer['body_mask'],
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position=(0, 0))
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return body_layer
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def organize_clothing(layer):
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# 起始坐标
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start_point = calculate_start_point(layer['keypoint'], layer['scale'], layer['clothes_keypoint'], layer['body_point_test'], layer["offset"], layer["resize_scale"])
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# 前片数据
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front_layer = dict(priority=layer['priority'] if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_front', None),
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name=f'{layer["name"].lower()}_front',
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image=layer["front_image"],
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# mask_image=layer['front_mask_image'],
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image_url=layer['front_image_url'],
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mask_url=layer['mask_url'],
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sacle=layer['scale'],
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clothes_keypoint=layer['clothes_keypoint'],
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position=start_point,
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resize_scale=layer["resize_scale"],
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mask=cv2.resize(layer['mask'], layer["front_image"].size),
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gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
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pattern_image_url=layer['pattern_image_url'],
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pattern_image=layer['pattern_image']
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)
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# 后片数据
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back_layer = dict(priority=-layer.get("priority", 0) if layer.get("layer_order", False) else PRIORITY_DICT.get(f'{layer["name"].lower()}_back', None),
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name=f'{layer["name"].lower()}_back',
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image=layer["back_image"],
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# mask_image=layer['back_mask_image'],
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image_url=layer['back_image_url'],
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mask_url=layer['mask_url'],
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sacle=layer['scale'],
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clothes_keypoint=layer['clothes_keypoint'],
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position=start_point,
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resize_scale=layer["resize_scale"],
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mask=cv2.resize(layer['mask'], layer["front_image"].size),
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gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "",
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pattern_image_url=layer['pattern_image_url'],
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)
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return front_layer, back_layer
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def calculate_start_point(keypoint_type, scale, clothes_point, body_point, offset, resize_scale):
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"""
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Align left
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Args:
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keypoint_type: string, "waistband" | "shoulder" | "ear_point"
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scale: float
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clothes_point: dict{'left': [x1, y1, z1], 'right': [x2, y2, z2]}
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body_point: dict, containing keypoint data of body figure
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Returns:
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start_point: tuple (x', y')
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x' = y_body - y1 * scale + offset
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y' = x_body - x1 * scale + offset
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"""
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side_indicator = f'{keypoint_type}_left'
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start_point = (
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int(body_point[side_indicator][1] + offset[1] - int(clothes_point[side_indicator][0]) * scale), # y
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int(body_point[side_indicator][0] + offset[0] - int(clothes_point[side_indicator][1]) * scale) # x
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)
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return start_point
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def update_base_size_priority(layers, size):
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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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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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max_x = max(x_list)
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new_width = max_x - min_x
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new_height = 700
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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 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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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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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 oss_upload_json(json_data, object_name):
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try:
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with open(f"app/service/design/design_batch/response_json/{object_name}", 'w') as file:
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json.dump(json_data, file, indent=4)
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oss_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE)
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oss_client.fput_object("test", object_name, f"app/service/design/design_batch/response_json/{object_name}")
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except Exception as e:
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logger.warning(str(e))
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def generate_uuid():
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with id_lock:
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unique_id = str(uuid.uuid1())
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return unique_id
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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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def synthesis(data, size, basic_info):
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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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top = True
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bottom = 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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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 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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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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test_image.paste(layer['image'], (layer['adaptive_position'][1], layer['adaptive_position'][0]), layer['image'])
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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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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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except Exception as e:
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logging.warning(f"synthesis runtime exception : {e}")
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def design_generate(request_data):
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objects_data = request_data.dict()['objects']
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process_id = request_data.dict()['process_id']
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object_response = []
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threads = []
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active_threads = 0
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lock = threading.Lock()
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total = len(objects_data)
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def process_object(step, object):
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nonlocal active_threads
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basic = object['basic']
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items_response = {'layers': []}
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if basic['single_overall'] == "overall":
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item_results = []
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for item in object['items']:
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item_results.append(process_item(item, basic))
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layers = []
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body_size = None
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for item in item_results:
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body_size = process_layer(item, layers)
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layers = sorted(layers, key=lambda s: s.get("priority", float('inf')))
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layers, new_size = update_base_size_priority(layers, body_size)
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for lay in layers:
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items_response['layers'].append({
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'image_category': lay['name'],
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'position': lay['position'],
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'priority': lay.get("priority", None),
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'resize_scale': lay['resize_scale'] if "resize_scale" in lay.keys() else None,
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'image_size': lay['image'] if lay['image'] is None else lay['image'].size,
|
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'gradient_string': lay['gradient_string'] if 'gradient_string' in lay.keys() else "",
|
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'mask_url': lay['mask_url'],
|
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'image_url': lay['image_url'] if 'image_url' in lay.keys() else None,
|
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'pattern_image_url': lay['pattern_image_url'] if 'pattern_image_url' in lay.keys() else None,
|
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})
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items_response['synthesis_url'] = synthesis(layers, new_size, basic)
|
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else:
|
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item_result = process_item(object['items'][0], basic)
|
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items_response['layers'].append({
|
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'image_category': f"{item_result['name']}_front",
|
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'image_size': item_result['back_image'].size if item_result['back_image'] else None,
|
||
'position': None,
|
||
'priority': 0,
|
||
'image_url': item_result['front_image_url'],
|
||
'mask_url': item_result['mask_url'],
|
||
"gradient_string": item_result['gradient_string'] if 'gradient_string' in item_result.keys() else "",
|
||
'pattern_image_url': item_result['pattern_image_url'] if 'pattern_image_url' in item_result.keys() else None,
|
||
})
|
||
items_response['layers'].append({
|
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'image_category': f"{item_result['name']}_back",
|
||
'image_size': item_result['front_image'].size if item_result['front_image'] else None,
|
||
'position': None,
|
||
'priority': 0,
|
||
'image_url': item_result['back_image_url'],
|
||
'mask_url': item_result['mask_url'],
|
||
"gradient_string": item_result['gradient_string'] if 'gradient_string' in item_result.keys() else "",
|
||
'pattern_image_url': item_result['pattern_image_url'] if 'pattern_image_url' in item_result.keys() else None,
|
||
})
|
||
items_response['synthesis_url'] = synthesis_single(item_result['front_image'], item_result['back_image'])
|
||
update_progress(process_id, total)
|
||
|
||
with lock:
|
||
object_response.append(items_response)
|
||
active_threads -= 1
|
||
|
||
for step, object in enumerate(objects_data):
|
||
t = threading.Thread(target=process_object, args=(step, object))
|
||
threads.append(t)
|
||
t.start()
|
||
with lock:
|
||
active_threads += 1
|
||
|
||
for t in threads:
|
||
t.join()
|
||
final_progress(process_id)
|
||
return object_response
|
||
|
||
|
||
def update_progress(process_id, total):
|
||
logger.info(f"{process_id} , {total}")
|
||
r = Redis()
|
||
progress = r.read(key=process_id)
|
||
if progress and total != 1:
|
||
if int(progress) <= 100:
|
||
r.write(key=process_id, value=int(progress) + int(100 / total))
|
||
else:
|
||
r.write(key=process_id, value=99)
|
||
return progress
|
||
elif total == 1:
|
||
r.write(key=process_id, value=100)
|
||
return progress
|
||
else:
|
||
r.write(key=process_id, value=int(100 / total))
|
||
return progress
|
||
|
||
|
||
def final_progress(process_id):
|
||
r = Redis()
|
||
progress = r.read(key=process_id)
|
||
r.write(key=process_id, value=100)
|
||
return progress
|
||
|
||
|
||
if __name__ == '__main__':
|
||
object_data = {
|
||
"objects": [
|
||
{
|
||
"basic": {
|
||
"body_point_test": {
|
||
"waistband_right": [
|
||
203,
|
||
249
|
||
],
|
||
"hand_point_right": [
|
||
229,
|
||
343
|
||
],
|
||
"waistband_left": [
|
||
119,
|
||
248
|
||
],
|
||
"hand_point_left": [
|
||
97,
|
||
343
|
||
],
|
||
"shoulder_left": [
|
||
108,
|
||
107
|
||
],
|
||
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}
|
||
start_time = time.time()
|
||
X = design_generate(object_data)
|
||
print(time.time() - start_time)
|
||
print(X)
|