diff --git a/app/service/design_batch/design_batch.py b/app/service/design_batch/design_batch.py deleted file mode 100644 index 4a13c4e..0000000 --- a/app/service/design_batch/design_batch.py +++ /dev/null @@ -1,613 +0,0 @@ -import io -import json -import logging.config -import threading -import uuid - -import cv2 -import numpy as np -import urllib3 -from PIL import Image -from celery import Celery -from minio import Minio - -from app.core.config import * -from app.service.design_batch.item import BodyItem, TopItem, BottomItem - -id_lock = threading.Lock() -celery_app = Celery('tasks', broker='amqp://guest:guest@10.1.2.213:5672//', backend='rpc://') -celery_app.conf.worker_log_format = '%(asctime)s %(filename)s [line:%(lineno)d] %(levelname)s %(message)s' -celery_app.conf.worker_hijack_root_logger = False -logging.getLogger('pika').setLevel(logging.WARNING) -logger = logging.getLogger() - -timeout = urllib3.Timeout(connect=1, read=10.0) # 连接超时 5 秒，读取超时 10 秒 - - -# 自定义 Retry 类 -class CustomRetry(urllib3.Retry): - def increment(self, method=None, url=None, response=None, error=None, **kwargs): - # 调用父类的 increment 方法 - new_retry = super(CustomRetry, self).increment(method, url, response, error, **kwargs) - # 打印重试信息 - logger.info(f"重试连接: {method} {url}，错误: {error}，重试次数: {self.total - new_retry.total}") - return new_retry - - -http_client = urllib3.PoolManager( - num_pools=50, # 设置连接池大小 - maxsize=50, - timeout=timeout, - cert_reqs='CERT_REQUIRED', # 需要证书验证 - retries=CustomRetry( - total=5, - backoff_factor=0.2, - status_forcelist=[500, 502, 503, 504], - ), -) -minio_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE, http_client=http_client) - - -def oss_upload_image(bucket, object_name, image_bytes): - req = None - try: - oss_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) - req = oss_client.put_object(bucket_name=bucket, object_name=object_name, data=io.BytesIO(image_bytes), length=len(image_bytes), content_type='image/png') - except Exception as e: - logger.warning(f" 上传图片出现异常 ######: {e}") - return req - - -# 优先级 -priority_dict = { - 'earring_front': 99, - 'bag_front': 98, - 'hairstyle_front': 97, - 'outwear_front': 20, - 'tops_front': 19, - 'dress_front': 18, - 'blouse_front': 17, - 'skirt_front': 16, - 'trousers_front': 15, - 'bottoms_front': 14, - 'shoes_right': 1, - 'shoes_left': 1, - 'body': 0, - 'bottoms_back': -14, - 'trousers_back': -15, - 'skirt_back': -16, - 'blouse_back': -17, - 'dress_back': -18, - 'tops_back': -19, - 'outwear_back': -20, - 'hairstyle_back': -97, - 'bag_back': -98, - 'earring_back': -99, -} - - -def process_item(item, basic): - if item['type'] == "Body": - body_server = BodyItem(data=item, basic=basic, minio_client=minio_client) - item_data = body_server.process() - elif item['type'].lower() in ['blouse', 'outwear', 'dress', 'tops']: - top_server = TopItem(data=item, basic=basic, minio_client=minio_client) - item_data = top_server.process() - else: - bottom_server = BottomItem(data=item, basic=basic, minio_client=minio_client) - item_data = bottom_server.process() - return item_data - - -def process_layer(item, layers): - if item['name'] == "mannequin": - body_layer = organize_body(item) - layers.append(body_layer) - return item['body_image'].size - else: - front_layer, back_layer = organize_clothing(item) - layers.append(front_layer) - layers.append(back_layer) - - -def organize_body(layer): - body_layer = dict(priority=0, - name=layer["name"].lower(), - image=layer['body_image'], - image_url=layer['body_path'], - mask_image=None, - mask_url=None, - sacle=1, - # mask=layer['body_mask'], - position=(0, 0)) - return body_layer - - -def organize_clothing(layer): - # 起始坐标 - start_point = calculate_start_point(layer['keypoint'], layer['scale'], layer['clothes_keypoint'], layer['body_point_test'], layer["offset"], layer["resize_scale"]) - # 前片数据 - front_layer = dict(priority=layer['priority'] if layer.get("layer_order", False) else priority_dict.get(f'{layer["name"].lower()}_front', None), - name=f'{layer["name"].lower()}_front', - image=layer["front_image"], - # mask_image=layer['front_mask_image'], - image_url=layer['front_image_url'], - mask_url=layer['mask_url'], - sacle=layer['scale'], - clothes_keypoint=layer['clothes_keypoint'], - position=start_point, - resize_scale=layer["resize_scale"], - mask=cv2.resize(layer['mask'], layer["front_image"].size), - gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "", - pattern_image_url=layer['pattern_image_url'], - pattern_image=layer['pattern_image'] - - ) - # 后片数据 - back_layer = dict(priority=-layer.get("priority", 0) if layer.get("layer_order", False) else priority_dict.get(f'{layer["name"].lower()}_back', None), - name=f'{layer["name"].lower()}_back', - image=layer["back_image"], - # mask_image=layer['back_mask_image'], - image_url=layer['back_image_url'], - mask_url=layer['mask_url'], - sacle=layer['scale'], - clothes_keypoint=layer['clothes_keypoint'], - position=start_point, - resize_scale=layer["resize_scale"], - mask=cv2.resize(layer['mask'], layer["front_image"].size), - gradient_string=layer['gradient_string'] if 'gradient_string' in layer.keys() else "", - pattern_image_url=layer['pattern_image_url'], - ) - return front_layer, back_layer - - -def calculate_start_point(keypoint_type, scale, clothes_point, body_point, offset, resize_scale): - """ - Align left - Args: - keypoint_type: string, "waistband" | "shoulder" | "ear_point" - scale: float - clothes_point: dict{'left': [x1, y1, z1], 'right': [x2, y2, z2]} - body_point: dict, containing keypoint data of body figure - - Returns: - start_point: tuple (x', y') - x' = y_body - y1 * scale + offset - y' = x_body - x1 * scale + offset - - """ - side_indicator = f'{keypoint_type}_left' - start_point = ( - int(body_point[side_indicator][1] + offset[1] - int(clothes_point[side_indicator][0]) * scale), # y - int(body_point[side_indicator][0] + offset[0] - int(clothes_point[side_indicator][1]) * scale) # x - ) - return start_point - - -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) - - -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) - image_data = io.BytesIO() - result_image.save(image_data, format='PNG') - image_data.seek(0) - image_bytes = image_data.read() - bucket_name = 'aida-results' - object_name = f'result_{generate_uuid()}.png' - oss_upload_image(bucket=bucket_name, object_name=object_name, image_bytes=image_bytes) - return f"{bucket_name}/{object_name}" - - -def oss_upload_json(json_data, object_name): - try: - with open(f"app/service/design/design_batch/response_json/{object_name}", 'w') as file: - json.dump(json_data, file, indent=4) - - oss_client = Minio(MINIO_URL, access_key=MINIO_ACCESS, secret_key=MINIO_SECRET, secure=MINIO_SECURE) - oss_client.fput_object("test", object_name, f"app/service/design/design_batch/response_json/{object_name}") - except Exception as e: - logger.warning(str(e)) - - -def generate_uuid(): - with id_lock: - unique_id = str(uuid.uuid1()) - return unique_id - - -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 - - -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' - 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 publish_status(task_id, progress, result): - connection = pika.BlockingConnection(pika.ConnectionParameters('10.1.2.213')) - channel = connection.channel() - channel.queue_declare(queue='DesignBatch', durable=True) - message = {'task_id': task_id, 'progress': progress, "result": result} - channel.basic_publish(exchange='', - routing_key='DesignBatch', - body=json.dumps(message), - properties=pika.BasicProperties( - delivery_mode=2, - )) - connection.close() - - -@celery_app.task -def batch_design(objects_data, tasks_id, json_name): - object_response = [] - threads = [] - active_threads = 0 - lock = threading.Lock() - - def process_object(step, object): - nonlocal active_threads - basic = object['basic'] - items_response = {'layers': []} - if basic['single_overall'] == "overall": - item_results = [] - for item in object['items']: - item_results.append(process_item(item, basic)) - layers = [] - body_size = None - for item in item_results: - body_size = process_layer(item, layers) - layers = sorted(layers, key=lambda s: s.get("priority", float('inf'))) - - layers, new_size = update_base_size_priority(layers, body_size) - - for lay in layers: - items_response['layers'].append({ - 'image_category': lay['name'], - 'position': lay['position'], - 'priority': lay.get("priority", None), - 'resize_scale': lay['resize_scale'] if "resize_scale" in lay.keys() else None, - 'image_size': lay['image'] if lay['image'] is None else lay['image'].size, - 'gradient_string': lay['gradient_string'] if 'gradient_string' in lay.keys() else "", - 'mask_url': lay['mask_url'], - 'image_url': lay['image_url'] if 'image_url' in lay.keys() else None, - 'pattern_image_url': lay['pattern_image_url'] if 'pattern_image_url' in lay.keys() else None, - }) - items_response['synthesis_url'] = synthesis(layers, new_size, basic) - else: - item_result = process_item(object['items'][0], basic) - items_response['layers'].append({ - 'image_category': f"{item_result['name']}_front", - '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({ - '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']) - - with lock: - object_response.append(items_response) - publish_status(tasks_id, step + 1, 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() - - oss_upload_json(object_response, json_name) - publish_status(tasks_id, "ok", json_name) - return object_response - - -if __name__ == '__main__': - object_data = { - "objects": [ - { - "basic": { - "body_point_test": { - "waistband_right": [ - 199, - 239 - ], - "hand_point_right": [ - 220, - 308 - ], - "waistband_left": [ - 113, - 239 - ], - "hand_point_left": [ - 92, - 310 - ], - "shoulder_left": [ - 99, - 111 - ], - "shoulder_right": [ - 214, - 111 - ] - }, - "layer_order": False, - "scale_bag": 0.7, - "scale_earrings": 0.16, - "self_template": True, - "single_overall": "overall", - "switch_category": "" - }, - "items": [ - { - "color": "195 195 196", - "icon": "none", - "image_id": 116207, - "offset": [ - 1, - 1 - ], - "path": "aida-sys-image/images/female/trousers/trousers_973.jpg", - "print": { - "element": { - "element_angle_list": [], - "element_path_list": [], - "element_scale_list": [], - "location": [] - }, - "overall": { - "location": [ - [ - 0.0, - 0.0 - ] - ], - "print_angle_list": [ - 0.0, - 0.0 - ], - "print_path_list": [], - "print_scale_list": [ - 0.0, - 0.0 - ] - }, - "single": { - "location": [], - "print_angle_list": [], - "print_path_list": [], - "print_scale_list": [] - } - }, - "resize_scale": [ - 1.0, - 1.0 - ], - "type": "Trousers" - }, - { - "color": "203 204 202", - "icon": "none", - "image_id": 95825, - "offset": [ - 1, - 1 - ], - "path": "aida-sys-image/images/female/blouse/0902003606.jpg", - "print": { - "element": { - "element_angle_list": [], - "element_path_list": [], - "element_scale_list": [], - "location": [] - }, - "overall": { - "location": [ - [ - 0.0, - 0.0 - ] - ], - "print_angle_list": [ - 0.0, - 0.0 - ], - "print_path_list": [], - "print_scale_list": [ - 0.0, - 0.0 - ] - }, - "single": { - "location": [], - "print_angle_list": [], - "print_path_list": [], - "print_scale_list": [] - } - }, - "resize_scale": [ - 1.0, - 1.0 - ], - "type": "Blouse" - }, - { - "body_path": "aida-sys-image/models/female/23ecb158-7b70-4468-a9d1-bac3ded9da62.png", - "image_id": 116612, - "offset": [ - 1, - 1 - ], - "resize_scale": [ - 1.0, - 1.0 - ], - "type": "Body" - } - ] - } - ], - "process_id": "9062885798571902" - } - X = batch_design(object_data['objects'], "123", "test.json") - print(X) diff --git a/app/service/design_batch/item.py b/app/service/design_batch/item.py index e10320d..cad1488 100644 --- a/app/service/design_batch/item.py +++ b/app/service/design_batch/item.py @@ -1,4 +1,4 @@ -from app.service.design_fast.pipeline import LoadImage, KeyPoint, Segmentation, Color, PrintPainting, Scaling, Split, LoadBodyImage, ContourDetection +from app.service.design_batch.pipeline import * class BaseItem: diff --git a/app/service/design_batch/pipeline/keypoint.py b/app/service/design_batch/pipeline/keypoint.py index 243cf4e..313a613 100644 --- a/app/service/design_batch/pipeline/keypoint.py +++ b/app/service/design_batch/pipeline/keypoint.py @@ -4,7 +4,7 @@ import numpy as np from pymilvus import MilvusClient from app.core.config import * -from app.service.design.utils.design_ensemble import get_keypoint_result +from app.service.design_batch.utils.design_ensemble import get_keypoint_result logger = logging.getLogger(__name__) diff --git a/app/service/design_batch/pipeline/loading.py b/app/service/design_batch/pipeline/loading.py index 0ce0dfa..8f02378 100644 --- a/app/service/design_batch/pipeline/loading.py +++ b/app/service/design_batch/pipeline/loading.py @@ -1,9 +1,6 @@ -import io import logging import cv2 -import numpy as np -from PIL import Image from app.service.utils.new_oss_client import oss_get_image diff --git a/app/service/design_batch/pipeline/scale.py b/app/service/design_batch/pipeline/scale.py index 732fcd8..1908a9c 100644 --- a/app/service/design_batch/pipeline/scale.py +++ b/app/service/design_batch/pipeline/scale.py @@ -18,7 +18,7 @@ class Scaling: - int(result['body_point_test'][result['keypoint'] + '_right'][0])) ** 2 + 1 ) - + if distance_clo == 0: result['scale'] = 1 else: diff --git a/app/service/design_batch/pipeline/segmentation.py b/app/service/design_batch/pipeline/segmentation.py index 802487f..cba3446 100644 --- a/app/service/design_batch/pipeline/segmentation.py +++ b/app/service/design_batch/pipeline/segmentation.py @@ -5,7 +5,7 @@ import cv2 import numpy as np from app.core.config import SEG_CACHE_PATH -from app.service.design.utils.design_ensemble import get_seg_result +from app.service.design_batch.utils.design_ensemble import get_seg_result from app.service.utils.new_oss_client import oss_get_image logger = logging.getLogger() @@ -48,7 +48,7 @@ class Segmentation: @staticmethod def save_seg_result(seg_result, image_id): - file_path = f"{SEG_CACHE_PATH}{image_id}.npy" + file_path = f"seg_cache/{image_id}.npy" try: np.save(file_path, seg_result) logger.info(f"保存成功 ：{os.path.abspath(file_path)}") @@ -57,7 +57,7 @@ class Segmentation: @staticmethod def load_seg_result(image_id): - file_path = f"{SEG_CACHE_PATH}{image_id}.npy" + file_path = f"seg_cache/{image_id}.npy" logger.info(f"load seg file name is :{SEG_CACHE_PATH}{image_id}.npy") try: seg_result = np.load(file_path) diff --git a/app/service/design_batch/pipeline/split.py b/app/service/design_batch/pipeline/split.py index 35605b8..5dbcef5 100644 --- a/app/service/design_batch/pipeline/split.py +++ b/app/service/design_batch/pipeline/split.py @@ -7,8 +7,8 @@ from PIL import Image from cv2 import cvtColor, COLOR_BGR2RGBA from app.core.config import AIDA_CLOTHING -from app.service.design.utils.conversion_image import rgb_to_rgba -from app.service.design_fast.utils.upload_image import upload_png_mask +from app.service.design_batch.utils.conversion_image import rgb_to_rgba +from app.service.design_batch.utils.upload_image import upload_png_mask from app.service.utils.generate_uuid import generate_uuid from app.service.utils.new_oss_client import oss_upload_image diff --git a/app/service/design_batch/service.py b/app/service/design_batch/service.py index e69de29..ca6908e 100644 --- a/app/service/design_batch/service.py +++ b/app/service/design_batch/service.py @@ -0,0 +1,11 @@ +import json + +from app.service.design_batch.design_batch_celery import batch_design +from app.service.design_batch.utils.MQ import publish_status + + +async def start_design_batch_generate(data, file): + generate_clothes_task = batch_design.delay(json.loads(file.decode())['objects'], data.total, data.tasks_id) + print(generate_clothes_task) + publish_status(data.tasks_id, "0/100", "") + return {"task_id": data.tasks_id}