From 84fe2663f490ddf95fc1adae7fb69bd79645e628 Mon Sep 17 00:00:00 2001 From: zhouchengrong Date: Wed, 11 Dec 2024 13:51:22 +0800 Subject: [PATCH] Revert "design design batch" This reverts commit e6f0ee7f --- .../design_batch/design_batch_celery.py | 3 +- app/service/design_batch/item.py | 25 +----- app/service/design_batch/pipeline/__init__.py | 2 - .../design_batch/pipeline/back_perspective.py | 79 ------------------- app/service/design_batch/pipeline/color.py | 7 -- app/service/design_batch/pipeline/keypoint.py | 10 +-- app/service/design_batch/pipeline/loading.py | 5 -- app/service/design_batch/pipeline/scale.py | 12 --- .../design_batch/pipeline/segmentation.py | 29 ++----- app/service/design_batch/pipeline/split.py | 27 +------ app/service/design_batch/service.py | 2 +- app/service/design_batch/utils/MQ.py | 9 +-- .../design_batch/utils/design_ensemble.py | 2 +- app/service/design_batch/utils/organize.py | 44 +---------- .../design_batch/utils/synthesis_item.py | 21 +---- app/service/design_batch/utils/transparent.py | 26 ------ 16 files changed, 24 insertions(+), 279 deletions(-) delete mode 100644 app/service/design_batch/pipeline/back_perspective.py delete mode 100644 app/service/design_batch/utils/transparent.py diff --git a/app/service/design_batch/design_batch_celery.py b/app/service/design_batch/design_batch_celery.py index e8e4b9d..3f12862 100644 --- a/app/service/design_batch/design_batch_celery.py +++ b/app/service/design_batch/design_batch_celery.py @@ -12,7 +12,7 @@ from app.service.design_batch.utils.save_json import oss_upload_json from app.service.design_batch.utils.synthesis_item import update_base_size_priority, synthesis, synthesis_single id_lock = threading.Lock() -celery_app = Celery('tasks', broker='amqp://guest:guest@10.1.2.190:5672//', backend='rpc://') +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) @@ -108,7 +108,6 @@ def batch_design(objects_data, tasks_id, json_name): with lock: object_response.append(items_response) - # logger.info(items_response) publish_status(tasks_id, step + 1, items_response) active_threads -= 1 diff --git a/app/service/design_batch/item.py b/app/service/design_batch/item.py index ec18b17..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: @@ -9,27 +9,6 @@ class BaseItem: self.result.update(basic) -class AccessoriesItem(BaseItem): - def __init__(self, data, basic, minio_client): - super().__init__(data, basic) - self.Accessories_pipeline = [ - LoadImage(minio_client), - # KeyPoint(), - ContourDetection(), - # Segmentation(minio_client), - # BackPerspective(minio_client), - Color(minio_client), - PrintPainting(minio_client), - Scaling(), - Split(minio_client) - ] - - def process(self): - for item in self.Accessories_pipeline: - self.result = item(self.result) - return self.result - - class TopItem(BaseItem): def __init__(self, data, basic, minio_client): super().__init__(data, basic) @@ -37,7 +16,6 @@ class TopItem(BaseItem): LoadImage(minio_client), KeyPoint(), Segmentation(minio_client), - # BackPerspective(minio_client), Color(minio_client), PrintPainting(minio_client), Scaling(), @@ -58,7 +36,6 @@ class BottomItem(BaseItem): KeyPoint(), ContourDetection(), # Segmentation(), - # BackPerspective(minio_client), Color(minio_client), PrintPainting(minio_client), Scaling(), diff --git a/app/service/design_batch/pipeline/__init__.py b/app/service/design_batch/pipeline/__init__.py index f265bbe..ec55933 100644 --- a/app/service/design_batch/pipeline/__init__.py +++ b/app/service/design_batch/pipeline/__init__.py @@ -1,4 +1,3 @@ -from .back_perspective import BackPerspective from .color import Color from .contour_detection import ContourDetection from .keypoint import KeyPoint @@ -14,7 +13,6 @@ __all__ = [ 'KeyPoint', 'ContourDetection', 'Segmentation', - 'BackPerspective', 'Color', 'PrintPainting', 'Scaling', diff --git a/app/service/design_batch/pipeline/back_perspective.py b/app/service/design_batch/pipeline/back_perspective.py deleted file mode 100644 index 5ddd37c..0000000 --- a/app/service/design_batch/pipeline/back_perspective.py +++ /dev/null @@ -1,79 +0,0 @@ -import cv2 -import numpy as np - -from app.service.design_fast.utils.design_ensemble import get_seg_result -from app.service.utils.new_oss_client import oss_upload_image - - -class BackPerspective: - def __init__(self, minio_client): - self.minio_client = minio_client - - def __call__(self, result): - - # 如果sketch为系统图 查看是否有对应的 背后视角图 - if result['path'].split('/')[0] == 'aida-sys-image': - file_path = result['path'].replace("images", 'images_back', 1) - if self.is_file_exists(bucket_name='aida-sys-image', file_name=file_path[file_path.find('/') + 1:]): - result['back_perspective_url'] = file_path - return result - else: - seg_result = get_seg_result("1", result['image'])[0] - elif result['name'] in ['blouse', 'outwear', 'dress', 'tops']: - seg_result = result['seg_result'] - else: - seg_result = get_seg_result("1", result['image'])[0] - - m = self.thicken_contours_and_display(seg_result, thickness=10, color=(0, 0, 0)) - back_sketch = result['image'].copy() - back_sketch[m > 100] = 255 - # 上传背后视角图 - _, img_encoded = cv2.imencode(".jpg", back_sketch) - - resp = oss_upload_image(self.minio_client, bucket='test', object_name=result['path'], image_bytes=img_encoded.tobytes()) - result['back_perspective_url'] = f"{resp.bucket_name}/{resp.object_name}" - return result - - def thicken_contours_and_display(self, mask, thickness=10, color=(0, 0, 0)): - mask = mask.astype(np.uint8) * 255 - # 查找轮廓 - contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) - - # 创建一个彩色副本用于绘制轮廓 - mask_color = cv2.cvtColor(mask, cv2.COLOR_GRAY2BGR) - - def thicken_contour_inward(contour, thick): - # 创建一个空白的黑色图像与原始掩码大小相同 - blank = np.zeros_like(mask) - # 在空白图像上绘制白色的轮廓 - cv2.drawContours(blank, [contour], -1, 255, thickness=thick) - # 找到轮廓的中心（可以用重心等方法近似） - M = cv2.moments(contour) - cx = int(M['m10'] / M['m00']) - cy = int(M['m01'] / M['m00']) - # 进行距离变换，离中心越近的值越小 - dist_transform = cv2.distanceTransform(255 - blank, cv2.DIST_L2, 5) - # 根据距离变换的值来决定是否保留像素，离中心近的像素更容易被保留 - result = np.zeros_like(mask) - for i in range(dist_transform.shape[0]): - for j in range(dist_transform.shape[1]): - if dist_transform[i, j] < thick: - result[i, j] = 255 - return result - - for contour in contours: - thickened_contour = thicken_contour_inward(contour, thickness) - mask_color[thickened_contour > 0] = color - - _, binary_result = cv2.threshold(mask_color, 127, 255, cv2.THRESH_BINARY) - - # 转换为掩码形式 - mask_result = cv2.cvtColor(binary_result, cv2.COLOR_BGR2GRAY) - return mask_result - - def is_file_exists(self, bucket_name, file_name): - try: - self.minio_client.stat_object(bucket_name, file_name) - return True - except Exception: - return False diff --git a/app/service/design_batch/pipeline/color.py b/app/service/design_batch/pipeline/color.py index 3033bb5..546c671 100644 --- a/app/service/design_batch/pipeline/color.py +++ b/app/service/design_batch/pipeline/color.py @@ -14,18 +14,11 @@ class Color: def __call__(self, result): dim_image_h, dim_image_w = result['image'].shape[0:2] - # 渐变色 if "gradient" in result.keys() and result['gradient'] != "": bucket_name = result['gradient'].split('/')[0] object_name = result['gradient'][result['gradient'].find('/') + 1:] pattern = self.get_gradient(bucket_name=bucket_name, object_name=object_name) resize_pattern = cv2.resize(pattern, (dim_image_w, dim_image_h), interpolation=cv2.INTER_AREA) - # 无色 - elif "color" not in result.keys() or result['color'] == "": - result['final_image'] = result['pattern_image'] = result['single_image'] = result['image'] - result['alpha'] = 100 / 255.0 - return result - # 正常颜色 else: pattern = self.get_pattern(result['color']) resize_pattern = cv2.resize(pattern, (dim_image_w, dim_image_h), interpolation=cv2.INTER_AREA) diff --git a/app/service/design_batch/pipeline/keypoint.py b/app/service/design_batch/pipeline/keypoint.py index 73d7586..313a613 100644 --- a/app/service/design_batch/pipeline/keypoint.py +++ b/app/service/design_batch/pipeline/keypoint.py @@ -4,8 +4,7 @@ import numpy as np from pymilvus import MilvusClient from app.core.config import * -from app.service.design_fast.utils.design_ensemble import get_keypoint_result -from app.service.utils.decorator import ClassCallRunTime, RunTime +from app.service.design_batch.utils.design_ensemble import get_keypoint_result logger = logging.getLogger(__name__) @@ -17,15 +16,14 @@ class KeyPoint: def get_name(cls): return cls.name - @ClassCallRunTime def __call__(self, result): if result['name'] in ['blouse', 'skirt', 'dress', 'outwear', 'trousers', 'tops', 'bottoms']: # 查询是否有数据 且类别相同 相同则直接读 不同则推理后更新 # result['clothes_keypoint'] = self.infer_keypoint_result(result) site = 'up' if result['name'] in ['blouse', 'outwear', 'dress', 'tops'] else 'down' # keypoint_cache = search_keypoint_cache(result["image_id"], site) - # keypoint_cache = self.keypoint_cache(result, site) - keypoint_cache = False + keypoint_cache = self.keypoint_cache(result, site) # 取消向量查询 直接过模型推理 + # keypoint_cache = False if keypoint_cache is False: keypoint_infer_result, site = self.infer_keypoint_result(result) result['clothes_keypoint'] = self.save_keypoint_cache(result["image_id"], keypoint_infer_result, site) @@ -89,7 +87,7 @@ class KeyPoint: logger.info(f"save keypoint cache milvus error : {e}") return dict(zip(KEYPOINT_RESULT_TABLE_FIELD_SET, result.reshape(12, 2).astype(int).tolist())) - @RunTime + # @ RunTime def keypoint_cache(self, result, site): try: client = MilvusClient(uri=MILVUS_URL, token=MILVUS_TOKEN, db_name=MILVUS_ALIAS) diff --git a/app/service/design_batch/pipeline/loading.py b/app/service/design_batch/pipeline/loading.py index 5a55d9d..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 @@ -74,8 +71,6 @@ class LoadImage: keypoint = 'head_point' elif name == 'earring': keypoint = 'ear_point' - elif name == 'accessories': - keypoint = "accessories" else: raise KeyError(f"{name} does not belong to item category list: blouse, outwear, dress, trousers, skirt, " f"bag, shoes, hairstyle, earring.") diff --git a/app/service/design_batch/pipeline/scale.py b/app/service/design_batch/pipeline/scale.py index d1c7a36..1908a9c 100644 --- a/app/service/design_batch/pipeline/scale.py +++ b/app/service/design_batch/pipeline/scale.py @@ -46,16 +46,4 @@ class Scaling: result['scale'] = result['scale_bag'] elif result['keypoint'] == 'ear_point': result['scale'] = result['scale_earrings'] - elif result['keypoint'] == 'accessories': - # 由于没有识别配饰keypoint的模型 所以统一将配饰的两个关键点设定为 (0,0) (0,img.width) - # 模特的关键点设定为(0,0) (0,320/2) 距离比例简写为 160 / img.width - distance_clo = result['img_shape'][1] - distance_bdy = 320 / 2 - - if distance_clo == 0: - result['scale'] = 1 - else: - result['scale'] = distance_bdy / distance_clo - else: - result['scale'] = 1 return result diff --git a/app/service/design_batch/pipeline/segmentation.py b/app/service/design_batch/pipeline/segmentation.py index ebf02b4..cba3446 100644 --- a/app/service/design_batch/pipeline/segmentation.py +++ b/app/service/design_batch/pipeline/segmentation.py @@ -5,8 +5,7 @@ import cv2 import numpy as np from app.core.config import SEG_CACHE_PATH -from app.service.design_fast.utils.design_ensemble import get_seg_result -from app.service.utils.decorator import ClassCallRunTime +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() @@ -16,7 +15,6 @@ class Segmentation: def __init__(self, minio_client): self.minio_client = minio_client - @ClassCallRunTime def __call__(self, result): if "seg_mask_url" in result.keys() and result['seg_mask_url'] != "": seg_mask = oss_get_image(oss_client=self.minio_client, bucket=result['seg_mask_url'].split('/')[0], object_name=result['seg_mask_url'][result['seg_mask_url'].find('/') + 1:], data_type="cv2") @@ -33,26 +31,13 @@ class Segmentation: result['back_mask'] = np.array(green_mask, dtype=np.uint8) * 255 result['mask'] = result['front_mask'] + result['back_mask'] else: - # preview 过模型 不缓存 - if "preview_submit" in result.keys() and result['preview_submit'] == "preview": - # 推理获得seg 结果 - seg_result = get_seg_result(result["image_id"], result['image'])[0] - # submit 过模型 缓存 - elif "preview_submit" in result.keys() and result['preview_submit'] == "submit": + # 本地查询seg 缓存是否存在 + _, seg_result = self.load_seg_result(result["image_id"]) + result['seg_result'] = seg_result + if not _: # 推理获得seg 结果 seg_result = get_seg_result(result["image_id"], result['image'])[0] self.save_seg_result(seg_result, result['image_id']) - # null 正常流程 加载本地缓存 无缓存则过模型 - else: - # 本地查询seg 缓存是否存在 - _, seg_result = self.load_seg_result(result["image_id"]) - # 判断缓存和实际图片size是否相同 - if not _ or result["image"].shape[:2] != seg_result.shape: - # 推理获得seg 结果 - seg_result = get_seg_result(result["image_id"], result['image'])[0] - self.save_seg_result(seg_result, result['image_id']) - result['seg_result'] = seg_result - # 处理前片后片 temp_front = seg_result == 1.0 result['front_mask'] = (255 * (temp_front + 0).astype(np.uint8)) @@ -63,7 +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)}") @@ -72,7 +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 344c5c5..5dbcef5 100644 --- a/app/service/design_batch/pipeline/split.py +++ b/app/service/design_batch/pipeline/split.py @@ -7,11 +7,10 @@ from PIL import Image from cv2 import cvtColor, COLOR_BGR2RGBA from app.core.config import AIDA_CLOTHING -from app.service.design_fast.utils.conversion_image import rgb_to_rgba -from app.service.design_fast.utils.transparent import sketch_to_transparent -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, oss_get_image +from app.service.utils.new_oss_client import oss_upload_image class Split(object): @@ -21,7 +20,7 @@ class Split(object): def __call__(self, result): try: - if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms','accessories'): + if result['name'] in ('outwear', 'dress', 'blouse', 'skirt', 'trousers', 'tops', 'bottoms'): front_mask = result['front_mask'] back_mask = result['back_mask'] rgba_image = rgb_to_rgba(result['final_image'], front_mask + back_mask) @@ -31,24 +30,6 @@ class Split(object): front_mask = cv2.resize(front_mask, new_size) result_front_image[front_mask != 0] = rgba_image[front_mask != 0] result_front_image_pil = Image.fromarray(cvtColor(result_front_image, COLOR_BGR2RGBA)) - if 'transparent' in result.keys(): - # 用户自选区域transparent - transparent = result['transparent'] - if transparent['mask_url'] is not None and transparent['mask_url'] != "": - # 预处理用户自选区mask - seg_mask = oss_get_image(oss_client=self.minio_client, bucket=transparent['mask_url'].split('/')[0], object_name=transparent['mask_url'][transparent['mask_url'].find('/') + 1:], data_type="cv2") - seg_mask = cv2.resize(seg_mask, new_size, interpolation=cv2.INTER_NEAREST) - # 转换颜色空间为 RGB（OpenCV 默认是 BGR） - image_rgb = cv2.cvtColor(seg_mask, cv2.COLOR_BGR2RGB) - - r, g, b = cv2.split(image_rgb) - blue_mask = b > r - - # 创建红色和绿色掩码 - transparent_mask = np.array(blue_mask, dtype=np.uint8) * 255 - result_front_image_pil = sketch_to_transparent(result_front_image_pil, transparent_mask, transparent["scale"]) - else: - result_front_image_pil = sketch_to_transparent(result_front_image_pil, front_mask, transparent["scale"]) result['front_image'], result["front_image_url"], _ = upload_png_mask(self.minio_client, result_front_image_pil, f'{generate_uuid()}', mask=None) height, width = front_mask.shape diff --git a/app/service/design_batch/service.py b/app/service/design_batch/service.py index e2a9b23..ca6908e 100644 --- a/app/service/design_batch/service.py +++ b/app/service/design_batch/service.py @@ -5,7 +5,7 @@ from app.service.design_batch.utils.MQ import publish_status async def start_design_batch_generate(data, file): - generate_clothes_task = batch_design(json.loads(file.decode())['objects'], data.total, data.file_name) + 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} diff --git a/app/service/design_batch/utils/MQ.py b/app/service/design_batch/utils/MQ.py index d787bcb..50e98c2 100644 --- a/app/service/design_batch/utils/MQ.py +++ b/app/service/design_batch/utils/MQ.py @@ -2,12 +2,9 @@ import json import pika -from app.core.config import RABBITMQ_PARAMS - def publish_status(task_id, progress, result): - connection = pika.BlockingConnection(pika.ConnectionParameters(**RABBITMQ_PARAMS)) - # connection = pika.BlockingConnection(pika.ConnectionParameters('10.1.2.190')) + 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} @@ -18,7 +15,3 @@ def publish_status(task_id, progress, result): delivery_mode=2, )) connection.close() - - -if __name__ == '__main__': - publish_status("1", "1", "1") diff --git a/app/service/design_batch/utils/design_ensemble.py b/app/service/design_batch/utils/design_ensemble.py index 267ea00..f4f6a34 100644 --- a/app/service/design_batch/utils/design_ensemble.py +++ b/app/service/design_batch/utils/design_ensemble.py @@ -85,7 +85,7 @@ def seg_preprocess(img_path): if ori_shape != (img_scale_w, img_scale_h): # mmcv.imresize(img, img_scale_h, img_scale_w) # 老代码 引以为戒！哈哈哈~ h和w写反了 img = cv2.resize(img, (img_scale_h, img_scale_w)) - # img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True) + img = mmcv.imnormalize(img, mean=np.array([123.675, 116.28, 103.53]), std=np.array([58.395, 57.12, 57.375]), to_rgb=True) preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0) return preprocessed_img, ori_shape diff --git a/app/service/design_batch/utils/organize.py b/app/service/design_batch/utils/organize.py index 33edc4f..8190de0 100644 --- a/app/service/design_batch/utils/organize.py +++ b/app/service/design_batch/utils/organize.py @@ -33,8 +33,8 @@ def organize_clothing(layer): 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'], - # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else "" + 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), @@ -50,46 +50,6 @@ def organize_clothing(layer): 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'], - # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else "" - ) - return front_layer, back_layer - - -def organize_accessories(layer): - # 起始坐标 - start_point = (0, 0) - # 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=(0, 0), - 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'], - # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else "" - ) - # 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=(0, 0), - 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'], - # back_perspective_url=layer['back_perspective_url'] if 'back_perspective_url' in layer.keys() else "" ) return front_layer, back_layer diff --git a/app/service/design_batch/utils/synthesis_item.py b/app/service/design_batch/utils/synthesis_item.py index d7711f3..272ab23 100644 --- a/app/service/design_batch/utils/synthesis_item.py +++ b/app/service/design_batch/utils/synthesis_item.py @@ -79,11 +79,9 @@ def synthesis(data, size, basic_info): _, 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) - accessories_outer_mask = np.array(binary_body_mask) top = True bottom = True - accessories = True i = len(data) while i: i -= 1 @@ -100,7 +98,7 @@ def synthesis(data, size, basic_info): 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 + 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 @@ -111,23 +109,10 @@ def synthesis(data, size, basic_info): 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 accessories and data[i]['name'] in ['accessories_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] - accessories_outer_mask = background + accessories_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, accessories_outer_mask) for layer in data: if layer['image'] is not None: @@ -200,14 +185,12 @@ 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 = [] - 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) 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) diff --git a/app/service/design_batch/utils/transparent.py b/app/service/design_batch/utils/transparent.py deleted file mode 100644 index 3f73807..0000000 --- a/app/service/design_batch/utils/transparent.py +++ /dev/null @@ -1,26 +0,0 @@ -from PIL import Image - - -def sketch_to_transparent(image, mask, transparency): - # 打开原始图片 - image = image.convert("RGBA") - # 打开mask图片，假设mask图片是灰度图，白色区域为要处理的区域，黑色区域为保留的区域 - mask = Image.fromarray(mask) - - # 根据透明度调整因子，将透明度转换为0-255之间的值 - alpha_value = int((1 - transparency) * 255.0) - - # 获取图片的像素数据 - image_pixels = image.load() - mask_pixels = mask.load() - - width, height = image.size - - for y in range(height): - for x in range(width): - # 如果mask区域对应的像素为白色（值大于128，这里假设白色为要处理的区域，可根据实际情况调整） - if mask_pixels[x, y] > 128: - r, g, b, a = image_pixels[x, y] - image_pixels[x, y] = (r, g, b, alpha_value) - - return image