feat design 功能迁移
This commit is contained in:
zhouchengrong
0
app/service/design/utils/__init__.py
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0
app/service/design/utils/__init__.py
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23
app/service/design/utils/conversion_image.py
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app/service/design/utils/conversion_image.py
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#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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"""
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@Project ：trinity_client
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@File ：conversion_image.py
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@Author ：周成融
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@Date ：2023/8/21 10:40:29
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@detail ：
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"""
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import numpy as np
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def rgb_to_rgba(rgb_size, rgb_image, mask):
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alpha_channel = np.full(rgb_size, 255, dtype=np.uint8)
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# 创建四通道的结果图像
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rgba_image = np.dstack((rgb_image, alpha_channel))
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alpha_channel = np.where(mask > 0, 255, 0)
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# 更新RGBA图像的透明度通道
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rgba_image[:, :, 3] = alpha_channel
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return rgba_image
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if __name__ == '__main__':
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image = open("")
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138
app/service/design/utils/design_ensemble.py
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138
app/service/design/utils/design_ensemble.py
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#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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"""
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@Project ：trinity_client
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@File ：design_ensemble.py
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@Author ：周成融
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@Date ：2023/8/16 19:36:21
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@detail ：发起请求 获取推理结果
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"""
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import logging
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import cv2
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import mmcv
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import numpy as np
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import tritonclient.http as httpclient
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import torch
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import torch.nn.functional as F
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from app.core.config import *
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"""
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keypoint
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预处理 推理 后处理
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"""
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def keypoint_preprocess(img_path):
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img = mmcv.imread(img_path)
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img_scale = (256, 256)
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img, w_scale, h_scale = mmcv.imresize(img, img_scale, return_scale=True)
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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)
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preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
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return preprocessed_img, (w_scale, h_scale)
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# @ RunTime
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# 推理
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def get_keypoint_result(image, site):
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keypoint_result = None
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try:
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image, scale_factor = keypoint_preprocess(image)
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client = httpclient.InferenceServerClient(url=KEYPOINT_MODEL_URL)
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transformed_img = image.astype(np.float32)
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inputs = [httpclient.InferInput(f"input", transformed_img.shape, datatype="FP32")]
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inputs[0].set_data_from_numpy(transformed_img, binary_data=True)
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outputs = [httpclient.InferRequestedOutput(f"output", binary_data=True)]
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results = client.infer(model_name=f"keypoint_{site}_ocrnet_hr18", inputs=inputs, outputs=outputs)
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inference_output = torch.from_numpy(results.as_numpy(f'output'))
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keypoint_result = keypoint_postprocess(inference_output, scale_factor)
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except Exception as e:
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logging.warning(f"get_keypoint_result : {e}")
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return keypoint_result
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def keypoint_postprocess(output, scale_factor):
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max_indices = torch.argmax(output.view(output.size(0), output.size(1), -1), dim=2).unsqueeze(dim=2)
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max_coords = torch.cat((max_indices / output.size(3), max_indices % output.size(3)), dim=2)
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segment_result = max_coords.numpy()
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scale_factor = [1 / x for x in scale_factor[::-1]]
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scale_matrix = np.diag(scale_factor)
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nan = np.isinf(scale_matrix)
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scale_matrix[nan] = 0
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return np.ceil(np.dot(segment_result, scale_matrix) * 4)
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"""
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seg
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预处理 推理 后处理
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"""
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# KNet
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def seg_preprocess(img_path):
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img = mmcv.imread(img_path)
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ori_shape = img.shape[:2]
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img_scale_w, img_scale_h = ori_shape
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if ori_shape[0] > 1024:
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img_scale_w = 1024
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if ori_shape[1] > 1024:
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img_scale_h = 1024
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scale_factor = []
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img, x, y = mmcv.imresize(img, (img_scale_w, img_scale_h), return_scale=True)
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scale_factor.append(x)
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scale_factor.append(y)
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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)
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preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
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return preprocessed_img, ori_shape
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# @ RunTime
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def get_seg_result(image_id, image):
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image, ori_shape = seg_preprocess(image)
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client = httpclient.InferenceServerClient(url=f"{DESIGN_MODEL_URL}")
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transformed_img = image.astype(np.float32)
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# 输入集
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inputs = [
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httpclient.InferInput(SEGMENTATION['input'], transformed_img.shape, datatype="FP32")
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]
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inputs[0].set_data_from_numpy(transformed_img, binary_data=True)
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# 输出集
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outputs = [
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httpclient.InferRequestedOutput(SEGMENTATION['output'], binary_data=True),
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]
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results = client.infer(model_name=SEGMENTATION['new_model_name'], inputs=inputs, outputs=outputs)
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# 推理
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# 取结果
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inference_output1 = results.as_numpy(SEGMENTATION['output'])
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seg_result = seg_postprocess(int(image_id), inference_output1, ori_shape)
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return seg_result
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# no cache
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def seg_postprocess(image_id, output, ori_shape):
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seg_logit = F.interpolate(torch.tensor(output).float(), size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False)
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seg_pred = seg_logit.cpu().numpy()
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return seg_pred[0]
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def key_point_show(image_path, key_point_result=None):
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img = cv2.imread(image_path)
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points_list = key_point_result
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point_size = 1
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point_color = (0, 0, 255) # BGR
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thickness = 4 # 可以为 0 、4、8
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for point in points_list:
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cv2.circle(img, point[::-1], point_size, point_color, thickness)
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cv2.imshow("0", img)
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cv2.waitKey(0)
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if __name__ == '__main__':
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image = cv2.imread("./14162b58-f259-4833-98cb-89b9b496b251.jfif")
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a = get_keypoint_result(image, "up")
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new_list = []
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print(list)
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for i in a[0]:
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new_list.append((int(i[0]), int(i[1])))
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key_point_show("./14162b58-f259-4833-98cb-89b9b496b251.jfif", new_list)
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# a = get_seg_result(1, image)
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print(a)
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99
app/service/design/utils/redis_utils.py
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app/service/design/utils/redis_utils.py
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import redis
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from app.core.config import REDIS_HOST, REDIS_PORT
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class Redis(object):
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"""
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redis数据库操作
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"""
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@staticmethod
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def _get_r():
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host = REDIS_HOST
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port = REDIS_PORT
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db = 0
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r = redis.StrictRedis(host, port, db)
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return r
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@classmethod
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def write(cls, key, value, expire=None):
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"""
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写入键值对
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"""
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# 判断是否有过期时间，没有就设置默认值
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if expire:
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expire_in_seconds = expire
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else:
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expire_in_seconds = 100
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r = cls._get_r()
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r.set(key, value, ex=expire_in_seconds)
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@classmethod
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def read(cls, key):
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"""
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读取键值对内容
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"""
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r = cls._get_r()
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value = r.get(key)
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return value.decode('utf-8') if value else value
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@classmethod
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def hset(cls, name, key, value):
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"""
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写入hash表
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"""
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r = cls._get_r()
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r.hset(name, key, value)
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@classmethod
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def hget(cls, name, key):
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"""
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读取指定hash表的键值
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"""
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r = cls._get_r()
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value = r.hget(name, key)
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return value.decode('utf-8') if value else value
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@classmethod
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def hgetall(cls, name):
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"""
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获取指定hash表所有的值
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"""
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r = cls._get_r()
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return r.hgetall(name)
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@classmethod
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def delete(cls, *names):
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"""
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删除一个或者多个
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"""
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r = cls._get_r()
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r.delete(*names)
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@classmethod
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def hdel(cls, name, key):
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"""
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删除指定hash表的键值
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"""
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r = cls._get_r()
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r.hdel(name, key)
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@classmethod
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def expire(cls, name, expire=None):
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"""
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设置过期时间
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"""
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if expire:
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expire_in_seconds = expire
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else:
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expire_in_seconds = 100
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r = cls._get_r()
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r.expire(name, expire_in_seconds)
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if __name__ == '__main__':
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redis_client = Redis()
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# print(redis_client.write(key="1230", value=0))
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redis_client.write(key="1230", value=10)
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# print(redis_client.read(key="1230"))
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174
app/service/design/utils/synthesis_item.py
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app/service/design/utils/synthesis_item.py
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#!/usr/bin/env python
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# -*- coding: UTF-8 -*-
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"""
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@Project ：trinity_client
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@File ：synthesis_item.py
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@Author ：周成融
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@Date ：2023/8/26 14:13:04
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@detail ：
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"""
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import io
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import logging
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import time
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import boto3
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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.service.utils.decorator import RunTime
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from app.service.utils.generate_uuid import generate_uuid
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# minio_client = Minio(
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# f"{MINIO_IP}:{MINIO_PORT}",
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# access_key=MINIO_ACCESS,
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# secret_key=MINIO_SECRET,
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# secure=MINIO_SECURE)
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s3 = boto3.client(
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's3',
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aws_access_key_id="AKIAVD3OJIMF6UJFLSHZ",
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aws_secret_access_key="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",
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region_name="ap-east-1"
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)
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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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@RunTime
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def synthesis(data, size):
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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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top_outer_mask = np.zeros(all_mask_shape, dtype=np.uint8)
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bottom_outer_mask = np.zeros(all_mask_shape, dtype=np.uint8)
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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]['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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top_outer_mask[all_y_start:all_y_end, all_x_start:all_x_end] = data[i]['mask'][mask_y_start:mask_y_end, mask_x_start:mask_x_end]
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elif bottom and data[i]['name'] in ["trousers_front", "skirt_front", "bottoms_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]['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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bottom_outer_mask[all_y_start:all_y_end, all_x_start:all_x_end] = data[i]['mask'][mask_y_start:mask_y_end, mask_x_start:mask_x_end]
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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['position'][1], layer['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(cropped_image, (0, 0), cropped_image)
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else:
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base_image.paste(layer['image'], (layer['position'][1], layer['position'][0]), layer['image'])
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result_image = base_image
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with io.BytesIO() as output:
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result_image.save(output, format='PNG')
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data = output.getvalue()
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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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# 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}"
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object_name = f'result_{generate_uuid()}.png'
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response = s3.put_object(Bucket="aida-results", Key=object_name, Body=data, ContentType='image/png')
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object_url = f"aida-results/{object_name}"
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if response['ResponseMetadata']['HTTPStatusCode'] == 200:
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return object_url
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else:
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return ""
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except Exception as e:
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logging.warning(f"synthesis runtime exception : {e}")
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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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with io.BytesIO() as output:
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result_image.save(output, format='PNG')
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data = output.getvalue()
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# 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}"
|
||||
|
||||
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 ""
|
||||
160
app/service/design/utils/upload_image.py
Normal file
160
app/service/design/utils/upload_image.py
Normal file
@@ -0,0 +1,160 @@
|
||||
#!/usr/bin/env python
|
||||
# -*- coding: UTF-8 -*-
|
||||
"""
|
||||
@Project ：trinity_client
|
||||
@File ：upload_image.py
|
||||
@Author ：周成融
|
||||
@Date ：2023/8/28 13:49:20
|
||||
@detail ：
|
||||
"""
|
||||
import io
|
||||
import logging
|
||||
import time
|
||||
|
||||
import boto3
|
||||
import cv2
|
||||
from minio import Minio
|
||||
|
||||
from app.core.config import *
|
||||
from app.service.utils.decorator import RunTime
|
||||
|
||||
minio_client = Minio(
|
||||
f"{MINIO_URL}",
|
||||
access_key=MINIO_ACCESS,
|
||||
secret_key=MINIO_SECRET,
|
||||
secure=MINIO_SECURE)
|
||||
|
||||
"""S3 上传"""
|
||||
s3 = boto3.client(
|
||||
's3',
|
||||
aws_access_key_id="AKIAVD3OJIMF6UJFLSHZ",
|
||||
aws_secret_access_key="LNIwFFB27/QedtZ+Q/viVUoX9F5x1DbuM8N0DkD8",
|
||||
region_name="ap-east-1"
|
||||
)
|
||||
|
||||
|
||||
@RunTime
|
||||
def upload_png_mask(front_image, object_name, mask=None):
|
||||
start_time = time.time()
|
||||
mask_url = None
|
||||
if mask is not None:
|
||||
# 反转掩模
|
||||
mask_inverted = cv2.bitwise_not(mask)
|
||||
# 将掩模转换为 RGBA 格式
|
||||
rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA)
|
||||
rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0]
|
||||
# 将图像数据保存到内存中的 BytesIO 对象中
|
||||
image_bytes = io.BytesIO()
|
||||
image_bytes.write(cv2.imencode('.png', rgba_image)[1].tobytes())
|
||||
image_bytes.seek(0)
|
||||
try:
|
||||
key = f"mask/mask_{object_name}.png"
|
||||
mask_url = f"{AIDA_CLOTHING}/{key}"
|
||||
s3.put_object(Bucket=AIDA_CLOTHING, Key=key, Body=image_bytes, ContentType='image/png')
|
||||
except Exception as e:
|
||||
print(f'上传到 S3 失败: {e}')
|
||||
with io.BytesIO() as output:
|
||||
front_image.save(output, format='PNG')
|
||||
data = output.getvalue()
|
||||
# 创建一个 S3 客户端
|
||||
try:
|
||||
key = f"image/image_{object_name}.png"
|
||||
image_url = f"{AIDA_CLOTHING}/{key}"
|
||||
s3.put_object(Bucket=AIDA_CLOTHING, Key=key, Body=data, ContentType='image/png')
|
||||
return front_image, image_url, mask_url
|
||||
except Exception as e:
|
||||
print(f'上传到 S3 失败: {e}')
|
||||
|
||||
|
||||
@RunTime
|
||||
def upload_layer_image(image, object_name):
|
||||
with io.BytesIO() as output:
|
||||
image.save(output, format='PNG')
|
||||
data = output.getvalue()
|
||||
# 创建一个 S3 客户端
|
||||
try:
|
||||
key = f"image/image_{object_name}.png"
|
||||
image_url = f"{AIDA_CLOTHING}/{key}"
|
||||
s3.put_object(Bucket=AIDA_CLOTHING, Key=key, Body=data, ContentType='image/png')
|
||||
return image_url
|
||||
except Exception as e:
|
||||
print(f'上传到 S3 失败: {e}')
|
||||
|
||||
|
||||
@RunTime
|
||||
def upload_mask_image(mask, object_name):
|
||||
# 反转掩模
|
||||
mask_inverted = cv2.bitwise_not(mask)
|
||||
# 将掩模转换为 RGBA 格式
|
||||
rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA)
|
||||
rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0]
|
||||
# 将图像数据保存到内存中的 BytesIO 对象中
|
||||
image_bytes = io.BytesIO()
|
||||
image_bytes.write(cv2.imencode('.png', rgba_image)[1].tobytes())
|
||||
image_bytes.seek(0)
|
||||
try:
|
||||
key = f"mask/mask_{object_name}.png"
|
||||
mask_url = f"{AIDA_CLOTHING}/{key}"
|
||||
s3.put_object(Bucket=AIDA_CLOTHING, Key=key, Body=image_bytes, ContentType='image/png')
|
||||
return mask_url
|
||||
except Exception as e:
|
||||
print(f'上传到 S3 失败: {e}')
|
||||
|
||||
|
||||
"""minio 上传"""
|
||||
|
||||
# @RunTime
|
||||
# def upload_png_mask(front_image, object_name, mask=None):
|
||||
# start_time = time.time()
|
||||
# try:
|
||||
# mask_url = None
|
||||
# if mask is not None:
|
||||
# mask_inverted = cv2.bitwise_not(mask)
|
||||
# # 将掩模的3通道转换为4通道，白色部分不透明，黑色部分透明
|
||||
# rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA)
|
||||
# rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0]
|
||||
# image_bytes = io.BytesIO()
|
||||
# image_bytes.write(cv2.imencode('.png', rgba_image)[1].tobytes())
|
||||
#
|
||||
# image_bytes.seek(0)
|
||||
# mask_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'mask/mask_{object_name}.png', image_bytes, len(image_bytes.getvalue()), content_type='image/png').object_name}"
|
||||
#
|
||||
# image_data = io.BytesIO()
|
||||
# front_image.save(image_data, format='PNG')
|
||||
# image_data.seek(0)
|
||||
# image_bytes = image_data.read()
|
||||
# image_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'image/image_{object_name}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
|
||||
# # print(f"upload_png_mask {object_name} = {time.time() - start_time}")
|
||||
# return front_image, image_url, mask_url
|
||||
# except Exception as e:
|
||||
# logging.warning(f"upload_png_mask runtime exception : {e}")
|
||||
#
|
||||
#
|
||||
# @RunTime
|
||||
# def upload_layer_image(image, object_name):
|
||||
# try:
|
||||
# image_data = io.BytesIO()
|
||||
# image.save(image_data, format='PNG')
|
||||
# image_data.seek(0)
|
||||
# image_bytes = image_data.read()
|
||||
# image_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'image/image_{object_name}.png', io.BytesIO(image_bytes), len(image_bytes), content_type='image/png').object_name}"
|
||||
# return image_url
|
||||
# except Exception as e:
|
||||
# logging.warning(f"upload_png_mask runtime exception : {e}")
|
||||
#
|
||||
#
|
||||
# @RunTime
|
||||
# def upload_mask_image(mask, object_name):
|
||||
# try:
|
||||
# mask_inverted = cv2.bitwise_not(mask)
|
||||
# # 将掩模的3通道转换为4通道，白色部分不透明，黑色部分透明
|
||||
# rgba_image = cv2.cvtColor(mask_inverted, cv2.COLOR_BGR2BGRA)
|
||||
# rgba_image[rgba_image[:, :, 0] == 0] = [0, 0, 0, 0]
|
||||
# image_bytes = io.BytesIO()
|
||||
# image_bytes.write(cv2.imencode('.png', rgba_image)[1].tobytes())
|
||||
#
|
||||
# image_bytes.seek(0)
|
||||
# mask_url = f"{AIDA_CLOTHING}/{minio_client.put_object('aida-clothing', f'mask/mask_{object_name}.png', image_bytes, len(image_bytes.getvalue()), content_type='image/png').object_name}"
|
||||
# return mask_url
|
||||
# except Exception as e:
|
||||
# logging.warning(f"upload_png_mask runtime exception : {e}")
|
||||
Reference in New Issue
Block a user