AiDA_Python/app/service/lineart/service.py

import logging

import cv2
import mmcv
import numpy as np
import torch
import torch.nn.functional as F
import tritonclient.http as httpclient
from minio import Minio
from app.core.config import settings
from app.core.config import DESIGN_MODEL_URL
from app.schemas.image2sketch import Image2SketchModel
from app.service.utils.new_oss_client import oss_get_image, oss_upload_image

logger = logging.getLogger()
minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)


class LineArtService:
    def __init__(self, request_item):
        self.line_style = int(request_item.default_style)
        self.image_url = request_item.image_url
        self.sketch_bucket = request_item.sketch_bucket
        self.sketch_name = request_item.sketch_name
        self.weights = [(0.7, 0.3), (0.5, 0.5), (0.3, 0.7), (0.1, 0.9), (0, 1)]

    def get_result(self):
        client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL)
        input_image = self.get_image()
        input_img, ori_shape = self.line_art_preprocess(input_image)
        transformed_img = input_img.astype(np.float32)

        inputs = [httpclient.InferInput(f"input__0", transformed_img.shape, datatype="FP32")]
        inputs[0].set_data_from_numpy(transformed_img, binary_data=True)
        outputs = [httpclient.InferRequestedOutput(f"output__0", binary_data=True)]
        results = client.infer(model_name=f"lineart", inputs=inputs, outputs=outputs)
        inference_output1 = results.as_numpy("output__0")
        line_art_result = self.line_art_postprocess(inference_output1, ori_shape)

        line_art_result = (line_art_result[0] * 255.0).round().astype(np.uint8)
        if self.line_style != 0:
            logger.info(self.line_style)
            kernel = np.ones((3, 3), np.uint8)
            dilated = cv2.erode(line_art_result, kernel, iterations=1)
            # 将原图与膨胀后的图像进行混合，使用不同的权重
            line_art_result = cv2.addWeighted(line_art_result, self.weights[self.line_style][0], dilated, self.weights[self.line_style][1], 0)
        # cv2.imshow("", line_art_result)
        # cv2.waitKey(0)
        return self.put_image(line_art_result)

    def get_image(self):
        image = oss_get_image(bucket=self.image_url.split('/')[0], object_name=self.image_url[self.image_url.find('/') + 1:], data_type="cv2")
        # 将其转换为彩色图像
        if len(image.shape) == 3 and image.shape[2] == 4:
            image = cv2.cvtColor(image, cv2.COLOR_BGRA2BGR)
        elif len(image.shape) == 2:
            image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
        return image

    def put_image(self, image):
        try:
            image_bytes = cv2.imencode('.jpg', image)[1].tobytes()
            oss_upload_image(oss_client=minio_client, bucket=self.sketch_bucket, object_name=f"{self.sketch_name}.jpg", image_bytes=image_bytes)
            return f"{self.sketch_bucket}/{self.sketch_name}.jpg"
        except Exception as e:
            logger.warning(e)

    @staticmethod
    def line_art_preprocess(image):
        img = mmcv.imread(image)
        ori_shape = img.shape[:2]
        img_scale_w, img_scale_h = ori_shape
        if ori_shape[0] > 1024:
            img_scale_w = 1024
        if ori_shape[1] > 1024:
            img_scale_h = 1024
        # 如果图片size任意一边 大于 1024， 则会resize 成1024
        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)
        preprocessed_img = np.expand_dims(img.transpose(2, 0, 1), axis=0)
        return preprocessed_img, ori_shape

    @staticmethod
    def line_art_postprocess(output, ori_shape):
        seg_logit = F.interpolate(torch.tensor(output).float(), size=ori_shape, scale_factor=None, mode='bilinear', align_corners=False)
        seg_pred = seg_logit.cpu().numpy()
        return seg_pred[0]


if __name__ == '__main__':
    request_item = Image2SketchModel(
        image_url="aida-collection-element/87/Sketchboard/555a443f-fd6b-4cd7-8147-b92d55513af0.png",
        default_style="4",
        sketch_bucket="test",
        sketch_name="test123"
    )
    service = LineArtService(request_item)
    result_url = service.get_result()
    print(result_url)