95 lines
4.0 KiB
Python
95 lines
4.0 KiB
Python
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 torch
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import torch.nn.functional as F
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import tritonclient.http as httpclient
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from app.core.config import DESIGN_MODEL_URL
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from app.schemas.image2sketch import Image2SketchModel
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from app.service.utils.oss_client import oss_get_image, oss_upload_image
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logger = logging.getLogger()
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class LineArtService:
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def __init__(self, request_item):
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self.line_style = int(request_item.default_style)
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self.image_url = request_item.image_url
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self.sketch_bucket = request_item.sketch_bucket
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self.sketch_name = request_item.sketch_name
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self.weights = [(0.7, 0.3), (0.5, 0.5), (0.3, 0.7), (0.1, 0.9), (0, 1)]
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def get_result(self):
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client = httpclient.InferenceServerClient(url=DESIGN_MODEL_URL)
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input_image = self.get_image()
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input_img, ori_shape = self.line_art_preprocess(input_image)
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transformed_img = input_img.astype(np.float32)
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inputs = [httpclient.InferInput(f"input__0", 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__0", binary_data=True)]
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results = client.infer(model_name=f"lineart", inputs=inputs, outputs=outputs)
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inference_output1 = results.as_numpy("output__0")
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line_art_result = self.line_art_postprocess(inference_output1, ori_shape)
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line_art_result = (line_art_result[0] * 255.0).round().astype(np.uint8)
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if self.line_style != 0:
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logger.info(self.line_style)
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kernel = np.ones((3, 3), np.uint8)
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dilated = cv2.erode(line_art_result, kernel, iterations=1)
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# 将原图与膨胀后的图像进行混合，使用不同的权重
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line_art_result = cv2.addWeighted(line_art_result, self.weights[self.line_style][0], dilated, self.weights[self.line_style][1], 0)
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# cv2.imshow("", line_art_result)
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# cv2.waitKey(0)
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return self.put_image(line_art_result)
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def get_image(self):
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image = oss_get_image(bucket=self.image_url.split('/')[0], object_name=self.image_url[self.image_url.find('/') + 1:], data_type="cv2")
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return image
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def put_image(self, image):
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try:
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image_bytes = cv2.imencode('.jpg', image)[1].tobytes()
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oss_upload_image(bucket=self.sketch_bucket, object_name=f"{self.sketch_name}.jpg", image_bytes=image_bytes)
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return f"{self.sketch_bucket}/{self.sketch_name}.jpg"
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except Exception as e:
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logger.warning(e)
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@staticmethod
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def line_art_preprocess(image):
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img = mmcv.imread(image)
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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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# 如果图片size任意一边 大于 1024， 则会resize 成1024
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if ori_shape != (img_scale_w, img_scale_h):
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# 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
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img = cv2.resize(img, (img_scale_h, img_scale_w))
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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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@staticmethod
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def line_art_postprocess(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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if __name__ == '__main__':
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request_item = Image2SketchModel(
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image_url="aida-users/89/relight_image/d5f0d967-f8e8-424d-98f9-a8ad8313deec-0-89.png",
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default_style="4",
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sketch_bucket="test",
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sketch_name="test123.jpg"
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)
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service = LineArtService(request_item)
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result_url = service.get_result()
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print(result_url)
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