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115 lines
4.4 KiB
Python
115 lines
4.4 KiB
Python
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.core.config import settings
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from app.schemas.mannequin_edit import MannequinModel
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from app.service.utils.new_oss_client import oss_get_image, oss_upload_image
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minio_client = Minio(settings.MINIO_URL, access_key=settings.MINIO_ACCESS, secret_key=settings.MINIO_SECRET, secure=settings.MINIO_SECURE)
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class MannequinEditService:
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def __init__(self, request_data):
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self.resize_pixel = request_data.resize_pixel
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self.top = request_data.top
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self.bottom = request_data.bottom
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self.image = oss_get_image(oss_client=minio_client, bucket=request_data.mannequins.split('/')[0], object_name=request_data.mannequins[request_data.mannequins.find('/') + 1:], data_type="cv2")
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self.mannequin_name = request_data.mannequin_name
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self.bucket_name = request_data.bucket_name
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if self.image.shape[2] == 4:
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self.bgr = self.image[:, :, :3]
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self.alpha = self.image[:, :, 3]
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self.bgr = cv2.bitwise_and(self.bgr, self.bgr, mask=cv2.normalize(self.alpha, None, 0, 1, cv2.NORM_MINMAX))
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self.h, self.w, _ = self.bgr.shape
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else:
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self.bgr = self.image
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self.h, self.w, _ = self.bgr.shape
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self.alpha = None
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def __call__(self, *args, **kwargs):
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new_mannequin = self.resize_leg(self.top, self.bottom)
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_, encoded_image = cv2.imencode('.png', new_mannequin)
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image_bytes = encoded_image.tobytes()
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req = oss_upload_image(oss_client=minio_client, bucket=self.bucket_name, object_name=f"{self.mannequin_name}.png", image_bytes=image_bytes)
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return req.bucket_name + "/" + req.object_name
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def post_processing(self, image):
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# 原始图片的尺寸
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original_width, original_height = image.size
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# 计算宽度和高度的缩放比例
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width_ratio = self.w / original_width
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height_ratio = self.h / original_height
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# 选择较小的缩放比例，确保图片能完整放入目标图片中
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scale_ratio = min(width_ratio, height_ratio)
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# 计算调整后的尺寸
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new_width = int(original_width * scale_ratio)
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new_height = int(original_height * scale_ratio)
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# 调整图片大小
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resized_image = image.resize((new_width, new_height))
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# 创建一个 512x768 的透明图片
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result_image = Image.new("RGBA", (self.w, self.h), (255, 255, 255, 0))
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# 计算需要粘贴的位置，使图片居中
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x_offset = (self.w - new_width) // 2
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y_offset = (self.h - new_height) // 2
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# 将调整大小后的图片粘贴到透明图片上
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if resized_image.mode == "RGBA":
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result_image.paste(resized_image, (x_offset, y_offset), mask=resized_image.split()[3])
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else:
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result_image.paste(resized_image, (x_offset, y_offset))
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image = np.array(result_image)
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return image
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def resize_leg(self, top, bottom):
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# 上部
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top_part = self.bgr[:top, :]
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top_part_alpha = self.alpha[:top, :]
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# 需要resize 部分
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part_resize = self.bgr[top:bottom, :]
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part_resize_alpha = self.alpha[top:bottom, :]
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# 下部
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part_bottom = self.bgr[bottom:, :]
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part_bottom_alpha = self.alpha[bottom:, :]
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new_height = int((bottom - top) + self.resize_pixel)
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resized_thigh = cv2.resize(part_resize, (self.w, new_height), interpolation=cv2.INTER_LINEAR)
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resized_thigh_alpha = cv2.resize(part_resize_alpha, (self.w, new_height), interpolation=cv2.INTER_LINEAR)
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# 组合
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new_bgr = np.vstack((top_part, resized_thigh, part_bottom))
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new_bgr_alpha = np.vstack((top_part_alpha, resized_thigh_alpha, part_bottom_alpha))
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if self.alpha is not None:
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# 拼接 alpha 通道
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# 合并 BGR 通道和 alpha 通道
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new_image = np.dstack((new_bgr, new_bgr_alpha))
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else:
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new_image = new_bgr
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new_image = self.post_processing(Image.fromarray(new_image))
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return new_image
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if __name__ == '__main__':
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request_data = MannequinModel(
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mannequins="aida-sys-image/models/male/dc36ce58-46c3-4b6f-8787-5ca7d6fc26e6.png",
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resize_pixel=-100,
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bucket_name="test",
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mannequin_name="mannequin_name",
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top=270,
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bottom=432
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)
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service = MannequinEditService(request_data)
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print(service())
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