如果我想加一个图片映射，根据类别不同划分不同颜色的边界框，例：如果是孔洞，希望能画一个红色的框，如果是裂痕，希望能画一个绿色的框。请在如下代码中进行添加，要求整个完整rt cv2 import numpy as np from ultralytics import YOLO from PIL import Image def split_image(image, tile_size=640): """ 将大图分割为指定尺寸的切片 Args: image: 输入图像(numpy数组) tile_size: 切片尺寸，默认640x640 Returns: tiles: 切片列表，每个元素为(image_tile, x_offset, y_offset) """ height, width = image.shape[:2] tiles = [] # 遍历图像网格 for y in range(0, height, tile_size): for x in range(0, width, tile_size): # 计算当前切片坐标 y1 = y y2 = min(y + tile_size, height) x1 = x x2 = min(x + tile_size, width) # 提取切片并记录偏移量 tile = image[y1:y2, x1:x2] tiles.append((tile, x1, y1)) return tiles # 加载YOLOv8分割模型 model = YOLO('yolov8n-seg.pt') # 请确认模型路径正确 # 读取输入图像 image = cv2.imread('large_image.jpg') # 替换为你的图像路径 original_image = image.copy() # 存储所有检测结果 all_boxes = [] all_scores = [] all_class_ids = [] all_masks = [] # 分割图像为640x640切片 tiles = split_image(image) for tile, x_offset, y_offset in tiles: # 转换为RGB格式 tile_rgb = cv2.cvtColor(tile, cv2.COLOR_BGR2RGB) # 执行推理 results = model(tile_rgb) # 解析当前切片结果 for result in results: if result.masks is not None: # 获取检测结果 boxes = result.boxes.xyxy.cpu().numpy() scores = result.boxes.conf.cpu().numpy() class_ids = result.boxes.cls.cpu().numpy().astype(int) masks = result.masks.data.cpu().numpy() # 调整坐标到原始图像坐标系 boxes[:, [0, 2]] += x_offset boxes[:, [1, 3]] += y_offset # 调整掩码坐标 for mask in masks: # 创建与原图大小相同的全零矩阵 full_mask = np.zeros(original_image.shape[:2], dtype=np.uint8) # 将切片区域的掩码放入正确位置 full_mask[y_offset:y_offset+tile.shape[0], x_off