python实例分割代码

以下是一个使用 Mask R-CNN 进行实例分割的 Python 代码示例：

import os
import sys

import numpy as np
import skimage.draw

ROOT_DIR = os.path.abspath("../../")

# 导入 Mask RCNN
sys.path.append(ROOT_DIR)  
from mrcnn.config import Config
from mrcnn import model as modellib, utils

MODEL_DIR = os.path.join(ROOT_DIR, "logs")

COCO_MODEL_PATH = os.path.join(ROOT_DIR, "mask_rcnn_coco.h5")

class ShapesConfig(Config):
    NAME = "shapes"
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1
    NUM_CLASSES = 1 + 3  # background + 3 shapes
    IMAGE_MIN_DIM = 128
    IMAGE_MAX_DIM = 128
    RPN_ANCHOR_SCALES = (8, 16, 32, 64, 128)

config = ShapesConfig()
config.display()

class ShapesDataset(utils.Dataset):

    def __init__(self):
        super().__init__()

    def load_shapes(self, count, height, width):
        self.add_class("shapes", 1, "square")
        self.add_class("shapes", 2, "circle")
        self.add_class("shapes", 3, "triangle")

        for i in range(count):
            bg_color, shapes = self.random_image(height, width)
            self.add_image("shapes", image_id=i, path=None,
                           width=width, height=height,
                           bg_color=bg_color, shapes=shapes)

    def load_image(self, image_id):
        info = self.image_info[image_id]
        bg_color = info['bg_color']
        image = np.ones([info['height'], info['width'], 3], dtype=np.uint8)
        image = image * bg_color.astype(np.uint8)
        return image

    def image_reference(self, image_id):
        info = self.image_info[image_id]
        if info["source"] == "shapes":
            return info["shapes"]
        else:
            super(self.__class__).image_reference(self, image_id)

    def load_mask(self, image_id):
        info = self.image_info[image_id]
        shapes = info['shapes']
        count = len(shapes)
        mask = np.zeros([info['height'], info['width'], count], dtype=np.uint8)
        for i, (shape, dims) in enumerate(shapes):
            if shape == 'square':
                x, y = dims
                rr, cc = skimage.draw.polygon([y-5, y-5, y+5, y+5], [x-5, x+5, x+5, x-5])
                mask[rr, cc, i] = 1
            elif shape == 'circle':
                x, y = dims
                rr, cc = skimage.draw.circle(x, y, 5)
                mask[rr, cc, i] = 1
            elif shape == "triangle":
                x, y, x1, y1, x2, y2 = dims
                rr, cc = skimage.draw.polygon([x, x1, x2], [y, y1, y2])
                mask[rr, cc, i] = 1

        return mask, np.ones([count], dtype=np.int32)

# 训练集样本数为10，图片宽高为128
dataset_train = ShapesDataset()
dataset_train.load_shapes(10, 128, 128)
dataset_train.prepare()

# 测试集样本数为3，图片宽高为128
dataset_val = ShapesDataset()
dataset_val.load_shapes(3, 128, 128)
dataset_val.prepare()

# 建立模型
model = modellib.MaskRCNN(mode="training", config=config,
                          model_dir=MODEL_DIR)

# 使用 COCO 预训练模型并对其进行训练
model.load_weights(COCO_MODEL_PATH, by_name=True,
                   exclude=["mrcnn_class_logits", "mrcnn_bbox_fc", "mrcnn_bbox", "mrcnn_mask"])

model.train(dataset_train, dataset_val,
            learning_rate=config.LEARNING_RATE,
            epochs=1,
            layers='heads')

具体的代码解释可以参考代码注释，本示例假设已经安装好了 `mrcnn` 库，如果未安装需要先进行安装。