python工具——pixellib

pixellib 可以非常简单的实现图像分割

图像分割分为：

语义分割：将图像中每个像素赋予一个类别标签，用不同的颜色来表示
实例分割：无需对每个像素进行标记，只需要找到感兴趣物体的边缘轮廓

安装需要的库

pip3  install tensorflow
pip3  install pillow
pip3  install opencv-python
pip3  install scikit-image
pip3  install pixellib

语义分隔

步骤：

导入PixelLib模块

创建用于执行语义分割的类实例

调用load_pascalvoc_model()函数加载在Pascal voc上训练的Xception模型

调用segmentAsPascalvoc()函数对图像进行分割，并且分割采用pascalvoc的颜色格式进行

segmentAsPascalvoc()的参数

　　path_to_image：分割的目标图像的路径

　　path_to_output_image：保存分割后输出图像的路径

eg：

image.py

import pixellib
from pixellib.semantic import semantic_segmentation

segment_image = semantic_segmentation()
segment_image.load_pascalvoc_model("deeplabv3_xception_tf_dim_ordering_tf_kernels.h5")
segment_image.segmentAsPascalvoc("test.jpg", output_image_name = "new.jpg")

带有分段叠加层的图像

添加 overlay=True

import pixellib
from pixellib.semantic import semantic_segmentation
segment_image = semantic_segmentation()
segment_image.load_pascalvoc_model("deeplabv3_xception_tf_dim_ordering_tf_kernels.h5")

segment_image.segmentAsPascalvoc("test.jpg", output_image_name = "new1.jpg", overlay = True)

执行分割所需的推理时间

import pixellib
from pixellib.semantic import semantic_segmentation
import time
segment_image = semantic_segmentation()
segment_image.load_pascalvoc_model("deeplabv3_xception_tf_dim_ordering_tf_kernels.h5")
start = time.time()
segment_image.segmentAsPascalvoc("test.jpg", output_image_name = "new1.jpg", overlay = True)
end = time.time()
print(f"Inference Time: {end-start:.2f}seconds")

xception模型下载地址：

https://github.com/bonlime/keras-deeplab-v3-plus/releases/download/1.1/deeplabv3_xception_tf_dim_ordering_tf_kernels.h5

下载后放在image.py所在目录下

实例分割

import pixellib
from pixellib.instance import instance_segmentation
import time
segment_image = instance_segmentation()
segment_image.load_model("mask_rcnn_coco.h5")
start = time.time()
segment_image.segmentImage("22.jpeg", output_image_name = "22new.jpg")
end = time.time()
print(f"Inference Time: {end-start:.2f}seconds")

用边界框(bounding box)来实现分割

import pixellib
from pixellib.instance import instance_segmentation
import time
segment_image = instance_segmentation()
segment_image.load_model("mask_rcnn_coco.h5")
start = time.time()
segment_image.segmentImage("22.jpeg", output_image_name = "22new1.jpg",show_bboxes = True)
end = time.time()
print(f"Inference Time: {end-start:.2f}seconds")

耗时

Tensorflow在Windows下使用踩坑

https://gitee.com/babybeibeili/python-tool/tree/master/image