图像分割有何实际意义_图卷积神经网络和图神经网络区别

Tags: ComputerVision

编译

src/caffe/layers/contrastive_loss_layer.cpp:56:30: error: no matching function for call to ‘max(double, float)’
Dtype dist = std::max(margin – sqrt(dist_sq_.cpu_data()[i]), Dtype(0.0));

Replace line 56 by this one :
Dtype dist = std::max(margin – (float)sqrt(dist_sq_.cpu_data()[i]), Dtype(0.0));

.build_release/lib/libcaffe.so: undefined reference to `cv::imread(cv::String const&, int)’

Change Makefile:
LIBRARIES += glog gflags protobuf leveldb snappy
lmdb boost_system hdf5_hl hdf5 m
opencv_core opencv_highgui opencv_imgproc
add :opencv_imgcodecs

数据处理

median frequency balancing的计算
图片分割经常会遇到class unbalance的情况，如果你的target是要求每个类别的accuracy 都很高那么在训练的时候做class balancing 很重要，如果你的target要求只要求图片总体的pixel accuracy好，那么class balancing 此时就不是很重要，因为占比小的class, accuray 虽然小，但是对总体的Pixel accuracy影响也较小。
那么看下本文中的meidan frequency balancing是如何计算的：
对于一个多类别图片数据库，每个类别都会有一个class frequency, 该类别像素数目除以数据库总像素数目, 求出所有class frequency 的median 值，除以该类别对应的frequency 得到weight：

weighti=median(weights)/weightiweighti=median(weights)/weighti

这样可以保证占比小的class, 权重大于1，占比大的class，权重小于1，达到balancing的效果.
如对我自己的数据有两类分别为0,1，一共55张500500训练图片，统计55张图片中0,1像素的个数：
count1 227611
count0 13522389
freq1 = 227611/(50050055) = 0.0166
freq0 = 13522389/(500500*55) = 0.9834
median = 0.5
weight1 = 30.12
weight0 = 0.508
webdemo权重
作者训练的webdemo和他给出的模型文件的类别数目和label 是对不上号的，因此可以使用webdemo跑测试，但是最好不要在上面finetune, 直接在VGG-16上面finetune 就行
rgb label 转换为 gray label

一些数据集给出的label是rgb的，如下图，但是训练过程中输入网络的label一般是0 – class_num-1标记的label map, 因此需要一个转换过程，下面给出一个python2转换脚本：

#!/usr/bin/env python
import os
import numpy as np
from itertools import izip
from argparse import ArgumentParser
from collections import OrderedDict
from skimage.io import ImageCollection, imsave
from skimage.transform import resize


camvid_colors = OrderedDict([
    ("Animal", np.array([64, 128, 64], dtype=np.uint8)),
    ("Archway", np.array([192, 0, 128], dtype=np.uint8)),
    ("Bicyclist", np.array([0, 128, 192], dtype=np.uint8)),
    ("Bridge", np.array([0, 128, 64], dtype=np.uint8)),
    ("Building", np.array([128, 0, 0], dtype=np.uint8)),
    ("Car", np.array([64, 0, 128], dtype=np.uint8)),
    ("CartLuggagePram", np.array([64, 0, 192], dtype=np.uint8)),
    ("Child", np.array([192, 128, 64], dtype=np.uint8)),
    ("Column_Pole", np.array([192, 192, 128], dtype=np.uint8)),
    ("Fence", np.array([64, 64, 128], dtype=np.uint8)),
    ("LaneMkgsDriv", np.array([128, 0, 192], dtype=np.uint8)),
    ("LaneMkgsNonDriv", np.array([192, 0, 64], dtype=np.uint8)),
    ("Misc_Text", np.array([128, 128, 64], dtype=np.uint8)),
    ("MotorcycleScooter", np.array([192, 0, 192], dtype=np.uint8)),
    ("OtherMoving", np.array([128, 64, 64], dtype=np.uint8)),
    ("ParkingBlock", np.array([64, 192, 128], dtype=np.uint8)),
    ("Pedestrian", np.array([64, 64, 0], dtype=np.uint8)),
    ("Road", np.array([128, 64, 128], dtype=np.uint8)),
    ("RoadShoulder", np.array([128, 128, 192], dtype=np.uint8)),
    ("Sidewalk", np.array([0, 0, 192], dtype=np.uint8)),
    ("SignSymbol", np.array([192, 128, 128], dtype=np.uint8)),
    ("Sky", np.array([128, 128, 128], dtype=np.uint8)),
    ("SUVPickupTruck", np.array([64, 128, 192], dtype=np.uint8)),
    ("TrafficCone", np.array([0, 0, 64], dtype=np.uint8)),
    ("TrafficLight", np.array([0, 64, 64], dtype=np.uint8)),
    ("Train", np.array([192, 64, 128], dtype=np.uint8)),
    ("Tree", np.array([128, 128, 0], dtype=np.uint8)),
    ("Truck_Bus", np.array([192, 128, 192], dtype=np.uint8)),
    ("Tunnel", np.array([64, 0, 64], dtype=np.uint8)),
    ("VegetationMisc", np.array([192, 192, 0], dtype=np.uint8)),
    ("Wall", np.array([64, 192, 0], dtype=np.uint8)),
    ("Void", np.array([0, 0, 0], dtype=np.uint8))
])


def convert_label_to_grayscale(im):
    out = (np.ones(im.shape[:2]) * 255).astype(np.uint8)
    for gray_val, (label, rgb) in enumerate(camvid_colors.items()):
        match_pxls = np.where((im == np.asarray(rgb)).sum(-1) == 3)
        out[match_pxls] = gray_val
    assert (out != 255).all(), "rounding errors or missing classes in camvid_colors"
    return out.astype(np.uint8)


def make_parser():
    parser = ArgumentParser()
    parser.add_argument(
        'label_dir',
        help="Directory containing all RGB camvid label images as PNGs"
    )
    parser.add_argument(
        'out_dir',
        help="""Directory to save grayscale label images.
        Output images have same basename as inputs so be careful not to
        overwrite original RGB labels""")
    return parser


if __name__ == '__main__':
    parser = make_parser()
    args = parser.parse_args()
    labs = ImageCollection(os.path.join(args.label_dir, "*"))
    os.makedirs(args.out_dir)
    for i, (inpath, im) in enumerate(izip(labs.files, labs)):
        print(i + 1, "of", len(labs))
        # resize to caffe-segnet input size and preserve label values
        resized_im = (resize(im, (360, 480), order=0) * 255).astype(np.uint8)
        out = convert_label_to_grayscale(resized_im)
        outpath = os.path.join(args.out_dir, os.path.basename(inpath))
        imsave(outpath, out)