From 32d7baa2061103a2477576b6299b8018d7767b4b Mon Sep 17 00:00:00 2001 From: FlyingQianMM <245467267@qq.com> Date: Mon, 17 Aug 2020 22:41:30 +0800 Subject: [PATCH] add remote_sensing --- docs/apis/models/semantic_segmentation.md | 46 +++++++++ docs/examples/index.rst | 1 + docs/examples/remote_sensing.md | 76 ++++++++++++++ docs/examples/remote_sensing/index.rst | 5 - examples/remote_sensing/README.md | 82 +++++++++++++++ examples/remote_sensing/eval.py | 50 +++++++++ .../remote_sensing/images/overlap_tile.png | Bin 0 -> 443023 bytes .../images/visualize_compare.png | Bin 0 -> 955080 bytes examples/remote_sensing/predict.py | 30 ++++++ examples/remote_sensing/prepara_data.py | 95 ++++++++++++++++++ examples/remote_sensing/train.py | 55 ++++++++++ paddlex/cv/models/deeplabv3p.py | 59 ++++++++++- 12 files changed, 489 insertions(+), 10 deletions(-) create mode 100644 docs/examples/remote_sensing.md delete mode 100755 docs/examples/remote_sensing/index.rst create mode 100644 examples/remote_sensing/README.md create mode 100644 examples/remote_sensing/eval.py create mode 100644 examples/remote_sensing/images/overlap_tile.png create mode 100644 examples/remote_sensing/images/visualize_compare.png create mode 100644 examples/remote_sensing/predict.py create mode 100644 examples/remote_sensing/prepara_data.py create mode 100644 examples/remote_sensing/train.py diff --git a/docs/apis/models/semantic_segmentation.md b/docs/apis/models/semantic_segmentation.md index 82b758d..a78cffb 100755 --- a/docs/apis/models/semantic_segmentation.md +++ b/docs/apis/models/semantic_segmentation.md @@ -112,6 +112,46 @@ batch_predict(self, img_file_list, transforms=None, thread_num=2): +### tile_predict + +> DeepLabv3p模型无重叠的大图切小图预测接口。将大图像切分成互不重叠多个小块,分别对每个小块进行预测,最后将小块预测结果拼接成大图预测结果。由于每个小块边缘部分的预测效果会比中间部分的差,因此每个小块拼接处可能会有明显的裂痕感。 + +> 需要注意的是,只有在训练过程中定义了eval_dataset,模型在保存时才会将预测时的图像处理流程保存在`DeepLabv3p.test_transforms`和`DeepLabv3p.eval_transforms`中。如未在训练时定义eval_dataset,那在调用预测`tile_predict`接口时,用户需要再重新定义test_transforms传入给`tile_predict`接口。 + +> **参数** +> > +> > - **img_file** (str|np.ndarray): 预测图像路径或numpy数组(HWC排列,BGR格式)。 +> > - **tile_size** (list|tuple): 切分小块的大小,格式为(W,H)。默认值为[512, 512]。 +> > - **batch_size** (int):对小块进行批量预测时的批量大小。默认值为32。 +> > - **thread_num** (int): 并发执行各小块预处理时的线程数。默认值为8。 +> > - **transforms** (paddlex.seg.transforms): 数据预处理操作。 + +> **返回值** +> > +> > - **dict**: 包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图,像素值表示对应的类别,'score_map'存储各类别的概率,shape=(h, w, num_classes)。 + +### overlap_tile_predict + +> DeepLabv3p模型有重叠的大图切小图预测接口。Unet论文作者提出一种有重叠的大图切小图策略(Overlap-tile strategy)来消除拼接处的裂痕感。每次划分小块时向四周扩展面积,例如下图中的蓝色部分区域,到拼接大图时取小块中间部分的预测结果,例如下图中的黄色部分区域,对于处于原始图像边缘处的小块,其扩展面积下的像素则通过将边缘部分像素镜像填补得到。 + +![](../../../examples/remote_sensing/images/overlap_tile.png) + +> 需要注意的是,只有在训练过程中定义了eval_dataset,模型在保存时才会将预测时的图像处理流程保存在`DeepLabv3p.test_transforms`和`DeepLabv3p.eval_transforms`中。如未在训练时定义eval_dataset,那在调用预测`overlap_tile_predict`接口时,用户需要再重新定义test_transforms传入给`overlap_tile_predict`接口。 + +> **参数** +> > +> > - **img_file** (str|np.ndarray): 预测图像路径或numpy数组(HWC排列,BGR格式)。 +> > - **tile_size** (list|tuple): 切分小块中间部分用于拼接预测结果的大小,格式为(W,H)。默认值为[512, 512]。 +> > - **pad_size** (list|tuple): 切分小块向四周扩展的大小,格式为(W,H)。 +> > - **batch_size** (int):对小块进行批量预测时的批量大小。默认值为32。 +> > - **thread_num** (int): 并发执行各小块预处理时的线程数。默认值为8。 +> > - **transforms** (paddlex.seg.transforms): 数据预处理操作。 + +> **返回值** +> > +> > - **dict**: 包含关键字'label_map'和'score_map', 'label_map'存储预测结果灰度图,像素值表示对应的类别,'score_map'存储各类别的概率,shape=(h, w, num_classes)。 + + ## paddlex.seg.UNet ```python @@ -133,6 +173,8 @@ paddlex.seg.UNet(num_classes=2, upsample_mode='bilinear', use_bce_loss=False, us > - evaluate 评估接口说明同 [DeepLabv3p模型evaluate接口](#evaluate) > - predict 预测接口说明同 [DeepLabv3p模型predict接口](#predict) > - batch_predict 批量预测接口说明同 [DeepLabv3p模型predict接口](#batch-predict) +> - tile_predict 无重叠的大图切小图预测接口同 [DeepLabv3p模型tile_predict接口](#tile-predict) +> - overlap_tile_predict 有重叠的大图切小图预测接口同 [DeepLabv3p模型poverlap_tile_predict接口](#overlap-tile-predict) ## paddlex.seg.HRNet @@ -155,6 +197,8 @@ paddlex.seg.HRNet(num_classes=2, width=18, use_bce_loss=False, use_dice_loss=Fal > - evaluate 评估接口说明同 [DeepLabv3p模型evaluate接口](#evaluate) > - predict 预测接口说明同 [DeepLabv3p模型predict接口](#predict) > - batch_predict 批量预测接口说明同 [DeepLabv3p模型predict接口](#batch-predict) +> - tile_predict 无重叠的大图切小图预测接口同 [DeepLabv3p模型tile_predict接口](#tile-predict) +> - overlap_tile_predict 有重叠的大图切小图预测接口同 [DeepLabv3p模型poverlap_tile_predict接口](#overlap-tile-predict) ## paddlex.seg.FastSCNN @@ -177,3 +221,5 @@ paddlex.seg.FastSCNN(num_classes=2, use_bce_loss=False, use_dice_loss=False, cla > - evaluate 评估接口说明同 [DeepLabv3p模型evaluate接口](#evaluate) > - predict 预测接口说明同 [DeepLabv3p模型predict接口](#predict) > - batch_predict 批量预测接口说明同 [DeepLabv3p模型predict接口](#batch-predict) +> - tile_predict 无重叠的大图切小图预测接口同 [DeepLabv3p模型tile_predict接口](#tile-predict) +> - overlap_tile_predict 有重叠的大图切小图预测接口同 [DeepLabv3p模型poverlap_tile_predict接口](#overlap-tile-predict) diff --git a/docs/examples/index.rst b/docs/examples/index.rst index 0d660c9..8001afe 100755 --- a/docs/examples/index.rst +++ b/docs/examples/index.rst @@ -12,3 +12,4 @@ PaddleX精选飞桨视觉开发套件在产业实践中的成熟模型结构, solutions.md meter_reader.md human_segmentation.md + remote_sensing.md diff --git a/docs/examples/remote_sensing.md b/docs/examples/remote_sensing.md new file mode 100644 index 0000000..f99a3b6 --- /dev/null +++ b/docs/examples/remote_sensing.md @@ -0,0 +1,76 @@ +# 遥感影像分割 + +本案例基于PaddleX实现遥感影像分割,提供无重叠的大图切小图以及有重叠的大图切小图两种预测方式。 + +## 前置依赖 + +* Paddle paddle >= 1.8.4 +* Python >= 3.5 +* PaddleX >= 1.1.0 + +安装的相关问题参考[PaddleX安装](../install.md) + +下载PaddleX源码: + +``` +git clone https://github.com/PaddlePaddle/PaddleX +``` + +该案例所有脚本均位于`PaddleX/examples/remote_sensing/`,进入该目录: + +``` +cd PaddleX/examples/remote_sensing/ +``` + +## 数据准备 + +本案例使用2015 CCF大数据比赛提供的高清遥感影像,包含5张带标注的RGB图像,图像尺寸最大有7969 × 7939、最小有4011 × 2470。该数据集共标注了5类物体,分别是背景(标记为0)、植被(标记为1)、建筑(标记为2)、水体(标记为3)、道路 (标记为4)。 + +本案例将前4张图片划分入训练集,第5张图片作为验证集。为增加训练时的批量大小,以滑动窗口为(1024,1024)、步长为(512, 512)对前4张图片进行切分,加上原本的4张大尺寸图片,训练集一共有688张图片。直接对大图片进行验证会导致显存不足,为避免此类问题的出现,针对验证集,以滑动窗口为(769, 769)、步长为(769,769)对第5张图片进行切分,得到40张子图片。 + +运行以下脚本,下载原始数据集,并完成数据集的切分: + +``` +python3 prepare_data.py +``` + +## 模型训练 + +分割模型选择Backbone为MobileNetv3_large_ssld的Deeplabv3模型,该模型兼备高性能高精度的优点。运行以下脚本,进行模型训练: +``` +python3 train.py +``` + +## 模型预测 + +直接对大图片进行预测会导致显存不足,为避免此类问题的出现,本案例提供了两种预测方式:无重叠的大图切小图和有重叠的大图切小图。 + +* 无重叠的大图切小图 + +将大图像切分成互不重叠多个小块,分别对每个小块进行预测,最后将小块预测结果拼接成大图预测结果。由于每个小块边缘部分的预测效果会比中间部分的差,因此每个小块拼接处可能会有明显的裂痕感。 + +该预测方式的API接口详见[tile_predict](../apis/models/semantic_segmentation.html#tile-predict)。 + +* 有重叠的大图切小图 + +Unet论文作者提出一种有重叠的大图切小图策略(Overlap-tile strategy)来消除拼接处的裂痕感。每次划分小块时向四周扩展面积,例如下图中的蓝色部分区域,到拼接大图时取小块中间部分的预测结果,例如下图中的黄色部分区域,对于处于原始图像边缘处的小块,其扩展面积下的像素则通过将边缘部分像素镜像填补得到。 + +该预测方式的API接口说明详见[overlap_tile_predict](../apis/models/semantic_segmentation.html#overlap-tile-predict)。 + +![](../../examples/remote_sensing/images/overlap_tile.png) + +相比无重叠的大图切小图,有重叠的大图切小图策略将本案例的模型精度miou从80.58%提升至81.52%,并且将预测可视化结果中裂痕感显著消除,可见下图中两种预测方式的效果对比。 + +![](../../examples/remote_sensing/images/visualize_compare.png) + +运行以下脚本使用有重叠的大图切小图预测方式进行预测。如需使用无重叠的大图切小图的预测方式,参考以下脚本中的注释修改模型预测接口: +``` +python3 predict.py +``` + +## 模型评估 + +在训练过程中,每隔10个迭代轮数会评估一次模型在验证集的精度。由于已事先将原始大尺寸图片切分成小块,此时相当于使用无重叠的大图切小图预测方式,最优模型精度miou为80.58%。运行以下脚本,将采用有重叠的大图切小图的预测方式,重新评估原始大尺寸图片的模型精度,此时miou为81.52%。 +``` +python3 eval.py +``` diff --git a/docs/examples/remote_sensing/index.rst b/docs/examples/remote_sensing/index.rst deleted file mode 100755 index dc37565..0000000 --- a/docs/examples/remote_sensing/index.rst +++ /dev/null @@ -1,5 +0,0 @@ -遥感分割案例 -======================================= - - -这里面写遥感分割案例,可根据需求拆分为多个文档 diff --git a/examples/remote_sensing/README.md b/examples/remote_sensing/README.md new file mode 100644 index 0000000..d6b00d0 --- /dev/null +++ b/examples/remote_sensing/README.md @@ -0,0 +1,82 @@ +# 遥感影像分割 + +本案例基于PaddleX实现遥感影像分割,提供无重叠的大图切小图以及有重叠的大图切小图两种预测方式。 + +## 目录 +* [数据准备](#1) +* [模型训练](#2) +* [模型预测](#3) +* [模型评估](#4) + +#### 前置依赖 + +* Paddle paddle >= 1.8.4 +* Python >= 3.5 +* PaddleX >= 1.1.0 + +安装的相关问题参考[PaddleX安装](../install.md) + +下载PaddleX源码: + +``` +git clone https://github.com/PaddlePaddle/PaddleX +``` + +该案例所有脚本均位于`PaddleX/examples/remote_sensing/`,进入该目录: + +``` +cd PaddleX/examples/remote_sensing/ +``` + +##

数据准备

+ +本案例使用2015 CCF大数据比赛提供的高清遥感影像,包含5张带标注的RGB图像,图像尺寸最大有7969 × 7939、最小有4011 × 2470。该数据集共标注了5类物体,分别是背景(标记为0)、植被(标记为1)、建筑(标记为2)、水体(标记为3)、道路 (标记为4)。 + +本案例将前4张图片划分入训练集,第5张图片作为验证集。为增加训练时的批量大小,以滑动窗口为(1024,1024)、步长为(512, 512)对前4张图片进行切分,加上原本的4张大尺寸图片,训练集一共有688张图片。直接对大图片进行验证会导致显存不足,为避免此类问题的出现,针对验证集,以滑动窗口为(769, 769)、步长为(769,769)对第5张图片进行切分,得到40张子图片。 + +运行以下脚本,下载原始数据集,并完成数据集的切分: + +``` +python3 prepare_data.py +``` + +##

模型训练

+ +分割模型选择Backbone为MobileNetv3_large_ssld的Deeplabv3模型,该模型兼备高性能高精度的优点。运行以下脚本,进行模型训练: +``` +python3 train.py +``` + +##

模型预测

+ +直接对大图片进行预测会导致显存不足,为避免此类问题的出现,本案例提供了两种预测方式:无重叠的大图切小图和有重叠的大图切小图。 + +* 无重叠的大图切小图 + +将大图像切分成互不重叠多个小块,分别对每个小块进行预测,最后将小块预测结果拼接成大图预测结果。由于每个小块边缘部分的预测效果会比中间部分的差,因此每个小块拼接处可能会有明显的裂痕感。 + +该预测方式的API接口详见[tile_predict](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#tile-predict)。 + +* 有重叠的大图切小图 + +Unet论文作者提出一种有重叠的大图切小图策略(Overlap-tile strategy)来消除拼接处的裂痕感。每次划分小块时向四周扩展面积,例如下图中的蓝色部分区域,到拼接大图时取小块中间部分的预测结果,例如下图中的黄色部分区域,对于处于原始图像边缘处的小块,其扩展面积下的像素则通过将边缘部分像素镜像填补得到。 + +该预测方式的API接口说明详见[overlap_tile_predict](https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict)。 + +![](images/overlap_tile.png) + +相比无重叠的大图切小图,有重叠的大图切小图策略将本案例的模型精度miou从80.58%提升至81.52%,并且将预测可视化结果中裂痕感显著消除,可见下图中两种预测方式的效果对比。 + +![](images/visualize_compare.png) + +运行以下脚本使用有重叠的大图切小图预测方式进行预测。如需使用无重叠的大图切小图的预测方式,参考以下脚本中的注释修改模型预测接口: +``` +python3 predict.py +``` + +##

模型评估

+ +在训练过程中,每隔10个迭代轮数会评估一次模型在验证集的精度。由于已事先将原始大尺寸图片切分成小块,此时相当于使用无重叠的大图切小图预测方式,最优模型精度miou为80.58%。运行以下脚本,将采用有重叠的大图切小图的预测方式,重新评估原始大尺寸图片的模型精度,此时miou为81.52%。 +``` +python3 eval.py +``` diff --git a/examples/remote_sensing/eval.py b/examples/remote_sensing/eval.py new file mode 100644 index 0000000..98e74e4 --- /dev/null +++ b/examples/remote_sensing/eval.py @@ -0,0 +1,50 @@ +# 环境变量配置,用于控制是否使用GPU +# 说明文档:https://paddlex.readthedocs.io/zh_CN/develop/appendix/parameters.html#gpu +import os +os.environ['CUDA_VISIBLE_DEVICES'] = '0' +import numpy as np +import cv2 +from PIL import Image +from collections import OrderedDict + +import paddlex as pdx +import paddlex.utils.logging as logging +from paddlex.cv.models.utils.seg_eval import ConfusionMatrix + +# 导入模型参数 +model = pdx.load_model('output/deeplabv3p_mobilenetv3_large_ssld/best_model') + +# 指定待评估图像路径及其标注文件路径 +img_file = "dataset/JPEGImages/5.png" +label_file = "dataset/Annotations/5_class.png" + +# 定义用于计算miou、iou、macc、acc、kapp指标的混淆矩阵类 +conf_mat = ConfusionMatrix(model.num_classes, streaming=True) + +# 使用"无重叠的大图切小图"方式进行预测:将大图像切分成互不重叠多个小块,分别对每个小块进行预测 +# 最后将小块预测结果拼接成大图预测结果 +# API说明:https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#tile-predict +# tile_predict = model.tile_predict(img_file=img_file, tile_size=(769, 769)) +# pred = tile_predict["label_map"] + +# 使用"有重叠的大图切小图"策略进行预测:将大图像切分成相互重叠的多个小块, +# 分别对每个小块进行预测,将小块预测结果的中间部分拼接成大图预测结果 +# API说明:https://paddlex.readthedocs.io/zh_CN/develop/apis/models/semantic_segmentation.html#overlap-tile-predict +overlap_tile_predict = model.overlap_tile_predict( + img_file=img_file, tile_size=(769, 769)) +pred = overlap_tile_predict["label_map"] + +# 更新混淆矩阵 +pred = pred[np.newaxis, :, :, np.newaxis] +pred = pred.astype(np.int64) +label = np.asarray(Image.open("dataset/Annotations/5_class.png")) +label = label[np.newaxis, np.newaxis, :, :] +mask = label != model.ignore_index +conf_mat.calculate(pred=pred, label=label, ignore=mask) + +# 计算miou、iou、macc、acc、kapp +category_iou, miou = conf_mat.mean_iou() +category_acc, macc = conf_mat.accuracy() +logging.info( + "miou={:.6f} category_iou={} macc={:.6f} category_acc={} kappa={:.6f}". + format(miou, category_iou, macc, category_acc, conf_mat.kappa())) diff --git a/examples/remote_sensing/images/overlap_tile.png b/examples/remote_sensing/images/overlap_tile.png new file mode 100644 index 0000000000000000000000000000000000000000..60347caeb41b0807ad1cec84fac690e7318d20e1 GIT binary patch literal 443023 zcma&N1yt1C+CEGQh{S*b(jkmU!$WtMfQr(cB3(nbz>uSoN-2_(Lku9@4bnry(A{0% zpgtbYf35SbZ>=E~FnjO2uKT*~{Uhv|syyCZ^1CP~D0qqrGA~e2FhfvK(BiT0Ab)aY zsbY+Ra__N~wDdDYX=&PLPWBd7Hs&ZO3Sn`YSX%0xB&oU|-o5*Q_C*1w7Kgr80go-v z|7D|`0vZlu$Xi+yd6EZuxKBv(SHFSNtx?UO#*MJY@Qvoc@Dg?so6j|hXshcdGZ!;w zbIj8kHE&j2#7mTyIh4_l&{22=IV{BZ(c7NuqCsQ8!26O@ z7*HC`hIz>;g1wI9WGlkFNeP>nWBuevzm}&I(Vg>V_e{;%?>@&NQEDAeet2UkcwMn* z)*aHu8LIR^p~-YfuzMq(mrW`5iTp-H|_LM zGS|il2;$<$Io#hkect^1mL4U``;Qn2$=h9+C+WE;mmn&on z32)n;_;5y0u01nItdQu4CU1G~5qRogZfJ8i;p^#EBi0nY z3>pdcou{-xpYCJ@Jf*eSD%=S%?23QZfJ*cZ^|ZnG2lp_8;sAYMzrZu>>p(C;M)OtR z{Q~cI1Y3q71zp+b3YulhES_co*@P?p*zfS_&BG1S=Ld)yiC+V*{}Cc}>J52ff6>|Y zlBNV84Q_04FnK&c!ryS^KqEpVhW=S-A+>Uhu*m-UZT4HfCWat^(`ODQXwkCWNHn`%Z$mrV< zbS4m`bpyMx{F1x7oyhZ^s4oL154=l^DCgA{9PhNpYO1WiTo_&&7Kh*2KgIOeZ*BU- zlu8G%z}`;`aHv@38K*B?P{=BcDJ4ePuQjYHuQ7jo?DE!eKB?d}1xlSSIy$<_tPLOq zg>n(7DLaa7nTPyjmXa$y~l8d_)u``{Dh|N9X`LZZnb+6lUqJ_eUKX8RTbF zf`Q30eGIl8wu^Lw!8MX%U&)kY%bzn0-=zxnOb>XCK@L%sqne0oQAJT3Dk|K22c) zRF(*Qhq{mj90Ah))|ENs-J?_X8hr66!&VxE=&2@Jl#xO)(483m7U%7~CKoB3FM-8D z4v7wT3R6^@yy)L|(CQG2&P^yYSrAzgB@prxodDDbC&7@g+DAl+X=ZBsjIuET86q!m ztf;IA)&z1>h853q(!ZIcBl3y9eMqN&!Tcik*?57|(@g!Pk4iM!K{krVO!6@viw_sg z7q=GW_RXDuRgiQ=Xx8Zq1oOw(v)J-jovw#nqFuVLUcKUZ#WAw?O=`sBoAs-cyi|3c zob#}6vgKb(a$<70*I%rwuH&rRuIrLVS;*pk&it|eTG?u(OuY=N4D!Z>SDqI}u4Y@P zlc|%UQ$4}rTJk;9^Yi)rBdMdiM?vceBh|beI~x^9rtJfd+vg_qaW-1$N@^VQFDM%;Gv7V@RJ; 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