Tools to annotate images

ChangeImagesMaxSize
ExtractImagesFromVideo
CreateBoundingBoxes
ExportToClassification
ExportToCSV
ExportToPascal (🔼 2019-03-14)
Generate TFRecord - API Tensorflow (🔼 2019-03-19)
Import from txt ground truth - format Wider Face (🔼 2019-03-14)
Convert CityscapeMask annotatiton to XML Pascal.py (🔼 2019-06-10)

Tools to measures

Citation

@misc{marcelo_cabral_ghilardi_2019_2604909,
  author       = {Marcelo Cabral Ghilardi},
  title        = {kabrau/PyImageRoi: Tools to annotate images},
  month        = mar,
  year         = 2019,
  doi          = {10.5281/zenodo.2604909},
  url          = {https://doi.org/10.5281/zenodo.2604909}
}

ChangeImagesMaxSize

A tool to change images max size into folder

RUN

usage: ChangeImagesMaxSize.py [-h] -p PATH -s MAXSIZE

optional arguments:
  -h, --help            show this help message and exit
  -p PATH, --path PATH  images path
  -s MAXSIZE, --maxSize MAXSIZE
                        resize image (width ou height) to MAX SIZE

ExtractImagesFromVideo

A tool to extract images from videos

The images name is same video name with a sequential number, e.g.:

video: VID-20170817-WA0003.mp4
images: VID-20170817-WA0003-(F00001).txt, VID-20170817-WA0003-(F00002).txt, VID-20170817-WA0003-(F00003).txt

RUN

usage: ExtractImagesFromVideo.py [-h] -p VIDEOSPATH -o OUTPUTPATH [-f FPS]
                                 [-n]
                                 {portrait,landscape}

positional arguments:
  {portrait,landscape}  portrait (default) or landscape

optional arguments:
  -h, --help            show this help message and exit
  -p VIDEOSPATH, --videosPath VIDEOSPATH
                        videos input path
  -o OUTPUTPATH, --outputPath OUTPUTPATH
                        images output path
  -f FPS, --fps FPS     extract frames por seconds
  -n, --new             Extracts only from videos without extraction (new
                        video)              Extracts only from videos without extraction (new videos) <br>

CreateBoundingBoxes

A tool to Labeling object bounding boxes or ROI (Region of interest) in images
(adjusts the displayed image size to the screen size)

multiple boxes per image
multiple classes per image

The regions are saved in a text file with same name of image file, e.g.

IMG-20170807-WA0001.jpg
IMG-20170807-WA0001.txt
locaisvstdss.jpg
locaisvstdss.txt
phpiqa6ae.752.502.s.jpg
phpiqa6ae.752.502.s.txt

** Important: ** Do not put two pictures with the same name and different extensions in the same folder.

Each line of text file is a one region

class_number x1 y1 width height image_width image_height

Example:

1 426 679 55 99 1080 1920
1 440 839 30 59 1080 1920

RUN

usage: CreateBoundingBoxes.py [-h] -p PATH [-f] [-c CLASS]
                              [-className [CLASSNAME [CLASSNAME ...]]]

optional arguments:
  -h, --help            show this help message and exit
  -p PATH, --path PATH  images path
  -f, --first           starts on the first image (default: Jump to first
                        image without label)
  -c CLASS, --class CLASS
                        class number started (default = 0)
  -className [CLASSNAME [CLASSNAME ...]]
                        class name list (0..9 positions, max 10), e.g.
                        -className dog cat

USAGE

Left Click mouse to start marking an area
Right Click mouse to remove last area
'0..9' change class to new boxe (aaccent key = 0, too)
'N' or 'space-bar' to next image
'P' to previus image
'Q' Exit

Example

python CreateBoundingBoxes.py -p ...\image -className cat plant

0 227 111 662 359 1024 576 
1 647 255 114 173 1024 576 
1 756 257 115 172 1024 576 
1 4 180 164 316 1024 576

ExportToClassification

A tool to extract box from images and save imagebox to classification. Save the separated images by classes, each class in a subfolder.

RUN

usage: ExportToClassification.py [-h] -p PATH -d DEST

optional arguments:
  -h, --help            show this help message and exit
  -p PATH, --path PATH  images path
  -d DEST, --dest DEST  destination images path

Example

python ExportToClassification.py -p ...\image -f ...\image

..\.\tmp\0 MyCat-0.jpg [227, 111, 889, 470, '0'] (359, 662, 3)
..\.\tmp\1 MyCat-1.jpg [647, 255, 761, 428, '1'] (173, 114, 3)
..\.\tmp\1 MyCat-2.jpg [756, 257, 871, 429, '1'] (172, 115, 3)
..\.\tmp\1 MyCat-3.jpg [4, 180, 168, 496, '1'] (316, 164, 3)

ExportToCSV

A tool to create a cvs file with de bounding boxes

note: Use 1 classname only

RUN

usage: ExportToCSV.py [-h] -p PATH -c CVS_FILE

optional arguments:
  -h, --help            show this help message and exit
  -p PATH, --path PATH  images path
  -c CVS_FILE, --cvs_file CVS_FILE
                        cvs file

Example

python ExportToCSV.py -p E:\Datasets\pedestrian_signal\images\test -c E:\Datasets\pedestrian_signal\images\test.csv

filename,width,height,class,xmin,ymin,xmax,ymax
16431531.jpg,640,426,sinaleira,317,92,345,140
16431540.jpg,640,426,sinaleira,449,106,475,148
17074299.jpg,620,412,sinaleira,566,199,586,228
19156210.jpg,620,412,sinaleira,181,112,206,161
20170701_105311.jpg,768,1024,sinaleira,323,424,352,471
20170701_105442.jpg,768,1024,sinaleira,311,412,346,463

ExportToPascal

TOOL to Create a XML files (PASCAL FORMAT)

RUN

usage: ExportToPascal.py [-h] -p PATH -a ANNPATH 
                              [-className [CLASSNAME [CLASSNAME ...]]]

optional arguments:  
  -h, --help            show this help message and exit  
  -p PATH, --path PATH  images path  
  -a ANNPATH, --annpath ANNPATH  
                        annotation path  
  -className [CLASSNAME [CLASSNAME ...]]
                        class name list (0..9 positions, max 10), e.g.
                        -className dog cat

Example

python ExportToPascal.py -p "E:\Datasets\pedestrian_signal\images" -a "E:\Datasets\pedestrian_signal\images.ann_gostop" -className go stop off

Ps: At the end, it shows total images and classes

ExportToPascal.5971774

Specific converter for PedestrianLights dataset available at: http://www.uni-muenster.de/PRIA/en/forschung/index.shtml
TOOL to Create a XML files (PASCAL FORMAT)

RUN

usage: usage: ExportToPascal.5971774.py [-h] -p PATH -o GTFILE -a ANNPATH   

optional arguments:  
  -h, --help            show this help message and exit  
  -p PATH, --path PATH  images path  
  -o GTFILE, --gtfile GTFILE  
                        original ground truth file  
  -a ANNPATH, --annpath ANNPATH  
                        annotation path

Example

python ExportToPascal.5971774.py -p "E:\Datasets\pedestrianlights-5971774\pedestrianlights\download\imagesequences\01" -o "E:\Datasets\pedestrianlights-5971774\pedestrianlights\download\imagesequences\01\groundtruth.txt" -a "E:\Datasets\pedestrianlights-5971774\pedestrianlights\download\imagesequences\01.ann.GoStop"

Ps: At the end, it shows total images and classes

Generate TFRecord - API Tensorflow

First, convert from PASCAL to CSV, use: ExportPascal2csv.py
Second, generate TFRecord, use: Generate_TFRecord.py

RUN

usage: ExportPascal2csv.py [-h] -p PATH -o OUTPUT [-a]

optional arguments:
  -h, --help            show this help message and exit
  -p PATH, --path PATH  annotations path
  -o OUTPUT, --output OUTPUT
                        csv output file
  -a, --addPathCol      add path collumn

usage: Generate_TFRecord.py [-h] -c CSV_INPUT -o OUTPUT_PATH [-i IMAGES_PATH]

optional arguments:
  -h, --help            show this help message and exit
  -c CSV_INPUT, --csv_input CSV_INPUT
                        Path to the CSV input
  -o OUTPUT_PATH, --output_path OUTPUT_PATH
                        Path to output TFRecord
  -i IMAGES_PATH, --images_path IMAGES_PATH
                        Path for Images, If dont have into CSV

Example

python ExportPascal2csv.py -p E:\datasets\FaceDataset\Wider\WIDER_train\train.ann -o E:\datasets\FaceDataset\Wider\WIDER_train\train.csv -a

python Generate_TFRecord.py --csv_input=E:\datasets\FaceDataset\Wider\WIDER_train\train.csv --output_path=E:\datasets\FaceDataset\Wider\WIDER_train\train.record

mAP

Tool to calc mAP in Object Detection

Returns in the console the mAP values
And inside each method folder create a subfolder named _chart with a pdf of the Precision Recall method

You need a folder with Pascal VOC annotations, parameter --annpath

And a root folder with one ou more methods result folder, parameter --resultpath e.g:

c:\results <= root
c:\results\faster <= method result folder
c:\results\ssd <= method result folder
c:\results\yolo <= method result folder

Inside each method result folder, you need results files by classes
e.g:
cat.txt
dog.txt
mouse.txt

In the results files by class, the content is in this format:
filename confidence x1 y1 x2 y2
e.g:
file001 0.99862 441.5266 429.1418 504.2249 548.0778
file005 0.99757 466.8359 433.5500 531.3656 545.9105
file007 0.95728 495.3467 440.6576 554.5069 558.3262

RUN

usage: mAP.py [-h] -a ANNPATH -r RESULTPATH -c [CLASS [CLASS ...]] -i IOU [-v [VERBOSE]]  
  
  
optional arguments:  
  -h, --help            show this help message and exit   
  -a ANNPATH, --annpath ANNPATH  
                        Pascal VOC annotation path  
  -r RESULTPATH, --resultpath RESULTPATH  
                        Path of method results  
  -c [CLASS [CLASS ...]], --class [CLASS [CLASS ...]]  
                        list of class, e.g. --classes dog cat mouse  
  -i IOU, --IOU IOU     IOU confidence threshold, e.g. 0.5  
  -v [VERBOSE], --verbose [VERBOSE]  
                        show verbose

Example

python mAP.py -a E:\Datasets\signal\test.ann.GoStop -r E:\GitHub\PedestrialTrafficLight\accurace_calc\results\3C\ -c cat dog mouse -i 0.5

Import from txt ground truth - format Wider Face

Wider Face - A Face Detection dataset to Benchmark http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/index.html

The format of txt ground truth.

File name
Number of bounding box
x1, y1, w, h, blur, expression, illumination, invalid, occlusion, pose

RUN

usage: ImportFromTxtGT_01.py [-h] -p PATH -f FILE

optional arguments:
  -h, --help            show this help message and exit
  -p PATH, --path PATH  images path
  -f FILE, --file FILE  ground truth file

Example

python importFromTxtGT_01.py -p E:/datasets/FaceDataset/Wider/WIDER_train/images/ -f E:/datasets/FaceDataset/Wider/wider_face_split/wider_face_train_bbx_gt.txt

Import from txt ground truth - format Wider Face

Convert CityscapeMask annotatiton to XML Pascal.py

Open code CityscapeMask2Pascal.py and set folders and classes

RUN

python CityscapeMask2Pascal.py

andreeppinghaus / pyimageroi Goto Github PK

pyimageroi's Introduction

Tools to annotate images

Tools to measures

Citation

ChangeImagesMaxSize

RUN

ExtractImagesFromVideo

RUN

CreateBoundingBoxes

RUN

USAGE

Example

ExportToClassification

RUN

Example

ExportToCSV

RUN

Example

ExportToPascal

RUN

Example

ExportToPascal.5971774

RUN

Example

Generate TFRecord - API Tensorflow

RUN

Example

mAP

RUN

Example

Import from txt ground truth - format Wider Face

The format of txt ground truth.

RUN

Example

Import from txt ground truth - format Wider Face

RUN

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