Update reamde, change ImageRW_OpenCV file and add utils

Dockerfile

@@ -0,0 +1,44 @@
+FROM ubuntu:20.04
+
+WORKDIR /app
+ENV preset=2
+ENV mode=1
+ENV nolog=0
+ENV reverse=0
+ENV nomt=1
+ENV speed=0
+ENV nogui=1
+ENV DEBIAN_FRONTEND noninteractive
+RUN apt-get update  && \
+    apt install -y git
+RUN git clone https://github.com/IldarGreat/dso.git
+RUN apt-get install -y build-essential \
+    libsuitesparse-dev libeigen3-dev libboost-all-dev \
+    libopencv-dev 
+
+WORKDIR /app
+RUN git clone --recursive https://github.com/stevenlovegrove/Pangolin.git -b v0.6
+WORKDIR /app/Pangolin
+RUN apt-get install -y cmake && \
+    apt-get clean && \
+    apt install -y libglew-dev && \
+    apt-get install -y libegl1-mesa-dev
+
+RUN cmake -B build && \
+    cmake --build build
+
+RUN apt-get install -y zlib1g-dev
+WORKDIR /app/dso/thirdparty/
+RUN tar -zxvf libzip-1.1.1.tar.gz
+WORKDIR /app/dso/thirdparty/libzip-1.1.1/
+RUN ./configure && \
+    make && \
+    cp lib/zipconf.h /usr/local/include/zipconf.h
+
+RUN git submodule update --init
+
+WORKDIR /app/dso/build
+RUN cmake .. && \
+    make
+
+ENTRYPOINT ./bin/dso_dataset files=set/sequence calib=set/camera.txt mode=${mode} preset=${preset} nolog=${nolog} reverse=${reverse} nomt=${nomt} speed=${speed} nogui=${nogui}

README.md

@@ -8,12 +8,32 @@ For more information see
 * **A Photometrically Calibrated Benchmark For Monocular Visual Odometry**, *J. Engel, V. Usenko, D. Cremers*, In arXiv:1607.02555, 2016
 
 Get some datasets from [https://vision.in.tum.de/mono-dataset](https://vision.in.tum.de/mono-dataset) .
-
-### 2. Installation
-
-	git clone https://github.com/JakobEngel/dso.git
-
-#### 2.1 Required Dependencies
+### 2. Installation with docker
+
+#### 2.1 Get image
+##### Build image from Dockerfile.
+	git clone https://github.com/IldarGreat/dso.git
+	docker build . -t ildarthegreat/dso
+##### Or pull image from dockerhub
+	docker pull ildarthegreat/dso
+##### If you have arm based system
+	docker pull ildarthegreat/dsoarm64
+#### 2.2 Run container
+	docker run \
+	-v {path}:/app/dso/build/set \
+	-e mode=2 \
+	... other environment  (see Dockerfile)
+	ildarthegreat/dso (or ildarthegreat/dsoarm64)
+path - it's folder that contains the calib file camera.txt and another folder named sequence that contains a sequence of images<br>
+Example: .../path<br>
+		-camera.txt<br>
+		-sequence
+
+### 3. Installation from source code
+
+	git clone https://github.com/IldarGreat/dso.git
+
+#### 3.1 Required Dependencies
 
 ##### suitesparse and eigen3 (required).
 Required. Install with
@@ -22,7 +42,7 @@ Required. Install with
 
 
 
-#### 2.2 Optional Dependencies
+#### 3.2 Optional Dependencies
 
 ##### OpenCV (highly recommended).
 Used to read / write / display images.
@@ -37,7 +57,7 @@ Install with
 	sudo apt-get install libopencv-dev
 
 
-##### Pangolin (highly recommended).
+##### Pangolin v0.4! (highly recommended).
 Used for 3D visualization & the GUI.
 Pangolin is **only** used in `IOWrapper/Pangolin/*`. You can compile without Pangolin, 
 however then there is not going to be any visualization / GUI capability. 
@@ -46,6 +66,16 @@ and use it instead of `PangolinDSOViewer`
 
 Install from [https://github.com/stevenlovegrove/Pangolin](https://github.com/stevenlovegrove/Pangolin)
 
+Or install like bellow
+
+	git clone --recursive https://github.com/stevenlovegrove/Pangolin.git -b v0.6
+	apt-get clean
+        sudo apt install libglew-dev
+        sudo apt-get install libegl1-mesa-dev
+	cd Pangolin
+	cmake -B build
+	cmake --build build
+
 
 ##### ziplib (recommended).
 Used to read datasets with images as .zip, as e.g. in the TUM monoVO dataset. 
@@ -64,7 +94,7 @@ to unzip the dataset image archives before loading them).
 ##### sse2neon (required for ARM builds).
 After cloning, just run `git submodule update --init` to include this.  It translates Intel-native SSE functions to ARM-native NEON functions during the compilation process.
 
-#### 2.3 Build
+#### 3.3 Build
 
 		cd dso
 		mkdir build
@@ -76,12 +106,12 @@ this will compile a library `libdso.a`, which can be linked from external projec
 It will also build a binary `dso_dataset`, to run DSO on datasets. However, for this
 OpenCV and Pangolin need to be installed.
 
+If you have error with boost, please add #include <boost/bind.hpp> to IndexThreadReduce
 
 
 
 
-
-### 3 Usage
+### 4 Usage
 Run on a dataset from [https://vision.in.tum.de/mono-dataset](https://vision.in.tum.de/mono-dataset) using
 
 		bin/dso_dataset \
@@ -98,17 +128,17 @@ other camera drivers, to use DSO interactively without ROS.
 
 
 
-#### 3.1 Dataset Format.
+#### 4.1 Dataset Format.
 The format assumed is that of [https://vision.in.tum.de/mono-dataset](https://vision.in.tum.de/mono-dataset).
 However, it should be easy to adapt it to your needs, if required. The binary is run with:
 
-- `files=XXX` where XXX is either a folder or .zip archive containing images. They are sorted *alphabetically*. for .zip to work, need to comiple with ziplib support.
+- `files=XXX` where XXX is either a folder or .zip archive containing images. They are sorted *alphabetically*. for .zip to work, need to comiple with ziplib support. You can use video_to_images.py in utils folder to split video into images. For example python video_to_images.py -v "path to video" . It create a folder 'images' desired sequence of images
 
-- `gamma=XXX` where XXX is a gamma calibration file, containing a single row with 256 values, mapping [0..255] to the respective irradiance value, i.e. containing the *discretized inverse response function*. See TUM monoVO dataset for an example.
+- `gamma=XXX` where XXX is a gamma calibration file, containing a single row with 256 values, mapping [0..255] to the respective irradiance value, i.e. containing the *discretized inverse response function*. See TUM monoVO dataset for an example or read this https://github.com/tum-vision/online_photometric_calibration
 
 - `vignette=XXX` where XXX is a monochrome 16bit or 8bit image containing the vignette as pixelwise attenuation factors. See TUM monoVO dataset for an example.
 
-- `calib=XXX` where XXX is a geometric camera calibration file. See below.
+- `calib=XXX` where XXX is a geometric camera calibration file. See below. If you want you can use calibration.py in utils it use chessboard 6*9, to check camera calibration settings. For example python calibration.py -i "path to images of chessboard"
 
 
 
@@ -117,30 +147,30 @@ However, it should be easy to adapt it to your needs, if required. The binary is
 
 ###### Calibration File for Pre-Rectified Images
 
-    Pinhole fx fy cx cy 0
+    fx fy cx cy 0
     in_width in_height
     "crop" / "full" / "none" / "fx fy cx cy 0"
     out_width out_height
 
 ###### Calibration File for FOV camera model:
 
-    FOV fx fy cx cy omega
+    fx fy cx cy omega
     in_width in_height
     "crop" / "full" / "fx fy cx cy 0"
     out_width out_height
 
 
 ###### Calibration File for Radio-Tangential camera model
 
-    RadTan fx fy cx cy k1 k2 r1 r2
+    fx fy cx cy k1 k2 r1 r2
     in_width in_height
     "crop" / "full" / "fx fy cx cy 0"
     out_width out_height
 
 
 ###### Calibration File for Equidistant camera model
 
-    EquiDistant fx fy cx cy k1 k2 k3 k4
+    fx fy cx cy k1 k2 k3 k4
     in_width in_height
     "crop" / "full" / "fx fy cx cy 0"
     out_width out_height
@@ -183,7 +213,7 @@ outliers along those borders, and corrupt the scale-pyramid).
 
 
 
-#### 3.2 Commandline Options
+#### 4.2 Commandline Options
 there are many command line options available, see `main_dso_pangolin.cpp`. some examples include
 - `mode=X`: 
     -  `mode=0` use iff a photometric calibration exists (e.g. TUM monoVO dataset). 
@@ -210,11 +240,11 @@ there are many command line options available, see `main_dso_pangolin.cpp`. some
 
 
 
-#### 3.3 Runtime Options
+#### 4.3 Runtime Options
 Some parameters can be reconfigured from the Pangolin GUI at runtime. Feel free to add more.
 
 
-#### 3.4 Accessing Data.
+#### 4.4 Accessing Data.
 The easiest way to access the Data (poses, pointclouds, etc.) computed by DSO (in real-time)
 is to create your own `Output3DWrapper`, and add it to the system, i.e., to `FullSystem.outputWrapper`.
 The respective member functions will be called on various occations (e.g., when a new KF is created, 
@@ -233,7 +263,7 @@ using the TUM RGB-D / TUM monoVO format ([timestamp x y z qx qy qz qw] of the ca
 
 
 
-#### 3.5 Notes
+#### 4.5 Notes
 - the initializer is very slow, and does not work very reliably. Maybe replace by your own way to get an initialization.
 - see [https://github.com/JakobEngel/dso_ros](https://github.com/JakobEngel/dso_ros) for a minimal example project on how to use the library with your own input / output procedures.
 - see `settings.cpp` for a LOT of settings parameters. Most of which you shouldn't touch.
@@ -242,7 +272,7 @@ using the TUM RGB-D / TUM monoVO format ([timestamp x y z qx qy qz qw] of the ca
 
 
 
-### 4 General Notes for Good Results
+### 5 General Notes for Good Results
 
 #### Accurate Geometric Calibration
 - Please have a look at Chapter 4.3 from the DSO paper, in particular Figure 20 (Geometric Noise). Direct approaches suffer a LOT from bad geometric calibrations: Geometric distortions of 1.5 pixel already reduce the accuracy by factor 10.
@@ -272,7 +302,7 @@ little rotation) during initialization.
 Possibly replace by your own initializer.
 
 
-### 5 License
+### 6 License
 DSO was developed at the Technical University of Munich and Intel.
 The open-source version is licensed under the GNU General Public License
 Version 3 (GPLv3).

builddso.bash

@@ -0,0 +1,20 @@
+sudo apt-get install -y libsuitesparse-dev libeigen3-dev libboost-all-dev libopencv-dev libglew-dev libegl1-mesa-dev cmake zlib1g-dev curl lsb-release
+user=ildar
+cd /home/$user
+mkdir app
+cd app
+git clone --recursive https://github.com/stevenlovegrove/Pangolin.git -b v0.6
+cd Pangolin
+mkdir build
+cd build
+cmake ..
+make
+
+cd /home/$user/app
+git clone https://github.com/IldarGreat/dso.git
+cd dso
+git submodule update --init
+mkdir build
+cd build
+cmake ..
+make 

src/IOWrapper/OpenCV/ImageRW_OpenCV.cpp

@@ -34,7 +34,7 @@ namespace IOWrap
 {
 MinimalImageB* readImageBW_8U(std::string filename)
 {
-	cv::Mat m = cv::imread(filename, CV_LOAD_IMAGE_GRAYSCALE);
+	cv::Mat m = cv::imread(filename, cv::IMREAD_GRAYSCALE);
 	if(m.rows*m.cols==0)
 	{
 		printf("cv::imread could not read image %s! this may segfault. \n", filename.c_str());
@@ -52,7 +52,7 @@ MinimalImageB* readImageBW_8U(std::string filename)
 
 MinimalImageB3* readImageRGB_8U(std::string filename)
 {
-	cv::Mat m = cv::imread(filename, CV_LOAD_IMAGE_COLOR);
+	cv::Mat m = cv::imread(filename, cv::IMREAD_COLOR);
 	if(m.rows*m.cols==0)
 	{
 		printf("cv::imread could not read image %s! this may segfault. \n", filename.c_str());
@@ -70,7 +70,7 @@ MinimalImageB3* readImageRGB_8U(std::string filename)
 
 MinimalImage<unsigned short>* readImageBW_16U(std::string filename)
 {
-	cv::Mat m = cv::imread(filename, CV_LOAD_IMAGE_UNCHANGED);
+	cv::Mat m = cv::imread(filename, cv::IMREAD_UNCHANGED);
 	if(m.rows*m.cols==0)
 	{
 		printf("cv::imread could not read image %s! this may segfault. \n", filename.c_str());
@@ -88,7 +88,7 @@ MinimalImage<unsigned short>* readImageBW_16U(std::string filename)
 
 MinimalImageB* readStreamBW_8U(char* data, int numBytes)
 {
-	cv::Mat m = cv::imdecode(cv::Mat(numBytes,1,CV_8U, data), CV_LOAD_IMAGE_GRAYSCALE);
+	cv::Mat m = cv::imdecode(cv::Mat(numBytes,1,CV_8U, data), cv::IMREAD_GRAYSCALE);
 	if(m.rows*m.cols==0)
 	{
 		printf("cv::imdecode could not read stream (%d bytes)! this may segfault. \n", numBytes);

utils/calibration.py

@@ -0,0 +1,63 @@
+import cv2
+import numpy as np
+import os
+import glob
+import argparse
+
+ap = argparse.ArgumentParser()
+ap.add_argument("-i", "--images", required=True, help="path to input images")
+args = vars(ap.parse_args())
+
+# Определение размеров шахматной доски
+CHECKERBOARD = (6,9)
+criteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 0.001)
+# Создание вектора для хранения векторов трехмерных точек для каждого изображения шахматной доски
+objpoints = []
+# Создание вектора для хранения векторов 2D точек для каждого изображения шахматной доски
+imgpoints = [] 
+# Определение мировых координат для 3D точек
+objp = np.zeros((1, CHECKERBOARD[0] * CHECKERBOARD[1], 3), np.float32)
+objp[0,:,:2] = np.mgrid[0:CHECKERBOARD[0], 0:CHECKERBOARD[1]].T.reshape(-1, 2)
+prev_img_shape = None
+# Извлечение пути отдельного изображения, хранящегося в данном каталоге
+images = glob.glob(args["images"]+'/*.jpg')
+for fname in images:
+    img = cv2.imread(fname)
+    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
+    # Найти углы шахматной доски
+    # Если на изображении найдено нужное количество углов, тогда ret = true
+    ret, corners = cv2.findChessboardCorners(gray, CHECKERBOARD, cv2.CALIB_CB_ADAPTIVE_THRESH + cv2.CALIB_CB_FAST_CHECK + cv2.CALIB_CB_NORMALIZE_IMAGE)
+
+    """
+    Если желаемый номер угла обнаружен,
+    уточняем координаты пикселей и отображаем
+    их на изображениях шахматной доски
+    """
+    if ret == True:
+        objpoints.append(objp)
+        # уточнение координат пикселей для заданных 2d точек.
+        corners2 = cv2.cornerSubPix(gray, corners, (11,11),(-1,-1), criteria)
+
+        imgpoints.append(corners2)
+        # Нарисовать и отобразить углы
+        img = cv2.drawChessboardCorners(img, CHECKERBOARD, corners2, ret)
+
+    cv2.imshow('img',img)
+    cv2.waitKey(0)
+cv2.destroyAllWindows()
+h,w = img.shape[:2]
+"""
+Выполнение калибровки камеры с помощью
+Передача значения известных трехмерных точек (объектов)
+и соответствующие пиксельные координаты
+обнаруженные углы (imgpoints)
+"""
+ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpoints, imgpoints, gray.shape[::-1], None, None)
+print("Camera matrix : \n")
+print(mtx)
+print("dist : \n")
+print(dist)
+print("rvecs : \n")
+print(rvecs)
+print("tvecs : \n")
+print(tvecs)