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lhark:video-stab
lhark:optimise-flow
lhark:drone_control
lhark:greyscale-threshold
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lhark:video-stab
lhark:optimise-flow
lhark:drone_control
lhark:greyscale-threshold

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7 changed files with 364 additions and 264 deletions
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10
CMakeLists.txt
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@ -9,18 +9,13 @@ find_package(catkin REQUIRED COMPONENTS
message_generation
)
find_package(OpenCV)
find_package( PkgConfig REQUIRED )
pkg_check_modules ( ncurses++ REQUIRED ncurses++ )
add_message_files(
FILES
BoundingBox.msg
)
generate_messages(
DEPENDENCIES
std_msgs
)
generate_messages()
catkin_package(CATKIN_DEPENDS
roscpp
std_msgs
@ -33,10 +28,7 @@ catkin_package(CATKIN_DEPENDS
include_directories (${catkin_INCLUDE_DIRS})
add_executable (papillon src/papillon.cpp)
add_executable (control src/control.cpp)
add_dependencies( papillon ${PROJECT_NAME}_generate_messages_cpp )
target_link_libraries(papillon ${catkin_LIBRARIES})
target_link_libraries(control ${catkin_LIBRARIES} ncurses)
set_property (TARGET papillon APPEND PROPERTY INCLUDE_DIRECTORIES ${OpenCV_INCLUDE_DIRS})
set_property (TARGET papillon APPEND PROPERTY INCLUDE_DIRECTORIES ${catkin_INCLUDE_DIRS})
set_property (TARGET papillon APPEND PROPERTY LINK_LIBRARIES ${OpenCV_LIBRARIES})

21
README.md
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@ -1,22 +1,3 @@
# Papillon
Comportements émergents avec les drônes bebop
## Utilisation
Il faut en premier lieu se connecter en WiFi au drone.
Il faut ensuite bien entendu lancer `roscore`.
Une fois ce dernier lancé, on peut lancer le driver du drone afin qu'il soit connecté à ROS à l'aide de la commande :
```bash
$ roslaunch bebop_driver bebop_node.launch
```
Il faut ensuite lancer les deux noeuds de `papillon` à l'aide des commandes :
```bash
$ rosrun papillon papillon
$ rosrun papillon control
```
Enfin, sur le noeud `control`, lors de l'appui sur la touche **F** le drone décolle et commence à suivre les mouvements qu'il détecte.
Lors d'un appui sur la touche **Espace**, le drone arrête de suivre et se pose.
## Comportements émergents avec les drônes bebop

6
commandes
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@ -1,6 +0,0 @@
rosrun demo_teleop key_teleop.py --persist /cmd_vel:=/bebop/cmd_vel
rostopic pub --once /bebop/land std_msgs/Empty
rostopic pub --once /bebop/takeoff std_msgs/Empty
catkin_make --pkg=papillon
catkin_make -DCMAKE_BUILD_TYPE=Debug
roslaunch bebop_driver bebop_node.launch

1
package.xml
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@ -46,7 +46,6 @@
<build_depend>image_transport</build_depend>
<build_depend>cv_bridge</build_depend>
<build_depend>message_generation</build_depend>
<build_depend>ncurses++</build_depend>
<run_depend>roscpp</run_depend>
<run_depend>std_msgs</run_depend>
<run_depend>image_transport</run_depend>

137
src/control.cpp
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@ -1,137 +0,0 @@
#include "ros/ros.h"
#include <papillon/BoundingBox.h>
#include <geometry_msgs/Twist.h>
#include <std_msgs/Empty.h>
#include <ncurses.h>
#include <opencv/cv.h>
#include <sstream>
using namespace std;
class Drone_control {
public:
constexpr static float MAX_ANG_VEL = 0.3;
ros::NodeHandle n;
ros::Publisher pub_cmd;
ros::Publisher pub_takeoff;
ros::Publisher pub_land;
ros::Subscriber sub_box;
std_msgs::Empty nope;
float THRES_TURN = 0.1;
bool emergency = false;
// 1 left | 0 center | -1 right
vector<int> decision_acc;
const static int NB_ACC = 5;
Drone_control() : n("~") {
pub_cmd = n.advertise<geometry_msgs::Twist>("/bebop/cmd_vel", 1);
pub_takeoff = n.advertise<std_msgs::Empty>("/bebop/takeoff", 1);
pub_land = n.advertise<std_msgs::Empty>("/bebop/land", 1);
sub_box = n.subscribe<papillon::BoundingBox>("/papillon/bbox", 1, &Drone_control::on_msg, this);
// Initialise decision accumulator
for (size_t i = 0; i < NB_ACC; ++i)
decision_acc.push_back(0);
ROS_INFO("Built !");
}
// This processes an image and publishes the result.
void on_msg(const papillon::BoundingBox::ConstPtr& bbox) {
geometry_msgs::Twist twist = geometry_msgs::Twist();
cv::Point2f r_center = cv::Point2f(bbox->x + bbox->width/2, bbox->y + bbox->height/2);
if (!emergency) {
decision_acc.pop_back();
// Image is 1.0 in width and height
if (r_center.x < 0.5 - THRES_TURN)
decision_acc.insert(decision_acc.begin(), 1);
else if (r_center.x > 0.5 + THRES_TURN)
decision_acc.insert(decision_acc.begin(), -1);
else
decision_acc.insert(decision_acc.begin(), 0);
int moy = 0;
for (const auto& d : decision_acc)
moy += d;
twist.angular.z = moy / (float)NB_ACC * MAX_ANG_VEL;
}
pub_cmd.publish(twist);
}
void key_spin() {
// >>>>> nCurses initization
initscr();
keypad(stdscr, TRUE);
cbreak();
noecho();
timeout(0); // Non blocking getch()
// <<<<< nCurses initization
int c;
printw(" ------------------------\n");
printw("| Papillon HQ |\n");
printw(" ------------------------ \n");
printw("| Press F to take off. |\n");
printw("| Press SPACEBAR to land.|\n");
printw("| Press Q to exit. |\n");
printw(" ------------------------\n");
bool stop = false;
ros::Rate rate(1.0/30.0); // 30 Hz
while(!stop)
{
c = getch();
switch(c)
{
case 'f':
{
// Takeoff
ROS_DEBUG_STREAM("Taking off...\r");
pub_takeoff.publish(nope);
emergency = false;
printw("Taking off...\r");
break;
}
case ' ':
{
// Takeoff
ROS_DEBUG_STREAM("Landing...\r");
pub_land.publish(nope);
printw("Landing...\r");
emergency = true;
break;
}
case 'q':
case 'Q':
{
ROS_DEBUG_STREAM("EXIT\r");
stop = true;
}
}
ros::spinOnce();
}
printw("\nExiting\n");
}
};
int main(int argc, char **argv)
{
ros::init(argc, argv, "control");
Drone_control con=Drone_control();
con.key_spin();
return 0;
}

191
src/papillon.cpp
View file

@ -12,37 +12,35 @@ using namespace std;
class Traite_image {
public:
const static int SENSITIVITY_VALUE = 20;
const static int BLUR_SIZE = 9;
const static int CLOSE_SIZE = 30;
const static int ERODE_SIZE = 2;
const static int NB_FRAME_DROP = 1;
const static int SENSITIVITY_VALUE = 30;
const static int BLUR_Size = 15;
const static int CLOSE_SIZE = 50;
vector<cv::Mat> prevs;
cv::Mat prev;
cv::Mat last_T;
bool first = true;
int resize_f = 1;
int theObject[2] = {0,0};
cv::Rect objectBoundingRectangle = cv::Rect(0,0,0,0);
ros::NodeHandle n;
image_transport::ImageTransport it;
image_transport::Subscriber sub;
image_transport::Publisher pub_img;
image_transport::Publisher pub_thres;
ros::Publisher pub_cmd;
image_transport::Subscriber sub;
Traite_image() : n("~"),it(n) {
pub_img = it.advertise("/papillon/image_out", 1);
pub_thres = it.advertise("/papillon/thres_out", 1);
pub_cmd = n.advertise<papillon::BoundingBox>("/papillon/bbox", 1);
sub = it.subscribe("/bebop/image_raw", 1, [this](const sensor_msgs::ImageConstPtr& img) -> void { this->on_image(img);},ros::VoidPtr(),image_transport::TransportHints("compressed"));
pub_img = it.advertise("/image_out", 1);
pub_cmd = n.advertise<papillon::BoundingBox>("/bbox", 1);
sub = it.subscribe("/usb_cam/image_raw", 1, [this](const sensor_msgs::ImageConstPtr& img) -> void { this->on_image(img);},ros::VoidPtr(),image_transport::TransportHints("compressed"));
}
// Callback function executedwhen recieving an image from the camera
// Publishes to /papillon/image_out
// This processes an image and publishes the result.
void on_image(const sensor_msgs::ImageConstPtr& msg) {
cv_bridge::CvImageConstPtr bridge_input;
@ -55,24 +53,20 @@ class Traite_image {
return;
}
//cv::Mat& input = const_cast<cv::Mat&>(bridge_input->image);
const cv::Mat& input = bridge_input->image;
cv::Mat next;
resize(input, next, cv::Size(input.size().width/resize_f, input.size().height/resize_f));
cv::Mat output;
next.copyTo(output);
cv::cvtColor(next, next, cv::COLOR_BGR2GRAY);
cv::Mat output;// = input.clone(); // (input.rows, input.cols, CV_32FC2);
//ROS_INFO("got input");
if (first) {
for (int i = 0; i < NB_FRAME_DROP; ++i) {
prevs.push_back(next.clone());
}
prev = next.clone();
first = false;
ROS_INFO("Ready");
ROS_INFO("first done");
}
cv::Mat next_stab;
stabiliseImg(prevs.back(), next, next_stab);
stabiliseImg(prev, next, next_stab);
int crop_ratio = 6;
float crop_x = next_stab.size().width/crop_ratio;
float crop_y = next_stab.size().height/crop_ratio;
@ -80,19 +74,34 @@ class Traite_image {
float crop_h = next_stab.size().height*(1-2.0/crop_ratio);
cv::Rect myROI(crop_x, crop_y, crop_w, crop_h);
cv::Mat next_stab_cropped = next_stab(myROI);
cv::Mat prev_cropped = prevs.back()(myROI);
cv::Mat closed_thres;
searchForMovement(prev_cropped, next_stab_cropped, output, closed_thres);
cv::Mat prev_cropped = prev(myROI);
searchForMovement(prev_cropped, next_stab_cropped, output);
pub_img.publish(cv_bridge::CvImage(msg->header, "rgb8", output).toImageMsg());
pub_thres.publish(cv_bridge::CvImage(msg->header, "mono8", closed_thres).toImageMsg());
// bridge_input is handled by a smart-pointer. No explicit delete needed.
prevs.pop_back();
prevs.insert(prevs.begin(), next.clone());
//droneTracking(Rect(Point(0,0), output.size()));
//ROS_INFO("pub");
prev = next.clone();
}
void stabiliseImg(cv::Mat prev_grey, cv::Mat cur_grey, cv::Mat &output){
//int to string helper function
string intToString(int number){
//this function has a number input and string output
std::stringstream ss;
ss << number;
return ss.str();
}
void stabiliseImg(cv::Mat prev, cv::Mat cur, cv::Mat &output){
cv::Mat cur_grey, prev_grey;
cv::cvtColor(cur, cur_grey, cv::COLOR_BGR2GRAY);
cv::cvtColor(prev, prev_grey, cv::COLOR_BGR2GRAY);
// vector from prev to cur
vector <cv::Point2f> prev_corner, cur_corner;
vector <cv::Point2f> prev_corner2, cur_corner2;
@ -102,7 +111,7 @@ class Traite_image {
cv::goodFeaturesToTrack(prev_grey, prev_corner, 200, 0.01, 30);
cv::calcOpticalFlowPyrLK(prev_grey, cur_grey, prev_corner, cur_corner, status, err);
// weed out bad matches
// weed out bad cv::Matches
for(size_t i=0; i < status.size(); i++) {
if(status[i]) {
prev_corner2.push_back(prev_corner[i]);
@ -117,86 +126,90 @@ class Traite_image {
}
T.copyTo(last_T);
cv::warpAffine(cur_grey, output, T, cur_grey.size(),cv::INTER_CUBIC|cv::WARP_INVERSE_MAP);
cv::Mat cur2;
cv::warpAffine(cur, cur2, T, cur.size(),cv::INTER_CUBIC|cv::WARP_INVERSE_MAP);
cur2.copyTo(output);
}
void searchForMovement(cv::Mat prev_grey, cv::Mat cur_grey, cv::Mat &output, cv::Mat &out2){
cv::GaussianBlur(prev_grey, prev_grey, cv::Size(BLUR_SIZE,BLUR_SIZE), 3.0);
cv::GaussianBlur(cur_grey, cur_grey, cv::Size(BLUR_SIZE,BLUR_SIZE), 3.0);
void searchForMovement(cv::Mat prev, cv::Mat cur, cv::Mat &output){
cv::Mat cur_grey, prev_grey;
cur.copyTo(output);
cv::cvtColor(prev, prev_grey, cv::COLOR_BGR2GRAY);
cv::cvtColor(cur, cur_grey, cv::COLOR_BGR2GRAY);
cv::GaussianBlur(prev_grey, prev_grey, cv::Size(BLUR_Size,BLUR_Size), 3.0);
cv::GaussianBlur(cur_grey, cur_grey, cv::Size(BLUR_Size,BLUR_Size), 3.0);
//blur(prev_grey, prev_grey, cv::Size(BLUR_Size, BLUR_Size));
//blur(cur_grey, cur_grey, cv::Size(BLUR_Size, BLUR_Size));
// Subtract the 2 last frames and threshold them
cv::Mat thres;
cv::absdiff(prev_grey,cur_grey,thres);
cv::Mat element = getStructuringElement( cv::MORPH_ELLIPSE,
cv::Size( 2*ERODE_SIZE + 1, 2*ERODE_SIZE+1 ),
cv::Point( ERODE_SIZE, ERODE_SIZE ) );
// Apply the erode operation
cv::erode(thres, thres, element );
// threshold(thres, thres, SENSITIVITY_VALUE, 255, THRESH_BINARY);
// // Blur to eliminate noise
// blur(thres, thres, cv::Size(BLUR_Size, BLUR_Size));
cv::threshold(thres, thres, SENSITIVITY_VALUE, 255, cv::THRESH_BINARY);
// Intermediate output
thres.copyTo(out2);
//~ int dilation_Size = 2;
//~ cv::Mat element = getStructuringElement( MORPH_ELLIPSE,
//~ cv::Size( 2*dilation_Size + 1, 2*dilation_Size+1 ),
//~ Point( dilation_Size, dilation_Size ) );
//~ // Apply the dilation operation
//~ cv::Mat dilated_thres;
//~ dilate(thres, dilated_thres, element );
//~
//~ dilated_thres.copyTo(output);
cv::Mat closed_thres;
cv::Mat structuringElement = getStructuringElement(cv::MORPH_ELLIPSE, cv::Size(CLOSE_SIZE, CLOSE_SIZE));
cv::morphologyEx( thres, closed_thres, cv::MORPH_CLOSE, structuringElement );
//closed_thres.copyTo(output);
//notice how we use the '&' operator for objectDetected and output. This is because we wish
//to take the values passed into the function and manipulate them, rather than just working with a copy.
//eg. we draw to the output to be displayed in the main() function.
bool objectDetected = false;
cv::Mat temp;
closed_thres.copyTo(temp);
//these two vectors needed for output of findContours
vector< vector<cv::Point> > contours;
vector<cv::Vec4i> hierarchy;
//find contours of filtered image using openCV findContours function
cv::findContours(temp,contours,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE );// retrieves external contours
//findContours(temp,contours,hierarchy,CV_RETR_CCOMP,CV_CHAIN_APPROX_SIMPLE );// retrieves all contours
cv::findContours(temp,contours,hierarchy,CV_RETR_EXTERNAL,CV_CHAIN_APPROX_SIMPLE );// retrieves external contours
//if contours vector is not empty, we have found some objects
if(contours.size()>0){
vector<cv::Rect> nc_rects; // Non connected rectangles
for(size_t i=0; i<contours.size();i++)
{
nc_rects.push_back(cv::boundingRect(contours[i]));
}
if(contours.size()>0)objectDetected=true;
else objectDetected = false;
vector<cv::Rect> c_rects; // Connected rectangles
cleanBBoxes(nc_rects, cur_grey.size(), c_rects);
if (c_rects.size() > 0) {
for (const auto& rect : c_rects)
cv::rectangle(output, rect, cv::Scalar(0, 255, 0), 2);
if(objectDetected){
//the largest contour is found at the end of the contours vector
//we will simply assume that the biggest contour is the object we are looking for.
//vector< vector<Point> > largestContourVec;
//largestContourVec.push_back(contours.at(contours.size()-1));
//make a bounding rectangle around the largest contour then find its centroid
//this will be the object's final esticv::Mated position.
//~ for(int i=0; i<contours.size();i++)
//~ {
//~ objectBoundingRectangle = cv::boundingRect(contours[i]);
//~ cv::rectangle(output, objectBoundingRectangle, cv::Scalar(0, 255, 0), 2);
//~ }
objectBoundingRectangle = cv::boundingRect(contours.at(contours.size()-1));
cv::rectangle(output, objectBoundingRectangle, cv::Scalar(0, 255, 0), 2);
}
//make some temp x and y variables so we dont have to type out so much
//~ int x = objectBoundingRectangle.x;
//~ int y = objectBoundingRectangle.y;
//~ int width = objectBoundingRectangle.width;
//~ int height = objectBoundingRectangle.height;
cv::Rect objBRect = c_rects.front();
papillon::BoundingBox bbox = papillon::BoundingBox();
bbox.x = objBRect.x / (float)cur_grey.size().width;
bbox.y = objBRect.y / (float)cur_grey.size().height;
bbox.width = objBRect.width / (float)cur_grey.size().width;
bbox.height = objBRect.height / (float)cur_grey.size().height;
pub_cmd.publish(bbox);
}
}
}
//draw a rectangle around the object
//rectangle(output, Point(x,y), Point(x+width, y+height), cv::Scalar(0, 255, 0), 2);
void cleanBBoxes(vector<cv::Rect> nc_rects, cv::Size img, vector<cv::Rect> &c_rects) {
int max = 0;
for (auto r : nc_rects) {
cv::Point center = rectCenter(r);
if (r.width > img.width / 2 || r.height > img.height / 8)
continue;
if (center.y > img.height * 2 / 3 || center.y < img.height / 3)
continue;
int r_area = r.area();
if (max < r_area) {
max = r_area;
c_rects.insert(c_rects.begin(), r);
} else {
c_rects.push_back(r);
}
}
}
cv::Point rectCenter(cv::Rect r) {
return cv::Point(r.x + r.width/2, r.y + r.height / 2);
//write the position of the object to the screen
//putText(output,"Tracking object at (" + intToString(x)+","+intToString(y)+")",Point(x,y),1,1,cv::Scalar(255,0,0),2);
}
inline bool isFlowCorrect(cv::Point2f u)

258
src/videostab.cpp Normal file
View file

@ -0,0 +1,258 @@
#include <opencv2/opencv.hpp>
#include <iostream>
#include <cassert>
#include <cmath>
#include <fstream>
using namespace std;
using namespace cv;
// This video stablisation smooths the global trajectory using a sliding average window
const int SMOOTHING_RADIUS = 30; // In frames. The larger the more stable the video, but less reactive to sudden panning
const int HORIZONTAL_BORDER_CROP = 20; // In pixels. Crops the border to reduce the black borders from stabilisation being too noticeable.
// 1. Get previous to current frame transformation (dx, dy, da) for all frames
// 2. Accumulate the transformations to get the image trajectory
// 3. Smooth out the trajectory using an averaging window
// 4. Generate new set of previous to current transform, such that the trajectory ends up being the same as the smoothed trajectory
// 5. Apply the new transformation to the video
struct TransformParam
{
TransformParam() {}
TransformParam(double _dx, double _dy, double _da) {
dx = _dx;
dy = _dy;
da = _da;
}
double dx;
double dy;
double da; // angle
};
struct Trajectory
{
Trajectory() {}
Trajectory(double _x, double _y, double _a) {
x = _x;
y = _y;
a = _a;
}
double x;
double y;
double a; // angle
};
int main(int argc, char **argv)
{
if(argc < 2) {
cout << "./VideoStab [video.avi]" << endl;
return 0;
}
// For further analysis
ofstream out_transform("prev_to_cur_transformation.txt");
ofstream out_trajectory("trajectory.txt");
ofstream out_smoothed_trajectory("smoothed_trajectory.txt");
ofstream out_new_transform("new_prev_to_cur_transformation.txt");
VideoCapture cap(argv[1]);
assert(cap.isOpened());
Mat cur, cur_grey;
Mat prev, prev_grey;
cap >> prev;
cvtColor(prev, prev_grey, COLOR_BGR2GRAY);
// Step 1 - Get previous to current frame transformation (dx, dy, da) for all frames
vector <TransformParam> prev_to_cur_transform; // previous to current
int k=1;
int max_frames = cap.get(CV_CAP_PROP_FRAME_COUNT);
Mat last_T;
while(true) {
cap >> cur;
if(cur.data == NULL) {
break;
}
cvtColor(cur, cur_grey, COLOR_BGR2GRAY);
// vector from prev to cur
vector <Point2f> prev_corner, cur_corner;
vector <Point2f> prev_corner2, cur_corner2;
vector <uchar> status;
vector <float> err;
goodFeaturesToTrack(prev_grey, prev_corner, 200, 0.01, 30);
calcOpticalFlowPyrLK(prev_grey, cur_grey, prev_corner, cur_corner, status, err);
// weed out bad matches
for(size_t i=0; i < status.size(); i++) {
if(status[i]) {
prev_corner2.push_back(prev_corner[i]);
cur_corner2.push_back(cur_corner[i]);
}
}
// translation + rotation only
Mat T = estimateRigidTransform(prev_corner2, cur_corner2, false); // false = rigid transform, no scaling/shearing
// in rare cases no transform is found. We'll just use the last known good transform.
if(T.data == NULL) {
last_T.copyTo(T);
}
T.copyTo(last_T);
// decompose T
double dx = T.at<double>(0,2);
double dy = T.at<double>(1,2);
double da = atan2(T.at<double>(1,0), T.at<double>(0,0));
prev_to_cur_transform.push_back(TransformParam(dx, dy, da));
out_transform << k << " " << dx << " " << dy << " " << da << endl;
cur.copyTo(prev);
cur_grey.copyTo(prev_grey);
cout << "Frame: " << k << "/" << max_frames << " - good optical flow: " << prev_corner2.size() << endl;
k++;
}
// Step 2 - Accumulate the transformations to get the image trajectory
// Accumulated frame to frame transform
double a = 0;
double x = 0;
double y = 0;
vector <Trajectory> trajectory; // trajectory at all frames
for(size_t i=0; i < prev_to_cur_transform.size(); i++) {
x += prev_to_cur_transform[i].dx;
y += prev_to_cur_transform[i].dy;
a += prev_to_cur_transform[i].da;
trajectory.push_back(Trajectory(x,y,a));
out_trajectory << (i+1) << " " << x << " " << y << " " << a << endl;
}
// Step 3 - Smooth out the trajectory using an averaging window
vector <Trajectory> smoothed_trajectory; // trajectory at all frames
for(size_t i=0; i < trajectory.size(); i++) {
double sum_x = 0;
double sum_y = 0;
double sum_a = 0;
int count = 0;
for(int j=-SMOOTHING_RADIUS; j <= SMOOTHING_RADIUS; j++) {
if(i+j >= 0 && i+j < trajectory.size()) {
sum_x += trajectory[i+j].x;
sum_y += trajectory[i+j].y;
sum_a += trajectory[i+j].a;
count++;
}
}
double avg_a = sum_a / count;
double avg_x = sum_x / count;
double avg_y = sum_y / count;
smoothed_trajectory.push_back(Trajectory(avg_x, avg_y, avg_a));
out_smoothed_trajectory << (i+1) << " " << avg_x << " " << avg_y << " " << avg_a << endl;
}
// Step 4 - Generate new set of previous to current transform, such that the trajectory ends up being the same as the smoothed trajectory
vector <TransformParam> new_prev_to_cur_transform;
// Accumulated frame to frame transform
a = 0;
x = 0;
y = 0;
for(size_t i=0; i < prev_to_cur_transform.size(); i++) {
x += prev_to_cur_transform[i].dx;
y += prev_to_cur_transform[i].dy;
a += prev_to_cur_transform[i].da;
// target - current
double diff_x = smoothed_trajectory[i].x - x;
double diff_y = smoothed_trajectory[i].y - y;
double diff_a = smoothed_trajectory[i].a - a;
double dx = prev_to_cur_transform[i].dx + diff_x;
double dy = prev_to_cur_transform[i].dy + diff_y;
double da = prev_to_cur_transform[i].da + diff_a;
new_prev_to_cur_transform.push_back(TransformParam(dx, dy, da));
out_new_transform << (i+1) << " " << dx << " " << dy << " " << da << endl;
}
// Step 5 - Apply the new transformation to the video
cap.set(CV_CAP_PROP_POS_FRAMES, 0);
Mat T(2,3,CV_64F);
int vert_border = HORIZONTAL_BORDER_CROP * prev.rows / prev.cols; // get the aspect ratio correct
k=0;
while(k < max_frames-1) { // don't process the very last frame, no valid transform
cap >> cur;
if(cur.data == NULL) {
break;
}
T.at<double>(0,0) = cos(new_prev_to_cur_transform[k].da);
T.at<double>(0,1) = -sin(new_prev_to_cur_transform[k].da);
T.at<double>(1,0) = sin(new_prev_to_cur_transform[k].da);
T.at<double>(1,1) = cos(new_prev_to_cur_transform[k].da);
T.at<double>(0,2) = new_prev_to_cur_transform[k].dx;
T.at<double>(1,2) = new_prev_to_cur_transform[k].dy;
Mat cur2;
warpAffine(cur, cur2, T, cur.size());
cur2 = cur2(Range(vert_border, cur2.rows-vert_border), Range(HORIZONTAL_BORDER_CROP, cur2.cols-HORIZONTAL_BORDER_CROP));
// Resize cur2 back to cur size, for better side by side comparison
resize(cur2, cur2, cur.size());
// Now draw the original and stablised side by side for coolness
Mat canvas = Mat::zeros(cur.rows, cur.cols*2+10, cur.type());
cur.copyTo(canvas(Range::all(), Range(0, cur2.cols)));
cur2.copyTo(canvas(Range::all(), Range(cur2.cols+10, cur2.cols*2+10)));
// If too big to fit on the screen, then scale it down by 2, hopefully it'll fit :)
if(canvas.cols > 1920) {
resize(canvas, canvas, Size(canvas.cols/2, canvas.rows/2));
}
imshow("before and after", canvas);
//char str[256];
//sprintf(str, "images/%08d.jpg", k);
//imwrite(str, canvas);
waitKey(20);
k++;
}
return 0;
}