Update 'README.md'

git-subtree-dir: ros2_ws/src/perception_pcl
git-subtree-mainline: 0c9504b343
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README.md

@@ -1,3 +1,244 @@
 # greenhouse
 
-This repository contains code for detecting heat pipes in the greenhouse as well as estimating the pose of the pipes
+This repository contains code for detecting heat pipes in the greenhouse as well as estimating the pose of the pipes.
+
+Platform: ROS 2, Humble, Ubuntu 22.04
+
+## How to install dependencies??
+- Install `git`
+```
+sudo apt install git
+```
+- Install `ROS 2 Humble` on `Ubuntu 22.04` by following https://docs.ros.org/en/foxy/Installation/Ubuntu-Install-Debians.html
+- Install `colcon build` by following https://docs.ros.org/en/foxy/Tutorials/Beginner-CLI-Tools/Configuring-ROS2-Environment.html
+```
+sudo apt install python3-colcon-common-extensions
+```
+- Instal `pip` for python packages
+```
+sudo apt install python3-pip
+```
+- Now clone the repository
+```
+git clone https://tea.der-space.de/apoorva/greenhouse.git
+```
+- Install `ultralytics` for yolov3 package
+```
+pip install ultralytics
+```
+- For `yolov3_ros`, there are a bunch of other requirements. Go to `yolov3` folder and install using following commands:
+```
+cd ~/greenhouse/yolov3
+pip install -r requirements.txt
+```
+- Go to `ros2_ws` inside `greenhouse`. Make sure you only have `src` folder.
+```
+cd ~/greenhouse/ros2_ws
+ls
+src
+```
+- Inside `ros2_ws` folder, start building individual packages in the below sequence to avoid errors.
+```
+colcon build --packages-select pipe_msgs
+colcon build --packages-select pcl_ros
+colcon build --allow-overriding pcl_ros
+colcon build --packages-select pcl_conversions
+colcon build --allow-overriding pcl_conversions
+colcon build --packages-select find-pose
+colcon build --packages-select yolov3_ros
+. install/setup.bash
+```
+- The code should be ready to launch as explained in [How to run Live Detection?](#how-to-run-live-detection)
+This section explains what each module is responsible for.
+## perception_pcl
+This module is responsible for providing `pcl_conversions` and `pcl_ros` modules in `ros 2`.
+To build, run the following command:
+```
+cd ros2_ws/
+colcon build --packages-select perception_pcl
+. install/setup.bash
+```
+
+## pipe_msgs
+This module contains ros msgs for storing information about the detected object's bounding box.
+```
+cd ros2_ws/
+colcon build --packages-select pipe_msgs
+. install/setup.bash
+```
+To check if msgs are built properly, run following command
+```
+ros2 interface show pipe_msgs/msg/BoundingBox
+```
+The output will be:
+float64 probability
+int64 xmin
+int64 ymin
+int64 xmax
+int64 ymax
+int16 id
+string class_id
+
+
+## find-pose
+This ROS module is responsible for determining the position of the detected objects.
+The following input/ros topics are needed:
+- /rgb_img: RGB Image topic
+- /camera_info: Camera calibration parameters topic
+- /depth_img: Aligned depth image topic (aligned with rgb image)
+- /bboxes: Bounding box of each detected object. (Comes from yolov3 detection module)
+Output:
+- TF: Transform between camera_link and detected_object frame.
+How to build and run?
+This package is dependent on custom `pcl_conversion` and `pcl_ros` module. Make sure you have built those before building this package.
+```
+colcon build --packages-select find-pose
+. install/setup.bash
+ros2 launch find-pose find-pose-node.launch.py
+```
+All the topics can be remapped in the launch file.
+
+## yolov3_ros
+This ROS module is responsible for detecting the pipes from rgb image topic and also syncing the depth, rgb and camera_info topics.
+The following input/ros topics are needed:
+- /camera/color/image_raw: RGB Image topic
+- /camera/aligned_depth_to_color/image_raw: Aligned depth image topic (aligned with rgb image)
+- /camera/color/camera_info: Camera calibration parameters topic
+
+ROS Paramater Input:
+- best_weights: String that is the path to the best weights file of yolov3 detection
+Defaults: `'src/pipe_weights.pt'` inside `ros2_ws` folder
+
+The following are the output topics:
+- /detection_image: The RGB Image topic with bounding box drawn on it for visualization and debugging purpose
+- /bboxes: Bounding box of each detected object.
+- /rgb_img: Time Synced RGB Image topic
+- /camera_info: Time Synced Camera calibration parameters topic
+- /depth_img: Time Synced Aligned depth image topic (aligned with rgb image)
+
+How to build and run?
+```
+colcon build --packages-select yolov3_ros
+. install/setup.bash
+cd greenhouse/ros2_ws/
+ros2 launch yolov3_ros pipe_detection.launch.py
+```
+All the topics can be remapped in the launch file. The path to best_weights can also be changed inside the launch file.
+Launch file is stored in `yolov3_ros/launch/`.
+
+## yolov3
+This module contains code for yolov3. This method is being used to train the model to detect pipes in the greenhouse.
+- Ros bag was converted from ros 1 to ros 2 using rosbags module
+- Ros 2 bag was played, convert_2_img was used to subscribe to images and all the images were saved as .jpeg in a folder.
+- Images were labeled used labelImg.
+- Create a `custom-yolov3.yaml` file that has information about number of classes, location for labels,images.
+
+- Used google colab to run the yolov3 training.
+- Upload the label and images to drive and Mount the google drive in python notebook
+```
+from google.colab import drive
+drive.mount("/content/gdrive")
+```
+- Upload the yolov3 code and cd into the location of code
+```
+cd /content/gdrive/MyDrive/yolov3
+```
+- Run the training script
+```
+!python train.py --img 1280 --batch 16 --epochs 300 --data data/custom-yolov3.yaml --weights '' --cfg yolov3.yaml
+```
+- Important to note that we are not using any pre-trained weights.
+- Once training is finished, a `run` folder is created with exp number that stores the best weights (a .pt file).
+- This file is then used by detection to node to detect on new data. Update the image path and weights path to run detection.
+```
+!python detect.py --img 1280 --source ../pipe-dataset/validate/images/stereo_image103.jpeg --weights runs/train/exp14/weights/best.pt
+```
+- Once detect.py is finished, it create a new folder called `detect` inside `runs` folder that store the image with bounding box of detected object.
+More details: https://github.com/ultralytics/yolov3
+
+## convert_2_img
+This ROS module converts the rosbag data to images for yolo training purpose etc.
+- Make sure you have this module inside a ros workspace.
+- Create folder called `stereo` inside convert_2_img module.
+- Run following command to launch the node. Currently, this node listens for `/camera/color/image_raw` topic.
+```
+cd ros2_ws/src/convert_2_img
+python3 convert_2_img/convert_to_img.py
+```
+- Play the rosbag in another terminal
+```
+ros2 bag play bag/bag.db
+```
+- Once bag has finished playing, the images will be stored inside `stereo` folder.
+
+## labelImg
+This module is used to label images for yolo. The pre-defined custom classes file was changed to use new labels. This file is stored in `cd labelImg/data/predefined_classes.txt`
+To launch the gui, run
+```
+cd labelImg
+python3 labelImg.py 
+```
+More details: https://github.com/heartexlabs/labelImg
+
+## rosbags
+This module convert rosbags from ros 1 to ros 2. 
+```
+git clone https://gitlab.com/ternaris/rosbags.git
+cd rosbags
+python -m venv venv
+. venv/bin/activate
+
+pip install -r requirements-dev.txt
+pip install -e .
+
+rosbags-convert ../single_depth_color_640x480.bag
+```
+
+## flann_based
+Module that uses 3D model of the pipe to estimate pose. This method was not successful.
+
+## yolov7
+This module contains code for yolov7. This method was too heavy and didn't produce great results for detection.
+
+## darknet_ros2
+This module was provides interface to run neural network as rosnodes. The purpose was to use yolo model as ros node but this method was not successful
+
+## darknet_vendor
+This module was needed to build darknet_ros2.
+
+# How to run live detection?
+Once you have succefully built the ROS Modules specifically, `yolov3_ros` and `find-pose` along with dependencies like `pipe_msgs` and `perception_ros`, `perception_pcl`,you can do the following: 
+Assumption:
+You have the following required data in forms of ros topic:
+- /camera/color/image_raw: RGB Image topic
+- /camera/aligned_depth_to_color/image_raw: Aligned depth image topic (aligned with rgb image)
+- /camera/color/camera_info: Camera calibration parameters topic
+How do you get this data?
+- This data can come from a pre recorded Rosbag. If this is the case, do following:
+```
+ros2 bag play bag-folder/bag-name.db3
+```
+- This data can come directly from camera's (D455/ZED2I) ROS Node: Launch your node in a terminal. 
+- If your camera topics have different names than the default topic names mentioned above, update/remap ONLY the `pipe_detection.launch.py` script stored in `ros2_ws/src/yolov3_ros/launch` folder.
+- Once you have updated the launch file, build the code again using `colcon build --packages-select yolov3_ros` and proceedas mentioned below.
+
+Steps to run:
+- Open a terminal.
+- Run `$ cd greenhouse/ros2_ws/` and `$ . install/setup.bash`.
+- Launch node for object detection:
+```
+ros2 launch yolov3_ros pipe_detection.launch.py
+```
+- This node will output two topics: `/bboxes` and `/detection_image`.
+- Make sure that you run this launch file from `greenhouse/ros2_ws/` folder since the path of weights is relative (`/src/pipe_weights.pt`) inside the launch file.
+- Launch node for pose estimation:
+```
+ros2 launch find-pose find-pose-node.launch.py
+```
+- This node will output TF topics between `/camera_link` and `/${detected_object_name}`.
+- Open RVIZ by running `$ rviz2`. Change `fixed frame` from `map` to `camera_link`.
+    - Go to `Add` button. Under `By Display type`, select `tf`. Once tf is added, select required frames like `camera_link` and `l_trail` to see the tfs.
+    - To see the current image with detected object, Go to `Add` Button. Under `By Topic`, select topci called `detection_image`.
+    -You can add other topics as per the need and topic names. 
+- You can open launch files to update/remap topic name if different camera is being used. 
+- You can also update ros parameter from launch file. Currently, the `pipe_weights.pt` file is the one used. This file can be changed and you can update the parameter name `best_weights` inside the `pipe_detection.launch.py` file.

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flann_based/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/CsvReader.cpp

@@ -45,10 +45,14 @@ void CsvReader::readPLY(vector<Point3f> &list_vertex, vector<vector<int> > &list
              getline(liness, z);
 
              cv::Point3f tmp_p;
-             tmp_p.x = (float)StringToInt(x);
-             tmp_p.y = (float)StringToInt(y);
-             tmp_p.z = (float)StringToInt(z);
+            //  tmp_p.x = (float)StringToInt(x);
+            //  tmp_p.y = (float)StringToInt(y);
+            //  tmp_p.z = (float)StringToInt(z);
+             tmp_p.x = std::stod(x);
+             tmp_p.y = std::stod(y);
+             tmp_p.z = std::stod(z);
              list_vertex.push_back(tmp_p);
+             std::cout << "x: " << tmp_p.x << " y: " << tmp_p.y << " z: " << tmp_p.z << std::endl;
 
              count++;
              if(count == num_vertex)
@@ -70,6 +74,7 @@ void CsvReader::readPLY(vector<Point3f> &list_vertex, vector<vector<int> > &list
              tmp_triangle[0] = StringToInt(id0);
              tmp_triangle[1] = StringToInt(id1);
              tmp_triangle[2] = StringToInt(id2);
+             std::cout << "id0: " << tmp_triangle[0] << " id1: " << tmp_triangle[1] << " id2: " << tmp_triangle[2] << std::endl;
              list_triangles.push_back(tmp_triangle);
 
              count++;

flann_based/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/Mesh.cpp

@@ -75,4 +75,5 @@ void Mesh::load(const std::string& path)
     // Update mesh attributes
     num_vertices_ = (int)list_vertex_.size();
     num_triangles_ = (int)list_triangles_.size();
+    std::cout << "num_vertices_: " << num_vertices_ << std::endl;
 }

flann_based/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/Utils.cpp

@@ -19,6 +19,9 @@
 #include <opencv2/xfeatures2d.hpp>
 #endif
 
+#include <opencv2/line_descriptor.hpp>
+using namespace cv::line_descriptor;
+
 // For text
 const int fontFace = cv::FONT_ITALIC;
 const double fontScale = 0.75;
@@ -31,9 +34,9 @@ const int radius = 4;
 // Draw a text with the question point
 void drawQuestion(cv::Mat image, cv::Point3f point, cv::Scalar color)
 {
-    std::string x = IntToString((int)point.x);
-    std::string y = IntToString((int)point.y);
-    std::string z = IntToString((int)point.z);
+    std::string x = std::to_string(point.x);
+    std::string y = std::to_string(point.y);
+    std::string z = std::to_string(point.z);
 
     std::string text = " Where is point (" + x + ","  + y + "," + z + ") ?";
     cv::putText(image, text, cv::Point(25,50), fontFace, fontScale, color, thickness_font, 8);
@@ -71,7 +74,8 @@ void drawCounter(cv::Mat image, int n, int n_max, cv::Scalar color)
 {
     std::string n_str = IntToString(n);
     std::string n_max_str = IntToString(n_max);
-    std::string text = n_str + " of " + n_max_str + " points";
+    // std::string text = n_str + " of " + n_max_str + " points";
+    std::string text = "";
     cv::putText(image, text, cv::Point(500,50), fontFace, fontScale, color, thickness_font, 8);
 }
 

flann_based/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/main_detection.cpp

@@ -47,7 +47,7 @@ int main(int argc, char *argv[])
             "{inliers in        |30    | minimum inliers for Kalman update                                  }"
             "{method  pnp       |0     | PnP method: (0) ITERATIVE - (1) EPNP - (2) P3P - (3) DLS - (5) AP3P}"
             "{fast f            |true  | use of robust fast match                                           }"
-            "{feature           |ORB   | feature name (ORB, KAZE, AKAZE, BRISK, SIFT, SURF, BINBOOST, VGG)  }"
+            "{feature           |ORB   | feature name (Line, ORB, KAZE, AKAZE, BRISK, SIFT, SURF, BINBOOST, VGG)  }"
             "{FLANN             |false | use FLANN library for descriptors matching                         }"
             "{save              |      | path to the directory where to save the image results              }"
             "{displayFiltered   |false | display filtered pose (from Kalman filter)                         }"
@@ -62,14 +62,23 @@ int main(int argc, char *argv[])
     // samples::findFile("samples/cpp/tutorial_code/calib3d/real_time_pose_estimation/Data/box.ply");         // mesh
 
     // Intrinsic camera parameters: UVC WEBCAM
-    double f = 55;                           // focal length in mm
-    double sx = 22.3, sy = 14.9;             // sensor size
-    double width = 640, height = 480;        // image size
+    // double f = 55;                           // focal length in mm
+    // double sx = 22.3, sy = 14.9;             // sensor size
+    // double width = 640, height = 480;        // image size
 
-    double params_WEBCAM[] = { width*f/sx,   // fx
-                               height*f/sy,  // fy
-                               width/2,      // cx
-                               height/2};    // cy
+    // double params_WEBCAM[] = { width*f/sx,   // fx
+    //                            height*f/sy,  // fy
+    //                            width/2,      // cx
+    //                            height/2};    // cy
+
+    // Intrinsic camera parameters: Greenhouse Cameras
+    const double f = 45; // focal length in mm
+    const double sx = 22.3, sy = 14.9;
+    const double width = 1280, height = 720;
+    const double params_WEBCAM[] = { 643.008 ,   // fx
+                                642.201,  // fy
+                                648.812 ,      // cx
+                                360.262};    // cy
 
     // Some basic colors
     Scalar red(0, 0, 255);
@@ -92,7 +101,7 @@ int main(int argc, char *argv[])
 
     // PnP parameters
     int pnpMethod = SOLVEPNP_ITERATIVE;
-    string featureName = "ORB";
+    string featureName = "Line";
     bool useFLANN = false;
 
     // Save results
@@ -293,6 +302,7 @@ int main(int argc, char *argv[])
 
             // -- Step 6: Set estimated projection matrix
             pnp_detection_est.set_P_matrix(rotation_estimated, translation_estimated);
+            std::cout << "Current Pose!! x: " << translation_estimated.at<double>(0,0) << " y: " << translation_estimated.at<double>(1,0) << " z: " << translation_estimated.at<double>(2,0) << std::endl;
         }
 
         // -- Step X: Draw pose and coordinate frame

flann_based/cpp/tutorial_code/calib3d/real_time_pose_estimation/src/main_registration.cpp

@@ -13,15 +13,25 @@
 #include "ModelRegistration.h"
 #include "Utils.h"
 
+#include <opencv2/line_descriptor.hpp>
+
 using namespace cv;
 using namespace std;
-
+ using namespace cv::line_descriptor;
 /**  GLOBAL VARIABLES  **/
 
 // Boolean the know if the registration it's done
 bool end_registration = false;
-
 // Intrinsic camera parameters: UVC WEBCAM
+// const double f = 45; // focal length in mm
+// const double sx = 22.3, sy = 14.9;
+// const double width = 2592, height = 1944;
+// const double params_CANON[] = { width*f/sx,   // fx
+//                                 height*f/sy,  // fy
+//                                 width/2,      // cx
+//                                 height/2};    // cy
+
+// Intrinsic camera parameters: Greenhouse Cameras
 const double f = 45; // focal length in mm
 const double sx = 22.3, sy = 14.9;
 const double width = 1280, height = 720;
@@ -30,10 +40,16 @@ const double params_CANON[] = { 643.008 ,   // fx
                                 648.812 ,      // cx
                                 360.262};    // cy
 
+
 // Setup the points to register in the image
 // In the order of the *.ply file and starting at 1
-const int n = 8;
-const int pts[] = {1, 2, 3, 4, 5, 6, 7, 8}; // 3 -> 4
+// const int n = 8;
+// const int pts[] = {1, 2, 3, 4, 5, 6, 7, 8}; // 3 -> 4
+
+// Setup the points to register in the image
+// In the order of the *.ply file and starting at 1
+const int n = 16;
+const int pts[] = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}; // 3 -> 4
 
 /*
  * CREATE MODEL REGISTRATION OBJECT
@@ -89,7 +105,7 @@ int main(int argc, char *argv[])
             "{model         |      | path to output yml model                                           }"
             "{mesh          |      | path to ply mesh                                                   }"
             "{keypoints k   |2000  | number of keypoints to detect (only for ORB)                       }"
-            "{feature       |ORB   | feature name (ORB, KAZE, AKAZE, BRISK, SIFT, SURF, BINBOOST, VGG)  }"
+            "{feature       |ORB   | feature name (Line, ORB, KAZE, AKAZE, BRISK, SIFT, SURF, BINBOOST, VGG)  }"
             ;
     CommandLineParser parser(argc, argv, keys);
 
@@ -129,7 +145,15 @@ int main(int argc, char *argv[])
     //Instantiate robust matcher: detector, extractor, matcher
     RobustMatcher rmatcher;
     Ptr<Feature2D> detector, descriptor;
-    createFeatures(featureName, numKeyPoints, detector, descriptor);
+    Ptr<BinaryDescriptor> bd = BinaryDescriptor::createBinaryDescriptor();
+    if(featureName == "Line")
+    {
+        bd = BinaryDescriptor::createBinaryDescriptor();
+    }
+    else
+    {
+        createFeatures(featureName, numKeyPoints, detector, descriptor);
+    }
     rmatcher.setFeatureDetector(detector);
     rmatcher.setDescriptorExtractor(descriptor);
 
@@ -235,26 +259,110 @@ int main(int argc, char *argv[])
     vector<KeyPoint> keypoints_model;
     Mat descriptors;
 
-    // Compute keypoints and descriptors
-    rmatcher.computeKeyPoints(img_in, keypoints_model);
-    rmatcher.computeDescriptors(img_in, keypoints_model, descriptors);
     
-    // Check if keypoints are on the surface of the registration image and add to the model
-    for (unsigned int i = 0; i < keypoints_model.size(); ++i) {
-        Point2f point2d(keypoints_model[i].pt);
-        Point3f point3d;
-        bool on_surface = pnp_registration.backproject2DPoint(&mesh, point2d, point3d);
-        if (on_surface)
+    /* compute lines */
+    std::vector<KeyLine> keylines;
+
+    cv::Rect bbox(78, 152, 430, 550); // define the bounding box
+
+    cv::Mat roi = img_in(bbox); // extract the region of interest (ROI) inside the bounding box
+    imshow( "roi", roi );
+    waitKey();
+
+    if(featureName == "Line")
+    {
+        /* create a binary mask */
+        cv::Mat mask = Mat::ones( roi.size(), CV_8UC1 );
+        
+        bd->detect( roi, keylines, mask );
+        
+        bd->compute( roi, keylines, descriptors);
+    
+        cv::Mat output = img_in.clone();
+        /* draw lines extracted from octave 0 */
+        if( output.channels() == 1 )
+            cvtColor( output, output, COLOR_GRAY2BGR );
+        for ( size_t i = 0; i < keylines.size(); i++ )
         {
-            model.add_correspondence(point2d, point3d);
-            model.add_descriptor(descriptors.row(i));
-            model.add_keypoint(keypoints_model[i]);
+            KeyLine kl = keylines[i];
+            std::cout << "keyline length: " << kl.lineLength << std::endl;
+            if( kl.octave == 0 && kl.lineLength > 100)
+            {
+                /* get a random color */
+                int R = ( rand() % (int) ( 255 + 1 ) );
+                int G = ( rand() % (int) ( 255 + 1 ) );
+                int B = ( rand() % (int) ( 255 + 1 ) );
+            
+                /* get extremes of line */
+                Point pt1 = Point2f( kl.startPointX + bbox.x, kl.startPointY + bbox.y);
+                Point pt2 = Point2f( kl.endPointX + bbox.x, kl.endPointY + bbox.y);
+            
+                /* draw line */
+                line( output, pt1, pt2, Scalar( B, G, R ), 3 );
+            }
+        
         }
-        else
-        {
-            model.add_outlier(point2d);
+        
+        /* show lines on image */
+        imshow( "LSD lines", output );
+        imwrite( "lsd_lines.jpeg", output);
+        waitKey();
+        // Check if keypoints are on the surface of the registration image and add to the model
+        for (unsigned int i = 0; i < keylines.size(); ++i) {
+            KeyLine kl = keylines[i];
+            if( kl.octave == 0 && kl.lineLength > 100)
+            {
+                /* get extremes of line */
+                Point pt1 = Point2f( kl.startPointX + bbox.x, kl.startPointY + bbox.y);
+                Point pt2 = Point2f( kl.endPointX + bbox.x, kl.endPointY + bbox.y);
+                std::vector<Point2f> point2d;
+                // point2d.push_back(keylines[i].pt); // mid point of the line
+                point2d.push_back(pt1);
+                point2d.push_back(pt2);
+
+                for (unsigned int j = 0; j < point2d.size(); j++)
+                {
+                    Point3f point3d;
+                    bool on_surface = pnp_registration.backproject2DPoint(&mesh, point2d[j], point3d);
+                    if (on_surface)
+                    {
+                        model.add_correspondence(point2d[j], point3d);
+                        model.add_descriptor(descriptors.row(i));
+                        KeyPoint mid_point;
+                        mid_point.pt = point2d[j];
+                        model.add_keypoint(mid_point);
+                    }
+                    else
+                    {
+                        model.add_outlier(point2d[j]);
+                    }
+                }
+            }
         }
     }
+    else
+    {
+        // Compute keypoints and descriptors
+        rmatcher.computeKeyPoints(roi, keypoints_model);
+        rmatcher.computeDescriptors(roi, keypoints_model, descriptors);
+        // Check if keypoints are on the surface of the registration image and add to the model
+        for (unsigned int i = 0; i < keypoints_model.size(); ++i) {
+            Point2f point2d(keypoints_model[i].pt);
+            Point3f point3d;
+            bool on_surface = pnp_registration.backproject2DPoint(&mesh, point2d, point3d);
+            if (on_surface)
+            {
+                model.add_correspondence(point2d, point3d);
+                model.add_descriptor(descriptors.row(i));
+                model.add_keypoint(keypoints_model[i]);
+            }
+            else
+            {
+                model.add_outlier(point2d);
+            }
+        }
+
+    }
 
     model.set_trainingImagePath(img_path);
     // save the model into a *.yaml file

flann_based/sq_2.yml

@@ -0,0 +1,75 @@
+%YAML:1.0
+---
+points_3d: !!opencv-matrix
+   rows: 9
+   cols: 1
+   dt: "3f"
+   data: [ 5.56015968e-03, 1.42522305e-02, -2.98023224e-08,
+       3.35794687e-03, 1.43547952e-02, -2.98023224e-08, 4.15065885e-03,
+       1.42303258e-02, 0., 0., 4.48415509e-44, 0., 1.32452548e-02,
+       1.17547214e-02, 9.76203918e-01, -1.85193717e-02, 5.06480634e-01,
+       3.08125377e-01, 1.08953416e-02, 1.41046643e-02, 9.83510494e-01,
+       -2.08262205e-02, 5.04173756e-01, 3.06661725e-01, -2.39754319e-02,
+       5.01024604e-01, 1.64968729e-01 ]
+points_2d: !!opencv-matrix
+   rows: 9
+   cols: 1
+   dt: "2f"
+   data: [ 288., 6.45119995e+02, 2.88576019e+02, 6.48000061e+02,
+       2.88230438e+02, 6.46963318e+02, 8.65935547e+02, 4.77757568e+02,
+       5.25035645e+02, 3.18505035e+02, 8.68921570e+02, 4.77757568e+02,
+       5.26727722e+02, 3.18903198e+02, 8.67130005e+02, 4.80146362e+02,
+       9.17294556e+02, 5.51809998e+02 ]
+keypoints:
+   - [ 288., 6.4511999511718750e+02, 4.4640003204345703e+01,
+       2.0814511108398438e+02, 2.2853157133795321e-04, 2, -1 ]
+   - [ 2.8857601928710938e+02, 6.4800006103515625e+02,
+       5.3568004608154297e+01, 2.0699720764160156e+02,
+       2.5694066425785422e-04, 3, -1 ]
+   - [ 2.8823043823242188e+02, 6.4696331787109375e+02,
+       6.4281608581542969e+01, 2.0898962402343750e+02,
+       2.3757420422043651e-04, 4, -1 ]
+   - [ 8.6593554687500000e+02, 4.7775756835937500e+02,
+       7.7137939453125000e+01, 3.2710388183593750e+02,
+       2.0695610146503896e-04, 5, -1 ]
+   - [ 5.2503564453125000e+02, 3.1850503540039062e+02,
+       7.7137939453125000e+01, 1.6490226745605469e+01,
+       1.4561964781023562e-04, 5, -1 ]
+   - [ 8.6892156982421875e+02, 4.7775756835937500e+02,
+       9.2565528869628906e+01, 3.3250491333007812e+02,
+       1.7157701950054616e-04, 6, -1 ]
+   - [ 5.2672772216796875e+02, 3.1890319824218750e+02,
+       1.1107863616943359e+02, 4.8507041931152344e+01,
+       1.9299793348181993e-04, 7, -1 ]
+   - [ 8.6713000488281250e+02, 4.8014636230468750e+02,
+       1.1107863616943359e+02, 3.3898977661132812e+02,
+       1.3654734357260168e-04, 7, -1 ]
+   - [ 9.1729455566406250e+02, 5.5180999755859375e+02,
+       1.1107863616943359e+02, 3.0569372558593750e+02,
+       1.0311038204235956e-04, 7, -1 ]
+descriptors: !!opencv-matrix
+   rows: 9
+   cols: 32
+   dt: u
+   data: [ 59, 123, 112, 225, 229, 191, 248, 87, 15, 161, 190, 15, 143,
+       11, 86, 136, 238, 189, 223, 29, 156, 186, 81, 200, 215, 221, 154,
+       179, 143, 220, 219, 159, 186, 255, 112, 227, 245, 157, 254, 247,
+       13, 45, 254, 15, 175, 15, 22, 132, 238, 187, 159, 221, 222, 184,
+       80, 230, 255, 217, 142, 243, 39, 156, 216, 207, 42, 253, 114, 243,
+       53, 157, 252, 182, 173, 12, 190, 15, 174, 27, 118, 133, 238, 163,
+       223, 21, 238, 188, 80, 239, 255, 217, 158, 211, 6, 12, 217, 207,
+       9, 151, 175, 129, 244, 118, 189, 87, 60, 77, 222, 231, 156, 54,
+       162, 236, 109, 158, 191, 156, 149, 153, 49, 192, 255, 103, 190,
+       192, 138, 76, 165, 249, 90, 35, 19, 168, 59, 105, 25, 105, 173,
+       172, 139, 169, 206, 214, 104, 84, 246, 21, 237, 32, 16, 3, 163,
+       212, 144, 244, 43, 255, 34, 6, 37, 119, 19, 155, 167, 241, 246,
+       190, 60, 87, 53, 89, 222, 102, 185, 63, 180, 109, 230, 31, 63,
+       156, 147, 217, 33, 196, 127, 251, 246, 194, 166, 204, 181, 255,
+       67, 26, 205, 189, 114, 136, 105, 19, 62, 73, 82, 245, 189, 7, 146,
+       88, 6, 45, 63, 157, 135, 156, 197, 69, 196, 58, 186, 192, 44, 132,
+       1, 89, 3, 147, 63, 249, 182, 140, 104, 55, 63, 25, 82, 114, 176,
+       31, 180, 97, 230, 27, 63, 145, 211, 216, 67, 213, 12, 58, 254,
+       194, 148, 128, 241, 173, 92, 205, 106, 206, 157, 210, 254, 232,
+       196, 128, 207, 2, 107, 233, 61, 96, 120, 254, 139, 234, 72, 163,
+       28, 253, 251, 237, 143, 73, 51, 59, 124, 42 ]
+training_image_path: "/media/psf/freelancing/greenhouse/flann_based/stereo_image782.jpeg"

flann_based/sq_2_try.ply

@@ -0,0 +1,38 @@
+ply
+format ascii 1.0
+comment Created by Blender 3.4.1 - www.blender.org
+element vertex 16
+property float x
+property float y
+property float z
+element face 12
+property list uchar uint vertex_indices
+end_header
+0.000000 0.025000 0.000000
+0.000000 0.025000 1.000000
+0.025000 0.000000 1.000000
+0.025000 0.000000 0.000000
+0.000000 -0.025000 1.000000
+0.000000 -0.025000 0.000000
+-0.025000 0.000000 1.000000
+-0.025000 0.000000 0.000000
+0.000000 0.525000 0.000000
+0.000000 0.525000 1.000000
+0.025000 0.500000 1.000000
+0.025000 0.500000 0.000000
+0.000000 0.475000 1.000000
+0.000000 0.475000 0.000000
+-0.025000 0.500000 1.000000
+-0.025000 0.500000 0.000000
+4 0 1 2 3
+4 3 2 4 5
+4 2 1 6 4
+4 5 4 6 7
+4 7 6 1 0
+4 0 3 5 7
+4 8 9 10 11
+4 11 10 12 13
+4 10 9 14 12
+4 13 12 14 15
+4 15 14 9 8
+4 8 11 13 15

labelImg/.github/no-response.yml

@@ -0,0 +1,8 @@
+daysUntilClose: 14
+responseRequiredLabel: more-information-needed
+closeComment: >
+  This issue has been automatically closed because there has been no response
+  to our request for more information from the original author. With only the
+  information that is currently in the issue, we don't have enough information
+  to take action or reproduce the issue. Please reach out if you have or find
+  the answers we need so that we can investigate further.

labelImg/.github/workflows/package.yml

@@ -0,0 +1,66 @@
+name: Package
+on: [push, pull_request]
+
+jobs:
+  package-macos:
+    runs-on: macos-latest
+    env:
+      PIPENV_VENV_IN_PROJECT: 1
+      PIPENV_IGNORE_VIRTUALENVS: 1
+    steps:
+      - uses: actions/checkout@v3
+      - name: Setup Python Environment
+        run: |
+          pip3 install pipenv
+          pipenv install pyqt5 lxml
+          pipenv run pip install pyqt5==5.15.6 lxml
+      - name: Build LabelImg
+        run: |
+          pipenv run make qt5py3
+          rm -rf build dist
+      - name: Package LabelImg
+        run: |
+          pipenv run python setup.py py2app
+          open dist/labelImg.app
+      - name: Archive macOS app
+        run: |
+          cd dist/
+          tar czf labelImg.tgz labelImg.app
+      - uses: actions/upload-artifact@v3
+        with:
+          name: macOS artifact
+          path: dist/labelImg.tgz
+  package-windows:
+    runs-on: windows-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Setup Python Environment
+        run: |
+          pip3 install pyinstaller pyqt5==5.15.6 lxml
+      - name: Build LabelImg
+        run: |
+          pyrcc5 -o libs/resources.py resources.qrc
+      - name: Package LabelImg
+        run: |
+          pyinstaller --hidden-import=pyqt5 --hidden-import=lxml -F -n "labelImg" -c labelImg.py -p ./libs -p ./
+      - uses: actions/upload-artifact@v3
+        with:
+          name: Windows artifact
+          path: dist/labelImg.exe
+  package-linux:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Setup Python Environment
+        run: |
+          pip3 install pyinstaller pyqt5==5.15.6 lxml
+      - name: Build LabelImg
+        run: |
+          pyrcc5 -o libs/resources.py resources.qrc
+      - name: Package LabelImg
+        run: |
+          pyinstaller --hidden-import=pyqt5 --hidden-import=lxml -F -n "labelImg" -c labelImg.py -p ./libs -p ./
+      - uses: actions/upload-artifact@v3
+        with:
+          name: Linux artifact
+          path: dist/labelImg

labelImg/.gitignore

@@ -0,0 +1,43 @@
+resources/icons/.DS_Store
+resources.py
+labelImg.egg-info*
+
+*.pyc
+.*.swp
+
+build/
+dist/
+
+tags
+cscope*
+.ycm_extra_conf.py
+.subvimrc
+.vscode
+*.pkl
+Pipfile
+*.xml
+
+# MacOS System-Generated
+.DS_Store
+.DS_Store?
+._*
+.Spotlight-V100
+.Trashes
+ehthumbs.db
+Thumbs.db
+
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# IDE
+*.DS_Store
+*.iml
+.idea
+.unison*
+.attach*
+tmp.*

labelImg/CONTRIBUTING.rst

@@ -0,0 +1,3 @@
+TzuTa Lin
+[LabelMe](http://labelme2.csail.mit.edu/Release3.0/index.php)
+Ryan Flynn

labelImg/HISTORY.rst

@@ -0,0 +1,130 @@
+History
+=======
+
+1.8.6 (2021-10-10)
+------------------
+
+* Display box width and height
+
+
+1.8.5 (2021-04-11)
+------------------
+
+* Merged a couple of PRs
+* Fixed issues
+* Support CreateML format
+
+
+1.8.4 (2020-11-04)
+------------------
+
+* Merged a couple of PRs
+* Fixed issues
+
+1.8.2 (2018-12-02)
+------------------
+
+* Fix pip depolyment issue
+
+
+1.8.1 (2018-12-02)
+------------------
+
+* Fix issues
+* Support zh-Tw strings
+
+
+1.8.0 (2018-10-21)
+------------------
+
+* Support drawing sqaure rect
+* Add item single click slot
+* Fix issues
+
+1.7.0 (2018-05-18)
+------------------
+
+* Support YOLO
+* Fix minor issues
+
+
+1.6.1 (2018-04-17)
+------------------
+
+* Fix issue
+
+1.6.0 (2018-01-29)
+------------------
+
+* Add more pre-defined labels
+* Show cursor pose in status bar
+* Fix minor issues
+
+1.5.2 (2017-10-24)
+------------------
+
+* Assign different colors to different lablels
+
+1.5.1 (2017-9-27)
+------------------
+
+* Show a autosaving dialog
+
+1.5.0 (2017-9-14)
+------------------
+
+* Fix the issues
+* Add feature: Draw a box easier
+
+
+1.4.3 (2017-08-09)
+------------------
+
+* Refactor setting
+* Fix the issues
+
+
+1.4.0 (2017-07-07)
+------------------
+
+* Add feature: auto saving
+* Add feature: single class mode
+* Fix the issues
+
+1.3.4 (2017-07-07)
+------------------
+
+* Fix issues and improve zoom-in
+
+1.3.3 (2017-05-31)
+------------------
+
+* Fix issues
+
+1.3.2 (2017-05-18)
+------------------
+
+* Fix issues
+
+
+1.3.1 (2017-05-11)
+------------------
+
+* Fix issues
+
+1.3.0 (2017-04-22)
+------------------
+
+* Fix issues
+* Add difficult tag
+* Create new files for pypi
+
+1.2.3 (2017-04-22)
+------------------
+
+* Fix issues
+
+1.2.2 (2017-01-09)
+------------------
+
+* Fix issues

labelImg/LICENSE

@@ -0,0 +1,9 @@
+Copyright (c) <2015-Present> Tzutalin
+
+Copyright (C) 2013  MIT, Computer Science and Artificial Intelligence Laboratory. Bryan Russell, Antonio Torralba, William T. Freeman
+
+Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

labelImg/MANIFEST.in

@@ -0,0 +1,15 @@
+include CONTRIBUTING.rst
+include HISTORY.rst
+include LICENSE
+include README.rst
+
+include resources.qrc
+
+recursive-include data *
+recursive-include icons *
+recursive-include libs *
+
+recursive-exclude build-tools *
+recursive-exclude tests *
+recursive-exclude * __pycache__
+recursive-exclude * *.py[co]

labelImg/Makefile

@@ -0,0 +1,35 @@
+# ex: set ts=8 noet:
+
+all: qt5 test
+
+test: testpy3
+
+testpy2:
+	python -m unittest discover tests
+
+testpy3:
+	python3 -m unittest discover tests
+
+qt4: qt4py2
+
+qt5: qt5py3
+
+qt4py2:
+	pyrcc4 -py2 -o libs/resources.py resources.qrc
+
+qt4py3:
+	pyrcc4 -py3 -o libs/resources.py resources.qrc
+
+qt5py3:
+	pyrcc5 -o libs/resources.py resources.qrc
+
+clean:
+	rm -rf ~/.labelImgSettings.pkl *.pyc dist labelImg.egg-info __pycache__ build
+
+pip_upload:
+	python3 setup.py upload
+
+long_description:
+	restview --long-description
+
+.PHONY: all

labelImg/README.rst

@@ -0,0 +1,317 @@
+.. image:: /readme/images/labelimg.png
+        :target: https://github.com/heartexlabs/label-studio
+
+Label Studio is a modern, multi-modal data annotation tool
+=======
+
+LabelImg, the popular image annotation tool created by Tzutalin with the help of dozens contributors, is no longer actively being developed and has become part of the Label Studio community. Check out `Label Studio <https://github.com/heartexlabs/label-studio>`__, the most flexible open source data labeling tool for images, text, hypertext, audio, video and time-series data. `Install <https://labelstud.io/guide/install.html>`__ Label Studio and join the `slack community <https://label-studio.slack.com/>`__ to get started.
+
+.. image:: /readme/images/label-studio-1-6-player-screenshot.png
+        :target: https://github.com/heartexlabs/label-studio
+
+About LabelImg
+========
+
+.. image:: https://img.shields.io/pypi/v/labelimg.svg
+        :target: https://pypi.python.org/pypi/labelimg
+
+.. image:: https://img.shields.io/github/workflow/status/tzutalin/labelImg/Package?style=for-the-badge
+        :alt: GitHub Workflow Status
+
+.. image:: https://img.shields.io/badge/lang-en-blue.svg
+        :target: https://github.com/tzutalin/labelImg
+
+.. image:: https://img.shields.io/badge/lang-zh-green.svg
+        :target: https://github.com/tzutalin/labelImg/blob/master/readme/README.zh.rst
+
+.. image:: https://img.shields.io/badge/lang-jp-green.svg
+        :target: https://github.com/tzutalin/labelImg/blob/master/readme/README.jp.rst
+
+LabelImg is a graphical image annotation tool.
+
+It is written in Python and uses Qt for its graphical interface.
+
+Annotations are saved as XML files in PASCAL VOC format, the format used
+by `ImageNet <http://www.image-net.org/>`__.  Besides, it also supports YOLO and CreateML formats.
+
+.. image:: https://raw.githubusercontent.com/tzutalin/labelImg/master/demo/demo3.jpg
+     :alt: Demo Image
+
+.. image:: https://raw.githubusercontent.com/tzutalin/labelImg/master/demo/demo.jpg
+     :alt: Demo Image
+
+`Watch a demo video <https://youtu.be/p0nR2YsCY_U>`__
+
+Installation
+------------------
+
+Get from PyPI but only python3.0 or above
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+This is the simplest (one-command) install method on modern Linux distributions such as Ubuntu and Fedora.
+
+.. code:: shell
+
+    pip3 install labelImg
+    labelImg
+    labelImg [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
+
+
+Build from source
+~~~~~~~~~~~~~~~~~
+
+Linux/Ubuntu/Mac requires at least `Python
+2.6 <https://www.python.org/getit/>`__ and has been tested with `PyQt
+4.8 <https://www.riverbankcomputing.com/software/pyqt/intro>`__. However, `Python
+3 or above <https://www.python.org/getit/>`__ and  `PyQt5 <https://pypi.org/project/PyQt5/>`__ are strongly recommended.
+
+
+Ubuntu Linux
+^^^^^^^^^^^^
+
+Python 3 + Qt5
+
+.. code:: shell
+
+    sudo apt-get install pyqt5-dev-tools
+    sudo pip3 install -r requirements/requirements-linux-python3.txt
+    make qt5py3
+    python3 labelImg.py
+    python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
+
+macOS
+^^^^^
+
+Python 3 + Qt5
+
+.. code:: shell
+
+    brew install qt  # Install qt-5.x.x by Homebrew
+    brew install libxml2
+
+    or using pip
+
+    pip3 install pyqt5 lxml # Install qt and lxml by pip
+
+    make qt5py3
+    python3 labelImg.py
+    python3 labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
+
+
+Python 3 Virtualenv (Recommended)
+
+Virtualenv can avoid a lot of the QT / Python version issues
+
+.. code:: shell
+
+    brew install python3
+    pip3 install pipenv
+    pipenv run pip install pyqt5==5.15.2 lxml
+    pipenv run make qt5py3
+    pipenv run python3 labelImg.py
+    [Optional] rm -rf build dist; pipenv run python setup.py py2app -A;mv "dist/labelImg.app" /Applications
+
+Note: The Last command gives you a nice .app file with a new SVG Icon in your /Applications folder. You can consider using the script: build-tools/build-for-macos.sh
+
+
+Windows
+^^^^^^^
+
+Install `Python <https://www.python.org/downloads/windows/>`__,
+`PyQt5 <https://www.riverbankcomputing.com/software/pyqt/download5>`__
+and `install lxml <http://lxml.de/installation.html>`__.
+
+Open cmd and go to the `labelImg <#labelimg>`__ directory
+
+.. code:: shell
+
+    pyrcc4 -o libs/resources.py resources.qrc
+    For pyqt5, pyrcc5 -o libs/resources.py resources.qrc
+
+    python labelImg.py
+    python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
+
+If you want to package it into a separate EXE file
+
+.. code:: shell
+
+    Install pyinstaller and execute:
+
+    pip install pyinstaller
+    pyinstaller --hidden-import=pyqt5 --hidden-import=lxml -F -n "labelImg" -c labelImg.py -p ./libs -p ./
+
+Windows + Anaconda
+^^^^^^^^^^^^^^^^^^
+
+Download and install `Anaconda <https://www.anaconda.com/download/#download>`__ (Python 3+)
+
+Open the Anaconda Prompt and go to the `labelImg <#labelimg>`__ directory
+
+.. code:: shell
+
+    conda install pyqt=5
+    conda install -c anaconda lxml
+    pyrcc5 -o libs/resources.py resources.qrc
+    python labelImg.py
+    python labelImg.py [IMAGE_PATH] [PRE-DEFINED CLASS FILE]
+
+Use Docker
+~~~~~~~~~~~~~~~~~
+.. code:: shell
+
+    docker run -it \
+    --user $(id -u) \
+    -e DISPLAY=unix$DISPLAY \
+    --workdir=$(pwd) \
+    --volume="/home/$USER:/home/$USER" \
+    --volume="/etc/group:/etc/group:ro" \
+    --volume="/etc/passwd:/etc/passwd:ro" \
+    --volume="/etc/shadow:/etc/shadow:ro" \
+    --volume="/etc/sudoers.d:/etc/sudoers.d:ro" \
+    -v /tmp/.X11-unix:/tmp/.X11-unix \
+    tzutalin/py2qt4
+
+    make qt4py2;./labelImg.py
+
+You can pull the image which has all of the installed and required dependencies. `Watch a demo video <https://youtu.be/nw1GexJzbCI>`__
+
+
+Usage
+-----
+
+Steps (PascalVOC)
+~~~~~~~~~~~~~~~~~
+
+1. Build and launch using the instructions above.
+2. Click 'Change default saved annotation folder' in Menu/File
+3. Click 'Open Dir'
+4. Click 'Create RectBox'
+5. Click and release left mouse to select a region to annotate the rect
+   box
+6. You can use right mouse to drag the rect box to copy or move it
+
+The annotation will be saved to the folder you specify.
+
+You can refer to the below hotkeys to speed up your workflow.
+
+Steps (YOLO)
+~~~~~~~~~~~~
+
+1. In ``data/predefined_classes.txt`` define the list of classes that will be used for your training.
+
+2. Build and launch using the instructions above.
+
+3. Right below "Save" button in the toolbar, click "PascalVOC" button to switch to YOLO format.
+
+4. You may use Open/OpenDIR to process single or multiple images. When finished with a single image, click save.
+
+A txt file of YOLO format will be saved in the same folder as your image with same name. A file named "classes.txt" is saved to that folder too. "classes.txt" defines the list of class names that your YOLO label refers to.
+
+Note:
+
+- Your label list shall not change in the middle of processing a list of images. When you save an image, classes.txt will also get updated, while previous annotations will not be updated.
+
+- You shouldn't use "default class" function when saving to YOLO format, it will not be referred.
+
+- When saving as YOLO format, "difficult" flag is discarded.
+
+Create pre-defined classes
+~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+You can edit the
+`data/predefined\_classes.txt <https://github.com/tzutalin/labelImg/blob/master/data/predefined_classes.txt>`__
+to load pre-defined classes
+
+Annotation visualization
+~~~~~~~~~~~~~~~~~~~~~~~~
+
+1. Copy the existing lables file to same folder with the images. The labels file name must be same with image file name.
+
+2. Click File and choose 'Open Dir' then Open the image folder.
+
+3. Select image in File List, it will appear the bounding box and label for all objects in that image.
+
+(Choose Display Labels mode in View to show/hide lablels)
+
+
+Hotkeys
+~~~~~~~
+
++--------------------+--------------------------------------------+
+| Ctrl + u           | Load all of the images from a directory    |
++--------------------+--------------------------------------------+
+| Ctrl + r           | Change the default annotation target dir   |
++--------------------+--------------------------------------------+
+| Ctrl + s           | Save                                       |
++--------------------+--------------------------------------------+
+| Ctrl + d           | Copy the current label and rect box        |
++--------------------+--------------------------------------------+
+| Ctrl + Shift + d   | Delete the current image                   |
++--------------------+--------------------------------------------+
+| Space              | Flag the current image as verified         |
++--------------------+--------------------------------------------+
+| w                  | Create a rect box                          |
++--------------------+--------------------------------------------+
+| d                  | Next image                                 |
++--------------------+--------------------------------------------+
+| a                  | Previous image                             |
++--------------------+--------------------------------------------+
+| del                | Delete the selected rect box               |
++--------------------+--------------------------------------------+
+| Ctrl++             | Zoom in                                    |
++--------------------+--------------------------------------------+
+| Ctrl--             | Zoom out                                   |
++--------------------+--------------------------------------------+
+| ↑→↓←               | Keyboard arrows to move selected rect box  |
++--------------------+--------------------------------------------+
+
+**Verify Image:**
+
+When pressing space, the user can flag the image as verified, a green background will appear.
+This is used when creating a dataset automatically, the user can then through all the pictures and flag them instead of annotate them.
+
+**Difficult:**
+
+The difficult field is set to 1 indicates that the object has been annotated as "difficult", for example, an object which is clearly visible but difficult to recognize without substantial use of context.
+According to your deep neural network implementation, you can include or exclude difficult objects during training.
+
+How to reset the settings
+~~~~~~~~~~~~~~~~~~~~~~~~~
+
+In case there are issues with loading the classes, you can either:
+
+1. From the top menu of the labelimg click on Menu/File/Reset All
+2. Remove the `.labelImgSettings.pkl` from your home directory. In Linux and Mac you can do:
+    `rm ~/.labelImgSettings.pkl`
+
+
+How to contribute
+~~~~~~~~~~~~~~~~~
+
+Send a pull request
+
+License
+~~~~~~~
+`Free software: MIT license <https://github.com/tzutalin/labelImg/blob/master/LICENSE>`_
+
+Citation: Tzutalin. LabelImg. Git code (2015). https://github.com/tzutalin/labelImg
+
+Related and additional tools
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+1. `Label Studio <https://github.com/heartexlabs/label-studio>`__ to label images, text, audio, video and time-series data for machine learning and AI
+2. `ImageNet Utils <https://github.com/tzutalin/ImageNet_Utils>`__ to
+   download image, create a label text for machine learning, etc
+3. `Use Docker to run labelImg <https://hub.docker.com/r/tzutalin/py2qt4>`__
+4. `Generating the PASCAL VOC TFRecord files <https://github.com/tensorflow/models/blob/4f32535fe7040bb1e429ad0e3c948a492a89482d/research/object_detection/g3doc/preparing_inputs.md#generating-the-pascal-voc-tfrecord-files>`__
+5. `App Icon based on Icon by Nick Roach (GPL) <https://www.elegantthemes.com/>`__
+6. `Setup python development in vscode <https://tzutalin.blogspot.com/2019/04/set-up-visual-studio-code-for-python-in.html>`__
+7. `The link of this project on iHub platform <https://code.ihub.org.cn/projects/260/repository/labelImg>`__
+8. `Convert annotation files to CSV format or format for Google Cloud AutoML <https://github.com/tzutalin/labelImg/tree/master/tools>`__
+
+
+
+Stargazers over time
+~~~~~~~~~~~~~~~~~~~~
+
+.. image:: https://starchart.cc/tzutalin/labelImg.svg
+

labelImg/build-tools/.gitignore

@@ -0,0 +1,12 @@
+*.spec
+build
+dist
+pyinstaller
+python-2.*
+pywin32*
+virtual-wine
+venv_wine
+PyQt4-*
+lxml-*
+windows_v*
+linux_v*

labelImg/build-tools/README.md

@@ -0,0 +1,35 @@
+### Deploy to PyPI
+
+```
+cd [ROOT]
+sh build-tools/build-for-pypi.sh
+```
+
+### Build for Ubuntu
+
+```
+cd build-tools
+sh run-in-container.sh
+sh envsetup.sh
+sh build-ubuntu-binary.sh
+```
+
+### Build for Windows
+
+```
+cd build-tools
+sh run-in-container.sh
+sh envsetup.sh
+sh build-windows-binary.sh
+```
+
+### Build for macOS High Sierra
+```
+cd build-tools
+./build-for-macos.sh
+```
+
+Note: If there are some problems, try to
+```
+sudo rm -rf virtual-wne venv_wine
+```

labelImg/build-tools/build-for-macos.sh

@@ -0,0 +1,30 @@
+#!/bin/sh
+
+brew install python@2
+pip install --upgrade virtualenv
+
+# clone labelimg source
+rm -rf /tmp/labelImgSetup
+mkdir /tmp/labelImgSetup
+cd /tmp/labelImgSetup
+curl https://codeload.github.com/tzutalin/labelImg/zip/master --output labelImg.zip
+unzip labelImg.zip
+rm labelImg.zip
+
+# setup python3 space
+virtualenv --system-site-packages  -p python3 /tmp/labelImgSetup/labelImg-py3
+source /tmp/labelImgSetup/labelImg-py3/bin/activate
+cd labelImg-master
+
+# build labelImg app
+pip install py2app
+pip install PyQt5 lxml
+make qt5py3
+rm -rf build dist
+python setup.py py2app -A
+mv "/tmp/labelImgSetup/labelImg-master/dist/labelImg.app" /Applications
+# deactivate python3
+deactivate
+cd ../
+rm -rf /tmp/labelImgSetup
+echo 'DONE'