updated to readme wip

README.md

@@ -1,3 +1,107 @@
 # 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 build the workspace?
+
+How to install dependencies??
+
+
+This section explains what each module is responsible for.
+
+## pipe_msgs
+
+## find-pose
+
+## yolov3_ros
+
+## 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
+```
+
+## pcl_conversions
+
+## pcl_ros
+
+
+## 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.
+
+
+
+