diff --git a/README.md b/README.md index c8cca31d325ca823adcdd66221c9cc15b32bb27c..32d20ac7fe680714772597bee5233a60e450033f 100644 --- a/README.md +++ b/README.md @@ -1,22 +1,22 @@ # Segment IPP -## This is a repository with the code for segmentation of pellet object from the image data ### +## This is a repository with the code for the segmentation of a pellet object from the image data ### -The code is built on the recently released Meta model, segment-anything. It is capable to segment different objects in the image data with high precision. Objects are then analyzed using another computer vision algorithms and only the pellet is taken as an output for the further processing. +The code is built on Meta's recently released neural network model, segment-anything. It can segment different objects in the image data with high precision. Objects are then analysed using other computer vision algorithms, and only the pellet is taken as an output for further processing. --- ## Installation --- -Clone the repository on your disk. Then use the Conda package and environment managenent system for Python. Using the *.yaml files you can create a Python environment on your system and install the required modules. There are two versions, a regular CPU version (environment.yaml) and a GPU version (environment_GPU.yaml). If you have a CUDA compatible GPU use the GPU yaml file to install the GPU support. It will provide faster segmentation of image data. +Clone the repository on your disk. Then, use the Conda package and environment management system for Python. Using the *.yaml files, you can create a Python environment on your system and install the required modules. There are two versions: a regular CPU (environment.yaml) and a GPU (environment_GPU.yaml). If you have a CUDA-compatible GPU, use the GPU yaml file to install the GPU support. It will provide faster segmentation of image data. -To create CPU environment use: +To create a CPU environment, use: ``` conda env create -f environment.yaml ``` -To create GPU environment use: +To create a GPU environment, use: ``` conda env create -f environment_GPU.yaml @@ -26,7 +26,7 @@ conda env create -f environment_GPU.yaml ## Measure the pellet --- -To measure the size of the pellet in the images and create a time of a size evolution use either the eval_images.py or eval_images_GPU.py. These should be run as a python main script that expects four arguments as an input *Path to model*, *Path to data*, *Time stamp of a measured sample*, and *Sample size*. Path should be provided as an absolute path. Time stamp should be in the format "Y:m:d H:M:S" and should match the capture time of one of the images submitted as a data. Sample size should be provided with units as well to assign the correct units in the final plot. Individual arguments should be provided as strings, i.e. in quotes. +To measure the size of the pellet in the images and create a time of size evolution, use either the eval_images.py or eval_images_GPU.py. These should be run as a Python main script that expects four arguments as input: *Path to model*, *Path to data*, *Time stamp of a measured sample*, and *Sample size*. The path should be provided as an absolute path. The time stamp should be in the format "Y:m:d H:M:S" and should match the capture time of one of the images submitted as data. Sample size should also be provided with units to assign the correct units in the final plot. Individual arguments should be provided as strings, i.e. in quotes. Example: ``` @@ -38,7 +38,3 @@ python eval_images.py "Path\\to\\model\\model.pth" "Path\\to\\image_data\\folder --- The model is licensed under the [Apache 2.0 license](LICENSE.txt). - - - -