A collection of Python scripts to isolate and segment individual chips from scanning electron microscopy (SEM) images of drill cutting samples mounted on thin sections. 
Clone this repository to your local machine using your tool of choice. Install Anaconda –if you don't have it– and open the Anaconda Prompt. Navigate to the location of the cloned repository and install the environment using the command:
conda env create -f environment.yml
Installing the environment.yml might take some time.
Then, follow the instructions to activate the newly installed environment:
conda activate py_env
The terminal should look something like this:
Now you should be able to segment the individual drill cutting chips using the chip_identification script. Don't forget to use the py_env environment.
Open your local chip_identification and modify the input and output parameters.
The program expects img_names as a list containing one or more image names without the extension. Inside the loop, the variable fname_in uses the elements in img_names to assemble the full file name. The variable data_out_dir indicates where the output data will be saved. Other parameters that can be used for tuning include:
downscale_factor: to reduce the size of the original image;min_intensity: pixels with values smaller thanmin_intensityare considered background;max_intensity: pixels with values larger thanmax_intensityare considered as part of a chip;scale_nm_per_pixel: identifies the size of the pixels;min_holeandmin_chip: the smallest hole and smallest chip thresholds for topological operations.
Moreover, save_steps can be set to True so the program saves all steps of the segmentation, including the before and after for topological operations.
After setting the parameters, the program can be executed calling:
python chip_identification.py
The isolated and segmented chips are saved in the output folder specified in data_out_dir
Software here is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.