update app

.gitattributes

@@ -1,35 +1,2 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ckpt filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
+FastSAM-x.pt filter=lfs diff=lfs merge=lfs -text
 *.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tar filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
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+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

LICENSE

@@ -0,0 +1,201 @@
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README.md

@@ -1,13 +1,75 @@
----
-title: GrainSight
-emoji: 🏆
-colorFrom: green
-colorTo: gray
-sdk: streamlit
-sdk_version: 1.33.0
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# GrainSight: README
+
+GrainSight is a user-friendly application designed for petrographers and microscopists to perform real-time grain segmentation and analysis on microscopic thin section images. Built on top of the powerful FastSAM segmentation model, GrainSight allows you to extract quantitative data and insights from your images, aiding in various petrographic studies.
+
+## Importance in Petrographic Studies:
+
+- **Automated Grain Segmentation**: GrainSight eliminates the need for manual grain boundary tracing, saving significant time and effort.
+- **Quantitative Analysis**: Extract object-specific parameters such as area, perimeter, roundness, aspect ratio, and longest length, enabling quantitative analysis of grain characteristics.
+- **Mineral Identification and Classification**: The extracted parameters can assist in mineral identification and classification based on their morphological properties.
+- **Textural Analysis**: Grain size distribution and spatial arrangement of grains can be studied to understand the depositional and diagenetic history of rocks.
+
+## Installation and Usage:
+
+### 1. Create a Virtual Environment (Recommended):
+
+It's recommended to use a virtual environment to manage project-specific dependencies. You can create one using venv or conda:
+
+```bash
+# Using venv
+python3 -m venv grainsight_env
+
+# Using conda
+conda create -n grainsight_env python=3.8  # Replace 3.8 with your desired Python version
+```
+
+### 2. Activate the Virtual Environment:
+
+```bash
+# For venv
+source grainsight_env/bin/activate  # On Linux/macOS
+grainsight_env\Scripts\activate  # On Windows
+
+# For conda
+conda activate grainsight_env
+```
+
+### 3. Clone the GrainSight Repository:
+
+```bash
+git clone https://github.com/fazzam12345/grainsight.git
+```     
+
+### 4. Install Requirements:
+
+Install the required libraries from the requirements.txt file:
+
+```bash
+pip install -r requirements.txt
+```
+
+### 5. Run the Application:
+
+Start the Streamlit application:
+
+```bash
+streamlit run app.py
+```
+
+This will open the GrainSight application in your web browser.
+
+### Usage:
+
+- **Upload an Image**: Select a microscopic thin section image in JPG, PNG, or JPEG format.
+- **Set Parameters (Optional)**: Adjust segmentation parameters like input size, IOU threshold, and confidence threshold as needed.
+- **Draw a Line for Scale**: Draw a line on the image and enter its real-world length (in micrometers) to set the scale for measurements.
+- **Run Segmentation**: Click the "Run Segmentation" button to segment the image and extract grain parameters.
+- **Analyze Results**: View the segmented image and the table of calculated grain parameters. You can also download the data as a CSV file.
+- **Visualize Distributions**: Select a parameter to plot its distribution and gain further insights into grain characteristics.
+
+### Additional Notes:
+
+- **Dependencies**: Make sure you have the required versions of Python, PyTorch, Torchvision, and other libraries installed. Refer to the requirements.txt file for details.
+- **GPU Acceleration**: For faster processing, you can use a CUDA-enabled GPU with the appropriate drivers and PyTorch version.
+- **Customization**: The code is modular and can be easily extended or customized to suit your specific needs.
+

__init__.py

@@ -0,0 +1,3 @@
+from .src.ui import streamlit_ui, drawable_canvas
+from .src.segmentation import segment_everything, fast_process
+from .src.utils import calculate_parameters, plot_distribution, calculate_pixel_length

app.py

@@ -0,0 +1,110 @@
+
+import streamlit as st
+import logging
+from PIL import Image
+from src.ui.drawable_canvas import drawable_canvas
+from src.ui.streamlit_ui import streamlit_ui
+from src.segmentation import segment_everything
+from src.utils import calculate_parameters, plot_distribution, calculate_pixel_length, plot_cumulative_frequency
+from ultralytics import YOLO
+import torch
+import cv2
+
+logging.basicConfig(filename="grainsight.log", level=logging.INFO)
+
+# Cache the model and device
+@st.cache_data()
+def load_model_and_initialize():
+    model_path = "src\\model\\FastSAM-x.pt"
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model = YOLO(model_path)
+    return model, device
+
+def main():
+    """Main application logic."""
+    uploaded_image, input_size, iou_threshold, conf_threshold, better_quality, contour_thickness, real_world_length, max_det = streamlit_ui()
+    if uploaded_image is not None:
+        try:
+            canvas_result = drawable_canvas(uploaded_image, input_size)
+            pixel_length = None
+            if canvas_result.json_data is not None and "objects" in canvas_result.json_data:
+                if len(canvas_result.json_data["objects"]) > 0:
+                    line_object = canvas_result.json_data["objects"][0]
+                    start_point = [line_object['x1'], line_object['y1']]
+                    end_point = [line_object['x2'], line_object['y2']]
+
+                    # Get image dimensions for calculating the scaling factor
+                    image_width, image_height = Image.open(uploaded_image).size
+                    scale_factor = input_size / max(image_width, image_height)
+
+                    # Calculate pixel length with the scaling factor
+                    pixel_length = calculate_pixel_length(start_point, end_point)
+                    st.write(f"Pixel length of the line: {pixel_length}")
+                else:
+                    st.write("Please draw a line to set the scale or enter the real-world length.")
+            else:
+                st.write("Please draw a line to set the scale or enter the real-world length.")
+
+            if pixel_length is not None and real_world_length is not None:
+                scale_factor = real_world_length / pixel_length
+            else:
+                st.write("Scale factor could not be calculated. Make sure to draw a line and enter the real-world length.")
+                return
+
+            input_image = Image.open(uploaded_image)
+
+            # Load the model and device from cache
+            model, device = load_model_and_initialize()
+
+            segmented_image, annotations = segment_everything(
+                input_image,
+                model=model,
+                device=device,
+                input_size=input_size,
+                iou_threshold=iou_threshold,
+                conf_threshold=conf_threshold,
+                better_quality=better_quality,
+                contour_thickness=contour_thickness,
+                max_det=max_det
+            )
+
+            st.image(segmented_image, caption="Segmented Image", use_column_width=True)
+
+            # Calculate and display object parameters
+            df = calculate_parameters(annotations, scale_factor)
+
+            if not df.empty:
+                st.write("Summary of Object Parameters:")
+                st.dataframe(df)
+
+                csv = df.to_csv(index=False)
+                st.download_button(
+                    label="Download data as CSV",
+                    data=csv,
+                    file_name='grain_parameters.csv',
+                    mime='text/csv',
+                )
+
+                plot_cumulative_frequency(df)
+                filtered_columns = [col for col in df.columns.tolist() if col != 'Object']
+                selected_parameter = st.selectbox("Select a parameter to see its distribution:", filtered_columns)
+
+                if selected_parameter:
+                    plot_distribution(df, selected_parameter)
+                else:
+                    st.write("No parameter selected for plotting.")
+
+            else:
+                st.write("No objects detected.")
+
+        except Exception as e:
+            logging.error(f"An error occurred: {e}")
+            st.error("An error occurred during processing. Please check the logs for details.")
+
+    else:
+        st.write("Please upload an image.")
+
+if __name__ == "__main__":
+    main()
+    
+    

env.yml

@@ -0,0 +1,152 @@
+# environment.yml
+
+name: c:\Users\fares\Documents\interpreterwithgemini\test_gemini
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - asttokens=2.4.1=pyhd8ed1ab_0
+  - backcall=0.2.0=pyh9f0ad1d_0
+  - ca-certificates=2024.2.2=h56e8100_0
+  - colorama=0.4.6=pyhd8ed1ab_0
+  - comm=0.2.1=pyhd8ed1ab_0
+  - debugpy=1.6.7=py39hd77b12b_0
+  - decorator=5.1.1=pyhd8ed1ab_0
+  - executing=2.0.1=pyhd8ed1ab_0
+  - importlib-metadata=7.0.1=pyha770c72_0
+  - importlib_metadata=7.0.1=hd8ed1ab_0
+  - ipykernel=6.29.3=pyha63f2e9_0
+  - ipython=8.12.0=pyh08f2357_0
+  - jedi=0.19.1=pyhd8ed1ab_0
+  - jupyter_client=8.6.0=pyhd8ed1ab_0
+  - jupyter_core=4.12.0=py39hcbf5309_0
+  - libsodium=1.0.18=h8d14728_1
+  - matplotlib-inline=0.1.6=pyhd8ed1ab_0
+  - nest-asyncio=1.6.0=pyhd8ed1ab_0
+  - openssl=3.0.13=h2bbff1b_0
+  - packaging=23.2=pyhd8ed1ab_0
+  - parso=0.8.3=pyhd8ed1ab_0
+  - pickleshare=0.7.5=py_1003
+  - pip=23.3.1=py39haa95532_0
+  - prompt-toolkit=3.0.42=pyha770c72_0
+  - prompt_toolkit=3.0.42=hd8ed1ab_0
+  - psutil=5.9.0=py39h2bbff1b_0
+  - pure_eval=0.2.2=pyhd8ed1ab_0
+  - pygments=2.17.2=pyhd8ed1ab_0
+  - python=3.9.18=h1aa4202_0
+  - python-dateutil=2.8.2=pyhd8ed1ab_0
+  - python_abi=3.9=2_cp39
+  - pywin32=227=py39hb82d6ee_1
+  - pyzmq=25.1.2=py39hd77b12b_0
+  - setuptools=68.2.2=py39haa95532_0
+  - six=1.16.0=pyh6c4a22f_0
+  - sqlite=3.41.2=h2bbff1b_0
+  - stack_data=0.6.2=pyhd8ed1ab_0
+  - tornado=6.2=py39hb82d6ee_0
+  - traitlets=5.14.1=pyhd8ed1ab_0
+  - typing_extensions=4.10.0=pyha770c72_0
+  - vc=14.2=h21ff451_1
+  - vs2015_runtime=14.27.29016=h5e58377_2
+  - wcwidth=0.2.13=pyhd8ed1ab_0
+  - wheel=0.41.2=py39haa95532_0
+  - zeromq=4.3.5=hd77b12b_0
+  - zipp=3.17.0=pyhd8ed1ab_0
+  - pip:
+      - aiohttp==3.9.3
+      - aiosignal==1.3.1
+      - altair==5.3.0
+      - async-timeout==4.0.3
+      - attrs==23.2.0
+      - backports-tarfile==1.0.0
+      - blinker==1.7.0
+      - build==1.2.1
+      - cachetools==5.3.3
+      - certifi==2024.2.2
+      - charset-normalizer==3.3.2
+      - click==8.1.7
+      - clip==0.2.0
+      - contourpy==1.2.1
+      - cycler==0.12.1
+      - docutils==0.21.1
+      - filelock==3.13.4
+      - fonttools==4.51.0
+      - frozenlist==1.4.1
+      - fsspec==2024.3.1
+      - genai==2.1.0
+      - gitdb==4.0.11
+      - gitpython==3.1.43
+      - google-ai-generativelanguage==0.4.0
+      - google-api-core==2.17.1
+      - google-auth==2.28.1
+      - google-generativeai==0.3.2
+      - googleapis-common-protos==1.62.0
+      - grpcio==1.62.0
+      - grpcio-status==1.62.0
+      - idna==3.6
+      - importlib-resources==6.4.0
+      - jaraco-classes==3.4.0
+      - jaraco-context==5.3.0
+      - jaraco-functools==4.0.0
+      - jinja2==3.1.3
+      - jsonschema==4.21.1
+      - jsonschema-specifications==2023.12.1
+      - keyring==25.1.0
+      - kiwisolver==1.4.5
+      - markdown-it-py==3.0.0
+      - markupsafe==2.1.5
+      - matplotlib==3.8.4
+      - mdurl==0.1.2
+      - more-itertools==10.2.0
+      - mpmath==1.3.0
+      - multidict==6.0.5
+      - networkx==3.2.1
+      - nh3==0.2.17
+      - numpy==1.26.4
+      - openai==0.27.10
+      - opencv-python==4.9.0.80
+      - pandas==2.2.2
+      - pillow==10.3.0
+      - pkginfo==1.10.0
+      - proto-plus==1.23.0
+      - protobuf==4.25.3
+      - pyarrow==15.0.2
+      - pyasn1==0.5.1
+      - pyasn1-modules==0.3.0
+      - pydeck==0.8.1b0
+      - pyparsing==3.1.2
+      - pyproject-hooks==1.0.0
+      - pytz==2024.1
+      - pywin32-ctypes==0.2.2
+      - pyyaml==6.0.1
+      - readme-renderer==43.0
+      - referencing==0.34.0
+      - regex==2023.12.25
+      - requests==2.31.0
+      - requests-toolbelt==1.0.0
+      - rfc3986==2.0.0
+      - rich==13.7.1
+      - rpds-py==0.18.0
+      - rsa==4.9
+      - scipy==1.13.0
+      - seaborn==0.13.2
+      - smmap==5.0.1
+      - streamlit==1.33.0
+      - streamlit-drawable-canvas==0.9.3
+      - sympy==1.12
+      - tabulate==0.9.0
+      - tenacity==8.2.3
+      - tiktoken==0.3.3
+      - toml==0.10.2
+      - tomli==2.0.1
+      - toolz==0.12.1
+      - torch==2.2.2
+      - torchvision==0.17.2
+      - tqdm==4.66.2
+      - twine==5.0.0
+      - tzdata==2024.1
+      - ultralytics==8.0.120
+      - ultralytics-yolo==0.0.1
+      - urllib3==2.2.1
+      - watchdog==4.0.0
+      - yarl==1.9.4
+prefix: c:\Users\fares\Documents\interpreterwithgemini\test_gemini

gitignore

@@ -0,0 +1,163 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# .pt files are produced by PyInstaller
+*.pt
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/#use-with-ide
+.pdm.toml
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

grainsight.log

@@ -0,0 +1,189 @@
+ERROR:root:An error occurred: name 'model' is not defined
+ERROR:root:An error occurred: segment_everything() got an unexpected keyword argument 'model'
+ERROR:root:An error occurred: segment_everything() got an unexpected keyword argument 'model'
+ERROR:root:An error occurred: segment_everything() got an unexpected keyword argument 'model'
+ERROR:root:An error occurred: segment_everything() got an unexpected keyword argument 'model'
+ERROR:root:An error occurred: segment_everything() got an unexpected keyword argument 'model'
+ERROR:root:An error occurred: segment_everything() got an unexpected keyword argument 'model'
+ERROR:root:An error occurred: Cannot hash argument 'model' (of type `ultralytics.models.yolo.model.YOLO`) in 'segment_everything'.
+
+To address this, you can tell Streamlit not to hash this argument by adding a
+leading underscore to the argument's name in the function signature:
+
+```
+@st.cache_data
+def segment_everything(_model, ...):
+    ...
+```
+            
+ERROR:root:An error occurred: Cannot hash argument 'model' (of type `ultralytics.models.yolo.model.YOLO`) in 'segment_everything'.
+
+To address this, you can tell Streamlit not to hash this argument by adding a
+leading underscore to the argument's name in the function signature:
+
+```
+@st.cache_resource
+def segment_everything(_model, ...):
+    ...
+```
+            
+ERROR:root:An error occurred: Cannot hash argument 'annotations' (of type `torch.Tensor`) in 'fast_process'.
+
+To address this, you can tell Streamlit not to hash this argument by adding a
+leading underscore to the argument's name in the function signature:
+
+```
+@st.cache_resource
+def fast_process(_annotations, ...):
+    ...
+```
+            
+ERROR:root:An error occurred: tuple index out of range
+ERROR:root:An error occurred: drawable_canvas() missing 2 required positional arguments: 'annotations' and 'update_segmentation_results'
+ERROR:root:An error occurred: tuple index out of range
+ERROR:root:An error occurred: tuple index out of range
+ERROR:root:An error occurred: tuple index out of range
+ERROR:root:An error occurred: cannot unpack non-iterable int object
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 8791780800 bytes.
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 23004463104 bytes.
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 4685345280 bytes.
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 7493472768 bytes.
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 8282112000 bytes.
+ERROR:root:An error occurred: tuple index out of range
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 12423168000 bytes.
+ERROR:root:An error occurred: tuple index out of range
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 15366574080 bytes.
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 10655563776 bytes.
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 22683435008 bytes.
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 5753401600 bytes.
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 20216166400 bytes.
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 4364006400 bytes.
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 15180774400 bytes.
+ERROR:root:An error occurred: [enforce fail at ..\c10\core\impl\alloc_cpu.cpp:72] data. DefaultCPUAllocator: not enough memory: you tried to allocate 25800000000 bytes.
+ERROR:root:An error occurred: Cannot hash argument 'annotations' (of type `torch.Tensor`) in 'calculate_parameters'.
+
+To address this, you can tell Streamlit not to hash this argument by adding a
+leading underscore to the argument's name in the function signature:
+
+```
+@st.cache_data
+def calculate_parameters(_annotations, ...):
+    ...
+```
+            
+ERROR:root:An error occurred: Cannot hash argument 'annotations' (of type `torch.Tensor`) in 'calculate_parameters'.
+
+To address this, you can tell Streamlit not to hash this argument by adding a
+leading underscore to the argument's name in the function signature:
+
+```
+@st.cache_resource
+def calculate_parameters(_annotations, ...):
+    ...
+```
+            
+ERROR:root:An error occurred: OpenCV(4.9.0) D:\a\opencv-python\opencv-python\opencv\modules\imgproc\src\color.cpp:196: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'
+
+ERROR:root:An error occurred: st_canvas() got an unexpected keyword argument 'background_image_aspect_ratio'
+ERROR:root:An error occurred: Cannot hash argument 'annotations' (of type `torch.Tensor`) in 'calculate_parameters'.
+
+To address this, you can tell Streamlit not to hash this argument by adding a
+leading underscore to the argument's name in the function signature:
+
+```
+@st.cache_data
+def calculate_parameters(_annotations, ...):
+    ...
+```
+            
+ERROR:root:An error occurred: name 'device' is not defined
+ERROR:root:An error occurred: name 'device' is not defined
+ERROR:root:An error occurred: 'numpy.ndarray' object has no attribute 'cpu'
+ERROR:root:An error occurred: 'numpy.ndarray' object has no attribute 'cpu'
+ERROR:root:An error occurred: 'numpy.ndarray' object has no attribute 'cpu'
+ERROR:root:An error occurred: 'numpy.ndarray' object has no attribute 'cpu'
+ERROR:root:An error occurred: 'numpy.ndarray' object has no attribute 'numpy'
+ERROR:root:An error occurred: 'numpy.ndarray' object has no attribute 'cpu'
+ERROR:root:An error occurred: Cannot hash argument 'annotations' (of type `torch.Tensor`) in 'calculate_parameters'.
+
+To address this, you can tell Streamlit not to hash this argument by adding a
+leading underscore to the argument's name in the function signature:
+
+```
+@st.cache_data
+def calculate_parameters(_annotations, ...):
+    ...
+```
+            
+ERROR:root:An error occurred: Cannot hash argument 'annotations' (of type `torch.Tensor`) in 'calculate_parameters'.
+
+To address this, you can tell Streamlit not to hash this argument by adding a
+leading underscore to the argument's name in the function signature:
+
+```
+@st.cache_data
+def calculate_parameters(_annotations, ...):
+    ...
+```
+            
+ERROR:root:An error occurred: Cannot hash argument 'model' (of type `ultralytics.yolo.engine.model.YOLO`) in 'segment_everything'.
+
+To address this, you can tell Streamlit not to hash this argument by adding a
+leading underscore to the argument's name in the function signature:
+
+```
+@st.cache_data
+def segment_everything(_model, ...):
+    ...
+```
+            
+ERROR:root:An error occurred: Cannot hash argument 'model' (of type `ultralytics.yolo.engine.model.YOLO`) in 'segment_everything'.
+
+To address this, you can tell Streamlit not to hash this argument by adding a
+leading underscore to the argument's name in the function signature:
+
+```
+@st.cache_data
+def segment_everything(_model, ...):
+    ...
+```
+            
+ERROR:root:An error occurred: [enforce fail at alloc_cpu.cpp:114] data. DefaultCPUAllocator: not enough memory: you tried to allocate 27472896000 bytes.
+ERROR:root:An error occurred: [enforce fail at alloc_cpu.cpp:114] data. DefaultCPUAllocator: not enough memory: you tried to allocate 27472896000 bytes.
+ERROR:root:An error occurred: cannot unpack non-iterable int object
+ERROR:root:An error occurred: cannot unpack non-iterable int object
+ERROR:root:An error occurred: cannot unpack non-iterable int object
+ERROR:root:An error occurred: cannot unpack non-iterable int object
+ERROR:root:An error occurred: cannot unpack non-iterable int object
+ERROR:root:An error occurred: cannot unpack non-iterable int object
+ERROR:root:An error occurred: cannot unpack non-iterable int object
+ERROR:root:An error occurred: local variable 'segmented_image' referenced before assignment
+ERROR:root:An error occurred: local variable 'segmented_image' referenced before assignment
+ERROR:root:An error occurred: local variable 'segmented_image' referenced before assignment
+ERROR:root:An error occurred: local variable 'segmented_image' referenced before assignment
+ERROR:root:An error occurred: local variable 'segmented_image' referenced before assignment
+ERROR:root:An error occurred: drawable_canvas() missing 1 required positional argument: 'input_size'
+ERROR:root:An error occurred: drawable_canvas() missing 1 required positional argument: 'input_size'
+ERROR:root:An error occurred: local variable 'pixel_length' referenced before assignment
+ERROR:root:An error occurred: Boolean value of Tensor with more than one value is ambiguous
+ERROR:root:An error occurred: float division by zero
+ERROR:root:An error occurred: float division by zero
+ERROR:root:An error occurred: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
+ERROR:root:An error occurred: local variable 'feret_diameter_micron' referenced before assignment
+ERROR:root:An error occurred: name 'min_feret_diameter_micron' is not defined
+ERROR:root:An error occurred: [enforce fail at alloc_cpu.cpp:114] data. DefaultCPUAllocator: not enough memory: you tried to allocate 29915208704 bytes.
+ERROR:asyncio:Task exception was never retrieved
+future: <Task finished name='Task-25015' coro=<WebSocketProtocol13.write_message.<locals>.wrapper() done, defined at c:\Users\fares\Documents\interpreterwithgemini\test_gemini\lib\site-packages\tornado\websocket.py:1090> exception=WebSocketClosedError()>
+Traceback (most recent call last):
+  File "c:\Users\fares\Documents\interpreterwithgemini\test_gemini\lib\site-packages\tornado\websocket.py", line 1092, in wrapper
+    await fut
+tornado.iostream.StreamClosedError: Stream is closed
+
+During handling of the above exception, another exception occurred:
+
+Traceback (most recent call last):
+  File "c:\Users\fares\Documents\interpreterwithgemini\test_gemini\lib\site-packages\tornado\websocket.py", line 1094, in wrapper
+    raise WebSocketClosedError()
+tornado.websocket.WebSocketClosedError
+ERROR:root:An error occurred: [enforce fail at alloc_cpu.cpp:114] data. DefaultCPUAllocator: not enough memory: you tried to allocate 16398000000 bytes.
+ERROR:root:An error occurred: [enforce fail at alloc_cpu.cpp:114] data. DefaultCPUAllocator: not enough memory: you tried to allocate 39407287296 bytes.

packages.txt

@@ -0,0 +1 @@
+libgl1

requirements.txt

@@ -0,0 +1,16 @@
+matplotlib==3.8.4
+opencv-python==4.9.0.80
+Pillow==9.5.0
+PyYAML==6.0.1
+requests==2.31.0
+scipy==1.13.0
+torch==2.2.2
+torchvision==0.17.2
+tqdm==4.66.2
+pandas==2.2.2
+seaborn==0.13.2
+streamlit==1.24.0
+ultralytics==8.0.120
+streamlit-drawable-canvas==0.9.3
+clip==0.2.0
+ultralytics-yolo==0.0.1

setup.py

@@ -0,0 +1,28 @@
+from setuptools import setup, find_packages
+
+setup(
+    name='Grainsight',  
+    version='0.1.0',  
+    author='Your Name',  
+    author_email='your_email@example.com',  
+    description='A Streamlit app for segmenting grains using FastSAM',  
+    packages=find_packages(where="src"),  
+    install_requires=[
+        'streamlit',
+        'pillow',
+        'ultralytics',
+        'torch',
+        'numpy',
+        'opencv-python',
+        'matplotlib',
+        'pandas',
+        'seaborn',
+        'streamlit-drawable-canvas',
+        'pyyaml'  
+    ],
+    entry_points={
+        'console_scripts': [
+            'grainsight = grainsight.app:main'
+        ]
+    }
+)

src/model/.gitattributes

@@ -0,0 +1 @@
+*.pt filter=lfs diff=lfs merge=lfs -text

src/segmentation/__init__.py

@@ -0,0 +1 @@
+from .segment import segment_everything, fast_process

src/segmentation/segment.py

@@ -0,0 +1,207 @@
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from PIL import Image
+
+
+def segment_everything(_input, model, device, input_size=1024, iou_threshold=0.7, conf_threshold=0.25, better_quality=False, contour_thickness=1, max_det = 500):
+    """
+    Performs segmentation on the input image and returns the segmented image and annotations.
+    """
+    
+    input_image = _input
+    input_size = int(input_size)
+    w, h = input_image.size
+    scale = input_size / max(w, h)
+    new_w = int(w * scale)
+    new_h = int(h * scale)
+    input_image = input_image.resize((new_w, new_h))
+
+    results = model(input_image,                    
+                   retina_masks=True,                    
+                   iou=iou_threshold,                    
+                   conf=conf_threshold,                    
+                   imgsz=input_size,
+                   max_det=max_det)
+
+    annotations = results[0].masks.data
+    segmented_image = fast_process(annotations=annotations,                       
+                       device=device,                       
+                       image=input_image,                       
+                       scale=(1024 // input_size),                       
+                       better_quality=better_quality,                       
+                       contour_thickness=contour_thickness)
+
+    return segmented_image, annotations
+
+
+def fast_process(annotations,
+                  image,
+                  device,
+                  scale,
+                  better_quality=False,
+                  mask_random_color=True,
+                  bbox=None,
+                  use_retina=True,
+                  withContours=True,
+                  contour_thickness=2):
+    if isinstance(annotations[0], dict):
+        annotations = [annotation['segmentation'] for annotation in annotations]
+    original_h = image.height
+    original_w = image.width
+    if better_quality:
+        if isinstance(annotations[0], torch.Tensor):
+            annotations = np.array(annotations.cpu())
+        for i, mask in enumerate(annotations):
+            mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((3, 3), np.uint8))
+            annotations[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))
+    if device == 'cpu':
+        annotations = np.array(annotations)
+        inner_mask = fast_show_mask(
+            annotations,
+            plt.gca(),
+            random_color=mask_random_color,
+            bbox=bbox,
+            retinamask=use_retina,
+            target_height=original_h,
+            target_width=original_w,
+        )
+    else:
+        if isinstance(annotations[0], np.ndarray):
+            annotations = torch.from_numpy(annotations)
+        inner_mask = fast_show_mask_gpu(
+            annotations,
+            plt.gca(),
+            random_color=mask_random_color,
+            bbox=bbox,
+            retinamask=use_retina,
+            target_height=original_h,
+            target_width=original_w,
+        )
+    if isinstance(annotations, torch.Tensor):
+        annotations = annotations.cpu().numpy()
+    kernel = np.ones((5, 5), np.uint8)
+    if withContours:
+        contour_all = []
+        temp = np.zeros((original_h, original_w, 1))
+        for i, mask in enumerate(annotations):
+            if type(mask) == dict:
+                mask = mask['segmentation']
+            annotation = mask.astype(np.uint8)
+            # Perform morphological operations for separating connected objects and smoothing contours
+            kernel = np.ones((5, 5), np.uint8)
+            annotation = cv2.morphologyEx(annotation, cv2.MORPH_OPEN, kernel)
+            annotation = cv2.GaussianBlur(annotation, (5, 5), 0)  
+            # Find contours
+            contours, _ = cv2.findContours(annotation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+            for contour in contours:
+                hull = cv2.convexHull(contour)
+                epsilon = 0.005 * cv2.arcLength(contour, True)
+                approx = cv2.approxPolyDP(contour, epsilon, True)
+                contour_all.append(approx) 
+        for i, contour in enumerate(contour_all):
+            M = cv2.moments(contour)
+            if M["m00"] != 0:
+                cX = int(M["m10"] / M["m00"])
+                cY = int(M["m01"] / M["m00"])
+            else:
+                cX, cY = 0, 0
+            cv2.putText(temp, str(i), (cX, cY), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 125, 255), 2)
+        cv2.drawContours(temp, contour_all, -1, (255, 255, 255), contour_thickness)
+        color = np.array([255 / 255, 0 / 255, 0 / 255, 1])  # RGBA
+        contour_mask = temp / 255 * color.reshape(1, 1, -1)
+    image = image.convert('RGBA')
+    overlay_inner = Image.fromarray((inner_mask * 255).astype(np.uint8), 'RGBA')
+    image.paste(overlay_inner, (0, 0), overlay_inner)
+    if withContours:  
+        overlay_contour = Image.fromarray((contour_mask * 255).astype(np.uint8), 'RGBA')
+        image.paste(overlay_contour, (0, 0), overlay_contour)
+    return image
+
+
+# CPU post process
+def fast_show_mask(
+        annotation,
+        ax,
+        random_color=False,
+        bbox=None,
+        retinamask=True,
+        target_height=960,
+        target_width=960,
+):
+    mask_sum = annotation.shape[0]
+    height = annotation.shape[1]
+    weight = annotation.shape[2]
+    # Sort annotation by area
+    areas = np.sum(annotation, axis=(1, 2))
+    sorted_indices = np.argsort(areas)[::1]
+    annotation = annotation[sorted_indices]
+    index = (annotation != 0).argmax(axis=0)
+    if random_color:
+        color = np.random.random((mask_sum, 1, 1, 3))
+    else:
+        color = np.ones((mask_sum, 1, 1, 3)) * np.array([30 / 255, 144 / 255, 255 / 255])
+    transparency = np.ones((mask_sum, 1, 1, 1)) * 0.6
+    visual = np.concatenate([color, transparency], axis=-1)
+    mask_image = np.expand_dims(annotation, -1) * visual
+    mask = np.zeros((height, weight, 4))
+    h_indices, w_indices = np.meshgrid(np.arange(height), np.arange(weight), indexing='ij')
+    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
+    mask[h_indices, w_indices, :] = mask_image[indices]
+    if bbox is not None:
+        x1, y1, x2, y2 = bbox
+        ax.add_patch(plt.Rectangle((x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor='b', linewidth=1))
+    if not retinamask:
+        mask = cv2.resize(mask, (target_width, target_height), interpolation=cv2.INTER_NEAREST)
+    return mask
+
+
+def fast_show_mask_gpu(
+        annotation,
+        ax,
+        random_color=False,
+        bbox=None,
+        retinamask=True,
+        target_height=960,
+        target_width=960,
+):
+    device = annotation.device
+    mask_sum = annotation.shape[0]
+    height = annotation.shape[1]
+    weight = annotation.shape[2]
+    areas = torch.sum(annotation, dim=(1, 2))
+    sorted_indices = torch.argsort(areas, descending=False)
+    annotation = annotation[sorted_indices]
+    # Find the first non-zero value index for each position
+    index = (annotation != 0).to(torch.long).argmax(dim=0)
+    if random_color:
+        color = torch.rand((mask_sum, 1, 1, 3)).to(device)
+    else:
+        color = torch.ones((mask_sum, 1, 1, 3)).to(device) * torch.tensor(
+            [30 / 255, 144 / 255, 255 / 255]
+        ).to(device)
+    transparency = torch.ones((mask_sum, 1, 1, 1)).to(device) * 0.6
+    visual = torch.cat([color, transparency], dim=-1)
+    mask_image = torch.unsqueeze(annotation, -1) * visual
+    # Use vectorization to get the value of the batch
+    mask = torch.zeros((height, weight, 4)).to(device)
+    h_indices, w_indices = torch.meshgrid(torch.arange(height), torch.arange(weight))
+    indices = (index[h_indices, w_indices], h_indices, w_indices, slice(None))
+    # Use vectorized indexing to update the show values
+    mask[h_indices, w_indices, :] = mask_image[indices]
+    mask_cpu = mask.cpu().numpy()
+    if bbox is not None:
+        x1, y1, x2, y2 = bbox
+        ax.add_patch(
+            plt.Rectangle(
+                (x1, y1), x2 - x1, y2 - y1, fill=False, edgecolor="b", linewidth=1
+            )
+        )
+    if not retinamask:
+        mask_cpu = cv2.resize(
+            mask_cpu, (target_width, target_height), interpolation=cv2.INTER_NEAREST
+        )
+    return mask_cpu
+
+

src/ui/__init__.py

@@ -0,0 +1,2 @@
+from .streamlit_ui import streamlit_ui
+from .drawable_canvas import drawable_canvas

src/ui/drawable_canvas.py

@@ -0,0 +1,29 @@
+# src\ui\drawable_canvas.py
+import streamlit as st
+from streamlit_drawable_canvas import st_canvas
+from PIL import Image
+
+def drawable_canvas(uploaded_image, input_size):
+    """Creates a Streamlit drawable canvas with the resized image as the background."""
+    # Generate a unique key for the canvas based on the input size
+    canvas_key = f"canvas_{input_size}"
+
+    st.write("Draw a line to set the scale:")
+    original_image = Image.open(uploaded_image)
+    image_width, image_height = original_image.size
+    scale = input_size / max(image_width, image_height)
+    new_w = int(image_width * scale)
+    new_h = int(image_height * scale)
+    resized_image = original_image.resize((new_w, new_h))
+    canvas_result = st_canvas(
+        fill_color="rgba(255, 165, 0, 0.3)",
+        stroke_width=10,
+        stroke_color="#e00",
+        background_image=resized_image,
+        height=new_h,
+        width=new_w,
+        drawing_mode="line",
+        key=canvas_key,
+    )
+    return canvas_result
+

src/ui/streamlit_ui.py

@@ -0,0 +1,46 @@
+import streamlit as st
+
+def streamlit_ui():
+    """Creates the Streamlit user interface with input controls."""
+
+    st.sidebar.title("Segmentation Parameters")
+
+    uploaded_image = st.sidebar.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+    input_size = st.sidebar.slider(
+        "Input Size", 512, 3000, 1024, 64,
+        help="Size of the input image. Higher values may improve detection but will be slower."
+    )
+
+    iou_threshold = st.sidebar.slider(
+        "IOU Threshold", 0.0, 0.9, 0.7, 0.1, 
+        help="Intersection over Union threshold for object detection. Higher values reduce false positives."
+    )
+
+    conf_threshold = st.sidebar.slider(
+        "Confidence Threshold", 0.0, 0.9, 0.5, 0.01, 
+        help="Minimum confidence level for detected objects. Lower values may detect more objects but increase false positives."
+    )
+
+    better_quality = st.sidebar.checkbox(
+        "Better Visual Quality", True, 
+        help="Check to improve the visual quality of the segmentation. May be slower."
+    )
+
+    contour_thickness = st.sidebar.slider(
+        "Contour Thickness", 1, 50, 1,
+        help="Thickness of the contour lines around detected objects."
+    )
+
+    real_world_length = st.sidebar.number_input(
+        "Enter the real-world length of the line in micrometers:", 
+        min_value=1, value=100,
+        help="Length of the reference line in the real world, used for scaling object parameters."
+    )
+    
+    max_det = st.sidebar.number_input(
+        "Maximum Number of Detected Objects", 
+        min_value=1, value=500,
+        help="Maximum number of detected objects. Higher values may have significant impact on performance."
+    )
+    return uploaded_image, input_size, iou_threshold, conf_threshold, better_quality, contour_thickness, real_world_length, max_det

src/utils/__init__.py

@@ -0,0 +1,3 @@
+from .parameters import calculate_parameters
+from .visualization import plot_distribution, plot_cumulative_frequency
+from .calculations import calculate_pixel_length

src/utils/calculations.py

@@ -0,0 +1,7 @@
+import math
+
+def calculate_pixel_length(start_point, end_point):
+    """Calculates the pixel length of a line."""
+    x1, y1 = start_point
+    x2, y2 = end_point
+    return math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)

src/utils/parameters.py

@@ -0,0 +1,53 @@
+import cv2
+import numpy as np
+import pandas as pd
+import streamlit as st
+
+def calculate_parameters(annotations, scale_factor):
+    """Calculates parameters for each segmented object, including Feret diameter."""
+    df = pd.DataFrame(columns=['Object', 'Area', 'Perimeter', 'Roundness', 
+                                'Aspect Ratio (Elongation)', 'Longest Feret Diameter'])
+    if len(annotations) > 0:
+        for i, mask in enumerate(annotations):
+            binary_mask = mask.cpu().numpy().astype(np.uint8)
+            area_pixel = np.sum(binary_mask)
+            area_micron = area_pixel * (scale_factor ** 2)
+
+            # Find contours with all points (no approximation)
+            contours, _ = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+
+            if contours:  # Check if any contours were found
+                perimeter_pixel = cv2.arcLength(contours[0], True)
+                perimeter_micron = perimeter_pixel * scale_factor
+
+                # Fit ellipse for roundness and aspect ratio (check for sufficient points)
+                if len(contours[0]) >= 5:
+                    ellipse = cv2.fitEllipse(contours[0])
+                    major_axis, minor_axis = ellipse[1]
+                else:
+                    major_axis = minor_axis = 0
+
+                major_axis_micron = major_axis * scale_factor
+                minor_axis_micron = minor_axis * scale_factor
+                roundness = (4 * np.pi * area_micron) / (perimeter_micron ** 2)
+                aspect_ratio = major_axis_micron / minor_axis_micron if minor_axis_micron != 0 else "Undefined"
+
+                # Calculate Feret diameter and Elongation
+                hull = cv2.convexHull(contours[0])
+                distances = np.linalg.norm(hull - hull[:, 0, :], axis=2)
+                max_feret_diameter_micron = np.max(distances) * scale_factor
+
+                new_row = pd.DataFrame({
+                    'Object': [f"Object {i}"],
+                    'Area': [area_micron],
+                    'Perimeter': [perimeter_micron],
+                    'Roundness': [roundness],
+                    'Aspect Ratio (Elongation)': [aspect_ratio],
+                    'Longest Feret Diameter': [max_feret_diameter_micron],
+                })
+                df = pd.concat([df, new_row], ignore_index=True)
+
+        # Eliminate artifacts with undefined parameters
+        df = df[(df['Longest Feret Diameter'] != 0) & (df['Roundness'] >= 0) & (df['Roundness'] <= 1)]
+
+    return df

src/utils/visualization.py

@@ -0,0 +1,28 @@
+import seaborn as sns
+import matplotlib.pyplot as plt
+import streamlit as st
+
+def plot_distribution(df, selected_parameter):
+    """Plots the distribution of a selected parameter."""
+    
+    try:
+        fig, ax = plt.subplots()
+        sns.histplot(df[selected_parameter], kde=True, ax=ax)
+        ax.set_title(f'Distribution of {selected_parameter}')
+        ax.set_xlabel(selected_parameter)
+        ax.set_ylabel('Frequency')
+        st.pyplot(fig)
+    except Exception as e:
+        st.write(f"An error occurred while plotting: {e}")
+        
+def plot_cumulative_frequency(df):
+    try:
+        fig, ax = plt.subplots()
+        sns.ecdfplot(df['Longest Feret Diameter'], ax=ax)
+        ax.set_title(f'Cumulative Frequency Plot')
+        ax.set_xlabel('Grains diameter')
+        ax.set_ylabel('Cumulative Frequency')
+        st.pyplot(fig)
+    except Exception as e:
+        st.write(f"An error occurred while plotting: {e}")
+