Merge pull request #7 from idso-fa1-pathology/release/v1.1.2

Release/v1.1.2
idso-fa1-pathology · Jun 24, 2024 · a7274eb · a7274eb
2 parents 0806c98 + 1284a51
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diff --git a/.DS_Store b/.DS_Store
diff --git a/.vscode/settings.json b/.vscode/settings.json
@@ -0,0 +1,5 @@
+{
+    "python.analysis.extraPaths": [
+        "./src"
+    ]
+}
diff --git a/README.md b/README.md
@@ -1,12 +1,11 @@
+
 # cell-detection-package
 
-#Goal 
-##work on the traing parts
-The cell-detection-package is a Python package designed for detecting and segmenting cells in histology images. This package leverages deep learning models to provide accurate and efficient cell detection, facilitating research and analysis in pathology.
-any images size
-## Installation
+## Goal
+
+The cell-detection-package is a Python package designed for detecting and segmenting cells in histology images. This package leverages deep learning models to provide accurate and efficient cell detection, facilitating research and analysis in pathology. It supports images of any size, including whole slide images.
 
-whole slide images detection is last update and results will shows in same folder of predicted image
+## Installation
 
 First, clone the repository and install the necessary dependencies.
 
@@ -16,17 +15,14 @@ cd cell-sight-package
 pip install -r requirements.txt
 ```
 
-
-
 ## Usage
 
 ### Predict New Images
 To predict new images using the pre-trained model, follow these steps:
 
 Download Pre-trained Model:
 
-The pre-trained model weights will be automatically downloaded and saved in the runs/pretrained-model/weights directory when you run the prediction script.
-
+The pre-trained model weights will be automatically downloaded and saved in the `runs/pretrained-model/weights` directory when you run the prediction script.
 
 1. Upload your images to the `data/predict/images/` directory.
 
@@ -37,31 +33,40 @@ The pre-trained model weights will be automatically downloaded and saved in the
 
 3. The results will be saved in the specified `output_directory`, with annotated images and prediction data.
 
-### Data Format
-Your dataset should be organized into three main folders: `train`, `test`, and `valid`. Each of these folders should contain two subfolders: `images` and `labels`.
+### Train the Model
 
-- **images/**: Contains the image files.
-- **labels/**: Contains the label files, where each label file corresponds to an image file with the same name but a `.txt` extension. The format of each line in the label file should be as follows:
-    ```
-    class_id center_x center_y width height
-    ```
-    - `class_id`: The class ID of the object.
-    - `center_x`: The x-coordinate of the center of the bounding box, normalized to the [0, 1] range.
-    - `center_y`: The y-coordinate of the center of the bounding box, normalized to the [0, 1] range.
-    - `width`: The width of the bounding box, normalized to the [0, 1] range.
-    - `height`: The height of the bounding box, normalized to the [0, 1] range.
+To train the model on your own dataset, follow these steps:
 
-    Example content of a label file:
-    ```
-    0 0.942446 0.0212446 0.0742872 0.0422503
-    0 0.124674 0.327 0.0790858 0.078888
-    0 0.527016 0.342674 0.0800738 0.0796084
-    ```
-    All input images should be resized to 640x640 pixels.
+1. Organize your dataset into three main folders: `train`, `test`, and `valid`. Each of these folders should contain two subfolders: `images` and `labels`.
+    - **images/**: Contains the image files.
+    - **labels/**: Contains the label files, where each label file corresponds to an image file with the same name but a `.txt` extension. The format of each line in the label file should be as follows:
+        ```
+        class_id center_x center_y width height
+        ```
+        - `class_id`: The class ID of the object.
+        - `center_x`: The x-coordinate of the center of the bounding box, normalized to the [0, 1] range.
+        - `center_y`: The y-coordinate of the center of the bounding box, normalized to the [0, 1] range.
+        - `width`: The width of the bounding box, normalized to the [0, 1] range.
+        - `height`: The height of the bounding box, normalized to the [0, 1] range.
 
+        Example content of a label file:
+        ```
+        0 0.942446 0.0212446 0.0742872 0.0422503
+        0 0.124674 0.327 0.0790858 0.078888
+        0 0.527016 0.342674 0.0800738 0.0796084
+        ```
 
+2. Make sure all input images are resized to 640x640 pixels.
+
+3. Place your dataset in the `data/raw/` directory.
+
+4. Run the training script with the following command:
+    ```bash
+    python scripts/train.py --config_path data/raw/config.yaml
+    ```
+
+5. The model will be trained using the images and labels in the specified directories, and the results will be saved in the `runs/trained/` directory.
 
-
 ## Running with Docker
 
 To run the `cell-sight-package` Docker image, follow these steps:
@@ -88,14 +93,10 @@ This will start the container and run your application as defined in the Dockerf
     docker pull ghcr.io/idso-fa1-pathology/cell-sight-package:latest
    ```
 
-2. **Run the Docker container:**:
-
+2. **Run the Docker container**:
 
    ```bash
     docker run -it --rm ghcr.io/idso-fa1-pathology/cell-sight-package:latest
    ```
 
-
 This will start the container and run your application as defined in the Dockerfile.
-
-
diff --git a/data/.DS_Store b/data/.DS_Store
diff --git a/data/raw/.DS_Store b/data/raw/.DS_Store
diff --git a/data/raw/config.yaml b/data/raw/config.yaml
@@ -0,0 +1,10 @@
+path: .  # This path will be updated dynamically in the script
+train: train/images
+val: valid/images
+test: test/images
+
+# Classes
+names:
+  0: cd45
+  1: panCK
+  2: Others
diff --git a/data/raw/test/.DS_Store b/data/raw/test/.DS_Store
diff --git a/data/raw/test/images/16_roi_120.jpg b/data/raw/test/images/16_roi_120.jpg
diff --git a/data/raw/test/images/16_roi_366.jpg b/data/raw/test/images/16_roi_366.jpg
diff --git a/data/raw/test/labels/16_roi_120.txt b/data/raw/test/labels/16_roi_120.txt
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