Merge pull request #8 from computervision-xray-testing/release/0.3.0

Release/0.3.0

Author: cpieringer

.gitignore

@@ -102,3 +102,6 @@ venv.bak/
 
 # mypy
 .mypy_cache/
+
+# Ruff
+.ruff_cache

README.md

@@ -1,20 +1,69 @@
-# pybalu
+<a id="readme-top"></a>
 
-![Build and upload to PyPI](https://github.com/mbucchi/pybalu/workflows/Build%20and%20upload%20to%20PyPI/badge.svg)
 
-Python implementation for Balu, a computer vision, pattern recognition and image processing library. Originally implemented in matlab by Domingo Mery.
+<p align="right">(<a href="#readme-top">back to top</a>)</p>
+<!-- PROJECT LOGO -->
+<br />
+<div align="center">
+  <!-- <a href="https://github.com/othneildrew/Best-README-Template">
+    <img src="images/logo.png" alt="Logo" width="80" height="80">
+  </a> -->
 
-## Installing pybalu
+  <h3 align="center">pybalu</h3>
 
-Python 3.6 or higher is required to use this package. In order to install pybalu, simply run
+  <p align="center">
+    Python3 implementation of the computer vision and pattern recognition library Balu.
+    <br />
+    <a href="https://github.com/computervision-xray-testing/pybalu"><strong>Explore the docs »</strong></a>
+    <br />
+    <br />
+    <a href="https://github.com/computervision-xray-testing/pybalu/issues/new?labels=bug&template=bug-report---.md">Report Bug</a>
+    ·
+    <a href="https://github.com/computervision-xray-testing/pybalu/issues/new?labels=enhancement&template=feature-request---.md">Request Feature</a>
+  </p>
+</div>
+
+
+<!-- TABLE OF CONTENTS -->
+<details>
+  <summary>Table of Contents</summary>
+  <ol>
+    <li>
+      <a href="#about-the-project">About The Project</a>
+    </li>
+    <li>
+      <a href="#getting-started">Getting Started</a>
+      <ul>
+        <!-- <li><a href="#prerequisites">Prerequisites</a></li> -->
+        <li><a href="#installation">Installation</a></li>
+      </ul>
+    </li>
+    <li><a href="#contributing">Contributing</a></li>
+    <li><a href="#Roadmap">Contributing</a></li>
+  </ol>
+</details>
+
+
+![Build and upload to PyPI](hhttps://github.com/computervision-xray-testing/pybalu/workflows/Build%20and%20upload%20to%20PyPI/badge.svg)
+
+
+# About the Project
+
+Python implementation for Balu, a computer vision, pattern recognition and image processing library. Originally implemented in Matlab&reg; by Domingo Mery.
+
+
+# Installation
+
+Python 3.10 or higher is required to use this package. In order to install pybalu, simply run
 
 ```bash
 $ pip install pybalu
 ```
 
-## Development and contributing
 
-If you would like to contribute:
+# Contributing
+
+We follow github flow standard. If you would like to contribute:
 
 - Fork the repo
 - Create a new branch called `feature/<feature-desc>` or `fix/<fix-desc>` depending on the nature of your contribution
@@ -28,6 +77,12 @@ Possible and useful contributions:
 - Documentation
 - Examples
 
-## Documentation
+In case of contribution, once you have clone the repository, install the development version that includes optional dependencies specified in the pyproject.toml
+
+```bash
+$ pip install .[dev]
+```
+
+## Roadmap
 
-_TODO_
+- Documentation: _TODO_

examples/feature_extraction/ellipses.ipynb

@@ -8,8 +8,9 @@
    "source": [
     "# Change directory to VSCode workspace root so that relative path loads work correctly. Turn this addition off with the DataScience.changeDirOnImportExport setting\n",
     "import os\n",
+    "\n",
     "try:\n",
-    "    os.chdir(os.path.join(os.getcwd(), '..'))\n",
+    "    os.chdir(os.path.join(os.getcwd(), \"..\"))\n",
     "except:\n",
     "    pass"
    ]
@@ -34,7 +35,7 @@
     "from pybalu.feature_extraction.geometric_utils import bbox as _bbox\n",
     "from pybalu.feature_extraction import basic_geo_features\n",
     "from pybalu.io import imread\n",
-    "from skimage.measure import label\n"
+    "from skimage.measure import label"
    ]
   },
   {
@@ -53,6 +54,7 @@
    "outputs": [],
    "source": [
     "import matplotlib\n",
+    "\n",
     "matplotlib.rcParams[\"figure.figsize\"] = (7, 7)\n",
     "matplotlib.rcParams[\"axes.titlesize\"] = 20\n",
     "matplotlib.rcParams[\"axes.titlepad\"] = 15\n",
@@ -118,13 +120,15 @@
     "    region = (labeled_T == idx).astype(int)\n",
     "    box = _bbox(region)\n",
     "    feats = basic_geo_features(region[box])\n",
-    "    return np.array([\n",
-    "        box[0].start + feats[0],    # feats[0]:  center of grav i [px]\n",
-    "        box[1].start + feats[1],    # feats[1]:  center of grav j [px]\n",
-    "        feats[10],                  # feats[10]: MajorAxisLength  [px]\n",
-    "        feats[11],                  # feats[11]: MinorAxisLength  [px]\n",
-    "        feats[12]                   # feats[12]: Orientation      [deg]\n",
-    "    ])\n",
+    "    return np.array(\n",
+    "        [\n",
+    "            box[0].start + feats[0],  # feats[0]:  center of grav i [px]\n",
+    "            box[1].start + feats[1],  # feats[1]:  center of grav j [px]\n",
+    "            feats[10],  # feats[10]: MajorAxisLength  [px]\n",
+    "            feats[11],  # feats[11]: MinorAxisLength  [px]\n",
+    "            feats[12],  # feats[12]: Orientation      [deg]\n",
+    "        ]\n",
+    "    )\n",
     "\n",
     "\n",
     "with Pool() as pool:\n",
@@ -161,8 +165,9 @@
     "\n",
     "\n",
     "def draw_ellipse(x, y, height, width, angle, axes):\n",
-    "    ell = Ellipse(xy=(x, y), height=height, width=width,\n",
-    "                  angle=angle, edgecolor=\"red\", facecolor=\"none\")\n",
+    "    ell = Ellipse(\n",
+    "        xy=(x, y), height=height, width=width, angle=angle, edgecolor=\"red\", facecolor=\"none\"\n",
+    "    )\n",
     "    axes.add_artist(ell)\n",
     "    ell.set_clip_box(axes.bbox)\n",
     "    return ell\n",
@@ -205,8 +210,7 @@
     "major_25 = np.percentile(ellipses[:, 3], 25)\n",
     "major_75 = np.percentile(ellipses[:, 3], 75)\n",
     "\n",
-    "valid_labels = 1 + \\\n",
-    "    np.where((ellipses[:, 3] > major_25) & (ellipses[:, 3] < major_75))[0]\n",
+    "valid_labels = 1 + np.where((ellipses[:, 3] > major_25) & (ellipses[:, 3] < major_75))[0]\n",
     "im_mean = np.array(im)\n",
     "im_mean[np.where(~np.isin(labeled, valid_labels))] //= 7\n",
     "plt.imshow(im_mean, cmap=\"gray\")\n",
@@ -240,9 +244,7 @@
    ],
    "source": [
     "def draw_at_angle(theta):\n",
-    "    valid_labels = 1 + \\\n",
-    "        np.where((ellipses[:, 4] > theta - 10) &\n",
-    "                 (ellipses[:, 4] < theta + 10))[0]\n",
+    "    valid_labels = 1 + np.where((ellipses[:, 4] > theta - 10) & (ellipses[:, 4] < theta + 10))[0]\n",
     "    plt.title(f\"Orientation at {theta} deg\")\n",
     "    im_rotated = np.array(im)\n",
     "    im_rotated[np.where(~np.isin(labeled, valid_labels))] //= 7\n",

examples/feature_extraction/ellipses.py

@@ -7,17 +7,18 @@
 from matplotlib.patches import Ellipse
 from multiprocessing import Pool
 from pybalu.feature_extraction.geometric_utils import bbox as _bbox
-from pybalu.feature_extraction import basic_geo_features
+from pybalu.feature_extraction.basic_geo import basic_geo_features
 from pybalu.io import imread
 from skimage.measure import label
 
 # %% [markdown]
 # ## Matplotlib setup
-# The following code is used to set up the default parameters for all the 
+# The following code is used to set up the default parameters for all the
 # plots shown by matplotlib
 
 # %%
 import matplotlib
+
 matplotlib.rcParams["figure.figsize"] = (7, 7)
 matplotlib.rcParams["axes.titlesize"] = 20
 matplotlib.rcParams["axes.titlepad"] = 15
@@ -37,7 +38,7 @@
 # ## Recognizing rice grains
 # In order to recognize the grains, the following steps are followed:
 #
-#   1. Image is transformed to binary 
+#   1. Image is transformed to binary
 #   2. Rice grains are separated and labeled accordingly
 #   3. Geometric features are calculated for each grain by using `basic_geo_features` function
 #   4. An `Ellipse` object is built for each grain
@@ -49,20 +50,22 @@
 
 
 def calc_ellipse(idx):
-    region = (labeled_T == idx).astype(int)
-    box = _bbox(region)
-    feats = basic_geo_features(region[box])
-    return np.array([
-        box[0].start + feats[0],    # feats[0]:  center of grav i [px]
-        box[1].start + feats[1],    # feats[1]:  center of grav j [px]
-        feats[10],                  # feats[10]: MajorAxisLength  [px]
-        feats[11],                  # feats[11]: MinorAxisLength  [px]
-        feats[12]                   # feats[12]: Orientation      [deg]
-    ])
+	region = (labeled_T == idx).astype(int)
+	box = _bbox(region)
+	feats = basic_geo_features(region[box])
+	return np.array(
+		[
+			box[0].start + feats[0],  # feats[0]:  center of grav i [px]
+			box[1].start + feats[1],  # feats[1]:  center of grav j [px]
+			feats[10],  # feats[10]: MajorAxisLength  [px]
+			feats[11],  # feats[11]: MinorAxisLength  [px]
+			feats[12],  # feats[12]: Orientation      [deg]
+		]
+	)
 
 
 with Pool() as pool:
-    ellipses = np.vstack(pool.map(calc_ellipse, range(1, n)))
+	ellipses = np.vstack(pool.map(calc_ellipse, range(1, n)))
 
 # %% [markdown]
 # ## Displaying the ellipses over the original image
@@ -74,15 +77,16 @@ def calc_ellipse(idx):
 
 
 def draw_ellipse(x, y, height, width, angle, axes):
-    ell = Ellipse(xy=(x, y), height=height, width=width,
-                  angle=angle, edgecolor="red", facecolor="none")
-    axes.add_artist(ell)
-    ell.set_clip_box(axes.bbox)
-    return ell
+	ell = Ellipse(
+		xy=(x, y), height=height, width=width, angle=angle, edgecolor="red", facecolor="none"
+	)
+	axes.add_artist(ell)
+	ell.set_clip_box(axes.bbox)
+	return ell
 
 
 for ell in ellipses:
-    draw_ellipse(*ell, axes=ax)
+	draw_ellipse(*ell, axes=ax)
 
 plt.show()
 
@@ -98,8 +102,7 @@ def draw_ellipse(x, y, height, width, angle, axes):
 major_25 = np.percentile(ellipses[:, 3], 25)
 major_75 = np.percentile(ellipses[:, 3], 75)
 
-valid_labels = 1 + \
-    np.where((ellipses[:, 3] > major_25) & (ellipses[:, 3] < major_75))[0]
+valid_labels = 1 + np.where((ellipses[:, 3] > major_25) & (ellipses[:, 3] < major_75))[0]
 im_mean = np.array(im)
 im_mean[np.where(~np.isin(labeled, valid_labels))] //= 7
 plt.imshow(im_mean, cmap="gray")
@@ -108,21 +111,19 @@ def draw_ellipse(x, y, height, width, angle, axes):
 
 # %% [markdown]
 # ## Finding rice grains oriented at a specific angle
-# Rice grains rotation is within 10deg of the given angle are highlighted. Just as 
+# Rice grains rotation is within 10deg of the given angle are highlighted. Just as
 # before, this is done with the help of numpy matrix operations
 
 # %%
 
 
 def draw_at_angle(theta):
-    valid_labels = 1 + \
-        np.where((ellipses[:, 4] > theta - 10) &
-                 (ellipses[:, 4] < theta + 10))[0]
-    plt.title(f"Orientation at {theta} deg")
-    im_rotated = np.array(im)
-    im_rotated[np.where(~np.isin(labeled, valid_labels))] //= 7
-    plt.imshow(im_rotated, cmap="gray")
-    plt.show()
+	valid_labels = 1 + np.where((ellipses[:, 4] > theta - 10) & (ellipses[:, 4] < theta + 10))[0]
+	plt.title(f"Orientation at {theta} deg")
+	im_rotated = np.array(im)
+	im_rotated[np.where(~np.isin(labeled, valid_labels))] //= 7
+	plt.imshow(im_rotated, cmap="gray")
+	plt.show()
 
 
 draw_at_angle(45)

examples/feature_selection/sfs.ipynb

@@ -8,8 +8,9 @@
    "source": [
     "# Change directory to VSCode workspace root so that relative path loads work correctly. Turn this addition off with the DataScience.changeDirOnImportExport setting\n",
     "import os\n",
+    "\n",
     "try:\n",
-    "    os.chdir(os.path.join(os.getcwd(), '..'))\n",
+    "    os.chdir(os.path.join(os.getcwd(), \"..\"))\n",
     "except:\n",
     "    pass"
    ]
@@ -53,6 +54,7 @@
    "outputs": [],
    "source": [
     "import matplotlib\n",
+    "\n",
     "matplotlib.rcParams[\"figure.figsize\"] = (7, 7)\n",
     "matplotlib.rcParams[\"axes.titlesize\"] = 20\n",
     "matplotlib.rcParams[\"axes.titlepad\"] = 15\n",
@@ -98,7 +100,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "idx_train, idx_test = stratify(classes, .90)\n",
+    "idx_train, idx_test = stratify(classes, 0.90)\n",
     "f_train = features[idx_train]\n",
     "c_train = classes[idx_train]\n",
     "f_test = features[idx_test]\n",
@@ -146,8 +148,7 @@
    "source": [
     "N_FEATURES = 15\n",
     "\n",
-    "selected_feats = sfs(f_train_norm, c_train, n_features=N_FEATURES,\n",
-    "                     method=\"fisher\", show=True)"
+    "selected_feats = sfs(f_train_norm, c_train, n_features=N_FEATURES, method=\"fisher\", show=True)"
    ]
   },
   {
@@ -189,13 +190,12 @@
     "    return performance(prediction, c_test)\n",
     "\n",
     "\n",
-    "values = [performance_for_features(selected_feats[:i]) * 100\n",
-    "          for i in range(1, N_FEATURES + 1)]\n",
+    "values = [performance_for_features(selected_feats[:i]) * 100 for i in range(1, N_FEATURES + 1)]\n",
     "\n",
-    "plt.bar(*zip(*enumerate(values)), tick_label=range(1, N_FEATURES+1))\n",
+    "plt.bar(*zip(*enumerate(values)), tick_label=range(1, N_FEATURES + 1))\n",
     "plt.title(\"Performance vs. number of features\")\n",
-    "plt.xlabel('selected features')\n",
-    "plt.ylabel('accuracy [%]')\n",
+    "plt.xlabel(\"selected features\")\n",
+    "plt.ylabel(\"accuracy [%]\")\n",
     "plt.show()"
    ]
   },