Merge pull request #175 from cosanlab/dev

WIP Fixes
cosanlab · Aug 15, 2023 · f1073c1 · f1073c1
2 parents ac9681d + 5e7569c
commit f1073c1
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diff --git a/.github/workflows/tests_and_docs.yml b/.github/workflows/tests_and_docs.yml
@@ -27,7 +27,7 @@ jobs:
         # OSs to test
         os: [ubuntu-latest, macos-latest, windows-latest]
         # Python versions to test
-        python-version: [3.7, 3.8, 3.9]
+        python-version: [3.8, 3.9, "3.10"]
         # By default everything should pass for the workflow to pass
         ok-fail: [false]
     steps:

diff --git a/.github/workflows/tests_on_pr.yml b/.github/workflows/tests_on_pr.yml
@@ -25,7 +25,7 @@ jobs:
         # OSs to test
         os: [ubuntu-latest, macos-latest, windows-latest]
         # Python versions to test
-        python-version: [3.7, 3.8, 3.9]
+        python-version: [3.8, 3.9, "3.10"]
         # By default everything should pass for the workflow to pass
         ok-fail: [false]
     steps:

diff --git a/docs/basic_tutorials/04_plotting.ipynb b/docs/basic_tutorials/04_plotting.ipynb
@@ -7,7 +7,7 @@
     "# 4. Visualizing Facial Expressions\n",
     "*written by Eshin Jolly*\n",
     "\n",
-    "In this tutorial we'll explore plotting in Py-Feat using functions from the `feat.plotting` module along with plotting methods using the Fex data class. You can try it out interactively in Google Collab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cosanlab/py-feat/blob/master/notebooks/content/05_plotting.ipynb)\n",
+    "In this tutorial we'll explore plotting in Py-Feat using functions from the `feat.plotting` module along with plotting methods using the Fex data class. You can try it out interactively in Google Collab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cosanlab/py-feat/blob/master/notebooks/content/04_plotting.ipynb)\n",
     "\n",
     "\n",
     "To help visualize facial expressions in a standardized way, Py-Feat includes a pre-trained partial-least-squares (PLS) model that can map between an array of AU intensities (between 0-N) and facial landmark coordinates. Just pass in a numpy array of AU intensities to the `plot_face()` function to visualize the resulting facial expression. In general we find that a 4 by 5 aspect ratio seems to work best when plotting faces (default in `plot_face()`).\n",
@@ -620,7 +620,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.12"
+   "version": "3.8.13"
   },
   "toc": {
    "base_numbering": 1,

diff --git a/docs/pages/changelog.md b/docs/pages/changelog.md
@@ -1,5 +1,17 @@
 # Change Log
 
+# 0.6.1
+
+## Notes
+
+This version **drops support for Python 3.7** and fixes several dependency related issues:
+
+- [#162](https://github.com/cosanlab/py-feat/issues/162)
+- [#176](https://github.com/cosanlab/py-feat/issues/176)
+- We can now handle images with an alpha-channel by just grabbing the RGB channels (typically in png files)
+- Update minimum `scikit-learn` version requirement to ensure our viz models are loaded correctly
+- Soft-pin `numexpr` version until this upstream pandas issue is [fixed](https://github.com/pandas-dev/pandas/issues/54449)
+
 # 0.6.0
 
 ## Notes

diff --git a/feat/data.py b/feat/data.py
@@ -1914,6 +1914,10 @@ def __getitem__(self, idx):
             img = Image.open(self.images[idx])
             img = transforms.PILToTensor()(img)
 
+        # Drop alpha channel
+        if img.shape[0] == 4:
+            img = img[:3, ...]
+
         if img.shape[0] == 1:
             img = torch.cat([img, img, img], dim=0)
 

diff --git a/feat/detector.py b/feat/detector.py
@@ -367,12 +367,6 @@ def detect_faces(self, frame, threshold=0.5, **face_model_kwargs):
             item is a list containing the (x1, y1, x2, y2) coordinates of each detected
             face in that frame.
 
-        Examples:
-            >>> from feat import Detector
-            >>> from feat.utils import read_pictures
-            >>> img_data = read_pictures(['my_image.jpg'])
-            >>> detector = Detector()
-            >>> detector.detect_faces(frame)
         """
 
         logging.info("detecting faces...")

diff --git a/feat/tests/data/Image_with_alpha.png b/feat/tests/data/Image_with_alpha.png
diff --git a/feat/tests/test_detector_core.py b/feat/tests/test_detector_core.py
@@ -168,6 +168,11 @@ def test_detect_single_img_multi_face(default_detector, multi_face_img):
     assert out.shape == (5, 173)
 
 
+def test_detect_with_alpha(default_detector):
+    image = os.path.join(get_test_data_path(), "Image_with_alpha.png")
+    out = default_detector.detect_image(image)
+
+
 # Multiple images
 def test_detect_multi_img_single_face(default_detector, single_face_img):
     """Test detection of single face from multiple images"""

diff --git a/feat/version.py b/feat/version.py
@@ -1 +1 @@
-__version__ = "0.6.0"
+__version__ = "0.6.1"
diff --git a/requirements.txt b/requirements.txt
@@ -3,7 +3,8 @@ numpy>=1.9
 seaborn>=0.7.0
 matplotlib>=2.1
 nltools>=0.4.7
-scikit-learn>=1.0
+numexpr<2.8.5
+scikit-learn>=1.2
 pywavelets>=0.3.0
 h5py>=2.7.0
 Pillow>=6.0.0

diff --git a/setup.py b/setup.py
@@ -27,9 +27,9 @@
         "License :: OSI Approved :: MIT License",
         "Natural Language :: English",
         "Programming Language :: Python :: 3",
-        "Programming Language :: Python :: 3.7",
         "Programming Language :: Python :: 3.8",
         "Programming Language :: Python :: 3.9",
+        "Programming Language :: Python :: 3.10",
     ],
     test_suite="feat/tests",
     **extra_setuptools_args