Merge pull request #40 from fraunhoferportugal/dev

Patch version 0.1.8

CHANGELOG.md

@@ -3,6 +3,35 @@
 All notable changes to this project will be documented in this file.
 This format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/), and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+
+## [0.1.8] - 2025-02-21
+Minor patch release with Time Series standardization and documentation updates.
+
+### Changed
+ - Introduced `BaseTSExtractor` torch module for time_series
+ - Loading TSFEL config on class instanciation
+
+### Fixed
+ - Removed input layer logic from time_series example notebook (should not use internal methods or classes)
+
+
+## [0.1.7] - 2025-01-31
+Minor patch release with reduced dependency tree and documentaion updates.
+
+### Added
+ - `convert_grayscale` option in `PSNR`metric
+ - Explicitely listing `SSIM` and `MSSIM` params
+ - Non deterministic warning for MSID documentation
+
+### Changed
+ - Renamed `MSSIM` to `MSSSIM`
+ - GIQA documentation to indicate QS and DS as in paper
+ - Removed unnecessary torchmetrics dependency for image
+
+### Fixed
+ - Added `np.isclose` verification in hypersphere estimation functions for synthetic evaluation
+
+
 ## [0.1.6] - 2024-12-10
 Minor patch release with new image features extraction method and documentation updates.
 

Makefile

@@ -127,7 +127,7 @@ setup-all:
 	@echo -e "$(INFO) Creating development virtual environment...$(TERMINATOR)" && \
 	python3 -m venv .venv-dev && \
 	source .venv-dev/bin/activate && \
-	pip install -U poetry<2.0.0 && \
+	pip install -U "poetry<2.0.0" && \
 	poetry run pip install --upgrade pip setuptools && \
 	poetry install --with dev --all-extras && \
 	echo -e "$(SUCCESS) Virtual environment created successfully!$(TERMINATOR)" && \

README.md

@@ -73,7 +73,7 @@ Depending on the data modality you want to use, you may need to install addition
 ```bash
 pip install "pymdma[image] @ git+https://github.com/fraunhoferportugal/pymdma.git" # image dependencies
 pip install "pymdma[tabular] @ git+https://github.com/fraunhoferportugal/pymdma.git" # tabular dependencies
-pip install "pymdma[tabular] @ git+https://github.com/fraunhoferportugal/pymdma.git" # time series dependencies
+pip install "pymdma[time_series] @ git+https://github.com/fraunhoferportugal/pymdma.git" # time series dependencies
 ```
 
 For a minimal installation, you can install the package without CUDA support by forcing pip to install torch from the CPU index with the `--find-url` command.

VERSION

@@ -1 +1 @@
-0.1.7
+0.1.8

notebooks/image_examples.ipynb

@@ -6,7 +6,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install \"pymdma[image] @ https://github.com/fraunhoferportugal/pymdma.git\" --extra-index-url \"https://download.pytorch.org/whl/cpu/torch_stable.html\""
+    "%pip install \"pymdma[image]\" --extra-index-url \"https://download.pytorch.org/whl/cpu/torch_stable.html\""
    ]
   },
   {
@@ -260,6 +260,7 @@
     "            distorted.append((dst).astype(np.uint8))\n",
     "    return [np.array(x) for x in distorted]\n",
     "\n",
+    "\n",
     "distorted = generate_full_ref_dataset(dataset)\n",
     "\n",
     "mssim = MSSSIM()\n",
@@ -315,17 +316,15 @@
     "\n",
     "from pymdma.image.models.features import ExtractorFactory\n",
     "\n",
+    "N_SAMPLES = 2000\n",
     "random.seed(10)\n",
     "\n",
     "cifake_test_path = cifake_path / \"test\"\n",
     "test_images_ref = Path(cifake_test_path / \"REAL\")  # real images\n",
     "test_images_synth = Path(cifake_test_path / \"FAKE\")  # synthetic images\n",
     "\n",
-    "images_ref = [img for img in test_images_ref.iterdir() if img.is_file()]\n",
-    "images_synth = [img for img in test_images_synth.iterdir() if img.is_file()]\n",
-    "\n",
-    "# images_ref = random.sample([img for img in test_images_ref.iterdir() if img.is_file()], 5000)\n",
-    "# images_synth = random.sample([img for img in test_images_synth.iterdir() if img.is_file()], 5000)\n",
+    "images_ref = [img for img in test_images_ref.iterdir() if img.is_file()][:N_SAMPLES]\n",
+    "images_synth = [img for img in test_images_synth.iterdir() if img.is_file()][:N_SAMPLES]\n",
     "\n",
     "extractor = ExtractorFactory.model_from_name(name=\"dino_vits8\")\n",
     "ref_features = extractor.extract_features_from_files(images_ref)\n",
@@ -446,8 +445,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import matplotlib.pyplot as plt\n",
-    "\n",
     "giqa_result.plot(\"GIQA\", bins=50)\n",
     "plt.xlabel(\"Score\")\n",
     "plt.ylabel(\"Frequency\")\n",