Add README text, set fPIC by force

CMakeLists.txt

@@ -14,14 +14,17 @@ set(CMAKE_CXX_STANDARD 17)
 file(GLOB HOG_SOURCES "src/hog.cpp")
 if (CMAKE_SYSTEM_PROCESSOR MATCHES "(x86)|(X86)|(amd64)|(AMD64)")
   add_library(hog_sse4 OBJECT src/magnitude_orientation.cpp)
+  set_property(TARGET hog_sse4 PROPERTY POSITION_INDEPENDENT_CODE ON)
   target_compile_options(hog_sse4 PRIVATE -msse4 -DVEC_T=Vec2d)
   target_include_directories(hog_sse4 PRIVATE ${HOG_INCLUDES})
 
   add_library(hog_avx2 OBJECT src/magnitude_orientation.cpp)
+  set_property(TARGET hog_avx2 PROPERTY POSITION_INDEPENDENT_CODE ON)
   target_compile_options(hog_avx2 PUBLIC -mavx2 -mfma -DVEC_T=Vec4d)
   target_include_directories(hog_avx2 PRIVATE ${HOG_INCLUDES})
 
   add_library(hog_avx512 OBJECT src/magnitude_orientation.cpp)
+  set_property(TARGET hog_avx512 PROPERTY POSITION_INDEPENDENT_CODE ON)
   target_compile_options(hog_avx512 PUBLIC -mavx512f -mfma -DVEC_T=Vec8d)
   target_include_directories(hog_avx512 PRIVATE ${HOG_INCLUDES})
 

README.md

@@ -1,2 +1,46 @@
 # FastHOG
-Reasonably fast implementation of Histogram of Oriented Gradients.
+Reasonably fast implementation of Histogram of Oriented Gradients. Designed to be roughly equivalent to
+`skimage.feature.hog`. While slightly less flexible (it only supports `float64` single-channel images),
+it is _significantly_ faster, so ideal in workloads where `HOG` calculation is a bottleneck, as was in
+the https://github.com/flatironinstitute/ManifoldEM project that inspired this repository.
+
+
+## Installation
+
+```bash
+pip install fasthog
+```
+
+## Usage
+Example taken from https://scikit-image.org/docs/stable/auto_examples/features_detection/plot_hog.html
+
+```python3
+from fasthog import hog
+from skimage import data
+from skimage.color import rgb2gray
+
+image = rgb2gray(data.astronaut())
+
+fd, hog_image = hog(
+    image,
+    n_bins=8,
+    pixels_per_cell=(16, 16),
+    cells_per_block=(1, 1),
+)
+
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(8, 4), sharex=True, sharey=True)
+
+ax1.axis('off')
+ax1.imshow(image, cmap=plt.cm.gray)
+ax1.set_title('Input image')
+
+# Rescale histogram for better display
+hog_image_rescaled = exposure.rescale_intensity(hog_image, in_range=(0, 10))
+
+ax2.axis('off')
+ax2.imshow(hog_image_rescaled, cmap=plt.cm.gray)
+ax2.set_title('Histogram of Oriented Gradients')
+plt.show()
+
+
+```