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refactor: using opencv for faster convolution
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martibosch committed Feb 18, 2024
1 parent 6a7cabd commit 7334193
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Showing 3 changed files with 8 additions and 4 deletions.
4 changes: 2 additions & 2 deletions detectree/image_descriptor.py
Original file line number Diff line number Diff line change
@@ -1,7 +1,7 @@
"""Compute image descriptors."""
import cv2
import numpy as np
from PIL import Image
from scipy import ndimage as ndi
from skimage import color
from skimage.util import shape
from sklearn import preprocessing
Expand Down Expand Up @@ -60,7 +60,7 @@ def compute_image_descriptor(img_rgb, kernels, response_bins_per_axis, num_color
)

for i, kernel in enumerate(kernels):
filter_response = ndi.convolve(img_gray, kernel)
filter_response = cv2.filter2D(img_gray, ddepth=-1, kernel=kernel)
response_bins = shape.view_as_blocks(filter_response, block_shape)
bin_sum = response_bins.sum(axis=(2, 3)).flatten()
gist_descr[i * num_blocks : (i + 1) * num_blocks] = bin_sum
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7 changes: 5 additions & 2 deletions detectree/pixel_features.py
Original file line number Diff line number Diff line change
@@ -1,8 +1,8 @@
"""Build pixel features."""

import glob
from os import path

import cv2
import dask
import numpy as np
from dask import diagnostics
Expand Down Expand Up @@ -180,7 +180,10 @@ def build_features_from_arr(self, img_rgb):
# theta = orientation / num_orientations * np.pi
theta = orientation * 180 / self.num_orientations
oriented_kernel_arr = ndi.interpolation.rotate(base_kernel_arr, theta)
img_filtered = ndi.convolve(img_lab_l, oriented_kernel_arr)
# img_filtered = ndi.convolve(img_lab_l, oriented_kernel_arr)
img_filtered = cv2.filter2D(
img_lab_l, ddepth=-1, kernel=oriented_kernel_arr
)
img_filtered_vec = img_filtered.flatten()
X[
:, self.num_color_features + i * self.num_orientations + j
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1 change: 1 addition & 0 deletions pyproject.toml
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Expand Up @@ -27,6 +27,7 @@ dependencies = [
"joblib",
"laspy >= 2.0.0",
"numpy >= 1.15",
"opencv-python >= 4.0.0",
"pandas >= 0.23",
"pymaxflow >= 1.0.0",
"rasterio >= 1.0.0",
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