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Removing "filter" function overload #1526

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Removing "filter" function overload #1526

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4 changes: 2 additions & 2 deletions plantcv/plantcv/roi/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,9 +5,9 @@
from plantcv.plantcv.roi.roi_methods import auto_grid
from plantcv.plantcv.roi.roi_methods import multi
from plantcv.plantcv.roi.roi_methods import custom
from plantcv.plantcv.roi.roi_methods import filter
from plantcv.plantcv.roi.roi_methods import mask_filter
from plantcv.plantcv.roi.roi2mask import roi2mask
from plantcv.plantcv.roi.quick_filter import quick_filter

__all__ = ["circle", "ellipse", "from_binary_image", "rectangle", "auto_grid", "multi", "custom",
"filter", "roi2mask", "quick_filter"]
"mask_filter", "roi2mask", "quick_filter"]
2 changes: 1 addition & 1 deletion plantcv/plantcv/roi/roi_methods.py
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Expand Up @@ -425,7 +425,7 @@ def custom(img, vertices):


# Filter a mask based on a region of interest
def filter(mask, roi, roi_type="partial"):
def mask_filter(mask, roi, roi_type="partial"):
"""Filter a mask using a region of interest. Connected regions of non-zero pixels outside the ROI turn to zero

Inputs:
Expand Down
14 changes: 7 additions & 7 deletions tests/plantcv/roi/test_roi.py
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Expand Up @@ -2,7 +2,7 @@
import cv2
import numpy as np
from plantcv.plantcv import Objects
from plantcv.plantcv.roi import from_binary_image, rectangle, circle, ellipse, auto_grid, multi, custom, filter
from plantcv.plantcv.roi import from_binary_image, rectangle, circle, ellipse, auto_grid, multi, custom, mask_filter


def test_from_binary_image(roi_test_data):
Expand Down Expand Up @@ -247,7 +247,7 @@ def test_filter(mode, exp, test_data):
roi = [np.array([[[150, 150]], [[150, 174]], [[249, 174]], [[249, 150]]], dtype=np.int32)]
roi_str = np.array([[[-1, -1, -1, -1]]], dtype=np.int32)
roi_Obj = Objects(contours=[roi], hierarchy=[roi_str])
filtered_mask = filter(mask=mask, roi=roi_Obj, roi_type=mode)
filtered_mask = mask_filter(mask=mask, roi=roi_Obj, roi_type=mode)
area = cv2.countNonZero(filtered_mask)
# Assert that the contours were filtered as expected
assert area == exp
Expand All @@ -264,7 +264,7 @@ def test_filter_multi(test_data):
roi_str = np.array([[[-1, -1, -1, -1]]], dtype=np.int32)
# make a multi-ROI by repeating the same roi twice
roi_Obj = Objects(contours=[roi, roi], hierarchy=[roi_str, roi_str])
filtered_mask = filter(mask=mask, roi=roi_Obj, roi_type="partial")
filtered_mask = mask_filter(mask=mask, roi=roi_Obj, roi_type="partial")
area = cv2.countNonZero(filtered_mask)
# Assert that the contours were filtered as expected
assert area == 221
Expand All @@ -281,7 +281,7 @@ def test_filter_bad_input(test_data):
roi_str = np.array([[[-1, -1, -1, -1]]], dtype=np.int32)
roi_Obj = Objects(contours=[roi], hierarchy=[roi_str])
with pytest.raises(RuntimeError):
_ = filter(mask=mask, roi=roi_Obj, roi_type="cut")
_ = mask_filter(mask=mask, roi=roi_Obj, roi_type="cut")


def test_filter_grayscale_input(test_data):
Expand All @@ -294,7 +294,7 @@ def test_filter_grayscale_input(test_data):
roi = [np.array([[[150, 150]], [[150, 174]], [[249, 174]], [[249, 150]]], dtype=np.int32)]
roi_str = np.array([[[-1, -1, -1, -1]]], dtype=np.int32)
roi_Obj = Objects(contours=[roi], hierarchy=[roi_str])
filtered_mask = filter(mask=mask, roi=roi_Obj, roi_type="partial")
filtered_mask = mask_filter(mask=mask, roi=roi_Obj, roi_type="partial")
area = cv2.countNonZero(filtered_mask)
# Assert that the contours were filtered as expected
assert area == 221
Expand All @@ -310,7 +310,7 @@ def test_filter_no_overlap(test_data):
roi = [np.array([[[0, 0]], [[0, 24]], [[24, 24]], [[24, 0]]], dtype=np.int32)]
roi_str = np.array([[[-1, -1, -1, -1]]], dtype=np.int32)
roi_Obj = Objects(contours=[roi], hierarchy=[roi_str])
filtered_mask = filter(mask=mask, roi=roi_Obj, roi_type="partial")
filtered_mask = mask_filter(mask=mask, roi=roi_Obj, roi_type="partial")
area = cv2.countNonZero(filtered_mask)
# Assert that the contours were filtered as expected
assert area == 0
Expand All @@ -334,7 +334,7 @@ def test_filter_nested():
roi = [np.array([[[0, 0]], [[0, 99]], [[99, 99]], [[99, 0]]], dtype=np.int32)]
roi_str = np.array([[[-1, -1, -1, -1]]], dtype=np.int32)
roi_Obj = Objects(contours=[roi], hierarchy=[roi_str])
filtered_mask = filter(mask=mask, roi=roi_Obj, roi_type="largest")
filtered_mask = mask_filter(mask=mask, roi=roi_Obj, roi_type="largest")
filtered_area = cv2.countNonZero(filtered_mask)
assert area_pre == filtered_area
assert area_total > filtered_area