Create a logical operation helper function

docs/logical_xor.md

@@ -32,8 +32,8 @@ from plantcv import plantcv as pcv
 pcv.params.debug = "plot"
 
 # Combine two images that have had different thresholds applied to them.
-# For logical 'and' operation object pixel must be in both images 
-# to be included in 'and' image.
+# For logical 'xor' operation object pixel must be in either images 
+# but not both to be included in 'xor' image.
 xor_image = pcv.logical_xor(s_threshold, b_threshold)
 
 ```

plantcv/plantcv/_helpers.py

@@ -1,7 +1,6 @@
 import cv2
 import numpy as np
 from plantcv.plantcv.dilate import dilate
-from plantcv.plantcv.logical_and import logical_and
 from plantcv.plantcv.image_subtract import image_subtract
 from plantcv.plantcv import fatal_error, warn
 from plantcv.plantcv import params
@@ -47,7 +46,7 @@ def _find_segment_ends(skel_img, leaf_objects, plotting_img, size):
             segment_plot = np.zeros(skel_img.shape[:2], np.uint8)
             cv2.drawContours(segment_plot, obj, -1, 255, 1, lineType=8)
             segment_plot = dilate(segment_plot, 3, 1)
-            overlap_img = logical_and(segment_plot, tips)
+            overlap_img = _logical_operation(segment_plot, tips, 'and')
             x, y = segment_end_obj[j].ravel()[:2]
             coord = (int(x), int(y))
             coords.append(coord)
@@ -339,7 +338,7 @@ def _roi_filter(img, roi, obj, hierarchy, roi_type="partial"):
         for c, _ in enumerate(object_contour):
             filtering_mask = np.zeros(np.shape(img)[:2], dtype=np.uint8)
             cv2.fillPoly(filtering_mask, [np.vstack(object_contour[c])], (255))
-            overlap_img = logical_and(filtering_mask, roi_mask)
+            overlap_img = _logical_operation(filtering_mask, roi_mask, 'and')
             # Delete contours that do not overlap at all with the ROI
             if np.sum(overlap_img) == 0:
                 cv2.drawContours(mask, object_contour, c, (0), -1, lineType=8, hierarchy=obj_hierarchy)
@@ -581,3 +580,34 @@ def _rgb2gray(rgb_img):
     gray = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY)
 
     return gray
+
+
+def _logical_operation(bin_img1, bin_img2, operation):
+    """Perform a logical operation on two binary images.
+
+    Parameters
+    ----------
+    bin_img1 : numpy.ndarray
+        First binary image
+    bin_img2 : numpy.ndarray
+        Second binary image
+    operation : str
+        Logical operation to perform ('and', 'or', or 'xor')
+
+    Returns
+    -------
+    numpy.ndarray
+        Resulting binary image after the logical operation
+    """
+    # Dictionary of operations
+    operations = {
+        'and': cv2.bitwise_and,
+        'or': cv2.bitwise_or,
+        'xor': cv2.bitwise_xor
+    }
+    # Check if the operation is valid
+    if operation.lower() not in operations:
+        fatal_error(f"Operation '{operation}' is not supported. Use 'and', 'or', or 'xor'.")
+    # Perform the logical operation
+    mask = operations[operation.lower()](bin_img1, bin_img2)
+    return mask

plantcv/plantcv/kmeans_classifier.py

@@ -7,6 +7,7 @@
 from plantcv.plantcv._debug import _debug
 from plantcv.plantcv import params
 from plantcv.learn.train_kmeans import patch_extract
+from plantcv.plantcv._helpers import _logical_operation
 
 
 def predict_kmeans(img, model_path="./kmeansout.fit", patch_size=10):
@@ -83,7 +84,7 @@ def mask_kmeans(labeled_img, k, cat_list=None):
         if idx == 0:
             mask_light = np.where(labeled_img == i, 255, 0).astype("uint8")
         else:
-            mask_light = pcv.logical_or(mask_light, np.where(labeled_img == i, 255, 0).astype("uint8"))
+            mask_light = _logical_operation(mask_light, np.where(labeled_img == i, 255, 0).astype("uint8"), "or")
     params.debug = debug
     _debug(visual=mask_light, filename=os.path.join(params.debug_outdir, "_kmeans_combined_mask.png"))
     return mask_light

plantcv/plantcv/logical_and.py

@@ -1,30 +1,28 @@
 # Join images (AND)
 
-import cv2
 import os
 from plantcv.plantcv._debug import _debug
 from plantcv.plantcv import params
+from plantcv.plantcv._helpers import _logical_operation
 
 
 def logical_and(bin_img1, bin_img2):
+    """Join two images using the bitwise AND operator.
+
+    Parameters
+    ----------
+    bin_img1 : numpy.ndarray
+        First binary image.
+    bin_img2 : numpy.ndarray
+        Second binary image.
+
+    Returns
+    -------
+    numpy.ndarray
+        Joined binary image.
     """
-    Join two images using the bitwise AND operator.
+    merged = _logical_operation(bin_img1, bin_img2, 'and')
 
-    Inputs:
-    bin_img1   = Binary image data to be compared to bin_img2
-    bin_img2   = Binary image data to be compared to bin_img1
-
-    Returns:
-    merged     = joined binary image
-
-    :param bin_img1: numpy.ndarray
-    :param bin_img2: numpy.ndarray
-    :return merged: numpy.ndarray
-    """
-    merged = cv2.bitwise_and(bin_img1, bin_img2)
-
-    _debug(visual=merged,
-           filename=os.path.join(params.debug_outdir, str(params.device) + '_and_joined.png'),
-           cmap='gray')
+    _debug(visual=merged, filename=os.path.join(params.debug_outdir, f'{params.device}_and_joined.png'), cmap='gray')
 
     return merged

plantcv/plantcv/logical_or.py

@@ -1,30 +1,28 @@
 # Join images (OR)
 
-import cv2
 import os
 from plantcv.plantcv._debug import _debug
 from plantcv.plantcv import params
+from plantcv.plantcv._helpers import _logical_operation
 
 
 def logical_or(bin_img1, bin_img2):
+    """Join two images using the bitwise OR operator.
+
+    Parameters
+    ----------
+    bin_img1 : numpy.ndarray
+        First binary image.
+    bin_img2 : numpy.ndarray
+        Second binary image.
+
+    Returns
+    -------
+    numpy.ndarray
+        Joined binary image.
     """
-    Join two images using the bitwise OR operator.
+    merged = _logical_operation(bin_img1, bin_img2, 'or')
 
-    Inputs:
-    bin_img1   = Binary image data to be compared to bin_img2
-    bin_img2   = Binary image data to be compared to bin_img1
-
-    Returns:
-    merged     = joined binary image
-
-    :param bin_img1: numpy.ndarray
-    :param bin_img2: numpy.ndarray
-    :return merged: numpy.ndarray
-    """
-    merged = cv2.bitwise_or(bin_img1, bin_img2)
-
-    _debug(visual=merged,
-           filename=os.path.join(params.debug_outdir, str(params.device) + '_or_joined.png'),
-           cmap='gray')
+    _debug(visual=merged, filename=os.path.join(params.debug_outdir, f'{params.device}_or_joined.png'), cmap='gray')
 
     return merged

plantcv/plantcv/logical_xor.py

@@ -1,30 +1,28 @@
 # Join images (XOR)
 
-import cv2
 import os
 from plantcv.plantcv._debug import _debug
 from plantcv.plantcv import params
+from plantcv.plantcv._helpers import _logical_operation
 
 
 def logical_xor(bin_img1, bin_img2):
+    """Join two images using the bitwise XOR operator.
+
+    Parameters
+    ----------
+    bin_img1 : numpy.ndarray
+        First binary image.
+    bin_img2 : numpy.ndarray
+        Second binary image.
+
+    Returns
+    -------
+    numpy.ndarray
+        Joined binary image.
     """
-    Join two images using the bitwise XOR operator.
+    merged = _logical_operation(bin_img1, bin_img2, 'xor')
 
-    Inputs:
-    bin_img1   = Binary image data to be compared to bin_img2
-    bin_img2   = Binary image data to be compared to bin_img1
-
-    Returns:
-    merged     = joined binary image
-
-    :param bin_img1: numpy.ndarray
-    :param bin_img2: numpy.ndarray
-    :return merged: numpy.ndarray
-    """
-    merged = cv2.bitwise_xor(bin_img1, bin_img2)
-
-    _debug(visual=merged,
-           filename=os.path.join(params.debug_outdir, str(params.device) + '_xor_joined.png'),
-           cmap='gray')
+    _debug(visual=merged, filename=os.path.join(params.debug_outdir, f'{params.device}_xor_joined.png'), cmap='gray')
 
     return merged

plantcv/plantcv/morphology/segment_insertion_angle.py

@@ -6,12 +6,11 @@
 from plantcv.plantcv import dilate
 from plantcv.plantcv import closing
 from plantcv.plantcv import outputs
-from plantcv.plantcv import logical_and
 from plantcv.plantcv import fatal_error
 from plantcv.plantcv import color_palette
 from plantcv.plantcv.morphology.segment_tangent_angle import _slope_to_intesect_angle
 from plantcv.plantcv._debug import _debug
-from plantcv.plantcv._helpers import _cv2_findcontours, _find_tips, _iterative_prune
+from plantcv.plantcv._helpers import _cv2_findcontours, _find_tips, _iterative_prune, _logical_operation
 
 
 def segment_insertion_angle(skel_img, segmented_img, leaf_objects, stem_objects, size, label=None):
@@ -88,7 +87,7 @@ def segment_insertion_angle(skel_img, segmented_img, leaf_objects, stem_objects,
                 segment_plot = np.zeros(segmented_img.shape[:2], np.uint8)
                 cv2.drawContours(segment_plot, obj, -1, 255, 1, lineType=8)
                 segment_plot = dilate(segment_plot, 3, 1)
-                overlap_img = logical_and(segment_plot, tips)
+                overlap_img = _logical_operation(segment_plot, tips, "and")
 
                 # If none of the tips are within a segment_end then it's an insertion segment
                 if np.sum(overlap_img) == 0:

plantcv/plantcv/morphology/segment_sort.py

@@ -5,8 +5,7 @@
 import numpy as np
 from plantcv.plantcv import dilate
 from plantcv.plantcv import params
-from plantcv.plantcv import logical_and
-from plantcv.plantcv._helpers import _find_tips
+from plantcv.plantcv._helpers import _find_tips, _logical_operation
 from plantcv.plantcv._debug import _debug
 
 
@@ -50,7 +49,7 @@ def segment_sort(skel_img, objects, mask=None, first_stem=True):
     for i, cnt in enumerate(objects):
         segment_plot = np.zeros(skel_img.shape[:2], np.uint8)
         cv2.drawContours(segment_plot, objects, i, 255, 1, lineType=8)
-        overlap_img = logical_and(segment_plot, tips_img)
+        overlap_img = _logical_operation(segment_plot, tips_img, "and")
 
         # The first contour is the base, and while it contains a tip, it isn't a leaf
         if i == 0 and first_stem:

plantcv/plantcv/roi/quick_filter.py

@@ -7,7 +7,7 @@
 from plantcv.plantcv import params
 from plantcv.plantcv.roi import roi2mask
 from plantcv.plantcv._debug import _debug
-from plantcv.plantcv.logical_and import logical_and
+from plantcv.plantcv._helpers import _logical_operation
 
 
 def quick_filter(mask, roi):
@@ -107,7 +107,7 @@ def _quick_cutto(mask, roi):
         # Pixel intensity of (i+1) such that the first object has value
         cv2.drawContours(labeled_mask, roi.contours[i], -1, (i+1), -1)
         cv2.drawContours(bin_mask, roi.contours[i], -1, (255), -1)
-    cropped_mask = logical_and(mask_copy, bin_mask)
+    cropped_mask = _logical_operation(mask_copy, bin_mask, "and")
     # Make a labeled mask from the cropped objects
     label_mask_where = np.where(cropped_mask == 255, labeled_mask, 0)
 

tests/plantcv/test_helper_logical_operation.py

@@ -0,0 +1,10 @@
+import cv2
+import pytest
+from plantcv.plantcv._helpers import _logical_operation
+
+
+def test_logical_operation_bad_type(test_data):
+    """Test for PlantCV."""
+    mask = cv2.imread(test_data.small_bin_img, -1)
+    with pytest.raises(RuntimeError):
+        _ = _logical_operation(bin_img1=mask, bin_img2=mask, operation="invalid")