Revert "Revert "Develop (#315)""

contrib/common/lib/cv/annotations/CircularAnnotations.py

@@ -1,3 +1,4 @@
+from typing import TYPE_CHECKING
 import numpy as np
 import scipy.spatial.transform
 
@@ -13,6 +14,10 @@
 import opencsp.common.lib.render_control.RenderControlPointSeq as rcps
 import opencsp.common.lib.tool.log_tools as lt
 
+if TYPE_CHECKING:
+    # don't import at runtime in order to avoid cyclic dependencies
+    from opencsp.common.lib.cv.spot_analysis.SpotAnalysisOperable import SpotAnalysisOperable
+
 
 class CircularAnnotations(AbstractAnnotations):
     """
@@ -77,7 +82,13 @@ def scale(self) -> list[float]:
             )
         return [d * self.meters_per_pixel for d in self.size]
 
-    def render_to_figure(self, fig: rcfr.RenderControlFigureRecord, image: np.ndarray = None, include_label=False):
+    def render_to_figure(
+        self,
+        fig: rcfr.RenderControlFigureRecord,
+        image: np.ndarray = None,
+        include_label=False,
+        operable: "SpotAnalysisOperable" = None,
+    ):
         label = self.get_label(include_label)
 
         # draw the circles
@@ -93,6 +104,8 @@ def render_to_figure(self, fig: rcfr.RenderControlFigureRecord, image: np.ndarra
             for seg in range(nverticies):
                 p = (np.sin(seg / nverticies * np.pi * 2) * r) + x
                 q = (np.cos(seg / nverticies * np.pi * 2) * r) + y
+                if operable is not None:
+                    p, q = operable.transform_coordinates(p2.Pxy((p, q)))[1].astuple()
                 pq_list.append((p, q))
 
             fig.view.draw_pq_list(pq_list, close=True, style=self.style, label=label)

contrib/common/lib/cv/annotations/EnclosedEnergyAnnotations.py

@@ -1,3 +1,4 @@
+from typing import TYPE_CHECKING
 import numpy as np
 import scipy.spatial.transform
 
@@ -10,6 +11,10 @@
 import opencsp.common.lib.render_control.RenderControlFigureRecord as rcfr
 import opencsp.common.lib.render_control.RenderControlPointSeq as rcps
 
+if TYPE_CHECKING:
+    # don't import at runtime in order to avoid cyclic dependencies
+    from opencsp.common.lib.cv.spot_analysis.SpotAnalysisOperable import SpotAnalysisOperable
+
 
 class EnclosedEnergyAnnotations(AbstractAnnotations):
     """
@@ -84,11 +89,17 @@ def size(self) -> list[float]:
     def scale(self) -> list[float]:
         return self._representative_circle.scale
 
-    def render_to_figure(self, fig: rcfr.RenderControlFigureRecord, image: np.ndarray = None, include_label=False):
+    def render_to_figure(
+        self,
+        fig: rcfr.RenderControlFigureRecord,
+        image: np.ndarray = None,
+        include_label=False,
+        operable: "SpotAnalysisOperable" = None,
+    ):
         if self.enclosed_shape == "circle":
-            return self._representative_circle.render_to_figure(fig, image, include_label)
+            return self._representative_circle.render_to_figure(fig, image, include_label, operable)
         elif self.enclosed_shape == "square":
-            return self._representative_square.render_to_figure(fig, image, include_label)
+            return self._representative_square.render_to_figure(fig, image, include_label, operable)
         else:
             raise RuntimeError(
                 "Error in EnclosedEnergyAnnotations.render_to_figure() "

contrib/common/lib/cv/annotations/MomentsAnnotation.py

@@ -1,5 +1,6 @@
 import copy
 from functools import cache, cached_property
+from typing import TYPE_CHECKING
 
 import numpy as np
 import scipy.spatial.transform
@@ -15,6 +16,10 @@
 import opencsp.common.lib.tool.image_tools as it
 import opencsp.common.lib.tool.log_tools as lt
 
+if TYPE_CHECKING:
+    # don't import at runtime in order to avoid cyclic dependencies
+    from opencsp.common.lib.cv.spot_analysis.SpotAnalysisOperable import SpotAnalysisOperable
+
 
 class MomentsAnnotation(AbstractAnnotations):
     def __init__(
@@ -252,11 +257,18 @@ def central_moment(self, p: int, q: int) -> float:
     # other uses of moments...
 
     def render_to_figure(
-        self, fig_record: rcfr.RenderControlFigureRecord, image: np.ndarray = None, include_label: bool = False
+        self,
+        fig_record: rcfr.RenderControlFigureRecord,
+        image: np.ndarray = None,
+        include_label: bool = False,
+        operable: "SpotAnalysisOperable" = None,
     ):
         # draw the centroid marker
         label = None if not include_label else "centroid"
-        fig_record.view.draw_pq(([self.cX], [self.cY]), self.style, label=label)
+        cX, cY = self.cX, self.cY
+        if operable is not None:
+            cX, cY = operable.transform_coordinates(p2.Pxy((cX, cY)))[1].astuple()
+        fig_record.view.draw_pq(([cX], [cY]), self.style, label=label)
 
         # start by assuming that the plot is >= 30 pixels
         height, width, rotation_arrow_dist = None, None, 30
@@ -289,6 +301,11 @@ def render_to_figure(
             if len(intersections) > 0:
                 rotation_endpoints_list = [(intersections.x[i], intersections.y[i]) for i in range(len(intersections))]
 
+        # transform coordinates for the operable
+        if operable is not None:
+            for i, (p, q) in enumerate(rotation_endpoints_list):
+                rotation_endpoints_list[i] = operable.transform_coordinates(p2.Pxy((p, q)))[1].astuple()
+
         # draw the rotation as an arrow
         style = copy.deepcopy(self.rotation_style)
         style.marker = "arrow"

contrib/common/lib/cv/annotations/RectangleAnnotations.py

@@ -1,3 +1,4 @@
+from typing import TYPE_CHECKING
 import numpy as np
 import scipy.spatial.transform
 
@@ -13,6 +14,10 @@
 import opencsp.common.lib.render_control.RenderControlPointSeq as rcps
 import opencsp.common.lib.tool.log_tools as lt
 
+if TYPE_CHECKING:
+    # don't import at runtime in order to avoid cyclic dependencies
+    from opencsp.common.lib.cv.spot_analysis.SpotAnalysisOperable import SpotAnalysisOperable
+
 
 class RectangleAnnotations(AbstractAnnotations):
     """
@@ -82,7 +87,13 @@ def scale(self) -> list[float]:
             )
         return [self.size * self.meters_per_pixel]
 
-    def render_to_figure(self, fig: rcfr.RenderControlFigureRecord, image: np.ndarray = None, include_label=False):
+    def render_to_figure(
+        self,
+        fig: rcfr.RenderControlFigureRecord,
+        image: np.ndarray = None,
+        include_label=False,
+        operable: "SpotAnalysisOperable" = None,
+    ):
         label = self.get_label(include_label)
 
         # get the corner vertices for each bounding box
@@ -92,6 +103,8 @@ def render_to_figure(self, fig: rcfr.RenderControlFigureRecord, image: np.ndarra
             for loop in bbox.loops:
                 loop_verts = list(zip(loop.vertices.x, loop.vertices.y))
                 loop_verts = [(int(x), int(y)) for x, y in loop_verts]
+                if operable is not None:
+                    loop_verts = [operable.transform_coordinates(p2.Pxy((x, y)))[1].astuple() for x, y in loop_verts]
                 draw_loops.append(loop_verts)
 
         # draw the bounding boxes

contrib/common/lib/cv/annotations/SpotWidthAnnotation.py

@@ -1,3 +1,4 @@
+from typing import TYPE_CHECKING
 import matplotlib.patches
 import numpy as np
 import scipy.spatial.transform
@@ -10,6 +11,10 @@
 import opencsp.common.lib.render_control.RenderControlFigureRecord as rcfr
 import opencsp.common.lib.render_control.RenderControlSpotSize as rcss
 
+if TYPE_CHECKING:
+    # don't import at runtime in order to avoid cyclic dependencies
+    from opencsp.common.lib.cv.spot_analysis.SpotAnalysisOperable import SpotAnalysisOperable
+
 
 class SpotWidthAnnotation(AbstractAnnotations):
     def __init__(
@@ -94,7 +99,13 @@ def rotation(self) -> scipy.spatial.transform.Rotation:
     def size(self) -> list[float]:
         raise NotImplementedError
 
-    def render_to_figure(self, fig: rcfr.RenderControlFigureRecord, image: np.ndarray, include_label=False):
+    def render_to_figure(
+        self,
+        fig: rcfr.RenderControlFigureRecord,
+        image: np.ndarray,
+        include_label=False,
+        operable: "SpotAnalysisOperable" = None,
+    ):
         label = self.get_label(include_label)
 
         # draw the full-width boundary
@@ -109,17 +120,23 @@ def render_to_figure(self, fig: rcfr.RenderControlFigureRecord, image: np.ndarra
             }
             label = None
             if self.spot_width_technique == "fwhm":
+                center = self.long_axis_center
+                if operable is not None:
+                    center = operable.transform_coordinates(center)[1]
                 ellipse = matplotlib.patches.Ellipse(
-                    xy=self.long_axis_center.astuple(),
+                    xy=center.astuple(),
                     width=self.width,
                     height=self.orthogonal_axis_width,
                     angle=np.rad2deg(self.long_axis_rotation),
                     **style_params
                 )
                 fig.view.axis.add_patch(ellipse)
             else:
+                center = self.centroid_loc
+                if operable is not None:
+                    center = operable.transform_coordinates(center)[1]
                 ellipse = matplotlib.patches.Ellipse(
-                    xy=self.centroid_loc.astuple(), width=self.width, height=self.width, **style_params
+                    xy=center.astuple(), width=self.width, height=self.width, **style_params
                 )
                 fig.view.axis.add_patch(ellipse)
 
@@ -129,10 +146,15 @@ def render_to_figure(self, fig: rcfr.RenderControlFigureRecord, image: np.ndarra
             for loop in bbox.loops:
                 loop_verts = list(zip(loop.vertices.x, loop.vertices.y))
                 loop_verts = [(int(x), int(y)) for x, y in loop_verts]
+                if operable is not None:
+                    loop_verts = [operable.transform_coordinates(p2.Pxy((x, y)))[1].astuple() for x, y in loop_verts]
                 fig.view.draw_pq_list(loop_verts, close=True, style=self.style.bounding_box_style, label=label)
                 label = None
 
         # draw the centroid
         if self.style.center_style != "None":
-            fig.view.draw_pq(self.centroid_loc.data, self.style.center_style, label=label)
+            xy = self.centroid_loc
+            if operable is not None:
+                xy = operable.transform_coordinates(xy)[1]
+            fig.view.draw_pq(xy.data, self.style.center_style, label=label)
             label = None

contrib/common/lib/cv/spot_analysis/image_processor/DiscardAnnotationsImageProcessor.py

@@ -0,0 +1,130 @@
+import copy
+import dataclasses
+from typing import Callable, Type
+
+import cv2
+import numpy as np
+
+from opencsp.common.lib.cv.CacheableImage import CacheableImage
+from opencsp.common.lib.cv.fiducials.AbstractFiducials import AbstractFiducials
+from opencsp.common.lib.cv.spot_analysis.SpotAnalysisOperable import SpotAnalysisOperable
+from opencsp.common.lib.cv.spot_analysis.image_processor.AbstractSpotAnalysisImageProcessor import (
+    AbstractSpotAnalysisImageProcessor,
+)
+import opencsp.common.lib.geometry.LoopXY as l2
+import opencsp.common.lib.geometry.Pxy as p2
+import opencsp.common.lib.tool.log_tools as lt
+import opencsp.common.lib.tool.image_tools as it
+
+
+class DiscardAnnotationsImageProcessor(AbstractSpotAnalysisImageProcessor):
+    """
+    Discards fiducials and/or annotations from operables that pass through
+    based on given criteria.
+
+    TODO: extend this class to discard single instances of fiducials for instances where multiple fiducials are included in a single "AbstractFiducials" instance.
+    """
+
+    def __init__(
+        self,
+        outside_boundaries: bool = False,
+        matching_types: list[Type[AbstractFiducials]] = None,
+        custom_criteria: Callable[[SpotAnalysisOperable], list[AbstractFiducials]] = None,
+    ):
+        """
+        Parameters
+        ----------
+        outside_boundaries : bool, optional
+            If True, then any fiducials and/or annotations that are centered
+            outside the boundaries of the image are discarded.
+        matching_types : list[Type[AbstractFiducials]], optional
+            All fiducials and/or annotations that are instances of one of the
+            given types are discarded.
+        custom_criteria : Callable[[SpotAnalysisOperable], np.ndarray], optional
+            The function to be provided that takes in an operable and returns a
+            list of fiducials and/or annotations to be discarded.
+        """
+        super().__init__()
+
+        self.outside_boundaries = outside_boundaries
+        self.matching_types = matching_types or []
+        self.custom_criteria = custom_criteria
+
+    def get_fiducials_outside_boundaries(self, operable: SpotAnalysisOperable) -> list[AbstractFiducials]:
+        """
+        Get the annotations that fall outside the boundaries of the image. This can happen, for example, after a CroppingImageProcessor has trimmed down an image area and the new area excludes an existing fiducial. If self.outside_boundaries is False, then returns an empty list.
+        """
+        if not self.outside_boundaries:
+            return []
+        fiducials_to_discard: list[AbstractFiducials] = []
+
+        (height, width), _ = it.dims_and_nchannels(operable.primary_image.nparray)
+        image_bounds_tltrbrbl = p2.Pxy([[0, width, width, 0], [0, 0, height, height]])
+        transformed_bounds_tltrbrbl = operable.transform_coordinates(image_bounds_tltrbrbl)[1]
+        transformed_loop = l2.LoopXY.from_vertices(transformed_bounds_tltrbrbl)
+
+        for fiducials_list in [operable.given_fiducials, operable.found_fiducials, operable.annotations]:
+            for fiducials in fiducials_list:
+                fiducials: AbstractFiducials = fiducials
+                origins = operable.transform_coordinates(fiducials.origin)[1]
+                origins_inside = transformed_loop.is_inside_or_on_border(origins)
+                if not np.all(origins_inside):
+                    fiducials_to_discard.append(fiducials)
+
+        return fiducials_to_discard
+
+    def get_fiducials_matching_types(self, operable: SpotAnalysisOperable):
+        """Get the fiducials to discard based on the types of the fiducials."""
+        fiducials_to_discard: list[AbstractFiducials] = []
+
+        for fiducials_list in [operable.given_fiducials, operable.found_fiducials, operable.annotations]:
+            for fiducials in fiducials_list:
+                fiducials: AbstractFiducials = fiducials
+                for fiducials_type in self.matching_types:
+                    if isinstance(fiducials, fiducials_type):
+                        fiducials_to_discard.append(fiducials)
+
+        return fiducials_to_discard
+
+    def get_fiducials_by_custom_criteria(self, operable: SpotAnalysisOperable) -> list[AbstractFiducials]:
+        """Get the annotations to be discarded based on a custom function."""
+        if self.custom_criteria is None:
+            return []
+        return self.custom_criteria(operable)
+
+    def discard_fiducials(
+        self, operable: SpotAnalysisOperable, fiducials_to_discard: list[AbstractFiducials]
+    ) -> SpotAnalysisOperable:
+        """
+        Discards the fiducials in the given list and returns a new operable with
+        newly updated annotations.
+        """
+        # Make copies so that we don't change the lists on the input operable
+        given_fiducials = copy.copy(operable.given_fiducials)
+        found_fiducials = copy.copy(operable.found_fiducials)
+        annotations = copy.copy(operable.annotations)
+
+        # Remove all fiducials in the list to be discarded
+        for fiducials_list in [given_fiducials, found_fiducials, annotations]:
+            fiducials_list: list[AbstractFiducials] = fiducials_list
+            for fiducials in copy.copy(fiducials_list):
+                if fiducials in fiducials_to_discard:
+                    fiducials_list.remove(fiducials)
+
+        # Update with a new operable instance
+        return dataclasses.replace(
+            operable, given_fiducials=given_fiducials, found_fiducials=found_fiducials, annotations=annotations
+        )
+
+    def _execute(self, operable: SpotAnalysisOperable, is_last: bool) -> list[SpotAnalysisOperable]:
+        fiducials_to_discard: list[AbstractFiducials] = []
+
+        # Get the fiducials to discard
+        fiducials_to_discard += self.get_fiducials_outside_boundaries(operable)
+        fiducials_to_discard += self.get_fiducials_matching_types(operable)
+        fiducials_to_discard += self.get_fiducials_by_custom_criteria(operable)
+
+        # Build a new operable instance, keeping all fiducials not in the discard list
+        operable = self.discard_fiducials(operable, fiducials_to_discard)
+
+        return [operable]

contrib/common/lib/cv/spot_analysis/image_processor/ViewAnnotationsImageProcessor.py

@@ -113,12 +113,17 @@ def visualize_operable(
 
         # initialize the figure
         self.prepare_figure_records([self.figure], [image.shape])
-        self.figure.view.imshow(image)
+        tc = lambda x, y: operable.transform_coordinates(p2.Pxy((x, y)))[1].astuple()
+        (height, width), _ = it.dims_and_nchannels(image)
+        img_xy, img_xy2 = tc(0, 0), tc(width, height)
+        self.figure.view.draw_image(
+            base_image.nparray, img_xy, (img_xy2[0] - img_xy[0], img_xy2[1] - img_xy[1]), invert_ylim=True
+        )
 
         # render
         include_label = len(to_render) > 1
         for fiducials in to_render:
-            fiducials.render_to_figure(self.figure, image, include_label)
+            fiducials.render_to_figure(self.figure, image, include_label, operable)
 
         # show the legend
         if include_label: