Fixes #7: ROI rectangles with negative coords

Author: PierreRaybaut

CHANGELOG.md

@@ -13,6 +13,10 @@ for future and past milestones.
     The result was an image positioned at the origin (0, 0) instead of the expected
     position (x0, y0) and the ROI rectangle itself was not removed as expected.
     This is now fixed (see [Issue #6](https://github.com/Codra-Ingenierie-Informatique/DataLab/issues/6) - 'Extract multiple ROI' feature: unexpected result for a single ROI)
+  * ROI rectangles with negative coordinates were not properly handled:
+    ROI extraction was raising a `ValueError` exception, and the image mask was not
+    displayed properly.
+    This is now fixed (see [Issue #7](https://github.com/Codra-Ingenierie-Informatique/DataLab/issues/7) - Image ROI extraction: `ValueError: zero-size array to reduction operation minimum which has no identity`)
   * ROI extraction was not taking into account the pixel size (dx, dy) and the origin
     (x0, y0) of the image.
     This is now fixed (see [Issue #8](https://github.com/Codra-Ingenierie-Informatique/DataLab/issues/8) - Image ROI extraction: take into account pixel size)

cdl/core/computation/image/__init__.py

@@ -527,8 +527,8 @@ def extract_multiple_roi(src: ImageObj, group: gds.DataSetGroup) -> ImageObj:
     Returns:
         ImageObj: output image object
     """
-    x0 = min(p.x0 for p in group.datasets)
-    y0 = min(p.y0 for p in group.datasets)
+    x0 = max(min(p.x0 for p in group.datasets), 0)
+    y0 = max(min(p.y0 for p in group.datasets), 0)
     x1 = max(p.x1 for p in group.datasets)
     y1 = max(p.y1 for p in group.datasets)
     suffix = None
@@ -540,12 +540,11 @@ def extract_multiple_roi(src: ImageObj, group: gds.DataSetGroup) -> ImageObj:
     dst.y0 += y0 * src.dy
     dst.roi = None
     if len(group.datasets) == 1:
-        p = group.datasets[0]
-        dst.data = src.data.copy()[p.y0 : p.y1, p.x0 : p.x1]
+        dst.data = src.data.copy()[y0:y1, x0:x1]
         return dst
     out = np.zeros_like(src.data)
     for p in group.datasets:
-        slice1, slice2 = slice(p.y0, p.y1 + 1), slice(p.x0, p.x1 + 1)
+        slice1, slice2 = slice(max(p.y0, 0), p.y1 + 1), slice(max(p.x0, 0), p.x1 + 1)
         out[slice1, slice2] = src.data[slice1, slice2]
     dst.data = out[y0:y1, x0:x1]
     return dst
@@ -560,9 +559,10 @@ def extract_single_roi(src: ImageObj, p: gds.DataSet) -> ImageObj:
         ImageObj: output image object
     """
     dst = dst_11(src, "extract_single_roi", p.get_suffix())
-    dst.data = src.data.copy()[p.y0 : p.y1, p.x0 : p.x1]
-    dst.x0 += p.x0 * src.dx
-    dst.y0 += p.y0 * src.dy
+    x0, y0, x1, y1 = max(p.x0, 0), max(p.y0, 0), p.x1, p.y1
+    dst.data = src.data.copy()[y0:y1, x0:x1]
+    dst.x0 += x0 * src.dx
+    dst.y0 += y0 * src.dy
     dst.roi = None
     if p.geometry is RoiDataGeometries.CIRCLE:
         # Circular ROI

cdl/core/model/image.py

@@ -166,7 +166,7 @@ def apply_mask(self, data: np.ndarray, yxratio: float) -> np.ndarray:
         roi_mask = np.ones_like(data, dtype=bool)
         x0, y0, x1, y1 = self.get_rect()
         if self.geometry is RoiDataGeometries.RECTANGLE:
-            roi_mask[y0:y1, x0:x1] = False
+            roi_mask[max(y0, 0) : y1, max(x0, 0) : x1] = False
         else:
             xc, yc = 0.5 * (x0 + x1), 0.5 * (y0 + y1)
             radius = 0.5 * (x1 - x0)