Fix contrast adjustment for high dynamic range float images

Fix #46

Author: PierreRaybaut

CHANGELOG.md

@@ -11,6 +11,7 @@
 🛠️ Bug fixes:
 
 * [Issue #44](https://github.com/PlotPyStack/PlotPy/issues/44) - Incorrect calculation method for "∑(y)" in `CurveStatsTool`: replaced `spt.trapezoid` with `np.sum`, which is more consistent with the summation operation
+* [Issue #46](https://github.com/PlotPyStack/PlotPy/issues/46) - Contrast adjustment with 'Eliminate outliers' failed for float images with high dynamic range
 
 Other changes:
 

plotpy/lutrange.py

@@ -29,25 +29,47 @@
 def hist_range_threshold(
     hist: np.ndarray, bin_edges: np.ndarray, percent: float
 ) -> tuple[float, float]:
-    """Return the range corresponding to the given percent of the histogram
+    """Return the range corresponding to the central `percent` of the histogram mass.
+    This can be used to eliminate outliers symmetrically, e.g. for contrast adjustment.
+
+    Notes:
+        - If the histogram comes from an image with integer values (e.g. 0-255),
+          the first bin is assumed to correspond to value 0 and will be ignored.
+        - For floating-point images, all bins are considered.
 
     Args:
-        hist (numpy.ndarray): The histogram
-        bin_edges (numpy.ndarray): The bin edges
-        percent (float): The percent of the histogram
+        hist: The histogram (length N)
+        bin_edges: The bin edges (length N+1)
+        percent: Percent of histogram mass to retain (between 0 and 100)
 
     Returns:
-        tuple[float, float]: The range
+        A tuple containing the minimum and maximum values of the range
+        corresponding to the central `percent` of the histogram mass.
     """
-    hist = np.concatenate((hist, [0]))
-    hist = hist[1:]
-    bin_edges = bin_edges[1:]
+    if not (0 <= percent <= 100):
+        raise ValueError("percent must be in (0, 100]")
+
+    hist_len = len(hist)
+    i_offset = 0
+
+    # 1. Remove the first bin (typically corresponding to 0), only for integer images
+    if np.issubdtype(bin_edges.dtype, np.integer):
+        hist = hist[1:]
+        i_offset = 1
+
+    # 3. Threshold: keep `percent`% of the mass → remove (1 - percent)% symmetrically
     threshold = 0.5 * percent / 100 * hist.sum()
 
-    i_bin_min = np.cumsum(hist).searchsorted(threshold)
-    i_bin_max = -1 - np.cumsum(np.flipud(hist)).searchsorted(threshold)
+    # 4. Find index where left cumulative sum exceeds threshold
+    i_bin_min = max(np.cumsum(hist).searchsorted(threshold) - i_offset, 0)
+
+    # 5. Find index where right cumulative sum exceeds threshold
+    i_bin_max = hist_len - np.searchsorted(np.cumsum(np.flipud(hist)), threshold)
+
+    # 6. Return bounds as [bin_edges[i_min], bin_edges[i_max + 1]]
+    vmin, vmax = bin_edges[i_bin_min], bin_edges[i_bin_max]
 
-    return bin_edges[i_bin_min], bin_edges[i_bin_max]
+    return vmin, vmax
 
 
 def lut_range_threshold(

plotpy/tests/features/test_contrast.py

@@ -10,12 +10,13 @@
 import os
 import os.path as osp
 
+import numpy as np
 from guidata.env import execenv
 from guidata.qthelpers import qt_app_context
 
 from plotpy.builder import make
 from plotpy.tests import get_path
-from plotpy.tests.data import gen_image1
+from plotpy.tests.data import gen_image1, gen_image4
 
 
 def __create_dialog_with_contrast(item):
@@ -72,6 +73,20 @@ def test_contrast2():
         plot.set_active_item(item2)
 
 
+def test_contrast3():
+    """Contrast test 3
+
+    Test if level histogram works properly when the image has a really high dynamic
+    range (the validation is not automatic)
+    """
+    with qt_app_context(exec_loop=True):
+        data = gen_image4(512, 512)
+        data = np.fft.fftshift(np.fft.fft2(data)).real
+        item = make.image(data, colormap="viridis", eliminate_outliers=2.0)
+        win = __create_dialog_with_contrast(item)
+
+
 if __name__ == "__main__":
-    # test_contrast1()
+    test_contrast1()
     test_contrast2()
+    test_contrast3()