Update screenshots and enhance blob detection scenario in beautiful_app

- Replace outdated screenshot with a new blob detection image.
- Modify the beautiful_app scenario to include blob detection on flower images.
- Update documentation to reflect the new screenshot and scenario changes.

Author: PierreRaybaut

README.md

@@ -12,7 +12,7 @@ See [DataLab website](https://datalab-platform.com/) for more details.
 
 ℹ️ Created by [CODRA](https://codra.net/)/[Pierre Raybaut](https://github.com/PierreRaybaut) in 2023, developed and maintained by DataLab Platform Developers.
 
-![DataLab](https://raw.githubusercontent.com/DataLab-Platform/DataLab/main/doc/images/DataLab-Screenshot.png)
+![DataLab](https://raw.githubusercontent.com/DataLab-Platform/DataLab/main/doc/images/shots/i_blob_detection_flower.png)
 
 ℹ️ DataLab is powered by [PlotPyStack](https://github.com/PlotPyStack) 🚀.
 

datalab/tests/scenarios/beautiful_app.py

@@ -21,9 +21,10 @@
 # guitest: show,skip
 
 import sigima.objects
-import sigima.params as sigima_param
+import sigima.params
 from sigima.tests.data import get_test_image
 
+from datalab.config import Conf
 from datalab.tests import datalab_test_app_context
 
 
@@ -39,7 +40,7 @@ def run_beautiful_scenario(screenshots: bool = False) -> None:
         panel.processor.run_feature("wiener")
         panel.processor.run_feature("derivative")
         panel.processor.run_feature("integral")
-        panel.processor.run_feature("gaussian_filter", sigima_param.GaussianParam())
+        panel.processor.run_feature("gaussian_filter", sigima.params.GaussianParam())
         panel.processor.run_feature("fft")
         panel.processor.run_feature("derivative")
         if screenshots:
@@ -51,16 +52,16 @@ def run_beautiful_scenario(screenshots: bool = False) -> None:
         ima = sigima.objects.create_image_from_param(param)
         ima.set_metadata_option("colormap", "jet")
         panel.add_object(ima)
-        panel.processor.run_feature("equalize_hist", sigima_param.EqualizeHistParam())
+        panel.processor.run_feature("equalize_hist", sigima.params.EqualizeHistParam())
         panel.processor.run_feature(
-            "equalize_adapthist", sigima_param.EqualizeAdaptHistParam()
+            "equalize_adapthist", sigima.params.EqualizeAdaptHistParam()
         )
-        panel.processor.run_feature("denoise_tv", sigima_param.DenoiseTVParam())
+        panel.processor.run_feature("denoise_tv", sigima.params.DenoiseTVParam())
         panel.processor.run_feature(
-            "denoise_wavelet", sigima_param.DenoiseWaveletParam()
+            "denoise_wavelet", sigima.params.DenoiseWaveletParam()
         )
-        panel.processor.run_feature("white_tophat", sigima_param.MorphologyParam())
-        panel.processor.run_feature("denoise_tv", sigima_param.DenoiseTVParam())
+        panel.processor.run_feature("white_tophat", sigima.params.MorphologyParam())
+        panel.processor.run_feature("denoise_tv", sigima.params.DenoiseTVParam())
         n = data_size // 3
         roi = sigima.objects.create_image_roi(
             "rectangle", [n, n, data_size - 2 * n, data_size - 2 * n]
@@ -71,13 +72,15 @@ def run_beautiful_scenario(screenshots: bool = False) -> None:
             win.take_menu_screenshots()
 
 
-def run_circle_detection_scenario(screenshots: bool = False) -> None:
+def run_blob_detection_on_flower_image(screenshots: bool = False) -> None:
     """High-level test scenario for flower image with ROI extraction
 
     This scenario creates:
     - A flower test image
     - Roberts edge detection filter applied
     - A rectangular ROI extraction
+    - A closing morphological filter to clean up the result
+    - Blob detection using OpenCV algorithm
     """
     with datalab_test_app_context(console=False, exec_loop=not screenshots) as win:
         # Create an image panel
@@ -86,59 +89,33 @@ def run_circle_detection_scenario(screenshots: bool = False) -> None:
         # Load the flower test image
         ima = get_test_image("flower.npy")
         ima.title = "Test image 'flower.npy'"
-        ima.set_metadata_option("colormap", "jet")
         panel.add_object(ima)
 
         # Apply Roberts filter for edge detection
         panel.processor.run_feature("roberts")
 
         # Extract a rectangular ROI
-        roi = sigima.objects.create_image_roi("rectangle", [32, 128, 448, 256])
+        roi = sigima.objects.create_image_roi("rectangle", [32, 64, 448, 384])
         panel.processor.compute_roi_extraction(roi)
 
-        if screenshots:
-            win.statusBar().hide()
-            win.take_screenshot("i_flower_roi")
-
-
-def test_contour_detection_limits() -> None:
-    """Test scenario to verify result truncation limits work correctly
-
-    This scenario tests:
-    - Contour detection on flower.npy (generates many contours)
-    - Result truncation at max_result_rows limit
-    - Shape drawing truncation at max_shapes_to_draw limit
-    - Label display truncation at max_cells_in_label & max_cols_in_label limits
-    - Warning dialog at max_cells_in_dialog limit
-    """
-    with datalab_test_app_context(console=False, exec_loop=False) as win:
-        # Create an image panel
-        panel = win.imagepanel
-
-        # Load the flower test image
-        ima = get_test_image("flower.npy")
-        ima.title = "Test image 'flower.npy' - Contour Detection Limit Test"
-        ima.set_metadata_option("colormap", "jet")
-        panel.add_object(ima)
-
-        # Run contour detection which should trigger the limits
-        # This will detect many contours and test our safety mechanisms
-        print("\nRunning contour detection on flower.npy...")
-        print("This should trigger result truncation and shape drawing limits.")
-        panel.processor.run_feature("contour_shape", sigima_param.ContourShapeParam())
-
-        print("\nTest completed successfully!")
-        print("Expected behavior:")
-        print("  1. Results truncated to max_result_rows (default: 1000)")
-        print("  2. Only max_shapes_to_draw shapes drawn (default: 50)")
-        print("  3. Warning label on plot showing truncation")
-        print("  4. Result dialog warning if > max_cells_in_dialog (default: 50000)")
-        print("  5. Merged label: max_cells_in_label (100) & max_cols_in_label (15)")
+        # Apply a closing morphological filter to clean up the result
+        closing_param = sigima.params.MorphologyParam.create(radius=10)
+        panel.processor.run_feature("closing", closing_param)
+
+        # Detect blobs using OpenCV algorithm
+        param = sigima.params.BlobOpenCVParam()
+        param.filter_by_color = False
+        param.min_area = 400
+        param.max_area = 1000
+        param.filter_by_circularity = True
+        param.min_circularity = 0.7
+        with Conf.proc.show_result_dialog.temp(False):
+            with Conf.view.show_result_label.temp(False):
+                panel.processor.run_feature("blob_opencv", param)
+                if screenshots:
+                    win.statusBar().hide()
+                    win.take_screenshot("i_blob_detection_flower")
 
 
 if __name__ == "__main__":
-    # Uncomment to run the original scenarios:
-    # run_circle_detection_scenario()
-
-    # Run the test for result limits
-    test_contour_detection_limits()
+    run_blob_detection_on_flower_image(screenshots=False)

doc/features/index.rst

@@ -24,7 +24,7 @@ data analysis and visualization platform.
 Overview & Common features
 --------------------------
 
-.. figure:: /images/DataLab-Screenshot.png
+.. figure:: /images/shots/i_blob_detection_flower.png
 
     DataLab main window
 

doc/images/DataLab-Screenshot.png

@@ -15,5 +15,6 @@
     launcher1_app_test.test_launcher1(screenshots=True)
     config.reset()
     beautiful_app.run_beautiful_scenario(screenshots=True)
+    beautiful_app.run_blob_detection_on_flower_image(screenshots=True)
     settings_unit_test.capture_settings_screenshots()
     print("done.")