modify bioimage_agent eval cases

.gitattributes

@@ -11,6 +11,4 @@
 *.vtu filter=lfs diff=lfs merge=lfs -text
 *.vti filter=lfs diff=lfs merge=lfs -text
 *.cif filter=lfs diff=lfs merge=lfs -text
-eval_cases/napari/1_workflows/GT/dataset002_1.jpg filter=lfs diff=lfs merge=lfs -text
-eval_cases/napari/1_workflows/GT/dataset002_2.jpg filter=lfs diff=lfs merge=lfs -text
 *.nc filter=lfs diff=lfs merge=lfs -text

eval_cases/napari/1_workflows/eval_analysis_workflows.yaml

@@ -5,15 +5,18 @@
 # Test: Cell Counting and Measurement Analysis
 - vars:
     question: |
-      1. Load the image "data/dataset_002/dataset_002.tif" and set it to red colormap for channel 0 and green colormap for channel 1.
-      2. Take a screenshot and analyze it to count how many complete cells are visible (not cut off by edges).
-      3. Add point annotations to mark the center of each counted cell.
-      4. Measure the distance between the two most distant cells.
-      5. Respond with the number of complete cells you counted, for example "5" if you see 5 complete cells.
+      Load the image "data/dataset_001/dataset_001.tiff" and set it to magenta colormap.
+      Take a screenshot and analyze it to count how many complete cells are visible (not cut off by edges).
+      Add point annotations to mark the center of each counted cell.
+      Measure the distance between the two most distant cells.
+      Q1: answer with the number of complete cells you counted, for example "5" if you see 5 complete cells.
+      Save the answer of Q1 to the questions in plain text as "eval_analysis_workflows/Q1_answer.txt".
   assert:
     - type: llm-rubric
-      value: 
-        - It counted 2 complete cells
+      subtype: text
+      value: |
+        1. Q1 correct answer: 2
+      rs-file: eval_analysis_workflows/Q1_answer.txt
   options:
     cache: false
     runSerially: true
@@ -21,17 +24,16 @@
 # Test: Statistical Analysis and Data Export
 - vars:
     question: |
-      1. Get basic statistics (min, max, mean, std) for the loaded layer.
-      2. Extract the raw layer data and examine its properties.
-      3. Save the current layer to a file for further analysis.
-      4. Export a screenshot of the current view for documentation.
+      Get basic statistics (min, max, mean, std) for the loaded layer.
+      Extract the raw layer data and examine its properties.
+      Save the current layer to a file for further analysis.
+      Export a screenshot of the current view for documentation.
+      Respond with <1> if the statistical analysis and data export were successful, or <0> if it failed. Only respond with <1> or <0>.
   assert:
-    - type: llm-rubric
-      value:
-        - The statistical analysis was successfully performed and reported min, max, mean, and std values
-        - The layer data was extracted and its properties were examined
-        - The layer was saved to a file
-        - A screenshot was exported for documentation
+    - type: contains-all
+      value: "<1>"
+    - type: not-contains
+      value: "<0>"
   options:
     cache: false
     runSerially: true
@@ -42,14 +44,12 @@
       1. Add point annotations to mark specific features of interest in the image.
       2. Add shape annotations (rectangles or circles) to highlight regions of interest.
       3. Measure distances between multiple pairs of points.
-      4. Take a screenshot showing all annotations and measurements.
+      4. Take a screenshot showing all annotations and measurements, save it to "eval_analysis_workflows/screenshot_1.png".
   assert:
     - type: llm-rubric
-      value:
-        - Point annotations were successfully added to mark features of interest
-        - Shape annotations (rectangles or circles) were added to highlight regions
-        - Distance measurements were performed between points
-        - A screenshot was taken showing the annotations and measurements
+      value: |
+          1. The screenshot shows the point and shape annotations, and measurements
+      rs-file: eval_analysis_workflows/screenshot_1.png
   options:
     cache: false
     runSerially: true
@@ -57,16 +57,16 @@
 # Test: Time Series Analysis (if applicable)
 - vars:
     question: |
-      1. If the data has time dimensions, navigate through different time points.
-      2. Compare cellular structures between different time points.
-      3. Take screenshots at different time points to show temporal changes.
-      4. If no time dimension exists, simulate time series analysis by adjusting the current view and taking multiple screenshots.
+      If the data has time dimensions, navigate through different time points.
+      Compare cellular structures between different time points.
+      Take screenshots at different time points to show temporal changes.
+      If no time dimension exists, simulate time series analysis by adjusting the current view and taking multiple screenshots.
+      Respond with <1> if the time series analysis was successful, or <0> if it failed. Only respond with <1> or <0>.
   assert:
-    - type: llm-rubric
-      value:
-        - Time series analysis was attempted, either by navigating time dimensions or simulating temporal changes
-        - Cellular structures were compared between different time points or views
-        - Multiple screenshots were taken to document temporal or view changes
+    - type: contains-all
+      value: "<1>"
+    - type: not-contains
+      value: "<0>"
   options:
     cache: false
     runSerially: true
@@ -77,14 +77,14 @@
       1. Define a region of interest by cropping the layer to a specific area.
       2. Analyze the cropped region separately from the full dataset.
       3. Compare statistics between the full dataset and the cropped region.
-      4. Take screenshots of both the full view and the cropped region.
+      4. Take a screenshot of the full view and save it to "eval_analysis_workflows/screenshot_2.png".
+      5. Take a screenshot of the cropped region and save it to "eval_analysis_workflows/screenshot_3.png".
   assert:
     - type: llm-rubric
-      value:
-        - A region of interest was defined by cropping the layer to a specific area
-        - The cropped region was analyzed separately from the full dataset
-        - Statistics were compared between the full dataset and the cropped region
-        - Screenshots were taken of both the full view and the cropped region
+      subtype: vision
+      value: |
+          1. This screenshot shows the full dataset with the cropped region highlighted.
+      rs-file: eval_analysis_workflows/screenshot_2.png
   options:
     cache: false
     runSerially: true
@@ -92,13 +92,13 @@
 # Test: Cleanup - Reset for next test run
 - vars:
     question: |
-      1. Delete all loaded layers and remove any annotations to prepare for the next test run.
+      Delete all loaded layers and remove any annotations to prepare for the next test run.
+      Respond with <1> if all layers and annotations were successfully removed, or <0> if it failed. Only respond with <1> or <0>.
   assert:
-    - type: llm-rubric
-      value:
-        - All loaded layers were successfully deleted
-        - All annotations were removed
-        - The environment is clean and ready for the next test run
+    - type: contains-all
+      value: "<1>"
+    - type: not-contains
+      value: "<0>"
   options:
     cache: false
-    runSerially: true
+    runSerially: true

eval_cases/napari/1_workflows/eval_figure_recreation.yaml

@@ -13,11 +13,16 @@
       6. Take a screenshot of your recreation.
       7. If the recreation does not match the target figure, adjust the visualization settings and take a screenshot again.
       8. Stop when the recreation matches the target figure or you have tried five different visualization settings.
+      9. Save the final screenshot to "eval_figure_recreation/screenshot.png".
   assert:
     - type: llm-rubric
       subtype: vision
       value: |
-          1. Does the rendering look similar to GT/dataset001.jpg?                 
+          1. Does the result screenshot look similar to the ground truth image?
+          2. Are the same colormaps and blending modes used as in the target figure?
+          3. Is the contrast and gamma adjusted to match the target figure?
+      gs-file: GS/dataset_001.png
+      rs-file: eval_figure_recreation/screenshot.png                 
   options:
     cache: false
     runSerially: true

eval_cases/napari/1_workflows/eval_iso_surface_determination.yaml

@@ -11,15 +11,15 @@
       5. Rotate the camera to several angles and take a screenshot of the result each time to check if the target structure is clearly visible from different angles.
       6. If the target structure is not clearly visible, adjust the iso surface value and take a screenshot again.
       7. Stop when the target structure is clearly visible or you have tried five different iso surface values.
+      8. Save the final screenshot to "eval_iso_surface_determination/screenshot.png".
   assert:
     - type: llm-rubric
       subtype: vision
       value: |
-          1. Does the visualization show the target structure clearly? 
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Does the rendering look similar to GT/dataset003_1.jpg?                 
+          1. Does the result rendering look similar to ground truth? 
+          2. Does the visualization show the target structure clearly?
+      gs-file: GS/dataset_003.png
+      rs-file: eval_iso_surface_determination/screenshot.png                          
   options:
     cache: false
     runSerially: true

eval_cases/napari/1_workflows/eval_visualization_workflows.yaml

@@ -10,20 +10,21 @@
       4. Use additive blending for all channels to create an overlay visualization.
       5. Go the timestep 14. 
         Q1: Does the cell show protrusions? (Yes/No)
-      6. Take a screenshot of the result.
+      6. Take a screenshot of the result, save it to "eval_visualization_workflows/screenshot_1.png"
+      7. Answer Q1 in a plain text file "eval_visualization_workflows/Q1_answer.txt".
   assert:
     - type: llm-rubric
       subtype: vision
       value: |
           1. Does the visualization show a green cell with red blobs on the inside? 
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Does the rendering look similar to GT/dataset002_1.jpg
+          2. Does the result rendering look similar to ground truth?
+      gs-file: GS/dataset_002_1.png
+      rs-file: eval_visualization_workflows/screenshot_1.png
     - type: llm-rubric
       subtype: text
       value: |
-          1. Q1 correct answer: Yes                    
+          1. Q1 correct answer: Yes
+      rs-file: eval_visualization_workflows/Q1_answer.txt                    
   options:
     cache: false
     runSerially: true
@@ -32,12 +33,13 @@
 - vars:
     question: |
       1. Set all layers invisible except for the layer that contains the individual cells. 
-      2. Take a screenshots of the result. 
+      2. Take a screenshots of the result, save it to "eval_visualization_workflows/screenshot_2.png".
   assert:
     - type: llm-rubric
       subtype: vision
       value: |
-          1. Are there only the green cells visible with no red blobs on the inside? 
+          1. Are there only the green cells visible with no red blobs on the inside?
+      rs-file: eval_visualization_workflows/screenshot_2.png
   options:
     cache: false
     runSerially: true
@@ -48,16 +50,15 @@
       1. Start in the 3D view
       2. Zoom into the cell with protrusions until the cell fills up the entire viewport. 
       3. Rotate the camera to show the 3D data from a different perspective (side view).
-      4. Take a screenshot.
+      4. Take a screenshot of the result, save it to "eval_visualization_workflows/screenshot_3.png".
   assert:
     - type: llm-rubric
       subtype: vision
       value: |
           1. Does the visualization show the green cell from the side? 
-    - type: llm-rubric
-      subtype: vision
-      value: |
-          1. Does the rendering look similar to GT/dataset002_2.jpg
+          2. Does the result rendering look similar to the ground truth image?
+      gs-file: GS/dataset_002_2.jpg
+      rs-file: eval_visualization_workflows/screenshot_3.png
              
   options:
     cache: false