eval_cases/napari/eval_visualization_tasks.yaml

# Basic Visualization Workflow Tests
# Use https://www.ebi.ac.uk/bioimage-archive/galleries/S-BIAD573.html IM1 to test the workflows.

# Case 1: Multi-channel Overlay with Colormaps with channels
- vars:
    question: |
      1. Load the "data/dataset_002/dataset_002_ch0.tif" dataset into napari as channel 0 and "data/dataset_002/dataset_002_ch1.tif" as channel 1. 
      2. Set the colormap for channel 0 to red and channel 1 to green.
      3. Switch to the 3D view. 
      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, save it to "eval_visualization_tasks/case_1/results/{agent_mode}/screenshot_1.png"
      7. Answer Q1 in a plain text file "eval_visualization_tasks/case_1/results/{agent_mode}/multi_channel_answer.txt".
  assert:
    - type: llm-rubric
      subtype: vision
      value: |
          1. Does the visualization show a green cell with red blobs on the inside? 
          2. Does the result rendering look similar to ground truth?
      gs-file: GS/dataset_002_1.png
      rs-file: eval_visualization_tasks/case_1/results/{agent_mode}/screenshot_1.png
    - type: llm-rubric
      subtype: text
      value: |
          1. Q1 correct answer: Yes
      rs-file: eval_visualization_tasks/case_1/results/{agent_mode}/multi_channel_answer.txt                    
  options:
    cache: false
    runSerially: true


# Case 2: ingesting points
- vars:
    question: |
      1. Load the "data/dataset_002/Points.csv" dataset into napari.
      2. Check if the points layer has been created.
        Q1: Was the points layer created successfully? (Yes/No)
      3. Answer Q1 in a plain text file "eval_visualization_tasks/case_2/results/{agent_mode}/points_answer.txt".
  assert:
    - type: llm-rubric
      subtype: text
      value: |
          1. Q1 correct answer: Yes
      rs-file: eval_visualization_tasks/case_2/results/{agent_mode}/points_answer.txt
  options:
    cache: false
    runSerially: true

# Case 3: ingesting shapes
- vars:
    question: |
      1. Load the "data/dataset_002/Shapes.csv" dataset into napari.
      2. Check if the shapes layer has been created.
        Q1: Was the shapes layer created successfully? (Yes/No)
      3. Answer Q1 in a plain text file "eval_visualization_tasks/case_3/results/{agent_mode}/shapes_answer.txt".
  assert:
    - type: llm-rubric
      subtype: text
      value: |
          1. Q1 correct answer: Yes
      rs-file: eval_visualization_tasks/case_3/results/{agent_mode}/shapes_answer.txt
  options:
    cache: false
    runSerially: true

# Case 4: ingesting labels
- vars:
    question: |
      1. Load the "data/dataset_002/Labels.tif" dataset into napari.
      2. Check if a new layer called "Labels" has been created.
        Q1: Was the layer created successfully? (Yes/No)
      3. Answer Q1 in a plain text file "eval_visualization_tasks/case_4/results/{agent_mode}/labels_answer.txt".
  assert:
    - type: llm-rubric
      subtype: text
      value: |
        1. Q1 correct answer: Yes
      rs-file: eval_visualization_tasks/case_4/results/{agent_mode}/labels_answer.txt
  options:
    cache: false
    runSerially: true


# Case 5: Recreate a figure from a dataset. 
- vars:
    question: |
      1. Load the dataset into napari: data/dataset_001/dataset_001.tiff
      2. Read the target figure: data/dataset_001/dataset_001.png but don't load it into napari.
      3. Read the dataset description: data/dataset_001/dataset_001.yaml. 
      4. Set the same colormaps and blending modes as the target figure.
      5. Adjust contrast and gamma as needed to match the target figure.
      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_visualization_tasks/case_5/results/{agent_mode}/screenshot.png".
  assert:
    - type: llm-rubric
      subtype: vision
      value: |
          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_visualization_tasks/case_5/results/{agent_mode}/screenshot.png                 
  options:
    cache: false
    runSerially: true

# Case 6: Iso surface determination for a target
- vars:
    question: |
      1. Read the file "data/dataset_003/eval_iso_surface_determination_target_1.txt" to get the target iso-surface values for different tooth structures.
      2. Load data/dataset_003/dataset_003.tif into napari. 
      3. Switch to 3D view mode and set the rendering to iso. 
      4. Find the iso surface value that shows the target clearly.
      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_visualization_tasks/case_6/results/{agent_mode}/screenshot.png".
  assert:
    - type: llm-rubric
      subtype: vision
      value: |
          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_visualization_tasks/case_6/results/{agent_mode}/screenshot.png                          
  options:
    cache: false
    runSerially: true


# Case 7: Cell Counting and Measurement Analysis
- vars:
    question: |
      1. Load the image "data/dataset_002/dataset_002_ch0.tif" and set channel 0 to a magenta colormap.
      2. Switch to a 3D MIP view. 
      3. Take a screenshot and analyze it to count how many complete cells are visible (not cut off by edges).
      Q1: answer with the number of complete cells you counted, for example "5" if you see 5 complete cells.
      4. Save the answer of Q1 to the questions in plain text as "eval_visualization_tasks/case_7/results/{agent_mode}/Q1_answer.txt".
  assert:
    - type: llm-rubric
      subtype: text
      value: |
        1. Q1 correct answer: 2
      rs-file: eval_visualization_tasks/case_7/results/{agent_mode}/Q1_answer.txt
  options:
    cache: false
    runSerially: true

# Case 8: Statistical Analysis and Data Export
- vars:
    question: |
      1. Load the image "data/dataset_001/dataset_001.tiff". 
      2. Get basic statistics (min, max, mean, std) for the loaded layer.
      3. Extract the raw layer data and examine its properties.
      4. Save the current layer to a file for further analysis.
       Q1: Was the statistical analysis and data export successful? (Yes/No)
      6. Save the answer of Q1 in plain text as "eval_visualization_tasks/case_8/results/{agent_mode}/layer_statistics_answer.txt".
  assert:
    - type: llm-rubric
      subtype: text
      value: |
        1. Q1 correct answer: Yes
      rs-file: eval_visualization_tasks/case_8/results/{agent_mode}/layer_statistics_answer.txt
  options:
    cache: false
    runSerially: true

# Case 9: Annotation Workflow
- vars:
    question: |
      1. Load the image "data/dataset_001/dataset_001.tiff". 
      2. Add point annotations at random locations on the image.
      3. Add shape annotations (rectangles or circles) at random locations on the image.
       Q1: Check if layers have been generated. (Yes/No)
      4. Save the answer of Q1 in plain text as "eval_visualization_tasks/case_9/results/{agent_mode}/annotation_answer.txt".
  assert:
    - type: llm-rubric
      subtype: text
      value: |
          1. Q1 correct answer: Yes
      rs-file: eval_visualization_tasks/case_9/results/{agent_mode}/annotation_answer.txt
  options:
    cache: false
    runSerially: true

# Case 10: Advanced Annotation Workflow: Cell Surface Trace (This will likely fail)
- vars:
    question: |
      1. Load the image "data/dataset_002/dataset_002_ch0.tif" into napari.
      2. Trace the cell surface on the current slice by adding a polygon shape in a new shape layer.
      3. Use a screenshot to validate whether the polygon correctly traces the cell surface.
      4. If the trace is not accurate, adjust the polygon and take a new screenshot to validate.
      5. Stop when the trace is accurate or you have tried five different attempts.
      6. Save the results and the final screenshot to "eval_visualization_tasks/case_10/results/{agent_mode}/cell_surface_trace.png".
  assert:
    - type: llm-rubric
      subtype: vision
      value: |
          1. Does the final screenshot show a polygon shape that accurately traces the outline of the cell surface?
          2. Is the polygon layer correctly overlaid on the image?
      rs-file: eval_visualization_tasks/case_10/results/{agent_mode}/cell_surface_trace.png
  options:
    cache: false
    runSerially: true

# Case 11: Camera Operations (Zoom and Rotate)
- vars:
    question: |
      1. Load the "data/dataset_002/dataset_002_ch0.tif" dataset into napari as channel 0 and "data/dataset_002/dataset_002_ch1.tif" as channel 1.
      2. Depending on the number of channels, set the colormap for the first channel 0 to red and channel 1 to green.
      3. Switch to the 3D view. 
      4. Zoom in to the cell in the middle.
      5. Rotate the camera to a side view.
      6. Take a screenshot of the zoomed-in view and save it to "eval_visualization_tasks/case_11/results/{agent_mode}/zoom_screenshot.png".
      7. Take a screenshot of the side view and save it to "eval_visualization_tasks/case_11/results/{agent_mode}/rotate_screenshot.png".
  assert:
    - type: llm-rubric
      subtype: vision
      value: |
          1. Does the visualization show a zoomed-in view of the cell in the middle?
          2. Does the result rendering look similar to ground truth?
      gs-file: GS/dataset_002_zoom.jpg
      rs-file: eval_visualization_tasks/case_11/results/{agent_mode}/zoom_screenshot.png
    - type: llm-rubric
      subtype: vision
      value: |
          1. Does the visualization show a side view of the cell?
          2. Does the result rendering look similar to ground truth?
      gs-file: GS/dataset_002_camera_side.png
      rs-file: eval_visualization_tasks/case_11/results/{agent_mode}/rotate_screenshot.png
  options:
    cache: false
    runSerially: true