| |
| |
|
|
| |
| - 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 |
|
|
|
|
| |
| - 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 |
|
|
| |
| - 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 |
|
|
| |
| - 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 |
|
|
|
|
| |
| - 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 |
|
|
| |
| - 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 |
|
|
|
|
| |
| - 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 |
|
|
| |
| - 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 |
|
|
| |
| - 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 |
|
|
| |
| - 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 |
|
|
| |
| - 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 |
|
|
