GRABSEEDS: How to tune accuracy
Examples below show the command line usage of GRABSEEDS, in particular, how to set parameters for more accurate feature extractions.
Please note: the following image examples can be downloaded here.
The easiest fix is to use a more powerful, deep learning based segmentation - Segment Anything. For example, the following image can be difficult given the white background.
python -m jcvi.graphics.grabseeds seeds tobacco3.png --filter sam --sam-checkpoint sam_vit_h_4b8939.pth
You will need to download sam_vit_h_4b8939.pth from segment-anything.
In the following example, the default settings identified certain features that interfere with the real objects.
When the background is non-uniform and contain texture (for example, cloth), increase --sigma value.
python -m jcvi.graphics.grabseeds seeds noisy.JPG --sigma=2
In the following example, the default settings correctly identified the objects but not the entire region, so the area calculation will be inaccurate.
Increase --kernel value to ensure that the objects are properly filled.
In the following example, the default settings identified a smaller object (ruler).
Use --minsize cutoff effectively removes the artifact, default --minsize=.05, --maxsize=50 which corresponds to any object with pixel counts that are between 0.05% to 50% of the entire photo.
In the log file, we noticed the following information:
12:27:59 [grabseeds] Find objects with pixels between 270 (0.05%) and 270000 (50%)
12:27:59 [grabseeds] A total of 8 objects identified.
12:27:59 [grabseeds] Seed #1: 11618 pixels (676 sampled) - 2.15%
12:28:00 [grabseeds] Seed #2: 15956 pixels (1024 sampled) - 2.95%
12:28:00 [grabseeds] Seed #3: 14238 pixels (900 sampled) - 2.64%
12:28:00 [grabseeds] Seed #4: 14577 pixels (1024 sampled) - 2.70%
12:28:00 [grabseeds] Seed #5: 14639 pixels (1024 sampled) - 2.71%
12:28:00 [grabseeds] Seed #6: 13622 pixels (1156 sampled) - 2.52%
12:28:00 [grabseeds] Seed #7: 582 pixels (16 sampled) - 0.11%
12:28:00 [grabseeds] Seed #8: 342 pixels (36 sampled) - 0.06%
The command below changes the lower cutoff to 1, which is 1%, this cutoff would be able to trim off seed #7 and #8 that are false positives.
python -m jcvi.graphics.grabseeds seeds sizeselection.JPG --minsize=1
In the following example, the shadow connect the seeds into one giant object.
Use --watershed to run the watershed algorithm to separate overlapping objects.
python -m jcvi.graphics.grabseeds seeds touching.JPG --watershed
