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.
In the following example, the default settings identified certain features that interfere with the real objects.
--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.
--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.
--watershed to run the watershed algorithm to separate overlapping objects.
python -m jcvi.graphics.grabseeds seeds touching.JPG --watershed
