GRABSEEDS
Phenotyping plant organs (seeds, flowers, leaves, fruits, nuts) is important in Quantitative Trait Loci (QTL) mapping and whole genome association studies. Computerized phenotyping of digital images promises rapid, robust and reproducible typing of length and color traits. We highlight several features of GRABSEEDS.
- Work on seeds, leaves and flowers from a set of organisms
- Easy to use, command line interface, runs fast
- Robust against lighting condition, noisy background and overlapping seeds
- Label recognition towards full automation of seed phenotyping projects
Please check out our installation guide.
To process single image, for example, test.JPG, run this command:
python -m jcvi.graphics.grabseeds seeds test.JPG
This command generates a PDF file for debugging purposes while all the identified objects will be written to stdout. The PDF output contains object information layered onto original image. The four panels contain Original picture, Edge detection, Object detection, Object list, respectively. These panels are useful in finding the best parameter for object detection.
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
In the following example, the image contains a package that interfere with the seed recognition.
--rows=:800 will extract the first 800 rows. Please use the coordinates on the axes of the image as a guide.
python -m jcvi.graphics.grabseeds seeds label.JPG --rows=:800
To fully automate phenotyping pipeline, placing a text label next to the seeds is a good idea. The packaging in the previous example contains text. Similarly to cropping the image area, --labelrows=1200: tells the program to run OCR starting on row 1200. Consequently the following command extracts the label as 2013 Sh Backcross 401O2B.
python -m jcvi.graphics.grabseeds seeds label.JPG --rows=:800 --labelrows=1200:
See more examples at the GRABSEEDS Gallery.