nfahlgren
commented
4 years ago Hi @AdamDimech!
The blob files (e.g. blob49) on our system are zip files containing a file called data. Our data files are raw 8-bit binary files. The data encoded in the raw image file is the raw camera sensor data, which is a Bayer filter.
In OpenCV there are a lot of Bayer demosaicing methods but they can be grouped into some basic sets. First, you could ignore all the COLOR_BayerXX2RGB methods as OpenCV functions (and PlantCV since we are using OpenCV primarily) want the images in BGR order. Unless you use an alpha or transparency channel, all the COLOR_BayerXX2RGBA can be ignored. The COLOR_BayerXX2GRAY can also be ignored since you're trying to convert to a color image.
All that's left are a few groups. The first part codes the Bayer filter pattern type (COLOR_BayerXX2BGR) and different sensors may have different patterns. The RG pattern has worked with our data. The COLOR_BayerXX2BGR methods use the default interpolation method, which I think is bilinear. The other two methods with the _VNG and _EG extensions do the same thing but use either the Variable Number of Gradients or Edge-Aware interpolation methods.
When you use cv2.cvtColor it uses a demosaicing algorithm if provided one of the Bayer methods. I ran both to compare and I got the same results:
img = cv2.cvtColor(raw_img, cv2.COLOR_BAYER_RG2BGR)
imgd = cv2.demosaicing(raw_img, cv2.COLOR_BAYER_RG2BGR)
(img == imgd).all()
# TrueIt looks like we need a method to specify the type of Bayer filter so the correct method can be used. I have not compared the Bilinear, VNG, or EA algorithm results but sounds like you are getting better results with VNG, which is interesting! I need to do some comparisons on our end too.
LemnaTec might not be doing the conversion with OpenCV. They could be using a different interpolation algorithm and/or it could be an algorithm from the camera/camera sensor manufacturer that uses a proprietary method. It's also possible they might do some other preprocessing to tweak the color gains in the raw image before demosaicing.
dschneiderch
AdamDimech
I have been using PlantCV for a while now to analyse LemnaTec Scanalyser visible-spectrum images, but it has come to my attention that the quality of the images generated by PlantCV (using OpenCV functions) is not as good as the native LemnaTec software and I am unsure why.
Images are downloaded using a forked version of Data Science Tools (LT-db-extractor.py). For our system, we initially found that OpenCV image conversion number 47 (cv::COLOR_BayerGB2BGR) worked best for us. Here's a close-up:
You can see, the quality isn't great around the edges of the object. Let's look at the same image exported via LemnaBase:
Here the colours are better and edges sharper. In the past few days, I have played around with LT-db-extractor.py and changed the function on line 184 from:
img = cv2.cvtColor(raw_img, db['colour'])toimg = cv2.demosaicing(raw_img, db['colour'])I am not entirely sure what the difference between these functions is, but this change along with a modification in the OpenCV conversion format to #63 (cv::COLOR_BayerGB2BGR_VNG) has lead to considerable improvement:
Whilst better, it's still not quite as good as the LemnaTec software version (ie there is still some colour artifacts on the edges). I am trying to work out whether there is a problem with OpenCV's image demosaicing process or some other factor. I have written a small Python script that will open a LemnaTec blob file and attempt to make all 143 conversions for easy comparison (of course most fail, but it generates a PNG of those that work for easy assessment).
The other thing I am trying to figure out is the format of the blobs. Do you know are they encoded and what actually sits within? Each seems to contain multiple images, encoded in some unknown format and the metadata seems to be obtained from the database via a SQL query. There is a "data" file in there, but what does that contain? In what format is it encoded?
So many questions, but some insight would be a great help.