VIS/NIR Tutorial¶

For dual VIS/NIR workflows, a visible image is used to identify an image mask for the plant material. The get nir function is used to get the NIR image that matches the VIS image (must be in same folder, with similar naming scheme), then functions are used to size and place the VIS image mask over the NIR image. This allows two workflows to be done at once and also allows plant material to be identified in low-quality images. We do not recommend this approach if there is a lot of plant movement between capture of NIR and VIS images.

To run a VIS/NIR workflow over a single VIS image there are two required inputs:

Image: Images can be processed regardless of what type of VIS camera was used (high-throughput platform, digital camera, cell phone camera).

Image processing will work with adjustments if images are well lit and free of background that is similar in color to plant material.
2. Output directory: If debug mode is set to 'print' output images from each intermediate step are produced.

In [1]:

# Import Libraries 
import cv2
from plantcv import plantcv as pcv

In [2]:

class options:
    def __init__(self):
        self.image = "./img/VIS_SV_image.jpg"
        self.debug = "plot"
        self.writeimg= False 
        self.result = "vis_nir_tutorial_results.json"
        self.outdir = "." # Store the output to the current directory

# Get options
args = options()

# Set debug to the global parameter 
pcv.params.debug = args.debug

In [3]:

# Read image 

# Inputs:
#   filename - Image file to be read in 
#   mode - Return mode of image; either 'native' (default), 'rgb', 'gray', or 'csv' 
img, path, filename = pcv.readimage(filename=args.image)

In [4]:

# Convert RGB to HSV and extract the saturation channel

# Inputs:
#   rgb_img - RGB image data 
#   channel - Split by 'h' (hue), 's' (saturation), or 'v' (value) channel
s = pcv.rgb2gray_hsv(rgb_img=img, channel='s')

In [5]:

# Threshold the Saturation image

# Inputs:
#   gray_img - Grayscale image data 
#   threshold- Threshold value (between 0-255)
#   max_value - Value to apply above threshold (255 = white) 
#   object_type - 'light' (default) or 'dark'. If the object is lighter than the background then standard threshold is done.
#                 If the object is darker than the background then inverse thresholding is done. 
s_thresh = pcv.threshold.binary(gray_img=s, threshold=50, max_value=255, object_type='light')

In [6]:

# Median Blur

# Inputs: 
#   gray_img - Grayscale image data 
#   ksize - Kernel size (integer or tuple), (ksize, ksize) box if integer input,
#           (n, m) box if tuple input 
s_mblur = pcv.median_blur(gray_img=s_thresh, ksize=5)

In [7]:

# Convert RGB to LAB and extract the blue-yellow channel

# Input:
#   rgb_img - RGB image data 
#   channel- Split by 'l' (lightness), 'a' (green-magenta), or 'b' (blue-yellow) channel
b = pcv.rgb2gray_lab(rgb_img=img, channel='b')

# Threshold the blue-yellow channel image
b_thresh = pcv.threshold.binary(gray_img=b, threshold=129, max_value=255, object_type='light')