#!/usr/bin/env python
# coding: utf-8

# # PSII Tutorial 
# 
# PSII images (3 in a set; F0, Fmin, and Fmax) are captured directly following a saturating fluorescence pulse 
# (red light; 630 nm). These three PSII images can be used to calculate Fv/Fm (efficiency of photosystem II) 
# for each pixel of the plant. Unfortunately, our PSII imaging cabinet has a design flaw when capturing images 
# of plants with vertical architecture. You can read more about how we validated this flaw using our PSII 
# analysis workflows in the [PlantCV Paper](http://dx.doi.org/10.1016/j.molp.2015.06.005).
# 
# To run a PSII workflow over a single PSII image set (3 images) there are 4 required inputs:
# 
# 1.  **Image 1:** F0 (a.k.a Fdark/null) image.
# 2.  **Image 2:** Fmin image.
# 3.  **Image 3:** Fmax image. 
# 5.  **Output directory:** If debug mode is set to 'print' output images from each step are produced.

# In[1]:


# Import libraries 
from plantcv import plantcv as pcv


# In[2]:


class options:
    def __init__(self):
        self.image = "./img/PSII_PSD_supopt_temp_btx623_22_rep1.DAT"
        self.debug = "plot"
        self.writeimg= False 
        self.result = "psII_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 fluorescence image data

# Inputs:
#   filename - Image file to be read in 
#   mode - How to read in the image; either 'native' (default), 'rgb', 'gray', or 'csv'
fdark1, fmin1, fmax1 = pcv.photosynthesis.read_cropreporter(args.image)



# In[4]:


# Rotate each frame so that plant is upright

# Inputs:
#   img             - Image data
#   rotation_deg    - Rotation angle in degrees, can be a negative number, positive values move counter clockwise
#   crop            - If crop is set to True, image will be cropped to original image dimensions. If set to False, the image size will be adjusted to accommodate new image dimensions.
fdark = pcv.transform.rotate(img=fdark1, rotation_deg=-90, crop=False)
fmin = pcv.transform.rotate(img=fmin1, rotation_deg=-90, crop=False)
fmax = pcv.transform.rotate(img=fmax1, rotation_deg=-90, crop=False)


# In[5]:


# Threshold the `fmax` image

# Inputs:
#   gray_img        - Grayscale image data
#   threshold       - Threshold value (usually 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.
plant_mask = pcv.threshold.binary(gray_img=fmax, threshold=855, max_value=255, object_type="light")


# In[6]:


# Fill small objects

# Inputs:
#   bin_img         - Binary image data
#   size            - Minimum object area size in pixels (integer), smaller objects get filled in.
cleaned_mask = pcv.fill(bin_img=plant_mask, size=20)


# In[7]:


# Identify objects

# Inputs:
#   img             - RGB or grayscale image data for plotting
#   mask            - Binary mask used for detecting contours
id_objects, obj_hierarchy = pcv.find_objects(img=fmax, mask=cleaned_mask)


# In[8]:


# Object combine kept objects

# Inputs:
#   img - RGB or grayscale image data for plotting
#   contours        - Contour list
#   hierarchy       - Contour hierarchy array
obj, masked = pcv.object_composition(img=cleaned_mask, contours=id_objects, hierarchy=obj_hierarchy)



# Now we can analyze the plant objects for traits such as shape, or PSII signal. 

# In[9]:


################ Analysis ################  

# Find shape properties, output shape image (optional)

# Inputs:
#   img             - RGB or grayscale image data
#   obj             - Single or grouped contour object
#   mask            - Binary image mask to use as mask for moments analysis
#   label           - Optional label parameter, modifies the variable name of observations recorded. (default `label="default"`)filled_img = pcv.morphology.fill_segments(mask=cropped_mask, objects=edge_objects)

shape_img = pcv.analyze_object(img=fmax, obj=obj, mask=cleaned_mask, label="default")



# In[10]:


# Analyze fv/fm fluorescence properties

# Inputs:
#   fdark           - Grayscale image
#   fmin            - Grayscale image
#   fmax            - Grayscale image
#   mask            - Binary mask of selected contours
#   bins            - Number of grayscale bins (0-256 for 8-bit img, 0-65536 for 16-bit). Default bins = 256
#   label           - Optional label parameter, modifies the variable name of observations recorded. (default `label="default"`)filled_img = pcv.morphology.fill_segments(mask=cropped_mask, objects=edge_objects)

fvfm_images = pcv.photosynthesis.analyze_fvfm(fdark=fdark, fmin=fmin, fmax=fmax, mask=cleaned_mask, bins=256, label="default")


# In[11]:


# Store the two fv_fm images
fvfm_img = fvfm_images[0]
fvfm_hist = fvfm_images[1]


# In[12]:


# Pseudocolor the Fv/Fm grayscale image that is calculated inside the fluor_fvfm function

# Inputs:
#     gray_img      - Grayscale image data
#     obj           - Single or grouped contour object (optional), if provided the pseudocolored image gets cropped down to the region of interest.
#     mask          - Binary mask (optional)
#     background    - Background color/type. Options are "image" (gray_img), "white", or "black". A mask must be supplied.
#     cmap          - Colormap (https://matplotlib.org/tutorials/colors/colormaps.html)
#     min_value     - Minimum value for range of interest
#     max_value     - Maximum value for range of interest
#     dpi           - Dots per inch for image if printed out (optional, if dpi=None then the default is set to 100 dpi).
#     axes          - If False then the title, x-axis, and y-axis won't be displayed (default axes=True).
#     colorbar      - If False then the colorbar won't be displayed (default colorbar=True)
pseudocolored_img = pcv.visualize.pseudocolor(gray_img=fvfm_img, mask=cleaned_mask, cmap='jet')

simple_pseudo_img = pcv.visualize.pseudocolor(gray_img=fvfm_img, obj=None, mask=cleaned_mask, background="image", 
                                              axes=False, cmap='rainbow', min_value=0.5, max_value=0.9)


# In[13]:


# The save results function will take the measurements stored when running any PlantCV analysis functions, format, 
# and print an output text file for data analysis. The Outputs class stores data whenever any of the following functions
# are ran: analyze_bound_horizontal, analyze_bound_vertical, analyze_color, analyze_nir_intensity, analyze_object, 
# fluor_fvfm, report_size_marker_area, watershed. If no functions have been run, it will print an empty text file 
pcv.outputs.save_results(filename=args.result)



# To view and/or download the text file output (saved in JSON format)...
# 1) To see the text file with data that got saved out, click “File” tab in top left corner.
# 2) Click “Open…”
# 3) Open the file named “psII_tutorial_results.txt”
# 
# Check out documentation on how to [convert JSON](https://plantcv.readthedocs.io/en/latest/tools/#convert-output-json-data-files-to-csv-tables) format output into table formatted output. Depending on the analysis steps a PlantCV user may have two CSV files (single value traits and multivalue traits). 
# 

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