Merge pull request #1415 from danforthcenter/updated_color_correction_tutorial

Updated color correction tutorial

Author: nfahlgren

docs/analyze_grayscale.md

@@ -18,7 +18,7 @@ the values out to the [Outputs class](outputs.md). Can also return/plot/print ou
 - **Example use:**
     * [Grayscale Image Tutorial](tutorials/grayscale_tutorial.md)
 
-- **Output data stored:** Data ('gray_frequencies', 'gray_mean', 'gray_median', 'nir_stdev') automatically gets stored to
+- **Output data stored:** Data ('gray_frequencies', 'gray_mean', 'gray_median', 'gray_stdev') automatically gets stored to
 the [`Outputs` class](outputs.md) when this function is ran. These data can always get accessed during a workflow (example
 below). For more detail about data output see [Summary of Output Observations](output_measurements.md#summary-of-output-observations)
 

docs/get_color_matrix.md

@@ -8,7 +8,7 @@ Computes the average *R*, *G*, *B* values for each region in the RGB image denot
 
 - **Parameters**
     - rgb_img - RGB image with color chips visualized
-    - mask    - a gray-scale img with unique values for each segmented space, representing unique, discrete color chips.
+    - mask    - a gray-scale img with unique values for each segmented space, representing unique, discrete color chips. Likely created with [`pcv.transform.detect_color_card`](transform_detect_color_card.md). 
 
 - **Returns**
     - color_matrix - a *n* x 4 matrix containing the average red value, average green value, and average blue value for each color chip.

docs/output_measurements.md

@@ -147,32 +147,35 @@ suggestions for additional metadata we should track that would be useful to you,
 
 Functions that automatically store data to the [`Outputs` class](outputs.md) are
 [analyze.bound_horizontal](analyze_bound_horizontal2.md), 
-[analyze.bound_vertical](analyze_bound_vertical2.md),
-[analyze.color](analyze_color2.md),
-[analyze.grayscale](analyze_grayscale.md),
-[analyze.size](analyze_size.md),
-[analyze.spectral_index](analyze_spectral_index.md),
-[analyze.spectral_reflectance](analyze_spectral_reflectance.md),
-[analyze.thermal](analyze_thermal.md),
+[analyze.bound_vertical](analyze_bound_vertical2.md), 
+[analyze.color](analyze_color2.md), 
+[analyze.grayscale](analyze_grayscale.md), 
+[analyze.size](analyze_size.md), 
+[analyze.spectral_index](analyze_spectral_index.md), 
+[analyze.spectral_reflectance](analyze_spectral_reflectance.md), 
+[analyze.thermal](analyze_thermal.md), 
 [analyze.yii](analyze_yii.md), 
-[analyze.npq](analyze_npq.md),
-[homology.landmark_reference_pt_dist](homology_landmark_reference_pt_dist.md),
+[analyze.npq](analyze_npq.md), 
+[homology.acute](homology_landmark_reference_pt_dist.md), 
+[homology.landmark_reference_pt_dist](homology_landmark_reference_pt_dist.md), 
 [homology.x_axis_pseudolandmarks](homology_x_axis_pseudolandmarks.md), 
-[homology.y_axis_pseudolandmarks](homology_y_axis_pseudolandmarks.md),
-[morphology.check_cycles](check_cycles.md),
+[homology.y_axis_pseudolandmarks](homology_y_axis_pseudolandmarks.md), 
+[morphology.analyze_stem](analyze_stem.md), 
+[morphology.check_cycles](check_cycles.md), 
 [morphology.fill_segments](fill_segments.md), 
-[morphology.find_tips](find_tips.md),
+[morphology.find_tips](find_tips.md), 
 [morphology.find_branch_pts](find_branch_pts.md), 
-[morphology.segment_angle](segment_angle.md),
+[morphology.segment_angle](segment_angle.md), 
 [morphology.segment_curvature](segment_curvature.md), 
-[morphology.segment_euclidean_length](segment_euclidean_length.md),
+[morphology.segment_euclidean_length](segment_euclidean_length.md), 
 [morphology.segment_insertion_angle](segment_insertion_angle.md), 
-[morphology.segment_path_length](segment_pathlength.md),
-[morphology.segment_tangent_angle](segment_tangent_angle.md),
-[report_size_marker_area](report_size_marker.md),
-[transform.find_color_card](find_color_card.md),
+[morphology.segment_path_length](segment_pathlength.md), 
+[morphology.segment_tangent_angle](segment_tangent_angle.md), 
+[report_size_marker_area](report_size_marker.md), 
+[transform.find_color_card](find_color_card.md), 
+[transform.detect_color_card](transform_detect_color_card.md)
 [watershed_segmentation](watershed.md), and
-[within_frame](within_frame.md)
+[within_frame](within_frame.md).
 
 All of these functions include an optional `label` parameter 
 that allows users to append custom prefixes to the unique variable identifier. 

docs/transform_affine_color_correction.md

@@ -15,7 +15,8 @@ Euclidean distance between the transformed source color values and the target co
     - rgb_img       - an RGB image with color chips visualized
     - source_matrix - array of RGB color values (intensity in the range [0-1]) from the image to be corrected where each row is one color reference and the columns are organized as index,R,G,B
     - target_matrix - array of target RGB color values (intensity in the range [0-1]) where each row is one color reference and the columns are organized as index,R,G,B
-
+- **Example use:**
+    - [Color Correction Tutorial](tutorials/transform_color_correction_tutorial.md)
 
 **Reference Images**
 

docs/transform_detect_color_card.md

@@ -15,6 +15,8 @@ Automatically detects a color card and creates a labeled mask.
         - radius         - Radius of circle to make the color card labeled mask (default = 20).
 - **Returns**
     - labeled_mask     - Labeled color card mask (useful downstream of this step in `pcv.transform.get_color_matrix` and `pcv.transform.correct_color`)
+- **Example use:**
+    - [Color Correction Tutorial](tutorials/transform_color_correction_tutorial.md)
 
 !!! note
     Color chip size can only be used reasonably as a scaling factor (converting pixels to a known real world scale like cms)
@@ -31,6 +33,8 @@ rgb_img, path, filename = pcv.readimage("target_img.png")
 cc_mask = pcv.transform.detect_color_card(rgb_img=rgb_img)
 
 avg_chip_size = pcv.outputs.observations['default']['median_color_chip_size']['value']
+avg_chip_w = pcv.outputs.observations['default']['median_color_chip_width']['value']
+avg_chip_h = pcv.outputs.observations['default']['median_color_chip_height']['value']
 
 ```
 

plantcv/plantcv/photosynthesis/reassign_frame_labels.py

@@ -1,3 +1,4 @@
+"""Reassign PSII frame labels based on induction curve"""
 import numpy as np
 from plantcv.plantcv import fatal_error
 from plantcv.plantcv.classes import PSII_data