1704 cmyk plotting and threshold

docs/threshold_dual_channels.md

@@ -11,9 +11,9 @@ Pixels in the plane above and below the straight line are assigned two different
 - **Parameters:**
     - rgb_img - RGB image
     - x_channel - Channel to use for the horizontal coordinate.
-      Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'gray', and 'index'
+      Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray', and 'index'
     - y_channel - Channel to use for the vertical coordinate.
-      Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'gray', and 'index'
+      Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray', and 'index'
     - points - List containing two points as tuples defining the segmenting straight line
     - above - Whether the pixels above the line are given the value of 0 or 255
 

docs/visualize_pixel_scatter_vis.md

@@ -9,9 +9,9 @@ This function plots a 2D pixel scatter plot visualization for a dataset of image
 - **Parameters:**
     - paths_to_imgs   - List of paths to the images.
     - x_channel       - Channel to use for the horizontal coordinate of the scatter plot.
-    Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'gray', and 'index'.
+    Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray', and 'index'.
     - y_channel       - Channel to use for the vertical coordinate of the scatter plot.
-    Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'gray', and 'index'.
+    Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray', and 'index'.
 
 
 - **Context:**

plantcv/plantcv/threshold/threshold_methods.py

@@ -4,10 +4,9 @@
 import math
 import numpy as np
 from matplotlib import pyplot as plt
-from plantcv.plantcv import fatal_error, warn
-from plantcv.plantcv import params
+from plantcv.plantcv import fatal_error, warn, params
 from plantcv.plantcv._debug import _debug
-from plantcv.plantcv._helpers import _rgb2lab, _rgb2hsv, _rgb2gray
+from plantcv.plantcv._helpers import _rgb2lab, _rgb2hsv, _rgb2gray, _rgb2cmyk
 from skimage.feature import graycomatrix, graycoprops
 from scipy.ndimage import generic_filter
 
@@ -795,7 +794,7 @@ def mask_bad(float_img, bad_type='native'):
 
 
 # functions to get a given channel with parameters compatible
-# with _rgb2lab and _rgb2hsv to use in the dict
+# with rgb2gray_lab, rgb2gray_hsv, and rgb2gray_cmyk to use in the dict
 def _get_R(rgb_img, _):
     """Get the red channel from a RGB image"""
     return rgb_img[:, :, 2]
@@ -824,24 +823,27 @@ def _get_index(rgb_img, _):
 
 def _not_valid(*args):
     """Error for a non valid channel"""
-    return fatal_error("channel not valid, use R, G, B, l, a, b, h, s, v, gray, or index")
+    return fatal_error("channel not valid, use R, G, B, l, a, b, h, s, v, c, m, y, k, gray, or index")
 
 
 def dual_channels(rgb_img, x_channel, y_channel, points, above=True):
-    """Create a binary image from an RGB image based on the pixels values in two channels.
+    """
+    Create a binary image from an RGB image based on the pixels values in two channels.
     The x and y channels define a 2D plane and the two input points define a straight line.
     Pixels in the plane above and below the straight line are assigned two different values.
+
     Inputs:
     rgb_img   = RGB image
-    ch_x      = Channel to use for the horizontal coordinate.
-                Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'gray', and 'index'
-    ch_y      = Channel to use for the vertical coordinate.
-                Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'gray', and 'index'
+    x_channel = Channel to use for the horizontal coordinate.
+                Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray', and 'index'
+    y_channel = Channel to use for the vertical coordinate.
+                Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray', and 'index'
     points    = List containing two points as tuples defining the segmenting straight line
     above     = Whether the pixels above the line are given the value of 0 or max_value
 
     Returns:
-    bin_img      = Thresholded, binary image
+    bin_img = Thresholded, binary image
+
     :param rgb_img: numpy.ndarray
     :param x_channel: str
     :param y_channel: str
@@ -862,6 +864,10 @@ def dual_channels(rgb_img, x_channel, y_channel, points, above=True):
         's': _rgb2hsv,
         'v': _rgb2hsv,
         'index': _get_index,
+        'c': _rgb2cmyk,
+        'm': _rgb2cmyk,
+        'y': _rgb2cmyk,
+        'k': _rgb2cmyk
     }
 
     debug = params.debug

plantcv/plantcv/visualize/pixel_scatter_vis.py

@@ -1,19 +1,19 @@
 # Visualize a scatter plot of pixels
 
 import numpy as np
-import cv2 as cv
+import cv2
 from matplotlib import pyplot as plt
-from plantcv import plantcv as pcv
 from plantcv.plantcv import fatal_error, params
-from plantcv.plantcv._helpers import _rgb2lab, _rgb2hsv, _rgb2gray
+from plantcv.plantcv.readimage import readimage
+from plantcv.plantcv._helpers import _rgb2lab, _rgb2hsv, _rgb2cmyk, _rgb2gray
 
 
 MAX_MARKER_SIZE = 20
 IMG_WIDTH = 128
 
 
 # functions to get a given channel with parameters compatible
-# with _rgb2lab and _rgb2hsv to use in the dict
+# with rgb2gray_lab, rgb2gray_hsv, and rgb2gray_cmyk to use in the dict
 def _get_R(rgb_img, _):
     """Get the red channel from a RGB image."""
     return rgb_img[:, :, 2]
@@ -42,7 +42,7 @@ def _get_index(rgb_img, _):
 
 def _not_valid(*args):
     """Error for a non valid channel."""
-    return fatal_error("channel not valid, use R, G, B, l, a, b, h, s, v, gray, or index")
+    return fatal_error("channel not valid, use R, G, B, l, a, b, h, s, v, c, m, y, k, gray, or index")
 
 
 def pixel_scatter_plot(paths_to_imgs, x_channel, y_channel):
@@ -55,15 +55,15 @@ def pixel_scatter_plot(paths_to_imgs, x_channel, y_channel):
     Inputs:
     paths_to_imgs  = List of paths to the images
     x_channel      = Channel to use for the horizontal coordinate of the scatter plot.
-                     Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'gray', and 'index'
+                     Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray', and 'index'
     y_channel      = Channel to use for the vertical coordinate of the scatter plot.
-                     Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'gray', and 'index'
+                     Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray', and 'index'
 
     Returns:
     fig = matplotlib pyplot Figure object of the visualization
     ax  = matplotlib pyplot Axes object of the visualization
 
-    :param paths_to_imgs: str
+    :param paths_to_imgs: list of str
     :param x_channel: str
     :param y_channel: str
     :return fig: matplotlib.pyplot Figure object
@@ -82,6 +82,10 @@ def pixel_scatter_plot(paths_to_imgs, x_channel, y_channel):
         's': _rgb2hsv,
         'v': _rgb2hsv,
         'index': _get_index,
+        'c': _rgb2cmyk,
+        'm': _rgb2cmyk,
+        'y': _rgb2cmyk,
+        'k': _rgb2cmyk
     }
 
     # store debug mode
@@ -93,17 +97,17 @@ def pixel_scatter_plot(paths_to_imgs, x_channel, y_channel):
     fig, ax = plt.subplots()
     # load and plot the set of images sequentially
     for p in paths_to_imgs:
-        img, _, _ = pcv.readimage(filename=p, mode="native")
+        img, _, _ = readimage(filename=p)
         h, _, c = img.shape
 
         # resizing to predetermined width to reduce the number of pixels
         ratio = h/IMG_WIDTH
         img_height = int(IMG_WIDTH*ratio)
         # nearest interpolation avoids mixing pixel values
-        sub_img = cv.resize(img, (IMG_WIDTH, img_height), interpolation=cv.INTER_NEAREST)
+        sub_img = cv2.resize(img, (IMG_WIDTH, img_height), interpolation=cv2.INTER_NEAREST)
 
         # organize the channels as RGB to use as facecolor for the markers
-        sub_img_rgb = cv.cvtColor(sub_img, cv.COLOR_BGR2RGB)
+        sub_img_rgb = cv2.cvtColor(sub_img, cv2.COLOR_BGR2RGB)
         fcolors = sub_img_rgb.reshape(img_height*IMG_WIDTH, c)/255
 
         # get channels

tests/plantcv/visualize/test_pixel_scatter_plot.py

@@ -2,16 +2,16 @@
 import cv2
 import os
 import numpy as np
-from plantcv.plantcv.visualize.pixel_scatter_vis import pixel_scatter_plot
+from plantcv.plantcv.visualize import pixel_scatter_plot
 
 
-@pytest.mark.parametrize("ch", ['R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'gray'])
+@pytest.mark.parametrize("ch", ['R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray'])
 def test_pixel_scatter_plot(ch, tmpdir):
     """Test for PlantCV."""
     # Create a tmp directory
     cache_dir = tmpdir.mkdir("cache")
     rng = np.random.default_rng()
-    img_size = (10,10,3)
+    img_size = (10, 10, 3)
     # create a random image and write it to the temp directory
     img = rng.integers(low=0, high=255, size=img_size, dtype=np.uint8, endpoint=True)
     path_to_img = os.path.join(cache_dir, 'tmp_img.png')
@@ -26,12 +26,11 @@ def test_pixel_scatter_plot_wrong_ch(tmpdir):
     # Create a tmp directory
     cache_dir = tmpdir.mkdir("cache")
     rng = np.random.default_rng()
-    img_size = (10,10,3)
+    img_size = (10, 10, 3)
     # create a random image and write it to the temp directory
     img = rng.integers(low=0, high=255, size=img_size, dtype=np.uint8, endpoint=True)
     path_to_img = os.path.join(cache_dir, 'tmp_img.png')
     cv2.imwrite(path_to_img, img)
     # test the function with channel parameter that is not an option
     with pytest.raises(RuntimeError):
         _, _ = pixel_scatter_plot(paths_to_imgs=[path_to_img], x_channel='wrong_ch', y_channel='index')
-