Pixel scatter path flexibility

docs/updating.md

@@ -92,6 +92,11 @@ Removed `label` parameter since size marker data is now stored as metadata in th
 Deprecated the function in favor of the new
 [`plantcv.transform.detect_color_card`](transform_detect_color_card.md) function.
 
+#### plantcv.visualize.pixel_scatter_plot
+
+Changed `paths_to_imgs` argument to `source` to reflect that it can use a `numpy.ndarray`, `str` path,
+or a list of paths where it previously only could use a list of paths.
+
 #### plantcv.visualize.time_lapse_video
 
 Deprecated the function to enable compatibility with the opencv-headless package. Will be readded in a future release.
@@ -1575,7 +1580,8 @@ pages for more details on the input and output variable types.
 #### plantcv.visualize.pixel_scatter_plot
 
 * pre v4.0: NA
-* post v4.0: fig, ax = **pcv.visualize.pixel_scatter_plot**(*paths_to_imgs, x_channel, y_channel*)
+* pre v5.0: fig, ax = **pcv.visualize.pixel_scatter_plot**(*paths_to_imgs, x_channel, y_channel*)
+* post v5.0: fig, ax = **pcv.visualize.pixel_scatter_plot**(*source, x_channel, y_channel, n=20, ext="png"*)
 
 #### plantcv.visualize.tile
 

docs/visualize_pixel_scatter_vis.md

@@ -2,16 +2,18 @@
 
 This function plots a 2D pixel scatter plot visualization for a dataset of images. The horizontal and vertical coordinates are defined by the intensity of the pixels in the specified channels. The color of each dot is given by the original RGB color of the pixel.
 
-**plantcv.visualize.pixel_scatter_plot**(*paths_to_imgs, x_channel, y_channel*)
+**plantcv.visualize.pixel_scatter_plot**(*source, x_channel, y_channel, n=20, ext="png"*)
 
 **returns** fig, ax
 
 - **Parameters:**
-    - paths_to_imgs   - List of paths to the images.
+    - source   - Image as a numpy array, string file path to a directory of images, or 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', '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', 'c', 'm', 'y', 'k', 'gray', and 'index'.
+    - n   - Max number of images to use if `source` is a filepath.
+	- ext   - Image file extension to search for if `source` is a filepath.
 
 
 - **Context:**
@@ -37,9 +39,9 @@ This function plots a 2D pixel scatter plot visualization for a dataset of image
 
 from plantcv import plantcv as pcv
 
-fig1, ax1 = pcv.visualize.pixel_scatter_plot(paths_to_imgs=file_paths, x_channel='index', y_channel='G')
+fig1, ax1 = pcv.visualize.pixel_scatter_plot(source=file_paths, x_channel='index', y_channel='G')
 
-fig2, ax2 = pcv.visualize.pixel_scatter_plot(paths_to_imgs=file_paths, x_channel='index', y_channel='s')
+fig2, ax2 = pcv.visualize.pixel_scatter_plot(source=file_paths, x_channel='index', y_channel='s')
 
 ```
 
@@ -61,9 +63,11 @@ fig2, ax2 = pcv.visualize.pixel_scatter_plot(paths_to_imgs=file_paths, x_channel
 
 from plantcv import plantcv as pcv
 
-fig1, ax1 = pcv.visualize.pixel_scatter_plot(paths_to_imgs=file_paths, x_channel='b', y_channel='a')
+fig1, ax1 = pcv.visualize.pixel_scatter_plot(source=file_paths, x_channel='b', y_channel='a')
 
-fig2, ax2 = pcv.visualize.pixel_scatter_plot(paths_to_imgs=file_paths, x_channel='G', y_channel='b')
+fig2, ax2 = pcv.visualize.pixel_scatter_plot(source="/path/to/images/", x_channel='G', y_channel='b')
+
+fig3, ax3 = pcv.visualize.pixel_scatter_plot(source=img, x_channel='G', y_channel='b')
 
 ```
 

plantcv/plantcv/visualize/pixel_scatter_vis.py

@@ -1,5 +1,5 @@
 # Visualize a scatter plot of pixels
-
+import os
 import numpy as np
 import cv2
 from matplotlib import pyplot as plt
@@ -45,29 +45,32 @@ def _not_valid(*args):
     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):
+def pixel_scatter_plot(source, x_channel, y_channel, n=20, ext="png"):
     """
     Plot a 2D pixel scatter plot visualization for a dataset of images.
     The horizontal and vertical coordinates are defined by the intensity of the
     pixels in the specified channels.
     The color of each dot is given by the original RGB color of the pixel.
 
-    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', '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', '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: list of str
-    :param x_channel: str
-    :param y_channel: str
-    :return fig: matplotlib.pyplot Figure object
-    :return ax: matplotlib.pyplot Axes object
+    Parameters
+    ----------
+    source : list, numpy.ndarray, or str,
+        List of paths to the images, an image as a numpy array, or a path to a starting directory to find images in
+    x_channel : str,
+        Channel to use for the horizontal coordinate of the scatter plot.
+        Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray', and 'index'
+    y_channel : str,
+        Channel to use for the vertical coordinate of the scatter plot.
+        Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray', and 'index'
+    n : int,
+        max number of images to use if source is a string
+    ext : str,
+        image file extension to search for if source is a string
+
+    Returns
+    -------
+    fig : matplotlib pyplot Figure object of the visualization
+    ax : matplotlib pyplot Axes object of the visualization
     """
     # dictionary returns the function that gets the required image channel
     channel_dict = {
@@ -87,13 +90,24 @@ def pixel_scatter_plot(paths_to_imgs, x_channel, y_channel):
         'y': _rgb2cmyk,
         'k': _rgb2cmyk
     }
+    if isinstance(source, np.ndarray):
+        fig, ax = _px_scatter_from_img(source, x_channel, y_channel, channel_dict)
+        return fig, ax
+    # if not an image then keep going
+    paths_to_imgs = source
+    N = len(paths_to_imgs)
+    if isinstance(source, str):
+        N = n
+        paths_to_imgs = []
+        for root, _, files in os.walk(source):
+            for file in files:
+                if file.lower().endswith(ext) and len(paths_to_imgs) < n:
+                    paths_to_imgs.append(os.path.join(root, file))
 
     # store debug mode
     debug = params.debug
     params.debug = None
 
-    N = len(paths_to_imgs)
-
     fig, ax = plt.subplots()
     # load and plot the set of images sequentially
     for p in paths_to_imgs:
@@ -126,3 +140,51 @@ def pixel_scatter_plot(paths_to_imgs, x_channel, y_channel):
     params.debug = debug
 
     return fig, ax
+
+
+def _px_scatter_from_img(source, x_channel, y_channel, channel_dict):
+    """Make pixel scatter plot from an image
+
+    Parameters
+    ----------
+    source : numpy.ndarray
+        List of paths to the images, an image as a numpy array, or a path to a starting directory to find images in
+    x_channel : str,
+        Channel to use for the horizontal coordinate of the scatter plot.
+        Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray', and 'index'
+    y_channel : str,
+        Channel to use for the vertical coordinate of the scatter plot.
+        Options:  'R', 'G', 'B', 'l', 'a', 'b', 'h', 's', 'v', 'c', 'm', 'y', 'k', 'gray', and 'index'
+    channel_dict : dict,
+        dictionary of functions to pull channels. Defined internally in user facing function.
+
+    Returns
+    -------
+    fig : matplotlib pyplot Figure object of the visualization
+    ax : matplotlib pyplot Axes object of the visualization
+    """
+    fig, ax = plt.subplots()
+    h, _, c = source.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 = cv2.resize(source, (IMG_WIDTH, img_height), interpolation=cv2.INTER_NEAREST)
+
+    # organize the channels as RGB to use as facecolor for the markers
+    sub_img_rgb = cv2.cvtColor(sub_img, cv2.COLOR_BGR2RGB)
+    fcolors = sub_img_rgb.reshape(img_height*IMG_WIDTH, c)/255
+
+    # get channels
+    sub_img_x_ch = channel_dict.get(x_channel, _not_valid)(sub_img, x_channel)
+    sub_img_y_ch = channel_dict.get(y_channel, _not_valid)(sub_img, y_channel)
+
+    ax.scatter(sub_img_x_ch.reshape(-1),
+               sub_img_y_ch.reshape(-1),
+               alpha=0.05, s=MAX_MARKER_SIZE,
+               edgecolors=None, facecolors=fcolors)
+
+    plt.xlabel(x_channel)
+    plt.ylabel(y_channel)
+
+    return fig, ax

tests/plantcv/visualize/test_pixel_scatter_plot.py

@@ -17,7 +17,7 @@ def test_pixel_scatter_plot(ch, tmpdir):
     path_to_img = os.path.join(cache_dir, 'tmp_img.png')
     cv2.imwrite(path_to_img, img)
     # test the function with a list of one path to the random image
-    _, _ = pixel_scatter_plot(paths_to_imgs=[path_to_img], x_channel=ch, y_channel='index')
+    _, _ = pixel_scatter_plot(source=[path_to_img], x_channel=ch, y_channel='index')
     assert 1
 
 
@@ -33,4 +33,32 @@ def test_pixel_scatter_plot_wrong_ch(tmpdir):
     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')
+        _, _ = pixel_scatter_plot(source=[path_to_img], x_channel='wrong_ch', y_channel='index')
+
+
+def test_pixel_scatter_plot_str_source(tmpdir):
+    """Test for PlantCV."""
+    # Create a tmp directory
+    cache_dir = tmpdir.mkdir("cache")
+    for i in range(2):
+        rng = np.random.default_rng()
+        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, f'tmp_img_{i}.png')
+        cv2.imwrite(path_to_img, img)
+    # test the function with an str path
+    _, _ = pixel_scatter_plot(source=str(cache_dir), x_channel="R", y_channel="index")
+    assert 1
+
+
+def test_pixel_scatter_plot_img_source():
+    """Test for PlantCV."""
+    # Create an image
+    rng = np.random.default_rng()
+    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)
+    # test the function with an str path
+    _, _ = pixel_scatter_plot(source=img, x_channel="l", y_channel="a")
+    assert 1