Create helpers for color conversion

plantcv/plantcv/_helpers.py

@@ -457,3 +457,127 @@ def _grayscale_to_rgb(img):
         img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
 
     return img
+
+
+def _rgb2lab(rgb_img, channel):
+    """Convert image from RGB colorspace to LAB colorspace. Returns the specified subchannel as a gray image.
+
+    Parameters
+    ----------
+    rgb_img : numpy.ndarray
+        RGB image data
+    channel : str
+        color subchannel (l = lightness, a = green-magenta, b = blue-yellow)
+
+    Returns
+    -------
+    numpy.ndarray
+        grayscale image from one LAB color channel
+    """
+    # The allowable channel inputs are l, a or b
+    channel = channel.lower()
+    if channel not in ["l", "a", "b"]:
+        fatal_error("Channel " + str(channel) + " is not l, a or b!")
+
+    # Convert the input BGR image to LAB colorspace
+    lab = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2LAB)
+    # Split LAB channels
+    l, a, b = cv2.split(lab)
+    # Create a channel dictionaries for lookups by a channel name index
+    channels = {"l": l, "a": a, "b": b}
+    return channels[channel]
+
+
+def _rgb2hsv(rgb_img, channel):
+    """Convert image from RGB colorspace to HSV colorspace. Returns the specified subchannel as a gray image.
+
+    Parameters
+    ----------
+    rgb_img : numpy.ndarray
+        RGB image data
+    channel : str
+        color subchannel (h = hue, s = saturation, v = value/intensity/brightness)
+
+    Returns
+    -------
+    numpy.ndarray
+        grayscale image from one HSV color channel
+    """
+    # The allowable channel inputs are h, s or v
+    channel = channel.lower()
+    if channel not in ["h", "s", "v"]:
+        fatal_error("Channel " + str(channel) + " is not h, s or v!")
+
+    # Convert the input BGR image to HSV colorspace
+    hsv = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2HSV)
+    # Split HSV channels
+    h, s, v = cv2.split(hsv)
+    # Create a channel dictionaries for lookups by a channel name index
+    channels = {"h": h, "s": s, "v": v}
+
+    return channels[channel]
+
+
+def _rgb2cmyk(rgb_img, channel):
+    """Convert image from RGB colorspace to CMYK colorspace. Returns the specified subchannel as a gray image.
+
+    Parameters
+    ----------
+    rgb_img : numpy.ndarray
+        RGB image data
+    channel : str
+        color subchannel (c = cyan, m = magenta, y = yellow, k=black)
+
+    Returns
+    -------
+    numpy.ndarray
+        grayscale image from one CMYK color channel
+    """
+    # Set NumPy to ignore divide by zero errors
+    _ = np.seterr(divide='ignore', invalid='ignore')
+    # The allowable channel inputs are c, m , y or k
+    channel = channel.lower()
+    if channel not in ["c", "m", "y", "k"]:
+        fatal_error("Channel " + str(channel) + " is not c, m, y or k!")
+
+    # Create float
+    bgr = rgb_img.astype(float)/255.
+
+    # K channel
+    k = 1 - np.max(bgr, axis=2)
+
+    # C Channel
+    c = (1 - bgr[..., 2] - k) / (1 - k)
+
+    # M Channel
+    m = (1 - bgr[..., 1] - k) / (1 - k)
+
+    # Y Channel
+    y = (1 - bgr[..., 0] - k) / (1 - k)
+
+    # Convert the input BGR image to LAB colorspace
+    cmyk = (np.dstack((c, m, y, k)) * 255).astype(np.uint8)
+    # Split CMYK channels
+    y, m, c, k = cv2.split(cmyk)
+    # Create a channel dictionaries for lookups by a channel name index
+    channels = {"c": c, "m": m, "y": y, "k": k}
+
+    return channels[channel]
+
+
+def _rgb2gray(rgb_img):
+    """Convert image from RGB colorspace to Gray.
+
+    Parameters
+    ----------
+    rgb_img : numpy.ndarray
+        RGB image data
+
+    Returns
+    -------
+    numpy.ndarray
+        grayscale image
+    """
+    gray = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY)
+
+    return gray

plantcv/plantcv/report_size_marker_area.py

@@ -2,10 +2,10 @@
 import cv2
 import numpy as np
 import os
-from plantcv.plantcv import params, outputs, fatal_error, apply_mask, rgb2gray_hsv
+from plantcv.plantcv import params, outputs, fatal_error, apply_mask
 from plantcv.plantcv.threshold import binary as binary_threshold
 from plantcv.plantcv._debug import _debug
-from plantcv.plantcv._helpers import _cv2_findcontours, _object_composition, _roi_filter
+from plantcv.plantcv._helpers import _cv2_findcontours, _object_composition, _roi_filter, _rgb2hsv
 
 
 def report_size_marker_area(img, roi, marker='define', objcolor='dark', thresh_channel=None,
@@ -66,7 +66,7 @@ def report_size_marker_area(img, roi, marker='define', objcolor='dark', thresh_c
             # Mask the input image
             masked = apply_mask(img=ref_img, mask=roi_mask, mask_color="black")
             # Convert the masked image to hue, saturation, or value
-            marker_hsv = rgb2gray_hsv(rgb_img=masked, channel=thresh_channel)
+            marker_hsv = _rgb2hsv(rgb_img=masked, channel=thresh_channel)
             # Threshold the HSV image
             marker_bin = binary_threshold(gray_img=marker_hsv, threshold=thresh, object_type=objcolor)
             # Identify contours in the masked image

plantcv/plantcv/rgb2gray.py

@@ -1,25 +1,26 @@
 # RGB -> Gray
 
-import cv2
 import os
 from plantcv.plantcv import params
 from plantcv.plantcv._debug import _debug
+from plantcv.plantcv._helpers import _rgb2gray
 
 
 def rgb2gray(rgb_img):
     """Convert image from RGB colorspace to Gray.
 
-    Inputs:
-    rgb_img    = RGB image data
+    Parameters
+    ----------
+    rgb_img : numpy.ndarray
+        RGB image data
 
-    Returns:
-    gray   = grayscale image
-
-    :param rgb_img: numpy.ndarray
-    :return gray: numpy.ndarray
+    Returns
+    -------
+    numpy.ndarray
+        grayscale image
     """
-    gray = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2GRAY)
+    gray = _rgb2gray(rgb_img=rgb_img)
 
-    _debug(visual=gray, filename=os.path.join(params.debug_outdir, str(params.device) + "_gray.png"))
+    _debug(visual=gray, filename=os.path.join(params.debug_outdir, f"{params.device}_gray.png"))
 
     return gray

plantcv/plantcv/rgb2gray_cmyk.py

@@ -1,59 +1,34 @@
 """Convert RGB to CMYK colorspace and return the specified subchannel as a grayscale image."""
 
-import cv2
 import os
 from plantcv.plantcv._debug import _debug
-from plantcv.plantcv import fatal_error
 from plantcv.plantcv import params
-import numpy as np
+from plantcv.plantcv._helpers import _rgb2cmyk
 
 
 def rgb2gray_cmyk(rgb_img, channel):
     """Convert image from RGB colorspace to CMYK colorspace. Returns the specified subchannel as a gray image.
 
-    Inputs:
-    rgb_img   = RGB image data
-    channel   = color subchannel (c = cyan, m = magenta, y = yellow, k=black)
-
-    Returns:
-    c | m | y | k = grayscale image from one CMYK color channel
-
-    :param rgb_img: numpy.ndarray
-    :param channel: str
-    :return channel: numpy.ndarray
+    Parameters
+    ----------
+    rgb_img : numpy.ndarray
+        RGB image data
+    channel : str
+        color subchannel (c = cyan, m = magenta, y = yellow, k=black)
+
+    Returns
+    -------
+    numpy.ndarray
+        grayscale image from one CMYK color channel
     """
-    # Set NumPy to ignore divide by zero errors
-    _ = np.seterr(divide='ignore', invalid='ignore')
+    # Convert RGB to CMYK and return the specified subchannel as a gray image
+    gray_img = _rgb2cmyk(rgb_img=rgb_img, channel=channel)
+
     # The allowable channel inputs are c, m , y or k
     names = {"c": "cyan", "m": "magenta", "y": "yellow", "k": "black"}
-    channel = channel.lower()
-    if channel not in names:
-        fatal_error("Channel " + str(channel) + " is not c, m, y or k!")
-
-    # Create float
-    bgr = rgb_img.astype(float)/255.
-
-    # K channel
-    k = 1 - np.max(bgr, axis=2)
-
-    # C Channel
-    c = (1 - bgr[..., 2] - k) / (1 - k)
-
-    # M Channel
-    m = (1 - bgr[..., 1] - k) / (1 - k)
-
-    # Y Channel
-    y = (1 - bgr[..., 0] - k) / (1 - k)
-
-    # Convert the input BGR image to LAB colorspace
-    cmyk = (np.dstack((c, m, y, k)) * 255).astype(np.uint8)
-    # Split CMYK channels
-    y, m, c, k = cv2.split(cmyk)
-    # Create a channel dictionaries for lookups by a channel name index
-    channels = {"c": c, "m": m, "y": y, "k": k}
 
     # Save or display the grayscale image
-    _debug(visual=channels[channel], filename=os.path.join(params.debug_outdir,
-                                                           str(params.device) + "_cmyk_" + names[channel] + ".png"))
+    _debug(visual=gray_img,
+           filename=os.path.join(params.debug_outdir, f"{params.device}_cmyk_{names[channel.lower()]}.png"), cmap="gray")
 
-    return channels[channel]
+    return gray_img

plantcv/plantcv/rgb2gray_hsv.py

@@ -1,43 +1,33 @@
 # RGB -> HSV -> Gray
 
-import cv2
 import os
 from plantcv.plantcv._debug import _debug
-from plantcv.plantcv import fatal_error
 from plantcv.plantcv import params
+from plantcv.plantcv._helpers import _rgb2hsv
 
 
 def rgb2gray_hsv(rgb_img, channel):
+    """Convert image from RGB colorspace to HSV colorspace. Returns the specified subchannel as a gray image.
+
+    Parameters
+    ----------
+    rgb_img : numpy.ndarray
+        RGB image data
+    channel : str
+        color subchannel (h = hue, s = saturation, v = value/intensity/brightness)
+
+    Returns
+    -------
+    numpy.ndarray
+        grayscale image from one HSV color channel
     """
-    Convert an RGB color image to HSV colorspace and return a gray image (one channel).
+    # Convert RGB to HSV and return the specified subchannel as a gray image
+    gray_img = _rgb2hsv(rgb_img=rgb_img, channel=channel)
 
-    Inputs:
-    rgb_img = RGB image data
-    channel = color subchannel (h = hue, s = saturation, v = value/intensity/brightness)
-
-    Returns:
-    h | s | v = image from single HSV channel
-
-    :param rgb_img: numpy.ndarray
-    :param channel: str
-    :return channel: numpy.ndarray
-    """
     # The allowable channel inputs are h, s or v
     names = {"h": "hue", "s": "saturation", "v": "value"}
-    channel = channel.lower()
-    if channel not in names:
-        fatal_error("Channel " + str(channel) + " is not h, s or v!")
-
-    # Convert the input BGR image to HSV colorspace
-    hsv = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2HSV)
-    # Split HSV channels
-    h, s, v = cv2.split(hsv)
-    # Create a channel dictionaries for lookups by a channel name index
-    channels = {"h": h, "s": s, "v": v}
-
-    _debug(visual=channels[channel],
-           filename=os.path.join(params.debug_outdir,
-                                 str(params.device) + "_hsv_" + names[channel] + ".png"),
-           cmap='gray')
-
-    return channels[channel]
+
+    _debug(visual=gray_img,
+           filename=os.path.join(params.debug_outdir, f"{params.device}_hsv_{names[channel.lower()]}.png"), cmap='gray')
+
+    return gray_img

plantcv/plantcv/rgb2gray_lab.py

@@ -1,43 +1,34 @@
 # RGB -> LAB -> Gray
 
-import cv2
 import os
 from plantcv.plantcv._debug import _debug
-from plantcv.plantcv import fatal_error
 from plantcv.plantcv import params
+from plantcv.plantcv._helpers import _rgb2lab
 
 
 def rgb2gray_lab(rgb_img, channel):
+    """Convert image from RGB colorspace to LAB colorspace. Returns the specified subchannel as a gray image.
+
+    Parameters
+    ----------
+    rgb_img : numpy.ndarray
+        RGB image data
+    channel : str
+        color subchannel (l = lightness, a = green-magenta, b = blue-yellow)
+
+    Returns
+    -------
+    numpy.ndarray
+        grayscale image from one LAB color channel
     """
-    Convert image from RGB colorspace to LAB colorspace. Returns the specified subchannel as a gray image.
+    # Convert RGB to LAB and return the specified subchannel as a gray image
+    gray_img = _rgb2lab(rgb_img=rgb_img, channel=channel)
 
-    Inputs:
-    rgb_img   = RGB image data
-    channel   = color subchannel (l = lightness, a = green-magenta, b = blue-yellow)
-
-    Returns:
-    l | a | b = grayscale image from one LAB color channel
-
-    :param rgb_img: numpy.ndarray
-    :param channel: str
-    :return channel: numpy.ndarray
-    """
     # The allowable channel inputs are l, a or b
     names = {"l": "lightness", "a": "green-magenta", "b": "blue-yellow"}
-    channel = channel.lower()
-    if channel not in names:
-        fatal_error("Channel " + str(channel) + " is not l, a or b!")
-
-    # Convert the input BGR image to LAB colorspace
-    lab = cv2.cvtColor(rgb_img, cv2.COLOR_BGR2LAB)
-    # Split LAB channels
-    l, a, b = cv2.split(lab)
-    # Create a channel dictionaries for lookups by a channel name index
-    channels = {"l": l, "a": a, "b": b}
-
-    _debug(visual=channels[channel],
-           filename=os.path.join(params.debug_outdir,
-                                 str(params.device) + "_lab_" + names[channel] + ".png"),
-           cmap="gray")
-
-    return channels[channel]
+
+    # Display debug image
+    _debug(visual=gray_img,
+           filename=os.path.join(params.debug_outdir, f"{params.device}_lab_{names[channel.lower()]}.png"), cmap="gray")
+
+    return gray_img