Merge pull request #807 from danforthcenter/convert_debug

Convert to _debug

plantcv/plantcv/_debug.py

@@ -5,7 +5,7 @@
 from plantcv.plantcv import plot_image
 
 
-def _debug(visual, filename=None):
+def _debug(visual, filename=None, **kwargs):
     """Save or display a visual for debugging.
 
     Inputs:
@@ -23,4 +23,4 @@ def _debug(visual, filename=None):
         print_image(img=visual, filename=filename)
     elif params.debug == "plot":
         # If debug is plot, print to the plotting device
-        plot_image(img=visual)
+        plot_image(img=visual, **kwargs)

plantcv/plantcv/acute_vertex.py

@@ -4,10 +4,9 @@
 import cv2
 import numpy as np
 import math
-from plantcv.plantcv import print_image
-from plantcv.plantcv import plot_image
 from plantcv.plantcv import params
 from plantcv.plantcv import outputs
+from plantcv.plantcv._debug import _debug
 
 
 def acute_vertex(img, obj, win, thresh, sep, label="default"):
@@ -50,15 +49,17 @@ def acute_vertex(img, obj, win, thresh, sep, label="default"):
         post_x, post_y = obj[i + win].ravel()
 
         # Angle in radians derived from Law of Cosines, converted to degrees
-        P12 = np.sqrt((x-pre_x)*(x-pre_x)+(y-pre_y)*(y-pre_y))
-        P13 = np.sqrt((x-post_x)*(x-post_x)+(y-post_y)*(y-post_y))
-        P23 = np.sqrt((pre_x-post_x)*(pre_x-post_x)+(pre_y-post_y)*(pre_y-post_y))
-        if (2*P12*P13) > 0.001:
-            dot = (P12*P12 + P13*P13 - P23*P23)/(2*P12*P13)
-        elif (2*P12*P13) < 0.001:
-            dot = (P12*P12 + P13*P13 - P23*P23)/0.001
-
-        if dot < -1:     # If float exceeds -1 prevent arcos error and force to equal -1
+        P12 = np.sqrt((x - pre_x) * (x - pre_x) + (y - pre_y) * (y - pre_y))
+        P13 = np.sqrt((x - post_x) * (x - post_x) + (y - post_y) *
+                      (y - post_y))
+        P23 = np.sqrt((pre_x - post_x) * (pre_x - post_x) + (pre_y - post_y) *
+                      (pre_y - post_y))
+        if (2 * P12 * P13) > 0.001:
+            dot = (P12 * P12 + P13 * P13 - P23 * P23) / (2 * P12 * P13)
+        elif (2 * P12 * P13) < 0.001:
+            dot = (P12 * P12 + P13 * P13 - P23 * P23) / 0.001
+
+        if dot < -1:  # If float exceeds -1 prevent arcos error and force to equal -1
             dot = -1
         ang = math.degrees(math.acos(dot))
         chain.append(ang)
@@ -73,11 +74,11 @@ def acute_vertex(img, obj, win, thresh, sep, label="default"):
     # Sep is the number of points to evaluate the number of vertices
     out = []
     tester = []
-    for i in range(len(index)-1):
+    for i in range(len(index) - 1):
         # print str(index[i])
-        if index[i+1] - index[i] < sep:
+        if index[i + 1] - index[i] < sep:
             tester.append(index[i])
-        if index[i+1] - index[i] >= sep:
+        if index[i + 1] - index[i] >= sep:
             tester.append(index[i])
             # print(tester)
             angles = ([chain[d] for d in tester])
@@ -99,14 +100,18 @@ def acute_vertex(img, obj, win, thresh, sep, label="default"):
         x, y = i.ravel()
         cv2.circle(img2, (x, y), params.line_thickness, (255, 0, 255), -1)
 
-    if params.debug == 'print':
-        print_image(img2, os.path.join(params.debug_outdir, str(params.device) + '_acute_vertices.png'))
-    elif params.debug == 'plot':
-        plot_image(img2)
+    _debug(visual=img2,
+           filename=os.path.join(params.debug_outdir,
+                                 str(params.device) + '_acute_vertices.png'))
 
     # Store into global measurements
-    outputs.add_observation(sample=label, variable='tip_coordinates', trait='tip coordinates',
-                            method='plantcv.plantcv.acute_vertex', scale='none', datatype=list,
-                            value=acute_points, label='none')
+    outputs.add_observation(sample=label,
+                            variable='tip_coordinates',
+                            trait='tip coordinates',
+                            method='plantcv.plantcv.acute_vertex',
+                            scale='none',
+                            datatype=list,
+                            value=acute_points,
+                            label='none')
 
     return acute_points, img2

plantcv/plantcv/analyze_bound_horizontal.py

@@ -3,14 +3,14 @@
 import os
 import cv2
 import numpy as np
-from plantcv.plantcv import print_image
-from plantcv.plantcv import plot_image
+from plantcv.plantcv._debug import _debug
 from plantcv.plantcv import params
 from plantcv.plantcv import outputs
 
 
 def analyze_bound_horizontal(img, obj, mask, line_position, label="default"):
-    """User-input boundary line tool
+    """
+    User-input boundary line tool
 
     Inputs:
     img             = RGB or grayscale image data for plotting
@@ -29,7 +29,6 @@ def analyze_bound_horizontal(img, obj, mask, line_position, label="default"):
     :param label: str
     :return analysis_images: list
     """
-
     ori_img = np.copy(img)
 
     # Draw line horizontal line through bottom of image, that is adjusted to user input height
@@ -139,12 +138,10 @@ def analyze_bound_horizontal(img, obj, mask, line_position, label="default"):
                          params.line_thickness)
                 cv2.line(wback, (int(cmx), y_coor - 2), (int(cmx), y_coor + height_below_bound), (0, 255, 0),
                          params.line_thickness)
-        if params.debug == 'print':
-            print_image(wback, os.path.join(params.debug_outdir, str(params.device) + '_boundary_on_white.png'))
-            print_image(ori_img, os.path.join(params.debug_outdir, str(params.device) + '_boundary_on_img.png'))
-        if params.debug == 'plot':
-            plot_image(wback)
-            plot_image(ori_img)
+        _debug(visual=wback,
+               filename=os.path.join(params.debug_outdir, str(params.device) + '_boundary_on_white.png'))
+        _debug(visual=ori_img,
+               filename=os.path.join(params.debug_outdir, str(params.device) + '_boundary_on_img.png'))
 
     outputs.add_observation(sample=label, variable='horizontal_reference_position',
                             trait='horizontal reference position',

plantcv/plantcv/analyze_bound_vertical.py

@@ -3,21 +3,19 @@
 import os
 import cv2
 import numpy as np
-from plantcv.plantcv import print_image
-from plantcv.plantcv import plot_image
+from plantcv.plantcv._debug import _debug
 from plantcv.plantcv import params
 from plantcv.plantcv import outputs
 
 
 def analyze_bound_vertical(img, obj, mask, line_position, label="default"):
-    """User-input boundary line tool
+    """
+    User-input boundary line tool
 
     Inputs:
     img             = RGB or grayscale image data for plotting
     obj             = single or grouped contour object
     mask            = Binary mask made from selected contours
-    shape_header    = pass shape header data to function
-    shape_data      = pass shape data so that analyze_bound data can be appended to it
     line_position   = position of boundary line (a value of 0 would draw the line through the left side of the image)
     label           = optional label parameter, modifies the variable name of observations recorded
 
@@ -92,7 +90,7 @@ def analyze_bound_vertical(img, obj, mask, line_position, label="default"):
         cv2.line(ori_img, point3, point4, (255, 0, 255), params.line_thickness)
         cv2.line(wback, point3, point4, (255, 0, 255), params.line_thickness)
         m = cv2.moments(mask, binaryImage=True)
-        cmx, cmy = (m['m10'] / m['m00'], m['m01'] / m['m00'])
+        _, cmy = (m['m10'] / m['m00'], m['m01'] / m['m00'])
         if x_coor - x <= 0:
             cv2.line(ori_img, (x, int(cmy)), (x + width, int(cmy)), (0, 255, 0), params.line_thickness)
             cv2.line(wback, (x, int(cmy)), (x + width, int(cmy)), (0, 255, 0), params.line_thickness)
@@ -121,7 +119,7 @@ def analyze_bound_vertical(img, obj, mask, line_position, label="default"):
         cv2.line(ori_img, point3, point4, (255, 0, 255), params.line_thickness)
         cv2.line(wback, point3, point4, (255, 0, 255), params.line_thickness)
         m = cv2.moments(mask, binaryImage=True)
-        cmx, cmy = (m['m10'] / m['m00'], m['m01'] / m['m00'])
+        _, cmy = (m['m10'] / m['m00'], m['m01'] / m['m00'])
         if x_coor - x <= 0:
             cv2.line(ori_img, (x, int(cmy)), (x + width, int(cmy)), (0, 255, 0), params.line_thickness)
             cv2.line(wback, (x, int(cmy)), (x + width, int(cmy)), (0, 255, 0), params.line_thickness)
@@ -139,12 +137,10 @@ def analyze_bound_vertical(img, obj, mask, line_position, label="default"):
                          params.line_thickness)
                 cv2.line(wback, (x_coor + 2, int(cmy)), (x_coor - width_right_bound, int(cmy)), (0, 255, 0),
                          params.line_thickness)
-        if params.debug == 'print':
-            print_image(wback, os.path.join(params.debug_outdir, str(params.device) + '_boundary_on_white.png'))
-            print_image(ori_img, os.path.join(params.debug_outdir, str(params.device) + '_boundary_on_img.png'))
-        if params.debug == 'plot':
-            plot_image(wback)
-            plot_image(ori_img)
+        _debug(visual=wback,
+               filename=os.path.join(params.debug_outdir, str(params.device) + '_boundary_on_white.png'))
+        _debug(visual=ori_img,
+               filename=os.path.join(params.debug_outdir, str(params.device) + '_boundary_on_img.png'))
 
     outputs.add_observation(sample=label, variable='vertical_reference_position', trait='vertical reference position',
                             method='plantcv.plantcv.analyze_bound_vertical', scale='none', datatype=int,

plantcv/plantcv/apply_mask.py

@@ -4,8 +4,7 @@
 import cv2
 import numpy as np
 from plantcv.plantcv import params
-from plantcv.plantcv import plot_image
-from plantcv.plantcv import print_image
+from plantcv.plantcv._debug import _debug
 from plantcv.plantcv import fatal_error
 from plantcv.plantcv.transform import rescale
 
@@ -27,8 +26,6 @@ def apply_mask(img, mask, mask_color):
     :return masked_img: numpy.ndarray
     """
 
-    params.device += 1
-
     if mask_color.upper() == "WHITE":
         color_val = 255
     elif mask_color.upper() == "BLACK":
@@ -53,14 +50,10 @@ def apply_mask(img, mask, mask_color):
                                 rescale(array_data[:, :, num_bands - 1])))
         params.debug = debug
 
-        if params.debug == 'print':
-            print_image(pseudo_rgb, os.path.join(params.debug_outdir, str(params.device) + '_masked.png'))
-        elif params.debug == 'plot':
-            plot_image(pseudo_rgb)
+        _debug(visual=pseudo_rgb,
+               filename=os.path.join(params.debug_outdir, str(params.device) + '_masked.png'))
     else:
-        if params.debug == 'print':
-            print_image(array_data, os.path.join(params.debug_outdir, str(params.device) + '_masked.png'))
-        elif params.debug == 'plot':
-            plot_image(array_data)
+        _debug(visual=array_data,
+               filename=os.path.join(params.debug_outdir, str(params.device) + '_masked.png'))
 
     return array_data

plantcv/plantcv/auto_crop.py

@@ -3,14 +3,14 @@
 import os
 import cv2
 import numpy as np
-from plantcv.plantcv import print_image
-from plantcv.plantcv import plot_image
+from plantcv.plantcv._debug import _debug
 from plantcv.plantcv import params
 from plantcv.plantcv import fatal_error
 
 
 def auto_crop(img, obj, padding_x=0, padding_y=0, color='black'):
-    """Resize image.
+    """
+    Resize image.
 
     Inputs:
     img       = RGB or grayscale image data
@@ -59,30 +59,32 @@ def auto_crop(img, obj, padding_x=0, padding_y=0, color='black'):
 
     if color.upper() == 'BLACK':
         colorval = (0, 0, 0)
-        cropped = cv2.copyMakeBorder(crop_img, offsety_top, offsety_bottom, offsetx_left, offsetx_right, cv2.BORDER_CONSTANT, value=colorval)
+        cropped = cv2.copyMakeBorder(crop_img, offsety_top, offsety_bottom, offsetx_left,
+                                     offsetx_right, cv2.BORDER_CONSTANT, value=colorval)
     elif color.upper() == 'WHITE':
         colorval = (255, 255, 255)
-        cropped = cv2.copyMakeBorder(crop_img, offsety_top, offsety_bottom, offsetx_left, offsetx_right, cv2.BORDER_CONSTANT, value=colorval)
+        cropped = cv2.copyMakeBorder(crop_img, offsety_top, offsety_bottom, offsetx_left,
+                                     offsetx_right, cv2.BORDER_CONSTANT, value=colorval)
     elif color.upper() == 'IMAGE':
         # Check whether the ROI is correctly bounded inside the image
-        if x - offsetx_right < 0  or y - offsety_top < 0 or x + w + offsetx_right > width or y + h + offsety_bottom > height:
+        if x - offsetx_right < 0 or y - offsety_top < 0 or x + w + offsetx_right > width or y + h + offsety_bottom > height:
             cropped = img_copy2[y:y + h, x:x + w]
         else:
             # If padding is the image, crop the image with a buffer rather than cropping and adding a buffer
             cropped = img_copy2[y - offsety_top:y + h + offsety_bottom, x - offsetx_left:x + w + offsetx_right]
     else:
         fatal_error('Color was provided but ' + str(color) + ' is not "white", "black", or "image"!')
 
-
-    if params.debug == 'print':
-        print_image(img_copy, os.path.join(params.debug_outdir, str(params.device) + "_crop_area.png"))
-        print_image(cropped, os.path.join(params.debug_outdir, str(params.device) + "_auto_cropped.png"))
-    elif params.debug == 'plot':
-        if len(np.shape(img_copy)) == 3:
-            plot_image(img_copy)
-            plot_image(cropped)
-        else:
-            plot_image(img_copy, cmap='gray')
-            plot_image(cropped, cmap='gray')
+    if len(np.shape(img_copy)) == 3:
+        cmap = None
+    else:
+        cmap = 'gray'
+
+    _debug(visual=img_copy,
+           filename=os.path.join(params.debug_outdir, str(params.device) + "_crop_area.png"),
+           cmap=cmap)
+    _debug(visual=cropped,
+           filename=os.path.join(params.debug_outdir, str(params.device) + "_auto_cropped.png"),
+           cmap=cmap)
 
     return cropped

plantcv/plantcv/background_subtraction.py

@@ -4,14 +4,14 @@
 import os
 import cv2
 import numpy as np
-from plantcv.plantcv import print_image
-from plantcv.plantcv import plot_image
+from plantcv.plantcv._debug import _debug
 from plantcv.plantcv import fatal_error
 from plantcv.plantcv import params
 
 
 def background_subtraction(background_image, foreground_image):
-    """Creates a binary image from a background subtraction of the foreground using cv2.BackgroundSubtractorMOG().
+    """
+    Creates a binary image from a background subtraction of the foreground using cv2.BackgroundSubtractorMOG().
     The binary image returned is a mask that should contain mostly foreground pixels.
     The background image should be the same background as the foreground image except not containing the object
     of interest.
@@ -31,8 +31,6 @@ def background_subtraction(background_image, foreground_image):
     :param foreground_image: numpy.ndarray
     :return fgmask: numpy.ndarray
     """
-
-    params.device += 1
     # Copying images to make sure not alter originals
     bg_img = np.copy(background_image)
     fg_img = np.copy(foreground_image)
@@ -57,10 +55,8 @@ def background_subtraction(background_image, foreground_image):
     # Applying the foreground image to the background subtractor (therefore removing the background)
     fgmask = bgsub.apply(fg_img)
 
-    # Debug options
-    if params.debug == "print":
-        print_image(fgmask, os.path.join(params.debug_outdir, str(params.device) + "_background_subtraction.png"))
-    elif params.debug == "plot":
-        plot_image(fgmask, cmap="gray")
+    _debug(visual=fgmask,
+           filename=os.path.join(params.debug_outdir, str(params.device) + "_background_subtraction.png"),
+           cmap='gray')
 
     return fgmask

plantcv/plantcv/canny_edge_detect.py

@@ -1,7 +1,6 @@
 # Canny edge detection
 
-from plantcv.plantcv import print_image
-from plantcv.plantcv import plot_image
+from plantcv.plantcv._debug import _debug
 from plantcv.plantcv import dilate
 from plantcv.plantcv import params
 from plantcv.plantcv import fatal_error
@@ -13,7 +12,8 @@
 
 def canny_edge_detect(img, mask=None, sigma=1.0, low_thresh=None, high_thresh=None, thickness=1,
                       mask_color=None, use_quantiles=False):
-    """Edge filter an image using the Canny algorithm.
+    """
+    Edge filter an image using the Canny algorithm.
 
     Inputs:
     img           = RGB or grayscale image data
@@ -52,8 +52,6 @@ def canny_edge_detect(img, mask=None, sigma=1.0, low_thresh=None, high_thresh=No
     Original author: Lee Kamentsky
     """
 
-    params.device += 1
-
     # Check if the image is grayscale; if color img then make it grayscale
     dimensions = np.shape(img)
     if len(dimensions) == 3:
@@ -83,9 +81,8 @@ def canny_edge_detect(img, mask=None, sigma=1.0, low_thresh=None, high_thresh=No
         params.debug = debug
 
     # Print or plot the binary image
-    if params.debug == 'print':
-        print_image(bin_img, os.path.join(params.debug_outdir, (str(params.device) + '_canny_edge_detect.png')))
-    elif params.debug == 'plot':
-        plot_image(bin_img, cmap='gray')
+    _debug(visual=bin_img,
+           filename=os.path.join(params.debug_outdir, (str(params.device) + '_canny_edge_detect.png')),
+           cmap='gray')
 
     return bin_img