Merge branch 'master' into cluster_functions

docs/multi-plant_tutorial.md

@@ -178,7 +178,7 @@ Convert the image from [RGB to LAB](rgb2lab.md) and select single color channel
 # STEP 5: Convert image from RGB colorspace to LAB colorspace
 # Keep only the green-magenta channel (grayscale)
 # Inputs:
-# img  = image object, RGB colorspace
+# rgb_img = image object, RGB colorspace
 # channel = color subchannel ('l' = lightness, 'a' = green-magenta , 'b' = blue-yellow)
 
 a = pcv.rgb2gray_lab(img1, 'a')
@@ -194,7 +194,7 @@ Use the [binary threshold](binary_threshold.md) function to threshold green-mage
 
 # STEP 6: Set a binary threshold on the saturation channel image
 # Inputs:
-# img  = img object, grayscale
+# gray_img = img object, grayscale
 # threshold = threshold value (0-255)
 # maxValue = value to apply above threshold (usually 255 = white)
 # object_type = light or dark
@@ -217,8 +217,8 @@ img_binary = pcv.threshold.binary(a, 120, 255, 'dark')
 
 # STEP 7: Fill in small objects (speckles)
 # Inputs:
-# img = image object, grayscale. img will be returned after filling
-# size = minimum object area size in pixels (integer)
+# bin_img = image object, grayscale. img will be returned after filling
+# size  = minimum object area size in pixels (integer)
 
 fill_image = pcv.fill(img_binary, 100)
 # ^
@@ -236,9 +236,9 @@ fill_image = pcv.fill(img_binary, 100)
 
 # STEP 8: Dilate so that you don't lose leaves (just in case)
 # Inputs:
-# img  = input image
-# kernel = integer
-# i = iterations, i.e. number of consecutive filtering passes
+# gray_img = input image
+# ksize = kernel size, integer
+# i  = iterations, i.e. number of consecutive filtering passes
 
 dilated = pcv.dilate(fill_image, 1, 1)
 ```
@@ -269,17 +269,17 @@ Define a [rectangular region of interest](roi_rectangle.md) in the image.
 
 # STEP 10: Define region of interest (ROI)
 # Inputs:
+# img = img to overlay roi
 # x_adj = adjust center along x axis
 # y_adj = adjust center along y axis
 # w_adj = adjust width
 # h_adj = adjust height
-# img = img to overlay roi
-# roi_contour, roi_hierarchy = pcv.roi.rectangle(10, 500, -10, -100, img1)
-# ^ ^
-# |________________|
+# roi_contour, roi_hierarchy = pcv.roi.rectangle(img1, 10, 500, -10, -100)
+# ^ ^
+# |________________|
 # adjust these four values
 
-roi_contour, roi_hierarchy = pcv.roi.rectangle(10, 500, -10, -100, img1)
+roi_contour, roi_hierarchy = pcv.roi.rectangle(img1, 10, 500, -10, -100)
 ```
 
 **Figure 10.** Define ROI.
@@ -320,7 +320,7 @@ roi_objects, roi_obj_hierarchy, kept_mask, obj_area = pcv.roi_objects(img1, 'par
 # clusters them based on user input of rows and columns
 
 # Inputs:
-# img = An RGB image
+# img = An RGB or grayscale image
 # roi_objects = object contours in an image that are needed to be clustered.
 # roi_obj_hierarchy = object hierarchy
 # nrow = number of rows to cluster (this should be the approximate number of desired rows in the entire image even if there isn't a literal row of plants)
@@ -530,17 +530,17 @@ def main():
 
  # STEP 10: Define region of interest (ROI)
  # Inputs:
+ # img = img to overlay roi
  # x_adj = adjust center along x axis
  # y_adj = adjust center along y axis
  # w_adj = adjust width
  # h_adj = adjust height
- # img = img to overlay roi
- # roi_contour, roi_hierarchy = pcv.roi.rectangle(10, 500, -10, -100, img1)
- # ^ ^
- # |________________|
+ # roi_contour, roi_hierarchy = pcv.roi.rectangle(img1, 10, 500, -10, -100)
+ # ^ ^
+ # |________________|
  # adjust these four values
 
- roi_contour, roi_hierarchy = pcv.roi.rectangle(10, 500, -10, -100, img1)
+ roi_contour, roi_hierarchy = pcv.roi.rectangle(img1, 10, 500, -10, -100)
 
  # STEP 11: Keep objects that overlap with the ROI
  # Inputs:

docs/nir_tutorial.md

@@ -136,13 +136,13 @@ the amount of plant material captured, and it is particularly useful if estimati
 ```python
 # Laplace filtering (identify edges based on 2nd derivative)
 lp_img = pcv.laplace_filter(img, 1, 1)
-if args.debug:
- pcv.plot_hist(lp_img, 'hist_lp')
+# Plot histogram of grayscale values 
+pcv.plot_hist(lp_img)
 
 # Lapacian image sharpening, this step will enhance the darkness of the edges detected
 lp_shrp_img = pcv.image_subtract(img, lp_img)
-if args.debug:
- pcv.plot_hist(lp_sharp_img, 'hist_lp_sharp')
+# Plot histogram of grayscale values, this helps to determine thresholding value 
+pcv.plot_hist(lp_sharp_img)
 ```
 
 **Figure 3.** (Top) Result after second derivative Laplacian filter is applied to the original grayscale image.
@@ -439,13 +439,11 @@ def main():
 
  # Laplace filtering (identify edges based on 2nd derivative)
  lp_img = pcv.laplace_filter(img, 1, 1)
- if args.debug:
- pcv.plot_hist(lp_img, 'hist_lp')
+ pcv.plot_hist(lp_img)
 
  # Lapacian image sharpening, this step will enhance the darkness of the edges detected
  lp_shrp_img = pcv.image_subtract(img, lp_img)
- if args.debug:
- pcv.plot_hist(lp_shrp_img, 'hist_lp_sharp')
+ pcv.plot_hist(lp_shrp_img)
 
  # Sobel filtering
  # 1st derivative sobel filtering along horizontal axis, kernel = 1)

docs/plot_hist.md

@@ -2,31 +2,36 @@
 
 This is a plotting method used to examine the distribution of signal within an image.
 
-**plantcv.plot_hist**(*img, name=False*)
+**plantcv.plot_hist**(*gray_img, mask=None, bins=256*)
 
 **returns** bins, hist
 
 - **Parameters:**
- - img - RGB or grayscale image data, the original image for analysis.
- - name - the name of the output plot as a string (default: name=False)
+ - gray_img - Grayscale image data, the original image for analysis.
+ - mask - Optional binary mask made from selected contours (default mask=None)
+ - bins - Number of class to divide spectrum into
 - **Context:**
- - Examine the distribution of the signal, this help you select a value for binary thresholding.
+ - Examine the distribution of the signal, this helps you select a value for binary thresholding.
 - **Example use:**
  - [Use In NIR Tutorial](nir_tutorial.md)
 
 **Grayscale image**
 
-![Screenshot](img/documentation_images/plot_hist/grayscale_image.jpg)

+![Screenshot](img/documentation_images/plot_hist/01_hsv_saturation.jpg)

+
+**Mask**
+
+![Screenshot](img/documentation_images/plot_hist/mask.jpg)
 
 ```python
 
 from plantcv import plantcv as pcv
 
 # Examine signal distribution within an image
 # prints out an image histogram of signal within image
-bins, hist = pcv.plot_hist(img, 'histogram')
+header, hist_data, hist_figure = pcv.plot_hist(gray_img, mask=mask, bins=256)
 ```
 
 **Histogram of signal intensity**
 
-![Screenshot](img/documentation_images/plot_hist/histogram.jpg)

+![Screenshot](img/documentation_images/plot_hist/hist.jpg)


docs/roi_multi.md

@@ -67,7 +67,7 @@ for i in range(0, len(rois1)):
  plant_contour, plant_mask = pcv.object_composition(img=img, contours=filtered_contours, hierarchy=filtered_hierarchy) 
 
  # Analyze the shape of each plant 
- shape_data_headers, shape_data, analysis_images = pcv.analyze_object(img=img_copy, obj=obj, mask=img_mask)
+ shape_data_headers, shape_data, analysis_images = pcv.analyze_object(img=img_copy, obj=plant_contour, mask=plant_mask)
 
  # Save the image with shape characteristics 
  img_copy = analysis_images[0]

docs/updating.md

@@ -361,7 +361,7 @@ post v3.0: new_img = **plantcv.image_subtract**(*gray_img1, gray_img2*)
 
 * pre v3.0dev2: bins, hist = **plantcv.plot_hist**(*img, name=False*)
 * post v3.0dev2: bins, hist = **plantcv.plot_hist**(*img, name=False*)
-* post v3.2: header, hist_data, hist_figure = **pcv.plot_hist**(*gray_img, mask=None, bins=256*)
+* post v3.2: hist_header, hist_data, fig_hist = **plantcv.plot_hist**(*gray_img, mask=None, bins=256*)
 
 #### plantcv.plot_image
 

docs/vis_nir_tutorial.md

@@ -207,7 +207,7 @@ Next, a [rectangular region of interest](roi_rectangle.md) is defined (this can
 ```python
 
  # Define ROI
- roi1, roi_hierarchy= pcv.roi.rectangle(600,450,-600,-700, img)
+ roi1, roi_hierarchy= pcv.roi.rectangle(img,600,450,-600,-700)
 ```
 
 **Figure 9.** Region of interest drawn onto image. 
@@ -350,7 +350,7 @@ Write co-result data out to a file.
  pcv.print_result(filename=args.coresult)
 
 if __name__ == '__main__':
- main()
+  main()
 ```
 
 To deploy a pipeline over a full image set please see tutorial on 
@@ -426,7 +426,7 @@ def main():
  id_objects,obj_hierarchy = pcv.find_objects(masked, bs)
 
  # Define ROI
- roi1, roi_hierarchy= pcv.roi.rectangle(600,450,-600,-700, img)
+ roi1, roi_hierarchy= pcv.roi.rectangle(img,600,450,-600,-700)
 
  # Decide which objects to keep
  roi_objects, hierarchy, kept_mask, obj_area = pcv.roi_objects(img,'partial',roi1,roi_hierarchy,id_objects,obj_hierarchy)

docs/vis_tutorial.md

@@ -265,7 +265,7 @@ Next a [rectangular region of interest](roi_rectangle.md) is defined (this can b
 ```python
 
  # Define ROI
- roi1, roi_hierarchy= pcv.roi.rectangle(x=100, y=100, h=200, w=200, img=masked2)
+ roi1, roi_hierarchy= pcv.roi.rectangle(img=masked2, x=100, y=100, h=200, w=200)
 ```
 
 **Figure 12.** Region of interest drawn onto image. 

plantcv/plantcv/analyze_nir_intensity.py

@@ -23,17 +23,17 @@ def analyze_nir_intensity(gray_img, mask, bins, histplot=False):
  histplot = if True plots histogram of intensity values
 
  Returns:
- hist_header = NIR histogram data table headers
- hist_data = NIR histogram data table values
- nir_hist  = NIR histogram image
+ hist_header  = NIR histogram data table headers
+ hist_data  = NIR histogram data table values
+ analysis_images = NIR histogram image
 
  :param gray_img: numpy array
  :param mask: numpy array
  :param bins: int
  :param histplot: bool
  :return hist_header: list
  :return hist_data: list
- :return nir_hist: str
+ :return analysis_images: list
  """
 
  params.device += 1
@@ -109,11 +109,10 @@ def analyze_nir_intensity(gray_img, mask, bins, histplot=False):
  + scale_x_continuous(breaks=list(range(0, bins, 25))))
 
  analysis_images.append(fig_hist)
- if params.debug is not None:
- if params.debug == "print":
- fig_hist.save(os.path.join(params.debug_outdir, str(params.device) + '_nir_hist.png'))
- if params.debug == "plot":
- print(fig_hist)
+ if params.debug == "print":
+ fig_hist.save(os.path.join(params.debug_outdir, str(params.device) + '_nir_hist.png'))
+ elif params.debug == "plot":
+ print(fig_hist)
 
  # Store into global measurements
  if not 'nir_histogram' in outputs.measurements:

plantcv/plantcv/plot_hist.py

@@ -1,52 +1,85 @@
 # Plot histogram
 
 import cv2
+import os
 import numpy as np
-from matplotlib import pyplot as plt
+from plantcv.plantcv.threshold import binary as binary_threshold
+from plantcv.plantcv import params
+import pandas as pd
+from plotnine import ggplot, aes, geom_line, scale_x_continuous
 
 
-def plot_hist(img, name=False):
- """Plot a histogram using the pyplot library.
+
+def plot_hist(gray_img, mask=None, bins=256):
+ """Plot a histogram using ggplot.
 
  Inputs:
- img = image to analyze
- name = name for plot output
+ gray_img = image to analyze
+ mask = binary mask made from selected contours
+ bins = number of classes to divide spectrum into
 
- :param img: numpy.ndarray
- :param name: str
+ :param gray_img: numpy.ndarray
+ :param mask: numpy.ndarray
+ :param bins: int
  :return bins: list
  :return hist: list
+ :return fig_hist: ggplot
  """
 
- # get histogram
- if img.dtype == 'uint8':
- hist = cv2.calcHist([img], [0], None, [256], [0, 255])
- bins = range(0, 256, 1)
-
- if name is not False:
- # open pyplot plotting window using hist data
- plt.plot(hist)
- # set range of x-axis
- xaxis = plt.xlim([0, 255])
- fig_name = name + '.png'
- # write the figure to current directory
- plt.savefig(fig_name)
- # close pyplot plotting window
- plt.clf()
+ params.device += 1
+ debug = params.debug
+ # Apply mask if one is supplied
+ if mask is not None:
+ # apply plant shaped mask to image
+ params.debug=None
+ mask1 = binary_threshold(mask, 0, 255, 'light')
+ mask1 = (mask1 / 255)
+ masked = np.multiply(gray_img, mask1)
+ else:
+ masked = gray_img
 
+ if gray_img.dtype == 'uint16':
+ maxval = 65536
  else:
- hist, bins = np.histogram(img, bins='auto')
-
- if name is not False:
- # open pyplot plotting window using hist data
- plt.plot(bins[:-1], hist)
- plt.xticks(bins[:-1], rotation='vertical', fontsize=4)
- # set range of x-axis
- # xaxis = plt.xlim([0, bins.max()])
- fig_name = name + '.png'
- # write the figure to current directory
- plt.savefig(fig_name)
- # close pyplot plotting window
- plt.clf()
-
- return bins, hist
+ maxval = 256
+
+ # Store histogram data
+ hist_gray_data, hist_bins = np.histogram(masked, bins, (1, maxval), False, None, None)
+ hist_bins1 = hist_bins[:-1]
+ hist_bins2 = [l for l in hist_bins1]
+ hist_gray = [l for l in hist_gray_data]
+ # make hist percentage for plotting
+ pixels = cv2.countNonZero(masked)
+ hist_percent = (hist_gray_data / float(pixels)) * 100
+
+ # report histogram data
+ hist_header = [
+ 'HEADER_HISTOGRAM',
+ 'bin-number',
+ 'bin-values',
+ 'gray_img'
+ ]
+
+ hist_data = [
+ 'HISTOGRAM_DATA',
+ bins,
+ hist_bins2,
+ hist_gray
+ ]
+ hist_x = hist_percent
+ bin_labels = np.arange(0, bins)
+ dataset = pd.DataFrame({'Grayscale pixel intensity': bin_labels,
+ 'Proportion of pixels (%)': hist_x})
+ fig_hist = (ggplot(data=dataset,
+ mapping=aes(x='Grayscale pixel intensity',
+ y='Proportion of pixels (%)'))
+ + geom_line(color='red')
+ + scale_x_continuous(breaks=list(range(0, bins, 25))))
+ params.debug=debug
+ if params.debug is not None:
+ if params.debug == "print":
+ fig_hist.save(os.path.join(params.debug_outdir, str(params.device) + '_hist.png'))
+ if params.debug == "plot":
+ print(fig_hist)
+
+ return hist_header, hist_data, fig_hist