def test_plantcv_triangle_threshold():
img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1)
device, thresholded = pcv.triangle_auto_threshold(0,
img1,
255,
"light",
10,
debug=None)
thresholdedavg = np.average(thresholded)
imgavg = np.average(img1)
assert thresholdedavg > imgavg
def main():
# Initialize device
device = 0
# Parse command-line options
args = options()
# Read image
img, path, filename = pcv.readimage(filename=args.image, debug=args.debug)
# Convert RGB to LAB and extract the Blue-Yellow channel
device, blue_channel = pcv.rgb2gray_lab(img=img,
channel="b",
device=device,
debug=args.debug)
# Threshold the blue image using the triangle autothreshold method
device, blue_tri = pcv.triangle_auto_threshold(device=device,
img=blue_channel,
maxvalue=255,
object_type="light",
xstep=1,
debug=args.debug)
# Extract core plant region from the image to preserve delicate plant features during filtering
device += 1
plant_region = blue_tri[0:1750, 600:2080]
if args.debug is not None:
pcv.print_image(filename=str(device) + "_extract_plant_region.png",
img=plant_region)
# Use a Gaussian blur to disrupt the strong edge features in the cabinet
device, blur_gaussian = pcv.gaussian_blur(device=device,
img=blue_tri,
ksize=(3, 3),
sigmax=0,
sigmay=None,
debug=args.debug)
# Threshold the blurred image to remove features that were blurred
device, blur_thresholded = pcv.binary_threshold(img=blur_gaussian,
threshold=250,
maxValue=255,
object_type="light",
device=device,
debug=args.debug)
# Add the plant region back in to the filtered image
device += 1
blur_thresholded[0:1750, 600:2080] = plant_region
if args.debug is not None:
pcv.print_image(filename=str(device) + "_replace_plant_region.png",
img=blur_thresholded)
# Fill small noise
device, blue_fill_50 = pcv.fill(img=np.copy(blur_thresholded),
mask=np.copy(blur_thresholded),
size=50,
device=device,
debug=args.debug)
# Apply a small median blur to break up pot edges
device, med_blur = pcv.median_blur(img=np.copy(blue_fill_50),
ksize=3,
device=device,
debug=args.debug)
# Define an ROI for the barcode label
device, label_roi, label_hierarchy = pcv.define_roi(img=img,
shape="rectangle",
device=device,
roi=None,
roi_input="default",
debug=args.debug,
adjust=True,
x_adj=1100,
y_adj=1350,
w_adj=-1070,
h_adj=-590)
# Identify all remaining contours in the binary image
device, contours, hierarchy = pcv.find_objects(img=img,
mask=np.copy(med_blur),
device=device,
debug=args.debug)
# Remove contours completely contained within the label region of interest
device, remove_label_mask = remove_countors_roi(mask=med_blur,
contours=contours,
hierarchy=hierarchy,
roi=label_roi,
device=device,
debug=args.debug)
# Identify objects
device, contours, contour_hierarchy = pcv.find_objects(
img=img, mask=remove_label_mask, device=device, debug=args.debug)
# Define ROI
device, roi, roi_hierarchy = pcv.define_roi(img=img,
shape="rectangle",
device=device,
roi=None,
roi_input="default",
debug=args.debug,
adjust=True,
x_adj=565,
y_adj=0,
w_adj=-490,
h_adj=-600)
# Decide which objects to keep
device, roi_contours, roi_contour_hierarchy, _, _ = pcv.roi_objects(
img=img,
roi_type="partial",
roi_contour=roi,
roi_hierarchy=roi_hierarchy,
object_contour=contours,
obj_hierarchy=contour_hierarchy,
device=device,
debug=args.debug)
# If there are no contours left we cannot measure anything
if len(roi_contours) > 0:
# Object combine kept objects
device, plant_contour, plant_mask = pcv.object_composition(
img=img,
contours=roi_contours,
hierarchy=roi_contour_hierarchy,
device=device,
debug=args.debug)
outfile = False
if args.writeimg:
outfile = args.outdir + "/" + filename
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img=img,
imgname=args.image,
obj=plant_contour,
mask=plant_mask,
device=device,
debug=args.debug,
filename=outfile)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img = pcv.analyze_bound(
img=img,
imgname=args.image,
obj=plant_contour,
mask=plant_mask,
line_position=690,
device=device,
debug=args.debug,
filename=outfile)
# Determine color properties: Histograms, Color Slices and Pseudocolored Images,
# output color analyzed images (optional)
device, color_header, color_data, color_img = pcv.analyze_color(
img=img,
imgname=args.image,
mask=plant_mask,
bins=256,
device=device,
debug=args.debug,
hist_plot_type=None,
pseudo_channel="v",
pseudo_bkg="img",
resolution=300,
filename=outfile)
# Output shape and color data
result = open(args.result, "a")
result.write('\t'.join(map(str, shape_header)) + "\n")
result.write('\t'.join(map(str, shape_data)) + "\n")
for row in shape_img:
result.write('\t'.join(map(str, row)) + "\n")
result.write('\t'.join(map(str, color_header)) + "\n")
result.write('\t'.join(map(str, color_data)) + "\n")
result.write('\t'.join(map(str, boundary_header)) + "\n")
result.write('\t'.join(map(str, boundary_data)) + "\n")
result.write('\t'.join(map(str, boundary_img)) + "\n")
for row in color_img:
result.write('\t'.join(map(str, row)) + "\n")
result.close()
def main():
# Initialize device
device = 0
# Parse command-line options
args = options()
# Read image
img, path, filename = pcv.readimage(filename=args.image, debug=args.debug)
# Convert RGB to LAB and extract the Blue-Yellow channel
device, blue_channel = pcv.rgb2gray_lab(img=img,
channel="b",
device=device,
debug=args.debug)
# Threshold the blue image using the triangle autothreshold method
device, blue_tri = pcv.triangle_auto_threshold(device=device,
img=blue_channel,
maxvalue=255,
object_type="light",
xstep=1,
debug=args.debug)
# Extract core plant region from the image to preserve delicate plant features during filtering
device += 1
plant_region = blue_tri[0:1750, 600:2080]
if args.debug is not None:
pcv.print_image(filename=str(device) + "_extract_plant_region.png",
img=plant_region)
# Use a Gaussian blur to disrupt the strong edge features in the cabinet
device, blur_gaussian = pcv.gaussian_blur(device=device,
img=blue_tri,
ksize=(3, 3),
sigmax=0,
sigmay=None,
debug=args.debug)
# Threshold the blurred image to remove features that were blurred
device, blur_thresholded = pcv.binary_threshold(img=blur_gaussian,
threshold=250,
maxValue=255,
object_type="light",
device=device,
debug=args.debug)
# Add the plant region back in to the filtered image
device += 1
blur_thresholded[0:1750, 600:2080] = plant_region
if args.debug is not None:
pcv.print_image(filename=str(device) + "_replace_plant_region.png",
img=blur_thresholded)
# Fill small noise
device, blue_fill_50 = pcv.fill(img=np.copy(blur_thresholded),
mask=np.copy(blur_thresholded),
size=50,
device=device,
debug=args.debug)
# Identify objects
device, contours, contour_hierarchy = pcv.find_objects(img=img,
mask=blue_fill_50,
device=device,
debug=args.debug)
# Define ROI
device, roi, roi_hierarchy = pcv.define_roi(img=img,
shape="rectangle",
device=device,
roi=None,
roi_input="default",
debug=args.debug,
adjust=True,
x_adj=565,
y_adj=0,
w_adj=-490,
h_adj=-250)
# Decide which objects to keep
device, roi_contours, roi_contour_hierarchy, _, _ = pcv.roi_objects(
img=img,
roi_type="partial",
roi_contour=roi,
roi_hierarchy=roi_hierarchy,
object_contour=contours,
obj_hierarchy=contour_hierarchy,
device=device,
debug=args.debug)
# If there are no contours left we cannot measure anything
if len(roi_contours) > 0:
# Object combine kept objects
device, plant_contour, plant_mask = pcv.object_composition(
img=img,
contours=roi_contours,
hierarchy=roi_contour_hierarchy,
device=device,
debug=args.debug)
outfile = False
if args.writeimg:
outfile = args.outdir + "/" + filename
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img=img,
imgname=args.image,
obj=plant_contour,
mask=plant_mask,
device=device,
debug=args.debug,
filename=outfile)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img = pcv.analyze_bound(
img=img,
imgname=args.image,
obj=plant_contour,
mask=plant_mask,
line_position=440,
device=device,
debug=args.debug,
filename=outfile)
# Determine color properties: Histograms, Color Slices and Pseudocolored Images,
# output color analyzed images (optional)
device, color_header, color_data, color_img = pcv.analyze_color(
img=img,
imgname=args.image,
mask=plant_mask,
bins=256,
device=device,
debug=args.debug,
hist_plot_type=None,
pseudo_channel="v",
pseudo_bkg="img",
resolution=300,
filename=outfile)
# Output shape and color data
result = open(args.result, "a")
result.write('\t'.join(map(str, shape_header)) + "\n")
result.write('\t'.join(map(str, shape_data)) + "\n")
for row in shape_img:
result.write('\t'.join(map(str, row)) + "\n")
result.write('\t'.join(map(str, color_header)) + "\n")
result.write('\t'.join(map(str, color_data)) + "\n")
result.write('\t'.join(map(str, boundary_header)) + "\n")
result.write('\t'.join(map(str, boundary_data)) + "\n")
result.write('\t'.join(map(str, boundary_img)) + "\n")
for row in color_img:
result.write('\t'.join(map(str, row)) + "\n")
result.close()
# Find matching NIR image
device, nirpath = pcv.get_nir(path=path,
filename=filename,
device=device,
debug=args.debug)
nir_rgb, nir_path, nir_filename = pcv.readimage(nirpath)
nir_img = cv2.imread(nirpath, 0)
# Make mask glovelike in proportions via dilation
device, d_mask = pcv.dilate(plant_mask,
kernel=1,
i=0,
device=device,
debug=args.debug)
# Resize mask
prop2, prop1 = conv_ratio()
device, nmask = pcv.resize(img=d_mask,
resize_x=prop1,
resize_y=prop2,
device=device,
debug=args.debug)
# Convert the resized mask to a binary mask
device, bmask = pcv.binary_threshold(img=nmask,
threshold=0,
maxValue=255,
object_type="light",
device=device,
debug=args.debug)
device, crop_img = crop_sides_equally(mask=bmask,
nir=nir_img,
device=device,
debug=args.debug)
# position, and crop mask
device, newmask = pcv.crop_position_mask(img=nir_img,
mask=crop_img,
device=device,
x=34,
y=9,
v_pos="top",
h_pos="right",
debug=args.debug)
# Identify objects
device, nir_objects, nir_hierarchy = pcv.find_objects(img=nir_rgb,
mask=newmask,
device=device,
debug=args.debug)
# Object combine kept objects
device, nir_combined, nir_combinedmask = pcv.object_composition(
img=nir_rgb,
contours=nir_objects,
hierarchy=nir_hierarchy,
device=device,
debug=args.debug)
if args.writeimg:
outfile = args.outdir + "/" + nir_filename
# Analyze NIR signal data
device, nhist_header, nhist_data, nir_imgs = pcv.analyze_NIR_intensity(
img=nir_img,
rgbimg=nir_rgb,
mask=nir_combinedmask,
bins=256,
device=device,
histplot=False,
debug=args.debug,
filename=outfile)
# Analyze the shape of the plant contour from the NIR image
device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(
img=nir_img,
imgname=nir_filename,
obj=nir_combined,
mask=nir_combinedmask,
device=device,
debug=args.debug,
filename=outfile)
# Write NIR data to co-results file
coresult = open(args.coresult, "a")
coresult.write('\t'.join(map(str, nhist_header)) + "\n")
coresult.write('\t'.join(map(str, nhist_data)) + "\n")
for row in nir_imgs:
coresult.write('\t'.join(map(str, row)) + "\n")
coresult.write('\t'.join(map(str, nshape_header)) + "\n")
coresult.write('\t'.join(map(str, nshape_data)) + "\n")
coresult.write('\t'.join(map(str, nir_shape)) + "\n")
coresult.close()
# Pipeline step
device = 0
# White balance
device, corr_img = pcv.white_balance(device, img, debug, [1450, 400, 5, 5])
# Modify the 4 numbers in square brackets to draw a square that rests on a white color card, or white color marker.
# Convert RBG to Grey using Blue-Yellow Channel
device, b = pcv.rgb2gray_lab(corr_img, 'b', device, debug)
# Channel 'b' should make the plant brighter over background. Otherwise, use a different channel.
# Gaussian blur
device, g_blur = pcv.gaussian_blur(device, b, (5,5), 0, None, debug)
# Threshold the blue image
device, img_thresh = pcv.triangle_auto_threshold(device, g_blur, 255, 'light', 20, args.debug)
device, img_cnt = pcv.triangle_auto_threshold(device, g_blur, 255, 'light', 20, args.debug)
# Modify the number 20 in both lines to an appropriate threshold that best separates plant from background
# Fill small objects
device, img_fill = pcv.fill(img_thresh, img_cnt, 50, device, args.debug)
# Modify the number 50 to what best fills holes in the plant (white) and background (black)
# Mask white-balanced image (here, based on blue channel)
device, masked = pcv.apply_mask(corr_img, b_fill, 'white', device, debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(masked, img_fill, device, args.debug)
# Define region of interest
device, roi1, roi_hierarchy = pcv.define_roi(masked, 'rectangle', device, None, 'default', debug, True,
def test_plantcv_triangle_threshold():
img1 = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1)
device, thresholded = pcv.triangle_auto_threshold(0, img1, 255, "light", 10, debug=None)
thresholdedavg = np.average(thresholded)
imgavg = np.average(img1)
assert thresholdedavg > imgavg
