def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 36, 255, 'light', device,
args.debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 0, device, args.debug)
device, s_cnt = pcv.median_blur(s_thresh, 0, device, args.debug)
# Fill small objects
#device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 137, 255, 'light', device,
args.debug)
device, b_cnt = pcv.binary_threshold(b, 137, 255, 'light', device,
args.debug)
# Fill small objects
#device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_mblur, b_cnt, device, args.debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, 'white', device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, args.debug)
device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, args.debug)
# Threshold the green-magenta and blue images
device, maskeda_thresh = pcv.binary_threshold(masked_a, 127, 255, 'dark',
device, args.debug)
device, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, 'light',
device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device,
args.debug)
device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device,
args.debug)
# Fill small noise
device, ab_fill1 = pcv.fill(ab, ab_cnt, 2, device, args.debug)
# Dilate to join small objects with larger ones
device, ab_cnt1 = pcv.dilate(ab_fill1, 3, 2, device, args.debug)
device, ab_cnt2 = pcv.dilate(ab_fill1, 3, 2, device, args.debug)
# Fill dilated image mask
device, ab_cnt3 = pcv.fill(ab_cnt2, ab_cnt1, 150, device, args.debug)
img2 = np.copy(img)
device, masked2 = pcv.apply_mask(img2, ab_cnt3, 'white', device,
args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked2_a = pcv.rgb2gray_lab(masked2, 'a', device, args.debug)
device, masked2_b = pcv.rgb2gray_lab(masked2, 'b', device, args.debug)
# Threshold the green-magenta and blue images
device, masked2a_thresh = pcv.binary_threshold(masked2_a, 127, 255, 'dark',
device, args.debug)
device, masked2b_thresh = pcv.binary_threshold(masked2_b, 128, 255,
'light', device, args.debug)
device, ab_fill = pcv.logical_or(masked2a_thresh, masked2b_thresh, device,
args.debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(
masked2, ab_fill, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(masked2, 'rectangle', device,
None, 'default', args.debug,
True, 650, 10, -600, -340)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img, 'partial', roi1, roi_hierarchy, id_objects, obj_hierarchy, device,
args.debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3,
device, args.debug)
############## Landmarks ################
device, points = pcv.acute_vertex(obj, 40, 30, 40, img, device, args.debug)
boundary_line = 290
# Use acute fxn to estimate tips
device, points_r, centroid_r, bline_r = pcv.scale_features(
obj, mask, points, boundary_line, device, args.debug)
# Get number of points
tips = len(points_r)
# Use turgor_proxy fxn to get distances
device, vert_ave_c, hori_ave_c, euc_ave_c, ang_ave_c, vert_ave_b, hori_ave_b, euc_ave_b, ang_ave_b = pcv.turgor_proxy(
points_r, centroid_r, bline_r, device, args.debug)
# Get pseudomarkers along the y-axis
device, left, right, center_h = pcv.y_axis_pseudolandmarks(
obj, mask, img, device, args.debug)
# Re-scale the points
device, left_r, left_cr, left_br = pcv.scale_features(
obj, mask, left, boundary_line, device, args.debug)
device, right_r, right_cr, right_br = pcv.scale_features(
obj, mask, right, boundary_line, device, args.debug)
device, center_hr, center_hcr, center_hbr = pcv.scale_features(
obj, mask, center_h, boundary_line, device, args.debug)
# Get pseudomarkers along the x-axis
device, top, bottom, center_v = pcv.x_axis_pseudolandmarks(
obj, mask, img, device, args.debug)
# Re-scale the points
device, top_r, top_cr, top_br = pcv.scale_features(obj, mask, top,
boundary_line, device,
args.debug)
device, bottom_r, bottom_cr, bottom_br = pcv.scale_features(
obj, mask, bottom, boundary_line, device, args.debug)
device, center_vr, center_vcr, center_vbr = pcv.scale_features(
obj, mask, center_v, boundary_line, device, args.debug)
## Need to convert the points into a list of tuples format to match the scaled points
points = points.reshape(len(points), 2)
points = points.tolist()
temp_out = []
for p in points:
p = tuple(p)
temp_out.append(p)
points = temp_out
left = left.reshape(20, 2)
left = left.tolist()
temp_out = []
for l in left:
l = tuple(l)
temp_out.append(l)
left = temp_out
right = right.reshape(20, 2)
right = right.tolist()
temp_out = []
for r in right:
r = tuple(r)
temp_out.append(r)
right = temp_out
center_h = center_h.reshape(20, 2)
center_h = center_h.tolist()
temp_out = []
for ch in center_h:
ch = tuple(ch)
temp_out.append(ch)
center_h = temp_out
## Need to convert the points into a list of tuples format to match the scaled points
top = top.reshape(20, 2)
top = top.tolist()
temp_out = []
for t in top:
t = tuple(t)
temp_out.append(t)
top = temp_out
bottom = bottom.reshape(20, 2)
bottom = bottom.tolist()
temp_out = []
for b in bottom:
b = tuple(b)
temp_out.append(b)
bottom = temp_out
center_v = center_v.reshape(20, 2)
center_v = center_v.tolist()
temp_out = []
for cvr in center_v:
cvr = tuple(cvr)
temp_out.append(cvr)
center_v = temp_out
#Store Landmark Data
landmark_header = ('HEADER_LANDMARK', 'tip_points', 'tip_points_r',
'centroid_r', 'baseline_r', 'tip_number', 'vert_ave_c',
'hori_ave_c', 'euc_ave_c', 'ang_ave_c', 'vert_ave_b',
'hori_ave_b', 'euc_ave_b', 'ang_ave_b', 'left_lmk',
'right_lmk', 'center_h_lmk', 'left_lmk_r',
'right_lmk_r', 'center_h_lmk_r', 'top_lmk',
'bottom_lmk', 'center_v_lmk', 'top_lmk_r',
'bottom_lmk_r', 'center_v_lmk_r')
landmark_data = ('LANDMARK_DATA', points, points_r, centroid_r, bline_r,
tips, vert_ave_c, hori_ave_c, euc_ave_c, ang_ave_c,
vert_ave_b, hori_ave_b, euc_ave_b, ang_ave_b, left, right,
center_h, left_r, right_r, center_hr, top, bottom,
center_v, top_r, bottom_r, center_vr)
############## VIS Analysis ################
outfile = False
#if args.writeimg==True:
#outfile=args.outdir+"/"+filename
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img, args.image, obj, mask, device, args.debug, outfile)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(
img, args.image, obj, mask, 290, device, args.debug, 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, args.image, mask, 256, device, args.debug, None, 'v', 'img', 300,
outfile)
# Output shape and color data
result = open(args.result, "a")
result.write('\t'.join(map(str, shape_header)))
result.write("\n")
result.write('\t'.join(map(str, shape_data)))
result.write("\n")
for row in shape_img:
result.write('\t'.join(map(str, row)))
result.write("\n")
result.write('\t'.join(map(str, color_header)))
result.write("\n")
result.write('\t'.join(map(str, color_data)))
result.write("\n")
result.write('\t'.join(map(str, boundary_header)))
result.write("\n")
result.write('\t'.join(map(str, boundary_data)))
result.write("\n")
result.write('\t'.join(map(str, boundary_img1)))
result.write("\n")
for row in color_img:
result.write('\t'.join(map(str, row)))
result.write("\n")
result.write('\t'.join(map(str, landmark_header)))
result.write("\n")
result.write('\t'.join(map(str, landmark_data)))
result.write("\n")
result.close()
def process_sv_images(vis_img, nir_img, debug=None):
"""Process side-view images.
Inputs:
vis_img = An RGB image.
nir_img = An NIR grayscale image.
debug = None, print, or plot. Print = save to file, Plot = print to screen.
:param vis_img: str
:param nir_img: str
:param debug: str
:return:
"""
# Read VIS image
img, path, filename = pcv.readimage(vis_img)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 36, 255, 'light', device, debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, debug)
# Fill small objects
# device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 137, 255, 'light', device, debug)
device, b_cnt = pcv.binary_threshold(b, 137, 255, 'light', device, debug)
# Fill small objects
# device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_mblur, b_cnt, device, debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, 'white', device, debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, debug)
device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, debug)
# Threshold the green-magenta and blue images
device, maskeda_thresh = pcv.binary_threshold(masked_a, 127, 255, 'dark', device, debug)
device, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, 'light', device, debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, debug)
device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, debug)
# Fill small noise
device, ab_fill1 = pcv.fill(ab, ab_cnt, 200, device, debug)
# Dilate to join small objects with larger ones
device, ab_cnt1 = pcv.dilate(ab_fill1, 3, 2, device, debug)
device, ab_cnt2 = pcv.dilate(ab_fill1, 3, 2, device, debug)
# Fill dilated image mask
device, ab_cnt3 = pcv.fill(ab_cnt2, ab_cnt1, 150, device, debug)
device, masked2 = pcv.apply_mask(masked, ab_cnt3, 'white', device, debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked2_a = pcv.rgb2gray_lab(masked2, 'a', device, debug)
device, masked2_b = pcv.rgb2gray_lab(masked2, 'b', device, debug)
# Threshold the green-magenta and blue images
device, masked2a_thresh = pcv.binary_threshold(masked2_a, 127, 255, 'dark', device, debug)
device, masked2b_thresh = pcv.binary_threshold(masked2_b, 128, 255, 'light', device, debug)
device, masked2a_thresh_blur = pcv.median_blur(masked2a_thresh, 5, device, debug)
device, masked2b_thresh_blur = pcv.median_blur(masked2b_thresh, 13, device, debug)
device, ab_fill = pcv.logical_or(masked2a_thresh_blur, masked2b_thresh_blur, device, debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(masked2, ab_fill, device, debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(masked2, 'rectangle', device, None, 'default', debug, True, 700,
0, -600, -300)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img, 'partial', roi1, roi_hierarchy,
id_objects, obj_hierarchy, device,
debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, debug)
############## VIS Analysis ################
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(img, vis_img, obj, mask, device, debug)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(img, vis_img, obj, mask, 384, device,
debug)
# 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, vis_img, mask, 256, device, debug,
None, 'v', 'img', 300)
# Output shape and color data
print('\t'.join(map(str, shape_header)) + '\n')
print('\t'.join(map(str, shape_data)) + '\n')
for row in shape_img:
print('\t'.join(map(str, row)) + '\n')
print('\t'.join(map(str, color_header)) + '\n')
print('\t'.join(map(str, color_data)) + '\n')
print('\t'.join(map(str, boundary_header)) + '\n')
print('\t'.join(map(str, boundary_data)) + '\n')
print('\t'.join(map(str, boundary_img1)) + '\n')
for row in color_img:
print('\t'.join(map(str, row)) + '\n')
############################# Use VIS image mask for NIR image#########################
# Read NIR image
nir, path1, filename1 = pcv.readimage(nir_img)
nir2 = cv2.imread(nir_img, -1)
# Flip mask
device, f_mask = pcv.flip(mask, "vertical", device, debug)
# Reize mask
device, nmask = pcv.resize(f_mask, 0.1154905775, 0.1154905775, device, debug)
# position, and crop mask
device, newmask = pcv.crop_position_mask(nir, nmask, device, 30, 4, "top", "right", debug)
# Identify objects
device, nir_objects, nir_hierarchy = pcv.find_objects(nir, newmask, device, debug)
# Object combine kept objects
device, nir_combined, nir_combinedmask = pcv.object_composition(nir, nir_objects, nir_hierarchy, device, debug)
####################################### Analysis #############################################
device, nhist_header, nhist_data, nir_imgs = pcv.analyze_NIR_intensity(nir2, filename1, nir_combinedmask, 256,
device, False, debug)
device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(nir2, filename1, nir_combined, nir_combinedmask,
device, debug)
print('\t'.join(map(str, nhist_header)) + '\n')
print('\t'.join(map(str, nhist_data)) + '\n')
for row in nir_imgs:
print('\t'.join(map(str, row)) + '\n')
print('\t'.join(map(str, nshape_header)) + '\n')
print('\t'.join(map(str, nshape_data)) + '\n')
print('\t'.join(map(str, nir_shape)) + '\n')
def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
# roi = cv2.imread(args.roi)
# Pipeline step
device = 0
## Convert RGB to HSV and extract the Saturation channel
# device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug)
#
## Threshold the Saturation image
# device, s_thresh = pcv.binary_threshold(s, 90, 255, 'dark', device, args.debug)
#
## Median Filter
# device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug)
# device, s_cnt = pcv.median_blur(s_thresh, 5, device, args.debug)
#
## Fill small objects
##device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug)
#
## Convert RGB to LAB and extract the Blue channel
# device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug)
#
## Threshold the blue image
# device, b_thresh = pcv.binary_threshold(b, 135, 255, 'light', device, args.debug)
# device, b_cnt = pcv.binary_threshold(b, 135, 255, 'light', device, args.debug)
#
##Fill small objects
# device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, args.debug)
#
## Join the thresholded saturation and blue-yellow images
# device, bs = pcv.logical_or(s_mblur, b_cnt, device, args.debug)
#
## Apply Mask (for vis images, mask_color=white)
# device, masked = pcv.apply_mask(img, bs, 'white', device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked_a = pcv.rgb2gray_lab(img, "a", device, args.debug)
device, masked_b = pcv.rgb2gray_lab(img, "b", device, args.debug)
# Threshold the green-magenta and blue images
device, maskeda_thresh = pcv.binary_threshold(masked_a, 135, 255, "dark", device, args.debug)
device, maskedb_thresh = pcv.binary_threshold(masked_b, 140, 255, "light", device, args.debug)
#
# # Join the thresholded saturation and blue-yellow images (OR)
device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug)
device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug)
# Fill small objects
device, ab_fill = pcv.fill(ab, ab_cnt, 1000, device, args.debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(img, ab_fill, "white", device, args.debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(masked2, ab_fill, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(
masked2, "rectangle", device, None, "default", args.debug, True, 550, 0, -500, -300
)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img, "partial", roi1, roi_hierarchy, id_objects, obj_hierarchy, device, args.debug
)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, args.debug)
############### Analysis ################
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img, args.image, obj, mask, device, args.debug, args.outdir + "/" + filename
)
# Shape properties relative to user boundary line (optional)
# device, boundary_header,boundary_data, boundary_img1= pcv.analyze_bound(img, args.image,obj, mask, 1680, device,args.debug,args.outdir+'/'+filename)
# Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional)
device, color_header, color_data, norm_slice = pcv.analyze_color(
img, args.image, kept_mask, 256, device, args.debug, "all", "rgb", "v", "img", 300, args.outdir + "/" + filename
)
# Output shape and color data
pcv.print_results(args.image, shape_header, shape_data)
pcv.print_results(args.image, color_header, color_data)
def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
brass_mask = cv2.imread(args.roi)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, "s", device, args.debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 49, 255, "light", device, args.debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, args.debug)
# Fill small objects
device, s_fill = pcv.fill(s_mblur, s_cnt, 150, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, "b", device, args.debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 138, 255, "light", device, args.debug)
device, b_cnt = pcv.binary_threshold(b, 138, 255, "light", device, args.debug)
# Fill small objects
device, b_fill = pcv.fill(b_thresh, b_cnt, 150, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_fill, b_fill, device, args.debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, "white", device, args.debug)
# Mask pesky brass piece
device, brass_mask1 = pcv.rgb2gray_hsv(brass_mask, "v", device, args.debug)
device, brass_thresh = pcv.binary_threshold(brass_mask1, 0, 255, "light", device, args.debug)
device, brass_inv = pcv.invert(brass_thresh, device, args.debug)
device, brass_masked = pcv.apply_mask(masked, brass_inv, "white", device, args.debug)
# Further mask soil and car
device, masked_a = pcv.rgb2gray_lab(brass_masked, "a", device, args.debug)
device, soil_car1 = pcv.binary_threshold(masked_a, 128, 255, "dark", device, args.debug)
device, soil_car2 = pcv.binary_threshold(masked_a, 128, 255, "light", device, args.debug)
device, soil_car = pcv.logical_or(soil_car1, soil_car2, device, args.debug)
device, soil_masked = pcv.apply_mask(brass_masked, soil_car, "white", device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, soil_a = pcv.rgb2gray_lab(soil_masked, "a", device, args.debug)
device, soil_b = pcv.rgb2gray_lab(soil_masked, "b", device, args.debug)
# Threshold the green-magenta and blue images
device, soila_thresh = pcv.binary_threshold(soil_a, 124, 255, "dark", device, args.debug)
device, soilb_thresh = pcv.binary_threshold(soil_b, 148, 255, "light", device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, soil_ab = pcv.logical_or(soila_thresh, soilb_thresh, device, args.debug)
device, soil_ab_cnt = pcv.logical_or(soila_thresh, soilb_thresh, device, args.debug)
# Fill small objects
device, soil_cnt = pcv.fill(soil_ab, soil_ab_cnt, 250, device, args.debug)
# Median Filter
# device, soil_mblur = pcv.median_blur(soil_fill, 5, device, args.debug)
# device, soil_cnt = pcv.median_blur(soil_fill, 5, device, args.debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(soil_masked, soil_cnt, "white", device, args.debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(masked2, soil_cnt, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(
img, "rectangle", device, None, "default", args.debug, True, 600, 450, -600, -350
)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img, "partial", roi1, roi_hierarchy, id_objects, obj_hierarchy, device, args.debug
)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, args.debug)
############## VIS Analysis ################
outfile = False
if args.writeimg == True:
outfile = args.outdir + "/" + filename
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img, args.image, obj, mask, device, args.debug, 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, args.image, mask, 256, device, args.debug, None, "v", "img", 300, outfile
)
# Output shape and color data
result = open(args.result, "a")
result.write("\t".join(map(str, shape_header)))
result.write("\n")
result.write("\t".join(map(str, shape_data)))
result.write("\n")
for row in shape_img:
result.write("\t".join(map(str, row)))
result.write("\n")
result.write("\t".join(map(str, color_header)))
result.write("\n")
result.write("\t".join(map(str, color_data)))
result.write("\n")
for row in color_img:
result.write("\t".join(map(str, row)))
result.write("\n")
def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
brass_mask = cv2.imread(args.roi)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 49, 255, 'light', device, args.debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, args.debug)
# Fill small objects
device, s_fill = pcv.fill(s_mblur, s_cnt, 150, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug)
device, b_cnt = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug)
# Fill small objects
device, b_fill = pcv.fill(b_thresh, b_cnt, 100, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_fill, b_fill, device, args.debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs,'white', device, args.debug)
# Mask pesky brass piece
device, brass_mask1 = pcv.rgb2gray_hsv(brass_mask, 'v', device, args.debug)
device, brass_thresh = pcv.binary_threshold(brass_mask1, 0, 255, 'light', device, args.debug)
device, brass_inv=pcv.invert(brass_thresh, device, args.debug)
device, brass_masked = pcv.apply_mask(masked, brass_inv, 'white', device, args.debug)
# Further mask soil and car
device, masked_a = pcv.rgb2gray_lab(brass_masked, 'a', device, args.debug)
device, soil_car = pcv.binary_threshold(masked_a, 128, 255, 'dark', device, args.debug)
device, soil_masked = pcv.apply_mask(brass_masked, soil_car, 'white', device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, soil_a = pcv.rgb2gray_lab(soil_masked, 'a', device, args.debug)
device, soil_b = pcv.rgb2gray_lab(soil_masked, 'b', device, args.debug)
# Threshold the green-magenta and blue images
device, soila_thresh = pcv.binary_threshold(soil_a, 118, 255, 'dark', device, args.debug)
device, soilb_thresh = pcv.binary_threshold(soil_b, 155, 255, 'light', device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, soil_ab = pcv.logical_or(soila_thresh, soilb_thresh, device, args.debug)
device, soil_ab_cnt = pcv.logical_or(soila_thresh, soilb_thresh, device, args.debug)
# Fill small objects
device, soil_fill = pcv.fill(soil_ab, soil_ab_cnt, 75, device, args.debug)
# Median Filter
device, soil_mblur = pcv.median_blur(soil_fill, 5, device, args.debug)
device, soil_cnt = pcv.median_blur(soil_fill, 5, device, args.debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(soil_masked, soil_cnt, 'white', device, args.debug)
# Identify objects
device, id_objects,obj_hierarchy = pcv.find_objects(masked2, soil_cnt, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy= pcv.define_roi(img,'circle', device, None, 'default', args.debug,True, 0,0,-50,-50)
# Decide which objects to keep
device,roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img,'partial',roi1,roi_hierarchy,id_objects,obj_hierarchy,device, args.debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, args.debug)
############## Analysis ################
# Find shape properties, output shape image (optional)
device, shape_header,shape_data,shape_img = pcv.analyze_object(img, args.image, obj, mask, device,args.debug,args.outdir+'/'+filename)
# 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, args.image, kept_mask, 256, device, args.debug,'all','v','img',300,args.outdir+'/'+filename)
# Output shape and color data
pcv.print_results(args.image, shape_header, shape_data)
pcv.print_results(args.image, color_header, color_data)
def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
brass_mask = cv2.imread(args.roi)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, "s", device, args.debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 49, 255, "light", device, args.debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, args.debug)
# Fill small objects
device, s_fill = pcv.fill(s_mblur, s_cnt, 150, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, "b", device, args.debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 138, 255, "light", device, args.debug)
device, b_cnt = pcv.binary_threshold(b, 138, 255, "light", device, args.debug)
# Fill small objects
device, b_fill = pcv.fill(b_thresh, b_cnt, 150, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_fill, b_fill, device, args.debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, "white", device, args.debug)
# Mask pesky brass piece
device, brass_mask1 = pcv.rgb2gray_hsv(brass_mask, "v", device, args.debug)
device, brass_thresh = pcv.binary_threshold(brass_mask1, 0, 255, "light", device, args.debug)
device, brass_inv = pcv.invert(brass_thresh, device, args.debug)
device, brass_masked = pcv.apply_mask(masked, brass_inv, "white", device, args.debug)
# Further mask soil and car
device, masked_a = pcv.rgb2gray_lab(brass_masked, "a", device, args.debug)
device, soil_car = pcv.binary_threshold(masked_a, 128, 255, "dark", device, args.debug)
device, soil_masked = pcv.apply_mask(brass_masked, soil_car, "white", device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, soil_a = pcv.rgb2gray_lab(soil_masked, "a", device, args.debug)
device, soil_b = pcv.rgb2gray_lab(soil_masked, "b", device, args.debug)
# Threshold the green-magenta and blue images
device, soila_thresh = pcv.binary_threshold(soil_a, 118, 255, "dark", device, args.debug)
device, soilb_thresh = pcv.binary_threshold(soil_b, 150, 255, "light", device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, soil_ab = pcv.logical_or(soila_thresh, soilb_thresh, device, args.debug)
device, soil_ab_cnt = pcv.logical_or(soila_thresh, soilb_thresh, device, args.debug)
# Fill small objects
device, soil_cnt = pcv.fill(soil_ab, soil_ab_cnt, 75, device, args.debug)
# Median Filter
# device, soil_mblur = pcv.median_blur(soil_fill, 5, device, args.debug)
# device, soil_cnt = pcv.median_blur(soil_fill, 5, device, args.debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(soil_masked, soil_cnt, "white", device, args.debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(masked2, soil_cnt, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(
img, "circle", device, None, "default", args.debug, True, 0, 0, -200, -200
)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img, "partial", roi1, roi_hierarchy, id_objects, obj_hierarchy, device, args.debug
)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, args.debug)
############## VIS Analysis ################
outfile = False
if args.writeimg == True:
outfile = args.outdir + "/" + filename
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img, args.image, obj, mask, device, args.debug, 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, args.image, mask, 256, device, args.debug, None, "v", "img", 300, outfile
)
# Output shape and color data
result = open(args.result, "a")
result.write("\t".join(map(str, shape_header)))
result.write("\n")
result.write("\t".join(map(str, shape_data)))
result.write("\n")
for row in shape_img:
result.write("\t".join(map(str, row)))
result.write("\n")
result.write("\t".join(map(str, color_header)))
result.write("\n")
result.write("\t".join(map(str, color_data)))
result.write("\n")
for row in color_img:
result.write("\t".join(map(str, row)))
result.write("\n")
result.close()
############################# Use VIS image mask for NIR image#########################
# Find matching NIR image
device, nirpath = pcv.get_nir(path, filename, device, args.debug)
nir, path1, filename1 = pcv.readimage(nirpath)
nir2 = cv2.imread(nirpath, -1)
# Flip mask
device, f_mask = pcv.flip(mask, "horizontal", device, args.debug)
# Reize mask
device, nmask = pcv.resize(f_mask, 0.1304, 0.1304, device, args.debug)
# position, and crop mask
device, newmask = pcv.crop_position_mask(nir, nmask, device, 9, 12, "top", "left", args.debug)
# Identify objects
device, nir_objects, nir_hierarchy = pcv.find_objects(nir, newmask, device, args.debug)
# Object combine kept objects
device, nir_combined, nir_combinedmask = pcv.object_composition(nir, nir_objects, nir_hierarchy, device, args.debug)
####################################### Analysis #############################################
outfile1 = False
if args.writeimg == True:
outfile1 = args.outdir + "/" + filename1
device, nhist_header, nhist_data, nir_imgs = pcv.analyze_NIR_intensity(
nir2, filename1, nir_combinedmask, 256, device, False, args.debug, outfile1
)
device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(
nir2, filename1, nir_combined, nir_combinedmask, device, args.debug, outfile1
)
coresult = open(args.coresult, "a")
coresult.write("\t".join(map(str, nhist_header)))
coresult.write("\n")
coresult.write("\t".join(map(str, nhist_data)))
coresult.write("\n")
for row in nir_imgs:
coresult.write("\t".join(map(str, row)))
coresult.write("\n")
coresult.write("\t".join(map(str, nshape_header)))
coresult.write("\n")
coresult.write("\t".join(map(str, nshape_data)))
coresult.write("\n")
coresult.write("\t".join(map(str, nir_shape)))
coresult.write("\n")
coresult.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=380, 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()
def process_sv_images_core(vis_id, vis_img, nir_id, nir_rgb, nir_cv2, traits, debug=None):
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(vis_img, 's', device, debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 36, 255, 'light', device, debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, debug)
# Fill small objects
# device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(vis_img, 'b', device, debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 137, 255, 'light', device, debug)
device, b_cnt = pcv.binary_threshold(b, 137, 255, 'light', device, debug)
# Fill small objects
# device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_mblur, b_cnt, device, debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(vis_img, bs, 'white', device, debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, debug)
device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, debug)
# Threshold the green-magenta and blue images
device, maskeda_thresh = pcv.binary_threshold(masked_a, 127, 255, 'dark', device, debug)
device, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, 'light', device, debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, debug)
device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, debug)
# Fill small noise
device, ab_fill1 = pcv.fill(ab, ab_cnt, 200, device, debug)
# Dilate to join small objects with larger ones
device, ab_cnt1 = pcv.dilate(ab_fill1, 3, 2, device, debug)
device, ab_cnt2 = pcv.dilate(ab_fill1, 3, 2, device, debug)
# Fill dilated image mask
device, ab_cnt3 = pcv.fill(ab_cnt2, ab_cnt1, 150, device, debug)
device, masked2 = pcv.apply_mask(masked, ab_cnt3, 'white', device, debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked2_a = pcv.rgb2gray_lab(masked2, 'a', device, debug)
device, masked2_b = pcv.rgb2gray_lab(masked2, 'b', device, debug)
# Threshold the green-magenta and blue images
device, masked2a_thresh = pcv.binary_threshold(masked2_a, 127, 255, 'dark', device, debug)
device, masked2b_thresh = pcv.binary_threshold(masked2_b, 128, 255, 'light', device, debug)
device, masked2a_thresh_blur = pcv.median_blur(masked2a_thresh, 5, device, debug)
device, masked2b_thresh_blur = pcv.median_blur(masked2b_thresh, 13, device, debug)
device, ab_fill = pcv.logical_or(masked2a_thresh_blur, masked2b_thresh_blur, device, debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(masked2, ab_fill, device, debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(masked2, 'rectangle', device, None, 'default', debug, True, 700,
0, -600, -300)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(vis_img, 'partial', roi1, roi_hierarchy,
id_objects, obj_hierarchy, device,
debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(vis_img, roi_objects, hierarchy3, device, debug)
############## VIS Analysis ################
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(vis_img, vis_id, obj, mask, device, debug)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(vis_img, vis_id, obj, mask, 384, device,
debug)
# Determine color properties: Histograms, Color Slices and
# Pseudocolored Images, output color analyzed images (optional)
device, color_header, color_data, color_img = pcv.analyze_color(vis_img, vis_id, mask, 256, device, debug,
None, 'v', 'img', 300)
# Output shape and color data
vis_traits = {}
for i in range(1, len(shape_header)):
vis_traits[shape_header[i]] = shape_data[i]
for i in range(1, len(boundary_header)):
vis_traits[boundary_header[i]] = boundary_data[i]
for i in range(2, len(color_header)):
vis_traits[color_header[i]] = serialize_color_data(color_data[i])
############################# Use VIS image mask for NIR image#########################
# Flip mask
device, f_mask = pcv.flip(mask, "vertical", device, debug)
# Reize mask
device, nmask = pcv.resize(f_mask, 0.1154905775, 0.1154905775, device, debug)
# position, and crop mask
device, newmask = pcv.crop_position_mask(nir_rgb, nmask, device, 30, 4, "top", "right", debug)
# Identify objects
device, nir_objects, nir_hierarchy = pcv.find_objects(nir_rgb, newmask, device, debug)
# Object combine kept objects
device, nir_combined, nir_combinedmask = pcv.object_composition(nir_rgb, nir_objects, nir_hierarchy, device, debug)
####################################### Analysis #############################################
device, nhist_header, nhist_data, nir_imgs = pcv.analyze_NIR_intensity(nir_cv2, nir_id, nir_combinedmask, 256,
device, False, debug)
device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(nir_cv2, nir_id, nir_combined, nir_combinedmask,
device, debug)
nir_traits = {}
for i in range(1, len(nshape_header)):
nir_traits[nshape_header[i]] = nshape_data[i]
for i in range(2, len(nhist_header)):
nir_traits[nhist_header[i]] = serialize_color_data(nhist_data[i])
# Add data to traits table
traits['sv_area'].append(vis_traits['area'])
traits['hull_area'].append(vis_traits['hull-area'])
traits['solidity'].append(vis_traits['solidity'])
traits['height'].append(vis_traits['height_above_bound'])
traits['perimeter'].append(vis_traits['perimeter'])
return [vis_traits, nir_traits]
def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 36, 255, 'light', device,
args.debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 0, device, args.debug)
device, s_cnt = pcv.median_blur(s_thresh, 0, device, args.debug)
# Fill small objects
#device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 137, 255, 'light', device,
args.debug)
device, b_cnt = pcv.binary_threshold(b, 137, 255, 'light', device,
args.debug)
# Fill small objects
#device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_mblur, b_cnt, device, args.debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, 'white', device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, args.debug)
device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, args.debug)
# Threshold the green-magenta and blue images
device, maskeda_thresh = pcv.binary_threshold(masked_a, 127, 255, 'dark',
device, args.debug)
device, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, 'light',
device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device,
args.debug)
device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device,
args.debug)
# Fill small noise
device, ab_fill1 = pcv.fill(ab, ab_cnt, 2, device, args.debug)
# Dilate to join small objects with larger ones
device, ab_cnt1 = pcv.dilate(ab_fill1, 3, 2, device, args.debug)
device, ab_cnt2 = pcv.dilate(ab_fill1, 3, 2, device, args.debug)
# Fill dilated image mask
device, ab_cnt3 = pcv.fill(ab_cnt2, ab_cnt1, 150, device, args.debug)
device, masked2 = pcv.apply_mask(masked, ab_cnt3, 'white', device,
args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked2_a = pcv.rgb2gray_lab(masked2, 'a', device, args.debug)
device, masked2_b = pcv.rgb2gray_lab(masked2, 'b', device, args.debug)
# Threshold the green-magenta and blue images
device, masked2a_thresh = pcv.binary_threshold(masked2_a, 127, 255, 'dark',
device, args.debug)
device, masked2b_thresh = pcv.binary_threshold(masked2_b, 128, 255,
'light', device, args.debug)
device, ab_fill = pcv.logical_or(masked2a_thresh, masked2b_thresh, device,
args.debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(
masked2, ab_fill, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(masked2, 'rectangle', device,
None, 'default', args.debug,
True, 550, 0, -600, -925)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img, 'partial', roi1, roi_hierarchy, id_objects, obj_hierarchy, device,
args.debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3,
device, args.debug)
############## VIS Analysis ################
outfile = False
if args.writeimg == True:
outfile = args.outdir + "/" + filename
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img, args.image, obj, mask, device, args.debug, outfile)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(
img, args.image, obj, mask, 900, device, args.debug, 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, args.image, mask, 256, device, args.debug, None, 'v', 'img', 300,
outfile)
# Output shape and color data
result = open(args.result, "a")
result.write('\t'.join(map(str, shape_header)))
result.write("\n")
result.write('\t'.join(map(str, shape_data)))
result.write("\n")
for row in shape_img:
result.write('\t'.join(map(str, row)))
result.write("\n")
result.write('\t'.join(map(str, color_header)))
result.write("\n")
result.write('\t'.join(map(str, color_data)))
result.write("\n")
result.write('\t'.join(map(str, boundary_header)))
result.write("\n")
result.write('\t'.join(map(str, boundary_data)))
result.write("\n")
result.write('\t'.join(map(str, boundary_img1)))
result.write("\n")
for row in color_img:
result.write('\t'.join(map(str, row)))
result.write("\n")
def test_plantcv_analyze_color():
img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR))
mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1)
device, color_header, color_data, analysis_images = pcv.analyze_color(img, "img", mask, 256, 0, None, None)
assert np.sum(color_data[3]) != 0
def main():
# Get options
args = options()
path_mask = '/home/mfeldman/tester/mask/mask_brass_tv_z3000_L2.png'
# Pipeline step
device = 0
# Read image
img, path, filename = pcv.readimage(args.image)
brass_mask = cv2.imread(path_mask)
# Mask pesky brass piece
device, brass_mask1 = pcv.rgb2gray_hsv(brass_mask, 'v', device, args.debug)
device, brass_thresh = pcv.binary_threshold(brass_mask1, 0, 255, 'light',
device, args.debug)
device, brass_inv = pcv.invert(brass_thresh, device, args.debug)
device, masked_image = pcv.apply_mask(img, brass_inv, 'white', device,
args.debug)
# Looks like we can detect very bright soil particles with the h channel
device, h = pcv.rgb2gray_hsv(masked_image, 'h', device, args.debug)
h_soil = cv2.inRange(h, 100, 255)
# Make an image mask to cover these points
h_soil_inv = cv2.bitwise_not(h_soil)
# We can remove the outer ring of the pot using the s channel
device, s = pcv.rgb2gray_hsv(masked_image, 's', device, args.debug)
s_ring = cv2.inRange(s, 0, 40)
s_ring_inv = cv2.bitwise_not(s_ring)
# We can do a pretty good job of identifying the plant from the a channel
device, a = pcv.rgb2gray_lab(masked_image, 'a', device, args.debug)
a_thresh = cv2.inRange(a, 100, 117)
# Lets blur the result a bit to get rid of unwanted noise
blur = cv2.medianBlur(a_thresh, 5)
# Get cart to remove from b channel
device, b = pcv.rgb2gray_lab(masked_image, 'b', device, args.debug)
b_cart = cv2.inRange(b, 0, 110)
b_cart_inv = cv2.bitwise_not(b_cart)
# Now lets set of a series of filters to remove unwanted background
filter1 = cv2.bitwise_and(blur, b_cart_inv)
filter2 = cv2.bitwise_and(filter1, h_soil_inv)
plant_shape = cv2.bitwise_and(filter2, s_ring_inv)
# Now remove all remaining small points using erosion with a 3 x 3 kernel
kernel = np.ones((3, 3), np.uint8)
erosion = cv2.erode(plant_shape, kernel, iterations=1)
# Now dilate to fill in small holes
kernel = np.ones((3, 3), np.uint8)
dilation = cv2.dilate(erosion, kernel, iterations=1)
# Apply mask to the background image
device, masked = pcv.apply_mask(img, plant_shape, 'white', device,
args.debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(
img, erosion, device, args.debug)
# Get ROI contours
device, roi, roi_hierarchy = pcv.define_roi(masked_image,
'circle',
device,
None,
'default',
args.debug,
True,
x_adj=0,
y_adj=0,
w_adj=0,
h_adj=-1200)
# ROI
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
masked_image, 'partial', roi, roi_hierarchy, id_objects, obj_hierarchy,
device, args.debug)
# Get object contour and masked object
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3,
device, args.debug)
############## Landmarks ################
device, points = pcv.acute_vertex(obj, 20, 10, 40, img, device, args.debug)
boundary_line = 'NA'
# Use acute fxn to estimate tips
device, points_r, centroid_r, bline_r = pcv.scale_features(
obj, mask, points, boundary_line, device, args.debug)
# Get number of points
tips = len(points_r)
# Use turgor_proxy fxn to get distances
device, vert_ave_c, hori_ave_c, euc_ave_c, ang_ave_c, vert_ave_b, hori_ave_b, euc_ave_b, ang_ave_b = pcv.turgor_proxy(
points_r, centroid_r, bline_r, device, args.debug)
# Get pseudomarkers along the y-axis
device, left, right, center_h = pcv.y_axis_pseudolandmarks(
obj, mask, img, device, args.debug)
# Re-scale the points
device, left_r, left_cr, left_br = pcv.scale_features(
obj, mask, left, boundary_line, device, args.debug)
device, right_r, right_cr, right_br = pcv.scale_features(
obj, mask, right, boundary_line, device, args.debug)
device, center_hr, center_hcr, center_hbr = pcv.scale_features(
obj, mask, center_h, boundary_line, device, args.debug)
# Get pseudomarkers along the x-axis
device, top, bottom, center_v = pcv.x_axis_pseudolandmarks(
obj, mask, img, device, args.debug)
# Re-scale the points
device, top_r, top_cr, top_br = pcv.scale_features(obj, mask, top,
boundary_line, device,
args.debug)
device, bottom_r, bottom_cr, bottom_br = pcv.scale_features(
obj, mask, bottom, boundary_line, device, args.debug)
device, center_vr, center_vcr, center_vbr = pcv.scale_features(
obj, mask, center_v, boundary_line, device, args.debug)
## Need to convert the points into a list of tuples format to match the scaled points
points = points.reshape(len(points), 2)
points = points.tolist()
temp_out = []
for p in points:
p = tuple(p)
temp_out.append(p)
points = temp_out
left = left.reshape(20, 2)
left = left.tolist()
temp_out = []
for l in left:
l = tuple(l)
temp_out.append(l)
left = temp_out
right = right.reshape(20, 2)
right = right.tolist()
temp_out = []
for r in right:
r = tuple(r)
temp_out.append(r)
right = temp_out
center_h = center_h.reshape(20, 2)
center_h = center_h.tolist()
temp_out = []
for ch in center_h:
ch = tuple(ch)
temp_out.append(ch)
center_h = temp_out
## Need to convert the points into a list of tuples format to match the scaled points
top = top.reshape(20, 2)
top = top.tolist()
temp_out = []
for t in top:
t = tuple(t)
temp_out.append(t)
top = temp_out
bottom = bottom.reshape(20, 2)
bottom = bottom.tolist()
temp_out = []
for b in bottom:
b = tuple(b)
temp_out.append(b)
bottom = temp_out
center_v = center_v.reshape(20, 2)
center_v = center_v.tolist()
temp_out = []
for cvr in center_v:
cvr = tuple(cvr)
temp_out.append(cvr)
center_v = temp_out
#Store Landmark Data
landmark_header = ('HEADER_LANDMARK', 'tip_points', 'tip_points_r',
'centroid_r', 'baseline_r', 'tip_number', 'vert_ave_c',
'hori_ave_c', 'euc_ave_c', 'ang_ave_c', 'vert_ave_b',
'hori_ave_b', 'euc_ave_b', 'ang_ave_b', 'left_lmk',
'right_lmk', 'center_h_lmk', 'left_lmk_r',
'right_lmk_r', 'center_h_lmk_r', 'top_lmk',
'bottom_lmk', 'center_v_lmk', 'top_lmk_r',
'bottom_lmk_r', 'center_v_lmk_r')
landmark_data = ('LANDMARK_DATA', points, points_r, centroid_r, bline_r,
tips, vert_ave_c, hori_ave_c, euc_ave_c, ang_ave_c,
vert_ave_b, hori_ave_b, euc_ave_b, ang_ave_b, left, right,
center_h, left_r, right_r, center_hr, top, bottom,
center_v, top_r, bottom_r, center_vr)
############## VIS Analysis ################
outfile = False
#if args.writeimg==True:
#outfile=args.outdir+"/"+filename
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img, args.image, obj, mask, device, args.debug, outfile)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(
img, args.image, obj, mask, 330, device, args.debug, 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, args.image, mask, 256, device, args.debug, None, 'v', 'img', 300,
outfile)
# Output shape and color data
result = open(args.result, "a")
result.write('\t'.join(map(str, shape_header)))
result.write("\n")
result.write('\t'.join(map(str, shape_data)))
result.write("\n")
for row in shape_img:
result.write('\t'.join(map(str, row)))
result.write("\n")
result.write('\t'.join(map(str, color_header)))
result.write("\n")
result.write('\t'.join(map(str, color_data)))
result.write("\n")
result.write('\t'.join(map(str, boundary_header)))
result.write("\n")
result.write('\t'.join(map(str, boundary_data)))
result.write("\n")
result.write('\t'.join(map(str, boundary_img1)))
result.write("\n")
for row in color_img:
result.write('\t'.join(map(str, row)))
result.write("\n")
result.write('\t'.join(map(str, landmark_header)))
result.write("\n")
result.write('\t'.join(map(str, landmark_data)))
result.write("\n")
result.close()
def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
brass_mask = cv2.imread(args.roi)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 49, 255, 'light', device, args.debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, args.debug)
# Fill small objects
device, s_fill = pcv.fill(s_mblur, s_cnt, 150, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug)
device, b_cnt = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug)
# Fill small objects
device, b_fill = pcv.fill(b_thresh, b_cnt, 150, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_fill, b_fill, device, args.debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs,'white', device, args.debug)
# Mask pesky brass piece
device, brass_mask1 = pcv.rgb2gray_hsv(brass_mask, 'v', device, args.debug)
device, brass_thresh = pcv.binary_threshold(brass_mask1, 0, 255, 'light', device, args.debug)
device, brass_inv=pcv.invert(brass_thresh, device, args.debug)
device, brass_masked = pcv.apply_mask(masked, brass_inv, 'white', device, args.debug)
# Further mask soil and car
device, masked_a = pcv.rgb2gray_lab(brass_masked, 'a', device, args.debug)
device, soil_car1 = pcv.binary_threshold(masked_a, 128, 255, 'dark', device, args.debug)
device, soil_car2 = pcv.binary_threshold(masked_a, 128, 255, 'light', device, args.debug)
device, soil_car=pcv.logical_or(soil_car1, soil_car2,device, args.debug)
device, soil_masked = pcv.apply_mask(brass_masked, soil_car, 'white', device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, soil_a = pcv.rgb2gray_lab(soil_masked, 'a', device, args.debug)
device, soil_b = pcv.rgb2gray_lab(soil_masked, 'b', device, args.debug)
# Threshold the green-magenta and blue images
device, soila_thresh = pcv.binary_threshold(soil_a, 124, 255, 'dark', device, args.debug)
device, soilb_thresh = pcv.binary_threshold(soil_b, 148, 255, 'light', device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, soil_ab = pcv.logical_or(soila_thresh, soilb_thresh, device, args.debug)
device, soil_ab_cnt = pcv.logical_or(soila_thresh, soilb_thresh, device, args.debug)
# Fill small objects
device, soil_cnt = pcv.fill(soil_ab, soil_ab_cnt, 250, device, args.debug)
# Median Filter
#device, soil_mblur = pcv.median_blur(soil_fill, 5, device, args.debug)
#device, soil_cnt = pcv.median_blur(soil_fill, 5, device, args.debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(soil_masked, soil_cnt, 'white', device, args.debug)
# Identify objects
device, id_objects,obj_hierarchy = pcv.find_objects(masked2, soil_cnt, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy= pcv.define_roi(img,'rectangle', device, None, 'default', args.debug,True, 600,450,-600,-350)
# Decide which objects to keep
device,roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img,'partial',roi1,roi_hierarchy,id_objects,obj_hierarchy,device, args.debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, args.debug)
############## VIS Analysis ################
outfile=False
if args.writeimg==True:
outfile=args.outdir+"/"+filename
# Find shape properties, output shape image (optional)
device, shape_header,shape_data,shape_img = pcv.analyze_object(img, args.image, obj, mask, device,args.debug,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, args.image, mask, 256, device, args.debug,None,'v','img',300,outfile)
# Output shape and color data
result=open(args.result,"a")
result.write('\t'.join(map(str,shape_header)))
result.write("\n")
result.write('\t'.join(map(str,shape_data)))
result.write("\n")
for row in shape_img:
result.write('\t'.join(map(str,row)))
result.write("\n")
result.write('\t'.join(map(str,color_header)))
result.write("\n")
result.write('\t'.join(map(str,color_data)))
result.write("\n")
for row in color_img:
result.write('\t'.join(map(str,row)))
result.write("\n")
############################# Use VIS image mask for NIR image#########################
# Find matching NIR image
device, nirpath=pcv.get_nir(path,filename,device,args.debug)
nir, path1, filename1=pcv.readimage(nirpath)
nir2=cv2.imread(nirpath,-1)
# Flip mask
device, f_mask= pcv.flip(mask,"horizontal",device,args.debug)
# Reize mask
device, nmask = pcv.resize(f_mask, 0.11532,0.11532, device, args.debug)
# position, and crop mask
device,newmask=pcv.crop_position_mask(nir,nmask,device,22,9,"top","left",args.debug)
# Identify objects
device, nir_objects,nir_hierarchy = pcv.find_objects(nir, newmask, device, args.debug)
# Object combine kept objects
device, nir_combined, nir_combinedmask = pcv.object_composition(nir, nir_objects, nir_hierarchy, device, args.debug)
####################################### Analysis #############################################
outfile1=False
if args.writeimg==True:
outfile1=args.outdir+"/"+filename1
device,nhist_header, nhist_data,nir_imgs= pcv.analyze_NIR_intensity(nir2, filename1, nir_combinedmask, 256, device,False, args.debug, outfile1)
device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(nir2, filename1, nir_combined, nir_combinedmask, device, args.debug, outfile1)
coresult=open(args.coresult,"a")
coresult.write('\t'.join(map(str,nhist_header)))
coresult.write("\n")
coresult.write('\t'.join(map(str,nhist_data)))
coresult.write("\n")
for row in nir_imgs:
coresult.write('\t'.join(map(str,row)))
coresult.write("\n")
coresult.write('\t'.join(map(str,nshape_header)))
coresult.write("\n")
coresult.write('\t'.join(map(str,nshape_data)))
coresult.write("\n")
coresult.write('\t'.join(map(str,nir_shape)))
coresult.write("\n")
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 Green-Magenta channel
device, green_channel = pcv.rgb2gray_lab(img=img,
channel="a",
device=device,
debug=args.debug)
# Threshold the Green-Magenta image to isolate damaged tissues
device, green_thresh = pcv.binary_threshold(img=green_channel,
threshold=137,
maxValue=255,
object_type="light",
device=device,
debug=args.debug)
# Extract core plant region from the image to preserve delicate plant features during filtering
device += 1
plant_region = green_thresh[250:2000, 250:2250]
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=green_thresh,
ksize=(7, 7),
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[250:2000, 250:2250] = plant_region
if args.debug is not None:
pcv.print_image(filename=str(device) + "_replace_plant_region.png",
img=blur_thresholded)
# Define an ROI for the brass stopper
device, stopper_roi, stopper_hierarchy = pcv.define_roi(
img=img,
shape="rectangle",
device=device,
roi=None,
roi_input="default",
debug=args.debug,
adjust=True,
x_adj=1480,
y_adj=850,
w_adj=-870,
h_adj=-1075)
# Identify all remaining contours in the binary image
device, contours, hierarchy = pcv.find_objects(
img=img,
mask=np.copy(blur_thresholded),
device=device,
debug=args.debug)
# Remove stopper contours
device, remove_stopper_mask = remove_countors_roi(mask=blur_thresholded,
contours=contours,
hierarchy=hierarchy,
roi=stopper_roi,
device=device,
debug=args.debug)
# Threshold image
device, green_inv_thresh = pcv.binary_threshold(img=green_channel,
threshold=120,
maxValue=255,
object_type="dark",
device=device,
debug=args.debug)
# Merge the plant and damaged plant masks
device, green_merged = pcv.logical_or(img1=green_inv_thresh,
img2=remove_stopper_mask,
device=device,
debug=args.debug)
# Extract core plant region from the image to preserve delicate plant features during filtering
device += 1
plant_region = green_merged[250:2000, 250:2250]
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=green_merged,
ksize=(7, 7),
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
blur_thresholded[250:2000, 250:2250] = plant_region
if args.debug is not None:
pcv.print_image(filename=str(device) + "_replace_plant_region.png",
img=blur_thresholded)
# Use a median blur to breakup the horizontal and vertical lines caused by shadows from the track edges
device, med_blur = pcv.median_blur(img=blur_thresholded,
ksize=7,
device=device,
debug=args.debug)
# Fill in small contours
device, green_fill_50 = pcv.fill(img=np.copy(med_blur),
mask=np.copy(med_blur),
size=100,
device=device,
debug=args.debug)
# Identify remaining objects
device, contours, contour_hierarchy = pcv.find_objects(
img=img, mask=np.copy(green_fill_50), device=device, debug=args.debug)
# Define an ROI for the brass stopper
device, stopper_roi, stopper_hierarchy = pcv.define_roi(
img=img,
shape="rectangle",
device=device,
roi=None,
roi_input="default",
debug=args.debug,
adjust=True,
x_adj=1480,
y_adj=850,
w_adj=-870,
h_adj=-1075)
device, remove_stopper_mask = remove_countors_roi(
mask=green_fill_50,
contours=contours,
hierarchy=contour_hierarchy,
roi=stopper_roi,
device=device,
debug=args.debug)
# Define an ROI for a screw hole
device, screw_roi, screw_hierarchy = pcv.define_roi(img=img,
shape="rectangle",
device=device,
roi=None,
roi_input="default",
debug=args.debug,
adjust=True,
x_adj=2000,
y_adj=945,
w_adj=-220,
h_adj=-880)
device, remove_screw_mask = remove_countors_roi(
mask=remove_stopper_mask,
contours=contours,
hierarchy=contour_hierarchy,
roi=screw_roi,
device=device,
debug=args.debug)
# Define an ROI for a screw hole
device, screw_roi, screw_hierarchy = pcv.define_roi(img=img,
shape="rectangle",
device=device,
roi=None,
roi_input="default",
debug=args.debug,
adjust=True,
x_adj=1660,
y_adj=990,
w_adj=-600,
h_adj=-1000)
device, remove_screw_mask = remove_countors_roi(
mask=remove_screw_mask,
contours=contours,
hierarchy=contour_hierarchy,
roi=screw_roi,
device=device,
debug=args.debug)
# Identify objects
device, contours, contour_hierarchy = pcv.find_objects(
img=img, mask=remove_screw_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=200,
w_adj=-520,
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)
# 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")
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=2,
y=0,
v_pos="bottom",
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)
# 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()
def process_sv_images(session, url, vis_id, nir_id, traits, debug=None):
"""Process side-view images from Clowder.
Inputs:
session = requests session object
url = Clowder URL
vis_id = The Clowder ID of an RGB image
nir_img = The Clowder ID of an NIR grayscale image
traits = traits table (dictionary)
debug = None, print, or plot. Print = save to file, Plot = print to screen
:param session: requests session object
:param url: str
:param vis_id: str
:param nir_id: str
:param traits: dict
:param debug: str
:return traits: dict
"""
# Read VIS image from Clowder
vis_r = session.get(posixpath.join(url, "api/files", vis_id), stream=True)
img_array = np.asarray(bytearray(vis_r.content), dtype="uint8")
img = cv2.imdecode(img_array, -1)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 36, 255, 'light', device, debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, debug)
# Fill small objects
# device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 137, 255, 'light', device, debug)
device, b_cnt = pcv.binary_threshold(b, 137, 255, 'light', device, debug)
# Fill small objects
# device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_mblur, b_cnt, device, debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, 'white', device, debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, debug)
device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, debug)
# Threshold the green-magenta and blue images
device, maskeda_thresh = pcv.binary_threshold(masked_a, 127, 255, 'dark', device, debug)
device, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, 'light', device, debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, debug)
device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, debug)
# Fill small noise
device, ab_fill1 = pcv.fill(ab, ab_cnt, 200, device, debug)
# Dilate to join small objects with larger ones
device, ab_cnt1 = pcv.dilate(ab_fill1, 3, 2, device, debug)
device, ab_cnt2 = pcv.dilate(ab_fill1, 3, 2, device, debug)
# Fill dilated image mask
device, ab_cnt3 = pcv.fill(ab_cnt2, ab_cnt1, 150, device, debug)
device, masked2 = pcv.apply_mask(masked, ab_cnt3, 'white', device, debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked2_a = pcv.rgb2gray_lab(masked2, 'a', device, debug)
device, masked2_b = pcv.rgb2gray_lab(masked2, 'b', device, debug)
# Threshold the green-magenta and blue images
device, masked2a_thresh = pcv.binary_threshold(masked2_a, 127, 255, 'dark', device, debug)
device, masked2b_thresh = pcv.binary_threshold(masked2_b, 128, 255, 'light', device, debug)
device, masked2a_thresh_blur = pcv.median_blur(masked2a_thresh, 5, device, debug)
device, masked2b_thresh_blur = pcv.median_blur(masked2b_thresh, 13, device, debug)
device, ab_fill = pcv.logical_or(masked2a_thresh_blur, masked2b_thresh_blur, device, debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(masked2, ab_fill, device, debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(masked2, 'rectangle', device, None, 'default', debug, True, 700,
0, -600, -300)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img, 'partial', roi1, roi_hierarchy,
id_objects, obj_hierarchy, device,
debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, debug)
############## VIS Analysis ################
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(img, vis_id, obj, mask, device, debug)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(img, vis_id, obj, mask, 384, device,
debug)
# 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, vis_id, mask, 256, device, debug,
None, 'v', 'img', 300)
# Output shape and color data
vis_traits = {}
for i in range(1, len(shape_header)):
vis_traits[shape_header[i]] = shape_data[i]
for i in range(1, len(boundary_header)):
vis_traits[boundary_header[i]] = boundary_data[i]
for i in range(2, len(color_header)):
vis_traits[color_header[i]] = serialize_color_data(color_data[i])
#print(vis_traits)
add_plantcv_metadata(session, url, vis_id, vis_traits)
############################# Use VIS image mask for NIR image#########################
# Read NIR image from Clowder
nir_r = session.get(posixpath.join(url, "api/files", nir_id), stream=True)
nir_array = np.asarray(bytearray(nir_r.content), dtype="uint8")
nir = cv2.imdecode(nir_array, -1)
nir_rgb = cv2.cvtColor(nir, cv2.COLOR_GRAY2BGR)
# Flip mask
device, f_mask = pcv.flip(mask, "vertical", device, debug)
# Reize mask
device, nmask = pcv.resize(f_mask, 0.1154905775, 0.1154905775, device, debug)
# position, and crop mask
device, newmask = pcv.crop_position_mask(nir_rgb, nmask, device, 30, 4, "top", "right", debug)
# Identify objects
device, nir_objects, nir_hierarchy = pcv.find_objects(nir_rgb, newmask, device, debug)
# Object combine kept objects
device, nir_combined, nir_combinedmask = pcv.object_composition(nir_rgb, nir_objects, nir_hierarchy, device, debug)
####################################### Analysis #############################################
device, nhist_header, nhist_data, nir_imgs = pcv.analyze_NIR_intensity(nir, nir_id, nir_combinedmask, 256,
device, False, debug)
device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(nir, nir_id, nir_combined, nir_combinedmask,
device, debug)
nir_traits = {}
for i in range(1, len(nshape_header)):
nir_traits[nshape_header[i]] = nshape_data[i]
for i in range(2, len(nhist_header)):
nir_traits[nhist_header[i]] = serialize_color_data(nhist_data[i])
#print(nir_traits)
add_plantcv_metadata(session, url, nir_id, nir_traits)
# Add data to traits table
traits['sv_area'].append(vis_traits['area'])
traits['hull_area'].append(vis_traits['hull-area'])
traits['solidity'].append(vis_traits['solidity'])
traits['height'].append(vis_traits['height_above_bound'])
traits['perimeter'].append(vis_traits['perimeter'])
return traits
def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
#roi = cv2.imread(args.roi)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 36, 255, 'light', device,
args.debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 0, device, args.debug)
device, s_cnt = pcv.median_blur(s_thresh, 0, device, args.debug)
# Fill small objects
#device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 137, 255, 'light', device,
args.debug)
device, b_cnt = pcv.binary_threshold(b, 137, 255, 'light', device,
args.debug)
# Fill small objects
#device, b_fill = pcv.fill(b_thresh, b_cnt, 150, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_mblur, b_cnt, device, args.debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, 'white', device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, args.debug)
device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, args.debug)
# Threshold the green-magenta and blue images
device, maskeda_thresh = pcv.binary_threshold(masked_a, 127, 255, 'dark',
device, args.debug)
device, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, 'light',
device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device,
args.debug)
device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device,
args.debug)
# Fill small objects
device, ab_fill = pcv.fill(ab, ab_cnt, 2, device, args.debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(masked, ab_fill, 'white', device,
args.debug)
# Select area with black bars and find overlapping plant material
device, roi1, roi_hierarchy1 = pcv.define_roi(masked2, 'rectangle', device,
None, 'default', args.debug,
True, 0, 0, -1900, 0)
device, id_objects1, obj_hierarchy1 = pcv.find_objects(
masked2, ab_fill, device, args.debug)
device, roi_objects1, hierarchy1, kept_mask1, obj_area1 = pcv.roi_objects(
masked2, 'cutto', roi1, roi_hierarchy1, id_objects1, obj_hierarchy1,
device, args.debug)
device, masked3 = pcv.apply_mask(masked2, kept_mask1, 'white', device,
args.debug)
device, masked_a1 = pcv.rgb2gray_lab(masked3, 'a', device, args.debug)
device, masked_b1 = pcv.rgb2gray_lab(masked3, 'b', device, args.debug)
device, maskeda_thresh1 = pcv.binary_threshold(masked_a1, 122, 255, 'dark',
device, args.debug)
device, maskedb_thresh1 = pcv.binary_threshold(masked_b1, 170, 255,
'light', device, args.debug)
device, ab1 = pcv.logical_and(maskeda_thresh1, maskedb_thresh1, device,
args.debug)
device, ab_cnt1 = pcv.logical_or(maskeda_thresh1, maskedb_thresh1, device,
args.debug)
device, ab_fill1 = pcv.fill(ab1, ab_cnt1, 1500, device, args.debug)
device, roi2, roi_hierarchy2 = pcv.define_roi(masked2, 'rectangle', device,
None, 'default', args.debug,
True, 1900, 0, 0, 0)
device, id_objects2, obj_hierarchy2 = pcv.find_objects(
masked2, ab_fill, device, args.debug)
device, roi_objects2, hierarchy2, kept_mask2, obj_area2 = pcv.roi_objects(
masked2, 'cutto', roi2, roi_hierarchy2, id_objects2, obj_hierarchy2,
device, args.debug)
device, masked4 = pcv.apply_mask(masked2, kept_mask2, 'white', device,
args.debug)
device, masked_a2 = pcv.rgb2gray_lab(masked4, 'a', device, args.debug)
device, masked_b2 = pcv.rgb2gray_lab(masked4, 'b', device, args.debug)
device, maskeda_thresh2 = pcv.binary_threshold(masked_a2, 122, 255, 'dark',
device, args.debug)
device, maskedb_thresh2 = pcv.binary_threshold(masked_b2, 170, 255,
'light', device, args.debug)
device, ab2 = pcv.logical_and(maskeda_thresh2, maskedb_thresh2, device,
args.debug)
device, ab_cnt2 = pcv.logical_or(maskeda_thresh2, maskedb_thresh2, device,
args.debug)
device, ab_fill2 = pcv.fill(ab2, ab_cnt2, 1500, device, args.debug)
device, ab_cnt3 = pcv.logical_or(ab_fill1, ab_fill2, device, args.debug)
device, masked3 = pcv.apply_mask(masked2, ab_cnt3, 'white', device,
args.debug)
# Identify objects
device, id_objects3, obj_hierarchy3 = pcv.find_objects(
masked2, ab_fill, device, args.debug)
# Define ROI
device, roi3, roi_hierarchy3 = pcv.define_roi(masked2, 'rectangle', device,
None, 'default', args.debug,
True, 500, 0, -450, -750)
# Decide which objects to keep and combine with objects overlapping with black bars
device, roi_objects3, hierarchy3, kept_mask3, obj_area1 = pcv.roi_objects(
img, 'cutto', roi3, roi_hierarchy3, id_objects3, obj_hierarchy3,
device, args.debug)
device, kept_mask4_1 = pcv.logical_or(ab_cnt3, kept_mask3, device,
args.debug)
device, kept_cnt = pcv.logical_or(ab_cnt3, kept_mask3, device, args.debug)
device, kept_mask4 = pcv.fill(kept_mask4_1, kept_cnt, 12, device,
args.debug)
device, masked5 = pcv.apply_mask(masked2, kept_mask4, 'white', device,
args.debug)
device, id_objects4, obj_hierarchy4 = pcv.find_objects(
masked5, kept_mask4, device, args.debug)
device, roi4, roi_hierarchy4 = pcv.define_roi(masked2, 'rectangle', device,
None, 'default', args.debug,
False, 0, 0, 0, 0)
device, roi_objects4, hierarchy4, kept_mask4, obj_area = pcv.roi_objects(
img, 'partial', roi4, roi_hierarchy4, id_objects4, obj_hierarchy4,
device, args.debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects4, hierarchy4,
device, args.debug)
############## Analysis ################
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img, args.image, obj, mask, device, args.debug,
args.outdir + '/' + filename)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(
img, args.image, obj, mask, 920, device, args.debug,
args.outdir + '/' + filename)
# Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional)
device, color_header, color_data, norm_slice = pcv.analyze_color(
img, args.image, kept_mask4, 256, device, args.debug, 'all', 'rgb',
'v', 'img', 300, args.outdir + '/' + filename)
# Output shape and color data
pcv.print_results(args.image, shape_header, shape_data)
pcv.print_results(args.image, color_header, color_data)
pcv.print_results(args.image, boundary_header, boundary_data)
def process_tv_images(session, url, vis_id, nir_id, traits, debug=False):
"""Process top-view images.
Inputs:
session = requests session object
url = Clowder URL
vis_id = The Clowder ID of an RGB image
nir_img = The Clowder ID of an NIR grayscale image
traits = traits table (dictionary)
debug = None, print, or plot. Print = save to file, Plot = print to screen.
:param session: requests session object
:param url: str
:param vis_id: str
:param nir_id: str
:param traits: dict
:param debug: str
:return traits: dict
"""
# Read VIS image from Clowder
vis_r = session.get(posixpath.join(url, "api/files", vis_id), stream=True)
img_array = np.asarray(bytearray(vis_r.content), dtype="uint8")
img = cv2.imdecode(img_array, -1)
# Read the VIS top-view image mask for zoom = 1 from Clowder
mask_r = session.get(posixpath.join(url, "api/files/57451b28e4b0efbe2dc3d4d5"), stream=True)
mask_array = np.asarray(bytearray(mask_r.content), dtype="uint8")
brass_mask = cv2.imdecode(mask_array, -1)
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 75, 255, 'light', device, debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, debug)
# Fill small objects
device, s_fill = pcv.fill(s_mblur, s_cnt, 150, device, debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 138, 255, 'light', device, debug)
device, b_cnt = pcv.binary_threshold(b, 138, 255, 'light', device, debug)
# Fill small objects
device, b_fill = pcv.fill(b_thresh, b_cnt, 100, device, debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_fill, b_fill, device, debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, 'white', device, debug)
# Mask pesky brass piece
device, brass_mask1 = pcv.rgb2gray_hsv(brass_mask, 'v', device, debug)
device, brass_thresh = pcv.binary_threshold(brass_mask1, 0, 255, 'light', device, debug)
device, brass_inv = pcv.invert(brass_thresh, device, debug)
device, brass_masked = pcv.apply_mask(masked, brass_inv, 'white', device, debug)
# Further mask soil and car
device, masked_a = pcv.rgb2gray_lab(brass_masked, 'a', device, debug)
device, soil_car1 = pcv.binary_threshold(masked_a, 128, 255, 'dark', device, debug)
device, soil_car2 = pcv.binary_threshold(masked_a, 128, 255, 'light', device, debug)
device, soil_car = pcv.logical_or(soil_car1, soil_car2, device, debug)
device, soil_masked = pcv.apply_mask(brass_masked, soil_car, 'white', device, debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, soil_a = pcv.rgb2gray_lab(soil_masked, 'a', device, debug)
device, soil_b = pcv.rgb2gray_lab(soil_masked, 'b', device, debug)
# Threshold the green-magenta and blue images
device, soila_thresh = pcv.binary_threshold(soil_a, 124, 255, 'dark', device, debug)
device, soilb_thresh = pcv.binary_threshold(soil_b, 148, 255, 'light', device, debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, soil_ab = pcv.logical_or(soila_thresh, soilb_thresh, device, debug)
device, soil_ab_cnt = pcv.logical_or(soila_thresh, soilb_thresh, device, debug)
# Fill small objects
device, soil_cnt = pcv.fill(soil_ab, soil_ab_cnt, 300, device, debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(soil_masked, soil_cnt, 'white', device, debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(masked2, soil_cnt, device, debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(img, 'rectangle', device, None, 'default', debug, True, 600, 450, -600,
-350)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img, 'partial', roi1, roi_hierarchy,
id_objects, obj_hierarchy, device, debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, debug)
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(img, vis_id, obj, mask, device, debug)
# Determine color properties
device, color_header, color_data, color_img = pcv.analyze_color(img, vis_id, mask, 256, device, debug, None,
'v', 'img', 300)
# Output shape and color data
vis_traits = {}
for i in range(1, len(shape_header)):
vis_traits[shape_header[i]] = shape_data[i]
for i in range(2, len(color_header)):
vis_traits[color_header[i]] = serialize_color_data(color_data[i])
#print(vis_traits)
add_plantcv_metadata(session, url, vis_id, vis_traits)
############################# Use VIS image mask for NIR image#########################
# Read NIR image from Clowder
nir_r = session.get(posixpath.join(url, "api/files", nir_id), stream=True)
nir_array = np.asarray(bytearray(nir_r.content), dtype="uint8")
nir = cv2.imdecode(nir_array, -1)
nir_rgb = cv2.cvtColor(nir, cv2.COLOR_GRAY2BGR)
# Flip mask
device, f_mask = pcv.flip(mask, "horizontal", device, debug)
# Reize mask
device, nmask = pcv.resize(f_mask, 0.116148, 0.116148, device, debug)
# position, and crop mask
device, newmask = pcv.crop_position_mask(nir_rgb, nmask, device, 15, 5, "top", "right", debug)
# Identify objects
device, nir_objects, nir_hierarchy = pcv.find_objects(nir_rgb, newmask, device, debug)
# Object combine kept objects
device, nir_combined, nir_combinedmask = pcv.object_composition(nir_rgb, nir_objects, nir_hierarchy, device, debug)
####################################### Analysis #############################################
device, nhist_header, nhist_data, nir_imgs = pcv.analyze_NIR_intensity(nir, nir_id, nir_combinedmask, 256,
device, False, debug)
device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(nir, nir_id, nir_combined, nir_combinedmask,
device, debug)
nir_traits = {}
for i in range(1, len(nshape_header)):
nir_traits[nshape_header[i]] = nshape_data[i]
for i in range(2, len(nhist_header)):
nir_traits[nhist_header[i]] = serialize_color_data(nhist_data[i])
#print(nir_traits)
add_plantcv_metadata(session, url, nir_id, nir_traits)
# Add data to traits table
traits['tv_area'] = vis_traits['area']
return traits
def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
brass_mask = cv2.imread(args.roi)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 49, 255, 'light', device,
args.debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, args.debug)
# Fill small objects
device, s_fill = pcv.fill(s_mblur, s_cnt, 150, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 138, 255, 'light', device,
args.debug)
device, b_cnt = pcv.binary_threshold(b, 138, 255, 'light', device,
args.debug)
# Fill small objects
device, b_fill = pcv.fill(b_thresh, b_cnt, 50, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_fill, b_fill, device, args.debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, 'white', device, args.debug)
# Mask pesky brass piece
device, brass_mask1 = pcv.rgb2gray_hsv(brass_mask, 'v', device, args.debug)
device, brass_thresh = pcv.binary_threshold(brass_mask1, 0, 255, 'light',
device, args.debug)
device, brass_inv = pcv.invert(brass_thresh, device, args.debug)
device, brass_masked = pcv.apply_mask(masked, brass_inv, 'white', device,
args.debug)
# Further mask soil and car
device, masked_a = pcv.rgb2gray_lab(brass_masked, 'a', device, args.debug)
device, soil_car = pcv.binary_threshold(masked_a, 128, 255, 'dark', device,
args.debug)
device, soil_masked = pcv.apply_mask(brass_masked, soil_car, 'white',
device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, soil_a = pcv.rgb2gray_lab(soil_masked, 'a', device, args.debug)
device, soil_b = pcv.rgb2gray_lab(soil_masked, 'b', device, args.debug)
# Threshold the green-magenta and blue images
device, soila_thresh = pcv.binary_threshold(soil_a, 118, 255, 'dark',
device, args.debug)
device, soilb_thresh = pcv.binary_threshold(soil_b, 155, 255, 'light',
device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, soil_ab = pcv.logical_or(soila_thresh, soilb_thresh, device,
args.debug)
device, soil_ab_cnt = pcv.logical_or(soila_thresh, soilb_thresh, device,
args.debug)
# Fill small objects
device, soil_fill = pcv.fill(soil_ab, soil_ab_cnt, 50, device, args.debug)
# Median Filter
device, soil_mblur = pcv.median_blur(soil_fill, 5, device, args.debug)
device, soil_cnt = pcv.median_blur(soil_fill, 5, device, args.debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(soil_masked, soil_cnt, 'white', device,
args.debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(
masked2, soil_cnt, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(img, 'circle', device, None,
'default', args.debug, True,
0, 0, -50, -50)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img, 'partial', roi1, roi_hierarchy, id_objects, obj_hierarchy, device,
args.debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3,
device, args.debug)
############## Analysis ################
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img, args.image, obj, mask, device, args.debug,
args.outdir + '/' + filename)
# 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, args.image, kept_mask, 256, device, args.debug, 'all', 'v', 'img',
300, args.outdir + '/' + filename)
# Output shape and color data
pcv.print_results(args.image, shape_header, shape_data)
pcv.print_results(args.image, color_header, color_data)
def process_tv_images_core(vis_id, vis_img, nir_id, nir_rgb, nir_cv2, brass_mask, traits, debug=None):
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(vis_img, 's', device, debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 75, 255, 'light', device, debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, debug)
# Fill small objects
device, s_fill = pcv.fill(s_mblur, s_cnt, 150, device, debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(vis_img, 'b', device, debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 138, 255, 'light', device, debug)
device, b_cnt = pcv.binary_threshold(b, 138, 255, 'light', device, debug)
# Fill small objects
device, b_fill = pcv.fill(b_thresh, b_cnt, 100, device, debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_fill, b_fill, device, debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(vis_img, bs, 'white', device, debug)
# Mask pesky brass piece
device, brass_mask1 = pcv.rgb2gray_hsv(brass_mask, 'v', device, debug)
device, brass_thresh = pcv.binary_threshold(brass_mask1, 0, 255, 'light', device, debug)
device, brass_inv = pcv.invert(brass_thresh, device, debug)
device, brass_masked = pcv.apply_mask(masked, brass_inv, 'white', device, debug)
# Further mask soil and car
device, masked_a = pcv.rgb2gray_lab(brass_masked, 'a', device, debug)
device, soil_car1 = pcv.binary_threshold(masked_a, 128, 255, 'dark', device, debug)
device, soil_car2 = pcv.binary_threshold(masked_a, 128, 255, 'light', device, debug)
device, soil_car = pcv.logical_or(soil_car1, soil_car2, device, debug)
device, soil_masked = pcv.apply_mask(brass_masked, soil_car, 'white', device, debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, soil_a = pcv.rgb2gray_lab(soil_masked, 'a', device, debug)
device, soil_b = pcv.rgb2gray_lab(soil_masked, 'b', device, debug)
# Threshold the green-magenta and blue images
device, soila_thresh = pcv.binary_threshold(soil_a, 124, 255, 'dark', device, debug)
device, soilb_thresh = pcv.binary_threshold(soil_b, 148, 255, 'light', device, debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, soil_ab = pcv.logical_or(soila_thresh, soilb_thresh, device, debug)
device, soil_ab_cnt = pcv.logical_or(soila_thresh, soilb_thresh, device, debug)
# Fill small objects
device, soil_cnt = pcv.fill(soil_ab, soil_ab_cnt, 300, device, debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(soil_masked, soil_cnt, 'white', device, debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(masked2, soil_cnt, device, debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(vis_img, 'rectangle', device, None, 'default', debug, True, 600, 450, -600,
-350)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(vis_img, 'partial', roi1, roi_hierarchy,
id_objects, obj_hierarchy, device, debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(vis_img, roi_objects, hierarchy3, device, debug)
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(vis_img, vis_id, obj, mask, device, debug)
# Determine color properties
device, color_header, color_data, color_img = pcv.analyze_color(vis_img, vis_id, mask, 256, device, debug, None,
'v', 'img', 300)
# Output shape and color data
vis_traits = {}
for i in range(1, len(shape_header)):
vis_traits[shape_header[i]] = shape_data[i]
for i in range(2, len(color_header)):
vis_traits[color_header[i]] = serialize_color_data(color_data[i])
############################# Use VIS image mask for NIR image#########################
# Flip mask
device, f_mask = pcv.flip(mask, "horizontal", device, debug)
# Reize mask
device, nmask = pcv.resize(f_mask, 0.116148, 0.116148, device, debug)
# position, and crop mask
device, newmask = pcv.crop_position_mask(nir_rgb, nmask, device, 15, 5, "top", "right", debug)
# Identify objects
device, nir_objects, nir_hierarchy = pcv.find_objects(nir_rgb, newmask, device, debug)
# Object combine kept objects
device, nir_combined, nir_combinedmask = pcv.object_composition(nir_rgb, nir_objects, nir_hierarchy, device, debug)
####################################### Analysis #############################################
device, nhist_header, nhist_data, nir_imgs = pcv.analyze_NIR_intensity(nir_cv2, nir_id, nir_combinedmask, 256,
device, False, debug)
device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(nir_cv2, nir_id, nir_combined, nir_combinedmask,
device, debug)
nir_traits = {}
for i in range(1, len(nshape_header)):
nir_traits[nshape_header[i]] = nshape_data[i]
for i in range(2, len(nhist_header)):
nir_traits[nhist_header[i]] = serialize_color_data(nhist_data[i])
# Add data to traits table
traits['tv_area'] = vis_traits['area']
return [vis_traits, nir_traits]
def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
# roi = cv2.imread(args.roi)
device = 0
device, mask = pcv.naive_bayes_classifier(img, "naive_bayes.pdf.txt",
device, args.debug)
mask1 = np.uint8(mask)
mask_copy = np.copy(mask1)
# Fill small objects
device, soil_fill = pcv.fill(mask1, mask_copy, 200, device, args.debug)
# Median Filter
device, soil_mblur = pcv.median_blur(soil_fill, 5, device, args.debug)
device, soil_cnt = pcv.median_blur(soil_fill, 5, device, args.debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(img, soil_cnt, 'white', device,
args.debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(
masked2, soil_cnt, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(img, 'rectangle', device,
None, 'default', args.debug,
True, 0, 0, 0, -900)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img, 'partial', roi1, roi_hierarchy, id_objects, obj_hierarchy, device,
args.debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3,
device, args.debug)
# ############# Analysis ################
# output mask
device, maskpath, mask_images = pcv.output_mask(device, img, mask,
filename, args.outdir,
True, args.debug)
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img, args.image, obj, mask, device, args.debug)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(
img, args.image, obj, mask, 830, device)
# 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, args.image, mask, 256, device, args.debug, None, 'v', 'img', 300)
result = open(args.result, "a")
result.write('\t'.join(map(str, shape_header)))
result.write("\n")
result.write('\t'.join(map(str, shape_data)))
result.write("\n")
for row in mask_images:
result.write('\t'.join(map(str, row)))
result.write("\n")
result.write('\t'.join(map(str, color_header)))
result.write("\n")
result.write('\t'.join(map(str, color_data)))
result.write("\n")
result.write('\t'.join(map(str, boundary_header)))
result.write("\n")
result.write('\t'.join(map(str, boundary_data)))
result.write("\n")
result.close()
def main(): # Get options args = options() # Read image img, path, filename = pcv.readimage(args.image) brass_mask = cv2.imread(args.roi) # Pipeline step device = 0 # Convert RGB to HSV and extract the Saturation channel device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug) # Threshold the Saturation image device, s_thresh = pcv.binary_threshold(s, 49, 255, 'light', device, args.debug) #Median Filter device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug) #Apply Mask (for vis images, mask_color=white) device, masked = pcv.apply_mask(img, s_mblur, 'white', device, args.debug) # Convert RGB to LAB and extract the Green-Magenta device, soil_a = pcv.rgb2gray_lab(masked, 'a', device, args.debug) # # Threshold the green-magenta device, soila_thresh = pcv.binary_threshold(soil_a, 133, 255, 'light', device, args.debug) device, soila_cnt = pcv.binary_threshold(soil_a, 133, 255, 'light', device, args.debug) # # Fill small objects device, soil_fill = pcv.fill(soila_thresh, soila_cnt, 200, device, args.debug) # # Median Filter device, soil_mblur = pcv.median_blur(soil_fill, 13, device, args.debug) device, soil_cnt = pcv.median_blur(soil_fill, 13, device, args.debug) # # Apply mask (for vis images, mask_color=white) device, masked2 = pcv.apply_mask(soil_mblur, soil_cnt, 'white', device, args.debug) # # Identify objects device, id_objects,obj_hierarchy = pcv.find_objects(masked2, soil_cnt, device, args.debug) # # Define ROI device, roi1, roi_hierarchy= pcv.define_roi(img,'rectangle', device, None, 'default', args.debug,True, 400,400,-400,-400) # # Decide which objects to keep device,roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img,'partial',roi1,roi_hierarchy,id_objects,obj_hierarchy,device, args.debug) # # Object combine kept objects device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, args.debug) # ############## Analysis ################ # Find shape properties, output shape image (optional) device, shape_header,shape_data,shape_img = pcv.analyze_object(img, args.image, obj, mask, device,args.debug,args.outdir+'/'+filename) # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional) device, color_header,color_data,norm_slice= pcv.analyze_color(img, args.image, mask, 256, device, args.debug,'all','rgb','v','img',300,args.outdir+'/'+filename) # Output shape and color data pcv.print_results(args.image, shape_header, shape_data) pcv.print_results(args.image, color_header, color_data)
def main(): # Get options args = options() # Read image img, path, filename = pcv.readimage(args.image) #roi = cv2.imread(args.roi) # Pipeline step device = 0 # Convert RGB to HSV and extract the Saturation channel device, s = pcv.rgb2gray_lab(img, 'l', device, args.debug) # Threshold the Saturation image device, s_thresh = pcv.binary_threshold(s, 100, 255, 'light', device, args.debug) # Median Filter device, s_mblur = pcv.median_blur(s_thresh, 0, device, args.debug) device, s_cnt = pcv.median_blur(s_thresh, 0, device, args.debug) # Fill small objects #device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug) ## Convert RGB to LAB and extract the Blue channel #device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug) ## Threshold the blue image #device, b_thresh = pcv.binary_threshold(b, 145, 255, 'light', device, args.debug) #device, b_cnt = pcv.binary_threshold(b, 145, 255, 'light', device, args.debug) # # Fill small objects #device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, args.debug) ## Join the thresholded saturation and blue-yellow images #device, bs = pcv.logical_and(s_mblur, b_cnt, device, args.debug) # Apply Mask (for vis images, mask_color=white) device, masked = pcv.apply_mask(img, s_cnt, 'white', device, args.debug) # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, args.debug) device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, args.debug) # Threshold the green-magenta and blue images device, maskeda_thresh = pcv.binary_threshold(masked_a, 127, 255, 'dark', device, args.debug) device, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, 'light', device, args.debug) # Join the thresholded saturation and blue-yellow images (OR) device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug) device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug) # Fill small objects device, ab_fill = pcv.fill(ab, ab_cnt, 20, device, args.debug) # Apply mask (for vis images, mask_color=white) device, masked2 = pcv.apply_mask(masked, ab_fill, 'white', device, args.debug) # Identify objects device, id_objects,obj_hierarchy = pcv.find_objects(masked2, ab_fill, device, args.debug) # Define ROI device, roi1, roi_hierarchy= pcv.define_roi(img,'rectangle', device, None, 'default', args.debug,True, 25, 25,-10,-25) # Decide which objects to keep device,roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img,'partial',roi1,roi_hierarchy,id_objects,obj_hierarchy,device, args.debug) # Object combine kept objects device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, args.debug) # ############### Analysis ################ # Find shape properties, output shape image (optional) device, shape_header,shape_data,shape_img = pcv.analyze_object(img, args.image, obj, mask, device,args.debug,args.outdir+'/'+filename) # Shape properties relative to user boundary line (optional) device, boundary_header,boundary_data, boundary_img1= pcv.analyze_bound(img, args.image,obj, mask, 25, device,args.debug,args.outdir+'/'+filename) # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional) device, color_header,color_data,norm_slice= pcv.analyze_color(img, args.image, kept_mask, 256, device, args.debug,'all','rgb','v','img',300,args.outdir+'/'+filename) # Output shape and color data pcv.print_results(args.image, shape_header, shape_data) pcv.print_results(args.image, color_header, color_data) pcv.print_results(args.image, boundary_header, boundary_data)
def main(): # Get options args = options() # Read image img, path, filename = pcv.readimage(args.image) #roi = cv2.imread(args.roi) # Pipeline step device = 0 # Convert RGB to HSV and extract the Saturation channel device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug) # Threshold the Saturation image device, s_thresh = pcv.binary_threshold(s, 36, 255, 'light', device, args.debug) # Median Filter device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug) device, s_cnt = pcv.median_blur(s_thresh, 5, device, args.debug) # Fill small objects device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug) # Convert RGB to LAB and extract the Blue channel device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug) # Threshold the blue image device, b_thresh = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug) device, b_cnt = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug) # Fill small objects device, b_fill = pcv.fill(b_thresh, b_cnt, 150, device, args.debug) # Join the thresholded saturation and blue-yellow images device, bs = pcv.logical_and(s_fill, b_fill, device, args.debug) # Apply Mask (for vis images, mask_color=white) device, masked = pcv.apply_mask(img, bs, 'white', device, args.debug) # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, args.debug) device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, args.debug) # Threshold the green-magenta and blue images device, maskeda_thresh = pcv.binary_threshold(masked_a, 122, 255, 'dark', device, args.debug) device, maskedb_thresh = pcv.binary_threshold(masked_b, 133, 255, 'light', device, args.debug) # Join the thresholded saturation and blue-yellow images (OR) device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug) device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug) # Fill small objects device, ab_fill = pcv.fill(ab, ab_cnt, 200, device, args.debug) # Apply mask (for vis images, mask_color=white) device, masked2 = pcv.apply_mask(masked, ab_fill, 'white', device, args.debug) # Select area with black bars and find overlapping plant material device, roi1, roi_hierarchy1= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 0, 0,-1900,0) device, id_objects1,obj_hierarchy1 = pcv.find_objects(masked2, ab_fill, device, args.debug) device,roi_objects1, hierarchy1, kept_mask1, obj_area1 = pcv.roi_objects(masked2,'cutto',roi1,roi_hierarchy1,id_objects1,obj_hierarchy1,device, args.debug) device, masked3 = pcv.apply_mask(masked2, kept_mask1, 'white', device, args.debug) device, masked_a1 = pcv.rgb2gray_lab(masked3, 'a', device, args.debug) device, masked_b1 = pcv.rgb2gray_lab(masked3, 'b', device, args.debug) device, maskeda_thresh1 = pcv.binary_threshold(masked_a1, 122, 255, 'dark', device, args.debug) device, maskedb_thresh1 = pcv.binary_threshold(masked_b1, 170, 255, 'light', device, args.debug) device, ab1 = pcv.logical_or(maskeda_thresh1, maskedb_thresh1, device, args.debug) device, ab_cnt1 = pcv.logical_or(maskeda_thresh1, maskedb_thresh1, device, args.debug) device, ab_fill1 = pcv.fill(ab1, ab_cnt1, 300, device, args.debug) device, roi2, roi_hierarchy2= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 1900, 0,0,0) device, id_objects2,obj_hierarchy2 = pcv.find_objects(masked2, ab_fill, device, args.debug) device,roi_objects2, hierarchy2, kept_mask2, obj_area2 = pcv.roi_objects(masked2,'cutto',roi2,roi_hierarchy2,id_objects2,obj_hierarchy2,device, args.debug) device, masked4 = pcv.apply_mask(masked2, kept_mask2, 'white', device, args.debug) device, masked_a2 = pcv.rgb2gray_lab(masked4, 'a', device, args.debug) device, masked_b2 = pcv.rgb2gray_lab(masked4, 'b', device, args.debug) device, maskeda_thresh2 = pcv.binary_threshold(masked_a2, 122, 255, 'dark', device, args.debug) device, maskedb_thresh2 = pcv.binary_threshold(masked_b2, 170, 255, 'light', device, args.debug) device, ab2 = pcv.logical_or(maskeda_thresh2, maskedb_thresh2, device, args.debug) device, ab_cnt2 = pcv.logical_or(maskeda_thresh2, maskedb_thresh2, device, args.debug) device, ab_fill2 = pcv.fill(ab2, ab_cnt2, 200, device, args.debug) device, ab_cnt3 = pcv.logical_or(ab_fill1, ab_fill2, device, args.debug) device, masked3 = pcv.apply_mask(masked2, ab_cnt3, 'white', device, args.debug) # Identify objects device, id_objects3,obj_hierarchy3 = pcv.find_objects(masked2, ab_fill, device, args.debug) # Define ROI device, roi3, roi_hierarchy3= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 500, 0,-450,-530) # Decide which objects to keep and combine with objects overlapping with black bars device,roi_objects3, hierarchy3, kept_mask3, obj_area1 = pcv.roi_objects(img,'cutto',roi3,roi_hierarchy3,id_objects3,obj_hierarchy3,device, args.debug) device, kept_mask4_1 = pcv.logical_or(ab_cnt3, kept_mask3, device, args.debug) device, kept_cnt = pcv.logical_or(ab_cnt3, kept_mask3, device, args.debug) device, kept_mask4 = pcv.fill(kept_mask4_1, kept_cnt, 200, device, args.debug) device, masked5 = pcv.apply_mask(masked2, kept_mask4, 'white', device, args.debug) device, id_objects4,obj_hierarchy4 = pcv.find_objects(masked5, kept_mask4, device, args.debug) device, roi4, roi_hierarchy4= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,False, 0, 0,0,0) device,roi_objects4, hierarchy4, kept_mask4, obj_area = pcv.roi_objects(img,'partial',roi4,roi_hierarchy4,id_objects4,obj_hierarchy4,device, args.debug) # Object combine kept objects device, obj, mask = pcv.object_composition(img, roi_objects4, hierarchy4, device, args.debug) ############## Analysis ################ # Find shape properties, output shape image (optional) device, shape_header,shape_data,shape_img = pcv.analyze_object(img, args.image, obj, mask, device,args.debug,args.outdir+'/'+filename) # Shape properties relative to user boundary line (optional) device, boundary_header,boundary_data, boundary_img1= pcv.analyze_bound(img, args.image,obj, mask, 950, device,args.debug,args.outdir+'/'+filename) # Tiller Tool Test device, tillering_header, tillering_data, tillering_img= pcv.tiller_count(img, args.image,obj, mask, 965, device,args.debug,args.outdir+'/'+filename) # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional) device, color_header,color_data,norm_slice= pcv.analyze_color(img, args.image, kept_mask4, 256, device, args.debug,'all','rgb','v',args.outdir+'/'+filename) # Output shape and color data pcv.print_results(args.image, shape_header, shape_data) pcv.print_results(args.image, color_header, color_data) pcv.print_results(args.image, boundary_header, boundary_data) pcv.print_results(args.image, tillering_header,tillering_data)
def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, "s", device, args.debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 36, 255, "light", device, args.debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 0, device, args.debug)
device, s_cnt = pcv.median_blur(s_thresh, 0, device, args.debug)
# Fill small objects
# device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, "b", device, args.debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 137, 255, "light", device, args.debug)
device, b_cnt = pcv.binary_threshold(b, 137, 255, "light", device, args.debug)
# Fill small objects
# device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_mblur, b_cnt, device, args.debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, "white", device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked_a = pcv.rgb2gray_lab(masked, "a", device, args.debug)
device, masked_b = pcv.rgb2gray_lab(masked, "b", device, args.debug)
# Threshold the green-magenta and blue images
device, maskeda_thresh = pcv.binary_threshold(masked_a, 127, 255, "dark", device, args.debug)
device, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, "light", device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug)
device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug)
# Fill small noise
device, ab_fill1 = pcv.fill(ab, ab_cnt, 2, device, args.debug)
# Dilate to join small objects with larger ones
device, ab_cnt1 = pcv.dilate(ab_fill1, 3, 2, device, args.debug)
device, ab_cnt2 = pcv.dilate(ab_fill1, 3, 2, device, args.debug)
# Fill dilated image mask
device, ab_cnt3 = pcv.fill(ab_cnt2, ab_cnt1, 150, device, args.debug)
device, masked2 = pcv.apply_mask(masked, ab_cnt3, "white", device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked2_a = pcv.rgb2gray_lab(masked2, "a", device, args.debug)
device, masked2_b = pcv.rgb2gray_lab(masked2, "b", device, args.debug)
# Threshold the green-magenta and blue images
device, masked2a_thresh = pcv.binary_threshold(masked2_a, 127, 255, "dark", device, args.debug)
device, masked2b_thresh = pcv.binary_threshold(masked2_b, 128, 255, "light", device, args.debug)
device, ab_fill = pcv.logical_or(masked2a_thresh, masked2b_thresh, device, args.debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(masked2, ab_fill, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(
masked2, "rectangle", device, None, "default", args.debug, True, 500, 0, -600, -885
)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img, "partial", roi1, roi_hierarchy, id_objects, obj_hierarchy, device, args.debug
)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, args.debug)
############## VIS Analysis ################
outfile = False
if args.writeimg == True:
outfile = args.outdir + "/" + filename
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img, args.image, obj, mask, device, args.debug, outfile
)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(
img, args.image, obj, mask, 845, device, args.debug, 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, args.image, mask, 256, device, args.debug, None, "v", "img", 300, outfile
)
# Output shape and color data
result = open(args.result, "a")
result.write("\t".join(map(str, shape_header)))
result.write("\n")
result.write("\t".join(map(str, shape_data)))
result.write("\n")
for row in shape_img:
result.write("\t".join(map(str, row)))
result.write("\n")
result.write("\t".join(map(str, color_header)))
result.write("\n")
result.write("\t".join(map(str, color_data)))
result.write("\n")
result.write("\t".join(map(str, boundary_header)))
result.write("\n")
result.write("\t".join(map(str, boundary_data)))
result.write("\n")
result.write("\t".join(map(str, boundary_img1)))
result.write("\n")
for row in color_img:
result.write("\t".join(map(str, row)))
result.write("\n")
result.close()
############################# Use VIS image mask for NIR image#########################
# Find matching NIR image
device, nirpath = pcv.get_nir(path, filename, device, args.debug)
nir, path1, filename1 = pcv.readimage(nirpath)
nir2 = cv2.imread(nirpath, -1)
# Flip mask
device, f_mask = pcv.flip(mask, "vertical", device, args.debug)
# Reize mask
device, nmask = pcv.resize(f_mask, 0.1304, 0.1304, device, args.debug)
# position, and crop mask
device, newmask = pcv.crop_position_mask(nir, nmask, device, 65, 0, "top", "left", args.debug)
# Identify objects
device, nir_objects, nir_hierarchy = pcv.find_objects(nir, newmask, device, args.debug)
# Object combine kept objects
device, nir_combined, nir_combinedmask = pcv.object_composition(nir, nir_objects, nir_hierarchy, device, args.debug)
####################################### Analysis #############################################
outfile1 = False
if args.writeimg == True:
outfile1 = args.outdir + "/" + filename1
device, nhist_header, nhist_data, nir_imgs = pcv.analyze_NIR_intensity(
nir2, filename1, nir_combinedmask, 256, device, False, args.debug, outfile1
)
device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(
nir2, filename1, nir_combined, nir_combinedmask, device, args.debug, outfile1
)
coresult = open(args.coresult, "a")
coresult.write("\t".join(map(str, nhist_header)))
coresult.write("\n")
coresult.write("\t".join(map(str, nhist_data)))
coresult.write("\n")
for row in nir_imgs:
coresult.write("\t".join(map(str, row)))
coresult.write("\n")
coresult.write("\t".join(map(str, nshape_header)))
coresult.write("\n")
coresult.write("\t".join(map(str, nshape_data)))
coresult.write("\n")
coresult.write("\t".join(map(str, nir_shape)))
coresult.write("\n")
coresult.close()
def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 36, 255, 'light', device, args.debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 0, device, args.debug)
device, s_cnt = pcv.median_blur(s_thresh, 0, device, args.debug)
# Fill small objects
#device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 137, 255, 'light', device, args.debug)
device, b_cnt = pcv.binary_threshold(b, 137, 255, 'light', device, args.debug)
# Fill small objects
#device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_mblur, b_cnt, device, args.debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, 'white', device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, args.debug)
device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, args.debug)
# Threshold the green-magenta and blue images
device, maskeda_thresh = pcv.binary_threshold(masked_a, 127, 255, 'dark', device, args.debug)
device, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, 'light', device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug)
device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug)
# Fill small noise
device, ab_fill1 = pcv.fill(ab, ab_cnt, 2, device, args.debug)
# Dilate to join small objects with larger ones
device, ab_cnt1=pcv.dilate(ab_fill1, 3, 2, device, args.debug)
device, ab_cnt2=pcv.dilate(ab_fill1, 3, 2, device, args.debug)
# Fill dilated image mask
device, ab_cnt3=pcv.fill(ab_cnt2,ab_cnt1,150,device,args.debug)
device, masked2 = pcv.apply_mask(masked, ab_cnt3, 'white', device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, masked2_a = pcv.rgb2gray_lab(masked2, 'a', device, args.debug)
device, masked2_b = pcv.rgb2gray_lab(masked2, 'b', device, args.debug)
# Threshold the green-magenta and blue images
device, masked2a_thresh = pcv.binary_threshold(masked2_a, 127, 255, 'dark', device, args.debug)
device, masked2b_thresh = pcv.binary_threshold(masked2_b, 128, 255, 'light', device, args.debug)
device, ab_fill = pcv.logical_or(masked2a_thresh, masked2b_thresh, device, args.debug)
# Identify objects
device, id_objects,obj_hierarchy = pcv.find_objects(masked2, ab_fill, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy= pcv.define_roi(masked2,'rectangle', device, None, 'default', args.debug,True, 525, 0,-490,-150)
# Decide which objects to keep
device,roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img,'partial',roi1,roi_hierarchy,id_objects,obj_hierarchy,device, args.debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, args.debug)
############## VIS Analysis ################
outfile=False
if args.writeimg==True:
outfile=args.outdir+"/"+filename
# Find shape properties, output shape image (optional)
device, shape_header,shape_data,shape_img = pcv.analyze_object(img, args.image, obj, mask, device,args.debug,outfile)
# Shape properties relative to user boundary line (optional)
device, boundary_header,boundary_data, boundary_img1= pcv.analyze_bound(img, args.image,obj, mask, 325, device,args.debug,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, args.image, mask, 256, device, args.debug,None,'v','img',300,outfile)
# Output shape and color data
result=open(args.result,"a")
result.write('\t'.join(map(str,shape_header)))
result.write("\n")
result.write('\t'.join(map(str,shape_data)))
result.write("\n")
for row in shape_img:
result.write('\t'.join(map(str,row)))
result.write("\n")
result.write('\t'.join(map(str,color_header)))
result.write("\n")
result.write('\t'.join(map(str,color_data)))
result.write("\n")
result.write('\t'.join(map(str,boundary_header)))
result.write("\n")
result.write('\t'.join(map(str,boundary_data)))
result.write("\n")
result.write('\t'.join(map(str,boundary_img1)))
result.write("\n")
for row in color_img:
result.write('\t'.join(map(str,row)))
result.write("\n")
result.close()
def process_tv_images(vis_img, nir_img, debug=False):
"""Process top-view images.
Inputs:
vis_img = An RGB image.
nir_img = An NIR grayscale image.
debug = None, print, or plot. Print = save to file, Plot = print to screen.
:param vis_img: str
:param nir_img: str
:param debug: str
:return:
"""
# Read image
img, path, filename = pcv.readimage(vis_img)
brass_mask = cv2.imread('mask_brass_tv_z1_L1.png')
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 75, 255, 'light', device, debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, debug)
# Fill small objects
device, s_fill = pcv.fill(s_mblur, s_cnt, 150, device, debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 138, 255, 'light', device, debug)
device, b_cnt = pcv.binary_threshold(b, 138, 255, 'light', device, debug)
# Fill small objects
device, b_fill = pcv.fill(b_thresh, b_cnt, 100, device, debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_fill, b_fill, device, debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, 'white', device, debug)
# Mask pesky brass piece
device, brass_mask1 = pcv.rgb2gray_hsv(brass_mask, 'v', device, debug)
device, brass_thresh = pcv.binary_threshold(brass_mask1, 0, 255, 'light', device, debug)
device, brass_inv = pcv.invert(brass_thresh, device, debug)
device, brass_masked = pcv.apply_mask(masked, brass_inv, 'white', device, debug)
# Further mask soil and car
device, masked_a = pcv.rgb2gray_lab(brass_masked, 'a', device, debug)
device, soil_car1 = pcv.binary_threshold(masked_a, 128, 255, 'dark', device, debug)
device, soil_car2 = pcv.binary_threshold(masked_a, 128, 255, 'light', device, debug)
device, soil_car = pcv.logical_or(soil_car1, soil_car2, device, debug)
device, soil_masked = pcv.apply_mask(brass_masked, soil_car, 'white', device, debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, soil_a = pcv.rgb2gray_lab(soil_masked, 'a', device, debug)
device, soil_b = pcv.rgb2gray_lab(soil_masked, 'b', device, debug)
# Threshold the green-magenta and blue images
device, soila_thresh = pcv.binary_threshold(soil_a, 124, 255, 'dark', device, debug)
device, soilb_thresh = pcv.binary_threshold(soil_b, 148, 255, 'light', device, debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, soil_ab = pcv.logical_or(soila_thresh, soilb_thresh, device, debug)
device, soil_ab_cnt = pcv.logical_or(soila_thresh, soilb_thresh, device, debug)
# Fill small objects
device, soil_cnt = pcv.fill(soil_ab, soil_ab_cnt, 300, device, debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(soil_masked, soil_cnt, 'white', device, debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(masked2, soil_cnt, device, debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(img, 'rectangle', device, None, 'default', debug, True, 600, 450, -600,
-350)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img, 'partial', roi1, roi_hierarchy,
id_objects, obj_hierarchy, device, debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, debug)
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(img, vis_img, obj, mask, device, debug)
# Determine color properties
device, color_header, color_data, color_img = pcv.analyze_color(img, vis_img, mask, 256, device, debug, None,
'v', 'img', 300)
print('\t'.join(map(str, shape_header)) + '\n')
print('\t'.join(map(str, shape_data)) + '\n')
for row in shape_img:
print('\t'.join(map(str, row)) + '\n')
print('\t'.join(map(str, color_header)) + '\n')
print('\t'.join(map(str, color_data)) + '\n')
for row in color_img:
print('\t'.join(map(str, row)) + '\n')
############################# Use VIS image mask for NIR image#########################
# Read NIR image
nir, path1, filename1 = pcv.readimage(nir_img)
nir2 = cv2.imread(nir_img, -1)
# Flip mask
device, f_mask = pcv.flip(mask, "horizontal", device, debug)
# Reize mask
device, nmask = pcv.resize(f_mask, 0.116148, 0.116148, device, debug)
# position, and crop mask
device, newmask = pcv.crop_position_mask(nir, nmask, device, 15, 5, "top", "right", debug)
# Identify objects
device, nir_objects, nir_hierarchy = pcv.find_objects(nir, newmask, device, debug)
# Object combine kept objects
device, nir_combined, nir_combinedmask = pcv.object_composition(nir, nir_objects, nir_hierarchy, device, debug)
####################################### Analysis #############################################
device, nhist_header, nhist_data, nir_imgs = pcv.analyze_NIR_intensity(nir2, filename1, nir_combinedmask, 256,
device, False, debug)
device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(nir2, filename1, nir_combined, nir_combinedmask,
device, debug)
print('\t'.join(map(str,nhist_header)) + '\n')
print('\t'.join(map(str,nhist_data)) + '\n')
for row in nir_imgs:
print('\t'.join(map(str,row)) + '\n')
print('\t'.join(map(str,nshape_header)) + '\n')
print('\t'.join(map(str,nshape_data)) + '\n')
print('\t'.join(map(str,nir_shape)) + '\n')
def main(): # Get options args = options() # Read image img = cv2.imread(args.image) roi = cv2.imread(args.roi) # Pipeline step device = 0 # Convert RGB to HSV and extract the Saturation channel device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug) # Threshold the Saturation image device, s_thresh = pcv.binary_threshold(s, 36, 255, 'light', device, args.debug) # Median Filter device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug) device, s_cnt = pcv.median_blur(s_thresh, 5, device, args.debug) # Fill small objects device, s_fill = pcv.fill(s_mblur, s_cnt, 0, device, args.debug) # Convert RGB to LAB and extract the Blue channel device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug) # Threshold the blue image device, b_thresh = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug) device, b_cnt = pcv.binary_threshold(b, 138, 255, 'light', device, args.debug) # Fill small objects device, b_fill = pcv.fill(b_thresh, b_cnt, 150, device, args.debug) # Join the thresholded saturation and blue-yellow images device, bs = pcv.logical_and(s_fill, b_fill, device, args.debug) # Apply Mask (for vis images, mask_color=white) device, masked = pcv.apply_mask(img, bs, 'white', device, args.debug) # Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, args.debug) device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, args.debug) # Threshold the green-magenta and blue images device, maskeda_thresh = pcv.binary_threshold(masked_a, 122, 255, 'dark', device, args.debug) device, maskedb_thresh = pcv.binary_threshold(masked_b, 133, 255, 'light', device, args.debug) # Join the thresholded saturation and blue-yellow images (OR) device, ab = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug) device, ab_cnt = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, args.debug) # Fill small objects device, ab_fill = pcv.fill(ab, ab_cnt, 200, device, args.debug) # Apply mask (for vis images, mask_color=white) device, masked2 = pcv.apply_mask(masked, ab_fill, 'white', device, args.debug) # Identify objects device, id_objects,obj_hierarchy = pcv.find_objects(masked2, ab_fill, device, args.debug) # Define ROI device, roi1, roi_hierarchy= pcv.define_roi(img,'rectangle', device, None, 'default', args.debug,False, 0, 0,0,0) # Decide which objects to keep device,roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(img,'partial',roi1,roi_hierarchy,id_objects,obj_hierarchy,device, args.debug) # Object combine kept objects device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, args.debug) ############## Analysis ################ # Find shape properties, output shape image (optional) device, shape_header,shape_data,shape_img = pcv.analyze_object(img, args.image, obj, mask, device,args.debug,True) # Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional) device, color_header,color_data,norm_slice= pcv.analyze_color(img, args.image, kept_mask, 256, device, args.debug,'all','rgb','v') # Output shape and color data pcv.print_results(args.image, shape_header, shape_data) pcv.print_results(args.image, color_header, color_data)
def main():
print("step one")
"""
Step one: Background forground substraction
"""
# Get options
args = options()
debug = args.debug
# Read image
img, path, filename = pcv.readimage(args.image)
# Pipeline step
device = 0
device, resize_img = pcv.resize(img, 0.4, 0.4, device, debug)
# Classify the pixels as plant or background
device, mask_img = pcv.naive_bayes_classifier(
resize_img,
pdf_file=
"/home/matthijs/PycharmProjects/SMR1/src/vision/ML_background/Trained_models/model_5/naive_bayes_pdfs.txt",
device=0,
debug='print')
# Median Filter
device, blur = pcv.median_blur(mask_img.get('plant'), 5, device, debug)
print("step two")
"""
Step one: Identifiy the objects, extract and filter the objects
"""
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(resize_img,
blur,
device,
debug=None)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(resize_img,
'rectangle',
device,
roi=True,
roi_input='default',
debug=True,
adjust=True,
x_adj=50,
y_adj=10,
w_adj=-100,
h_adj=0)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
resize_img, 'cutto', roi1, roi_hierarchy, id_objects, obj_hierarchy,
device, debug)
# print(roi_objects[0])
# cv2.drawContours(resize_img, [roi_objects[0]], 0, (0, 255, 0), 3)
# cv2.imshow("img",resize_img)
# cv2.waitKey(0)
area_oud = 0
i = 0
index = 0
object_list = []
# a = np.array([[hierarchy3[0][0]]])
hierarchy = []
for cnt in roi_objects:
area = cv2.contourArea(cnt)
M = cv2.moments(cnt)
if M["m10"] or M["m01"]:
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
# check if the location of the contour is between the constrains
if cX > 200 and cX < 500 and cY > 25 and cY < 400:
# cv2.circle(resize_img, (cX, cY), 5, (255, 0, 255), thickness=1, lineType=1, shift=0)
# check if the size of the contour is bigger than 250
if area > 450:
obj = np.vstack(roi_objects)
object_list.append(roi_objects[i])
hierarchy.append(hierarchy3[0][i])
print(i)
i = i + 1
a = np.array([hierarchy])
#a = [[[-1,-1,-1,-1][-1,-1,-1,-1][-1,-1,-1,-1]]]
# Object combine kept objects
# device, obj, mask_2 = pcv.object_composition(resize_img, object_list, a, device, debug)
mask_contours = np.zeros(resize_img.shape, np.uint8)
cv2.drawContours(mask_contours, object_list, -1, (255, 255, 255), -1)
gray_image = cv2.cvtColor(mask_contours, cv2.COLOR_BGR2GRAY)
ret, mask_contours = cv2.threshold(gray_image, 127, 255, cv2.THRESH_BINARY)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(resize_img,
mask_contours,
device,
debug=None)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
resize_img,
'cutto',
roi1,
roi_hierarchy,
id_objects,
obj_hierarchy,
device,
debug=None)
# Object combine kept objects
device, obj, mask = pcv.object_composition(resize_img,
roi_objects,
hierarchy3,
device,
debug=None)
############### Analysis ################
outfile = False
if args.writeimg == True:
outfile = args.outdir + "/" + filename
print("step three")
"""
Step three: Calculate all the features
"""
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
resize_img, args.image, obj, mask, device, debug,
args.outdir + '/' + filename)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(
resize_img, args.image, obj, mask, 1680, device, debug,
args.outdir + '/' + filename)
# Determine color properties: Histograms, Color Slices and Pseudocolored Images, output color analyzed images (optional)
device, color_header, color_data, color_img = pcv.analyze_color(
resize_img, args.image, kept_mask, 256, device, debug, 'all', 'v',
'img', 300, args.outdir + '/' + filename)
maks_watershed = mask.copy()
kernel = np.zeros((5, 5), dtype=np.uint8)
device, mask_watershed, = pcv.erode(maks_watershed, 5, 1, device, debug)
device, watershed_header, watershed_data, analysis_images = pcv.watershed_segmentation(
device, resize_img, mask, 50, './examples', debug)
device, list_of_acute_points = pcv.acute_vertex(obj, 30, 60, 10,
resize_img, device, debug)
device, top, bottom, center_v = pcv.x_axis_pseudolandmarks(
obj, mask, resize_img, device, debug)
device, left, right, center_h = pcv.y_axis_pseudolandmarks(
obj, mask, resize_img, device, debug)
device, points_rescaled, centroid_rescaled, bottomline_rescaled = pcv.scale_features(
obj, mask, list_of_acute_points, 225, device, debug)
# Identify acute vertices (tip points) of an object
# Results in set of point values that may indicate tip points
device, vert_ave_c, hori_ave_c, euc_ave_c, ang_ave_c, vert_ave_b, hori_ave_b, euc_ave_b, ang_ave_b = pcv.landmark_reference_pt_dist(
points_rescaled, centroid_rescaled, bottomline_rescaled, device, debug)
landmark_header = [
'HEADER_LANDMARK', 'tip_points', 'tip_points_r', 'centroid_r',
'baseline_r', 'tip_number', 'vert_ave_c', 'hori_ave_c', 'euc_ave_c',
'ang_ave_c', 'vert_ave_b', 'hori_ave_b', 'euc_ave_b', 'ang_ave_b',
'left_lmk', 'right_lmk', 'center_h_lmk', 'left_lmk_r', 'right_lmk_r',
'center_h_lmk_r', 'top_lmk', 'bottom_lmk', 'center_v_lmk', 'top_lmk_r',
'bottom_lmk_r', 'center_v_lmk_r'
]
landmark_data = [
'LANDMARK_DATA', 0, 0, 0, 0,
len(list_of_acute_points), vert_ave_c, hori_ave_c, euc_ave_c,
ang_ave_c, vert_ave_b, hori_ave_b, euc_ave_b, ang_ave_b, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0
]
# Write shape and color data to results fil
result = open(args.result, "a")
result.write('\t'.join(map(str, shape_header)))
result.write("\n")
result.write('\t'.join(map(str, shape_data)))
result.write("\n")
result.write('\t'.join(map(str, watershed_header)))
result.write("\n")
result.write('\t'.join(map(str, watershed_data)))
result.write("\n")
result.write('\t'.join(map(str, landmark_header)))
result.write("\n")
result.write('\t'.join(map(str, landmark_data)))
result.write("\n")
for row in shape_img:
result.write('\t'.join(map(str, row)))
result.write("\n")
result.write('\t'.join(map(str, color_header)))
result.write("\n")
result.write('\t'.join(map(str, color_data)))
result.write("\n")
for row in color_img:
result.write('\t'.join(map(str, row)))
result.write("\n")
result.close()
print("done")
