def test_plantcv_analyze_bound():
img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR))
mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1)
contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS))
object_contours = contours_npz['arr_0']
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(
img, "img", object_contours[0], mask, 300, 0, None)
assert boundary_data[3] == 596347
def test_plantcv_analyze_bound():
img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_COLOR))
mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1)
contours_npz = np.load(os.path.join(TEST_DATA, TEST_INPUT_CONTOURS))
object_contours = contours_npz['arr_0']
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(img, "img", object_contours[0], mask,
300, 0, None)
assert boundary_data[3] == 596347
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, 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, 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, 30, 25, -10, -15)
# 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)
#print(watershed_data)
# 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, False,'all', 'v', 'img', 300,False)
# plt.plot(shape_img)
# plt.show()
# cv2.imshow('shape',shape_img)
# cv2.imshow('color',color_img)
# cv2.imshow('boundry',boundary_img1)
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(img, "Yucca", obj, masked2, device, debug = "plot")
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img1 = pcv.analyze_bound(img, "Yucca", obj, masked2, 1680, device, debug = "plot")
# 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, "Yucca", ma, 256, device, debug = "plot", 'all', 'v', 'img', 300, args.outdir + '/' + filename)
# Starting skeletoning----------------------------------------------------
print(
"Plant extracton done-----------------------------------------------------------------------------------Starting skeletoning")
size = np.size(masked2)
skel = np.zeros(masked2.shape, np.uint8)
#ret, mask_thresh = cv2.threshold(masked2, 127, 255, 0)
element = cv2.getStructuringElement(cv2.MORPH_CROSS, (3, 3))
print(element)
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, 550, 10, -600, -907)
# 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, 40, 40, img, device, args.debug)
boundary_line = 900
# 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, 935, 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) #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,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 ################ # 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, 830, 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)
args.debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, args.debug)
############## 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, 90, device,
args.debug, outfile)
# Determine color properties: Histograms, Color Slices and Pseudo-colored 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)))
def main():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
# Pipeline step
device = 0
debug = args.debug
# print('Original image')
# pcv.plot_image(img)
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, debug)
# print('Convert RGB to HSV and extract the Saturation channel')
# plt.imshow(s)
# plt.show()
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 100, 255, 'light', device,
debug)
# print('Threshold the Saturation image')
# plt.imshow(s_thresh)
# plt.show()
#
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, debug)
# print('Median Filter')
# plt.imshow(s_mblur)
# plt.show()
#
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, debug)
# print('Convert RGB to LAB and extract the Blue channel')
# plt.imshow(b)
# plt.show()
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 160, 255, 'light', device,
debug)
device, b_cnt = pcv.binary_threshold(b, 160, 255, 'light', device, debug)
# print('Threshold the blue image')
# plt.imshow(b_cnt)
# plt.show()
# Fill small objects
#device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, debug)
#
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_or(s_mblur, b_cnt, device, debug)
# print('Join the thresholded saturation and blue-yellow images')
# plt.imshow(bs)
# plt.show()
#
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, 'white', device, debug)
# print('Apply Mask 1 (for vis images, mask_color=white)')
# plt.imshow(masked)
# plt.show()
#
# 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, 115, 255, 'dark',
device, debug)
device, maskeda_thresh1 = pcv.binary_threshold(masked_a, 135, 255, 'light',
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, ab1 = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, debug)
device, ab = pcv.logical_or(maskeda_thresh1, ab1, device, debug)
device, ab_cnt = pcv.logical_or(maskeda_thresh1, ab1, device, debug)
# Fill small objects
device, ab_fill = pcv.fill(ab, ab_cnt, 200, device, debug)
# Apply mask (for vis images, mask_color=white)
device, masked2 = pcv.apply_mask(masked, ab_fill, 'white', device, debug)
# print('Apply Mask 2 (for vis images, mask_color=white)')
# plt.imshow(masked2)
# plt.show()
#
#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, 67, 377, -125, -368)
device, roi1, roi_hierarchy = pcv.define_roi(masked2, 'rectangle', device,
None, 'default', debug, True,
1, 1, -1, -1)
#
# 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)
############### 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, 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, 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, debug, 'all', 'v', 'img', 300,
args.outdir + '/' + filename)
# Write shape and color data to results file
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()
def main():
# Initialize device
device = 0
# Parse command-line options
args = options()
# Read image
img, path, filename = pcv.readimage(filename=args.image, debug=args.debug)
# Convert RGB to LAB and extract the Blue-Yellow channel
device, blue_channel = pcv.rgb2gray_lab(img=img,
channel="b",
device=device,
debug=args.debug)
# Threshold the blue image using the triangle autothreshold method
device, blue_tri = pcv.triangle_auto_threshold(device=device,
img=blue_channel,
maxvalue=255,
object_type="light",
xstep=1,
debug=args.debug)
# Extract core plant region from the image to preserve delicate plant features during filtering
device += 1
plant_region = blue_tri[0:1750, 600:2080]
if args.debug is not None:
pcv.print_image(filename=str(device) + "_extract_plant_region.png",
img=plant_region)
# Use a Gaussian blur to disrupt the strong edge features in the cabinet
device, blur_gaussian = pcv.gaussian_blur(device=device,
img=blue_tri,
ksize=(3, 3),
sigmax=0,
sigmay=None,
debug=args.debug)
# Threshold the blurred image to remove features that were blurred
device, blur_thresholded = pcv.binary_threshold(img=blur_gaussian,
threshold=250,
maxValue=255,
object_type="light",
device=device,
debug=args.debug)
# Add the plant region back in to the filtered image
device += 1
blur_thresholded[0:1750, 600:2080] = plant_region
if args.debug is not None:
pcv.print_image(filename=str(device) + "_replace_plant_region.png",
img=blur_thresholded)
# Fill small noise
device, blue_fill_50 = pcv.fill(img=np.copy(blur_thresholded),
mask=np.copy(blur_thresholded),
size=50,
device=device,
debug=args.debug)
# Apply a small median blur to break up pot edges
device, med_blur = pcv.median_blur(img=np.copy(blue_fill_50),
ksize=3,
device=device,
debug=args.debug)
# Define an ROI for the barcode label
device, label_roi, label_hierarchy = pcv.define_roi(img=img,
shape="rectangle",
device=device,
roi=None,
roi_input="default",
debug=args.debug,
adjust=True,
x_adj=1100,
y_adj=1350,
w_adj=-1070,
h_adj=-590)
# Identify all remaining contours in the binary image
device, contours, hierarchy = pcv.find_objects(img=img,
mask=np.copy(med_blur),
device=device,
debug=args.debug)
# Remove contours completely contained within the label region of interest
device, remove_label_mask = remove_countors_roi(mask=med_blur,
contours=contours,
hierarchy=hierarchy,
roi=label_roi,
device=device,
debug=args.debug)
# Identify objects
device, contours, contour_hierarchy = pcv.find_objects(
img=img, mask=remove_label_mask, device=device, debug=args.debug)
# Define ROI
device, roi, roi_hierarchy = pcv.define_roi(img=img,
shape="rectangle",
device=device,
roi=None,
roi_input="default",
debug=args.debug,
adjust=True,
x_adj=565,
y_adj=0,
w_adj=-490,
h_adj=-600)
# Decide which objects to keep
device, roi_contours, roi_contour_hierarchy, _, _ = pcv.roi_objects(
img=img,
roi_type="partial",
roi_contour=roi,
roi_hierarchy=roi_hierarchy,
object_contour=contours,
obj_hierarchy=contour_hierarchy,
device=device,
debug=args.debug)
# If there are no contours left we cannot measure anything
if len(roi_contours) > 0:
# Object combine kept objects
device, plant_contour, plant_mask = pcv.object_composition(
img=img,
contours=roi_contours,
hierarchy=roi_contour_hierarchy,
device=device,
debug=args.debug)
outfile = False
if args.writeimg:
outfile = args.outdir + "/" + filename
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img=img,
imgname=args.image,
obj=plant_contour,
mask=plant_mask,
device=device,
debug=args.debug,
filename=outfile)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img = pcv.analyze_bound(
img=img,
imgname=args.image,
obj=plant_contour,
mask=plant_mask,
line_position=690,
device=device,
debug=args.debug,
filename=outfile)
# Determine color properties: Histograms, Color Slices and Pseudocolored Images,
# output color analyzed images (optional)
device, color_header, color_data, color_img = pcv.analyze_color(
img=img,
imgname=args.image,
mask=plant_mask,
bins=256,
device=device,
debug=args.debug,
hist_plot_type=None,
pseudo_channel="v",
pseudo_bkg="img",
resolution=300,
filename=outfile)
# Output shape and color data
result = open(args.result, "a")
result.write('\t'.join(map(str, shape_header)) + "\n")
result.write('\t'.join(map(str, shape_data)) + "\n")
for row in shape_img:
result.write('\t'.join(map(str, row)) + "\n")
result.write('\t'.join(map(str, color_header)) + "\n")
result.write('\t'.join(map(str, color_data)) + "\n")
result.write('\t'.join(map(str, boundary_header)) + "\n")
result.write('\t'.join(map(str, boundary_data)) + "\n")
result.write('\t'.join(map(str, boundary_img)) + "\n")
for row in color_img:
result.write('\t'.join(map(str, row)) + "\n")
result.close()
def main():
# 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, 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)
############### 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, 900, 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, None, "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_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 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 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 1
args = options()
# lee imagen 2
img, path, filename = pcv.readimage(args.image)
# cv2.imshow("imagen",img)
# pasos del pipeline 3
device = 0
debug=args.debug
# Convert RGB to HSV and extract the Saturation channel 4
#convertir RGB a HSV y extraer el canal de saturacion
device, s = pcv.rgb2gray_hsv(img, 's', device, debug)
# cv2.imshow("rgb a hsv y extraer saturacion 4",s)
# Threshold the Saturation image 5
#sacar imagen binaria del canal de saturacion
device, s_thresh = pcv.binary_threshold(s, 85, 255, 'light', device, debug)
# cv2.imshow("imagen binaria de hsv",s_thresh)
# Median Filter 6
#sacar un filtro median_blur
device, s_mblur = pcv.median_blur(s_thresh, 5, device, debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, debug)
# cv2.imshow("s_mblur",s_mblur)
# cv2.imshow("s_cnt",s_cnt)
# Convert RGB to LAB and extract the Blue channel 7
#convertir RGB(imagen original) a LAB Y extraer el canal azul
device, b = pcv.rgb2gray_lab(img, 'b', device, debug)
# cv2.imshow("convertir RGB a LAB",b)
# Threshold the blue image 8
#sacar imagen binaria de LAB imagen blue
device, b_thresh = pcv.binary_threshold(b, 160, 255, 'light', device, debug)
device, b_cnt = pcv.binary_threshold(b, 160, 255, 'light', device, debug)
# cv2.imshow("imagen binaria de LAB",b_thresh)
# cv2.imshow("imagen binaria",b_cnt)
# Fill small objects
#device, b_fill = pcv.fill(b_thresh, b_cnt, 10, device, debug)
# Join the thresholded saturation and blue-yellow images 9
#
device, bs = pcv.logical_or(s_mblur, b_cnt, device, debug)
# cv2.imshow("suma logica s_mblur and b_cnt",bs)
# Apply Mask (for vis images, mask_color=white) 10
device, masked = pcv.apply_mask(img, bs, 'white', device, debug)
# cv2.imshow("aplicar mascara masked",masked)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels 11
device, masked_a = pcv.rgb2gray_lab(masked, 'a', device, debug)
device, masked_b = pcv.rgb2gray_lab(masked, 'b', device, debug)
# cv2.imshow("canal verde-magenta",masked_a)
# cv2.imshow("canal azul-amarillo",masked_b)
# Threshold the green-magenta and blue images 12
device, maskeda_thresh = pcv.binary_threshold(masked_a, 115, 255, 'dark', device, debug)
device, maskeda_thresh1 = pcv.binary_threshold(masked_a, 135, 255, 'light', device, debug)
device, maskedb_thresh = pcv.binary_threshold(masked_b, 128, 255, 'light', device, debug)
# cv2.imshow("threshold de canal verde-magenta dark",maskeda_thresh)
# cv2.imshow("threshold de canal verde-magenta light",maskeda_thresh1)
# cv2.imshow("threshold de canal azul-amarillo",maskedb_thresh)
# Join the thresholded saturation and blue-yellow images (OR) 13
device, ab1 = pcv.logical_or(maskeda_thresh, maskedb_thresh, device, debug)
device, ab = pcv.logical_or(maskeda_thresh1, ab1, device, debug)
device, ab_cnt = pcv.logical_or(maskeda_thresh1, ab1, device, debug)
# cv2.imshow("suma logica or 1",ab1)
# cv2.imshow("suma logica or 2 ab",ab)
# cv2.imshow("suma logica or 3 ab_cnt",ab_cnt)
# Fill small objects 14
device, ab_fill = pcv.fill(ab, ab_cnt, 200, device, debug)
# cv2.imshow("ab_fill",ab_fill)
# Apply mask (for vis images, mask_color=white) 15
device, masked2 = pcv.apply_mask(masked, ab_fill, 'white', device, debug)
# cv2.imshow("aplicar maskara2 white",masked2)
####################entendible hasta aqui######################
# Identify objects 16 solo print Se utiliza para identificar objetos (material vegetal) en una imagen.
#imprime la imagen si uso print o no si uso plot no almacena la imagen pero en pritn si la aguarda
#usa b_thresh y observa
device,id_objects,obj_hierarchy = pcv.find_objects(masked2,ab_fill, device, debug)
# Define ROI 17 solo print encierra el objeto detectato pero aun es manual aun no automatico
device, roi1, roi_hierarchy= pcv.define_roi(masked2, 'rectangle', device, None, 'default', debug, True, 92, 80, -127, -343)
# Decide which objects to keep 18
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 19
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3, device, debug)
############### 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,'image', obj, mask, device,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, 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, debug, 'all', 'v', 'img', 300, args.outdir + '/' + filename)
#Write shape and color data to results file
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()
cv2.waitKey()
cv2.destroyAllWindows()
def main():
# Get options
args = options()
path_mask = '/home/mfeldman/tester/mask/mask_brass_tv_z1_L0.png'
# Read image
img, path, filename = pcv.readimage(args.image)
brass_mask = cv2.imread(path_mask)
# Pipeline step
device = 0
# 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)
# We can do a pretty good job of identifying the plant from the s channel
device, s = pcv.rgb2gray_hsv(masked_image, 's', device, args.debug)
s_thresh = cv2.inRange(s, 100, 190)
# Lets blur the result a bit to get rid of unwanted noise
s_blur = cv2.medianBlur(s_thresh,5)
# The a channel is good too
device, a = pcv.rgb2gray_lab(masked_image, 'a', device, args.debug)
a_thresh = cv2.inRange(a, 90, 120)
a_blur = cv2.medianBlur(a_thresh,5)
# Now lets set of a series of filters to remove unwanted background
plant_shape = cv2.bitwise_and(a_blur, s_blur)
# Lets remove all the crap on the sides of the image
plant_shape[:,:330] = 0
plant_shape[:,2100:] = 0
plant_shape[:200,:] = 0
# 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(masked_image, plant_shape, 'white', device, args.debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(masked, dilation, 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, 40, 40, 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 get_feature(img):
print("step one")
"""
Step one: Background forground substraction
"""
# Get options
args = options()
debug = args.debug
# Read image
filename = args.result
# 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_3/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 ################
masked = mask.copy()
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, filename="/file")
print(shape_img)
# 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)
# 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)
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
]
shape_data_train = list(shape_data)
shape_data_train.pop(0)
shape_data_train.pop(10)
watershed_data_train = list(watershed_data)
watershed_data_train.pop(0)
landmark_data_train = [
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
]
X = shape_data_train + watershed_data_train + landmark_data_train
print("len X", len(X))
print(X)
# 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")
print(shape_img)
return X, shape_img, masked
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 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)
# roi = cv2.imread(args.roi)
# Pipeline step
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, -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)
# ############## 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, 900, 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():
# Initialize device
device = 0
# Parse command-line options
args = options()
# Read image
img, path, filename = pcv.readimage(filename=args.image, debug=args.debug)
# Convert RGB to LAB and extract the Blue-Yellow channel
device, blue_channel = pcv.rgb2gray_lab(img=img,
channel="b",
device=device,
debug=args.debug)
# Threshold the blue image using the triangle autothreshold method
device, blue_tri = pcv.triangle_auto_threshold(device=device,
img=blue_channel,
maxvalue=255,
object_type="light",
xstep=1,
debug=args.debug)
# Extract core plant region from the image to preserve delicate plant features during filtering
device += 1
plant_region = blue_tri[0:1750, 600:2080]
if args.debug is not None:
pcv.print_image(filename=str(device) + "_extract_plant_region.png",
img=plant_region)
# Use a Gaussian blur to disrupt the strong edge features in the cabinet
device, blur_gaussian = pcv.gaussian_blur(device=device,
img=blue_tri,
ksize=(3, 3),
sigmax=0,
sigmay=None,
debug=args.debug)
# Threshold the blurred image to remove features that were blurred
device, blur_thresholded = pcv.binary_threshold(img=blur_gaussian,
threshold=250,
maxValue=255,
object_type="light",
device=device,
debug=args.debug)
# Add the plant region back in to the filtered image
device += 1
blur_thresholded[0:1750, 600:2080] = plant_region
if args.debug is not None:
pcv.print_image(filename=str(device) + "_replace_plant_region.png",
img=blur_thresholded)
# Fill small noise
device, blue_fill_50 = pcv.fill(img=np.copy(blur_thresholded),
mask=np.copy(blur_thresholded),
size=50,
device=device,
debug=args.debug)
# Identify objects
device, contours, contour_hierarchy = pcv.find_objects(img=img,
mask=blue_fill_50,
device=device,
debug=args.debug)
# Define ROI
device, roi, roi_hierarchy = pcv.define_roi(img=img,
shape="rectangle",
device=device,
roi=None,
roi_input="default",
debug=args.debug,
adjust=True,
x_adj=565,
y_adj=0,
w_adj=-490,
h_adj=-250)
# Decide which objects to keep
device, roi_contours, roi_contour_hierarchy, _, _ = pcv.roi_objects(
img=img,
roi_type="partial",
roi_contour=roi,
roi_hierarchy=roi_hierarchy,
object_contour=contours,
obj_hierarchy=contour_hierarchy,
device=device,
debug=args.debug)
# If there are no contours left we cannot measure anything
if len(roi_contours) > 0:
# Object combine kept objects
device, plant_contour, plant_mask = pcv.object_composition(
img=img,
contours=roi_contours,
hierarchy=roi_contour_hierarchy,
device=device,
debug=args.debug)
outfile = False
if args.writeimg:
outfile = args.outdir + "/" + filename
# Find shape properties, output shape image (optional)
device, shape_header, shape_data, shape_img = pcv.analyze_object(
img=img,
imgname=args.image,
obj=plant_contour,
mask=plant_mask,
device=device,
debug=args.debug,
filename=outfile)
# Shape properties relative to user boundary line (optional)
device, boundary_header, boundary_data, boundary_img = pcv.analyze_bound(
img=img,
imgname=args.image,
obj=plant_contour,
mask=plant_mask,
line_position=440,
device=device,
debug=args.debug,
filename=outfile)
# Determine color properties: Histograms, Color Slices and Pseudocolored Images,
# output color analyzed images (optional)
device, color_header, color_data, color_img = pcv.analyze_color(
img=img,
imgname=args.image,
mask=plant_mask,
bins=256,
device=device,
debug=args.debug,
hist_plot_type=None,
pseudo_channel="v",
pseudo_bkg="img",
resolution=300,
filename=outfile)
# Output shape and color data
result = open(args.result, "a")
result.write('\t'.join(map(str, shape_header)) + "\n")
result.write('\t'.join(map(str, shape_data)) + "\n")
for row in shape_img:
result.write('\t'.join(map(str, row)) + "\n")
result.write('\t'.join(map(str, color_header)) + "\n")
result.write('\t'.join(map(str, color_data)) + "\n")
result.write('\t'.join(map(str, boundary_header)) + "\n")
result.write('\t'.join(map(str, boundary_data)) + "\n")
result.write('\t'.join(map(str, boundary_img)) + "\n")
for row in color_img:
result.write('\t'.join(map(str, row)) + "\n")
result.close()
# Find matching NIR image
device, nirpath = pcv.get_nir(path=path,
filename=filename,
device=device,
debug=args.debug)
nir_rgb, nir_path, nir_filename = pcv.readimage(nirpath)
nir_img = cv2.imread(nirpath, 0)
# Make mask glovelike in proportions via dilation
device, d_mask = pcv.dilate(plant_mask,
kernel=1,
i=0,
device=device,
debug=args.debug)
# Resize mask
prop2, prop1 = conv_ratio()
device, nmask = pcv.resize(img=d_mask,
resize_x=prop1,
resize_y=prop2,
device=device,
debug=args.debug)
# Convert the resized mask to a binary mask
device, bmask = pcv.binary_threshold(img=nmask,
threshold=0,
maxValue=255,
object_type="light",
device=device,
debug=args.debug)
device, crop_img = crop_sides_equally(mask=bmask,
nir=nir_img,
device=device,
debug=args.debug)
# position, and crop mask
device, newmask = pcv.crop_position_mask(img=nir_img,
mask=crop_img,
device=device,
x=34,
y=9,
v_pos="top",
h_pos="right",
debug=args.debug)
# Identify objects
device, nir_objects, nir_hierarchy = pcv.find_objects(img=nir_rgb,
mask=newmask,
device=device,
debug=args.debug)
# Object combine kept objects
device, nir_combined, nir_combinedmask = pcv.object_composition(
img=nir_rgb,
contours=nir_objects,
hierarchy=nir_hierarchy,
device=device,
debug=args.debug)
if args.writeimg:
outfile = args.outdir + "/" + nir_filename
# Analyze NIR signal data
device, nhist_header, nhist_data, nir_imgs = pcv.analyze_NIR_intensity(
img=nir_img,
rgbimg=nir_rgb,
mask=nir_combinedmask,
bins=256,
device=device,
histplot=False,
debug=args.debug,
filename=outfile)
# Analyze the shape of the plant contour from the NIR image
device, nshape_header, nshape_data, nir_shape = pcv.analyze_object(
img=nir_img,
imgname=nir_filename,
obj=nir_combined,
mask=nir_combinedmask,
device=device,
debug=args.debug,
filename=outfile)
# Write NIR data to co-results file
coresult = open(args.coresult, "a")
coresult.write('\t'.join(map(str, nhist_header)) + "\n")
coresult.write('\t'.join(map(str, nhist_data)) + "\n")
for row in nir_imgs:
coresult.write('\t'.join(map(str, row)) + "\n")
coresult.write('\t'.join(map(str, nshape_header)) + "\n")
coresult.write('\t'.join(map(str, nshape_data)) + "\n")
coresult.write('\t'.join(map(str, nir_shape)) + "\n")
coresult.close()
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, 34, 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, 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_thresh, 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, -1700, 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, 110, 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, 1700, 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, 110, 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, 650, 0, -450, -300)
# 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, 375, 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_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]
