def test_plantcv_output_mask():
img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1)
mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1)
device, imgpath, maskpath, analysis_images = pcv.output_mask(
0, img, mask, 'test.png', TEST_DATA, mask_only=False, debug=None)
path = str(TEST_DATA) + '/ori-images'
path1 = str(TEST_DATA) + '/mask-images'
assert all([os.path.exists(path) is True, os.path.exists(path1) is True])
shutil.rmtree(path)
shutil.rmtree(path1)
def test_plantcv_output_mask():
img = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_GRAY), -1)
mask = cv2.imread(os.path.join(TEST_DATA, TEST_INPUT_BINARY), -1)
device, imgpath, maskpath, analysis_images = pcv.output_mask(0, img, mask, 'test.png', TEST_DATA,
mask_only=False, debug=None)
path = str(TEST_DATA) + '/ori-images'
path1 = str(TEST_DATA) + '/mask-images'
assert all([os.path.exists(path) is True, os.path.exists(path1) is True])
shutil.rmtree(path)
shutil.rmtree(path1)
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():
# Get options
args = options()
# Read image
img, path, filename = pcv.readimage(args.image)
brass_mask = cv2.imread(args.roi)
# Pipeline step
device = 0
# Convert RGB to HSV and extract the Saturation channel
device, s = pcv.rgb2gray_hsv(img, 's', device, args.debug)
# Threshold the Saturation image
device, s_thresh = pcv.binary_threshold(s, 49, 255, 'light', device,
args.debug)
# Median Filter
device, s_mblur = pcv.median_blur(s_thresh, 5, device, args.debug)
device, s_cnt = pcv.median_blur(s_thresh, 5, device, args.debug)
# Fill small objects
device, s_fill = pcv.fill(s_mblur, s_cnt, 150, device, args.debug)
# Convert RGB to LAB and extract the Blue channel
device, b = pcv.rgb2gray_lab(img, 'b', device, args.debug)
# Threshold the blue image
device, b_thresh = pcv.binary_threshold(b, 138, 255, 'light', device,
args.debug)
device, b_cnt = pcv.binary_threshold(b, 138, 255, 'light', device,
args.debug)
# Fill small objects
device, b_fill = pcv.fill(b_thresh, b_cnt, 150, device, args.debug)
# Join the thresholded saturation and blue-yellow images
device, bs = pcv.logical_and(s_fill, b_fill, device, args.debug)
# Apply Mask (for vis images, mask_color=white)
device, masked = pcv.apply_mask(img, bs, 'white', device, args.debug)
# Mask pesky brass piece
device, brass_mask1 = pcv.rgb2gray_hsv(brass_mask, 'v', device, args.debug)
device, brass_thresh = pcv.binary_threshold(brass_mask1, 0, 255, 'light',
device, args.debug)
device, brass_inv = pcv.invert(brass_thresh, device, args.debug)
device, brass_masked = pcv.apply_mask(masked, brass_inv, 'white', device,
args.debug)
# Further mask soil and car
device, masked_a = pcv.rgb2gray_lab(brass_masked, 'a', device, args.debug)
device, soil_car = pcv.binary_threshold(masked_a, 128, 255, 'dark', device,
args.debug)
device, soil_masked = pcv.apply_mask(brass_masked, soil_car, 'white',
device, args.debug)
# Convert RGB to LAB and extract the Green-Magenta and Blue-Yellow channels
device, soil_a = pcv.rgb2gray_lab(soil_masked, 'a', device, args.debug)
device, soil_b = pcv.rgb2gray_lab(soil_masked, 'b', device, args.debug)
# Threshold the green-magenta and blue images
device, soila_thresh = pcv.binary_threshold(soil_a, 118, 255, 'dark',
device, args.debug)
device, soilb_thresh = pcv.binary_threshold(soil_b, 155, 255, 'light',
device, args.debug)
# Join the thresholded saturation and blue-yellow images (OR)
device, soil_ab = pcv.logical_or(soila_thresh, soilb_thresh, device,
args.debug)
device, soil_ab_cnt = pcv.logical_or(soila_thresh, soilb_thresh, device,
args.debug)
# Fill small objects
device, soil_fill = pcv.fill(soil_ab, soil_ab_cnt, 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(soil_masked, soil_cnt, 'white', device,
args.debug)
# Identify objects
device, id_objects, obj_hierarchy = pcv.find_objects(
masked2, soil_cnt, device, args.debug)
# Define ROI
device, roi1, roi_hierarchy = pcv.define_roi(img, 'circle', device, None,
'default', args.debug, True,
0, 0, -50, -50)
# Decide which objects to keep
device, roi_objects, hierarchy3, kept_mask, obj_area = pcv.roi_objects(
img, 'partial', roi1, roi_hierarchy, id_objects, obj_hierarchy, device,
args.debug)
# Object combine kept objects
device, obj, mask = pcv.object_composition(img, roi_objects, hierarchy3,
device, args.debug)
# ############# Analysis ################
# 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)
# 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.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)
# 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, 200, 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, -50)
# 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 ################
# 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, 280, 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()
