def test_with_ax(fake_points_of_interest):
fig, ax = plt.subplots(figsize=(20, 5))
points_interest, true_results_pix, true_results_mm = fake_points_of_interest
axes = [ax] + [None] * 6
dist_pix, dist_mm = measurement.main(points_interest, 2)
assert dist_pix == true_results_pix
assert dist_mm == true_results_mm
def test_t_space_unchanged():
# tests with simple t_space = 1 that everything remains the same.
pixel_points = [(0, 0), (5, 0), (0, 0), (0, 4)]
mm_points = measurement.main(pixel_points, 1)
assert mm_points == ((5, 4), (5, 4))
def test_with_ax():
fig, ax = plt.subplots(figsize=(20, 5))
pixel_points = [(0, 0), (5, 0), (0, 0), (0, 4)]
mm_points = measurement.main(pixel_points, 2, ax)
assert mm_points == ((5, 4), (2.5, 2))
def test_different_t_space():
pixel_points = [(0, 0), (5, 0), (0, 0), (0, 4)]
mm_points = measurement.main(pixel_points, 2)
assert mm_points == ((5, 4), (2.5, 2))
def main():
# Assign description to the help doc
parser = argparse.ArgumentParser(
description='Script to automate butterfly wings measurment')
# Add arguments
# Plotting
parser.add_argument('-p',
'--plot',
action='store_true',
help='If entered images are plotted to the output\
folder')
parser.add_argument('-pp',
'--detailed_plot',
action='store_true',
help='If entered detailed images are plotted to the\
output folder')
# Input path
parser.add_argument('-i',
'--input',
type=str,
help='Input path for folder or single image',
required=False,
default='raw_images')
# Output path
parser.add_argument('-o',
'--output_folder',
type=str,
help='Output path for raw image',
required=False,
default='outputs')
# Stage
parser.add_argument('-s',
'--stage',
type=str,
help="Stage name: 'ruler_detection', 'binarization',\
'tracing', 'measurements",
required=False,
default='measurements')
# Dots per inch
parser.add_argument('-dpi',
type=int,
help='Dots per inch of the saved figures',
default=300)
# CSV output path
parser.add_argument('-csv',
'--path_csv',
type=str,
help='Path of the resulting csv file',
default='results.csv')
# Grabcut
parser.add_argument('-g',
'--grabcut',
action='store_true',
help='Use grabcut in binarization step')
args = parser.parse_args()
# Initialization
if os.path.exists(args.output_folder):
oldList = os.listdir(args.output_folder)
for oldFile in oldList:
os.remove(args.output_folder + "/" + oldFile)
else:
os.mkdir(args.output_folder)
stages = ['ruler_detection', 'binarization', 'measurements']
if args.stage not in stages:
print("ERROR : Stage can only be 'ruler_detection', 'binarization',\
or 'measurements'")
return 0
plot_level = 0
if args.plot:
plot_level = 1
if args.detailed_plot:
plot_level = 2
# Initializing the csv file
if args.stage == 'measurements':
if os.path.exists(args.path_csv):
os.remove(args.path_csv)
with open(args.path_csv, 'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerow([
'image_id', 'left_wing (mm)', 'right_wing (mm)',
'left_wing_center (mm)', 'right_wing_center (mm)',
'wing_span (mm)'
])
stage_idx = stages.index(args.stage)
pipeline_process = stages[:stage_idx + 1]
raw_image_path = args.input
if (os.path.isdir(raw_image_path)):
image_names = os.listdir(raw_image_path)
image_paths = []
for image_name in image_names:
if not image_name.lower().endswith(('.png', '.jpg', '.jpeg')):
continue
image_path = os.path.join(raw_image_path, image_name)
image_paths.append(image_path)
else:
image_paths = [raw_image_path]
n = len(image_paths)
for i, image_path in enumerate(image_paths):
image_name = os.path.basename(image_path)
print(f'Image {i+1}/{n} : {image_name}')
image_rgb = imread(image_path, plugin='matplotlib')
axes = create_layout(len(pipeline_process), plot_level)
for step in pipeline_process:
if step == 'ruler_detection':
T_space, top_ruler = ruler_detection.main(image_rgb, axes)
elif step == 'binarization':
binary = binarization.main(image_rgb, top_ruler, args.grabcut,
axes)
elif step == 'measurements':
points_interest = tracing.main(binary, axes)
dist_pix, dist_mm = measurement.main(points_interest, T_space,
axes)
with open(args.path_csv, 'a') as csv_file:
writer = csv.writer(csv_file)
writer.writerow([
image_name, dist_mm["dist_l"], dist_mm["dist_r"],
dist_mm["dist_l_center"], dist_mm["dist_r_center"],
dist_mm["dist_span"]
])
if plot_level > 0:
output_path = os.path.normpath(
os.path.join(args.output_folder, image_name))
dpi = args.dpi
if plot_level == 2:
dpi = int(1.5 * args.dpi)
plt.savefig(output_path, dpi=dpi)
plt.close()
def test_different_t_space(fake_points_of_interest):
points_interest, true_results_pix, true_results_mm = fake_points_of_interest
dist_pix, dist_mm = measurement.main(points_interest, 2)
assert dist_pix == true_results_pix
assert dist_mm == true_results_mm
def test_t_space_unchanged(fake_points_of_interest):
# tests with simple t_space = 1 that everything remains the same.
points_interest, true_results_pix, true_results_mm = fake_points_of_interest
dist_pix, dist_mm = measurement.main(points_interest, 1)
assert dist_pix == true_results_pix
assert dist_mm == true_results_pix
def test_t_space_unchanged():
# tests with simple t_space = 1 that everything remains the same.
pixel_points = [(0, 0), (5, 0), (0, 0), (0, 4)]
dst_pix, dst_mm = measurement.main(pixel_points, 1, AXES)
assert (dst_pix, dst_mm) == ((5, 4), (5, 4))
def test_with_ax():
fig, ax = plt.subplots(figsize=(20, 5))
pixel_points = [(0, 0), (5, 0), (0, 0), (0, 4)]
axes = [ax] + [None] * 6
dst_pix, dst_mm = measurement.main(pixel_points, 2, axes)
assert (dst_pix, dst_mm) == ((5, 4), (2.5, 2))
def test_different_t_space():
pixel_points = [(0, 0), (5, 0), (0, 0), (0, 4)]
dst_pix, dst_mm = measurement.main(pixel_points, 2, AXES)
assert (dst_pix, dst_mm) == ((5, 4), (2.5, 2))
def main():
# Assign description to the help doc
parser = argparse.ArgumentParser(
description='Script to automate butterfly wings measurment')
# Add arguments
# Plotting
parser.add_argument('-p',
'--plot',
action='store_true',
help='If entered images are plotted to the output\
folder')
# Input path
parser.add_argument('-i',
'--input',
type=str,
help='Input path for folder or single image',
required=False,
default='raw_images')
# Output path
parser.add_argument('-o',
'--output_folder',
type=str,
help='Output path for raw image',
required=False,
default='outputs')
# Stage
parser.add_argument('-s',
'--stage',
type=str,
help="Stage name: 'ruler_detection', 'binarization',\
'tracing', 'measurements",
required=True)
# Dots per inch
parser.add_argument('-dpi',
type=int,
help='Dots per inch of the saved figures',
default=300)
parser.add_argument('-csv',
'--path_csv',
type=str,
help='Path of the resulting csv file',
default='results.csv')
args = parser.parse_args()
# Initialization
if os.path.exists(args.output_folder):
oldList = os.listdir(args.output_folder)
for oldFile in oldList:
os.remove(args.output_folder + "/" + oldFile)
else:
os.mkdir(args.output_folder)
stages = ['ruler_detection', 'binarization', 'tracing', 'measurements']
if args.stage not in stages:
print("ERROR : Stage can only be 'ruler_detection', 'binarization',\
'tracing' or 'measurements'")
return 0
# Initializing the csv file
if args.stage == 'measurements':
if os.path.exists(args.path_csv):
os.remove(args.path_csv)
with open(args.path_csv, 'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(['image_id', 'left_wing (mm)', 'right_wing (mm)'])
raw_image_path = args.input
stage_idx = stages.index(args.stage)
pipeline_process = stages[:stage_idx + 1]
raw_image_path = args.input
if (os.path.isdir(raw_image_path)):
image_names = os.listdir(raw_image_path)
else:
image_names = [""]
# For testing purpose, the pipeline is only applied to the first 10 images
for image_name in image_names[:10]:
print(raw_image_path + '/' + image_name)
image_path = os.path.normpath(raw_image_path + '/' + image_name)
image_rgb = imread(image_path)
ax = [None, None, None]
if args.plot:
ncols = min(len(pipeline_process), 3)
fig, ax = plt.subplots(ncols=ncols, figsize=(20, 5))
for step in pipeline_process:
if step == 'ruler_detection':
ax0 = ax
if len(pipeline_process) > 1:
ax0 = ax[0]
T_space, top_ruler = ruler_detection.main(image_rgb, ax0)
elif step == 'binarization':
binary = binarization.main(image_rgb, top_ruler, ax[1])
elif step == 'tracing':
points_interest = tracing.main(binary, ax[2])
else:
dst_pix, dst_mm = measurement.main(points_interest, T_space,
ax[0])
with open(args.path_csv, 'a') as csv_file:
writer = csv.writer(csv_file)
writer.writerow([image_name, dst_mm[0], dst_mm[1]])
if args.plot:
filename = args.output_folder + '/' + image_name
output_path = os.path.normpath(filename)
plt.savefig(output_path, dpi=args.dpi)
plt.close()