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(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_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 main():
# Assign description to the help doc
parser = argparse.ArgumentParser(
description='This is mothra, a software to automate different\
measurements on images of Lepidopterae.')
# 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 single image, folder or text\
file (extension txt) containing paths',
required=False,
default='input_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: 'binarization', 'ruler_detection',\
'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='outputs/results.csv')
# Disable cache
parser.add_argument(
'--cache',
action='store_true',
help='Enable computation cache (useful when developing algorithms)')
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((f"* mothra expects stage to be 'ruler_detection', "
f"binarization', or 'measurements'. Received '{args.stage}'"))
return None
plot_level = 0
if args.plot:
plot_level = 1
if args.detailed_plot:
plot_level = 2
# initializing the csv file.
if args.stage == 'measurements':
initialize_csv_file(csv_fname=args.path_csv)
stage_idx = stages.index(args.stage)
pipeline_process = stages[:stage_idx + 1]
# reading and processing input path.
input_name = args.input
image_paths = _process_paths_in_input(input_name)
number_of_images = len(image_paths)
# Set up caching and import motha modules
if args.cache:
from mothra import cache
import joblib
cache.memory = joblib.Memory('./cachedir', verbose=0)
from mothra import (ruler_detection, tracing, measurement, binarization,
identification)
for i, image_path in enumerate(image_paths):
try:
# creating axes layout for plotting.
axes = create_layout(len(pipeline_process), plot_level)
image_name = os.path.basename(image_path)
print(f'Image {i+1}/{number_of_images} : {image_name}')
image_rgb = imread(image_path)
# check image orientation and untilt it, if necessary.
angle = read_orientation(image_path)
if angle not in (None, 0): # angle == 0 does not need untilting
image_rgb = img_as_ubyte(
rotate(image_rgb, angle=angle, resize=True))
for step in pipeline_process:
# first, binarize the input image and return its components.
_, ruler_bin, lepidop_bin = binarization.main(image_rgb, axes)
if step == 'ruler_detection':
T_space, top_ruler = ruler_detection.main(
image_rgb, ruler_bin, axes)
elif step == 'binarization':
# already binarized in the beginning. Moving on...
pass
elif step == 'measurements':
points_interest = tracing.main(lepidop_bin, axes)
_, dist_mm = measurement.main(points_interest, T_space,
axes)
# measuring position and gender
position, gender = identification.main(image_rgb)
with open(args.path_csv, 'a') as csv_file:
_write_csv_data(csv_file, image_name, dist_mm,
position, gender)
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()
except Exception as exc:
print(
f"* Sorry, could not process {image_path}. More details:\n {exc}"
)
continue