images = io_functions.specimen_ids_from_images(os.listdir(input_folder))
with open(os.path.join(output_folder,'colors.csv'), 'wb') as csvfile:
writer = csv.writer(csvfile)
num_colors = [10, 5]
headers = ['Specimen']
for n in num_colors:
headers += ['Segment', 'Mean Hue', 'Mean Saturation']
headers += [channel + str(n) for n in range(n) for channel in ['R', 'G', 'B', 'P']]
writer.writerow(headers)
for filename in images:
color = io_functions.read_image(os.path.join(input_folder, 'color_' + filename))
abdomen = io_functions.read_image(os.path.join(input_folder, 'abdomen_' + filename))[:, :, 0]
wings = io_functions.read_image(os.path.join(input_folder, 'wings_' + filename))[:, :, 0]
if color is not None and abdomen is not None and wings is not None:
HSV = rgb2hsv(color)
segments = [Segment('wings', wings, 10),
Segment('abdomen', abdomen, 5)]
current_row = [os.path.splitext(filename)[0]]
for segment in segments:
current_row += [segment.name,
np.mean(HSV[:, :, 0][np.where(segment.mask > 128)]),
np.mean(HSV[:, :, 1][np.where(segment.mask > 128)])]
writer = csv.writer(csvfile)
num_colors = [10, 5]
headers = ['Specimen']
for n in num_colors:
headers += ['Segment', 'Mean Hue', 'Mean Saturation']
headers += [
channel + str(n) for n in range(n)
for channel in ['R', 'G', 'B', 'P']
]
writer.writerow(headers)
for filename in images:
color = io_functions.read_image(
os.path.join(input_folder, 'color_' + filename))
abdomen = io_functions.read_image(
os.path.join(input_folder, 'abdomen_' + filename))[:, :, 0]
wings = io_functions.read_image(
os.path.join(input_folder, 'wings_' + filename))[:, :, 0]
if color is not None and abdomen is not None and wings is not None:
HSV = rgb2hsv(color)
segments = [
Segment('wings', wings, 10),
Segment('abdomen', abdomen, 5)
]
current_row = [os.path.splitext(filename)[0]]
for segment in segments:
writer = csv.writer(csvfile)
writer.writerow(['Specimen', 'Right', 'Left'])
with open('computed_wing_lengths.csv', 'rU') as csvfile:
reader = csv.reader(csvfile)
specimen_ids = [row[0] for row in reader]
specimens = [
specimen for specimen in specimens if specimen[0] not in specimen_ids
]
for specimen_id, filename in specimens:
with open('computed_wing_lengths.csv', 'a') as csvfile:
writer = csv.writer(csvfile)
start = timeit.default_timer()
image = io_functions.read_image(filename)
height, width = image.shape[:2]
segmented_image, segmented_mask = segmentation.segment_butterfly(
image, saliency_threshold=96, approximate=False)
wing_mask, wing_paths, centre_of_mass = segmentation.segment_wing(
segmented_mask)
left_wing_length = wing_length(wing_mask[:, centre_of_mass[1]::-1],
wing_paths[0][:, centre_of_mass[1]::-1])
right_wing_length = wing_length(wing_mask[:, centre_of_mass[1]:],
wing_paths[1][:, centre_of_mass[1]:])
stop = timeit.default_timer()
print('{}: Left wing is {:.2f}px, right wing is {:.2f}px [{:.2f}s]'.
with open('computed_wing_lengths.csv', 'wb') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['Specimen', 'Right', 'Left'])
with open('computed_wing_lengths.csv', 'rU') as csvfile:
reader = csv.reader(csvfile)
specimen_ids = [row[0] for row in reader]
specimens = [specimen for specimen in specimens if specimen[0] not in specimen_ids]
for specimen_id, filename in specimens:
with open('computed_wing_lengths.csv', 'a') as csvfile:
writer = csv.writer(csvfile)
start = timeit.default_timer()
image = io_functions.read_image(filename)
height, width = image.shape[:2]
segmented_image, segmented_mask = segmentation.segment_butterfly(image,
saliency_threshold=96,
approximate=False)
wing_mask, wing_paths, centre_of_mass = segmentation.segment_wing(segmented_mask)
left_wing_length = wing_length(wing_mask[:, centre_of_mass[1]::-1], wing_paths[0][:, centre_of_mass[1]::-1])
right_wing_length = wing_length(wing_mask[:, centre_of_mass[1]:], wing_paths[1][:, centre_of_mass[1]:])
stop = timeit.default_timer()
print('{}: Left wing is {:.2f}px, right wing is {:.2f}px [{:.2f}s]'.format(specimen_id, left_wing_length, right_wing_length, stop - start))
writer.writerow([specimen_id, right_wing_length, left_wing_length])
