def cut_oct_2d(img_file, image_size=None):
if isinstance(img_file, str):
size = os.path.getsize(img_file)
if size < THRETHOLD_FILESIZE:
return None
img1 = cv2.imread(img_file)
else:
img1 = img_file
# img1.shape:(885,512,3) (height,width,3)
height, width = img1.shape[:-1]
if height > width: #TOPCON
array_sum = np.sum(img1, axis=(1, 2)) # shape (885)
top_n = heapq.nlargest(1, range(len(array_sum)), array_sum.take)
bottom = max(0, top_n[0] - (img1.shape[1] // 2))
top = min(img1.shape[0], top_n[0] + (img1.shape[1] // 2))
img_crop = img1[bottom:top, :, :]
if img_crop.shape[0] < img_crop.shape[1]: # width < height
add_black_pixel = img_crop.shape[1] - img_crop.shape[0]
img_height_0 = np.zeros((add_black_pixel, img_crop.shape[1], 3))
img_crop = np.concatenate((img_height_0, img_crop), axis=0)
elif img_crop.shape[0] > img_crop.shape[1]:
add_black_pixel = img_crop.shape[0] - img_crop.shape[1]
img_width_0 = np.zeros((img_crop.shape[0], 3), add_black_pixel)
img_crop = np.concatenate((img_width_0, img_crop), axis=1)
if image_size is not None:
img_crop = cv2.resize(img_crop, (image_size, image_size))
return img_crop
else: #Carl Zeiss Jena
return image_to_square(img1, image_size)
def do_preprocess_rop(img_source,
crop_size,
img_file_dest=None,
add_black_pixel_ratio=0):
if isinstance(img_source, str):
try:
img1 = cv2.imread(img_source)
except:
# Corrupt JPEG data1: 19 extraneous bytes before marker 0xc4
raise Exception("image file not found:" + img_source)
else:
img1 = img_source
if img1 is None: #file not exists or other errors
raise Exception("image file error:" + img_source)
img1 = del_black_or_white(img1)
# after delete black margin, image may be too small
min_width_height = min(img1.shape[:2])
if min_width_height < IMG_SMALL_THRETHOLD:
return None
from LIBS.ImgPreprocess.my_image_helper import image_to_square
img2 = image_to_square(img1, image_size=crop_size)
if add_black_pixel_ratio > 0:
img2 = add_black_margin(img2,
add_black_pixel_ratio=add_black_pixel_ratio)
img2 = cv2.resize(img2, (crop_size, crop_size))
if img_file_dest is not None:
os.makedirs(os.path.dirname(img_file_dest), exist_ok=True)
cv2.imwrite(img_file_dest, img2)
return img2
def crop_posterior(img_source, circle_center, circle_diameter, diameter_times=3,
crop_circle=True, image_size=None):
if isinstance(img_source, str):
img_source = cv2.imread(img_source)
else:
img_source = img_source
img_source = img_source.astype(np.uint8)
circle_posterior = int(circle_diameter * diameter_times)
'''
(height, width) = img_file_source.shape[:-1]
if (circle_center[0] - circle_posterior) < 0:
out_of_region_left = True
# circle_center[0] += abs(circle_center[0] - circle_posterior)
else:
out_of_region_left = False
if (circle_center[0] + circle_posterior) > width:
out_of_region_right = True
# circle_center[0] -= circle_center[0] + circle_posterior
else:
out_of_region_right = False
if (circle_center[1] - circle_posterior) < 0:
out_of_region_bottom = True
# circle_center[1] += abs(circle_center[1] - circle_posterior)
else:
out_of_region_bottom = False
if (circle_center[1] + circle_posterior) > height:
out_of_region_top = True
# circle_center[1] -= circle_center[1] + circle_posterior
else:
out_of_region_top = False
'''
add_border_padding = 200
img_source = image_border_padding(img_source, padding_left=add_border_padding, padding_right=add_border_padding,
padding_top=add_border_padding, padding_bottom=add_border_padding)
circle_center1 = (circle_center[0] + add_border_padding, circle_center[1] + add_border_padding)
if crop_circle:
image_circle = np.zeros(img_source.shape)
cv2.circle(image_circle, circle_center1, circle_posterior, (255, 255, 255), -1)
# cv2.imwrite('/tmp4/aa0.jpg', img_file_source)
# cv2.imwrite('/tmp4/aa1.jpg', image_circle)
image_circle = image_circle // 255
img_source = np.multiply(image_circle, img_source)
# cv2.imwrite('/tmp4/aa2.jpg', img_file_source)
img_crop_optic_disc = img_source[circle_center1[1] - circle_posterior:circle_center1[1] + circle_posterior,
circle_center1[0] - circle_posterior:circle_center1[0] + circle_posterior, :] # width, height, channel
from LIBS.ImgPreprocess.my_image_helper import image_to_square
img_crop_optic_disc = image_to_square(img_crop_optic_disc, image_size=image_size)
return img_crop_optic_disc
def detect_optic_disc(img_file_source,
preprocess=False,
img_file_blood_seg=None):
str_uuid = str(uuid.uuid1())
os.makedirs(os.path.join(dir_tmp, str_uuid), exist_ok=True)
if preprocess:
img_file_preprocess = os.path.join(dir_tmp, str_uuid,
'preproces384.jpg')
# from LIBS.ImgPreprocess.my_preprocess_dir import preprocess_image
# preprocess_image(img_file_source, img_file_preprocess,
# crop_size=384, is_rop=False, add_black_pixel_ratio=0.07)
from LIBS.ImgPreprocess.my_rop import resize_rop_image
img_preprocess = resize_rop_image(img_file_source,
image_to_square=True,
output_shape=(384, 384))
cv2.imwrite(img_file_preprocess, img_preprocess)
else:
img_file_preprocess = img_file_source
#mask rcnn (384,384,3)
img_file_mask_tmp = os.path.join(dir_tmp, str_uuid, 'mask.jpg')
(confidence, img_file_mask, circle_center, circle_diameter) = \
seg_optic_disc(model, img_file_preprocess,
img_file_mask_tmp, image_shape=image_shape, return_optic_disc_postition=True)
if confidence is not None:
img_file_draw_circle = os.path.join(dir_tmp, str_uuid,
'draw_circle.jpg')
img_file_crop_optic_disc = os.path.join(dir_tmp, str_uuid,
'crop_optic_dsc.jpg')
img_draw_circle = optic_disc_draw_circle(img_file_preprocess,
circle_center,
circle_diameter,
diameter_times=3)
cv2.imwrite(img_file_draw_circle, img_draw_circle)
img_crop_optic_disc = crop_posterior(
img_file_preprocess,
circle_center,
circle_diameter,
diameter_times=my_config.posterior_diameter_times,
image_size=299,
crop_circle=False)
cv2.imwrite(img_file_crop_optic_disc, img_crop_optic_disc)
if img_file_blood_seg is None:
return float(confidence[0]), img_file_mask_tmp, img_file_crop_optic_disc,\
img_file_draw_circle
else:
img_file_blood_vessel_seg_posterior = os.path.join(
dir_tmp, str_uuid, 'blood_vessel_seg_posterior.jpg')
from LIBS.ImgPreprocess.my_image_helper import image_to_square
img_tmp1 = image_to_square(img_file_blood_seg, image_size=384)
img_blood_vessel_seg_posterior = crop_posterior(
img_tmp1,
circle_center,
circle_diameter,
diameter_times=my_config.posterior_diameter_times,
image_size=299,
crop_circle=False)
cv2.imwrite(img_file_blood_vessel_seg_posterior,
img_blood_vessel_seg_posterior)
return float(confidence[0]), img_file_mask_tmp, img_file_crop_optic_disc, \
img_file_draw_circle, img_file_blood_vessel_seg_posterior
else:
if img_file_blood_seg is None:
# return None, None, None, None #TypeError: cannot marshal None unless allow_none is enabled
return -1, -1, -1, -1
else:
# return None, None, None, None, None
return -1, -1, -1, -1, -1
for dir_path, subpaths, files in os.walk(dir_preprocess, False):
for f in files:
image_file = os.path.join(dir_path, f)
(filepath, tempfilename) = os.path.split(image_file)
(filename, extension) = os.path.splitext(tempfilename)
if filename.endswith('_mask'):
continue
if not extension.upper() in [
'.BMP', '.PNG', '.JPEG', '.JPG', '.TIFF', '.TIF'
]:
continue
if IMAGE_TO_SQUARE:
img_input = image_to_square(image_file)
else:
img_input = image_file
from LIBS.ImgPreprocess.my_patches_based_seg import seg_blood_vessel
img_result = seg_blood_vessel(img_input,
dicts_models,
PATCH_H,
PATCH_W,
rop_resized=True,
threshold=127,
min_size=10,
tmp_dir='/tmp',
test_time_image_aug=True)
img_file_preprocess384,
circle_center,
circle_diameter,
diameter_times=3,
image_size=299,
crop_circle=CROP_POSTERIOR_CIRCLE)
img_file_crop_optic_disc = img_file_preprocess384.replace(
dir_preprocess384, dir_crop_optic_disc)
if not os.path.exists(
os.path.dirname(img_file_crop_optic_disc)):
os.makedirs(os.path.dirname(img_file_crop_optic_disc))
cv2.imwrite(img_file_crop_optic_disc, img_crop_optic_disc)
if DO_BLOOD_VESSEL_SEG_POSTERIOR:
img_blood_vessel_seg = image_to_square(
img_file_preprocess384.replace(dir_preprocess384,
dir_blood_vessel_seg))
img_blood_vessel_seg = cv2.resize(img_blood_vessel_seg,
(384, 384))
img_blood_vessel_seg_posterior = crop_posterior(
img_blood_vessel_seg,
circle_center,
circle_diameter,
diameter_times=3,
image_size=299,
crop_circle=CROP_POSTERIOR_CIRCLE)
img_file_blood_vessel_seg_posterior = img_file_preprocess384.replace(
dir_preprocess384, dir_blood_vessel_seg_posterior)
if not os.path.exists(
os.path.dirname(
row[0] + '.jpg')
if 'Testing' in file_type:
image_file_original = os.path.join(dir_original, 'TestingSet',
row[0] + '.jpg')
x, y = row[1], row[2]
image_original = cv2.imread(image_file_original)
(height, width) = image_original.shape[:-1]
(bottom, top, left, right) = get_position(image_file_original)
img_preprocess = crop_image(image_original, bottom, top, left,
right)
x, y = x - left, y - bottom
(height, width) = img_preprocess.shape[:-1]
img_preprocess = image_to_square(img_preprocess,
image_size=preprocess_image_size)
if width > height:
y += math.ceil((width - height) / 2)
elif height > width:
x += math.ceil((height - width) / 2)
rescale_ratio = max(width, height) / preprocess_image_size
x /= rescale_ratio
y /= rescale_ratio
image_file_preprocess = image_file_original.replace(
dir_original, dir_preprocess)
os.makedirs(os.path.dirname(image_file_preprocess), exist_ok=True)
cv2.imwrite(image_file_preprocess, img_preprocess)
csv_writer.writerow(
[image_file_preprocess,
round(x, 2), round(y, 2)])
# filename, file_extension = os.path.splitext(img_file_source)
#
# if file_extension == '.gif':
# img_file_dest = img_file_source.replace('.gif', '.png')
#
# img1 = imageio.imread(img_file_source)
# print(img_file_dest)
# imageio.imwrite(img_file_dest, img1)
filename_csv = 'BloodVessel384.csv'
df = pd.read_csv(filename_csv) # panda dataframe
count = len(df.index)
for i in range(count):
img_file = df.at[i, 'images']
img_mask = df.at[i, 'masks']
if not 'HRF' in img_file:
continue
print(img_file)
img1 = cv2.imread(img_file)
img2 = image_to_square(img1)
cv2.imwrite(img_file, img2)
img3 = cv2.imread(img_mask)
img4 = image_to_square(img3)
cv2.imwrite(img_mask, img4)
print('OK')