def optic_disk_detect_3(img):
'''
Method that seems to work well with DIARETDB1 database.
'''
hsi = rgb_to_hsi(img)
intensity = hsi[:, :, 2].copy()
#plt.axis('off'); show_image(intensity)
#i_sp = add_salt_and_pepper(intensity, 0.005)
i_med = mh.median_filter(intensity) # use Wiener filter instead?
i_clahe = skimage.exposure.equalize_adapthist(i_med)
#plt.axis('off'); show_image(i_clahe)
seeds = (i_clahe > 0.85)
seeds = skimage.morphology.remove_small_objects(seeds, min_size=300, connectivity=2)
#plt.axis('off'); show_image(seeds)
optic_disk_map = region_growing_1(i_clahe, radius=3, tol1=0.1, tol2=0.2, tol3=0.2, seeds=seeds)
optic_disk_map += 1
#plt.axis('off'); show_image(optic_disk_map)
_cl_areas = cl_areas(optic_disk_map)
print _cl_areas
optic_disk_map = leave_segments_by_mask(optic_disk_map,
(8000 < _cl_areas) & (_cl_areas < 30000))
#optic_disk_map = skimage.morphology.remove_small_objects(optic_disk_map, min_size=500, connectivity=2)
optic_disk_map = mh.close_holes(mh.close(optic_disk_map, Bc=np.ones((10, 10))))
#optic_disk_map = skimage.morphology.remove_small_objects(optic_disk_map, min_size=5000, connectivity=2)
if np.all(optic_disk_map == 0):
print 'Disk not found'
return optic_disk_map
def optic_disk_detect_2(img):
hsi = rgb_to_hsi(img)
intensity = hsi[:, :, 2].copy()
i_sp = add_salt_and_pepper(intensity, 0.005)
i_med = mh.median_filter(i_sp)
i_clahe = skimage.exposure.equalize_adapthist(i_med)
optic_disk_map = (i_clahe > 0.6) & (hsi[:, :, 1] < 0.3)
#show_image(optic_disk_map)
optic_disk_map = skimage.morphology.remove_small_objects(optic_disk_map, min_size=500, connectivity=2)
optic_disk_map = mh.close_holes(mh.close(optic_disk_map, Bc=np.ones((30, 30))))
optic_disk_map = skimage.morphology.remove_small_objects(optic_disk_map, min_size=10000, connectivity=2)
if np.all(optic_disk_map == 0):
print 'Disk not found'
return optic_disk_map
def exudates_get_features_3(folder, elements_folder, fundus_mask, num_imgs=10,
stride_width=6):
thr_high = 189.0 / 255.0
y = []
pxl_df = pd.DataFrame(columns=['hue', 'intensity', 'mean intensity', 'std intensity', 'dist to optic disk'])
for img_fn in image_names_in_folder(folder)[:num_imgs]:
print 'Image filename:', img_fn
img = load_image(img_fn)
short_fn = os.path.split(img_fn)[-1]
hard_exud = load_image(os.path.join(elements_folder, short_fn))
hsi = rgb_to_hsi(img)
intensity = hsi[:, :, 2].copy()
#i_sp = add_salt_and_pepper(intensity, 0.005)
i_med = mh.median_filter(intensity) # use Wiener filter instead?
i_clahe = skimage.exposure.equalize_adapthist(i_med)
optic_disk_map = optic_disk_detect_3(img)
mask, optic_disk_center = optic_disk_mask(optic_disk_map, return_center=True)
print 'Disk center: ', optic_disk_center
fundus_wo_disk_mask = fundus_mask & (~mask)
pxl_mask = np.zeros_like(i_clahe, dtype=np.bool)
pxl_mask[::stride_width, ::stride_width] = True
pxl_mask[~fundus_wo_disk_mask] = False
bbox = bounding_box(hard_exud >= thr_high, bbox_radius_ratio=2)
pxl_mask &= bbox
cur_pxl_df = pd.DataFrame(columns=['hue', 'intensity', 'mean intensity', 'std intensity', 'dist to optic disk'])
cur_pxl_df['hue'] = hsi[:, :, 0][pxl_mask].ravel()
cur_pxl_df['intensity'] = i_clahe[pxl_mask].ravel()
cur_pxl_df['mean intensity'] = pf.pxls_mean_intensity(i_clahe, stride_width, neighbd_rad=7, mask=pxl_mask).ravel()
cur_pxl_df['std intensity'] = pf.pxls_std_intensity(i_clahe, stride_width, neighbd_rad=3, mask=pxl_mask).ravel()
cur_pxl_df['dist to optic disk'] = pf.pxls_dist_to_pnt(i_clahe.shape, optic_disk_center,
stride_width, mask=pxl_mask).ravel()
print 'Accounted pixels: ', len(cur_pxl_df)
y.append(hard_exud[pxl_mask]) # we can use fraction of white points in a neighborhood instead,
# but it becomes regression task then
pxl_df = pd.concat((pxl_df, cur_pxl_df), axis=0, ignore_index=True)
y = np.hstack(y)
y = y >= thr_high
return pxl_df, y
def hemorrhages_detect_3(img, fundus_mask, clf, stride_width=6):
#thr_high = 189.0 / 255.0
#img = imh.load_image(img_fn)
#short_fn = os.path.split(img_fn)[-1]
#hard_exud = imh.load_image(os.path.join(hard_exud_folder, short_fn))
hsi = rgb_to_hsi(img)
intensity = hsi[:, :, 2].copy()
#i_sp = add_salt_and_pepper(intensity, 0.005)
i_med = mh.median_filter(intensity) # use Wiener filter instead?
i_clahe = skimage.exposure.equalize_adapthist(i_med)
optic_disk_map = optic_disk_detect_3(img)
mask, optic_disk_center = optic_disk_mask(optic_disk_map, return_center=True)
#print 'Disk center: ', optic_disk_center
fundus_wo_disk_mask = fundus_mask & (~mask)
pxl_mask = np.zeros_like(i_clahe, dtype=np.bool)
pxl_mask[::stride_width, ::stride_width] = True
pxl_mask[~fundus_wo_disk_mask] = False
cur_pxl_df = pd.DataFrame(columns=['hue', 'saturation', 'intensity', 'mean intensity', 'std intensity'])
cur_pxl_df['hue'] = hsi[:, :, 0][::stride_width, ::stride_width].ravel()
cur_pxl_df['saturation'] = hsi[:, :, 1][::stride_width, ::stride_width].ravel()
cur_pxl_df['intensity'] = i_clahe[::stride_width, ::stride_width].ravel()
cur_pxl_df['mean intensity'] = pf.pxls_mean_intensity(i_clahe, stride_width, neighbd_rad=3).ravel()
cur_pxl_df['std intensity'] = pf.pxls_std_intensity(i_clahe, stride_width, neighbd_rad=7).ravel()
print 'Accounted pixels: ', len(cur_pxl_df)
scaler = sklearn.preprocessing.StandardScaler()
cur_pxl_df_scaled = scaler.fit_transform(cur_pxl_df)
pred = clf.predict(cur_pxl_df_scaled)
'''
pred_map = np.zeros_like(pxl_mask, dtype=np.uint8)
k = 0
for i in xrange(pred_map.shape[0]):
for j in xrange(pred_map.shape[1]):
if pxl_mask[i, j]:
pred_map[i, j] = 255 * pred[k]
if pred[k]:
cv2.circle(pred_map, (i, j), 8, (255, 255, 255))
k += 1
'''
pred_map = pred.reshape(pxl_mask[::stride_width, ::stride_width].shape)
show_image(pred_map)
pred_map &= pxl_mask[::stride_width, ::stride_width]
pred_map_full = np.zeros_like(pxl_mask, dtype=np.uint8)
pred_map_full[::stride_width, ::stride_width] = pred_map
pred_map_full_enl = np.zeros_like(pred_map_full, dtype=np.uint8)
for i in xrange(pred_map_full.shape[0]):
for j in xrange(pred_map_full.shape[1]):
if pred_map_full[i, j]:
#cv2.circle(pred_map_full_enl, (i, j), 3, (255, 255, 255))
sz = stride_width
pred_map_full_enl[i - sz + 1:i + sz, j - sz + 1:j + sz] = 1
return pred_map_full_enl