def detect_vessels_3(img, sigma=16, gabor_perc=95.0, one_channel_img=False):
# Preprocessing
if one_channel_img:
intensity = img.copy()
else:
intensity = img.mean(axis=2) / 256.0
show_image(intensity); plt.axis('off'); plt.savefig('../presentation/pics/vessels/1.png')
intensity = skimage.exposure.equalize_adapthist(intensity)
intensity -= mh.median_filter(intensity, Bc=np.ones((25, 25)))
show_image(intensity); plt.axis('off'); plt.savefig('../presentation/pics/vessels/2.png')
intensity = normalize_image(intensity)
intensity_inv = 1.0 - intensity
show_image(intensity_inv); plt.axis('off'); plt.savefig('../presentation/pics/vessels/3.png')
vessels = modified_gabor_convolve(intensity_inv, sigma)
show_image(vessels); plt.axis('off'); plt.savefig('../presentation/pics/vessels/4.png')
# Thresholding result
gabor_thresholded = vessels > np.percentile(vessels.ravel(), gabor_perc)
gabor_thresholded = skimage.morphology.remove_small_objects(gabor_thresholded, min_size=200)
show_image(gabor_thresholded); plt.axis('off'); plt.savefig('../presentation/pics/vessels/5.png')
return gabor_thresholded
def hemorrhages_detect(img):
moat = moat_operator(img, sigma=5.0)
show_image(moat); plt.axis('off'); plt.savefig('../presentation/pics/hemorrhages/1.png')
m_clahe = skimage.exposure.equalize_adapthist(moat)
m_med = mh.median_filter(m_clahe, Bc=np.ones((5, 5)))
show_image(m_med); plt.axis('off'); plt.savefig('../presentation/pics/hemorrhages/2.png')
threshold_global_otsu = skimage.filters.threshold_otsu(m_med)
print 'Otsu threshold:', threshold_global_otsu
segmented = m_med <= 0.9 * threshold_global_otsu
show_image(segmented); plt.axis('off'); plt.savefig('../presentation/pics/hemorrhages/3.png')
#vessels = od.detect_vessels_3(img)
vessels = detect_vessels_3(m_med, one_channel_img=True, gabor_perc=88.0)
vessels = skimage.morphology.remove_small_objects(vessels, min_size=150)
segmented[mh.dilate(vessels, Bc=np.ones((10, 10)))] = False
show_image(segmented); plt.axis('off'); plt.savefig('../presentation/pics/hemorrhages/4.png')
segm_label = skimage.measure.label(segmented)
segm_eccen = np.array(map(attrgetter('eccentricity'), skimage.measure.regionprops(segm_label + 1)))
segm_area = cl_areas(segm_label)
#segm_circ = cf.cl_circularity(segm_label)
segm_filtered = leave_segments_by_mask(segm_label, (segm_eccen < 0.85) & (segm_area < 30000),
replace_value=0) != 0
return segm_filtered
selected |= (edges_fzw == segm)
segm_mean.append(img[(edges_fzw == segm) & (img < 0.95)].mean())
plt.figure(figsize=(15, 10))
plt.title("Mean color of segments")
plt.plot(segm_mean)
all_mean = img[selected & (img < 0.95)].mean()
all_var = img[selected & (img < 0.95)].var()
#print all_mean, all_var
#all_mean = np.mean(segm_mean)
#all_var = np.var(segm_mean)
#print all_mean, all_var
segments_to_leave_2 = []
for i, el in enumerate(segm_mean):
#if abs(el - all_mean) < mean_color_threshold:
if abs(el - all_mean) < 15 * all_var:
segments_to_leave_2.append(segments_to_leave_1[i])
blanked = leave_segments(img, edges_fzw, segments_to_leave_2, img_orig)
#print "error for train image: ", rmse(y_train[0], X_train[0])
#print "error for train image: ", rmse(y_train[0], blanked)
#show_image(blanked)
return blanked
img_idx = 0
blanked = blank_by_edges_and_fzw(X_train[img_idx], X_train_orig[img_idx])
print "error for train image: ", rmse(y_train[img_idx], X_train[img_idx])
print "error for train image: ", rmse(y_train[img_idx], blanked)
show_image(blanked)
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