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detector.py
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detector.py
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########################################################################################
# Tomas Sykora, 2017 #
# Optic disc in a retina image detection in TensorFlow #
########################################################################################
import os
import tensorflow as tf
import numpy as np
from scipy.misc import imread, imresize, imsave
import time
from scipy import misc, ndimage
import random
from scipy.signal import medfilt
import cv2
import math
class optic_disc_detector:
def __init__(self, sess=None):
self.imgs = tf.placeholder(tf.float32, [None, 45, 42, 1])
self.train_mode = tf.placeholder(tf.bool)
self.convlayers()
self.output = self.conv1_6
self.sess = sess
def convlayers(self):
print('\nconvlayers(): Initializing layers')
self.parameters = []
# normalize input
with tf.name_scope('preprocess') as scope:
images = self.imgs/255 - 0.5
with tf.name_scope('conv1_1') as scope:
kernel = tf.get_variable(initializer=tf.keras.initializers.he_normal(), shape=[3, 3, 1, 8], name='weights1_1')
conv = tf.nn.conv2d(images, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[8], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv1_1 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
self.pool1 = tf.nn.max_pool(self.conv1_1,
ksize=[1, 3, 3, 1],
strides=[1, 3, 3, 1],
padding='SAME',
name='pool1')
self.dropout1 = tf.layers.dropout(self.pool1,
rate=0.2,
training=self.train_mode,
name='dropout1')
with tf.name_scope('conv1_2') as scope:
kernel = tf.get_variable(initializer=tf.keras.initializers.he_normal(), shape=[3, 3, 8, 16], name='weights1_2')
conv = tf.nn.conv2d(self.dropout1, kernel, [1, 1, 1, 1], padding='SAME')
biases = tf.Variable(tf.constant(0.0, shape=[16], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv1_2 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
self.pool2 = tf.nn.max_pool(self.conv1_2,
ksize=[1, 3, 3, 1],
strides=[1, 3, 3, 1],
padding='SAME',
name='pool2')
self.dropout2 = tf.layers.dropout(self.pool2,
rate=0.2,
training=self.train_mode,
name='dropout2')
with tf.name_scope('conv1_3') as scope:
kernel = tf.get_variable(initializer=tf.keras.initializers.he_normal(), shape=[3, 3, 16, 32], name='weights1_3')
conv = tf.nn.conv2d(self.dropout2, kernel, [1, 1, 1, 1], padding='VALID')
biases = tf.Variable(tf.constant(0.0, shape=[32], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv1_3 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
self.pool3 = tf.nn.max_pool(self.conv1_3,
ksize=[1, 3, 3, 1],
strides=[1, 3, 3, 1],
padding='SAME',
name='pool3')
self.dropout3 = tf.layers.dropout(self.pool3,
rate=0.2,
training=self.train_mode,
name='dropout3')
with tf.name_scope('conv1_4') as scope:
kernel = tf.get_variable(initializer=tf.keras.initializers.he_normal(), shape=[1, 1, 32, 16], name='weights1_4')
conv = tf.nn.conv2d(self.dropout3, kernel, [1, 1, 1, 1], padding='VALID')
biases = tf.Variable(tf.constant(0.0, shape=[16], dtype=tf.float32),
trainable=True, name='biases')
out = tf.nn.bias_add(conv, biases)
self.conv1_4 = tf.nn.relu(out, name=scope)
self.parameters += [kernel, biases]
with tf.name_scope('conv1_6') as scope:
kernel = tf.get_variable(initializer=tf.keras.initializers.he_normal(), shape=[1, 1, 16, 2], name='weights1_6')
conv = tf.nn.conv2d(self.conv1_4, kernel, [1, 1, 1, 1], padding='VALID')
biases = tf.Variable(tf.constant(0.0, shape=[2], dtype=tf.float32),
trainable=True, name='biases')
self.conv1_6 = tf.nn.bias_add(conv, biases)
self.parameters += [kernel, biases]
self.saver = tf.train.Saver({'W1': self.parameters[0], 'b1': self.parameters[1],
'W2': self.parameters[2], 'b2': self.parameters[3],
'W3': self.parameters[4], 'b3': self.parameters[5],
'W4': self.parameters[6], 'b4': self.parameters[7],
'W5': self.parameters[8], 'b5': self.parameters[9]})
print('output: ', np.shape(self.conv1_6))
def extract_patches(self, image, patchshape, overlap_allowed=0.5, cropvalue=None,
crop_fraction_allowed=0.1):
"""
Given an image, extract patches of a given shape with a certain
amount of allowed overlap between patches, using a heuristic to
ensure maximum coverage.
If cropvalue is specified, it is treated as a flag denoting a pixel
that has been cropped. Patch will be rejected if it has more than
crop_fraction_allowed * prod(patchshape) pixels equal to cropvalue.
Likewise, patches will be rejected for having more overlap_allowed
fraction of their pixels contained in a patch already selected.
"""
jump_cols = int(patchshape[1] * overlap_allowed)
jump_rows = int(patchshape[0] * overlap_allowed)
# Restrict ourselves to the rectangle containing non-cropped pixels
if cropvalue is not None:
rows, cols = np.where(image != cropvalue)
rows.sort(); cols.sort()
active = image[rows[0]:rows[-1], cols[0]:cols[-1]]
else:
active = image
rowstart = 0; colstart = 0
# Array tracking where we've already taken patches.
covered = np.zeros(active.shape, dtype=bool)
patches = []
while rowstart < active.shape[0] - patchshape[0]:
# Record whether or not e've found a patch in this row,
# so we know whether to skip ahead.
got_a_patch_this_row = False
colstart = 0
while colstart < active.shape[1] - patchshape[1]:
# Slice tuple indexing the region of our proposed patch
region = (slice(rowstart, rowstart + patchshape[0]),
slice(colstart, colstart + patchshape[1]))
# The actual pixels in that region.
patch = active[region]
# The current mask value for that region.
cover_p = covered[region]
if cropvalue is None or \
frac_eq_to(patch, cropvalue) <= crop_fraction_allowed and \
frac_eq_to(cover_p, True) <= overlap_allowed:
# Accept the patch.
patches.append(patch)
# Mask the area.
covered[region] = True
# Jump ahead in the x direction.
colstart += jump_cols
got_a_patch_this_row = True
#print "Got a patch at %d, %d" % (rowstart, colstart)
else:
# Otherwise, shift window across by one pixel.
colstart += 1
if got_a_patch_this_row:
# Jump ahead in the y direction.
rowstart += jump_rows
else:
# Otherwise, shift the window down by one pixel.
rowstart += 1
# Return a 3D array of the patches with the patch index as the first
# dimension (so that patch pixels stay contiguous in memory, in a
# C-ordered array).
return np.concatenate([pat[np.newaxis, ...] for pat in patches], axis=0)
def preprocess(self, images):
new_images = []
for i, img in enumerate(images):
clahe = cv2.createCLAHE(clipLimit=10.0, tileGridSize=(40,40))
img = clahe.apply(img)
img = cv2.bilateralFilter(img, -1, 20, 20)
intensities = medfilt(img, (21, 21))
intensities = intensities.astype(np.float32)
intensities_smoothed = cv2.bilateralFilter(intensities, -1, 70, 13)
width, height = img.shape
img[0:width, 0:height] = img[0:width, 0:height] + (90) - intensities_smoothed[0:width, 0:height]
idx = img[:] > 210
img[idx] = 18
clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(1,1))
img = clahe.apply(img)
new_images.append(img)
return np.array(new_images)
def eval_test_img(self, sess, idx, img, gt):
test = img[np.newaxis, ... ]
pred = sess.run(self.output, feed_dict={
self.imgs: test,
self.train_mode: False
})
print('TEST_set[', idx, ']:')
print('gr_t: ', gt)
print('pred: ', pred)
def exctract_disc_patches(self, images, ground_truth):
""" Exctract patches with zero distance from otic disc. """
patches = []
img_h, img_w = images[0].shape
for i, img in enumerate(images):
center_x = int(ground_truth[i,1])
center_y = int(ground_truth[i,0])
half_win_h = 22
half_win_w = 21
x1 = center_x - half_win_w
y1 = center_y - half_win_h
if x1 < 0:
x1 = 0
if y1 < 0:
y1 = 0
if (x1 + 42) >= img_w:
x1 = img_w - 42
if (y1 + 45) >= img_h:
y1 = img_h - 45
x2 = x1 + 42
y2 = y1 + 45
patch = img[y1:y2, x1:x2]
patches.append(patch)
#patches.append(ndimage.rotate(patch, 180))
return np.array(patches)
def load_data(self, images_folder, gt_file):
images = []
for filename in os.listdir(images_folder):
img = imread(os.path.join(images_folder, filename), mode='L')
if img is not None:
images.append(imresize(img, (201, 233)))
train_images = np.array(images)
train_output = np.loadtxt(gt_file)
# delete images without annotations (values (-1, -1))
indices_to_delete = ~(train_output==-1).any(1)
train_images = train_images[indices_to_delete]
train_output = train_output[indices_to_delete]
# resize output
train_output = np.rint(train_output / 3)
return train_images, train_output
def make_patches(self, images, gt):
train_patches = []
patch_dists = [] # ground truth distance of a patch from the optic disc
print("Creating patches...")
for img_id, img in enumerate(images):
# the less overlap allowed, the more patches created
patches = self.extract_patches(img, (45, 42), overlap_allowed=0.2, cropvalue=None, crop_fraction_allowed=0.1)
train_patches.extend(patches)
for patch_id, patch in enumerate(patches):
patch_x2d = patch_id % 24 # patches in a row
patch_y2d = patch_id / 24 # patches in a column
mid_x = patch_x2d * 8 + 21 # column step + width/2
mid_y = patch_y2d * 9 + 23 # row step + height/2
x_offset = gt[img_id, 1] - mid_x
y_offset = gt[img_id, 0] - mid_y
offset = np.array([y_offset, x_offset])
patch_dists.append(offset)
return np.array(train_patches), np.array(patch_dists)
def shuffle(self, images, gt):
shuffle = list(zip(images, gt))
random.shuffle(shuffle)
images, gt = zip(*shuffle)
return np.array(images), np.array(gt)
def prepare_test_data(self):
images_folder = './images/test/'
ground_truth = './images/gt_test.txt'
train_images, train_output = self.load_data(images_folder, ground_truth)
train_images = self.preprocess(train_images)
disc_patches = self.exctract_disc_patches(train_images, train_output)
zero_dists = np.zeros((len(disc_patches), 2))
print('Disc patches: ', np.shape(disc_patches))
train_images, train_output = self.make_patches(train_images, train_output)
print('Orig patches: ', np.shape(train_images))
train_output = np.concatenate((train_output, zero_dists), axis=0)
train_images = np.concatenate((train_images, disc_patches), axis=0)
# randomly shuffle train array
train_images, train_output = self.shuffle(train_images, train_output)
print("Train samples:", np.shape(train_images))
print("Output samples:", np.shape(train_output))
# create tensors
train_output = train_output[:, np.newaxis, np.newaxis, : ]
train_images = train_images[..., np.newaxis]
return train_images, train_output
def train(self, images_folder, ground_truth):
print('train(): Images processing')
train_images, train_output = self.load_data(images_folder, ground_truth)
train_images = self.preprocess(train_images)
disc_patches = self.exctract_disc_patches(train_images, train_output)
zero_dists = np.zeros((len(disc_patches), 2))
print('Disc patches: ', np.shape(disc_patches))
train_images, train_output = self.make_patches(train_images, train_output)
print('Orig patches: ', np.shape(train_images))
train_output = np.concatenate((train_output, zero_dists), axis=0)
train_images = np.concatenate((train_images, disc_patches), axis=0)
# randomly shuffle train array
train_images, train_output = self.shuffle(train_images, train_output)
print("Train samples:", np.shape(train_images))
print("Output samples:", np.shape(train_output))
# create tensors
train_output = train_output[:, np.newaxis, np.newaxis, : ]
train_images = train_images[..., np.newaxis]
train_set = train_images
train_y = train_output
print("train_y: ", np.shape(train_y))
print("train_set: ", np.shape(train_set))
test_images, test_output = self.prepare_test_data()
test_set = test_images[:130, ...]
test_y = test_output[:130, ...]
print("test_y: ", np.shape(test_y))
print("test_set: ", np.shape(test_set))
y = tf.placeholder("float32", [None, 1, 1, 2])
cost = tf.reduce_mean(tf.losses.mean_squared_error(labels=y, predictions=self.output))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
training_epochs = 150
display_step = 4
batch_size = 130
with self.sess as sess:
print('train(): Training started')
sess.run(tf.global_variables_initializer())
for epoch in range(training_epochs):
avg_cost = 0.0
total_batch = int(len(train_set) / batch_size)
x_batches = np.array_split(train_set, total_batch)
y_batches = np.array_split(train_y, total_batch)
for i in range(total_batch):
batch_x, batch_y = x_batches[i], y_batches[i]
_, c = sess.run([optimizer, cost],
feed_dict={
self.imgs: batch_x,
y: batch_y,
self.train_mode: True
})
avg_cost += c / total_batch
if epoch % display_step == 0:
print("\nEpoch:", '%04d' % (epoch+1), "\nmse(train_set)=", \
"{:.9f}".format(avg_cost))
pred_y = sess.run(self.output,
feed_dict={
self.imgs: test_set,
self.train_mode: False
})
mse = tf.reduce_mean(tf.square(pred_y - test_y))
print("MSE(test_set): %.4f" % sess.run(mse))
self.eval_test_img(sess, 90, test_set[90], test_y[90])
self.eval_test_img(sess, 100, test_set[100], test_y[100])
self.eval_test_img(sess, 20, test_set[20], test_y[20])
self.saver.save(sess, './model')
def fine_tune(self, images_folder, ground_truth):
""" Fine tuning with patches containing disc patches (400),
and with normal patches (1000). This helps the model to
converge and stay on the position of otpic disc, if found.
"""
train_images, train_output = self.load_data(images_folder, ground_truth)
train_images = self.preprocess(train_images)
disc_patches = self.exctract_disc_patches(train_images, train_output)
zero_dists = np.zeros((len(disc_patches), 2))
print('Disc patches: ', np.shape(disc_patches))
train_images, train_output = self.make_patches(train_images, train_output)
print('Orig patches: ', np.shape(train_images))
train_images, train_output = self.shuffle(train_images, train_output)
print("Train samples:", np.shape(train_images))
print("Output samples:", np.shape(train_output))
train_images = train_images[:1000, ...]
train_output = train_output[:1000, ...]
train_output = np.concatenate((train_output, zero_dists), axis=0)
train_images = np.concatenate((train_images, disc_patches), axis=0)
train_images, train_output = self.shuffle(train_images, train_output)
# create tensors
train_y = train_output[:, np.newaxis, np.newaxis, : ]
train_set = train_images[..., np.newaxis]
print("train_y: ", np.shape(train_y))
print("train_set: ", np.shape(train_set))
y = tf.placeholder("float32", [None, 1, 1, 2])
cost = tf.reduce_mean(tf.losses.mean_squared_error(labels=y, predictions=self.output))
optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(cost)
training_epochs = 50
display_step = 4
batch_size = 130
print('Starting session')
with self.sess as sess:
sess.run(tf.global_variables_initializer())
print('train(): Training started')
self.saver.restore(sess, './model')
print('Model restored')
for epoch in range(training_epochs):
avg_cost = 0.0
total_batch = int(len(train_set) / batch_size)
x_batches = np.array_split(train_set, total_batch)
y_batches = np.array_split(train_y, total_batch)
for i in range(total_batch):
batch_x, batch_y = x_batches[i], y_batches[i]
_, c = sess.run([optimizer, cost],
feed_dict={
self.imgs: batch_x,
y: batch_y,
self.train_mode: True
})
avg_cost += c / total_batch
if epoch % display_step == 0:
print("\nEpoch:", '%04d' % (epoch+1), "\nmse(train_set)=", \
"{:.9f}".format(avg_cost))
self.saver.save(sess, './model_tuned')
def detect(self, image_file):
image_orig = cv2.imread(image_file, 0)
image_orig = imresize(image_orig, (201, 250))
""" Preprocessing """
clahe = cv2.createCLAHE(clipLimit=10.0, tileGridSize=(40,40))
img = clahe.apply(image_orig)
img = cv2.bilateralFilter(img, -1, 20, 20)
intensities = medfilt(img, (21, 21))
intensities = intensities.astype(np.float32)
intensities_smoothed = cv2.bilateralFilter(intensities, -1, 70, 13)
width, height = img.shape
img[0:width, 0:height] = img[0:width, 0:height] + (90) - intensities_smoothed[0:width, 0:height]
idx = img[:] > 210
img[idx] = 18
clahe = cv2.createCLAHE(clipLimit=1.0, tileGridSize=(1,1))
img = clahe.apply(img)
img_h, img_w = img.shape
patch_width = 42
patch_height = 45
start_idx = 60
start_idy = 40
input_img = img[start_idy:start_idy+patch_height, start_idx:start_idx+patch_width].copy()
input_img = input_img[np.newaxis, ..., np.newaxis]
iters = 20
with self.sess as sess:
self.saver.restore(sess, './model_tuned')
print('After displaying the image, press any key to continue. (', iters, ' steps)')
for i in range(iters):
print('Iter: ', i, '/', iters-1)
pred = sess.run(self.output,
feed_dict={
self.imgs: input_img,
self.train_mode: False
})
start_idx = start_idx + int(pred[0,0,0,1])
start_idy = start_idy + int(pred[0,0,0,0])
if start_idx < 0:
start_idx = 0
if start_idy < 0:
start_idy = 0
if (start_idx + patch_width) >= img_w:
start_idx = img_w - patch_width
if (start_idy + patch_height) >= img_h:
start_idy = img_h - patch_height
end_idx = start_idx + patch_width
end_idy = start_idy + patch_height
input_img = img[start_idy:end_idy, start_idx:end_idx].copy()
input_img = input_img[np.newaxis, ..., np.newaxis]
img_show = image_orig.copy()
cv2.imshow('detection', cv2.rectangle(img_show,(start_idx,start_idy),(end_idx,end_idy),(0,255,0),1))
cv2.waitKey(0)
def eval_model(self, images_folder, gt_file):
images = []
for filename in os.listdir(images_folder):
img = cv2.imread(os.path.join(images_folder, filename), 0)
if img is not None:
images.append(imresize(img, (201, 250)))
train_images = np.array(images)
train_images = self.preprocess(train_images)
img_h, img_w = images[0].shape
print(img_h, img_w)
with self.sess as sess:
self.saver.restore(sess, './model_tuned')
output = []
patch_width = 42
patch_height = 45
images_cnt = len(train_images)
for img in train_images:
start_idx = 60
start_idy = 40
input_img = img[start_idy:start_idy+patch_height, start_idx:start_idx+patch_width]
input_img = input_img[np.newaxis, ..., np.newaxis]
iters = 15
for i in range(iters):
pred = sess.run(self.output,
feed_dict={
self.imgs: input_img,
self.train_mode: False
})
start_idx = start_idx + int(pred[0,0,0,1])
start_idy = start_idy + int(pred[0,0,0,0])
if start_idx < 0:
start_idx = 0
if start_idy < 0:
start_idy = 0
if (start_idx + patch_width) >= img_w:
start_idx = img_w - patch_width
if (start_idy + patch_height) >= img_h:
start_idy = img_h - patch_height
end_idx = start_idx + patch_width
end_idy = start_idy + patch_height
input_img = img[start_idy:end_idy, start_idx:end_idx]
input_img = input_img[np.newaxis, ..., np.newaxis]
x_final = start_idx + 21
y_final = start_idy + 23
output.append(np.array([y_final, x_final]))
output = np.array(output)
ground_truth = np.loadtxt(gt_file)
total = 0
radius_10 = 0
radius_20 = 0
for i, x in enumerate(ground_truth):
d = math.sqrt((x[0]-output[i,0])**2 + ((x[1]-output[i,1])**2))
total += d
if d < 10:
radius_10 += 1
if d < 20:
radius_20 += 1
total = total / images_cnt
print('Avg. distance from gt: ', total)
print('Detections with distance under 10: ', radius_10)
print('Detections with distance under 20: ', radius_20)
def parseArguments():
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--train', help='Train mode.', action='store_true')
parser.add_argument('--detect', help='Single image detection.', action='store_true')
parser.add_argument('--finetune', help='Fine tuning the trainedmodel.', action='store_true')
parser.add_argument('--eval', help='Final evaluation on imageret database.', action='store_true')
args = parser.parse_args()
return args
if __name__ == '__main__':
sess = tf.Session()
detector = optic_disc_detector(sess)
args = parseArguments()
if args.train is True:
detector.train('.images/train/', '.images/gt_train.txt')
elif args.finetune is True:
detector.fine_tune('./images/train/', './images/gt_train.txt')
elif args.detect is True:
detector.detect('./test_image.png')
elif args.eval is True:
detector.eval_model('./images', './images/imageret_gt.txt')
else:
print('No argument set.')