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run.py
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run.py
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#!/usr/bin/env python
# Copyright Aaron Lee, University of Washington 2017
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
from keras.models import Model
from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D
from keras.layers.core import Lambda
from keras.models import load_model
import sys
import numpy as np
from PIL import Image
import tensorflow as tf
import matplotlib
import matplotlib.pyplot as plt
from keras import backend as K
K.set_image_dim_ordering('th') # Theano dimension ordering in this code
import cv2
import argparse
parser = argparse.ArgumentParser()
parser.add_argument('--mode', default="mask_blend", type=str, help="Output mode, 'mask_blend' (default) for masked heatmap output, 'mask' for binary mask output, 'blend' for blended heatmap" )
parser.add_argument('input_file', help='Input PNG file')
parser.add_argument('output_file', help='Output PNG file')
args = parser.parse_args()
if not (args.mode == "mask_blend" or args.mode == "blend" or args.mode == "mask"):
print("Invalid mode: %s" % args.mode)
sys.exit()
modelfile = 'weights.hdf5'
image_rows = 432
image_cols = 32
def make_parallel(model, gpu_count):
def get_slice(data, idx, parts):
shape = tf.shape(data)
size = tf.concat(0, [ shape[:1] // parts, shape[1:] ])
stride = tf.concat(0, [ shape[:1] // parts, shape[1:]*0 ])
start = stride * idx
return tf.slice(data, start, size)
outputs_all = []
for i in range(len(model.outputs)):
outputs_all.append([])
for i in range(gpu_count):
with tf.device('/gpu:%d' % i):
with tf.name_scope('tower_%d' % i) as scope:
inputs = []
for x in model.inputs:
input_shape = tuple(x.get_shape().as_list())[1:]
slice_n = Lambda(get_slice, output_shape=input_shape, arguments={'idx':i,'parts':gpu_count})(x)
inputs.append(slice_n)
outputs = model(inputs)
if not isinstance(outputs, list):
outputs = [outputs]
for l in range(len(outputs)):
outputs_all[l].append(outputs[l])
with tf.device('/cpu:0'):
return Model(input=model.inputs, output=outputs)
def get_unet():
inputs = Input((1, image_rows, image_cols))
conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(inputs)
conv1 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(pool1)
conv2 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(pool2)
conv3 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(pool3)
conv4 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)
conv5 = Convolution2D(512, 3, 3, activation='relu', border_mode='same')(pool4)
conv5 = Convolution2D(512, 3, 3, activation='relu', border_mode='same')(conv5)
up6 = merge([UpSampling2D(size=(2, 2))(conv5), conv4], mode='concat', concat_axis=1)
conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(up6)
conv6 = Convolution2D(256, 3, 3, activation='relu', border_mode='same')(conv6)
up7 = merge([UpSampling2D(size=(2, 2))(conv6), conv3], mode='concat', concat_axis=1)
conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(up7)
conv7 = Convolution2D(128, 3, 3, activation='relu', border_mode='same')(conv7)
up8 = merge([UpSampling2D(size=(2, 2))(conv7), conv2], mode='concat', concat_axis=1)
conv8 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(up8)
conv8 = Convolution2D(64, 3, 3, activation='relu', border_mode='same')(conv8)
up9 = merge([UpSampling2D(size=(2, 2))(conv8), conv1], mode='concat', concat_axis=1)
conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(up9)
conv9 = Convolution2D(32, 3, 3, activation='relu', border_mode='same')(conv9)
conv10 = Convolution2D(1, 1, 1, activation='sigmoid')(conv9)
model = Model(input=inputs, output=conv10)
return model
model = get_unet()
model = make_parallel(model, 1)
model.load_weights(modelfile)
params = []
with open("params.txt") as fin:
for l in fin:
arr = l.rstrip().split("\t")
params.append(np.float32(arr[1]))
pngfile = args.input_file
img = cv2.imread(pngfile, cv2.IMREAD_GRAYSCALE)
img = np.array([img])
imgori = np.copy(img)
imgori = imgori.reshape((img.shape[1], img.shape[2]))
ji = Image.fromarray(imgori)
img = img.astype('float32')
img -= params[0] # subtract by mean, divide by SD
img /= params[1]
totaloutput = np.zeros((img.shape[1], img.shape[2], 32))
for dx in range(0, img.shape[2] - 32):
imgs = img[0, 0:image_rows, dx:image_cols+dx]
imgs = imgs.reshape(( 1, image_rows,image_cols))
imgsbatch = np.zeros((1, 1, image_rows,image_cols))
imgsbatch[0] = imgs
output = model.predict(imgsbatch, batch_size=1) # inference step
for x in range(0, image_rows):
for y in range(0, image_cols):
totaloutput[x,dx + y,dx % 32] = output[0,0,x,y]
totaloutput = np.mean(totaloutput, 2)
if (args.mode == "mask"):
# for binary masks
mask = (totaloutput > 0.5)
mask = np.uint8(mask)
mask *= 255
mask = Image.fromarray(mask)
mask.save(args.output_file)
elif (args.mode == "mask_blend"):
# for masked heatmap overlay
mask = (totaloutput < 0.5)
mask = np.uint8(mask)
mask *= 255
mask = Image.fromarray(mask)
my_cm = matplotlib.cm.get_cmap('jet')
mapped_data = my_cm(totaloutput, bytes=True)
j = Image.fromarray(mapped_data).convert('RGBA')
ji = ji.convert("RGBA")
Image.composite(ji, j,mask).save(args.output_file)
elif (args.mode == "blend"):
# for blend overlay
my_cm = matplotlib.cm.get_cmap('jet')
mapped_data = my_cm(totaloutput, bytes=True)
j = Image.fromarray(mapped_data).convert('RGBA')
ji = ji.convert("RGBA")
Image.blend(ji, j,0.2).save(args.output_file)