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pspnet.py
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#!/usr/bin/env python
from __future__ import print_function
#from python_utils import utils
import os
from os.path import splitext, join
import argparse
import numpy as np
from scipy import misc, ndimage
from keras import backend as K
from keras.models import model_from_json, load_model
import tensorflow as tf
import layers_builder as layers
#from python_utils import utils
#from python_utils.preprocessing import preprocess_img
from keras.utils.generic_utils import CustomObjectScope
# These are the means for the ImageNet pretrained ResNet
DATA_MEAN = np.array([[[123.68, 116.779, 103.939]]]) # RGB order
class PSPNet(object):
"""Pyramid Scene Parsing Network by Hengshuang Zhao et al 2017"""
def __init__(self, nb_classes, resnet_layers, input_shape, weights):
self.input_shape = input_shape
json_path = weights + ".json" #join("weights", "keras", weights + ".json")
h5_path = weights + ".h5" #join("weights", "keras", weights + ".h5")
if 'pspnet' in weights:
if os.path.isfile(json_path) and os.path.isfile(h5_path):
print("Keras model & weights found, loading...")
with CustomObjectScope({'Interp': layers.Interp}):
with open(json_path, 'r') as file_handle:
self.model = model_from_json(file_handle.read())
self.model.load_weights(h5_path)
else:
print("No Keras model & weights found, import from npy weights.")
self.model = layers.build_pspnet(nb_classes=nb_classes,
resnet_layers=resnet_layers,
input_shape=self.input_shape)
self.set_npy_weights(weights)
else:
print('Load pre-trained weights')
self.model = load_model(weights)
def predict(self, img, flip_evaluation=False):
"""
Predict segementation for an image.
Arguments:
img: must be rowsxcolsx3
"""
h_ori, w_ori = img.shape[:2]
# Preprocess
img = misc.imresize(img, self.input_shape)
img = img - DATA_MEAN
img = img[:, :, ::-1] # RGB => BGR
img = img.astype('float32')
print("Predicting...")
probs = self.feed_forward(img)
h, w = probs.shape[:2]
probs = ndimage.zoom(probs, (1. * h_ori / h, 1. * w_ori / w, 1.),
order=1, prefilter=False)
print("Finished prediction...")
return probs
def feed_forward(self, input_data, flip_evaluation=False):
assert input_data.shape == (self.input_shape[0], self.input_shape[1], 3)
input_data = input_data[np.newaxis, :, :, :]
# utils.debug(self.model, data)
pred = self.model.predict(input_data)[0]
if flip_evaluation:
print("Predict flipped")
input_with_flipped = np.array(
[input_data, np.flip(input_data, axis=1)])
prediction_with_flipped = self.model.predict(input_with_flipped)
prediction = (prediction_with_flipped[
0] + np.fliplr(prediction_with_flipped[1])) / 2.0
else:
prediction = self.model.predict(np.expand_dims(input_data, 0))[0]
if img.shape[0:1] != self.input_shape: # upscale prediction if necessary
h, w = prediction.shape[:2]
prediction = ndimage.zoom(prediction, (1. * h_ori / h, 1. * w_ori / w, 1.),
order=1, prefilter=False)
return prediction
def set_npy_weights(self, weights_path):
npy_weights_path = join("weights", "npy", weights_path + ".npy")
json_path = join("weights", "keras", weights_path + ".json")
h5_path = join("weights", "keras", weights_path + ".h5")
print("Importing weights from %s" % npy_weights_path)
weights = np.load(npy_weights_path, encoding='bytes').item()
for layer in self.model.layers:
print(layer.name)
if layer.name[:4] == 'conv' and layer.name[-2:] == 'bn':
mean = weights[layer.name.encode()][
'mean'.encode()].reshape(-1)
variance = weights[layer.name.encode()][
'variance'.encode()].reshape(-1)
scale = weights[layer.name.encode()][
'scale'.encode()].reshape(-1)
offset = weights[layer.name.encode()][
'offset'.encode()].reshape(-1)
self.model.get_layer(layer.name).set_weights(
[scale, offset, mean, variance])
elif layer.name[:4] == 'conv' and not layer.name[-4:] == 'relu':
try:
weight = weights[layer.name.encode()]['weights'.encode()]
self.model.get_layer(layer.name).set_weights([weight])
except Exception as err:
biases = weights[layer.name.encode()]['biases'.encode()]
self.model.get_layer(layer.name).set_weights([weight,
biases])
print('Finished importing weights.')
print("Writing keras model & weights")
json_string = self.model.to_json()
with open(json_path, 'w') as file_handle:
file_handle.write(json_string)
self.model.save_weights(h5_path)
print("Finished writing Keras model & weights")
class PSPNet50(PSPNet):
"""Build a PSPNet based on a 50-Layer ResNet."""
def __init__(self, nb_classes, weights, input_shape):
PSPNet.__init__(self, nb_classes=nb_classes, resnet_layers=50,
input_shape=input_shape, weights=weights)
class PSPNet101(PSPNet):
"""Build a PSPNet based on a 101-Layer ResNet."""
def __init__(self, nb_classes, weights, input_shape):
PSPNet.__init__(self, nb_classes=nb_classes, resnet_layers=101,
input_shape=input_shape, weights=weights)
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-m', '--model', type=str, default='pspnet101_voc2012',
help='Model/Weights to use',
choices=['pspnet50_ade20k',
'pspnet101_cityscapes',
'pspnet101_voc2012'])
parser.add_argument('-w', '--weights', type=str, default=None)
parser.add_argument('-i', '--input_path', type=str, default='example_images/ade20k.jpg',
help='Path the input image')
parser.add_argument('-o', '--output_path', type=str, default='example_results/ade20k.jpg',
help='Path to output')
parser.add_argument('--id', default="0")
parser.add_argument('--input_size', type=int, default=500)
parser.add_argument('-f', '--flip', type=bool, default=False,
help="Whether the network should predict on both image and flipped image.")
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = args.id
sess = tf.Session()
K.set_session(sess)
with sess.as_default():
img = misc.imread(args.input_path, mode='RGB')
cimg = misc.imresize(img, (args.input_size, args.input_size))
print(args)
if not args.weights:
if "pspnet50" in args.model:
pspnet = PSPNet50(nb_classes=150, input_shape=(473, 473),
weights=args.model)
elif "pspnet101" in args.model:
if "cityscapes" in args.model:
pspnet = PSPNet101(nb_classes=19, input_shape=(713, 713),
weights=args.model)
if "voc2012" in args.model:
pspnet = PSPNet101(nb_classes=21, input_shape=(473, 473),
weights=args.model)
else:
print("Network architecture not implemented.")
else:
pspnet = PSPNet50(nb_classes=2, input_shape=(
768, 480), weights=args.weights)
probs = pspnet.predict(cimg, args.flip)
print("Writing results...")
# import ipdb; ipdb.set_trace()
cm = np.argmax(probs, axis=2)
pm = np.max(probs, axis=2)
color_cm = utils.add_color(cm)
# color cm is [0.0-1.0] img is [0-255]
alpha_blended = 0.5 * color_cm * 255 + 0.5 * img
filename, ext = splitext(args.output_path)
misc.imsave(filename + "_seg_read" + ext, cm)
misc.imsave(filename + "_seg" + ext, color_cm)
misc.imsave(filename + "_probs" + ext, pm)
misc.imsave(filename + "_seg_blended" + ext, alpha_blended)