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infer1.py
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Infer for ICNet model."""
from __future__ import print_function
import cityscape
import argparse
import functools
import sys
import os
import cv2
import paddle.fluid as fluid
import paddle
from icnet import icnet
from utils import add_arguments, print_arguments, get_feeder_data, check_gpu
from paddle.fluid.layers.learning_rate_scheduler import _decay_step_counter
from paddle.fluid.initializer import init_on_cpu
os.system("sudo rm -rf /home/wangye/wangye/icnet_paddle/cut_map/* & sudo rm -rf /home/wangye/wangye/icnet_paddle/comb_map/*")
import numpy as np
IMG_MEAN = np.array((103.939, 116.779, 123.68), dtype=np.float32)
parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('model_path', str, None, "Model path.")
add_arg('images_list', str, './dataset/infer.list', "List file with images to be infered.")
add_arg('images_path', str, "input.png", "The images path.")
add_arg('out_path', str, "./output_map", "Output path.")
add_arg('use_gpu', bool, True, "Whether use GPU to test.")
# yapf: enable
data_shape = [3, 256, 256]
num_classes = 5
label_colours = [
[255, 255, 255],
[0, 255, 0],
[0, 0, 0]
# 0 = road, 1 = sidewalk, 2 = building
,
[131, 139, 139],
[19, 69, 139],
[153, 153, 153]
# 3 = wall, 4 = fence, 5 = pole
,
[250, 170, 29],
[219, 219, 0],
[106, 142, 35]
# 6 = traffic light, 7 = traffic sign, 8 = vegetation
,
[152, 250, 152],
[69, 129, 180],
[219, 19, 60]
# 9 = terrain, 10 = sky, 11 = person
,
[255, 0, 0],
[0, 0, 142],
[0, 0, 69]
# 12 = rider, 13 = car, 14 = truck
,
[0, 60, 100],
[0, 79, 100],
[0, 0, 230]
# 15 = bus, 16 = train, 17 = motocycle
,
[119, 10, 32]
]
# 18 = bicycle
def color(input):
"""
Convert infered result to color image.
"""
result = []
s = []
for i in input.flatten():
if i not in s:
s.append(i)
# print(i)
result.append(
[label_colours[i][2], label_colours[i][1], label_colours[i][0]])
result = np.array(result).reshape([input.shape[0], input.shape[1], 3])
# print(s)
return result
def infer(args,cut_path,image_list,comb_path):
data_shape = cityscape.test_data_shape()
num_classes = cityscape.num_classes()
# define network
images = fluid.layers.data(name='image', shape=data_shape, dtype='float32')
_, _, sub124_out = icnet(images, num_classes,
np.array(data_shape[1:]).astype("float32"))
predict = fluid.layers.resize_bilinear(
sub124_out, out_shape=data_shape[1:3])
predict = fluid.layers.transpose(predict, perm=[0, 2, 3, 1])
predict = fluid.layers.reshape(predict, shape=[-1, num_classes])
_, predict = fluid.layers.topk(predict, k=1)
predict = fluid.layers.reshape(
predict,
shape=[data_shape[1], data_shape[2], -1]) # batch_size should be 1
inference_program = fluid.default_main_program().clone(for_test=True)
# prepare environment
place = fluid.CPUPlace()
if args.use_gpu:
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
assert os.path.exists(args.model_path)
fluid.io.load_params(exe, args.model_path)
print("loaded model from: %s" % args.model_path)
sys.stdout.flush()
if not os.path.isdir(args.out_path):
os.makedirs(args.out_path)
for line in image_list:
# image_file = args.images_path + "/" + line.strip()
# filename = os.path.basename(image_file)
# print(str(cut_path)+"/"+str(line))
# image = cv2.imread(cut_path+'/'+line)
#print(11111111,line)
image = paddle.dataset.image.load_image(
cut_path+"/" +line, is_color=True).astype("float32")
image -= IMG_MEAN
img = paddle.dataset.image.to_chw(image)[np.newaxis, :]
image_t = fluid.LoDTensor()
image_t.set(img, place)
result = exe.run(inference_program,
feed={"image": image_t},
fetch_list=[predict])
cv2.imwrite(comb_path + "/" + line + "_result.png",
color(result[0]))
print("predicted images saved in :"+comb_path)
def image_cut(input,output):
data_list =[]
path = input
path_out = output
img = cv2.imread(path)
img_shape = img.shape
h_step = img.shape[0] // 256
w_step = img.shape[1] // 256
#print(h_step, w_step)
h_rest = -(img.shape[0] - 256 * h_step)
w_rest = -(img.shape[1] - 256 * w_step)
#print(h_rest, w_rest)
image_list = []
for h in range(h_step):
for w in range(w_step):
image_sample = img[(h * 256):(h * 256 + 256),
(w * 256):(w * 256 + 256), :]
image_list.append(image_sample)
image_list.append(img[(h * 256):(h * 256 + 256), -256:, :])
for w in range(w_step - 1):
image_list.append(img[-256:, (w * 256):(w * 256 + 256), :])
image_list.append(img[-256:, -256:, :])
for i in range(len(image_list)):
cv2.imwrite(path_out + '/' + str(i) + '.png', image_list[i])
data_list.append(str(i) + '.png')
print("cut images saved in :" + path_out)
return h_step,w_step,h_rest,w_rest,img_shape,data_list
def image_comb(h_step,w_step,h_rest,w_rest,img_shape,outname,inpath):
path = inpath
ori_image = img_shape
predict_list = []
files = os.listdir(path)
for file in range(len(files)):
file_path = path + '/' + str(file) + '.png'+"_result.png"
im = cv2.imread(file_path)
# predict_list.append(im[:, :, :])
predict_list.append(im[:, :, :])
# predict_list.append(ori_image[-256:, -256:, :])
count_temp = 0
tmp = np.ones([ori_image[0], ori_image[1], ori_image[2]])
for h in range(h_step):
for w in range(w_step):
tmp[h * 256:(h + 1) * 256, w * 256:(w + 1) * 256, :] = predict_list[count_temp]
count_temp += 1
tmp[h * 256:(h + 1) * 256, w_rest:, :] = predict_list[count_temp][:, w_rest:, :]
count_temp += 1
for w in range(w_step - 1):
tmp[h_rest:, (w * 256):(w * 256 + 256), :] = predict_list[count_temp][h_rest:, :, :]
count_temp += 1
tmp[h_rest:, w_rest:, :] = predict_list[count_temp][h_rest:, w_rest:, :]
# print(tmp.shape)
print("combined image saved in :" + outname)
cv2.imwrite(outname, tmp)
def main():
args = parser.parse_args()
print_arguments(args)
in_path = args.images_path
print('cutting......')
# in_path = 'input.png'
cut_path = 'cut_map'
comb_path = 'comb_map'
outname = in_path + '_predict.png'
h_step,w_step,h_rest,w_rest,img_shape,data_list = image_cut(in_path,cut_path)
check_gpu(args.use_gpu)
print('predicting......')
infer(args,cut_path,data_list,comb_path)
print('combining......')
image_comb(h_step,w_step,h_rest,w_rest,img_shape,outname,comb_path)
if __name__ == "__main__":
main()