infer.py

#   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.
import os
import time
import numpy as np
import argparse
import functools
from PIL import Image
from PIL import ImageDraw
from PIL import ImageFont

import paddle
import paddle.fluid as fluid
import reader
from mobilenet_ssd import build_mobilenet_ssd
from utility import add_arguments, print_arguments, check_cuda

parser = argparse.ArgumentParser(description=__doc__)
add_arg = functools.partial(add_arguments, argparser=parser)
# yapf: disable
add_arg('dataset',          str,   'pascalvoc',    "coco and pascalvoc.")
add_arg('use_gpu',          bool,  True,      "Whether use GPU.")
add_arg('image_path',       str,   '',        "The image used to inference and visualize.")
add_arg('model_dir',        str,   '',     "The model path.")
add_arg('nms_threshold',    float, 0.45,   "NMS threshold.")
add_arg('confs_threshold',  float, 0.5,    "Confidence threshold to draw bbox.")
add_arg('resize_h',         int,   300,    "The resized image height.")
add_arg('resize_w',         int,   300,    "The resized image height.")
add_arg('mean_value_B',     float, 127.5,  "Mean value for B channel which will be subtracted.")  #123.68
add_arg('mean_value_G',     float, 127.5,  "Mean value for G channel which will be subtracted.")  #116.78
add_arg('mean_value_R',     float, 127.5,  "Mean value for R channel which will be subtracted.")  #103.94
# yapf: enable


def infer(args, data_args, image_path, model_dir):
    image_shape = [3, data_args.resize_h, data_args.resize_w]
    if 'coco' in data_args.dataset:
        num_classes = 91
        # cocoapi
        from pycocotools.coco import COCO
        from pycocotools.cocoeval import COCOeval
        label_fpath = os.path.join(data_dir, label_file)
        coco = COCO(label_fpath)
        category_ids = coco.getCatIds()
        label_list = {
            item['id']: item['name']
            for item in coco.loadCats(category_ids)
        }
        label_list[0] = ['background']
    elif 'pascalvoc' in data_args.dataset:
        num_classes = 21
        label_list = data_args.label_list

    image = fluid.layers.data(name='image', shape=image_shape, dtype='float32')
    locs, confs, box, box_var = build_mobilenet_ssd(image, num_classes,
                                                    image_shape)
    nmsed_out = fluid.layers.detection_output(
        locs, confs, box, box_var, nms_threshold=args.nms_threshold)

    place = fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace()
    exe = fluid.Executor(place)
    # yapf: disable
    if model_dir:
        def if_exist(var):
            return os.path.exists(os.path.join(model_dir, var.name))
        fluid.io.load_vars(exe, model_dir, predicate=if_exist)
    # yapf: enable
    infer_reader = reader.infer(data_args, image_path)
    feeder = fluid.DataFeeder(place=place, feed_list=[image])

    data = infer_reader()

    # switch network to test mode (i.e. batch norm test mode)
    test_program = fluid.default_main_program().clone(for_test=True)
    nmsed_out_v, = exe.run(test_program,
                           feed=feeder.feed([[data]]),
                           fetch_list=[nmsed_out],
                           return_numpy=False)
    nmsed_out_v = np.array(nmsed_out_v)
    draw_bounding_box_on_image(image_path, nmsed_out_v, args.confs_threshold,
                               label_list)


def draw_bounding_box_on_image(image_path, nms_out, confs_threshold,
                               label_list):
    image = Image.open(image_path)
    draw = ImageDraw.Draw(image)
    im_width, im_height = image.size

    for dt in nms_out:
        if dt[1] < confs_threshold:
            continue
        category_id = dt[0]
        bbox = dt[2:]
        xmin, ymin, xmax, ymax = clip_bbox(dt[2:])
        (left, right, top, bottom) = (xmin * im_width, xmax * im_width,
                                      ymin * im_height, ymax * im_height)
        draw.line(
            [(left, top), (left, bottom), (right, bottom), (right, top),
             (left, top)],
            width=4,
            fill='red')
        if image.mode == 'RGB':
            draw.text((left, top), label_list[int(category_id)], (255, 255, 0))
    image_name = image_path.split('/')[-1]
    print("image with bbox drawed saved as {}".format(image_name))
    image.save(image_name)


def clip_bbox(bbox):
    xmin = max(min(bbox[0], 1.), 0.)
    ymin = max(min(bbox[1], 1.), 0.)
    xmax = max(min(bbox[2], 1.), 0.)
    ymax = max(min(bbox[3], 1.), 0.)
    return xmin, ymin, xmax, ymax


if __name__ == '__main__':
    args = parser.parse_args()
    print_arguments(args)

    check_cuda(args.use_gpu)

    data_dir = 'data/pascalvoc'
    label_file = 'label_list'

    if not os.path.exists(args.model_dir):
        raise ValueError("The model path [%s] does not exist." %
                         (args.model_dir))
    if 'coco' in args.dataset:
        data_dir = 'data/coco'
        label_file = 'annotations/instances_val2014.json'

    data_args = reader.Settings(
        dataset=args.dataset,
        data_dir=data_dir,
        label_file=label_file,
        resize_h=args.resize_h,
        resize_w=args.resize_w,
        mean_value=[args.mean_value_B, args.mean_value_G, args.mean_value_R],
        apply_distort=False,
        apply_expand=False,
        ap_version='')
    infer(
        args,
        data_args=data_args,
        image_path=args.image_path,
        model_dir=args.model_dir)