# Copyright (c) 2019 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os

import paddle.fluid as fluid
import numpy as np
from PIL import Image
from collections import OrderedDict

import ppdet.utils.checkpoint as checkpoint
from ppdet.utils.cli import ArgsParser
from ppdet.utils.check import check_gpu
from ppdet.utils.widerface_eval_utils import get_shrink, bbox_vote, \
    save_widerface_bboxes, save_fddb_bboxes, to_chw_bgr
from ppdet.core.workspace import load_config, merge_config, create

import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)


def face_img_process(image,
                     mean=[104., 117., 123.],
                     std=[127.502231, 127.502231, 127.502231]):
    img = np.array(image)
    img = to_chw_bgr(img)
    img = img.astype('float32')
    img -= np.array(mean)[:, np.newaxis, np.newaxis].astype('float32')
    img /= np.array(std)[:, np.newaxis, np.newaxis].astype('float32')
    img = [img]
    img = np.array(img)
    return img


def face_eval_run(exe,
                  compile_program,
                  fetches,
                  image_dir,
                  gt_file,
                  pred_dir='output/pred',
                  eval_mode='widerface',
                  multi_scale=False):
    # load ground truth files
    with open(gt_file, 'r') as f:
        gt_lines = f.readlines()
    imid2path = []
    pos_gt = 0
    while pos_gt < len(gt_lines):
        name_gt = gt_lines[pos_gt].strip('\n\t').split()[0]
        imid2path.append(name_gt)
        pos_gt += 1
        n_gt = int(gt_lines[pos_gt].strip('\n\t').split()[0])
        pos_gt += 1 + n_gt
    logger.info('The ground truth file load {} images'.format(len(imid2path)))

    dets_dist = OrderedDict()
    for iter_id, im_path in enumerate(imid2path):
        image_path = os.path.join(image_dir, im_path)
        if eval_mode == 'fddb':
            image_path += '.jpg'
        assert os.path.exists(image_path)
        image = Image.open(image_path).convert('RGB')
        if multi_scale:
            shrink, max_shrink = get_shrink(image.size[1], image.size[0])
            det0 = detect_face(exe, compile_program, fetches, image, shrink)
            det1 = flip_test(exe, compile_program, fetches, image, shrink)
            [det2, det3] = multi_scale_test(exe, compile_program, fetches,
                                            image, max_shrink)
            det4 = multi_scale_test_pyramid(exe, compile_program, fetches,
                                            image, max_shrink)
            det = np.row_stack((det0, det1, det2, det3, det4))
            dets = bbox_vote(det)
        else:
            dets = detect_face(exe, compile_program, fetches, image, 1)
        if eval_mode == 'widerface':
            save_widerface_bboxes(image_path, dets, pred_dir)
        else:
            dets_dist[im_path] = dets
        if iter_id % 100 == 0:
            logger.info('Test iter {}'.format(iter_id))
    if eval_mode == 'fddb':
        save_fddb_bboxes(dets_dist, pred_dir)
    logger.info("Finish evaluation.")


def detect_face(exe, compile_program, fetches, image, shrink):
    image_shape = [3, image.size[1], image.size[0]]
    if shrink != 1:
        h, w = int(image_shape[1] * shrink), int(image_shape[2] * shrink)
        image = image.resize((w, h), Image.ANTIALIAS)
        image_shape = [3, h, w]

    img = face_img_process(image)
    detection, = exe.run(compile_program,
                         feed={'image': img},
                         fetch_list=[fetches['bbox']],
                         return_numpy=False)
    detection = np.array(detection)
    # layout: xmin, ymin, xmax. ymax, score
    if np.prod(detection.shape) == 1:
        logger.info("No face detected")
        return np.array([[0, 0, 0, 0, 0]])
    det_conf = detection[:, 1]
    det_xmin = image_shape[2] * detection[:, 2] / shrink
    det_ymin = image_shape[1] * detection[:, 3] / shrink
    det_xmax = image_shape[2] * detection[:, 4] / shrink
    det_ymax = image_shape[1] * detection[:, 5] / shrink

    det = np.column_stack((det_xmin, det_ymin, det_xmax, det_ymax, det_conf))
    return det


def flip_test(exe, compile_program, fetches, image, shrink):
    img = image.transpose(Image.FLIP_LEFT_RIGHT)
    det_f = detect_face(exe, compile_program, fetches, img, shrink)
    det_t = np.zeros(det_f.shape)
    # image.size: [width, height]
    det_t[:, 0] = image.size[0] - det_f[:, 2]
    det_t[:, 1] = det_f[:, 1]
    det_t[:, 2] = image.size[0] - det_f[:, 0]
    det_t[:, 3] = det_f[:, 3]
    det_t[:, 4] = det_f[:, 4]
    return det_t


def multi_scale_test(exe, compile_program, fetches, image, max_shrink):
    # Shrink detecting is only used to detect big faces
    st = 0.5 if max_shrink >= 0.75 else 0.5 * max_shrink
    det_s = detect_face(exe, compile_program, fetches, image, st)
    index = np.where(
        np.maximum(det_s[:, 2] - det_s[:, 0] + 1, det_s[:, 3] - det_s[:, 1] + 1)
        > 30)[0]
    det_s = det_s[index, :]
    # Enlarge one times
    bt = min(2, max_shrink) if max_shrink > 1 else (st + max_shrink) / 2
    det_b = detect_face(exe, compile_program, fetches, image, bt)

    # Enlarge small image x times for small faces
    if max_shrink > 2:
        bt *= 2
        while bt < max_shrink:
            det_b = np.row_stack((det_b, detect_face(exe, compile_program,
                                                     fetches, image, bt)))
            bt *= 2
        det_b = np.row_stack((det_b, detect_face(exe, compile_program, fetches,
                                                 image, max_shrink)))

    # Enlarged images are only used to detect small faces.
    if bt > 1:
        index = np.where(
            np.minimum(det_b[:, 2] - det_b[:, 0] + 1,
                       det_b[:, 3] - det_b[:, 1] + 1) < 100)[0]
        det_b = det_b[index, :]
    # Shrinked images are only used to detect big faces.
    else:
        index = np.where(
            np.maximum(det_b[:, 2] - det_b[:, 0] + 1,
                       det_b[:, 3] - det_b[:, 1] + 1) > 30)[0]
        det_b = det_b[index, :]
    return det_s, det_b


def multi_scale_test_pyramid(exe, compile_program, fetches, image, max_shrink):
    # Use image pyramids to detect faces
    det_b = detect_face(exe, compile_program, fetches, image, 0.25)
    index = np.where(
        np.maximum(det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1)
        > 30)[0]
    det_b = det_b[index, :]

    st = [0.75, 1.25, 1.5, 1.75]
    for i in range(len(st)):
        if st[i] <= max_shrink:
            det_temp = detect_face(exe, compile_program, fetches, image, st[i])
            # Enlarged images are only used to detect small faces.
            if st[i] > 1:
                index = np.where(
                    np.minimum(det_temp[:, 2] - det_temp[:, 0] + 1,
                               det_temp[:, 3] - det_temp[:, 1] + 1) < 100)[0]
                det_temp = det_temp[index, :]
            # Shrinked images are only used to detect big faces.
            else:
                index = np.where(
                    np.maximum(det_temp[:, 2] - det_temp[:, 0] + 1,
                               det_temp[:, 3] - det_temp[:, 1] + 1) > 30)[0]
                det_temp = det_temp[index, :]
            det_b = np.row_stack((det_b, det_temp))
    return det_b


def main():
    """
    Main evaluate function
    """
    cfg = load_config(FLAGS.config)
    if 'architecture' in cfg:
        main_arch = cfg.architecture
    else:
        raise ValueError("'architecture' not specified in config file.")

    merge_config(FLAGS.opt)

    # check if set use_gpu=True in paddlepaddle cpu version
    check_gpu(cfg.use_gpu)

    # define executor
    place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
    exe = fluid.Executor(place)

    # build program
    model = create(main_arch)
    startup_prog = fluid.Program()
    eval_prog = fluid.Program()
    with fluid.program_guard(eval_prog, startup_prog):
        with fluid.unique_name.guard():
            inputs_def = cfg['EvalReader']['inputs_def']
            inputs_def['use_dataloader'] = False
            feed_vars, _ = model.build_inputs(**inputs_def)
            fetches = model.eval(feed_vars)

    eval_prog = eval_prog.clone(True)

    # load model
    exe.run(startup_prog)
    if 'weights' in cfg:
        checkpoint.load_params(exe, eval_prog, cfg.weights)

    assert cfg.metric in ['WIDERFACE'], \
            "unknown metric type {}".format(cfg.metric)

    dataset = cfg['EvalReader']['dataset']

    annotation_file = dataset.get_anno()
    dataset_dir = dataset.dataset_dir
    image_dir = dataset.image_dir

    pred_dir = FLAGS.output_eval if FLAGS.output_eval else 'output/pred'
    face_eval_run(
        exe,
        eval_prog,
        fetches,
        image_dir,
        annotation_file,
        pred_dir=pred_dir,
        eval_mode=FLAGS.eval_mode,
        multi_scale=FLAGS.multi_scale)


if __name__ == '__main__':
    parser = ArgsParser()
    parser.add_argument(
        "-d",
        "--dataset_dir",
        default=None,
        type=str,
        help="Dataset path, same as DataFeed.dataset.dataset_dir")
    parser.add_argument(
        "-f",
        "--output_eval",
        default=None,
        type=str,
        help="Evaluation file directory, default is current directory.")
    parser.add_argument(
        "-e",
        "--eval_mode",
        default="widerface",
        type=str,
        help="Evaluation mode, include `widerface` and `fddb`, default is `widerface`."
    )
    parser.add_argument(
        "--multi_scale",
        action='store_true',
        default=False,
        help="If True it will select `multi_scale` evaluation. Default is `False`, it will select `single-scale` evaluation."
    )
    FLAGS = parser.parse_args()
    main()