#  Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#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 io
import six
import time
import json
import numpy as np
import paddle
import paddle.fluid as fluid
import reader
from models.yolov3 import YOLOv3
from utility import print_arguments, parse_args, check_gpu
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval, Params
from config import cfg


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

    if '2014' in cfg.dataset:
        test_list = 'annotations/instances_val2014.json'
    elif '2017' in cfg.dataset:
        test_list = 'annotations/instances_val2017.json'

    if cfg.debug:
        if not os.path.exists('output'):
            os.mkdir('output')

    model = YOLOv3(is_train=False)
    model.build_model()
    outputs = model.get_pred()
    place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
    exe = fluid.Executor(place)
    # yapf: disable
    if cfg.weights:
        def if_exist(var):
            return os.path.exists(os.path.join(cfg.weights, var.name))
        fluid.io.load_vars(exe, cfg.weights, predicate=if_exist)
    # yapf: enable

    # you can save inference model by following code
    # fluid.io.save_inference_model("./output/yolov3", 
    #                               feeded_var_names=['image', 'im_shape'],
    #                               target_vars=outputs,
    #                               executor=exe)

    input_size = cfg.input_size
    test_reader = reader.test(input_size, 1)
    label_names, label_ids = reader.get_label_infos()
    if cfg.debug:
        print("Load in labels {} with ids {}".format(label_names, label_ids))
    feeder = fluid.DataFeeder(place=place, feed_list=model.feeds())

    def get_pred_result(boxes, scores, labels, im_id):
        result = []
        for box, score, label in zip(boxes, scores, labels):
            x1, y1, x2, y2 = box
            w = x2 - x1 + 1
            h = y2 - y1 + 1
            bbox = [x1, y1, w, h]

            res = {
                'image_id': im_id,
                'category_id': label_ids[int(label)],
                'bbox': list(map(float, bbox)),
                'score': float(score)
            }
            result.append(res)
        return result

    dts_res = []
    fetch_list = [outputs]
    total_time = 0
    for batch_id, batch_data in enumerate(test_reader()):
        start_time = time.time()
        batch_outputs = exe.run(fetch_list=[v.name for v in fetch_list],
                                feed=feeder.feed(batch_data),
                                return_numpy=False,
                                use_program_cache=True)
        lod = batch_outputs[0].lod()[0]
        nmsed_boxes = np.array(batch_outputs[0])
        if nmsed_boxes.shape[1] != 6:
            continue
        for i in range(len(lod) - 1):
            im_id = batch_data[i][1]
            start = lod[i]
            end = lod[i + 1]
            if start == end:
                continue
            nmsed_box = nmsed_boxes[start:end, :]
            labels = nmsed_box[:, 0]
            scores = nmsed_box[:, 1]
            boxes = nmsed_box[:, 2:6]
            dts_res += get_pred_result(boxes, scores, labels, im_id)

        end_time = time.time()
        print("batch id: {}, time: {}".format(batch_id, end_time - start_time))
        total_time += end_time - start_time

    with io.open("yolov3_result.json", 'w') as outfile:
        encode_func = unicode if six.PY2 else str
        outfile.write(encode_func(json.dumps(dts_res)))
    print("start evaluate detection result with coco api")
    coco = COCO(os.path.join(cfg.data_dir, test_list))
    cocoDt = coco.loadRes("yolov3_result.json")
    cocoEval = COCOeval(coco, cocoDt, 'bbox')
    cocoEval.evaluate()
    cocoEval.accumulate()
    cocoEval.summarize()
    print("evaluate done.")

    print("Time per batch: {}".format(total_time / batch_id))


if __name__ == '__main__':
    args = parse_args()
    print_arguments(args)
    eval()
