# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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 argparse
import numpy as np
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval

CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
            'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
            'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
            'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
            'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
            'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
            'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
            'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
            'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
            'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
            'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
            'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
            'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
            'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush')


def coco_evaluation(annotation_json, result_json):
    cocoGt = COCO(annotation_json)
    cocoDt = cocoGt.loadRes(result_json)
    iou_thrs = np.linspace(.5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)
    iou_type = 'bbox'

    cocoEval = COCOeval(cocoGt, cocoDt, iou_type)
    cocoEval.params.catIds = cocoGt.get_cat_ids(cat_names=CLASSES)
    cocoEval.params.imgIds = cocoGt.get_img_ids()
    cocoEval.params.maxDets = [100, 300, 1000] # proposal number for evaluating recalls/mAPs.
    cocoEval.params.iouThrs = iou_thrs

    cocoEval.evaluate()
    cocoEval.accumulate()
    cocoEval.summarize()

    # mapping of cocoEval.stats
    coco_metric_names = {
        'mAP': 0,
        'mAP_50': 1,
        'mAP_75': 2,
        'mAP_s': 3,
        'mAP_m': 4,
        'mAP_l': 5,
        'AR@100': 6,
        'AR@300': 7,
        'AR@1000': 8,
        'AR_s@1000': 9,
        'AR_m@1000': 10,
        'AR_l@1000': 11
    }

    metric_items = ['mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l']
    eval_results = {}

    for metric_item in metric_items:
        key = f'bbox_{metric_item}'
        val = float(
            f'{cocoEval.stats[coco_metric_names[metric_item]]:.3f}'
        )
        eval_results[key] = val
    ap = cocoEval.stats[:6]
    eval_results['bbox_mAP_copypaste'] = (
        f'{ap[0]:.3f} {ap[1]:.3f} {ap[2]:.3f} {ap[3]:.3f} '
        f'{ap[4]:.3f} {ap[5]:.3f}')
    
    return eval_results


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument("--ground_truth", default="instances_val2017.json")
    parser.add_argument("--detection_result", default="coco_detection_result.json")
    args = parser.parse_args()
    result = coco_evaluation(args.ground_truth, args.detection_result)
    print(result)
    with open('./coco_detection_result.txt', 'w') as f:
        for key, value in result.items():
            f.write(key + ': ' + str(value) + '\n')