# Copyright (c) 2020 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 sys

__dir__ = os.path.dirname(os.path.abspath(__file__))
sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))

from utils import logger
from utils import config
from utils.predictor import Predictor
from utils.get_image_list import get_image_list
from det_preprocess import det_preprocess
from preprocess import create_operators

import os
import argparse
import time
import yaml
import ast
from functools import reduce
import cv2
import numpy as np
import paddle


class DetPredictor(Predictor):
    def __init__(self, config):
        super().__init__(config["Global"],
                         config["Global"]["det_inference_model_dir"])

        self.preprocess_ops = create_operators(config["DetPreProcess"][
            "transform_ops"])
        self.config = config

    def preprocess(self, img):
        im_info = {
            'scale_factor': np.array(
                [1., 1.], dtype=np.float32),
            'im_shape': np.array(
                img.shape[:2], dtype=np.float32),
            'input_shape': self.config["Global"]["image_shape"],
            "scale_factor": np.array(
                [1., 1.], dtype=np.float32)
        }
        im, im_info = det_preprocess(img, im_info, self.preprocess_ops)
        inputs = self.create_inputs(im, im_info)
        return inputs

    def create_inputs(self, im, im_info):
        """generate input for different model type
        Args:
            im (np.ndarray): image (np.ndarray)
            im_info (dict): info of image
            model_arch (str): model type
        Returns:
            inputs (dict): input of model
        """
        inputs = {}
        inputs['image'] = np.array((im, )).astype('float32')
        inputs['im_shape'] = np.array(
            (im_info['im_shape'], )).astype('float32')
        inputs['scale_factor'] = np.array(
            (im_info['scale_factor'], )).astype('float32')

        return inputs

    def parse_det_results(self, pred, threshold, label_list):
        max_det_results = self.config["Global"]["max_det_results"]
        keep_indexes = pred[:, 1].argsort()[::-1][:max_det_results]
        results = []
        for idx in keep_indexes:
            single_res = pred[idx]
            class_id = int(single_res[0])
            score = single_res[1]
            bbox = single_res[2:]
            if score < threshold:
                continue
            label_name = label_list[class_id]
            results.append({
                "class_id": class_id,
                "score": score,
                "bbox": bbox,
                "label_name": label_name,
            })
        return results

    def predict(self, image, threshold=0.5, run_benchmark=False):
        '''
        Args:
            image (str/np.ndarray): path of image/ np.ndarray read by cv2
            threshold (float): threshold of predicted box' score
        Returns:
            results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
                            matix element:[class, score, x_min, y_min, x_max, y_max]
                            MaskRCNN's results include 'masks': np.ndarray:
                            shape: [N, im_h, im_w]
        '''
        inputs = self.preprocess(image)
        np_boxes = None
        input_names = self.predictor.get_input_names()

        for i in range(len(input_names)):
            input_tensor = self.predictor.get_input_handle(input_names[i])
            input_tensor.copy_from_cpu(inputs[input_names[i]])

        t1 = time.time()
        self.predictor.run()
        output_names = self.predictor.get_output_names()
        boxes_tensor = self.predictor.get_output_handle(output_names[0])
        np_boxes = boxes_tensor.copy_to_cpu()
        t2 = time.time()

        print("Inference: {} ms per batch image".format((t2 - t1) * 1000.0))

        # do not perform postprocess in benchmark mode
        results = []
        if reduce(lambda x, y: x * y, np_boxes.shape) < 6:
            print('[WARNNING] No object detected.')
            results = np.array([])
        else:
            results = np_boxes

        results = self.parse_det_results(results,
                                         self.config["Global"]["threshold"],
                                         self.config["Global"]["labe_list"])
        return results


def main(config):
    det_predictor = DetPredictor(config)
    image_list = get_image_list(config["Global"]["infer_imgs"])

    assert config["Global"]["batch_size"] == 1
    for idx, image_file in enumerate(image_list):
        img = cv2.imread(image_file)[:, :, ::-1]
        output = det_predictor.predict(img)
        print(output)

    return


if __name__ == "__main__":
    args = config.parse_args()
    config = config.get_config(args.config, overrides=args.override, show=True)
    main(config)