import paddle.v2 as paddle
import data_provider
import vgg_ssd_net
import os, sys
import numpy as np
import gzip
from PIL import Image
from config.pascal_voc_conf import cfg


def _infer(inferer, infer_data, threshold):
    ret = []
    infer_res = inferer.infer(input=infer_data)
    keep_inds = np.where(infer_res[:, 2] >= threshold)[0]
    for idx in keep_inds:
        ret.append([
            infer_res[idx][0], infer_res[idx][1] - 1, infer_res[idx][2],
            infer_res[idx][3], infer_res[idx][4], infer_res[idx][5],
            infer_res[idx][6]
        ])
    return ret


def infer(eval_file_list, save_path, data_args, batch_size, model_path,
          threshold):
    detect_out = vgg_net_ssd_v2.net_conf(mode='infer')

    assert os.path.isfile(init_model_path), 'Invalid model.'
    parameters = paddle.parameters.Parameters.from_tar(gzip.open(model_path))

    inferer = paddle.inference.Inference(
        output_layer=detect_out, parameters=parameters)

    reader = data_provider.infer(data_args, eval_file_list)
    all_fname_list = [line.strip() for line in open(eval_file_list).readlines()]

    test_data = []
    fname_list = []
    img_w = []
    img_h = []
    idx = 0
    """Do inference batch by batch,
    coords of bbox will be scaled based on image size
    """
    with open(save_path, 'w') as fout:
        for img in reader():
            test_data.append([img])
            fname_list.append(all_fname_list[idx])
            w, h = \
                Image.open(os.path.join('./data', fname_list[-1])).size
            img_w.append(w)
            img_h.append(h)
            if len(test_data) == batch_size:
                ret_res = _infer(inferer, test_data, threshold)
                for det_res in ret_res:
                    img_idx = int(det_res[0])
                    label = int(det_res[1])
                    conf_score = det_res[2]
                    xmin = det_res[3] * img_w[img_idx]
                    ymin = det_res[4] * img_h[img_idx]
                    xmax = det_res[5] * img_w[img_idx]
                    ymax = det_res[6] * img_h[img_idx]
                    fout.write(fname_list[img_idx] + '\t' + str(label) + '\t' +
                               str(conf_score) + '\t' + str(xmin) + ' ' + str(
                                   ymin) + ' ' + str(xmax) + ' ' + str(
                                       ymax) + '\n')
                test_data = []
                fname_list = []
                img_w = []
                img_h = []
            idx += 1

        if len(test_data) > 0:
            ret_res = _infer(inferer, test_data, threshold)
            for det_res in ret_res:
                img_idx = int(det_res[0])
                label = int(det_res[1])
                conf_score = det_res[2]
                xmin = det_res[3] * img_w[img_idx]
                ymin = det_res[4] * img_h[img_idx]
                xmax = det_res[5] * img_w[img_idx]
                ymax = det_res[6] * img_h[img_idx]
                fout.write(fname_list[img_idx] + '\t' + str(label) + '\t' + str(
                    conf_score) + '\t' + str(xmin) + ' ' + str(ymin) + ' ' +
                           str(xmax) + ' ' + str(ymax) + '\n')


if __name__ == "__main__":
    paddle.init(use_gpu=True, trainer_count=1)

    data_args = data_provider.Settings(
        data_dir='./data',
        label_file='label_list',
        resize_h=cfg.IMG_HEIGHT,
        resize_w=cfg.IMG_WIDTH,
        mean_value=[104, 117, 124])

    infer(
        eval_file_list='./data/infer.txt',
        save_path='infer.res',
        data_args=data_args,
        batch_size=4,
        model_path='models/pass-00000.tar.gz',
        threshold=0.3)