#-*- coding:utf-8 -*-

# 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.

from __future__ import division

from __future__ import absolute_import

from __future__ import print_function



import os

import sys

import torch

import argparse

import torch.nn as nn

import torch.utils.data as data

import torch.backends.cudnn as cudnn

import torchvision.transforms as transforms



import cv2

import time

import numpy as np

from PIL import Image



from data.config import cfg

from pyramidbox import build_net

from torch.autograd import Variable

from utils.augmentations import to_chw_bgr



parser = argparse.ArgumentParser(description='pyramidbox evaluatuon fddb')

parser.add_argument('--model', 

                    type=str,

                    default='weights/pyramidbox.pth', help='trained model')

parser.add_argument('--thresh', 

                    default=0.1, type=float,

                    help='Final confidence threshold')

args = parser.parse_args()





use_cuda = torch.cuda.is_available()



if use_cuda:

    torch.set_default_tensor_type('torch.cuda.FloatTensor')

else:

    torch.set_default_tensor_type('torch.FloatTensor')





FDDB_IMG_DIR = os.path.join(cfg.FACE.FDDB_DIR, 'images')

FDDB_FOLD_DIR = os.path.join(cfg.FACE.FDDB_DIR, 'FDDB-folds')

FDDB_RESULT_DIR = os.path.join(cfg.FACE.FDDB_DIR, 'pyramidbox')

FDDB_RESULT_IMG_DIR = os.path.join(FDDB_RESULT_DIR, 'images')



if not os.path.exists(FDDB_RESULT_IMG_DIR):

    os.makedirs(FDDB_RESULT_IMG_DIR)





def detect_face(net, img, thresh):

    height, width, _ = img.shape

    x = to_chw_bgr(img)

    x = x.astype('float32')

    x -= cfg.img_mean

    x = x[[2, 1, 0], :, :]



    x = Variable(torch.from_numpy(x).unsqueeze(0))

    if use_cuda:

        x = x.cuda()



    y = net(x)

    detections = y.data

    scale = torch.Tensor([img.shape[1], img.shape[0],

                          img.shape[1], img.shape[0]])



    bboxes = []

    for i in range(detections.size(1)):

        j = 0

        while detections[0, i, j, 0] >= thresh:

            box = []

            score = detections[0, i, j, 0]

            pt = (detections[0, i, j, 1:] * scale).cpu().numpy().astype(np.int)

            j += 1

            box += [pt[0], pt[1], pt[2] - pt[0], pt[3] - pt[1], score]

            bboxes += [box]



    return bboxes





if __name__ == '__main__':

    net = build_net('test', cfg.NUM_CLASSES)

    net.load_state_dict(torch.load(args.model))

    net.eval()



    if use_cuda:

        net.cuda()

        cudnn.benckmark = True



    #transform = S3FDBasicTransform(cfg.INPUT_SIZE, cfg.MEANS)



    counter = 0



    for i in range(10):

        txt_in = os.path.join(FDDB_FOLD_DIR, 'FDDB-fold-%02d.txt' % (i + 1))

        txt_out = os.path.join(FDDB_RESULT_DIR, 'fold-%02d-out.txt' % (i + 1))

        answer_in = os.path.join(

            FDDB_FOLD_DIR, 'FDDB-fold-%02d-ellipseList.txt' % (i + 1))

        with open(txt_in, 'r') as fr:

            lines = fr.readlines()

        fout = open(txt_out, 'w')

        ain = open(answer_in, 'r')

        for line in lines:

            line = line.strip()

            img_file = os.path.join(FDDB_IMG_DIR, line + '.jpg')

            out_file = os.path.join(

                FDDB_RESULT_IMG_DIR, line.replace('/', '_') + '.jpg')

            counter += 1

            t1 = time.time()

            #img = cv2.imread(img_file, cv2.IMREAD_COLOR)

            img = Image.open(img_file)

            if img.mode == 'L':

                img = img.convert('RGB')

            img = np.array(img)

            bboxes = detect_face(net, img, args.thresh)

            t2 = time.time()

            print('Detect %04d th image costs %.4f' % (counter, t2 - t1))

            fout.write('%s\n' % line)

            fout.write('%d\n' % len(bboxes))

            for bbox in bboxes:

                x1, y1, w, h, score = bbox

                fout.write('%d %d %d %d %lf\n' % (x1, y1, w, h, score))

            ain.readline()

            n = int(ain.readline().strip())

            for i in range(n):

                line = ain.readline().strip()

                line_data = [float(_) for _ in line.split(' ')[:5]]

                major_axis_radius, minor_axis_radius, angle, center_x, center_y = line_data

                angle = angle / 3.1415926 * 180.

                center_x, center_y = int(center_x), int(center_y)

                major_axis_radius, minor_axis_radius = int(

                    major_axis_radius), int(minor_axis_radius)

                cv2.ellipse(img, (center_x, center_y), (major_axis_radius,

                                                        minor_axis_radius), angle, 0, 360, (255, 0, 0), 2)



            for bbox in bboxes:

                x1, y1, w, h, score = bbox

                x1, y1, x2, y2 = int(x1), int(y1), int(x1 + w), int(y1 + h)

                cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)

            cv2.imwrite(out_file, img)

        fout.close()

        ain.close()