# -*- coding:utf-8 -*-
# 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 argparse
import ast
import os
import os.path as osp

import cv2
import numpy as np
import paddle.jit
import paddle.static
from paddle.inference import Config
from paddle.inference import create_predictor

from .data_feed import preprocess_v
from .data_feed import reader
from .optimal import postprocess_v
from .optimal import threshold_mask
from .processor import base64_to_cv2
from .processor import check_dir
from .processor import cv2_to_base64
from .processor import postprocess
from paddlehub.module.module import moduleinfo
from paddlehub.module.module import runnable
from paddlehub.module.module import serving


@moduleinfo(name="humanseg_lite",
            type="CV/semantic_segmentation",
            author="paddlepaddle",
            author_email="",
            summary="humanseg_lite is a semantic segmentation model.",
            version="1.3.0")
class ShufflenetHumanSeg:

    def __init__(self):
        self.default_pretrained_model_path = os.path.join(self.directory, "humanseg_lite_inference", "model")
        self._set_config()

    def _set_config(self):
        """
        predictor config setting
        """
        model = self.default_pretrained_model_path + '.pdmodel'
        params = self.default_pretrained_model_path + '.pdiparams'
        cpu_config = Config(model, params)
        cpu_config.disable_glog_info()
        cpu_config.disable_gpu()
        self.cpu_predictor = create_predictor(cpu_config)

        try:
            _places = os.environ["CUDA_VISIBLE_DEVICES"]
            int(_places[0])
            use_gpu = True
        except:
            use_gpu = False

        if use_gpu:
            gpu_config = Config(model, params)
            gpu_config.disable_glog_info()
            gpu_config.enable_use_gpu(memory_pool_init_size_mb=1000, device_id=0)

            if paddle.get_cudnn_version() == 8004:
                gpu_config.delete_pass('conv_elementwise_add_act_fuse_pass')
                gpu_config.delete_pass('conv_elementwise_add2_act_fuse_pass')
            self.gpu_predictor = create_predictor(gpu_config)

    def segment(self,
                images=None,
                paths=None,
                batch_size=1,
                use_gpu=False,
                visualization=False,
                output_dir='humanseg_lite_output'):
        """
        API for human segmentation.

        Args:
            images (list(numpy.ndarray)): images data, shape of each is [H, W, C], the color space is BGR.
            paths (list[str]): The paths of images.
            batch_size (int): batch size.
            use_gpu (bool): Whether to use gpu.
            visualization (bool): Whether to save image or not.
            output_dir (str): The path to store output images.

        Returns:
            res (list[dict]): each element in the list is a dict, the keys and values are:
                save_path (str, optional): the path to save images. (Exists only if visualization is True)
                data (numpy.ndarray): data of post processed image.
        """

        if use_gpu:
            try:
                _places = os.environ["CUDA_VISIBLE_DEVICES"]
                int(_places[0])
            except:
                raise RuntimeError(
                    "Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. If you wanna use gpu, please set CUDA_VISIBLE_DEVICES as cuda_device_id."
                )

        all_data = list()
        for yield_data in reader(images, paths):
            all_data.append(yield_data)

        total_num = len(all_data)
        loop_num = int(np.ceil(total_num / batch_size))

        res = list()
        for iter_id in range(loop_num):
            batch_data = list()
            handle_id = iter_id * batch_size
            for image_id in range(batch_size):
                try:
                    batch_data.append(all_data[handle_id + image_id])
                except:
                    pass
            # feed batch image
            batch_image = np.array([data['image'] for data in batch_data])

            predictor = self.gpu_predictor if use_gpu else self.cpu_predictor
            input_names = predictor.get_input_names()
            input_handle = predictor.get_input_handle(input_names[0])
            input_handle.copy_from_cpu(batch_image.copy())
            predictor.run()
            output_names = predictor.get_output_names()
            output_handle = predictor.get_output_handle(output_names[1])
            output = output_handle.copy_to_cpu()

            output = np.expand_dims(output[:, 1, :, :], axis=1)
            # postprocess one by one
            for i in range(len(batch_data)):
                out = postprocess(data_out=output[i],
                                  org_im=batch_data[i]['org_im'],
                                  org_im_shape=batch_data[i]['org_im_shape'],
                                  org_im_path=batch_data[i]['org_im_path'],
                                  output_dir=output_dir,
                                  visualization=visualization)
                res.append(out)
        return res

    def video_stream_segment(self, frame_org, frame_id, prev_gray, prev_cfd, use_gpu=False):
        """
        API for human video segmentation.

        Args:
           frame_org (numpy.ndarray): frame data, shape of each is [H, W, C], the color space is BGR.
           frame_id (int): index of the frame to be decoded.
           prev_gray (numpy.ndarray): gray scale image of last frame, shape of each is [H, W]
           prev_cfd (numpy.ndarray): fusion image from optical flow image and segment result, shape of each is [H, W]
           use_gpu (bool): Whether to use gpu.

        Returns:
            img_matting (numpy.ndarray): data of segmentation mask.
            cur_gray (numpy.ndarray): gray scale image of current frame, shape of each is [H, W]
            optflow_map (numpy.ndarray): optical flow image of current frame, shape of each is [H, W]

        """
        resize_h = 192
        resize_w = 192
        is_init = True
        width = int(frame_org.shape[0])
        height = int(frame_org.shape[1])
        disflow = cv2.DISOpticalFlow_create(cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST)
        frame = preprocess_v(frame_org, resize_w, resize_h)

        predictor = self.gpu_predictor if use_gpu else self.cpu_predictor
        input_names = predictor.get_input_names()
        input_handle = predictor.get_input_handle(input_names[0])
        input_handle.copy_from_cpu(frame.copy()[None, ...])
        predictor.run()
        output_names = predictor.get_output_names()
        output_handle = predictor.get_output_handle(output_names[1])
        score_map = output_handle.copy_to_cpu()

        frame = np.transpose(frame, axes=[1, 2, 0])
        score_map = np.transpose(np.squeeze(score_map, 0), axes=[1, 2, 0])
        cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        cur_gray = cv2.resize(cur_gray, (resize_w, resize_h))
        score_map = 255 * score_map[:, :, 1]
        if frame_id == 1:
            prev_gray = np.zeros((resize_h, resize_w), np.uint8)
            prev_cfd = np.zeros((resize_h, resize_w), np.float32)
            optflow_map = postprocess_v(cur_gray, score_map, prev_gray, prev_cfd, disflow, is_init)
        else:
            optflow_map = postprocess_v(cur_gray, score_map, prev_gray, prev_cfd, disflow, is_init)

        optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
        optflow_map = threshold_mask(optflow_map, thresh_bg=0.2, thresh_fg=0.8)
        img_matting = cv2.resize(optflow_map, (height, width), cv2.INTER_LINEAR)

        return [img_matting, cur_gray, optflow_map]

    def video_segment(self, video_path=None, use_gpu=False, save_dir='humanseg_lite_video_result'):
        """
        API for human video segmentation.

        Args:
           video_path (str): The path to take the video under preprocess. If video_path is None, it will capture
           the vedio from your camera.
           use_gpu (bool): Whether to use gpu.
           save_dir (str): The path to store output video.

        """
        if use_gpu:
            try:
                _places = os.environ["CUDA_VISIBLE_DEVICES"]
                int(_places[0])
            except:
                raise RuntimeError("Environment Variable CUDA_VISIBLE_DEVICES is not set correctly. "
                                   "If you wanna use gpu, please set CUDA_VISIBLE_DEVICES as cuda_device_id.")

        resize_h = 192
        resize_w = 192
        if not video_path:
            cap_video = cv2.VideoCapture(0)
        else:
            cap_video = cv2.VideoCapture(video_path)

        if not cap_video.isOpened():
            raise IOError("Error opening video stream or file, "
                          "--video_path whether existing: {}"
                          " or camera whether working".format(video_path))

        width = int(cap_video.get(cv2.CAP_PROP_FRAME_WIDTH))
        height = int(cap_video.get(cv2.CAP_PROP_FRAME_HEIGHT))
        disflow = cv2.DISOpticalFlow_create(cv2.DISOPTICAL_FLOW_PRESET_ULTRAFAST)
        prev_gray = np.zeros((resize_h, resize_w), np.uint8)
        prev_cfd = np.zeros((resize_h, resize_w), np.float32)
        is_init = True
        fps = cap_video.get(cv2.CAP_PROP_FPS)

        if video_path is not None:
            print('Please wait. It is computing......')
            if not osp.exists(save_dir):
                os.makedirs(save_dir)
            save_path = osp.join(save_dir, 'result' + '.avi')
            cap_out = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), fps, (width, height))

            while cap_video.isOpened():
                ret, frame_org = cap_video.read()
                if ret:
                    frame = preprocess_v(frame_org, resize_w, resize_h)

                    predictor = self.gpu_predictor if use_gpu else self.cpu_predictor
                    input_names = predictor.get_input_names()
                    input_handle = predictor.get_input_handle(input_names[0])
                    input_handle.copy_from_cpu(frame.copy()[None, ...])
                    predictor.run()
                    output_names = predictor.get_output_names()
                    output_handle = predictor.get_output_handle(output_names[1])
                    score_map = output_handle.copy_to_cpu()

                    frame = np.transpose(frame, axes=[1, 2, 0])
                    score_map = np.transpose(np.squeeze(score_map, 0), axes=[1, 2, 0])
                    cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
                    cur_gray = cv2.resize(cur_gray, (resize_w, resize_h))
                    score_map = 255 * score_map[:, :, 1]
                    optflow_map = postprocess_v(cur_gray, score_map, prev_gray, prev_cfd, disflow, is_init)
                    prev_gray = cur_gray.copy()
                    prev_cfd = optflow_map.copy()

                    optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
                    optflow_map = threshold_mask(optflow_map, thresh_bg=0.2, thresh_fg=0.8)
                    img_matting = cv2.resize(optflow_map, (width, height), cv2.INTER_LINEAR)
                    img_matting = np.repeat(img_matting[:, :, np.newaxis], 3, axis=2)
                    bg_im = np.ones_like(img_matting) * 255
                    comb = (img_matting * frame_org + (1 - img_matting) * bg_im).astype(np.uint8)
                    cap_out.write(comb)
                else:
                    break
            cap_video.release()
            cap_out.release()
        else:
            while cap_video.isOpened():
                ret, frame_org = cap_video.read()
                if ret:
                    frame = preprocess_v(frame_org, resize_w, resize_h)

                    predictor = self.gpu_predictor if use_gpu else self.cpu_predictor
                    input_names = predictor.get_input_names()
                    input_handle = predictor.get_input_handle(input_names[0])
                    input_handle.copy_from_cpu(frame.copy()[None, ...])
                    predictor.run()
                    output_names = predictor.get_output_names()
                    output_handle = predictor.get_output_handle(output_names[1])
                    score_map = output_handle.copy_to_cpu()

                    frame = np.transpose(frame, axes=[1, 2, 0])
                    score_map = np.transpose(np.squeeze(score_map, 0), axes=[1, 2, 0])
                    cur_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
                    cur_gray = cv2.resize(cur_gray, (resize_w, resize_h))
                    score_map = 255 * score_map[:, :, 1]
                    optflow_map = postprocess_v(cur_gray, score_map, prev_gray, prev_cfd, disflow, is_init)
                    prev_gray = cur_gray.copy()
                    prev_cfd = optflow_map.copy()
                    optflow_map = cv2.GaussianBlur(optflow_map, (3, 3), 0)
                    optflow_map = threshold_mask(optflow_map, thresh_bg=0.2, thresh_fg=0.8)
                    img_matting = cv2.resize(optflow_map, (width, height), cv2.INTER_LINEAR)
                    img_matting = np.repeat(img_matting[:, :, np.newaxis], 3, axis=2)
                    bg_im = np.ones_like(img_matting) * 255
                    comb = (img_matting * frame_org + (1 - img_matting) * bg_im).astype(np.uint8)
                    cv2.imshow('HumanSegmentation', comb)
                    if cv2.waitKey(1) & 0xFF == ord('q'):
                        break
                else:
                    break
            cap_video.release()

    @serving
    def serving_method(self, images, **kwargs):
        """
        Run as a service.
        """
        images_decode = [base64_to_cv2(image) for image in images]
        results = self.segment(images=images_decode, **kwargs)
        results = [{'data': cv2_to_base64(result['data'])} for result in results]
        return results

    @runnable
    def run_cmd(self, argvs):
        """
        Run as a command.
        """
        self.parser = argparse.ArgumentParser(description="Run the {} module.".format(self.name),
                                              prog='hub run {}'.format(self.name),
                                              usage='%(prog)s',
                                              add_help=True)

        self.arg_input_group = self.parser.add_argument_group(title="Input options", description="Input data. Required")
        self.arg_config_group = self.parser.add_argument_group(
            title="Config options", description="Run configuration for controlling module behavior, not required.")
        self.add_module_config_arg()
        self.add_module_input_arg()
        args = self.parser.parse_args(argvs)
        results = self.segment(paths=[args.input_path],
                               batch_size=args.batch_size,
                               use_gpu=args.use_gpu,
                               output_dir=args.output_dir,
                               visualization=args.visualization)
        if args.save_dir is not None:
            check_dir(args.save_dir)
            self.save_inference_model(args.save_dir)

        return results

    def add_module_config_arg(self):
        """
        Add the command config options.
        """
        self.arg_config_group.add_argument('--use_gpu',
                                           type=ast.literal_eval,
                                           default=False,
                                           help="whether use GPU or not")
        self.arg_config_group.add_argument('--output_dir',
                                           type=str,
                                           default='humanseg_lite_output',
                                           help="The directory to save output images.")
        self.arg_config_group.add_argument('--save_dir',
                                           type=str,
                                           default='humanseg_lite_model',
                                           help="The directory to save model.")
        self.arg_config_group.add_argument('--visualization',
                                           type=ast.literal_eval,
                                           default=False,
                                           help="whether to save output as images.")
        self.arg_config_group.add_argument('--batch_size', type=ast.literal_eval, default=1, help="batch size.")

    def add_module_input_arg(self):
        """
        Add the command input options.
        """
        self.arg_input_group.add_argument('--input_path', type=str, help="path to image.")

    def create_gradio_app(self):
        import gradio as gr
        import tempfile
        import os
        from PIL import Image

        def inference(image, use_gpu=False):
            with tempfile.TemporaryDirectory() as temp_dir:
                self.segment(paths=[image], use_gpu=use_gpu, visualization=True, output_dir=temp_dir)
                return Image.open(os.path.join(temp_dir, os.listdir(temp_dir)[0]))

        interface = gr.Interface(
            inference,
            [gr.inputs.Image(type="filepath"), gr.Checkbox(label='use_gpu')],
            gr.outputs.Image(type="ndarray"),
            title='humanseg_lite')
        return interface