"""
SNU Integrated Module v3.0
    - Action Classification

"""
import os
import cv2
import numpy as np
import torch
from torchvision import transforms
import torch.nn as nn

def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None):
        super(Bottleneck, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        width = int(planes * (base_width / 64.)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = norm_layer(width)
        self.conv2 = conv3x3(width, width, stride, groups, dilation)
        self.bn2 = norm_layer(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = norm_layer(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):

    def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
                 groups=1, width_per_group=64, replace_stride_with_dilation=None,
                 norm_layer=None):
        super(ResNet, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        self._norm_layer = norm_layer

        self.inplanes = 64
        self.dilation = 1
        if replace_stride_with_dilation is None:
            # each element in the tuple indicates if we should replace
            # the 2x2 stride with a dilated convolution instead
            replace_stride_with_dilation = [False, False, False]
        if len(replace_stride_with_dilation) != 3:
            raise ValueError("replace_stride_with_dilation should be None "
                             "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
        self.groups = groups
        self.base_width = width_per_group
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = norm_layer(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
                                       dilate=replace_stride_with_dilation[0])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
                                       dilate=replace_stride_with_dilation[1])
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
                                       dilate=replace_stride_with_dilation[2])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc1 = nn.Linear(1, 4)
        self.fc2 = nn.Linear(512 * block.expansion + 4, 128)
        self.fc3 = nn.Linear(128, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
        norm_layer = self._norm_layer
        downsample = None
        previous_dilation = self.dilation
        if dilate:
            self.dilation *= stride
            stride = 1
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
                            self.base_width, previous_dilation, norm_layer))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, groups=self.groups,
                                base_width=self.base_width, dilation=self.dilation,
                                norm_layer=norm_layer))

        return nn.Sequential(*layers)

    def forward(self, x, ratio):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.reshape(x.size(0), -1)

        x2 = self.fc1(ratio)
        x = self.fc2(torch.cat((x, x2),dim=1))
        x = self.fc3(x)

        return x

def _resnet(arch, block, layers, pretrained, progress, **kwargs):
    model = ResNet(block, layers, **kwargs)
    if pretrained:
        state_dict = load_state_dict_from_url(model_urls[arch],
                                              progress=progress)
        model.load_state_dict(state_dict)
    return model


def resnet18(pretrained=False, progress=True, **kwargs):
    """Constructs a ResNet-18 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
        progress (bool): If True, displays a progress bar of the download to stderr
    """
    return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
                   **kwargs)

def load_model(opts):
    # Set CUDA Device
    cuda_device_str = "cuda:" + str(opts.aclassifier.device)
    device = torch.device(cuda_device_str if torch.cuda.is_available() else "cpu")

    # Find Model
    pth_file_list = []
    for f in os.listdir(opts.aclassifier.model_dir):
        if f.endswith(".pth"):
            pth_file_list.append(f)

    if len(pth_file_list) == 1:
        model_path = os.path.join(opts.aclassifier.model_dir, pth_file_list[0])
    else:
        assert 0, "Error"

    # Get Model
    model = resnet18(num_classes = 3)
    model.load_state_dict(torch.load(model_path))
    model = model.to(device)

    return model


def voting(pose_list):
    counter = 0
    num = pose_list[0]
    for i in pose_list:
        curr_freq = pose_list.count(i)
        if curr_freq > counter:
            counter = curr_freq
            num = i

    return num + 1


# Action Classification
def aclassify(model, sync_data_dict, trackers, opts):
    # Get Color Frame
    color_frame = sync_data_dict["color"].get_data()
    H, W = color_frame.shape[0], color_frame.shape[1]

    # Set CUDA Device
    cuda_device_str = "cuda:" + str(opts.aclassifier.device)
    device = torch.device(cuda_device_str if torch.cuda.is_available() else "cpu")

    # Classify Action for Human Label
    for tracker_idx, tracker in enumerate(trackers):
        if tracker.label == 1:
            # Get Tracklet State
            x3 = tracker.x3.reshape(len(tracker.x3))

            # Get BBOX Points
            a = max(0, int(x3[1] - (x3[6] / 2)))
            b = min(int(x3[1] + (x3[6] / 2)), H - 1)
            c = max(0, int(x3[0] - (x3[5] / 2)))
            d = min(int(x3[0] + (x3[5] / 2)), W - 1)

            if (a >= b) or (c >= d):
                tracker.pose_list.insert(0, 0)
            else:
                ratio = torch.tensor([[(d-c) / float(b-a)]]).to(device)
                crop_image = color_frame[a:b, c:d]
                cr_rsz_img = cv2.resize(crop_image, dsize=(60, 60), interpolation=cv2.INTER_AREA)
                b, g, r = np.split(cr_rsz_img, 3, axis=2)
                d = np.array([r, g, b]).squeeze() / 255.0
                e = d.tolist()
                x = torch.tensor(e, dtype=torch.float)
                x = transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])(x)
                x = (x.view(1, 3, 60, 60)).to(device)
                tmp = model(x, ratio)
                tracker.pose_list.insert(0, torch.max(tmp, 1)[1])

            # Pose List has a maximum window size
            if len(tracker.pose_list) > 5:
                tracker.pose_list.pop()
            tracker.pose = voting(tracker.pose_list)

            trackers[tracker_idx] = tracker

        # For non-human,
        else:
            tracker.pose = None

    return trackers


# Test Action Classification (for snu-osr-pil computer)
def test_aclassifier_snu_osr_pil():
    pass


if __name__ == "__main__":
    pass
