utils

import os
import glob
import random
import re
import platform
import subprocess
import time
from pathlib import Path
import time
import logging
import requests
import torch
import torch.nn as nn
import numpy as np
import torchvision
import math
import cv2
from threading import Thread

img_formats = ['bmp', 'jpg', 'jpeg', 'png', 'tif', 'tiff', 'dng', 'webp', 'mpo']  # acceptable image suffixes
vid_formats = ['mov', 'avi', 'mp4', 'mpg', 'mpeg', 'm4v', 'wmv', 'mkv']  # acceptable video suffixes

def time_synchronized():
    # pytorch-accurate time
    if torch.cuda.is_available():
        torch.cuda.synchronize()
    return time.time()

LOGGER = logging.getLogger("Timer")
logger = logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

def xywh2xyxy(x):
    # Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
    y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left x
    y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
    y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
    y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
    return y

def xyxy2xywh(x):
    # Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
    y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
    y[:, 0] = (x[:, 0] + x[:, 2]) / 2  # x center
    y[:, 1] = (x[:, 1] + x[:, 3]) / 2  # y center
    y[:, 2] = x[:, 2] - x[:, 0]  # width
    y[:, 3] = x[:, 3] - x[:, 1]  # height
    return y

def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None):
    # Rescale coords (xyxy) from img1_shape to img0_shape
    if ratio_pad is None:  # calculate from img0_shape
        gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1])  # gain  = old / new
        pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2  # wh padding
    else:
        gain = ratio_pad[0][0]
        pad = ratio_pad[1]

    coords[:, [0, 2]] -= pad[0]  # x padding
    coords[:, [1, 3]] -= pad[1]  # y padding
    coords[:, :4] /= gain
    clip_coords(coords, img0_shape)
    return coords

def clip_coords(boxes, img_shape):
    # Clip bounding xyxy bounding boxes to image shape (height, width)
    boxes[:, 0].clamp_(0, img_shape[1])  # x1
    boxes[:, 1].clamp_(0, img_shape[0])  # y1
    boxes[:, 2].clamp_(0, img_shape[1])  # x2
    boxes[:, 3].clamp_(0, img_shape[0])  # y2

def plot_one_box(x, img, color=None, label=None, line_thickness=3):
    # Plots one bounding box on image img
    tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thickness
    color = color or [random.randint(0, 255) for _ in range(3)]
    c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))
    cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)
    if label:
        tf = max(tl - 1, 1)  # font thickness
        t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
        c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3
        cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filled
        cv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)

def strip_optimizer(f='best.pt', s=''):  # from utils.general import *; strip_optimizer()
    # Strip optimizer from 'f' to finalize training, optionally save as 's'
    x = torch.load(f, map_location=torch.device('cpu'))
    if x.get('ema'):
        x['model'] = x['ema']  # replace model with ema
    for k in 'optimizer', 'training_results', 'wandb_id', 'ema', 'updates':  # keys
        x[k] = None
    x['epoch'] = -1
    x['model'].half()  # to FP16
    for p in x['model'].parameters():
        p.requires_grad = False
    torch.save(x, s or f)
    mb = os.path.getsize(s or f) / 1E6  # filesize
    print(f"Optimizer stripped from {f},{(' saved as %s,' % s) if s else ''} {mb:.1f}MB")

def apply_classifier(x, model, img, im0):
    # applies a second stage classifier to yolo outputs
    im0 = [im0] if isinstance(im0, np.ndarray) else im0
    for i, d in enumerate(x):  # per image
        if d is not None and len(d):
            d = d.clone()

            # Reshape and pad cutouts
            b = xyxy2xywh(d[:, :4])  # boxes
            b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1)  # rectangle to square
            b[:, 2:] = b[:, 2:] * 1.3 + 30  # pad
            d[:, :4] = xywh2xyxy(b).long()

            # Rescale boxes from img_size to im0 size
            scale_coords(img.shape[2:], d[:, :4], im0[i].shape)

            # Classes
            pred_cls1 = d[:, 5].long()
            ims = []
            for j, a in enumerate(d):  # per item
                cutout = im0[i][int(a[1]):int(a[3]), int(a[0]):int(a[2])]
                im = cv2.resize(cutout, (224, 224))  # BGR
                # cv2.imwrite('test%i.jpg' % j, cutout)

                im = im[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
                im = np.ascontiguousarray(im, dtype=np.float32)  # uint8 to float32
                im /= 255.0  # 0 - 255 to 0.0 - 1.0
                ims.append(im)

            pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1)  # classifier prediction
            x[i] = x[i][pred_cls1 == pred_cls2]  # retain matching class detections

    return x

def letterbox(img, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
    # Resize and pad image while meeting stride-multiple constraints
    shape = img.shape[:2]  # current shape [height, width]
    if isinstance(new_shape, int):
        new_shape = (new_shape, new_shape)

    # Scale ratio (new / old)
    r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
    if not scaleup:  # only scale down, do not scale up (for better test mAP)
        r = min(r, 1.0)

    # Compute padding
    ratio = r, r  # width, height ratios
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
    dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh padding
    if auto:  # minimum rectangle
        dw, dh = np.mod(dw, stride), np.mod(dh, stride)  # wh padding
    elif scaleFill:  # stretch
        dw, dh = 0.0, 0.0
        new_unpad = (new_shape[1], new_shape[0])
        ratio = new_shape[1] / shape[1], new_shape[0] / shape[0]  # width, height ratios

    dw /= 2  # divide padding into 2 sides
    dh /= 2

    if shape[::-1] != new_unpad:  # resize
        img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
    top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
    left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border
    return img, ratio, (dw, dh)

def check_imshow():
    # Check if environment supports image displays
    try:
        cv2.imshow('test', np.zeros((1, 1, 3)))
        cv2.waitKey(1)
        cv2.destroyAllWindows()
        cv2.waitKey(1)
        return True
    except Exception as e:
        print(f'WARNING: Environment does not support cv2.imshow() or PIL Image.show() image displays\n{e}')
        return False

def load_classifier(name='resnet101', n=2):
    # Loads a pretrained model reshaped to n-class output
    model = torchvision.models.__dict__[name](pretrained=True)

    # ResNet model properties
    # input_size = [3, 224, 224]
    # input_space = 'RGB'
    # input_range = [0, 1]
    # mean = [0.485, 0.456, 0.406]
    # std = [0.229, 0.224, 0.225]

    # Reshape output to n classes
    filters = model.fc.weight.shape[1]
    model.fc.bias = nn.Parameter(torch.zeros(n), requires_grad=True)
    model.fc.weight = nn.Parameter(torch.zeros(n, filters), requires_grad=True)
    model.fc.out_features = n
    return model

def make_divisible(x, divisor):
    # Returns x evenly divisible by divisor
    return math.ceil(x / divisor) * divisor

def check_img_size(img_size, s=32):
    # Verify img_size is a multiple of stride s
    new_size = make_divisible(img_size, int(s))  # ceil gs-multiple
    if new_size != img_size:
        print('WARNING: --img-size %g must be multiple of max stride %g, updating to %g' % (img_size, s, new_size))
    return new_size

def select_device(device='', batch_size=None):
    # device = 'cpu' or '0' or '0,1,2,3'
    cpu = device.lower() == 'cpu'
    if cpu:
        os.environ['CUDA_VISIBLE_DEVICES'] = '-1'  # force torch.cuda.is_available() = False
    elif device:  # non-cpu device requested
        os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
        assert torch.cuda.is_available(), f'CUDA unavailable, invalid device {device} requested'  # check availability

    cuda = not cpu and torch.cuda.is_available()
    if cuda:
        n = torch.cuda.device_count()
        if n > 1 and batch_size:  # check that batch_size is compatible with device_count
            assert batch_size % n == 0, f'batch-size {batch_size} not multiple of GPU count {n}'
        space = ' ' * len(s)
        for i, d in enumerate(device.split(',') if device else range(n)):
            p = torch.cuda.get_device_properties(i)
            s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / 1024 ** 2}MB)\n"  # bytes to MB
    else:
        s += 'CPU\n'

    logger.info(s.encode().decode('ascii', 'ignore') if platform.system() == 'Windows' else s)  # emoji-safe
    return torch.device('cuda:0' if cuda else 'cpu')

def autopad(k, p=None):  # kernel, padding
    # Pad to 'same'
    if p is None:
        p = k // 2 if isinstance(k, int) else [x // 2 for x in k]  # auto-pad
    return p

def increment_path(path, exist_ok=True, sep=''):
    # Increment path, i.e. runs/exp --> runs/exp{sep}0, runs/exp{sep}1 etc.
    path = Path(path)  # os-agnostic
    if (path.exists() and exist_ok) or (not path.exists()):
        return str(path)
    else:
        dirs = glob.glob(f"{path}{sep}*")  # similar paths
        matches = [re.search(rf"%s{sep}(\d+)" % path.stem, d) for d in dirs]
        i = [int(m.groups()[0]) for m in matches if m]  # indices
        n = max(i) + 1 if i else 2  # increment number
        return f"{path}{sep}{n}"  # update path



def box_iou(box1, box2):
    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
    """
    Return intersection-over-union (Jaccard index) of boxes.
    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
    Arguments:
        box1 (Tensor[N, 4])
        box2 (Tensor[M, 4])
    Returns:
        iou (Tensor[N, M]): the NxM matrix containing the pairwise
            IoU values for every element in boxes1 and boxes2
    """

    def box_area(box):
        # box = 4xn
        return (box[2] - box[0]) * (box[3] - box[1])

    area1 = box_area(box1.T)
    area2 = box_area(box2.T)

    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)
    return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)

class BatchNormXd(torch.nn.modules.batchnorm._BatchNorm):
    def _check_input_dim(self, input):
        # The only difference between BatchNorm1d, BatchNorm2d, BatchNorm3d, etc
        # is this method that is overwritten by the sub-class
        # This original goal of this method was for tensor sanity checks
        # If you're ok bypassing those sanity checks (eg. if you trust your inference
        # to provide the right dimensional inputs), then you can just use this method
        # for easy conversion from SyncBatchNorm
        # (unfortunately, SyncBatchNorm does not store the original class - if it did
        #  we could return the one that was originally created)
        return

def revert_sync_batchnorm(module):
    # this is very similar to the function that it is trying to revert:
    # https://github.com/pytorch/pytorch/blob/c8b3686a3e4ba63dc59e5dcfe5db3430df256833/torch/nn/modules/batchnorm.py#L679
    module_output = module
    if isinstance(module, torch.nn.modules.batchnorm.SyncBatchNorm):
        new_cls = BatchNormXd
        module_output = BatchNormXd(module.num_features,
                                               module.eps, module.momentum,
                                               module.affine,
                                               module.track_running_stats)
        if module.affine:
            with torch.no_grad():
                module_output.weight = module.weight
                module_output.bias = module.bias
        module_output.running_mean = module.running_mean
        module_output.running_var = module.running_var
        module_output.num_batches_tracked = module.num_batches_tracked
        if hasattr(module, "qconfig"):
            module_output.qconfig = module.qconfig
    for name, child in module.named_children():
        module_output.add_module(name, revert_sync_batchnorm(child))
    del module
    return module_output

def clean_str(s):
    # Cleans a string by replacing special characters with underscore _
    return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s)

class LoadImages:  # for inference
    def __init__(self, path, img_size=640, stride=32):
        p = str(Path(path).absolute())  # os-agnostic absolute path
        if '*' in p:
            files = sorted(glob.glob(p, recursive=True))  # glob
        elif os.path.isdir(p):
            files = sorted(glob.glob(os.path.join(p, '*.*')))  # dir
        elif os.path.isfile(p):
            files = [p]  # files
        else:
            raise Exception(f'ERROR: {p} does not exist')

        images = [x for x in files if x.split('.')[-1].lower() in img_formats]
        videos = [x for x in files if x.split('.')[-1].lower() in vid_formats]
        ni, nv = len(images), len(videos)

        self.img_size = img_size
        self.stride = stride
        self.files = images + videos
        self.nf = ni + nv  # number of files
        self.video_flag = [False] * ni + [True] * nv
        self.mode = 'image'
        if any(videos):
            self.new_video(videos[0])  # new video
        else:
            self.cap = None
        assert self.nf > 0, f'No images or videos found in {p}. '

    def __iter__(self):
        self.count = 0
        return self

    def __next__(self):
        if self.count == self.nf:
            raise StopIteration
        path = self.files[self.count]

        if self.video_flag[self.count]:
            # Read video
            self.mode = 'video'
            ret_val, img0 = self.cap.read()
            if not ret_val:
                self.count += 1
                self.cap.release()
                if self.count == self.nf:  # last video
                    raise StopIteration
                else:
                    path = self.files[self.count]
                    self.new_video(path)
                    ret_val, img0 = self.cap.read()

            self.frame += 1
            print(f'video {self.count + 1}/{self.nf} ({self.frame}/{self.nframes}) {path}: ', end='')

        else:
            # Read image
            self.count += 1
            img0 = cv2.imread(path)  # BGR
            assert img0 is not None, 'Image Not Found ' + path
            #print(f'image {self.count}/{self.nf} {path}: ', end='')

        # Padded resize
        img = letterbox(img0, self.img_size, stride=self.stride)[0]

        # Convert
        img = img[:, :, ::-1].transpose(2, 0, 1)  # BGR to RGB, to 3x416x416
        img = np.ascontiguousarray(img)

        return path, img, img0, self.cap

    def new_video(self, path):
        self.frame = 0
        self.cap = cv2.VideoCapture(path)
        self.nframes = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT))

    def __len__(self):
        return self.nf  # number of files

class LoadStreams:  # multiple IP or RTSP cameras
    def __init__(self, sources='streams.txt', img_size=640, stride=32):
        self.mode = 'stream'
        self.img_size = img_size
        self.stride = stride

        if os.path.isfile(sources):
            with open(sources, 'r') as f:
                sources = [x.strip() for x in f.read().strip().splitlines() if len(x.strip())]
        else:
            sources = [sources]

        n = len(sources)
        self.imgs = [None] * n
        self.sources = [clean_str(x) for x in sources]  # clean source names for later
        for i, s in enumerate(sources):
            # Start the thread to read frames from the video stream
            print(f'{i + 1}/{n}: {s}... ', end='')
            url = eval(s) if s.isnumeric() else s
            cap = cv2.VideoCapture(url)
            assert cap.isOpened(), f'Failed to open {s}'
            w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
            h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
            self.fps = cap.get(cv2.CAP_PROP_FPS) % 100

            _, self.imgs[i] = cap.read()  # guarantee first frame
            thread = Thread(target=self.update, args=([i, cap]), daemon=True)
            print(f' success ({w}x{h} at {self.fps:.2f} FPS).')
            thread.start()
        print('')  # newline

        # check for common shapes
        s = np.stack([letterbox(x, self.img_size, stride=self.stride)[0].shape for x in self.imgs], 0)  # shapes
        self.rect = np.unique(s, axis=0).shape[0] == 1  # rect inference if all shapes equal
        if not self.rect:
            print('WARNING: Different stream shapes detected. For optimal performance supply similarly-shaped streams.')

    def update(self, index, cap):
        # Read next stream frame in a daemon thread
        n = 0
        while cap.isOpened():
            n += 1
            # _, self.imgs[index] = cap.read()
            cap.grab()
            if n == 4:  # read every 4th frame
                success, im = cap.retrieve()
                self.imgs[index] = im if success else self.imgs[index] * 0
                n = 0
            time.sleep(1 / self.fps)  # wait time

    def __iter__(self):
        self.count = -1
        return self

    def __next__(self):
        self.count += 1
        img0 = self.imgs.copy()
        if cv2.waitKey(1) == ord('q'):  # q to quit
            cv2.destroyAllWindows()
            raise StopIteration

        # Letterbox
        img = [letterbox(x, self.img_size, auto=self.rect, stride=self.stride)[0] for x in img0]

        # Stack
        img = np.stack(img, 0)

        # Convert
        img = img[:, :, :, ::-1].transpose(0, 3, 1, 2)  # BGR to RGB, to bsx3x416x416
        img = np.ascontiguousarray(img)

        return self.sources, img, img0, None

    def __len__(self):
        return 0  # 1E12 frames = 32 streams at 30 FPS for 30 years


class TracedModel(nn.Module):

    def __init__(self, model=None, device=None, img_size=(640,640)):
        super(TracedModel, self).__init__()

        print(" Convert model to Traced-model... ")
        self.stride = model.stride
        self.names = model.names
        self.model = model

        self.model = revert_sync_batchnorm(self.model)
        self.model.to('cpu')
        self.model.eval()

        self.detect_layer = self.model.model[-1]
        self.model.traced = True

        rand_example = torch.rand(1, 3, img_size, img_size)

        traced_script_module = torch.jit.trace(self.model, rand_example, strict=False)
        #traced_script_module = torch.jit.script(self.model)
        traced_script_module.save("traced_model.pt")
        print(" traced_script_module saved! ")
        self.model = traced_script_module
        self.model.to(device)
        self.detect_layer.to(device)
        print(" model is traced! \n")

    def forward(self, x, augment=False, profile=False):
        out = self.model(x)
        out = self.detect_layer(out)
        return out

class Ensemble(nn.ModuleList):
    # Ensemble of models
    def __init__(self):
        super(Ensemble, self).__init__()

    def forward(self, x, augment=False):
        y = []
        for module in self:
            y.append(module(x, augment)[0])
        # y = torch.stack(y).max(0)[0]  # max ensemble
        # y = torch.stack(y).mean(0)  # mean ensemble
        y = torch.cat(y, 1)  # nms ensemble
        return y, None  # inference, train output


class Conv(nn.Module):
    # Standard convolution
    def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):  # ch_in, ch_out, kernel, stride, padding, groups
        super(Conv, self).__init__()
        self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False)
        self.bn = nn.BatchNorm2d(c2)
        self.act = nn.SiLU() if act is True else (act if isinstance(act, nn.Module) else nn.Identity())

    def forward(self, x):
        return self.act(self.bn(self.conv(x)))

    def fuseforward(self, x):
        return self.act(self.conv(x))


def timeIt(func):
    def wrapper(*args, **kwargs):
        start_time = time.time() * 1000
        data = func(*args, **kwargs)
        end_time = time.time() * 1000
        time_diff = end_time - start_time
        time_in_seconds = time_diff / 1000
        message = func.__name__ + " took " + str(time_in_seconds) + "s"
        LOGGER.warning(message)
        return data
    return wrapper

def attempt_download(file, repo='WongKinYiu/yolov7'):
    # Attempt file download if does not exist
    file = Path(str(file).strip().replace("'", '').lower())

    if not file.exists():
        try:
            response = requests.get(f'https://api.github.com/repos/{repo}/releases/latest').json()  # github api
            assets = [x['name'] for x in response['assets']]  # release assets
            tag = response['tag_name']  # i.e. 'v1.0'
        except:  # fallback plan
            assets = ['yolov7.pt', 'yolov7-tiny.pt', 'yolov7x.pt', 'yolov7-d6.pt', 'yolov7-e6.pt',
                      'yolov7-e6e.pt', 'yolov7-w6.pt']
            tag = subprocess.check_output('git tag', shell=True).decode().split()[-1]

        name = file.name
        if name in assets:
            msg = f'{file} missing, try downloading from https://github.com/{repo}/releases/'
            redundant = False  # second download option
            try:  # GitHub
                url = f'https://github.com/{repo}/releases/download/{tag}/{name}'
                print(f'Downloading {url} to {file}...')
                torch.hub.download_url_to_file(url, file)
                assert file.exists() and file.stat().st_size > 1E6  # check
            except Exception as e:  # GCP
                print(f'Download error: {e}')
                assert redundant, 'No secondary mirror'
                url = f'https://storage.googleapis.com/{repo}/ckpt/{name}'
                print(f'Downloading {url} to {file}...')
                os.system(f'curl -L {url} -o {file}')  # torch.hub.download_url_to_file(url, weights)
            finally:
                if not file.exists() or file.stat().st_size < 1E6:  # check
                    file.unlink(missing_ok=True)  # remove partial downloads
                    print(f'ERROR: Download failure: {msg}')
                print('')
                return

def attempt_load(weights, map_location=None):
    # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
    model = Ensemble()
    for w in weights if isinstance(weights, list) else [weights]:
        attempt_download(w)
        ckpt = torch.load(w, map_location=map_location)  # load
        model.append(ckpt['ema' if ckpt.get('ema') else 'model'].float().fuse().eval())  # FP32 model

    # Compatibility updates
    for m in model.modules():
        if type(m) in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
            m.inplace = True  # pytorch 1.7.0 compatibility
        elif type(m) is nn.Upsample:
            m.recompute_scale_factor = None  # torch 1.11.0 compatibility
        elif type(m) is Conv:
            m._non_persistent_buffers_set = set()  # pytorch 1.6.0 compatibility

    if len(model) == 1:
        return model[-1]  # return model
    else:
        print('Ensemble created with %s\n' % weights)
        for k in ['names', 'stride']:
            setattr(model, k, getattr(model[-1], k))
        return model  # return ensemble

def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, multi_label=False,
                        labels=()):
    """Runs Non-Maximum Suppression (NMS) on inference results

    Returns:
         list of detections, on (n,6) tensor per image [xyxy, conf, cls]
    """

    nc = prediction.shape[2] - 5  # number of classes
    xc = prediction[..., 4] > conf_thres  # candidates

    # Settings
    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height
    max_det = 300  # maximum number of detections per image
    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()
    time_limit = 10.0  # seconds to quit after
    redundant = True  # require redundant detections
    multi_label &= nc > 1  # multiple labels per box (adds 0.5ms/img)
    merge = False  # use merge-NMS

    t = time.time()
    output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]
    for xi, x in enumerate(prediction):  # image index, image inference
        # Apply constraints
        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height
        x = x[xc[xi]]  # confidence

        # Cat apriori labels if autolabelling
        if labels and len(labels[xi]):
            l = labels[xi]
            v = torch.zeros((len(l), nc + 5), device=x.device)
            v[:, :4] = l[:, 1:5]  # box
            v[:, 4] = 1.0  # conf
            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls
            x = torch.cat((x, v), 0)

        # If none remain process next image
        if not x.shape[0]:
            continue

        # Compute conf
        if nc == 1:
            x[:, 5:] = x[:, 4:5] # for models with one class, cls_loss is 0 and cls_conf is always 0.5,
                                 # so there is no need to multiplicate.
        else:
            x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf

        # Box (center x, center y, width, height) to (x1, y1, x2, y2)
        box = xywh2xyxy(x[:, :4])

        # Detections matrix nx6 (xyxy, conf, cls)
        if multi_label:
            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T
            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)
        else:  # best class only
            conf, j = x[:, 5:].max(1, keepdim=True)
            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]

        # Filter by class
        if classes is not None:
            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]

        # Apply finite constraint
        # if not torch.isfinite(x).all():
        #     x = x[torch.isfinite(x).all(1)]

        # Check shape
        n = x.shape[0]  # number of boxes
        if not n:  # no boxes
            continue
        elif n > max_nms:  # excess boxes
            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence

        # Batched NMS
        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes
        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores
        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS
        if i.shape[0] > max_det:  # limit detections
            i = i[:max_det]
        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)
            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix
            weights = iou * scores[None]  # box weights
            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes
            if redundant:
                i = i[iou.sum(1) > 1]  # require redundancy

        output[xi] = x[i]
        if (time.time() - t) > time_limit:
            print(f'WARNING: NMS time limit {time_limit}s exceeded')
            break  # time limit exceeded

    return output