import argparse
import glob
import json
import os
import shutil
from pathlib import Path
import cv2

import numpy as np
import torch
import yaml
from tqdm import tqdm
from models.yolo import Model

from models.experimental import attempt_load
from utils.datasets import create_dataloader
from utils.general import (
    coco80_to_coco91_class, check_dataset, check_file, check_img_size, compute_loss, non_max_suppression, scale_coords,
    xyxy2xywh, clip_coords, plot_images, xywh2xyxy, box_iou, output_to_target, ap_per_class, set_logging)
from utils.torch_utils import select_device, time_synchronized, intersect_dicts


def test(data,
         weights=None,
         batch_size=16,
         imgsz=640,
         conf_thres=0.001,
         iou_thres=0.6,  # for NMS
         save_json=False,
         single_cls=False,
         augment=False,
         verbose=False,
         model=None,
         dataloader=None,
         save_dir=Path(''),  # for saving images
         save_txt=False,  # for auto-labelling
         plots=True,
         seq_len=2,
         att_criterion=None,
         hyp=None,
         epoch=0,
         training = False,
         fp = True):

    # Initialize/load model and set device
    training = model is not None
    if training:  # called by train.py
        device = next(model.parameters()).device  # get model device

    else:  # called directly
        set_logging()
        device = select_device(opt.device, batch_size=batch_size)
        save_txt = opt.save_txt  # save *.txt labels
        if save_txt:
            out = Path('inference/output')
            if os.path.exists(out):
                shutil.rmtree(out)  # delete output folder
            os.makedirs(out)  # make new output folder

        # Remove previous
        for f in glob.glob(str(save_dir / 'test_batch*.jpg')):
            os.remove(f)

        # Load model

        hyp = check_file(hyp)
        with open(hyp) as f:
            hyp = yaml.load(f, Loader=yaml.FullLoader)  # load hyps
        # Load model
        model = attempt_load(weights, map_location=device)  # load FP32 model
        ckpt = torch.load(weights, map_location=device)  # load checkpoint
        # if hyp.get('anchors'):
        #     ckpt['model'].yaml['anchors'] = round(hyp['anchors'])  # force autoanchor
        # model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=3, seq_len=opt.seq_len,
        #               size=imgsz, batch_size=batch_size).to(device)  # create
        # exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else []  # exclude keys
        # state_dict = ckpt['model'].float().state_dict()  # to FP32
        # state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude)  # intersect
        # model.load_state_dict(state_dict, strict=False)  # load

        imgsz = check_img_size(imgsz, s=model.stride.max())  # check img_size

        # Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
        # if device.type != 'cpu' and torch.cuda.device_count() > 1:
        #     model = nn.DataParallel(model)

    # Half
    half = device.type != 'cpu' and not fp # half precision only supported on CUDA
    if half:
        # print('Using half')
        model.half()

    # Configure
    model.eval()
    with open(data) as f:
        data = yaml.load(f, Loader=yaml.FullLoader)  # model dict
    check_dataset(data)  # check
    nc = 1 if single_cls else int(data['nc'])  # number of classes
    iouv = torch.linspace(0.5, 0.95, 10).to(device)  # iou vector for mAP@0.5:0.95
    niou = iouv.numel()

    # Dataloader
    if not training:
        # img = torch.zeros((1, 3, imgsz, imgsz), device=device)  # init img
        # _ = model(img.half() if half else img) if device.type != 'cpu' else None  # run once
        path = data['test'] if opt.task == 'test' else data['val']  # path to val/test images
        dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt,
                                       hyp=None, augment=False, cache=False, pad=0.5, rect=True,
                                       seq_len=opt.seq_len, video_seq=20)[0]

    seen = 0
    # names = model.names if hasattr(model, 'names') else model.module.names
    names = ['phone', 'gun', 'hand']

    coco91class = coco80_to_coco91_class()
    s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
    p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
    loss = torch.zeros(3, device=device)
    jdict, stats, ap, ap_class = [], [], [], []
    for batch_i, (img, targets, paths, shapes, masks, masksOP) in enumerate(tqdm(dataloader, desc=s)):
        img = img.view(-1, 3, img.shape[3], img.shape[4])
        img = img.to(device, non_blocking=True)

        masksOP = masksOP.to(device, non_blocking=True)
        masksOP = masksOP.half() if half else masksOP.float()  # uint8 to fp16/32

        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        targets = targets.to(device)
        nb, _, height, width = img.shape  # batch size, channels, height, width
        whwh = torch.Tensor([width, height, width, height]).to(device)

        # Disable gradients
        with torch.no_grad():
            # Run model
            t = time_synchronized()
            inf_out, train_out = model(img, masksOP, augment=augment)  # inference and training outputs
            t0 += time_synchronized() - t

            # Compute loss
            if training:  # if model has loss hyperparameters
                loss += compute_loss([x.float() for x in train_out], targets, model, model.model[-1].maps_outputs,
                                     att_criterion, masks)[1][:3]  # GIoU, obj, cls
            # Run NMS
            t = time_synchronized()
            output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres)
            t1 += time_synchronized() - t
            output = output[seq_len - 1::seq_len]
            targets = targets[(targets[:,0] + 1) % (seq_len) == 0]

        img = img.view(-1, seq_len, 3, img.shape[2], img.shape[3])
        img = img[:, img.shape[1] - 1, :, :, :]

        # Statistics per image
        for si, pred in enumerate(output):
            new_si = (si + 1) * seq_len - 1
            labels = targets[targets[:, 0] == new_si, 1:]
            nl = len(labels)
            tcls = labels[:, 0].tolist() if nl else []  # target class
            seen += 1

            if pred is None:
                if nl:
                    stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
                continue

            # Append to text file
            if save_txt:
                gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]]  # normalization gain whwh
                x = pred.clone()
                x[:, :4] = scale_coords(img[si].shape[1:], x[:, :4], shapes[si][0], shapes[si][1])  # to original
                for *xyxy, conf, cls in x:
                    xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist()  # normalized xywh
                    with open(str(out / Path(paths[si]).stem) + '.txt', 'a') as f:
                        f.write(('%g ' * 5 + '\n') % (cls, *xywh))  # label format

            # Clip boxes to image bounds
            clip_coords(pred, (height, width))

            # Append to pycocotools JSON dictionary
            if save_json:
                # [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
                image_id = Path(paths[si]).stem
                box = pred[:, :4].clone()  # xyxy
                scale_coords(img[si].shape[1:], box, shapes[si][0], shapes[si][1])  # to original shape
                box = xyxy2xywh(box)  # xywh
                box[:, :2] -= box[:, 2:] / 2  # xy center to top-left corner
                for p, b in zip(pred.tolist(), box.tolist()):
                    jdict.append({'image_id': int(image_id) if image_id.isnumeric() else image_id,
                                  'category_id': coco91class[int(p[5])],
                                  'bbox': [round(x, 3) for x in b],
                                  'score': round(p[4], 5)})

            # Assign all predictions as incorrect
            correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool, device=device)
            if nl:
                detected = []  # target indices
                tcls_tensor = labels[:, 0]

                # target boxes
                tbox = xywh2xyxy(labels[:, 1:5]) * whwh

                # Per target class
                for cls in torch.unique(tcls_tensor):
                    ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1)  # prediction indices
                    pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1)  # target indices

                    # Search for detections
                    if pi.shape[0]:
                        # Prediction to target ious
                        ious, i = box_iou(pred[pi, :4], tbox[ti]).max(1)  # best ious, indices

                        # Append detections
                        detected_set = set()
                        for j in (ious > iouv[0]).nonzero(as_tuple=False):
                            d = ti[i[j]]  # detected target
                            if d.item() not in detected_set:
                                detected_set.add(d.item())
                                detected.append(d)
                                correct[pi[j]] = ious[j] > iouv  # iou_thres is 1xn
                                if len(detected) == nl:  # all targets already located in image
                                    break

            # Append statistics (correct, conf, pcls, tcls)
            stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))

        # # Plot images
        # if batch_i == 1 or batch_i == 10:
        # f = Path(save_dir) / ('test/%stest_batch%g_pred.jpg' % (epoch, batch_i))
        # plot_images(img, output_to_target(output, width, height), paths, str(f), names)  # predictions


    # Compute statistics
    stats = [np.concatenate(x, 0) for x in zip(*stats)]  # to numpy
    if len(stats) and stats[0].any():
        p, r, ap, f1, ap_class = ap_per_class(*stats, plot=plots, fname=save_dir / 'precision-recall_curve.png')
        p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1)  # [P, R, AP@0.5, AP@0.5:0.95]
        mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
        nt = np.bincount(stats[3].astype(np.int64), minlength=nc)  # number of targets per class
    else:
        nt = torch.zeros(1)

    # Print results
    pf = '%20s' + '%12.3g' * 6  # print format
    print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))

    # # Print results per class
    # if verbose and nc > 1 and len(stats):
    #     for i, c in enumerate(ap_class):
    #         print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))

    # Print speeds
    t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size)  # tuple
    if not training:
        print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)

    # Save JSON
    if save_json and len(jdict):
        f = 'detections_val2017_%s_results.json' % \
            (weights.split(os.sep)[-1].replace('.pt', '') if isinstance(weights, str) else '')  # filename
        print('\nCOCO mAP with pycocotools... saving %s...' % f)
        with open(f, 'w') as file:
            json.dump(jdict, file)

        try:  # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
            from pycocotools.coco import COCO
            from pycocotools.cocoeval import COCOeval

            imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]
            cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0])  # initialize COCO ground truth api
            cocoDt = cocoGt.loadRes(f)  # initialize COCO pred api
            cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
            cocoEval.params.imgIds = imgIds  # image IDs to evaluate
            cocoEval.evaluate()
            cocoEval.accumulate()
            cocoEval.summarize()
            map, map50 = cocoEval.stats[:2]  # update results (mAP@0.5:0.95, mAP@0.5)
        except Exception as e:
            print('ERROR: pycocotools unable to run: %s' % e)

    # Return results
    model.float()  # for training
    maps = np.zeros(nc) + map
    for i, c in enumerate(ap_class):
        maps[c] = ap[i]
    return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t


if __name__ == '__main__':
    parser = argparse.ArgumentParser(prog='test.py')
    parser.add_argument('--weights', type=str, default='', help='model.pt path(s)')
    parser.add_argument('--data', type=str, default='data/coco_7000_5000.yaml', help='*.data path')
    parser.add_argument('--cfg', type=str, default='models/yolov5xc.yaml', help='model.yaml path')
    parser.add_argument('--batch-size', type=int, default=1, help='size of each image batch')
    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float, default=0.65, help='IOU threshold for NMS')
    parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
    parser.add_argument('--task', default='val', help="'val', 'test', 'study'")
    parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
    parser.add_argument('--single-cls', action='store_true', help='treat as single-class dataset')
    parser.add_argument('--augment', action='store_true', help='augmented inference')
    parser.add_argument('--verbose', action='store_true', help='report mAP by class')
    parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
    parser.add_argument('--seq_len', type=int, default=2, help='maximum number frames')
    parser.add_argument('--hyp', type=str, default='data/hyp.finetune.yaml', help='hyperparameters path')
    parser.add_argument('--fp', action='store_true', help='resume most recent training')

    opt = parser.parse_args()
    opt.save_json |= opt.data.endswith('coco.yaml')
    opt.data = check_file(opt.data)  # check file
    print(opt)

    if opt.task in ['val', 'test']:  # run normally
        test(opt.data,
             opt.weights,
             opt.batch_size,
             opt.img_size,
             opt.conf_thres,
             opt.iou_thres,
             opt.save_json,
             opt.single_cls,
             opt.augment,
             opt.verbose,
             hyp = opt.hyp,
             seq_len = opt.seq_len,
             fp = opt.fp)

    elif opt.task == 'study':  # run over a range of settings and save/plot
        for weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
            f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem)  # filename to save to
            x = list(range(320, 800, 64))  # x axis
            y = []  # y axis
            for i in x:  # img-size
                print('\nRunning %s point %s...' % (f, i))
                r, _, t = test(opt.data, weights, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json)
                y.append(r + t)  # results and times
            np.savetxt(f, y, fmt='%10.4g')  # save
        os.system('zip -r study.zip study_*.txt')
        # utils.general.plot_study_txt(f, x)  # plot
