# 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 data import COCODetection, get_label_map, MEANS, COLORS

from yolact import Yolact

from utils.augmentations import BaseTransform, FastBaseTransform, Resize

from utils.functions import MovingAverage, ProgressBar

from layers.box_utils import jaccard, center_size, mask_iou

from utils import timer

from utils.functions import SavePath

from layers.output_utils import postprocess, undo_image_transformation

import pycocotools



from data import cfg, set_cfg, set_dataset

from tqdm import tqdm

import numpy as np

import torch

import torch.backends.cudnn as cudnn

from torch.autograd import Variable

import argparse

import time

import random

import cProfile

import pickle

import json

import os

from collections import defaultdict

from pathlib import Path

from collections import OrderedDict

from PIL import Image



import matplotlib.pyplot as plt

import cv2



def str2bool(v):

    if v.lower() in ('yes', 'true', 't', 'y', '1'):

        return True

    elif v.lower() in ('no', 'false', 'f', 'n', '0'):

        return False

    else:

        raise argparse.ArgumentTypeError('Boolean value expected.')



def parse_args(argv=None):

    parser = argparse.ArgumentParser(

        description='YOLACT COCO Evaluation')

    parser.add_argument('--trained_model',

                        default='weights/ssd300_mAP_77.43_v2.pth', type=str,

                        help='Trained state_dict file path to open. If "interrupt", this will open the interrupt file.')

    parser.add_argument('--data_path', default=None, type=str,

                        help='data path')

    parser.add_argument('--top_k', default=5, type=int,

                        help='Further restrict the number of predictions to parse')

    parser.add_argument('--cuda', default=True, type=str2bool,

                        help='Use cuda to evaulate model')

    parser.add_argument('--fast_nms', default=True, type=str2bool,

                        help='Whether to use a faster, but not entirely correct version of NMS.')

    parser.add_argument('--cross_class_nms', default=False, type=str2bool,

                        help='Whether compute NMS cross-class or per-class.')

    parser.add_argument('--display_masks', default=True, type=str2bool,

                        help='Whether or not to display masks over bounding boxes')

    parser.add_argument('--display_bboxes', default=True, type=str2bool,

                        help='Whether or not to display bboxes around masks')

    parser.add_argument('--display_text', default=True, type=str2bool,

                        help='Whether or not to display text (class [score])')

    parser.add_argument('--display_scores', default=True, type=str2bool,

                        help='Whether or not to display scores in addition to classes')

    parser.add_argument('--display', dest='display', action='store_true',

                        help='Display qualitative results instead of quantitative ones.')

    parser.add_argument('--shuffle', dest='shuffle', action='store_true',

                        help='Shuffles the images when displaying them. Doesn\'t have much of an effect when display is off though.')

    parser.add_argument('--ap_data_file', default='results/ap_data.pkl', type=str,

                        help='In quantitative mode, the file to save detections before calculating mAP.')

    parser.add_argument('--resume', dest='resume', action='store_true',

                        help='If display not set, this resumes mAP calculations from the ap_data_file.')

    parser.add_argument('--max_images', default=-1, type=int,

                        help='The maximum number of images from the dataset to consider. Use -1 for all.')

    parser.add_argument('--output_coco_json', dest='output_coco_json', action='store_true',

                        help='If display is not set, instead of processing IoU values, this just dumps detections into the coco json file.')

    parser.add_argument('--bbox_det_file', default='results/bbox_detections.json', type=str,

                        help='The output file for coco bbox results if --coco_results is set.')

    parser.add_argument('--mask_det_file', default='results/mask_detections.json', type=str,

                        help='The output file for coco mask results if --coco_results is set.')

    parser.add_argument('--config', default='yolact_plus_resnet50_config',

                        help='The config object to use.')

    parser.add_argument('--output_web_json', dest='output_web_json', action='store_true',

                        help='If display is not set, instead of processing IoU values, this dumps detections for usage with the detections viewer web thingy.')

    parser.add_argument('--web_det_path', default='web/dets/', type=str,

                        help='If output_web_json is set, this is the path to dump detections into.')

    parser.add_argument('--no_bar', dest='no_bar', action='store_true',

                        help='Do not output the status bar. This is useful for when piping to a file.')

    parser.add_argument('--display_lincomb', default=False, type=str2bool,

                        help='If the config uses lincomb masks, output a visualization of how those masks are created.')

    parser.add_argument('--benchmark', default=False, dest='benchmark', action='store_true',

                        help='Equivalent to running display mode but without displaying an image.')

    parser.add_argument('--no_sort', default=False, dest='no_sort', action='store_true',

                        help='Do not sort images by hashed image ID.')

    parser.add_argument('--seed', default=None, type=int,

                        help='The seed to pass into random.seed. Note: this is only really for the shuffle and does not (I think) affect cuda stuff.')

    parser.add_argument('--mask_proto_debug', default=False, dest='mask_proto_debug', action='store_true',

                        help='Outputs stuff for scripts/compute_mask.py.')

    parser.add_argument('--no_crop', default=False, dest='crop', action='store_false',

                        help='Do not crop output masks with the predicted bounding box.')

    parser.add_argument('--image', default=None, type=str,

                        help='A path to an image to use for display.')

    parser.add_argument('--images', default=None, type=str,

                        help='An input folder of images and output folder to save detected images. Should be in the format input->output.')

    parser.add_argument('--video', default=None, type=str,

                        help='A path to a video to evaluate on. Passing in a number will use that index webcam.')

    parser.add_argument('--video_multiframe', default=1, type=int,

                        help='The number of frames to evaluate in parallel to make videos play at higher fps.')

    parser.add_argument('--score_threshold', default=0, type=float,

                        help='Detections with a score under this threshold will not be considered. This currently only works in display mode.')

    parser.add_argument('--dataset', default=None, type=str,

                        help='If specified, override the dataset specified in the config with this one (example: coco2017_dataset).')

    parser.add_argument('--detect', default=False, dest='detect', action='store_true',

                        help='Don\'t evauluate the mask branch at all and only do object detection. This only works for --display and --benchmark.')

    parser.add_argument('--display_fps', default=False, dest='display_fps', action='store_true',

                        help='When displaying / saving video, draw the FPS on the frame')

    parser.add_argument('--emulate_playback', default=False, dest='emulate_playback', action='store_true',

                        help='When saving a video, emulate the framerate that you\'d get running in real-time mode.')

    parser.add_argument('--rank_id', default=0, type=int)

    parser.set_defaults(no_bar=False, display=False, resume=False, output_coco_json=False, output_web_json=False, shuffle=False,

                        benchmark=False, no_sort=False, no_hash=False, mask_proto_debug=False, crop=True, detect=False, display_fps=False,

                        emulate_playback=False)



    global args

    args = parser.parse_args(argv)



    if args.output_web_json:

        args.output_coco_json = True

    

    if args.seed is not None:

        random.seed(args.seed)



iou_thresholds = [x / 100 for x in range(50, 100, 5)]

coco_cats = {} # Call prep_coco_cats to fill this

coco_cats_inv = {}

color_cache = defaultdict(lambda: {})



def prep_display(dets_out, img, h, w, undo_transform=True, class_color=False, mask_alpha=0.45, fps_str=''):

    """

    Note: If undo_transform=False then im_h and im_w are allowed to be None.

    """

    if undo_transform:

        img_numpy = undo_image_transformation(img, w, h)

        if args.cuda:

            img_gpu = torch.Tensor(img_numpy).npu()

        else:

            img_gpu = torch.Tensor(img_numpy)

        print('img_shape:', img_gpu)

    else:

        img_gpu = img / 255.0

        h, w, _ = img.shape

        print('h, w, _ :', img.shape)

    

    with timer.env('Postprocess'):

        save = cfg.rescore_bbox

        cfg.rescore_bbox = True

        t = postprocess(dets_out, w, h, visualize_lincomb = args.display_lincomb,

                                        crop_masks        = args.crop,

                                        score_threshold   = args.score_threshold)

        cfg.rescore_bbox = save



    with timer.env('Copy'):

        idx = t[1].argsort(0, descending=True)[:args.top_k]

        

        if cfg.eval_mask_branch:

            # Masks are drawn on the GPU, so don't copy

            masks = t[3][idx]

        classes, scores, boxes = [x[idx].cpu().numpy() for x in t[:3]]



    num_dets_to_consider = min(args.top_k, classes.shape[0])

    for j in range(num_dets_to_consider):

        if scores[j] < args.score_threshold:

            num_dets_to_consider = j

            break



    # Quick and dirty lambda for selecting the color for a particular index

    # Also keeps track of a per-gpu color cache for maximum speed

    def get_color(j, on_gpu=None):

        global color_cache

        color_idx = (classes[j] * 5 if class_color else j * 5) % len(COLORS)

        

        if on_gpu is not None and color_idx in color_cache[on_gpu]:

            return color_cache[on_gpu][color_idx]

        else:

            color = COLORS[color_idx]

            if not undo_transform:

                # The image might come in as RGB or BRG, depending

                color = (color[2], color[1], color[0])

            if on_gpu is not None:

                color = torch.Tensor(color).to(on_gpu).float() / 255.

                color_cache[on_gpu][color_idx] = color

            return color



    # First, draw the masks on the GPU where we can do it really fast

    # Beware: very fast but possibly unintelligible mask-drawing code ahead

    # I wish I had access to OpenGL or Vulkan but alas, I guess Pytorch tensor operations will have to suffice

    if args.display_masks and cfg.eval_mask_branch and num_dets_to_consider > 0:

        # After this, mask is of size [num_dets, h, w, 1]

        masks = masks[:num_dets_to_consider, :, :, None]

        

        # Prepare the RGB images for each mask given their color (size [num_dets, h, w, 1])

        colors = torch.cat([get_color(j, on_gpu=img_gpu.device.index).view(1, 1, 1, 3) for j in range(num_dets_to_consider)], dim=0)

        masks_color = masks.repeat(1, 1, 1, 3) * colors * mask_alpha



        # This is 1 everywhere except for 1-mask_alpha where the mask is

        inv_alph_masks = masks * (-mask_alpha) + 1

        

        # I did the math for this on pen and paper. This whole block should be equivalent to:

        #    for j in range(num_dets_to_consider):

        #        img_gpu = img_gpu * inv_alph_masks[j] + masks_color[j]

        masks_color_summand = masks_color[0]

        if num_dets_to_consider > 1:

            inv_alph_cumul = inv_alph_masks[:(num_dets_to_consider-1)].cumprod(dim=0)

            masks_color_cumul = masks_color[1:] * inv_alph_cumul

            masks_color_summand += masks_color_cumul.sum(dim=0)



        img_gpu = img_gpu * inv_alph_masks.prod(dim=0) + masks_color_summand

    

    if args.display_fps:

            # Draw the box for the fps on the GPU

        font_face = cv2.FONT_HERSHEY_DUPLEX

        font_scale = 0.6

        font_thickness = 1



        text_w, text_h = cv2.getTextSize(fps_str, font_face, font_scale, font_thickness)[0]



        img_gpu[0:text_h+8, 0:text_w+8] *= 0.6 # 1 - Box alpha





    # Then draw the stuff that needs to be done on the cpu

    # Note, make sure this is a uint8 tensor or opencv will not anti alias text for whatever reason

    img_numpy = (img_gpu * 255).byte().cpu().numpy()



    if args.display_fps:

        # Draw the text on the CPU

        text_pt = (4, text_h + 2)

        text_color = [255, 255, 255]



        cv2.putText(img_numpy, fps_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)

    

    if num_dets_to_consider == 0:

        return img_numpy



    if args.display_text or args.display_bboxes:

        for j in reversed(range(num_dets_to_consider)):

            x1, y1, x2, y2 = boxes[j, :]

            color = get_color(j)

            score = scores[j]



            if args.display_bboxes:

                cv2.rectangle(img_numpy, (x1, y1), (x2, y2), color, 1)



            if args.display_text:

                _class = cfg.dataset.class_names[classes[j]]

                text_str = '%s: %.2f' % (_class, score) if args.display_scores else _class



                font_face = cv2.FONT_HERSHEY_DUPLEX

                font_scale = 0.6

                font_thickness = 1



                text_w, text_h = cv2.getTextSize(text_str, font_face, font_scale, font_thickness)[0]



                text_pt = (x1, y1 - 3)

                text_color = [255, 255, 255]



                cv2.rectangle(img_numpy, (x1, y1), (x1 + text_w, y1 - text_h - 4), color, -1)

                cv2.putText(img_numpy, text_str, text_pt, font_face, font_scale, text_color, font_thickness, cv2.LINE_AA)

            

    

    return img_numpy



def prep_benchmark(dets_out, h, w):

    with timer.env('Postprocess'):

        t = postprocess(dets_out, w, h, crop_masks=args.crop, score_threshold=args.score_threshold)



    with timer.env('Copy'):

        classes, scores, boxes, masks = [x[:args.top_k] for x in t]

        if isinstance(scores, list):

            box_scores = scores[0].cpu().numpy()

            mask_scores = scores[1].cpu().numpy()

        else:

            scores = scores.cpu().numpy()

        classes = classes.cpu().numpy()

        boxes = boxes.cpu().numpy()

        masks = masks.cpu().numpy()

    

    with timer.env('Sync'):

        # Just in case

        torch.npu.synchronize()



def prep_coco_cats():

    """ Prepare inverted table for category id lookup given a coco cats object. """

    for coco_cat_id, transformed_cat_id_p1 in get_label_map().items():

        transformed_cat_id = transformed_cat_id_p1 - 1

        coco_cats[transformed_cat_id] = coco_cat_id

        coco_cats_inv[coco_cat_id] = transformed_cat_id





def get_coco_cat(transformed_cat_id):

    """ transformed_cat_id is [0,80) as indices in cfg.dataset.class_names """

    return coco_cats[transformed_cat_id]



def get_transformed_cat(coco_cat_id):

    """ transformed_cat_id is [0,80) as indices in cfg.dataset.class_names """

    return coco_cats_inv[coco_cat_id]





class Detections:



    def __init__(self):

        self.bbox_data = []

        self.mask_data = []



    def add_bbox(self, image_id:int, category_id:int, bbox:list, score:float):

        """ Note that bbox should be a list or tuple of (x1, y1, x2, y2) """

        bbox = [bbox[0], bbox[1], bbox[2]-bbox[0], bbox[3]-bbox[1]]



        # Round to the nearest 10th to avoid huge file sizes, as COCO suggests

        bbox = [round(float(x)*10)/10 for x in bbox]



        self.bbox_data.append({

            'image_id': int(image_id),

            'category_id': get_coco_cat(int(category_id)),

            'bbox': bbox,

            'score': float(score)

        })



    def add_mask(self, image_id:int, category_id:int, segmentation:np.ndarray, score:float):

        """ The segmentation should be the full mask, the size of the image and with size [h, w]. """

        rle = pycocotools.mask.encode(np.asfortranarray(segmentation.astype(np.uint8)))

        rle['counts'] = rle['counts'].decode('ascii') # json.dump doesn't like bytes strings



        self.mask_data.append({

            'image_id': int(image_id),

            'category_id': get_coco_cat(int(category_id)),

            'segmentation': rle,

            'score': float(score)

        })

    

    def dump(self):

        dump_arguments = [

            (self.bbox_data, args.bbox_det_file),

            (self.mask_data, args.mask_det_file)

        ]



        for data, path in dump_arguments:

            with open(path, 'w') as f:

                json.dump(data, f)

    

    def dump_web(self):

        """ Dumps it in the format for my web app. Warning: bad code ahead! """

        config_outs = ['preserve_aspect_ratio', 'use_prediction_module',

                        'use_yolo_regressors', 'use_prediction_matching',

                        'train_masks']



        output = {

            'info' : {

                'Config': {key: getattr(cfg, key) for key in config_outs},

            }

        }



        image_ids = list(set([x['image_id'] for x in self.bbox_data]))

        image_ids.sort()

        image_lookup = {_id: idx for idx, _id in enumerate(image_ids)}



        output['images'] = [{'image_id': image_id, 'dets': []} for image_id in image_ids]



        # These should already be sorted by score with the way prep_metrics works.

        for bbox, mask in zip(self.bbox_data, self.mask_data):

            image_obj = output['images'][image_lookup[bbox['image_id']]]

            image_obj['dets'].append({

                'score': bbox['score'],

                'bbox': bbox['bbox'],

                'category': cfg.dataset.class_names[get_transformed_cat(bbox['category_id'])],

                'mask': mask['segmentation'],

            })



        with open(os.path.join(args.web_det_path, '%s.json' % cfg.name), 'w') as f:

            json.dump(output, f)

        



        



def _mask_iou(mask1, mask2, iscrowd=False):

    with timer.env('Mask IoU'):

        ret = mask_iou(mask1, mask2, iscrowd)

    return ret.cpu()



def _bbox_iou(bbox1, bbox2, iscrowd=False):

    with timer.env('BBox IoU'):

        ret = jaccard(bbox1, bbox2, iscrowd)

    return ret.cpu()



def prep_metrics(ap_data, dets, img, gt, gt_masks, h, w, num_crowd, image_id, detections:Detections=None):

    """ Returns a list of APs for this image, with each element being for a class  """

    if not args.output_coco_json:

        with timer.env('Prepare gt'):

            gt_boxes = torch.Tensor(gt[:, :4]) if args.cuda else torch.Tensor(gt[:, :4])

            gt_boxes[:, [0, 2]] *= w

            gt_boxes[:, [1, 3]] *= h

            gt_classes = list(gt[:, 4].astype(int))

            gt_masks = torch.Tensor(gt_masks).view(-1, h*w) if args.cuda else torch.Tensor(gt_masks).view(-1, h*w)



            if num_crowd > 0:

                split = lambda x: (x[-num_crowd:], x[:-num_crowd])

                crowd_boxes  , gt_boxes   = split(gt_boxes)

                crowd_masks  , gt_masks   = split(gt_masks)

                crowd_classes, gt_classes = split(gt_classes)



    with timer.env('Postprocess'):

        classes, scores, boxes, masks = postprocess(dets, w, h, crop_masks=args.crop, score_threshold=args.score_threshold)



        if classes.size(0) == 0:

            return



        classes = list(classes.cpu().numpy().astype(int))

        if isinstance(scores, list):

            box_scores = list(scores[0].cpu().numpy().astype(float))

            mask_scores = list(scores[1].cpu().numpy().astype(float))

        else:

            scores = list(scores.cpu().numpy().astype(float))

            box_scores = scores

            mask_scores = scores

        # if args.cuda:

        #     masks = masks.view(-1, h*w).npu()

        #     boxes = boxes.npu()

        # else:

        masks = masks.view(-1, h*w)





    if args.output_coco_json:

        with timer.env('JSON Output'):

            boxes = boxes.cpu().numpy()

            masks = masks.view(-1, h, w).cpu().numpy()

            for i in range(masks.shape[0]):

                # Make sure that the bounding box actually makes sense and a mask was produced

                if (boxes[i, 3] - boxes[i, 1]) * (boxes[i, 2] - boxes[i, 0]) > 0:

                    detections.add_bbox(image_id, classes[i], boxes[i,:],   box_scores[i])

                    detections.add_mask(image_id, classes[i], masks[i,:,:], mask_scores[i])

            return

    

    with timer.env('Eval Setup'):

        num_pred = len(classes)

        num_gt   = len(gt_classes)



        mask_iou_cache = _mask_iou(masks, gt_masks)

        bbox_iou_cache = _bbox_iou(boxes.float(), gt_boxes.float())



        if num_crowd > 0:

            crowd_mask_iou_cache = _mask_iou(masks, crowd_masks, iscrowd=True)

            crowd_bbox_iou_cache = _bbox_iou(boxes.float(), crowd_boxes.float(), iscrowd=True)

        else:

            crowd_mask_iou_cache = None

            crowd_bbox_iou_cache = None



        box_indices = sorted(range(num_pred), key=lambda i: -box_scores[i])

        mask_indices = sorted(box_indices, key=lambda i: -mask_scores[i])



        iou_types = [

            ('box',  lambda i,j: bbox_iou_cache[i, j].item(),

                     lambda i,j: crowd_bbox_iou_cache[i,j].item(),

                     lambda i: box_scores[i], box_indices),

            ('mask', lambda i,j: mask_iou_cache[i, j].item(),

                     lambda i,j: crowd_mask_iou_cache[i,j].item(),

                     lambda i: mask_scores[i], mask_indices)

        ]



    timer.start('Main loop')

    for _class in set(classes + gt_classes):

        ap_per_iou = []

        num_gt_for_class = sum([1 for x in gt_classes if x == _class])

        

        for iouIdx in range(len(iou_thresholds)):

            iou_threshold = iou_thresholds[iouIdx]



            for iou_type, iou_func, crowd_func, score_func, indices in iou_types:

                gt_used = [False] * len(gt_classes)

                

                ap_obj = ap_data[iou_type][iouIdx][_class]

                ap_obj.add_gt_positives(num_gt_for_class)



                for i in indices:

                    if classes[i] != _class:

                        continue

                    

                    max_iou_found = iou_threshold

                    max_match_idx = -1

                    for j in range(num_gt):

                        if gt_used[j] or gt_classes[j] != _class:

                            continue

                            

                        iou = iou_func(i, j)



                        if iou > max_iou_found:

                            max_iou_found = iou

                            max_match_idx = j

                    

                    if max_match_idx >= 0:

                        gt_used[max_match_idx] = True

                        ap_obj.push(score_func(i), True)

                    else:

                        # If the detection matches a crowd, we can just ignore it

                        matched_crowd = False



                        if num_crowd > 0:

                            for j in range(len(crowd_classes)):

                                if crowd_classes[j] != _class:

                                    continue

                                

                                iou = crowd_func(i, j)



                                if iou > iou_threshold:

                                    matched_crowd = True

                                    break



                        # All this crowd code so that we can make sure that our eval code gives the

                        # same result as COCOEval. There aren't even that many crowd annotations to

                        # begin with, but accuracy is of the utmost importance.

                        if not matched_crowd:

                            ap_obj.push(score_func(i), False)

    timer.stop('Main loop')





class APDataObject:

    """

    Stores all the information necessary to calculate the AP for one IoU and one class.

    Note: I type annotated this because why not.

    """



    def __init__(self):

        self.data_points = []

        self.num_gt_positives = 0



    def push(self, score:float, is_true:bool):

        self.data_points.append((score, is_true))

    

    def add_gt_positives(self, num_positives:int):

        """ Call this once per image. """

        self.num_gt_positives += num_positives



    def is_empty(self) -> bool:

        return len(self.data_points) == 0 and self.num_gt_positives == 0



    def get_ap(self) -> float:

        """ Warning: result not cached. """



        if self.num_gt_positives == 0:

            return 0



        # Sort descending by score

        self.data_points.sort(key=lambda x: -x[0])



        precisions = []

        recalls    = []

        num_true  = 0

        num_false = 0



        # Compute the precision-recall curve. The x axis is recalls and the y axis precisions.

        for datum in self.data_points:

            # datum[1] is whether the detection a true or false positive

            if datum[1]: num_true += 1

            else: num_false += 1

            

            precision = num_true / (num_true + num_false)

            recall    = num_true / self.num_gt_positives



            precisions.append(precision)

            recalls.append(recall)



        # Smooth the curve by computing [max(precisions[i:]) for i in range(len(precisions))]

        # Basically, remove any temporary dips from the curve.

        # At least that's what I think, idk. COCOEval did it so I do too.

        for i in range(len(precisions)-1, 0, -1):

            if precisions[i] > precisions[i-1]:

                precisions[i-1] = precisions[i]



        # Compute the integral of precision(recall) d_recall from recall=0->1 using fixed-length riemann summation with 101 bars.

        y_range = [0] * 101 # idx 0 is recall == 0.0 and idx 100 is recall == 1.00

        x_range = np.array([x / 100 for x in range(101)])

        recalls = np.array(recalls)



        # I realize this is weird, but all it does is find the nearest precision(x) for a given x in x_range.

        # Basically, if the closest recall we have to 0.01 is 0.009 this sets precision(0.01) = precision(0.009).

        # I approximate the integral this way, because that's how COCOEval does it.

        indices = np.searchsorted(recalls, x_range, side='left')

        for bar_idx, precision_idx in enumerate(indices):

            if precision_idx < len(precisions):

                y_range[bar_idx] = precisions[precision_idx]



        # Finally compute the riemann sum to get our integral.

        # avg([precision(x) for x in 0:0.01:1])

        return sum(y_range) / len(y_range)



def badhash(x):

    """

    Just a quick and dirty hash function for doing a deterministic shuffle based on image_id.



    Source:

    https://stackoverflow.com/questions/664014/what-integer-hash-function-are-good-that-accepts-an-integer-hash-key

    """

    x = (((x >> 16) ^ x) * 0x045d9f3b) & 0xFFFFFFFF

    x = (((x >> 16) ^ x) * 0x045d9f3b) & 0xFFFFFFFF

    x =  ((x >> 16) ^ x) & 0xFFFFFFFF

    return x



def evalimage(net:Yolact, path:str, save_path:str=None):

    if args.cuda:

        frame = torch.from_numpy(cv2.imread(path)).npu().float()

    else:

        frame = torch.from_numpy(cv2.imread(path)).float()

    print('frame: ', frame)

    batch = FastBaseTransform()(frame.unsqueeze(0))

    print('batch_pred:', batch)

    print('batch_shape:', batch.shape)

    preds = net(batch)



    img_numpy = prep_display(preds, frame, None, None, undo_transform=False)

    

    if save_path is None:

        img_numpy = img_numpy[:, :, (2, 1, 0)]



    if save_path is None:

        plt.imshow(img_numpy)

        plt.title(path)

        plt.show()

    else:

        cv2.imwrite(save_path, img_numpy)



def evalimages(net:Yolact, input_folder:str, output_folder:str):

    if not os.path.exists(output_folder):

        os.mkdir(output_folder)



    print()

    for p in Path(input_folder).glob('*'): 

        path = str(p)

        name = os.path.basename(path)

        name = '.'.join(name.split('.')[:-1]) + '.png'

        out_path = os.path.join(output_folder, name)



        evalimage(net, path, out_path)

        print(path + ' -> ' + out_path)

    print('Done.')



from multiprocessing.pool import ThreadPool

from queue import Queue



class CustomDataParallel(torch.nn.DataParallel):

    """ A Custom Data Parallel class that properly gathers lists of dictionaries. """

    def gather(self, outputs, output_device):

        # Note that I don't actually want to convert everything to the output_device

        return sum(outputs, [])



def evalvideo(net:Yolact, path:str, out_path:str=None):

    # If the path is a digit, parse it as a webcam index

    is_webcam = path.isdigit()

    

    # If the input image size is constant, this make things faster (hence why we can use it in a video setting).

    cudnn.benchmark = True

    

    if is_webcam:

        vid = cv2.VideoCapture(int(path))

    else:

        vid = cv2.VideoCapture(path)

    

    if not vid.isOpened():

        print('Could not open video "%s"' % path)

        exit(-1)



    target_fps   = round(vid.get(cv2.CAP_PROP_FPS))

    frame_width  = round(vid.get(cv2.CAP_PROP_FRAME_WIDTH))

    frame_height = round(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))

    

    if is_webcam:

        num_frames = float('inf')

    else:

        num_frames = round(vid.get(cv2.CAP_PROP_FRAME_COUNT))



    net = CustomDataParallel(net).npu()

    transform = torch.nn.DataParallel(FastBaseTransform()).npu()

    frame_times = MovingAverage(100)

    fps = 0

    frame_time_target = 1 / target_fps

    running = True

    fps_str = ''

    vid_done = False

    frames_displayed = 0



    if out_path is not None:

        out = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*"mp4v"), target_fps, (frame_width, frame_height))



    def cleanup_and_exit():

        print()

        pool.terminate()

        vid.release()

        if out_path is not None:

            out.release()

        cv2.destroyAllWindows()

        exit()



    def get_next_frame(vid):

        frames = []

        for idx in range(args.video_multiframe):

            frame = vid.read()[1]

            if frame is None:

                return frames

            frames.append(frame)

        return frames



    def transform_frame(frames):

        with torch.no_grad():

            frames = [torch.from_numpy(frame).npu().float() for frame in frames]

            return frames, transform(torch.stack(frames, 0))



    def eval_network(inp):

        with torch.no_grad():

            frames, imgs = inp

            num_extra = 0

            while imgs.size(0) < args.video_multiframe:

                imgs = torch.cat([imgs, imgs[0].unsqueeze(0)], dim=0)

                num_extra += 1

            out = net(imgs)

            if num_extra > 0:

                out = out[:-num_extra]

            return frames, out



    def prep_frame(inp, fps_str):

        with torch.no_grad():

            frame, preds = inp

            return prep_display(preds, frame, None, None, undo_transform=False, class_color=True, fps_str=fps_str)



    frame_buffer = Queue()

    video_fps = 0



    # All this timing code to make sure that 

    def play_video():

        try:

            nonlocal frame_buffer, running, video_fps, is_webcam, num_frames, frames_displayed, vid_done



            video_frame_times = MovingAverage(100)

            frame_time_stabilizer = frame_time_target

            last_time = None

            stabilizer_step = 0.0005

            progress_bar = ProgressBar(30, num_frames)



            while running:

                frame_time_start = time.time()



                if not frame_buffer.empty():

                    next_time = time.time()

                    if last_time is not None:

                        video_frame_times.add(next_time - last_time)

                        video_fps = 1 / video_frame_times.get_avg()

                    if out_path is None:

                        cv2.imshow(path, frame_buffer.get())

                    else:

                        out.write(frame_buffer.get())

                    frames_displayed += 1

                    last_time = next_time



                    if out_path is not None:

                        if video_frame_times.get_avg() == 0:

                            fps = 0

                        else:

                            fps = 1 / video_frame_times.get_avg()

                        progress = frames_displayed / num_frames * 100

                        progress_bar.set_val(frames_displayed)



                        print('\rProcessing Frames  %s %6d / %6d (%5.2f%%)    %5.2f fps        '

                            % (repr(progress_bar), frames_displayed, num_frames, progress, fps), end='')



                

                # This is split because you don't want savevideo to require cv2 display functionality (see #197)

                if out_path is None and cv2.waitKey(1) == 27:

                    # Press Escape to close

                    running = False

                if not (frames_displayed < num_frames):

                    running = False



                if not vid_done:

                    buffer_size = frame_buffer.qsize()

                    if buffer_size < args.video_multiframe:

                        frame_time_stabilizer += stabilizer_step

                    elif buffer_size > args.video_multiframe:

                        frame_time_stabilizer -= stabilizer_step

                        if frame_time_stabilizer < 0:

                            frame_time_stabilizer = 0



                    new_target = frame_time_stabilizer if is_webcam else max(frame_time_stabilizer, frame_time_target)

                else:

                    new_target = frame_time_target



                next_frame_target = max(2 * new_target - video_frame_times.get_avg(), 0)

                target_time = frame_time_start + next_frame_target - 0.001 # Let's just subtract a millisecond to be safe

                

                if out_path is None or args.emulate_playback:

                    # This gives more accurate timing than if sleeping the whole amount at once

                    while time.time() < target_time:

                        time.sleep(0.001)

                else:

                    # Let's not starve the main thread, now

                    time.sleep(0.001)

        except:

            # See issue #197 for why this is necessary

            import traceback

            traceback.print_exc()





    extract_frame = lambda x, i: (x[0][i] if x[1][i]['detection'] is None else x[0][i].to(x[1][i]['detection']['box'].device), [x[1][i]])



    # Prime the network on the first frame because I do some thread unsafe things otherwise

    print('Initializing model... ', end='')

    first_batch = eval_network(transform_frame(get_next_frame(vid)))

    print('Done.')



    # For each frame the sequence of functions it needs to go through to be processed (in reversed order)

    sequence = [prep_frame, eval_network, transform_frame]

    pool = ThreadPool(processes=len(sequence) + args.video_multiframe + 2)

    pool.apply_async(play_video)

    active_frames = [{'value': extract_frame(first_batch, i), 'idx': 0} for i in range(len(first_batch[0]))]



    print()

    if out_path is None: print('Press Escape to close.')

    try:

        while vid.isOpened() and running:

            # Hard limit on frames in buffer so we don't run out of memory >.>

            while frame_buffer.qsize() > 100:

                time.sleep(0.001)



            start_time = time.time()



            # Start loading the next frames from the disk

            if not vid_done:

                next_frames = pool.apply_async(get_next_frame, args=(vid,))

            else:

                next_frames = None

            

            if not (vid_done and len(active_frames) == 0):

                # For each frame in our active processing queue, dispatch a job

                # for that frame using the current function in the sequence

                for frame in active_frames:

                    _args =  [frame['value']]

                    if frame['idx'] == 0:

                        _args.append(fps_str)

                    frame['value'] = pool.apply_async(sequence[frame['idx']], args=_args)

                

                # For each frame whose job was the last in the sequence (i.e. for all final outputs)

                for frame in active_frames:

                    if frame['idx'] == 0:

                        frame_buffer.put(frame['value'].get())



                # Remove the finished frames from the processing queue

                active_frames = [x for x in active_frames if x['idx'] > 0]



                # Finish evaluating every frame in the processing queue and advanced their position in the sequence

                for frame in list(reversed(active_frames)):

                    frame['value'] = frame['value'].get()

                    frame['idx'] -= 1



                    if frame['idx'] == 0:

                        # Split this up into individual threads for prep_frame since it doesn't support batch size

                        active_frames += [{'value': extract_frame(frame['value'], i), 'idx': 0} for i in range(1, len(frame['value'][0]))]

                        frame['value'] = extract_frame(frame['value'], 0)

                

                # Finish loading in the next frames and add them to the processing queue

                if next_frames is not None:

                    frames = next_frames.get()

                    if len(frames) == 0:

                        vid_done = True

                    else:

                        active_frames.append({'value': frames, 'idx': len(sequence)-1})



                # Compute FPS

                frame_times.add(time.time() - start_time)

                fps = args.video_multiframe / frame_times.get_avg()

            else:

                fps = 0

            

            fps_str = 'Processing FPS: %.2f | Video Playback FPS: %.2f | Frames in Buffer: %d' % (fps, video_fps, frame_buffer.qsize())

            if not args.display_fps:

                print('\r' + fps_str + '    ', end='')



    except KeyboardInterrupt:

        print('\nStopping...')

    

    cleanup_and_exit()



def evaluate(net:Yolact, dataset, train_mode=False, trainCuda = True):

    net.detect.use_fast_nms = args.fast_nms

    net.detect.use_cross_class_nms = args.cross_class_nms

    cfg.mask_proto_debug = args.mask_proto_debug



    # TODO Currently we do not support Fast Mask Re-scroing in evalimage, evalimages, and evalvideo

    if args.image is not None:

        if ':' in args.image:

            inp, out = args.image.split(':')

            evalimage(net, inp, out)

        else:

            evalimage(net, args.image)

        return

    elif args.images is not None:

        inp, out = args.images.split(':')

        evalimages(net, inp, out)

        return

    elif args.video is not None:

        if ':' in args.video:

            inp, out = args.video.split(':')

            evalvideo(net, inp, out)

        else:

            evalvideo(net, args.video)

        return



    frame_times = MovingAverage()

    dataset_size = len(dataset) if args.max_images < 0 else min(args.max_images, len(dataset))

    progress_bar = ProgressBar(30, dataset_size)



    print()



    if not args.display and not args.benchmark:

        # For each class and iou, stores tuples (score, isPositive)

        # Index ap_data[type][iouIdx][classIdx]

        ap_data = {

            'box' : [[APDataObject() for _ in cfg.dataset.class_names] for _ in iou_thresholds],

            'mask': [[APDataObject() for _ in cfg.dataset.class_names] for _ in iou_thresholds]

        }

        detections = Detections()

    else:

        timer.disable('Load Data')



    dataset_indices = list(range(len(dataset)))

    

    if args.shuffle:

        random.shuffle(dataset_indices)

    elif not args.no_sort:

        # Do a deterministic shuffle based on the image ids

        #

        # I do this because on python 3.5 dictionary key order is *random*, while in 3.6 it's

        # the order of insertion. That means on python 3.6, the images come in the order they are in

        # in the annotations file. For some reason, the first images in the annotations file are

        # the hardest. To combat this, I use a hard-coded hash function based on the image ids

        # to shuffle the indices we use. That way, no matter what python version or how pycocotools

        # handles the data, we get the same result every time.

        hashed = [badhash(x) for x in dataset.ids]

        dataset_indices.sort(key=lambda x: hashed[x])



    dataset_indices = dataset_indices[:dataset_size]



    try:

        # Main eval loop

        for it, image_idx in enumerate(tqdm(dataset_indices)):

            timer.reset()



            with timer.env('Load Data'):

                img, gt, gt_masks, h, w, num_crowd = dataset.pull_item(image_idx)

                # Test flag, do not upvote

                if cfg.mask_proto_debug:

                    with open('scripts/info.txt', 'w') as f:

                        f.write(str(dataset.ids[image_idx]))

                    np.save('scripts/gt.npy', gt_masks)



                batch = Variable(img.unsqueeze(0))

                if args.cuda:

                    if train_mode:

                        batch = batch.npu() if trainCuda else batch

                    else:

                        batch = batch.npu()



            with timer.env('Network Extra'):

                preds = net(batch)

            if preds is not None and preds[0] is not None:

                if preds[0]['box'] is not None:

                    preds[0]['box'] = preds[0]['box'].cpu()

                if preds[0]['mask'] is not None:

                    preds[0]['mask'] = preds[0]['mask'].cpu()

                if preds[0]['class'] is not None:

                    preds[0]['class'] = preds[0]['class'].cpu()

                if preds[0]['score'] is not None:

                    preds[0]['score'] = preds[0]['score'].cpu()

                if preds[0]['proto'] is not None:

                    preds[0]['proto'] = preds[0]['proto'].cpu()



            # Perform the meat of the operation here depending on our mode.

            if args.display:

                img_numpy = prep_display(preds, img, h, w)

            elif args.benchmark:

                prep_benchmark(preds, h, w)

            else:

                prep_metrics(ap_data, preds, img, gt, gt_masks, h, w, num_crowd, dataset.ids[image_idx], detections)



            # First couple of images take longer because we're constructing the graph.

            # Since that's technically initialization, don't include those in the FPS calculations.

            if it > 1:

                frame_times.add(timer.total_time())

            

            if args.display:

                if it > 1:

                    print('Avg FPS: %.4f' % (1 / frame_times.get_avg()))

                plt.imshow(img_numpy)

                plt.title(str(dataset.ids[image_idx]))

                plt.show()

            elif not args.no_bar:

                if it > 1: fps = 1 / frame_times.get_avg()

                else: fps = 0

                progress = (it+1) / dataset_size * 100

                progress_bar.set_val(it+1)

                print('\rProcessing Images  %s %6d / %6d (%5.2f%%)    %5.2f fps        '

                    % (repr(progress_bar), it+1, dataset_size, progress, fps), end='')







        if not args.display and not args.benchmark:

            print()

            if args.output_coco_json:

                print('Dumping detections...')

                if args.output_web_json:

                    detections.dump_web()

                else:

                    detections.dump()

            else:

                if not train_mode:

                    print('Saving data...')

                    with open(args.ap_data_file, 'wb') as f:

                        pickle.dump(ap_data, f)



                return calc_map(ap_data)

        elif args.benchmark:

            print()

            print()

            print('Stats for the last frame:')

            timer.print_stats()

            avg_seconds = frame_times.get_avg()

            print('Average: %5.2f fps, %5.2f ms' % (1 / frame_times.get_avg(), 1000*avg_seconds))



    except KeyboardInterrupt:

        print('Stopping...')





def calc_map(ap_data):

    print('Calculating mAP...')

    aps = [{'box': [], 'mask': []} for _ in iou_thresholds]



    for _class in range(len(cfg.dataset.class_names)):

        for iou_idx in range(len(iou_thresholds)):

            for iou_type in ('box', 'mask'):

                ap_obj = ap_data[iou_type][iou_idx][_class]



                if not ap_obj.is_empty():

                    aps[iou_idx][iou_type].append(ap_obj.get_ap())



    all_maps = {'box': OrderedDict(), 'mask': OrderedDict()}



    # Looking back at it, this code is really hard to read :/

    for iou_type in ('box', 'mask'):

        all_maps[iou_type]['all'] = 0 # Make this first in the ordereddict

        for i, threshold in enumerate(iou_thresholds):

            mAP = sum(aps[i][iou_type]) / len(aps[i][iou_type]) * 100 if len(aps[i][iou_type]) > 0 else 0

            all_maps[iou_type][int(threshold*100)] = mAP

        all_maps[iou_type]['all'] = (sum(all_maps[iou_type].values()) / (len(all_maps[iou_type].values())-1))

    

    print_maps(all_maps)

    

    # Put in a prettier format so we can serialize it to json during training

    all_maps = {k: {j: round(u, 2) for j, u in v.items()} for k, v in all_maps.items()}

    return all_maps



def print_maps(all_maps):

    # Warning: hacky 

    make_row = lambda vals: (' %5s |' * len(vals)) % tuple(vals)

    make_sep = lambda n:  ('-------+' * n)



    print()

    print(make_row([''] + [('.%d ' % x if isinstance(x, int) else x + ' ') for x in all_maps['box'].keys()]))

    print(make_sep(len(all_maps['box']) + 1))

    for iou_type in ('box', 'mask'):

        print(make_row([iou_type] + ['%.2f' % x if x < 100 else '%.1f' % x for x in all_maps[iou_type].values()]))

    print(make_sep(len(all_maps['box']) + 1))

    print()







if __name__ == '__main__':

    parse_args()



    if args.config is not None:

        set_cfg(args.config)



    if args.trained_model == 'interrupt':



        args.trained_model = SavePath.get_interrupt('weights/')

    elif args.trained_model == 'latest':

        args.trained_model = SavePath.get_latest('weights/', cfg.name)



    if args.config is None:

        model_path = SavePath.from_str(args.trained_model)

        # TODO: Bad practice? Probably want to do a name lookup instead.

        args.config = model_path.model_name + '_config'

        print('Config not specified. Parsed %s from the file name.\n' % args.config)

        set_cfg(args.config)



    if args.detect:

        cfg.eval_mask_branch = False



    if args.dataset is not None:

        set_dataset(args.dataset)



    if args.data_path:

        cfg.dataset.valid_images = args.data_path + '/images/'

        cfg.dataset.valid_info = args.data_path + '/annotations/instances_val2017.json'



    with torch.no_grad():

        if not os.path.exists('results'):

            os.makedirs('results')



        if args.cuda:

            cudnn.fastest = True

            args.device = torch.device(f'npu:{args.rank_id}')

            torch.npu.set_device(args.device)

            #torch.set_default_tensor_type('torch.npu.FloatTensor')

        else:

            torch.set_default_tensor_type('torch.FloatTensor')



        if args.resume and not args.display:

            with open(args.ap_data_file, 'rb') as f:

                ap_data = pickle.load(f)

            calc_map(ap_data)

            exit()



        if args.image is None and args.video is None and args.images is None:

            dataset = COCODetection(cfg.dataset.valid_images, cfg.dataset.valid_info,

                                    transform=BaseTransform(), has_gt=cfg.dataset.has_gt)

            prep_coco_cats()

        else:

            dataset = None        



        print('Loading model...', end='')

        net = Yolact()

        net.load_weights(args.trained_model)

        net.eval()

        print(' Done.')



        if args.cuda:

            net = net.npu()



        evaluate(net, dataset)