# This code is basically a copy and paste from the original cocoapi repo:
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/cocoeval.py
# with the following changes have been made:
# * Replace the usage of mask (maskUtils) by MaskEvaluator.
# * Comment out prints in the evaluate() function.
# * Include a return of the function evaluate. Inspired
#      by @ybelkada (https://huggingface.co/spaces/ybelkada/cocoevaluate/)

__author__ = "tsungyi"

import copy
import datetime
import time
from collections import defaultdict
from packaging import version

import numpy as np

if version.parse(np.__version__) < version.parse("1.24"):
    dtype_float = np.float
else:
    dtype_float = np.float32
    
from .mask_utils import MaskEvaluator as maskUtils

class COCOeval:
    # Interface for evaluating detection on the Microsoft COCO dataset.
    #
    # The usage for CocoEval is as follows:
    #  cocoGt=..., cocoDt=...       # load dataset and results
    #  E = CocoEval(cocoGt,cocoDt); # initialize CocoEval object
    #  E.params.recThrs = ...;      # set parameters as desired
    #  E.evaluate();                # run per image evaluation
    #  E.accumulate();              # accumulate per image results
    #  E.summarize();               # display summary metrics of results
    # For example usage see evalDemo.m and http://mscoco.org/.
    #
    # The evaluation parameters are as follows (defaults in brackets):
    #  imgIds     - [all] N img ids to use for evaluation
    #  catIds     - [all] K cat ids to use for evaluation
    #  iouThrs    - [.5:.05:.95] T=10 IoU thresholds for evaluation
    #  recThrs    - [0:.01:1] R=101 recall thresholds for evaluation
    #  areaRng    - [...] A=4 object area ranges for evaluation
    #  maxDets    - [1 10 100] M=3 thresholds on max detections per image
    #  iouType    - ['segm'] set iouType to 'segm', 'bbox' or 'keypoints'
    #  iouType replaced the now DEPRECATED useSegm parameter.
    #  useCats    - [1] if true use category labels for evaluation
    # Note: if useCats=0 category labels are ignored as in proposal scoring.
    # Note: multiple areaRngs [Ax2] and maxDets [Mx1] can be specified.
    #
    # evaluate(): evaluates detections on every image and every category and
    # concats the results into the "evalImgs" with fields:
    #  dtIds      - [1xD] id for each of the D detections (dt)
    #  gtIds      - [1xG] id for each of the G ground truths (gt)
    #  dtMatches  - [TxD] matching gt id at each IoU or 0
    #  gtMatches  - [TxG] matching dt id at each IoU or 0
    #  dtScores   - [1xD] confidence of each dt
    #  gtIgnore   - [1xG] ignore flag for each gt
    #  dtIgnore   - [TxD] ignore flag for each dt at each IoU
    #
    # accumulate(): accumulates the per-image, per-category evaluation
    # results in "evalImgs" into the dictionary "eval" with fields:
    #  params     - parameters used for evaluation
    #  date       - date evaluation was performed
    #  counts     - [T,R,K,A,M] parameter dimensions (see above)
    #  precision  - [TxRxKxAxM] precision for every evaluation setting
    #  recall     - [TxKxAxM] max recall for every evaluation setting
    # Note: precision and recall==-1 for settings with no gt objects.
    #
    # See also coco, mask, pycocoDemo, pycocoEvalDemo
    #
    # Microsoft COCO Toolbox.      version 2.0
    # Data, paper, and tutorials available at:  http://mscoco.org/
    # Code written by Piotr Dollar and Tsung-Yi Lin, 2015.
    # Licensed under the Simplified BSD License [see coco/license.txt]
    def __init__(self, cocoGt=None, cocoDt=None, iouType="segm"):
        """
        Initialize CocoEval using coco APIs for gt and dt
        :param cocoGt: coco object with ground truth annotations
        :param cocoDt: coco object with detection results
        :return: None
        """
        if not iouType:
            print("iouType not specified. use default iouType segm")
        self.cocoGt = cocoGt  # ground truth COCO API
        self.cocoDt = cocoDt  # detections COCO API
        self.evalImgs = defaultdict(
            list
        )  # per-image per-category evaluation results [KxAxI] elements
        self.eval = {}  # accumulated evaluation results
        self._gts = defaultdict(list)  # gt for evaluation
        self._dts = defaultdict(list)  # dt for evaluation
        self.params = Params(iouType=iouType)  # parameters
        self._paramsEval = {}  # parameters for evaluation
        self.stats = []  # result summarization
        self.ious = {}  # ious between all gts and dts
        if not cocoGt is None:
            self.params.imgIds = sorted(cocoGt.getImgIds())
            self.params.catIds = sorted(cocoGt.getCatIds())

    def _prepare(self):
        """
        Prepare ._gts and ._dts for evaluation based on params
        :return: None
        """

        def _toMask(anns, coco):
            # modify ann['segmentation'] by reference
            for ann in anns:
                rle = coco.annToRLE(ann)
                ann["segmentation"] = rle

        p = self.params
        if p.useCats:
            gts = self.cocoGt.loadAnns(
                self.cocoGt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)
            )
            dts = self.cocoDt.loadAnns(
                self.cocoDt.getAnnIds(imgIds=p.imgIds, catIds=p.catIds)
            )
        else:
            gts = self.cocoGt.loadAnns(self.cocoGt.getAnnIds(imgIds=p.imgIds))
            dts = self.cocoDt.loadAnns(self.cocoDt.getAnnIds(imgIds=p.imgIds))

        # convert ground truth to mask if iouType == 'segm'
        if p.iouType == "segm":
            _toMask(gts, self.cocoGt)
            _toMask(dts, self.cocoDt)
        # set ignore flag
        for gt in gts:
            gt["ignore"] = gt["ignore"] if "ignore" in gt else 0
            gt["ignore"] = "iscrowd" in gt and gt["iscrowd"]
            if p.iouType == "keypoints":
                gt["ignore"] = (gt["num_keypoints"] == 0) or gt["ignore"]
        self._gts = defaultdict(list)  # gt for evaluation
        self._dts = defaultdict(list)  # dt for evaluation
        for gt in gts:
            self._gts[gt["image_id"], gt["category_id"]].append(gt)
        for dt in dts:
            self._dts[dt["image_id"], dt["category_id"]].append(dt)
        self.evalImgs = defaultdict(list)  # per-image per-category evaluation results
        self.eval = {}  # accumulated evaluation results

    def evaluate(self):
        """
        Run per image evaluation on given images and store results (a list of dict) in self.evalImgs
        :return: None
        """
        # tic = time.time()
        # print("Running per image evaluation...")
        p = self.params
        # add backward compatibility if useSegm is specified in params
        if not p.useSegm is None:
            p.iouType = "segm" if p.useSegm == 1 else "bbox"
        #     print(
        #         "useSegm (deprecated) is not None. Running {} evaluation".format(
        #             p.iouType
        #         )
        #     )
        # print("Evaluate annotation type *{}*".format(p.iouType))
        p.imgIds = list(np.unique(p.imgIds))
        if p.useCats:
            p.catIds = list(np.unique(p.catIds))
        p.maxDets = sorted(p.maxDets)
        self.params = p

        self._prepare()
        # loop through images, area range, max detection number
        catIds = p.catIds if p.useCats else [-1]

        if p.iouType == "segm" or p.iouType == "bbox":
            computeIoU = self.computeIoU
        elif p.iouType == "keypoints":
            computeIoU = self.computeOks
        self.ious = {
            (imgId, catId): computeIoU(imgId, catId)
            for imgId in p.imgIds
            for catId in catIds
        }

        evaluateImg = self.evaluateImg
        maxDet = p.maxDets[-1]
        self.evalImgs = [
            evaluateImg(imgId, catId, areaRng, maxDet)
            for catId in catIds
            for areaRng in p.areaRng
            for imgId in p.imgIds
        ]
        self._paramsEval = copy.deepcopy(self.params)
        ret_evalImgs = np.asarray(self.evalImgs).reshape(
            len(catIds), len(p.areaRng), len(p.imgIds)
        )
        # toc = time.time()
        # print("DONE (t={:0.2f}s).".format(toc - tic))
        return ret_evalImgs

    def computeIoU(self, imgId, catId):
        p = self.params
        if p.useCats:
            gt = self._gts[imgId, catId]
            dt = self._dts[imgId, catId]
        else:
            gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]
            dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]
        if len(gt) == 0 and len(dt) == 0:
            return []
        inds = np.argsort([-d["score"] for d in dt], kind="mergesort")
        dt = [dt[i] for i in inds]
        if len(dt) > p.maxDets[-1]:
            dt = dt[0 : p.maxDets[-1]]

        if p.iouType == "segm":
            g = [g["segmentation"] for g in gt]
            d = [d["segmentation"] for d in dt]
        elif p.iouType == "bbox":
            g = [g["bbox"] for g in gt]
            d = [d["bbox"] for d in dt]
        else:
            raise Exception("unknown iouType for iou computation")

        # compute iou between each dt and gt region
        iscrowd = [int(o["iscrowd"]) for o in gt]
        ious = maskUtils.iou(d, g, iscrowd)
        return ious

    def computeOks(self, imgId, catId):
        p = self.params
        # dimention here should be Nxm
        gts = self._gts[imgId, catId]
        dts = self._dts[imgId, catId]
        inds = np.argsort([-d["score"] for d in dts], kind="mergesort")
        dts = [dts[i] for i in inds]
        if len(dts) > p.maxDets[-1]:
            dts = dts[0 : p.maxDets[-1]]
        # if len(gts) == 0 and len(dts) == 0:
        if len(gts) == 0 or len(dts) == 0:
            return []
        ious = np.zeros((len(dts), len(gts)))
        sigmas = p.kpt_oks_sigmas
        vars = (sigmas * 2) ** 2
        k = len(sigmas)
        # compute oks between each detection and ground truth object
        for j, gt in enumerate(gts):
            # create bounds for ignore regions(double the gt bbox)
            g = np.array(gt["keypoints"])
            xg = g[0::3]
            yg = g[1::3]
            vg = g[2::3]
            k1 = np.count_nonzero(vg > 0)
            bb = gt["bbox"]
            x0 = bb[0] - bb[2]
            x1 = bb[0] + bb[2] * 2
            y0 = bb[1] - bb[3]
            y1 = bb[1] + bb[3] * 2
            for i, dt in enumerate(dts):
                d = np.array(dt["keypoints"])
                xd = d[0::3]
                yd = d[1::3]
                if k1 > 0:
                    # measure the per-keypoint distance if keypoints visible
                    dx = xd - xg
                    dy = yd - yg
                else:
                    # measure minimum distance to keypoints in (x0,y0) & (x1,y1)
                    z = np.zeros((k))
                    dx = np.max((z, x0 - xd), axis=0) + np.max((z, xd - x1), axis=0)
                    dy = np.max((z, y0 - yd), axis=0) + np.max((z, yd - y1), axis=0)
                e = (dx**2 + dy**2) / vars / (gt["area"] + np.spacing(1)) / 2
                if k1 > 0:
                    e = e[vg > 0]
                ious[i, j] = np.sum(np.exp(-e)) / e.shape[0]
        return ious

    def evaluateImg(self, imgId, catId, aRng, maxDet):
        """
        perform evaluation for single category and image
        :return: dict (single image results)
        """
        p = self.params
        if p.useCats:
            gt = self._gts[imgId, catId]
            dt = self._dts[imgId, catId]
        else:
            gt = [_ for cId in p.catIds for _ in self._gts[imgId, cId]]
            dt = [_ for cId in p.catIds for _ in self._dts[imgId, cId]]
        if len(gt) == 0 and len(dt) == 0:
            return None

        for g in gt:
            if g["ignore"] or (g["area"] < aRng[0] or g["area"] > aRng[1]):
                g["_ignore"] = 1
            else:
                g["_ignore"] = 0

        # sort dt highest score first, sort gt ignore last
        gtind = np.argsort([g["_ignore"] for g in gt], kind="mergesort")
        gt = [gt[i] for i in gtind]
        dtind = np.argsort([-d["score"] for d in dt], kind="mergesort")
        dt = [dt[i] for i in dtind[0:maxDet]]
        iscrowd = [int(o["iscrowd"]) for o in gt]
        # load computed ious
        ious = (
            self.ious[imgId, catId][:, gtind]
            if len(self.ious[imgId, catId]) > 0
            else self.ious[imgId, catId]
        )

        T = len(p.iouThrs)
        G = len(gt)
        D = len(dt)
        gtm = np.zeros((T, G))
        dtm = np.zeros((T, D))
        gtIg = np.array([g["_ignore"] for g in gt])
        dtIg = np.zeros((T, D))
        if not len(ious) == 0:
            for tind, t in enumerate(p.iouThrs):
                for dind, d in enumerate(dt):
                    # information about best match so far (m=-1 -> unmatched)
                    iou = min([t, 1 - 1e-10])
                    m = -1
                    for gind, g in enumerate(gt):
                        # if this gt already matched, and not a crowd, continue
                        if gtm[tind, gind] > 0 and not iscrowd[gind]:
                            continue
                        # if dt matched to reg gt, and on ignore gt, stop
                        if m > -1 and gtIg[m] == 0 and gtIg[gind] == 1:
                            break
                        # continue to next gt unless better match made
                        if ious[dind, gind] < iou:
                            continue
                        # if match successful and best so far, store appropriately
                        iou = ious[dind, gind]
                        m = gind
                    # if match made store id of match for both dt and gt
                    if m == -1:
                        continue
                    dtIg[tind, dind] = gtIg[m]
                    dtm[tind, dind] = gt[m]["id"]
                    gtm[tind, m] = d["id"]
        # set unmatched detections outside of area range to ignore
        a = np.array([d["area"] < aRng[0] or d["area"] > aRng[1] for d in dt]).reshape(
            (1, len(dt))
        )
        dtIg = np.logical_or(dtIg, np.logical_and(dtm == 0, np.repeat(a, T, 0)))
        # store results for given image and category
        return {
            "image_id": imgId,
            "category_id": catId,
            "aRng": aRng,
            "maxDet": maxDet,
            "dtIds": [d["id"] for d in dt],
            "gtIds": [g["id"] for g in gt],
            "dtMatches": dtm,
            "gtMatches": gtm,
            "dtScores": [d["score"] for d in dt],
            "gtIgnore": gtIg,
            "dtIgnore": dtIg,
        }

    def accumulate(self, p=None):
        """
        Accumulate per image evaluation results and store the result in self.eval
        :param p: input params for evaluation
        :return: None
        """
        print("Accumulating evaluation results...")
        tic = time.time()
        if not self.evalImgs:
            print("Please run evaluate() first")
        # allows input customized parameters
        if p is None:
            p = self.params
        p.catIds = p.catIds if p.useCats == 1 else [-1]
        T = len(p.iouThrs)
        R = len(p.recThrs)
        K = len(p.catIds) if p.useCats else 1
        A = len(p.areaRng)
        M = len(p.maxDets)
        precision = -np.ones(
            (T, R, K, A, M)
        )  # -1 for the precision of absent categories
        recall = -np.ones((T, K, A, M))
        scores = -np.ones((T, R, K, A, M))

        # create dictionary for future indexing
        _pe = self._paramsEval
        catIds = _pe.catIds if _pe.useCats else [-1]
        setK = set(catIds)
        setA = set(map(tuple, _pe.areaRng))
        setM = set(_pe.maxDets)
        setI = set(_pe.imgIds)
        # get inds to evaluate
        k_list = [n for n, k in enumerate(p.catIds) if k in setK]
        m_list = [m for n, m in enumerate(p.maxDets) if m in setM]
        a_list = [
            n for n, a in enumerate(map(lambda x: tuple(x), p.areaRng)) if a in setA
        ]
        i_list = [n for n, i in enumerate(p.imgIds) if i in setI]
        I0 = len(_pe.imgIds)
        A0 = len(_pe.areaRng)
        # retrieve E at each category, area range, and max number of detections
        for k, k0 in enumerate(k_list):
            Nk = k0 * A0 * I0
            for a, a0 in enumerate(a_list):
                Na = a0 * I0
                for m, maxDet in enumerate(m_list):
                    E = [self.evalImgs[Nk + Na + i] for i in i_list]
                    E = [e for e in E if not e is None]
                    if len(E) == 0:
                        continue
                    dtScores = np.concatenate([e["dtScores"][0:maxDet] for e in E])

                    # different sorting method generates slightly different results.
                    # mergesort is used to be consistent as Matlab implementation.
                    inds = np.argsort(-dtScores, kind="mergesort")
                    dtScoresSorted = dtScores[inds]

                    dtm = np.concatenate(
                        [e["dtMatches"][:, 0:maxDet] for e in E], axis=1
                    )[:, inds]
                    dtIg = np.concatenate(
                        [e["dtIgnore"][:, 0:maxDet] for e in E], axis=1
                    )[:, inds]
                    gtIg = np.concatenate([e["gtIgnore"] for e in E])
                    npig = np.count_nonzero(gtIg == 0)
                    if npig == 0:
                        continue
                    tps = np.logical_and(dtm, np.logical_not(dtIg))
                    fps = np.logical_and(np.logical_not(dtm), np.logical_not(dtIg))

                    tp_sum = np.cumsum(tps, axis=1).astype(dtype=dtype_float)
                    fp_sum = np.cumsum(fps, axis=1).astype(dtype=dtype_float)
                    for t, (tp, fp) in enumerate(zip(tp_sum, fp_sum)):
                        tp = np.array(tp)
                        fp = np.array(fp)
                        nd = len(tp)
                        rc = tp / npig
                        pr = tp / (fp + tp + np.spacing(1))
                        q = np.zeros((R,))
                        ss = np.zeros((R,))

                        if nd:
                            recall[t, k, a, m] = rc[-1]
                        else:
                            recall[t, k, a, m] = 0

                        # numpy is slow without cython optimization for accessing elements
                        # use python array gets significant speed improvement
                        pr = pr.tolist()
                        q = q.tolist()

                        for i in range(nd - 1, 0, -1):
                            if pr[i] > pr[i - 1]:
                                pr[i - 1] = pr[i]

                        inds = np.searchsorted(rc, p.recThrs, side="left")
                        try:
                            for ri, pi in enumerate(inds):
                                q[ri] = pr[pi]
                                ss[ri] = dtScoresSorted[pi]
                        except:
                            pass
                        precision[t, :, k, a, m] = np.array(q)
                        scores[t, :, k, a, m] = np.array(ss)
        self.eval = {
            "params": p,
            "counts": [T, R, K, A, M],
            "date": datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
            "precision": precision,
            "recall": recall,
            "scores": scores,
        }
        toc = time.time()
        print("DONE (t={:0.2f}s).".format(toc - tic))

    def summarize(self):
        """
        Compute and display summary metrics for evaluation results.
        Note this functin can *only* be applied on the default parameter setting
        """

        def _summarize(ap=1, iouThr=None, areaRng="all", maxDets=100):
            p = self.params
            iStr = " {:<18} {} @[ IoU={:<9} | area={:>6s} | maxDets={:>3d} ] = {:0.3f}"
            titleStr = "Average Precision" if ap == 1 else "Average Recall"
            typeStr = "(AP)" if ap == 1 else "(AR)"
            iouStr = (
                "{:0.2f}:{:0.2f}".format(p.iouThrs[0], p.iouThrs[-1])
                if iouThr is None
                else "{:0.2f}".format(iouThr)
            )

            aind = [i for i, aRng in enumerate(p.areaRngLbl) if aRng == areaRng]
            mind = [i for i, mDet in enumerate(p.maxDets) if mDet == maxDets]
            if ap == 1:
                # dimension of precision: [TxRxKxAxM]
                s = self.eval["precision"]
                # IoU
                if iouThr is not None:
                    t = np.where(iouThr == p.iouThrs)[0]
                    s = s[t]
                s = s[:, :, :, aind, mind]
            else:
                # dimension of recall: [TxKxAxM]
                s = self.eval["recall"]
                if iouThr is not None:
                    t = np.where(iouThr == p.iouThrs)[0]
                    s = s[t]
                s = s[:, :, aind, mind]
            if len(s[s > -1]) == 0:
                mean_s = -1
            else:
                mean_s = np.mean(s[s > -1])
            print(iStr.format(titleStr, typeStr, iouStr, areaRng, maxDets, mean_s))
            return mean_s

        def _summarizeDets():
            stats = np.zeros((12,))
            stats[0] = _summarize(1)
            stats[1] = _summarize(1, iouThr=0.5, maxDets=self.params.maxDets[2])
            stats[2] = _summarize(1, iouThr=0.75, maxDets=self.params.maxDets[2])
            stats[3] = _summarize(1, areaRng="small", maxDets=self.params.maxDets[2])
            stats[4] = _summarize(1, areaRng="medium", maxDets=self.params.maxDets[2])
            stats[5] = _summarize(1, areaRng="large", maxDets=self.params.maxDets[2])
            stats[6] = _summarize(0, maxDets=self.params.maxDets[0])
            stats[7] = _summarize(0, maxDets=self.params.maxDets[1])
            stats[8] = _summarize(0, maxDets=self.params.maxDets[2])
            stats[9] = _summarize(0, areaRng="small", maxDets=self.params.maxDets[2])
            stats[10] = _summarize(0, areaRng="medium", maxDets=self.params.maxDets[2])
            stats[11] = _summarize(0, areaRng="large", maxDets=self.params.maxDets[2])
            return stats

        def _summarizeKps():
            stats = np.zeros((10,))
            stats[0] = _summarize(1, maxDets=20)
            stats[1] = _summarize(1, maxDets=20, iouThr=0.5)
            stats[2] = _summarize(1, maxDets=20, iouThr=0.75)
            stats[3] = _summarize(1, maxDets=20, areaRng="medium")
            stats[4] = _summarize(1, maxDets=20, areaRng="large")
            stats[5] = _summarize(0, maxDets=20)
            stats[6] = _summarize(0, maxDets=20, iouThr=0.5)
            stats[7] = _summarize(0, maxDets=20, iouThr=0.75)
            stats[8] = _summarize(0, maxDets=20, areaRng="medium")
            stats[9] = _summarize(0, maxDets=20, areaRng="large")
            return stats

        if not self.eval:
            raise Exception("Please run accumulate() first")
        iouType = self.params.iouType
        if iouType == "segm" or iouType == "bbox":
            summarize = _summarizeDets
        elif iouType == "keypoints":
            summarize = _summarizeKps
        self.stats = summarize()

    def __str__(self):
        self.summarize()


class Params:
    """
    Params for coco evaluation api
    """

    def setDetParams(self):
        self.imgIds = []
        self.catIds = []
        # np.arange causes trouble.  the data point on arange is slightly larger than the true value
        self.iouThrs = np.linspace(
            0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True
        )
        self.recThrs = np.linspace(
            0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True
        )
        self.maxDets = [1, 10, 100]
        self.areaRng = [
            [0**2, 1e5**2],
            [0**2, 32**2],
            [32**2, 96**2],
            [96**2, 1e5**2],
        ]
        self.areaRngLbl = ["all", "small", "medium", "large"]
        self.useCats = 1

    def setKpParams(self):
        self.imgIds = []
        self.catIds = []
        # np.arange causes trouble.  the data point on arange is slightly larger than the true value
        self.iouThrs = np.linspace(
            0.5, 0.95, int(np.round((0.95 - 0.5) / 0.05)) + 1, endpoint=True
        )
        self.recThrs = np.linspace(
            0.0, 1.00, int(np.round((1.00 - 0.0) / 0.01)) + 1, endpoint=True
        )
        self.maxDets = [20]
        self.areaRng = [[0**2, 1e5**2], [32**2, 96**2], [96**2, 1e5**2]]
        self.areaRngLbl = ["all", "medium", "large"]
        self.useCats = 1
        self.kpt_oks_sigmas = (
            np.array(
                [
                    0.26,
                    0.25,
                    0.25,
                    0.35,
                    0.35,
                    0.79,
                    0.79,
                    0.72,
                    0.72,
                    0.62,
                    0.62,
                    1.07,
                    1.07,
                    0.87,
                    0.87,
                    0.89,
                    0.89,
                ]
            )
            / 10.0
        )

    def __init__(self, iouType="segm"):
        if iouType == "bbox":
            self.setDetParams()
        else:
            raise Exception("iouType not supported")
        self.iouType = iouType
        # useSegm is deprecated
        self.useSegm = None