import cv2
import numpy as np
import math
from scipy.ndimage.filters import gaussian_filter
import torch
from torchvision import transforms

from . import util
from .model import bodypose_model


class Body(object):
    def __init__(self, model_path, device=None):
        self.device = device or torch.device(
            "cuda" if torch.cuda.is_available() else "cpu"
        )
        self.model = bodypose_model().to(self.device)
        model_dict = util.transfer(self.model, torch.load(model_path))
        self.model.load_state_dict(model_dict)
        self.model.eval()

    def __call__(self, oriImg):
        # scale_search = [0.5, 1.0, 1.5, 2.0]
        scale_search = [0.5]
        boxsize = 368
        stride = 8
        padValue = 128
        thre1 = 0.1
        thre2 = 0.05
        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 19))
        paf_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 38))

        for m in range(len(multiplier)):
            scale = multiplier[m]
            imageToTest = cv2.resize(
                oriImg, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC
            )
            imageToTest_padded, pad = util.padRightDownCorner(
                imageToTest, stride, padValue
            )
            im = (
                np.transpose(
                    np.float32(imageToTest_padded[:, :, :, np.newaxis]), (3, 2, 0, 1)
                )
                / 256
                - 0.5
            )
            im = np.ascontiguousarray(im)

            data = torch.from_numpy(im).float().to(self.device)
            # data = data.permute([2, 0, 1]).unsqueeze(0).float()
            with torch.no_grad():
                Mconv7_stage6_L1, Mconv7_stage6_L2 = self.model(data)
            Mconv7_stage6_L1 = Mconv7_stage6_L1.cpu().numpy()
            Mconv7_stage6_L2 = Mconv7_stage6_L2.cpu().numpy()

            # extract outputs, resize, and remove padding
            # heatmap = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[1]].data), (1, 2, 0))  # output 1 is heatmaps
            heatmap = np.transpose(
                np.squeeze(Mconv7_stage6_L2), (1, 2, 0)
            )  # output 1 is heatmaps
            heatmap = cv2.resize(
                heatmap, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC
            )
            heatmap = heatmap[
                : imageToTest_padded.shape[0] - pad[2],
                : imageToTest_padded.shape[1] - pad[3],
                :,
            ]
            heatmap = cv2.resize(
                heatmap,
                (oriImg.shape[1], oriImg.shape[0]),
                interpolation=cv2.INTER_CUBIC,
            )

            # paf = np.transpose(np.squeeze(net.blobs[output_blobs.keys()[0]].data), (1, 2, 0))  # output 0 is PAFs
            paf = np.transpose(
                np.squeeze(Mconv7_stage6_L1), (1, 2, 0)
            )  # output 0 is PAFs
            paf = cv2.resize(
                paf, (0, 0), fx=stride, fy=stride, interpolation=cv2.INTER_CUBIC
            )
            paf = paf[
                : imageToTest_padded.shape[0] - pad[2],
                : imageToTest_padded.shape[1] - pad[3],
                :,
            ]
            paf = cv2.resize(
                paf, (oriImg.shape[1], oriImg.shape[0]), interpolation=cv2.INTER_CUBIC
            )

            heatmap_avg += heatmap_avg + heatmap / len(multiplier)
            paf_avg += +paf / len(multiplier)

        all_peaks = []
        peak_counter = 0

        for part in range(18):
            map_ori = heatmap_avg[:, :, part]
            one_heatmap = gaussian_filter(map_ori, sigma=3)

            map_left = np.zeros(one_heatmap.shape)
            map_left[1:, :] = one_heatmap[:-1, :]
            map_right = np.zeros(one_heatmap.shape)
            map_right[:-1, :] = one_heatmap[1:, :]
            map_up = np.zeros(one_heatmap.shape)
            map_up[:, 1:] = one_heatmap[:, :-1]
            map_down = np.zeros(one_heatmap.shape)
            map_down[:, :-1] = one_heatmap[:, 1:]

            peaks_binary = np.logical_and.reduce(
                (
                    one_heatmap >= map_left,
                    one_heatmap >= map_right,
                    one_heatmap >= map_up,
                    one_heatmap >= map_down,
                    one_heatmap > thre1,
                )
            )
            peaks = list(
                zip(np.nonzero(peaks_binary)[1], np.nonzero(peaks_binary)[0])
            )  # note reverse
            peaks_with_score = [x + (map_ori[x[1], x[0]],) for x in peaks]
            peak_id = range(peak_counter, peak_counter + len(peaks))
            peaks_with_score_and_id = [
                peaks_with_score[i] + (peak_id[i],) for i in range(len(peak_id))
            ]

            all_peaks.append(peaks_with_score_and_id)
            peak_counter += len(peaks)

        # find connection in the specified sequence, center 29 is in the position 15
        limbSeq = [
            [2, 3],
            [2, 6],
            [3, 4],
            [4, 5],
            [6, 7],
            [7, 8],
            [2, 9],
            [9, 10],
            [10, 11],
            [2, 12],
            [12, 13],
            [13, 14],
            [2, 1],
            [1, 15],
            [15, 17],
            [1, 16],
            [16, 18],
            [3, 17],
            [6, 18],
        ]
        # the middle joints heatmap correpondence
        mapIdx = [
            [31, 32],
            [39, 40],
            [33, 34],
            [35, 36],
            [41, 42],
            [43, 44],
            [19, 20],
            [21, 22],
            [23, 24],
            [25, 26],
            [27, 28],
            [29, 30],
            [47, 48],
            [49, 50],
            [53, 54],
            [51, 52],
            [55, 56],
            [37, 38],
            [45, 46],
        ]

        connection_all = []
        special_k = []
        mid_num = 10

        for k in range(len(mapIdx)):
            score_mid = paf_avg[:, :, [x - 19 for x in mapIdx[k]]]
            candA = all_peaks[limbSeq[k][0] - 1]
            candB = all_peaks[limbSeq[k][1] - 1]
            nA = len(candA)
            nB = len(candB)
            indexA, indexB = limbSeq[k]
            if nA != 0 and nB != 0:
                connection_candidate = []
                for i in range(nA):
                    for j in range(nB):
                        vec = np.subtract(candB[j][:2], candA[i][:2])
                        norm = math.sqrt(vec[0] * vec[0] + vec[1] * vec[1])
                        norm = max(0.001, norm)
                        vec = np.divide(vec, norm)

                        startend = list(
                            zip(
                                np.linspace(candA[i][0], candB[j][0], num=mid_num),
                                np.linspace(candA[i][1], candB[j][1], num=mid_num),
                            )
                        )

                        vec_x = np.array(
                            [
                                score_mid[
                                    int(round(startend[I][1])),
                                    int(round(startend[I][0])),
                                    0,
                                ]
                                for I in range(len(startend))
                            ]
                        )
                        vec_y = np.array(
                            [
                                score_mid[
                                    int(round(startend[I][1])),
                                    int(round(startend[I][0])),
                                    1,
                                ]
                                for I in range(len(startend))
                            ]
                        )

                        score_midpts = np.multiply(vec_x, vec[0]) + np.multiply(
                            vec_y, vec[1]
                        )
                        score_with_dist_prior = sum(score_midpts) / len(
                            score_midpts
                        ) + min(0.5 * oriImg.shape[0] / norm - 1, 0)
                        criterion1 = len(
                            np.nonzero(score_midpts > thre2)[0]
                        ) > 0.8 * len(score_midpts)
                        criterion2 = score_with_dist_prior > 0
                        if criterion1 and criterion2:
                            connection_candidate.append(
                                [
                                    i,
                                    j,
                                    score_with_dist_prior,
                                    score_with_dist_prior + candA[i][2] + candB[j][2],
                                ]
                            )

                connection_candidate = sorted(
                    connection_candidate, key=lambda x: x[2], reverse=True
                )
                connection = np.zeros((0, 5))
                for c in range(len(connection_candidate)):
                    i, j, s = connection_candidate[c][0:3]
                    if i not in connection[:, 3] and j not in connection[:, 4]:
                        connection = np.vstack(
                            [connection, [candA[i][3], candB[j][3], s, i, j]]
                        )
                        if len(connection) >= min(nA, nB):
                            break

                connection_all.append(connection)
            else:
                special_k.append(k)
                connection_all.append([])

        # last number in each row is the total parts number of that person
        # the second last number in each row is the score of the overall configuration
        subset = -1 * np.ones((0, 20))
        candidate = np.array([item for sublist in all_peaks for item in sublist])

        for k in range(len(mapIdx)):
            if k not in special_k:
                partAs = connection_all[k][:, 0]
                partBs = connection_all[k][:, 1]
                indexA, indexB = np.array(limbSeq[k]) - 1

                for i in range(len(connection_all[k])):  # = 1:size(temp,1)
                    found = 0
                    subset_idx = [-1, -1]
                    for j in range(len(subset)):  # 1:size(subset,1):
                        if (
                            subset[j][indexA] == partAs[i]
                            or subset[j][indexB] == partBs[i]
                        ):
                            subset_idx[found] = j
                            found += 1

                    if found == 1:
                        j = subset_idx[0]
                        if subset[j][indexB] != partBs[i]:
                            subset[j][indexB] = partBs[i]
                            subset[j][-1] += 1
                            subset[j][-2] += (
                                candidate[partBs[i].astype(int), 2]
                                + connection_all[k][i][2]
                            )
                    elif found == 2:  # if found 2 and disjoint, merge them
                        j1, j2 = subset_idx
                        membership = (
                            (subset[j1] >= 0).astype(int)
                            + (subset[j2] >= 0).astype(int)
                        )[:-2]
                        if len(np.nonzero(membership == 2)[0]) == 0:  # merge
                            subset[j1][:-2] += subset[j2][:-2] + 1
                            subset[j1][-2:] += subset[j2][-2:]
                            subset[j1][-2] += connection_all[k][i][2]
                            subset = np.delete(subset, j2, 0)
                        else:  # as like found == 1
                            subset[j1][indexB] = partBs[i]
                            subset[j1][-1] += 1
                            subset[j1][-2] += (
                                candidate[partBs[i].astype(int), 2]
                                + connection_all[k][i][2]
                            )

                    # if find no partA in the subset, create a new subset
                    elif not found and k < 17:
                        row = -1 * np.ones(20)
                        row[indexA] = partAs[i]
                        row[indexB] = partBs[i]
                        row[-1] = 2
                        row[-2] = (
                            sum(candidate[connection_all[k][i, :2].astype(int), 2])
                            + connection_all[k][i][2]
                        )
                        subset = np.vstack([subset, row])
        # delete some rows of subset which has few parts occur
        deleteIdx = []
        for i in range(len(subset)):
            if subset[i][-1] < 4 or subset[i][-2] / subset[i][-1] < 0.4:
                deleteIdx.append(i)
        subset = np.delete(subset, deleteIdx, axis=0)

        # subset: n*20 array, 0-17 is the index in candidate, 18 is the total score, 19 is the total parts
        # candidate: x, y, score, id
        return candidate, subset
