import cv2
import copy
import math
import numpy as np
from collections import OrderedDict
from scipy.ndimage.filters import gaussian_filter

import torch
import torch.nn as nn

import matplotlib
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
import matplotlib.pyplot as plt
from skimage.measure import label
from read_data import LoadImages, LoadStreams
import torch.backends.cudnn as cudnn

class PoseDetection():
    def __init__(self, video_path=None):
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.body_estimation = Body('weight/pose/body_pose_model.pth')
        self.hand_estimation = Hand('weight/pose/hand_pose_model.pth')
        self.source = video_path
        self.classes = 'pose_detection'

        if video_path is not None:
            self.video_name = video_path
        else:
            self.video_name = 'vid2.mp4'  # A default video file

        
        self.dataset = LoadImages(self.video_name)

    
    def use_webcam(self, source):
        # self.dataset.release()  # Release any existing video capture
        #self.cap = cv2.VideoCapture(0)  # Open default webcam
        # print('use_webcam')
        source = source
        self.imgsz = 640
        cudnn.benchmark = True
        self.dataset = LoadStreams(source, img_size=self.imgsz)
        self.flag = 1


    def get_frame(self):
        
        for im0s in self.dataset:
            # print(self.dataset.mode)
            # print(self.dataset)
            if self.dataset.mode == 'stream':
                oriImg = im0s[0].copy()
            else:
                oriImg = im0s.copy()
            # oriImg = torch.from_numpy(oriImg)
            # oriImg = oriImg.to(self.device)
            candidate, subset = self.body_estimation(oriImg)
            # print(candidate)
            canvas = copy.deepcopy(oriImg)
            canvas = draw_bodypose(canvas, candidate, subset)
            

            # # detect hand
            # hands_list = handDetect(candidate, subset, oriImg)
            # all_hand_peaks = []
            # for x, y, w, is_left in hands_list:
            #     peaks = self.hand_estimation(oriImg[y:y+w, x:x+w, :])
            #     peaks[:, 0] = np.where(peaks[:, 0]==0, peaks[:, 0], peaks[:, 0]+x)
            #     peaks[:, 1] = np.where(peaks[:, 1]==0, peaks[:, 1], peaks[:, 1]+y)

            #     all_hand_peaks.append(peaks)

            # canvas = draw_handpose(canvas, all_hand_peaks)


            img = np.array(canvas[:, :, [2, 1, 0]])
            img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)

            accuracy = 0
            num_people = 0

            ret, jpeg = cv2.imencode(".jpg", img)
            return jpeg.tobytes(), ''


def make_layers(block, no_relu_layers):
    layers = []
    for layer_name, v in block.items():
        if 'pool' in layer_name:
            layer = nn.MaxPool2d(kernel_size=v[0], stride=v[1],
                                    padding=v[2])
            layers.append((layer_name, layer))
        else:
            conv2d = nn.Conv2d(in_channels=v[0], out_channels=v[1],
                               kernel_size=v[2], stride=v[3],
                               padding=v[4])
            layers.append((layer_name, conv2d))
            if layer_name not in no_relu_layers:
                layers.append(('relu_'+layer_name, nn.ReLU(inplace=True)))

    return nn.Sequential(OrderedDict(layers))

class bodypose_model(nn.Module):
    def __init__(self):
        super(bodypose_model, self).__init__()

        # these layers have no relu layer
        no_relu_layers = ['conv5_5_CPM_L1', 'conv5_5_CPM_L2', 'Mconv7_stage2_L1',\
                          'Mconv7_stage2_L2', 'Mconv7_stage3_L1', 'Mconv7_stage3_L2',\
                          'Mconv7_stage4_L1', 'Mconv7_stage4_L2', 'Mconv7_stage5_L1',\
                          'Mconv7_stage5_L2', 'Mconv7_stage6_L1', 'Mconv7_stage6_L1']
        blocks = {}
        block0 = OrderedDict([
                      ('conv1_1', [3, 64, 3, 1, 1]),
                      ('conv1_2', [64, 64, 3, 1, 1]),
                      ('pool1_stage1', [2, 2, 0]),
                      ('conv2_1', [64, 128, 3, 1, 1]),
                      ('conv2_2', [128, 128, 3, 1, 1]),
                      ('pool2_stage1', [2, 2, 0]),
                      ('conv3_1', [128, 256, 3, 1, 1]),
                      ('conv3_2', [256, 256, 3, 1, 1]),
                      ('conv3_3', [256, 256, 3, 1, 1]),
                      ('conv3_4', [256, 256, 3, 1, 1]),
                      ('pool3_stage1', [2, 2, 0]),
                      ('conv4_1', [256, 512, 3, 1, 1]),
                      ('conv4_2', [512, 512, 3, 1, 1]),
                      ('conv4_3_CPM', [512, 256, 3, 1, 1]),
                      ('conv4_4_CPM', [256, 128, 3, 1, 1])
                  ])


        # Stage 1
        block1_1 = OrderedDict([
                        ('conv5_1_CPM_L1', [128, 128, 3, 1, 1]),
                        ('conv5_2_CPM_L1', [128, 128, 3, 1, 1]),
                        ('conv5_3_CPM_L1', [128, 128, 3, 1, 1]),
                        ('conv5_4_CPM_L1', [128, 512, 1, 1, 0]),
                        ('conv5_5_CPM_L1', [512, 38, 1, 1, 0])
                    ])

        block1_2 = OrderedDict([
                        ('conv5_1_CPM_L2', [128, 128, 3, 1, 1]),
                        ('conv5_2_CPM_L2', [128, 128, 3, 1, 1]),
                        ('conv5_3_CPM_L2', [128, 128, 3, 1, 1]),
                        ('conv5_4_CPM_L2', [128, 512, 1, 1, 0]),
                        ('conv5_5_CPM_L2', [512, 19, 1, 1, 0])
                    ])
        blocks['block1_1'] = block1_1
        blocks['block1_2'] = block1_2

        self.model0 = make_layers(block0, no_relu_layers)

        # Stages 2 - 6
        for i in range(2, 7):
            blocks['block%d_1' % i] = OrderedDict([
                    ('Mconv1_stage%d_L1' % i, [185, 128, 7, 1, 3]),
                    ('Mconv2_stage%d_L1' % i, [128, 128, 7, 1, 3]),
                    ('Mconv3_stage%d_L1' % i, [128, 128, 7, 1, 3]),
                    ('Mconv4_stage%d_L1' % i, [128, 128, 7, 1, 3]),
                    ('Mconv5_stage%d_L1' % i, [128, 128, 7, 1, 3]),
                    ('Mconv6_stage%d_L1' % i, [128, 128, 1, 1, 0]),
                    ('Mconv7_stage%d_L1' % i, [128, 38, 1, 1, 0])
                ])

            blocks['block%d_2' % i] = OrderedDict([
                    ('Mconv1_stage%d_L2' % i, [185, 128, 7, 1, 3]),
                    ('Mconv2_stage%d_L2' % i, [128, 128, 7, 1, 3]),
                    ('Mconv3_stage%d_L2' % i, [128, 128, 7, 1, 3]),
                    ('Mconv4_stage%d_L2' % i, [128, 128, 7, 1, 3]),
                    ('Mconv5_stage%d_L2' % i, [128, 128, 7, 1, 3]),
                    ('Mconv6_stage%d_L2' % i, [128, 128, 1, 1, 0]),
                    ('Mconv7_stage%d_L2' % i, [128, 19, 1, 1, 0])
                ])

        for k in blocks.keys():
            blocks[k] = make_layers(blocks[k], no_relu_layers)

        self.model1_1 = blocks['block1_1']
        self.model2_1 = blocks['block2_1']
        self.model3_1 = blocks['block3_1']
        self.model4_1 = blocks['block4_1']
        self.model5_1 = blocks['block5_1']
        self.model6_1 = blocks['block6_1']

        self.model1_2 = blocks['block1_2']
        self.model2_2 = blocks['block2_2']
        self.model3_2 = blocks['block3_2']
        self.model4_2 = blocks['block4_2']
        self.model5_2 = blocks['block5_2']
        self.model6_2 = blocks['block6_2']


    def forward(self, x):

        out1 = self.model0(x)

        out1_1 = self.model1_1(out1)
        out1_2 = self.model1_2(out1)
        out2 = torch.cat([out1_1, out1_2, out1], 1)

        out2_1 = self.model2_1(out2)
        out2_2 = self.model2_2(out2)
        out3 = torch.cat([out2_1, out2_2, out1], 1)

        out3_1 = self.model3_1(out3)
        out3_2 = self.model3_2(out3)
        out4 = torch.cat([out3_1, out3_2, out1], 1)

        out4_1 = self.model4_1(out4)
        out4_2 = self.model4_2(out4)
        out5 = torch.cat([out4_1, out4_2, out1], 1)

        out5_1 = self.model5_1(out5)
        out5_2 = self.model5_2(out5)
        out6 = torch.cat([out5_1, out5_2, out1], 1)

        out6_1 = self.model6_1(out6)
        out6_2 = self.model6_2(out6)

        return out6_1, out6_2

class handpose_model(nn.Module):
    def __init__(self):
        super(handpose_model, self).__init__()

        # these layers have no relu layer
        no_relu_layers = ['conv6_2_CPM', 'Mconv7_stage2', 'Mconv7_stage3',\
                          'Mconv7_stage4', 'Mconv7_stage5', 'Mconv7_stage6']
        # stage 1
        block1_0 = OrderedDict([
                ('conv1_1', [3, 64, 3, 1, 1]),
                ('conv1_2', [64, 64, 3, 1, 1]),
                ('pool1_stage1', [2, 2, 0]),
                ('conv2_1', [64, 128, 3, 1, 1]),
                ('conv2_2', [128, 128, 3, 1, 1]),
                ('pool2_stage1', [2, 2, 0]),
                ('conv3_1', [128, 256, 3, 1, 1]),
                ('conv3_2', [256, 256, 3, 1, 1]),
                ('conv3_3', [256, 256, 3, 1, 1]),
                ('conv3_4', [256, 256, 3, 1, 1]),
                ('pool3_stage1', [2, 2, 0]),
                ('conv4_1', [256, 512, 3, 1, 1]),
                ('conv4_2', [512, 512, 3, 1, 1]),
                ('conv4_3', [512, 512, 3, 1, 1]),
                ('conv4_4', [512, 512, 3, 1, 1]),
                ('conv5_1', [512, 512, 3, 1, 1]),
                ('conv5_2', [512, 512, 3, 1, 1]),
                ('conv5_3_CPM', [512, 128, 3, 1, 1])
            ])

        block1_1 = OrderedDict([
            ('conv6_1_CPM', [128, 512, 1, 1, 0]),
            ('conv6_2_CPM', [512, 22, 1, 1, 0])
        ])

        blocks = {}
        blocks['block1_0'] = block1_0
        blocks['block1_1'] = block1_1

        # stage 2-6
        for i in range(2, 7):
            blocks['block%d' % i] = OrderedDict([
                    ('Mconv1_stage%d' % i, [150, 128, 7, 1, 3]),
                    ('Mconv2_stage%d' % i, [128, 128, 7, 1, 3]),
                    ('Mconv3_stage%d' % i, [128, 128, 7, 1, 3]),
                    ('Mconv4_stage%d' % i, [128, 128, 7, 1, 3]),
                    ('Mconv5_stage%d' % i, [128, 128, 7, 1, 3]),
                    ('Mconv6_stage%d' % i, [128, 128, 1, 1, 0]),
                    ('Mconv7_stage%d' % i, [128, 22, 1, 1, 0])
                ])

        for k in blocks.keys():
            blocks[k] = make_layers(blocks[k], no_relu_layers)

        self.model1_0 = blocks['block1_0']
        self.model1_1 = blocks['block1_1']
        self.model2 = blocks['block2']
        self.model3 = blocks['block3']
        self.model4 = blocks['block4']
        self.model5 = blocks['block5']
        self.model6 = blocks['block6']

    def forward(self, x):
        out1_0 = self.model1_0(x)
        out1_1 = self.model1_1(out1_0)
        concat_stage2 = torch.cat([out1_1, out1_0], 1)
        out_stage2 = self.model2(concat_stage2)
        concat_stage3 = torch.cat([out_stage2, out1_0], 1)
        out_stage3 = self.model3(concat_stage3)
        concat_stage4 = torch.cat([out_stage3, out1_0], 1)
        out_stage4 = self.model4(concat_stage4)
        concat_stage5 = torch.cat([out_stage4, out1_0], 1)
        out_stage5 = self.model5(concat_stage5)
        concat_stage6 = torch.cat([out_stage5, out1_0], 1)
        out_stage6 = self.model6(concat_stage6)
        return out_stage6


class Body(object):
    def __init__(self, model_path):
        self.model = bodypose_model()
        if torch.cuda.is_available():
            self.model = self.model.cuda()
        model_dict = 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 = 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()
            if torch.cuda.is_available():
                data = data.cuda()
            # 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
    
    
    
class Hand(object):
    def __init__(self, model_path):
        self.model = handpose_model()
        if torch.cuda.is_available():
            self.model = self.model.cuda()
        model_dict = 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
        thre = 0.05
        multiplier = [x * boxsize / oriImg.shape[0] for x in scale_search]
        heatmap_avg = np.zeros((oriImg.shape[0], oriImg.shape[1], 22))
        # 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 = 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()
            if torch.cuda.is_available():
                data = data.cuda()
            # data = data.permute([2, 0, 1]).unsqueeze(0).float()
            with torch.no_grad():
                output = self.model(data).cpu().numpy()
                # output = self.model(data).numpy()q

            # extract outputs, resize, and remove padding
            heatmap = np.transpose(np.squeeze(output), (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)

            heatmap_avg += heatmap / len(multiplier)

        all_peaks = []
        for part in range(21):
            map_ori = heatmap_avg[:, :, part]
            one_heatmap = gaussian_filter(map_ori, sigma=3)
            binary = np.ascontiguousarray(one_heatmap > thre, dtype=np.uint8)
            # 全部小于阈值
            if np.sum(binary) == 0:
                all_peaks.append([0, 0])
                continue
            label_img, label_numbers = label(binary, return_num=True, connectivity=binary.ndim)
            max_index = np.argmax([np.sum(map_ori[label_img == i]) for i in range(1, label_numbers + 1)]) + 1
            label_img[label_img != max_index] = 0
            map_ori[label_img == 0] = 0

            y, x = npmax(map_ori)
            all_peaks.append([x, y])
        return np.array(all_peaks)
    
    
    

def padRightDownCorner(img, stride, padValue):
    h = img.shape[0]
    w = img.shape[1]

    pad = 4 * [None]
    pad[0] = 0 # up
    pad[1] = 0 # left
    pad[2] = 0 if (h % stride == 0) else stride - (h % stride) # down
    pad[3] = 0 if (w % stride == 0) else stride - (w % stride) # right

    img_padded = img
    pad_up = np.tile(img_padded[0:1, :, :]*0 + padValue, (pad[0], 1, 1))
    img_padded = np.concatenate((pad_up, img_padded), axis=0)
    pad_left = np.tile(img_padded[:, 0:1, :]*0 + padValue, (1, pad[1], 1))
    img_padded = np.concatenate((pad_left, img_padded), axis=1)
    pad_down = np.tile(img_padded[-2:-1, :, :]*0 + padValue, (pad[2], 1, 1))
    img_padded = np.concatenate((img_padded, pad_down), axis=0)
    pad_right = np.tile(img_padded[:, -2:-1, :]*0 + padValue, (1, pad[3], 1))
    img_padded = np.concatenate((img_padded, pad_right), axis=1)

    return img_padded, pad

# transfer caffe model to pytorch which will match the layer name
def transfer(model, model_weights):
    transfered_model_weights = {}
    for weights_name in model.state_dict().keys():
        transfered_model_weights[weights_name] = model_weights['.'.join(weights_name.split('.')[1:])]
    return transfered_model_weights

# draw the body keypoint and lims
def draw_bodypose(canvas, candidate, subset):
    stickwidth = 4
    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]]

    colors = [[255, 0, 0], [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0], [85, 255, 0], [0, 255, 0], \
              [0, 255, 85], [0, 255, 170], [0, 255, 255], [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], \
              [170, 0, 255], [255, 0, 255], [255, 0, 170], [255, 0, 85]]
    for i in range(18):
        for n in range(len(subset)):
            index = int(subset[n][i])
            if index == -1:
                continue
            x, y = candidate[index][0:2]
            cv2.circle(canvas, (int(x), int(y)), 4, colors[i], thickness=-1)
    for i in range(17):
        for n in range(len(subset)):
            index = subset[n][np.array(limbSeq[i]) - 1]
            if -1 in index:
                continue
            cur_canvas = canvas.copy()
            Y = candidate[index.astype(int), 0]
            X = candidate[index.astype(int), 1]
            mX = np.mean(X)
            mY = np.mean(Y)
            length = ((X[0] - X[1]) ** 2 + (Y[0] - Y[1]) ** 2) ** 0.5
            angle = math.degrees(math.atan2(X[0] - X[1], Y[0] - Y[1]))
            polygon = cv2.ellipse2Poly((int(mY), int(mX)), (int(length / 2), stickwidth), int(angle), 0, 360, 1)
            cv2.fillConvexPoly(cur_canvas, polygon, colors[i])
            canvas = cv2.addWeighted(canvas, 0.4, cur_canvas, 0.6, 0)
    # plt.imsave("preview.jpg", canvas[:, :, [2, 1, 0]])
    # plt.imshow(canvas[:, :, [2, 1, 0]])
    return canvas

def draw_handpose(canvas, all_hand_peaks, show_number=False):
    edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \
             [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]
    fig = Figure(figsize=plt.figaspect(canvas))

    fig.subplots_adjust(0, 0, 1, 1)
    fig.subplots_adjust(bottom=0, top=1, left=0, right=1)
    bg = FigureCanvas(fig)
    ax = fig.subplots()
    ax.axis('off')
    ax.imshow(canvas)

    width, height = ax.figure.get_size_inches() * ax.figure.get_dpi()

    for peaks in all_hand_peaks:
        for ie, e in enumerate(edges):
            if np.sum(np.all(peaks[e], axis=1)==0)==0:
                x1, y1 = peaks[e[0]]
                x2, y2 = peaks[e[1]]
                ax.plot([x1, x2], [y1, y2], color=matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0]))

        for i, keyponit in enumerate(peaks):
            x, y = keyponit
            ax.plot(x, y, 'r.')
            if show_number:
                ax.text(x, y, str(i))
    bg.draw()
    canvas = np.fromstring(bg.tostring_rgb(), dtype='uint8').reshape(int(height), int(width), 3)
    return canvas

# image drawed by opencv is not good.
def draw_handpose_by_opencv(canvas, peaks, show_number=False):
    edges = [[0, 1], [1, 2], [2, 3], [3, 4], [0, 5], [5, 6], [6, 7], [7, 8], [0, 9], [9, 10], \
             [10, 11], [11, 12], [0, 13], [13, 14], [14, 15], [15, 16], [0, 17], [17, 18], [18, 19], [19, 20]]
    # cv2.rectangle(canvas, (x, y), (x+w, y+w), (0, 255, 0), 2, lineType=cv2.LINE_AA)
    # cv2.putText(canvas, 'left' if is_left else 'right', (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2)
    for ie, e in enumerate(edges):
        if np.sum(np.all(peaks[e], axis=1)==0)==0:
            x1, y1 = peaks[e[0]]
            x2, y2 = peaks[e[1]]
            cv2.line(canvas, (x1, y1), (x2, y2), matplotlib.colors.hsv_to_rgb([ie/float(len(edges)), 1.0, 1.0])*255, thickness=2)

    for i, keyponit in enumerate(peaks):
        x, y = keyponit
        cv2.circle(canvas, (x, y), 4, (0, 0, 255), thickness=-1)
        if show_number:
            cv2.putText(canvas, str(i), (x, y), cv2.FONT_HERSHEY_SIMPLEX, 0.3, (0, 0, 0), lineType=cv2.LINE_AA)
    return canvas

# detect hand according to body pose keypoints
# please refer to https://github.com/CMU-Perceptual-Computing-Lab/openpose/blob/master/src/openpose/hand/handDetector.cpp
def handDetect(candidate, subset, oriImg):
    # right hand: wrist 4, elbow 3, shoulder 2
    # left hand: wrist 7, elbow 6, shoulder 5
    ratioWristElbow = 0.33
    detect_result = []
    image_height, image_width = oriImg.shape[0:2]
    for person in subset.astype(int):
        # if any of three not detected
        has_left = np.sum(person[[5, 6, 7]] == -1) == 0
        has_right = np.sum(person[[2, 3, 4]] == -1) == 0
        if not (has_left or has_right):
            continue
        hands = []
        #left hand
        if has_left:
            left_shoulder_index, left_elbow_index, left_wrist_index = person[[5, 6, 7]]
            x1, y1 = candidate[left_shoulder_index][:2]
            x2, y2 = candidate[left_elbow_index][:2]
            x3, y3 = candidate[left_wrist_index][:2]
            hands.append([x1, y1, x2, y2, x3, y3, True])
        # right hand
        if has_right:
            right_shoulder_index, right_elbow_index, right_wrist_index = person[[2, 3, 4]]
            x1, y1 = candidate[right_shoulder_index][:2]
            x2, y2 = candidate[right_elbow_index][:2]
            x3, y3 = candidate[right_wrist_index][:2]
            hands.append([x1, y1, x2, y2, x3, y3, False])

        for x1, y1, x2, y2, x3, y3, is_left in hands:
            # pos_hand = pos_wrist + ratio * (pos_wrist - pos_elbox) = (1 + ratio) * pos_wrist - ratio * pos_elbox
            # handRectangle.x = posePtr[wrist*3] + ratioWristElbow * (posePtr[wrist*3] - posePtr[elbow*3]);
            # handRectangle.y = posePtr[wrist*3+1] + ratioWristElbow * (posePtr[wrist*3+1] - posePtr[elbow*3+1]);
            # const auto distanceWristElbow = getDistance(poseKeypoints, person, wrist, elbow);
            # const auto distanceElbowShoulder = getDistance(poseKeypoints, person, elbow, shoulder);
            # handRectangle.width = 1.5f * fastMax(distanceWristElbow, 0.9f * distanceElbowShoulder);
            x = x3 + ratioWristElbow * (x3 - x2)
            y = y3 + ratioWristElbow * (y3 - y2)
            distanceWristElbow = math.sqrt((x3 - x2) ** 2 + (y3 - y2) ** 2)
            distanceElbowShoulder = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
            width = 1.5 * max(distanceWristElbow, 0.9 * distanceElbowShoulder)
            # x-y refers to the center --> offset to topLeft point
            # handRectangle.x -= handRectangle.width / 2.f;
            # handRectangle.y -= handRectangle.height / 2.f;
            x -= width / 2
            y -= width / 2  # width = height
            # overflow the image
            if x < 0: x = 0
            if y < 0: y = 0
            width1 = width
            width2 = width
            if x + width > image_width: width1 = image_width - x
            if y + width > image_height: width2 = image_height - y
            width = min(width1, width2)
            # the max hand box value is 20 pixels
            if width >= 20:
                detect_result.append([int(x), int(y), int(width), is_left])

    '''
    return value: [[x, y, w, True if left hand else False]].
    width=height since the network require squared input.
    x, y is the coordinate of top left 
    '''
    return detect_result

# get max index of 2d array
def npmax(array):
    arrayindex = array.argmax(1)
    arrayvalue = array.max(1)
    i = arrayvalue.argmax()
    j = arrayindex[i]
    return i, j