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| import cv2 | |
| import math | |
| import matplotlib | |
| import matplotlib.pyplot as plt | |
| import numpy as np | |
| import time | |
| import torch | |
| from scipy.ndimage.filters import gaussian_filter | |
| from torchvision import transforms | |
| from . import util | |
| from .model import bodypose_model | |
| class Body(object): | |
| def __init__(self, model_path): | |
| self.model = bodypose_model() | |
| if torch.cuda.is_available(): | |
| self.model = self.model.cuda() | |
| print('cuda') | |
| 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() | |
| 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 | |