import cv2 import numpy as np import math import time from scipy.ndimage.filters import gaussian_filter import matplotlib.pyplot as plt import matplotlib import torch from torchvision import transforms from src import util from src.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() 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 if __name__ == "__main__": body_estimation = Body('../model/body_pose_model.pth') test_image = '../images/ski.jpg' oriImg = cv2.imread(test_image) # B,G,R order candidate, subset = body_estimation(oriImg) canvas = util.draw_bodypose(oriImg, candidate, subset) plt.imshow(canvas[:, :, [2, 1, 0]]) plt.show()