import numpy as np import math import cv2 # draw the body keypoint and lims def draw_bodypose(img, poses,model_type = 'coco'): stickwidth = 4 limbSeq = [[1, 2], [1, 5], [2, 3], [3, 4], [5, 6], [6, 7], [1, 8], [8, 9], \ [9, 10], [1, 11], [11, 12], [12, 13], [1, 0], [0, 14], [14, 16], \ [0, 15], [15, 17]] njoint = 18 if model_type == 'body_25': limbSeq = [[1,0],[1,2],[2,3],[3,4],[1,5],[5,6],[6,7],[1,8],[8,9],[9,10],\ [10,11],[8,12],[12,13],[13,14],[0,15],[0,16],[15,17],[16,18],\ [11,24],[11,22],[14,21],[14,19],[22,23],[19,20]] njoint = 25 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], [255,255,0], [255,255,85], [255,255,170],\ [255,255,255],[170,255,255],[85,255,255],[0,255,255]] for i in range(njoint): for n in range(len(poses)): pose = poses[n][i] if pose[2] <= 0: continue x, y = pose[:2] cv2.circle(img, (int(x), int(y)), 4, colors[i], thickness=-1) for pose in poses: for limb,color in zip(limbSeq,colors): p1 = pose[limb[0]] p2 = pose[limb[1]] if p1[2] <=0 or p2[2] <= 0: continue cur_canvas = img.copy() X = [p1[1],p2[1]] Y = [p1[0],p2[0]] 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, color) img = cv2.addWeighted(img, 0.4, cur_canvas, 0.6, 0) return img 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