#-*-coding:utf-8-*- ''' DpCas-Light |||| ||||| |||| || ||||||| || || || || || || |||| || || || || || || || || || || || || || || || || ||====|| |||||| || || ||||| || || ||======|| || || || || || || || || || || |||| || |||| || || ||||||| /--------------------- HandPose_X ---------------------/ ''' # date:2019-12-10 # Author: Eric.Lee # function: handpose :rotation & translation import cv2 import numpy as np # 人脸外轮廓 def get_face_outline(img_crop,face_crop_region,obj_crop_points,face_w,face_h): face_mask = np.zeros((1,27,2),dtype = np.int32) for m in range(obj_crop_points.shape[0]): if m <=16: x = int(face_crop_region[0]+obj_crop_points[m][0]*face_w) y = int(face_crop_region[1]+obj_crop_points[m][1]*face_h) # face_mask.append((x,y)) face_mask[0,m,0]=x face_mask[0,m,1]=y for k in range(16,26): m = 42-k x = int(face_crop_region[0]+obj_crop_points[m][0]*face_w) y = int(face_crop_region[1]+obj_crop_points[m][1]*face_h) # face_mask.append((x,y)) face_mask[0,k+1,0]=x face_mask[0,k+1,1]=y # print(x,y) return face_mask # 人脸公共模型三维坐标 object_pts = np.float32([ [0., 0.4,0.],#掌心 [0., 5.,0.],#hand 根部 # [-2, 2.5,0.],#thumb 第一指节 # [-4, 0.5,0.],#thumb 第二指节 [-2.7, -4.5, 0.],# index 根部 [0., -5., 0.],# middle 根部 [2.6, -4., 0.], # ring 根部 [5.2, -3., 0.],# pink 根部 ] ) # object_pts = np.float32([[-2.5, -7.45, 0.5],# pink 根部 # # [-1.2, -7.45, 0.5], # ring 根部 # # # [1.2, -7.5, 0.5],# middle 根部 # # [2.5, -7.45, 0.5],# index 根部 # [4.2, -3.45, 0.5],# thumb 第二指节 # [2.5, -2.0, 0.5],# thumb 根部 # [0.00, -0.0,0.5],#hand 根部 # ] # ) # xyz 立体矩形框 # reprojectsrc = np.float32([[3.0, 11.0, 2.0], # [3.0, 11.0, -4.0], # [3.0, -7.0, -4.0], # [3.0, -7.0, 2.0], # [-3.0, 11.0, 2.0], # [-3.0, 11.0, -4.0], # [-3.0, -7.0, -4.0], # [-3.0, -7.0, 2.0]]) reprojectsrc = np.float32([[5.0, 8.0, 2.0], [5.0, 8.0, -2.0], [5.0, -8.0, -2.0], [5.0, -8.0, 2.0], [-5.0, 8.0, 2.0], [-5.0, 8.0, -2.0], [-5.0, -8.0, -2.0], [-5.0, -8.0, 2.0]]) # reprojectsrc = np.float32([[6.0, 4.0, 2.0], # [6.0, 4.0, -4.0], # [6.0, -3.0, -4.0], # [6.0, -3.0, 2.0], # [-6.0, 4.0, 2.0], # [-6.0, 4.0, -4.0], # [-6.0, -3.0, -4.0], # [-6.0, -3.0, 2.0]]) # reprojectsrc = np.float32([[6.0, 6.0, 6.0], # [6.0, 6.0, -6.0], # [6.0, -6.0, -6.0], # [6.0, -6.0, 6.0], # [-6.0, 6.0, 6.0], # [-6.0, 6.0, -6.0], # [-6.0, -6.0, -6.0], # [-6.0, -6.0, 6.0]]) # 立体矩形框连线，连接组合 line_pairs = [[0, 1], [1, 2], [2, 3], [3, 0], [4, 5], [5, 6], [6, 7], [7, 4], [0, 4], [1, 5], [2, 6], [3, 7]] def get_hand_pose(shape,img,vis = True): h,w,_=img.shape K = [w, 0.0, w//2, 0.0, w, h//2, 0.0, 0.0, 1.0] # Assuming no lens distortion D = [0, 0, 0.0, 0.0, 0] cam_matrix = np.array(K).reshape(3, 3).astype(np.float32)# 相机矩阵 # dist_coeffs = np.array(D).reshape(5, 1).astype(np.float32)#相机畸变矩阵，默认无畸变 dist_coeffs = np.float32([0.0, 0.0, 0.0, 0.0, 0.0]) # 选取的人脸关键点的二维图像坐标 # image_pts = np.float32([shape[17], shape[21], shape[22], shape[26], shape[36], # shape[39], shape[42], shape[45], # shape[27],shape[31], shape[35],shape[30],shape[33]]) image_pts = np.float32([shape[0], shape[1], shape[2], shape[3], shape[4], shape[5] ] ) # PNP 计算图像二维和三维实际关系，获得旋转和偏移矩阵 _, rotation_vec, translation_vec = cv2.solvePnP(object_pts, image_pts, cam_matrix, dist_coeffs) # _, rotation_vec, translation_vec = cv2.solvePnPRansac(object_pts, image_pts, cam_matrix, dist_coeffs) # print("translation_vec:",translation_vec) #print('translation_vec : {}'.format(translation_vec)) # 映射矩形框 reprojectdst, _ = cv2.projectPoints(reprojectsrc, rotation_vec, translation_vec, cam_matrix,dist_coeffs) reprojectdst = tuple(map(tuple, reprojectdst.reshape(8, 2))) # calc euler angle rotation_mat, _ = cv2.Rodrigues(rotation_vec)#旋转向量转为旋转矩阵 pose_mat = cv2.hconcat((rotation_mat, translation_vec))# 拼接操作 旋转 + 偏移 _, _, _, _, _, _, euler_angle = cv2.decomposeProjectionMatrix(pose_mat)#欧拉角估计 if vis: for i,line_pair in enumerate(line_pairs):# 显示立体矩形框 x1 = int(reprojectdst[line_pair[0]][0]) y1 = int(reprojectdst[line_pair[0]][1]) x2 = int(reprojectdst[line_pair[1]][0]) y2 = int(reprojectdst[line_pair[1]][1]) if line_pair[0] in [0,3,4,7] and line_pair[1] in [0,3,4,7]: cv2.line(img,(x1,y1),(x2,y2),(255,0,0),2) elif line_pair[0] in [1,2,5,6] and line_pair[1] in [1,2,5,6]: cv2.line(img,(x1,y1),(x2,y2),(250,150,0),2) else: cv2.line(img,(x1,y1),(x2,y2),(0,90,255),2) return reprojectdst, euler_angle,translation_vec