From 82126f1cb3d10e64ce6c022bb08cb9c3f937d1b0 Mon Sep 17 00:00:00 2001 From: "Eric.Lee2021" <305141918@qq.com> Date: Tue, 22 Jun 2021 04:20:12 +0800 Subject: [PATCH] update --- yolo_inference.py | 47 +++++++++++++++++++++++++++++++++++++++++------ 1 file changed, 41 insertions(+), 6 deletions(-) diff --git a/yolo_inference.py b/yolo_inference.py index 468c94c..0a1da30 100644 --- a/yolo_inference.py +++ b/yolo_inference.py @@ -36,7 +36,7 @@ from manopth import manolayer if __name__ == "__main__": parser = argparse.ArgumentParser(description=' Project Hand Pose 3D Inference') - parser.add_argument('--model_path', type=str, default = './if_package/e3d_handposex-resnet_50-size-128-loss-wing_loss-20210619.pth', + parser.add_argument('--model_path', type=str, default = './if_package/e3d_handposex-resnet_50-size-128-loss-wing_loss-20210620.pth', help = 'model_path') # e3d handpose 模型路径 parser.add_argument('--detect_model_path', type=str, default = './if_package/hand_detect_416-20210606.pt', help = 'model_path') # detect 模型路径 @@ -159,6 +159,10 @@ if __name__ == "__main__": img_yolo_x = img_o.copy() hand_bbox =hand_detect_model.predict(img_yolo_x,vis = False) # 检测手，获取手的边界框 if len(hand_bbox) == 1: + #---------------------------------- + finger_index = None # 食指UI的二维坐标 + finger_thumb = None # 大拇UI指的二维坐标 + #---------------------------------- x_min,y_min,x_max,y_max,_ = hand_bbox[0] w_ = max(abs(x_max-x_min),abs(y_max-y_min)) @@ -180,6 +184,8 @@ if __name__ == "__main__": img_show = img.copy() # 用于显示使用 pts_2d_ = handpose_2d_model.predict(img.copy()) # handpose_2d predict pts_2d_hand = {} + + kps_min_x,kps_min_y,kps_max_x,kps_max_y = 65535.,65535.,0.,0. for ptk in range(int(pts_2d_.shape[0]/2)): xh = pts_2d_[ptk*2+0]*float(img.shape[1]) @@ -188,9 +194,25 @@ if __name__ == "__main__": "x":xh, "y":yh, } + kps_min_x = (xh+x1) if (xh+x1)kps_max_x else kps_max_x + kps_max_y = (yh+y1) if (yh+y1)>kps_max_y else kps_max_y + if ptk == 3 or ptk == 4: + if finger_thumb is None: + finger_thumb = (int(xh+x1),int(yh+y1)) + else: + finger_thumb = (int((xh+x1+finger_thumb[0])/2),int((yh+y1+finger_thumb[1])/2)) + if ptk == 7 or ptk == 8: + if finger_index is None: + finger_index = (int(xh+x1),int(yh+y1)) + else: + finger_index = (int((xh+x1+finger_index[0])/2),int((yh+y1+finger_index[1])/2)) + # cv2.circle(img_show, finger_index, 9, (25,160,255),-1) if ops.vis: cv2.circle(img_show, (int(xh),int(yh)), 4, (255,50,60),-1) cv2.circle(img_show, (int(xh),int(yh)), 3, (25,160,255),-1) + hand2d_kps_bbox = (int(kps_min_x),int(kps_min_y),int(kps_max_x),int(kps_max_y)) # 手关键点边界框 if ops.vis: draw_bd_handpose_c(img_show,pts_2d_hand,0,0,2) cv2.namedWindow("handpose_2d",0) @@ -278,7 +300,7 @@ if __name__ == "__main__": pose_R = torch.from_numpy(pose_R).float() # reconstruction hand_verts, j3d_recon = mano(pose_R, opt_tensor_shape.float()) - hand_verts[:,:,:] = hand_verts[:,:,:]*(0.573) + hand_verts[:,:,:] = hand_verts[:,:,:]*(0.503) # print(j3d_recon.size()) mesh.triangles = open3d.utility.Vector3iVector(mano.th_faces) @@ -311,13 +333,19 @@ if __name__ == "__main__": # print(j3d_.shape,j3d_) test_pcd.points = open3d.utility.Vector3dVector(j3d_[0]) # 定义点云坐标位置 else: - # test_pcd.points = open3d.utility.Vector3dVector(hand_verts) + # 778*3 + print("hand_verts shape : {}".format(hand_verts.shape)) + # a = np.concatenate(( + # hand_verts[38:40,:], + # hand_verts[40:100,:] + # ),axis=0) + test_pcd.points = open3d.utility.Vector3dVector(hand_verts) pre_joints[:,1] *=-1. pre_joints = pre_joints*70 pre_joints[:,1] -= 40 pre_joints[:,0] -= 0 # print("pre_joints",pre_joints.shape) - test_pcd.points = open3d.utility.Vector3dVector(pre_joints) + # test_pcd.points = open3d.utility.Vector3dVector(pre_joints) # test_pcd.points = open3d.utility.Vector3dVector(pre_joints[1,:].reshape(1,3)) # rgb = np.asarray([250,0,250]) # rgb_t = np.transpose(rgb) @@ -360,7 +388,13 @@ if __name__ == "__main__": #---------- cv2.namedWindow("image_3d",0) cv2.imshow("image_3d",image_3d) - scale_ = ((x_max-x_min)/(xx2-xx1) + (y_max-y_min)/(yy2-yy1))/2.*1.01 + + + # cv2.rectangle(img_yolo_x, (hand2d_kps_bbox[0],hand2d_kps_bbox[1]), + # (hand2d_kps_bbox[2],hand2d_kps_bbox[3]), (0,165,255), 3) # 绘制2d 手关键点 + # scale_ = ((x_max-x_min)/(xx2-xx1) + (y_max-y_min)/(yy2-yy1))/2.*1.01 + scale_ = ((hand2d_kps_bbox[2]-hand2d_kps_bbox[0])/(xx2-xx1) + + (hand2d_kps_bbox[3]-hand2d_kps_bbox[1])/(yy2-yy1))/2.*1.2 w_3d_ = (xx2-xx1)*scale_ h_3d_ = (yy2-yy1)*scale_ x_mid_3d = (xx1+xx2)/2. @@ -377,6 +411,7 @@ if __name__ == "__main__": crop_mask = np.expand_dims(crop_mask, 2) try: + #------------ img_ff = img_yolo_x[int(y_mid - h_r ):int(y_mid + h_r ),int(x_mid - w_r ):int(x_mid + w_r ),:]*(1.-crop_mask) + crop_*crop_mask img_yolo_x[int(y_mid - h_r ):int(y_mid + h_r ),int(x_mid - w_r ):int(x_mid + w_r ),:] = img_ff.astype(np.uint8) except: @@ -387,7 +422,7 @@ if __name__ == "__main__": depth_z = (model_hand_h/real_hand_h + model_hand_w/real_hand_w)/2.# 相对深度 z # cv2.putText(img_yolo_x, " Relative Depth_Z :{:.3f} ".format(depth_z), (4,42),cv2.FONT_HERSHEY_DUPLEX, 1.1, (55, 0, 220),7) - cv2.putText(img_yolo_x, " Relative Depth_Z :{:.3f} ".format(depth_z), (4,42),cv2.FONT_HERSHEY_DUPLEX, 1.1, (25, 180, 250),2) + cv2.putText(img_yolo_x, " Relative Depth_Z :{:.3f} ".format(depth_z), (4,42),cv2.FONT_HERSHEY_DUPLEX, 1.1, (25, 180, 250),1) cv2.namedWindow("img_yolo_x",0) -- GitLab