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e3d_handpose_x
e3d_handpose_x

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e3d_handpose_x
e3d_handpose_x

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提交 b0dc9f74 编写于 作者: Eric.Lee2021's avatar Eric.Lee2021 🚴🏻
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Showing with 36 addition and 41 deletion
e3d_data_iter/datasets.py
浏览文件 @ b0dc9f74
......@@ -156,7 +156,8 @@ def draw_msra_gaussian(heatmap, center, sigma):
return heatmap
def get_heatmap(img_fix_size,x1y1x2y2,handpose_2d,ratio, dw, dh,offset_x1=0,offset_y1=0,radius=20,vis = False):
num_objs = 21
hm = np.zeros((256,256,num_objs), dtype=np.float32)
hm = np.zeros((img_fix_size.shape[0],img_fix_size.shape[1],num_objs), dtype=np.float32)
draw_gaussian = draw_msra_gaussian if False else draw_umich_gaussian
for k in range(num_objs):
......@@ -449,12 +450,12 @@ class LoadImagesAndLabels(Dataset):
hand_w = int((x2_-x1_)/8)
hand_h = int((y2_-y1_)/8)
offset_x1 = random.randint(-hand_w,int(hand_w/2))
offset_y1 = random.randint(-hand_h,int(hand_h/2))
offset_x2 = random.randint(-int(hand_w/2),hand_w)
offset_y2 = random.randint(-int(hand_h/2),hand_h)
hand_w = int((x2_-x1_)/2)
hand_h = int((y2_-y1_)/2)
offset_x1 = random.randint(-hand_w,int(hand_w/6))
offset_y1 = random.randint(-hand_h,int(hand_h/6))
offset_x2 = random.randint(-int(hand_w/6),hand_w)
offset_y2 = random.randint(-int(hand_h/6),hand_h)
# print(" A : x1_,y1_,x2_,y2_ ： ",x1_,y1_,x2_,y2_)
x1_new = x1_+offset_x1
y1_new = y1_+offset_y1
......
yolo_inference.py
浏览文件 @ b0dc9f74
......@@ -36,8 +36,12 @@ from manopth import manolayer
if __name__ == "__main__":
parser = argparse.ArgumentParser(description=' Project Hand Pose 3D Inference')
parser.add_argument('--model_path', type=str, default = './model_exp/2021-06-16_10-53-50/resnet_50-size-256-loss-wing_loss-model_epoch-225.pth',
help = 'model_path') # 模型路径
parser.add_argument('--model_path', type=str, default = '././model_exp/2021-06-19_14-20-56/resnet_50-size-128-loss-wing_loss-model_epoch-196.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
parser.add_argument('--handpose_x2d_model_path', type=str, default = './if_package/handposex_2d_resnet_50-size-256-wingloss102-0.119.pth',
help = 'model_path') # 手2维关键点 模型路径
parser.add_argument('--model', type=str, default = 'resnet_50',
help = '''model : resnet_18,resnet_34,resnet_50,resnet_101''') # 模型类型
parser.add_argument('--num_classes', type=int , default = 63,
......@@ -46,7 +50,7 @@ if __name__ == "__main__":
help = 'GPUS') # GPU选择
parser.add_argument('--test_path', type=str, default = './image/',
help = 'test_path') # 测试图片路径
parser.add_argument('--img_size', type=tuple , default = (256,256),
parser.add_argument('--img_size', type=tuple , default = (128,128),
help = 'img_size') # 输入模型图片尺寸
parser.add_argument('--vis', type=bool , default = True,
help = 'vis') # 是否可视化图片
......@@ -90,16 +94,17 @@ if __name__ == "__main__":
print('load test model : {}'.format(ops.model_path))
#----------------- 构建 handpose_x 2D关键点检测模型
handpose_2d_model = handpose_x_model()
handpose_2d_model = handpose_x_model(model_path = ops.handpose_x2d_model_path)
#----------------- 构建 yolo 检测模型
hand_detect_model = yolo_v3_hand_model()
hand_detect_model = yolo_v3_hand_model(model_path = ops.detect_model_path,model_arch = "yolo",conf_thres = 0.3)
# hand_detect_model = yolo_v3_hand_model()
#----------------- 构建 manopth
g_side = "right"
print('load model finished')
pose, shape = func.initiate("zero")
pre_useful_bone_len = np.zeros((1, 15)) # 骨架点信息
solver = LM_Solver(num_Iter=666, th_beta=shape.cpu(), th_pose=pose.cpu(), lb_target=pre_useful_bone_len,
solver = LM_Solver(num_Iter=99, th_beta=shape.cpu(), th_pose=pose.cpu(), lb_target=pre_useful_bone_len,
weight=1e-5)
pose0 = torch.eye(3).repeat(1, 16, 1, 1)
......@@ -110,8 +115,8 @@ if __name__ == "__main__":
root_rot_mode='rotmat',
joint_rot_mode='rotmat')
print('start ~')
point_fliter = smoother.OneEuroFilter(4.0, 0.0)
mesh_fliter = smoother.OneEuroFilter(4.0, 0.0)
point_fliter = smoother.OneEuroFilter(23.0, 0.0)
mesh_fliter = smoother.OneEuroFilter(23.0, 0.0)
shape_fliter = smoother.OneEuroFilter(1.5, 0.0)
#--------------------------- 配置点云
view_mat = np.array([[1.0, 0.0, 0.0],
......@@ -136,7 +141,6 @@ if __name__ == "__main__":
test_pcd = open3d.geometry.PointCloud() # 定义点云
viewer.add_geometry(test_pcd)
print('start pose estimate')
pre_uv = None
......@@ -145,7 +149,6 @@ if __name__ == "__main__":
shape_flag = True
#---------------------------------------------------------------- 预测图片
with torch.no_grad():
idx = 0
cap = cv2.VideoCapture(0) #一般usb默认相机号为 0，如果没有相机无法启动，如果相机不为0需要自行确定其编号。
......@@ -159,7 +162,7 @@ if __name__ == "__main__":
x_min,y_min,x_max,y_max,_ = hand_bbox[0]
w_ = max(abs(x_max-x_min),abs(y_max-y_min))
w_ = w_*1.5
w_ = w_*1.6
x_mid = (x_max+x_min)/2
y_mid = (y_max+y_min)/2
#
......@@ -195,9 +198,9 @@ if __name__ == "__main__":
#--------------------------------
img_lbox,ratio, dw, dh = letterbox(img.copy(), height=ops.img_size[0], color=(0,0,0))
if ops.vis:
cv2.namedWindow("letterbox",0)
cv2.imshow("letterbox",img_lbox)
# if ops.vis:
# cv2.namedWindow("letterbox",0)
# cv2.imshow("letterbox",img_lbox)
#-------------------------------- get heatmap
x1y1x2y2 = 0,0,0,0
......@@ -219,24 +222,23 @@ if __name__ == "__main__":
image_fusion = image_fusion.unsqueeze_(0)
if use_cuda:
image_fusion = image_fusion.cuda() # (bs, channel, h, w)
print("image_fusion size : {}".format(image_fusion.size()))
# print("image_fusion size : {}".format(image_fusion.size()))
#-------------------------------- # handpose_3d predict
pre_ = model_(image_fusion.float()) # 模型推理
output = pre_.cpu().detach().numpy()
output = np.squeeze(output)
print("handpose_3d output shape : {}".format(output.shape))
# print("handpose_3d output shape : {}".format(output.shape))
pre_3d_joints = output.reshape((21,3))
print("pre_3d_joints shape : {}".format(pre_3d_joints.shape))
# print("pre_3d_joints shape : {}".format(pre_3d_joints.shape))
if g_side == "left":
print("------------------->>. left")
pre_3d_joints[:,0] *=(-1.)
pre_3d_joints = torch.tensor(pre_3d_joints).squeeze(0)
pre_3d_joints= pre_3d_joints.cuda()
print(pre_3d_joints.size())
# print(pre_3d_joints.size())
#--------------------------------------------------------------------
# now_uv = result['uv'].clone().detach().cpu().numpy()[0, 0]
# now_uv = now_uv.astype(np.float)
......@@ -251,6 +253,8 @@ if __name__ == "__main__":
# fliter_ax.cla()
#
# filted_ax = vis.plot3d(flited_joints + new_tran, fliter_ax)
# pre_useful_bone_len = bone.caculate_length(pre_joints, label="useful")
pre_useful_bone_len = bone.caculate_length(pre_joints, label="useful")
NGEN = 0 # PSO 迭代次数
......@@ -274,7 +278,7 @@ 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[:,:,:]*(0.55)
hand_verts[:,:,:] = hand_verts[:,:,:]*(0.573)
# print(j3d_recon.size())
mesh.triangles = open3d.utility.Vector3iVector(mano.th_faces)
......@@ -312,7 +316,7 @@ if __name__ == "__main__":
pre_joints = pre_joints*70
pre_joints[:,1] -= 40
pre_joints[:,0] -= 0
print("pre_joints",pre_joints.shape)
# print("pre_joints",pre_joints.shape)
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])
......@@ -342,8 +346,8 @@ if __name__ == "__main__":
mask_2 = np.where(image_3d[:,:,2]!=120,1,0)
img_mask = np.logical_or(mask_0,mask_1)
img_mask = np.logical_or(img_mask,mask_2)
cv2.namedWindow("img_mask",0)
cv2.imshow("img_mask",img_mask.astype(np.float))
# cv2.namedWindow("img_mask",0)
# cv2.imshow("img_mask",img_mask.astype(np.float))
locs = np.where(img_mask != 0)
xx1 = np.min(locs[1])
......@@ -356,7 +360,7 @@ 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.
scale_ = ((x_max-x_min)/(xx2-xx1) + (y_max-y_min)/(yy2-yy1))/2.*1.01
w_3d_ = (xx2-xx1)*scale_
h_3d_ = (yy2-yy1)*scale_
x_mid_3d = (xx1+xx2)/2.
......@@ -372,7 +376,6 @@ if __name__ == "__main__":
crop_mask = np.where(crop_mask[:,:]>0.,1.,0.)
crop_mask = np.expand_dims(crop_mask, 2)
# print("-----------------------------ppppppppppppppwwww : ",crop_.shape,crop_mask.shape,h_r,w_r)
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)
......@@ -383,15 +386,6 @@ if __name__ == "__main__":
real_hand_h = h_r*2
depth_z = (model_hand_h/real_hand_h + model_hand_w/real_hand_w)/2.# 相对深度 z
#
# ff_img = (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_mask * crop_)
# img_yolo_x[int(y_mid - h_r ):int(y_mid + h_r ),int(x_mid - w_r ):int(x_mid + w_r ),:]
# cv2.namedWindow("crop_mask",0)
# cv2.imshow("crop_mask",crop_mask)
# cv2.rectangle(img_yolo_x, (x1,y1),(x2,y2), (0,255,0), 5) # 绘制空心矩形
# print("scale : {}".format(scale_))
# cv2.rectangle(image_3d, (xx1,yy1), (xx2,yy2), (255,0,255), 5) # 绘制空心矩形
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)
......
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