new show script
This commit is contained in:
fxia22
parent
2564328a9e
commit
43da7d3575
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import numpy as np
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import ctypes as ct
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import cv2
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import sys
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import argparse
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from datasets import ViewDataSet3D
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from completion import CompletionNet
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import torch
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from torchvision import datasets, transforms
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from torch.autograd import Variable
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import time
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from numpy import cos, sin
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import utils
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mousex,mousey=0.5,0.5
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changed=True
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pitch,yaw,x,y,z = 0,0,0,0,0
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roll = 0
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org_pitch, org_yaw, org_x, org_y, org_z = 0,0,0,0,0
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org_roll = 0
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mousedown = False
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clickstart = (0,0)
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fps = 0
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dll=np.ctypeslib.load_library('render_cuda_f','.')
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def onmouse(*args):
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global mousex,mousey,changed
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global pitch,yaw,x,y,z
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global org_pitch, org_yaw, org_x, org_y, org_z
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global org_roll, roll
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global clickstart
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if args[0] == cv2.EVENT_LBUTTONDOWN:
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org_pitch, org_yaw, org_x, org_y, org_z =\
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pitch,yaw,x,y,z
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clickstart = (mousex, mousey)
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if args[0] == cv2.EVENT_RBUTTONDOWN:
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org_roll = roll
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clickstart = (mousex, mousey)
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if (args[3] & cv2.EVENT_FLAG_LBUTTON):
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pitch = org_pitch + (mousex - clickstart[0])/10
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yaw = org_yaw + (mousey - clickstart[1])
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changed=True
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if (args[3] & cv2.EVENT_FLAG_RBUTTON):
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roll = org_roll + (mousex - clickstart[0])/50
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changed=True
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my=args[1]
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mx=args[2]
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mousex=mx/float(256)
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mousey=my/float(256 * 2)
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def showpoints(imgs, depths, poses, model, target, tdepth):
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global mousex,mousey,changed
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global pitch,yaw,x,y,z,roll
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global fps
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showsz = target.shape[0]
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show=np.zeros((showsz,showsz * 2,3),dtype='uint8')
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target_depth = np.zeros((showsz,showsz * 2)).astype(np.int32)
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target_depth[:] = (tdepth[:,:,0] * 12800).astype(np.int32)
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#from IPython import embed; embed()
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overlay = False
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show_depth = False
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cv2.namedWindow('show3d')
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cv2.moveWindow('show3d',0,0)
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cv2.setMouseCallback('show3d',onmouse)
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imgv = Variable(torch.zeros(1,3, showsz, showsz*2), volatile=True).cuda()
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maskv = Variable(torch.zeros(1,2, showsz, showsz*2), volatile=True).cuda()
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cpose = np.eye(4)
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def render(imgs, depths, pose, model, poses):
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global fps
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t0 = time.time()
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#target_depth[:] = 65535
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show[:] = 0
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before = time.time()
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for i in range(len(imgs)):
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#print(poses[0])
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pose_after = pose.dot(np.linalg.inv(poses[0])).dot(poses[i]).astype(np.float32)
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#from IPython import embed; embed()
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print('after',pose_after)
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dll.render(ct.c_int(imgs[i].shape[0]),
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ct.c_int(imgs[i].shape[1]),
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imgs[i].ctypes.data_as(ct.c_void_p),
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depths[i].ctypes.data_as(ct.c_void_p),
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pose_after.ctypes.data_as(ct.c_void_p),
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show.ctypes.data_as(ct.c_void_p),
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target_depth.ctypes.data_as(ct.c_void_p)
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)
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print('PC render time:', time.time() - before)
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if model:
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tf = transforms.ToTensor()
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before = time.time()
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source = tf(show)
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source_depth = tf(np.expand_dims(target_depth, 2).astype(np.float32)/65536 * 255)
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mean = torch.from_numpy(np.array([0.57441127, 0.54226291, 0.50356019]).astype(np.float32))
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mask = (torch.sum(source,0)==0).float().unsqueeze(0)
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print(source.size(), mask.size(), mean.size())
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source += mask.repeat(3,1,1) * mean.view(3,1,1).repeat(1,1024,2048)
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print(source_depth.size())
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print(mask.size())
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source_depth = torch.cat([source_depth, mask], 0)
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#print(source.size(), source_depth.size())
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imgv.data.copy_(source)
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maskv.data.copy_(source_depth)
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print('Transfer time', time.time() - before)
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before = time.time()
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recon = model(imgv, maskv)
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print('NNtime:', time.time() - before)
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before = time.time()
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show2 = recon.data.cpu().numpy()[0].transpose(1,2,0)
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show[:] = (show2[:] * 255).astype(np.uint8)
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print('Transfer to CPU time:', time.time() - before)
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t1 =time.time()
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t = t1-t0
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fps = 1/t
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cv2.waitKey(5)%256
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while True:
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if changed:
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alpha = yaw
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beta = pitch
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gamma = roll
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cpose = cpose.flatten()
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cpose[0] = cos(alpha) * cos(beta);
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cpose[1] = cos(alpha) * sin(beta) * sin(gamma) - sin(alpha) * cos(gamma);
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cpose[2] = cos(alpha) * sin(beta) * cos(gamma) + sin(alpha) * sin(gamma);
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cpose[3] = 0
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cpose[4] = sin(alpha) * cos(beta);
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cpose[5] = sin(alpha) * sin(beta) * sin(gamma) + cos(alpha) * cos(gamma);
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cpose[6] = sin(alpha) * sin(beta) * cos(gamma) - cos(alpha) * sin(gamma);
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cpose[7] = 0
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cpose[8] = -sin(beta);
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cpose[9] = cos(beta) * sin(gamma);
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cpose[10] = cos(beta) * cos(gamma);
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cpose[11] = 0
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cpose[12:16] = 0
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cpose[15] = 1
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cpose = cpose.reshape((4,4))
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cpose2 = np.eye(4)
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cpose2[0,3] = x
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cpose2[1,3] = y
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cpose2[2,3] = z
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cpose = np.dot(cpose, cpose2)
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print('cpose',cpose)
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render(imgs, depths, cpose.astype(np.float32), model, poses)
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changed = False
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if overlay:
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show_out = (show/2 + target/2).astype(np.uint8)
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elif show_depth:
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show_out = (target_depth * 10).astype(np.uint8)
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else:
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show_out = show
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#cv2.putText(show,'pitch %.3f yaw %.2f roll %.3f x %.2f y %.2f z %.2f'%(pitch, yaw, roll, x, y, z),(15,showsz-15),0,0.5,cv2.CV_RGB(255,255,255))
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cv2.putText(show,'pitch %.3f yaw %.2f roll %.3f x %.2f y %.2f z %.2f'%(pitch, yaw, roll, x, y, z),(15,showsz-15),0,0.5,(255,255,255))
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#cv2.putText(show,'fps %.1f'%(fps),(15,15),0,0.5,cv2.cv.CV_RGB(255,255,255))
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cv2.putText(show,'fps %.1f'%(fps),(15,15),0,0.5,(255,255,255))
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show_rgb = cv2.cvtColor(show_out, cv2.COLOR_BGR2RGB)
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cv2.imshow('show3d',show_rgb)
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cmd=cv2.waitKey(5)%256
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if cmd==ord('q'):
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break
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elif cmd == ord('w'):
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x -= 0.05
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changed = True
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elif cmd == ord('s'):
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x += 0.05
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changed = True
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elif cmd == ord('a'):
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y += 0.05
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changed = True
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elif cmd == ord('d'):
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y -= 0.05
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changed = True
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elif cmd == ord('z'):
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z += 0.01
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changed = True
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elif cmd == ord('x'):
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z -= 0.01
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changed = True
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elif cmd == ord('r'):
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pitch,yaw,x,y,z = 0,0,0,0,0
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roll = 0
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changed = True
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elif cmd == ord('t'):
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pose = poses[0]
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print('pose', pose)
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RT = pose.reshape((4,4))
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R = RT[:3,:3]
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T = RT[:3,-1]
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x,y,z = np.dot(np.linalg.inv(R),T)
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roll, pitch, yaw = (utils.rotationMatrixToEulerAngles(R))
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changed = True
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elif cmd == ord('o'):
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overlay = not overlay
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elif cmd == ord('f'):
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show_depth = not show_depth
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elif cmd == ord('v'):
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cv2.imwrite('save.jpg', show_rgb)
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def show_target(target_img):
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cv2.namedWindow('target')
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cv2.moveWindow('target',0,256 + 50)
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show_rgb = cv2.cvtColor(target_img, cv2.COLOR_BGR2RGB)
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cv2.imshow('target', show_rgb)
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if __name__=='__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--debug' , action='store_true', help='debug mode')
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parser.add_argument('--dataroot' , required = True, help='dataset path')
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parser.add_argument('--idx' , type = int, default = 0, help='index of data')
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parser.add_argument('--model' , type = str, default = '', help='path of model')
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opt = parser.parse_args()
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d = ViewDataSet3D(root=opt.dataroot, transform = np.array, mist_transform = np.array, seqlen = 5, off_3d = False)
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idx = opt.idx
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data = d[idx]
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sources = data[0]
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target = data[1]
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source_depths = data[2]
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target_depth = data[3]
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poses = [item.numpy() for item in data[-1]]
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print('target', np.max(target_depth[:]))
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model = None
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if opt.model != '':
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comp = CompletionNet()
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comp = torch.nn.DataParallel(comp).cuda()
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comp.load_state_dict(torch.load(opt.model))
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model = comp.module
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model.eval()
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print(model)
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print(poses[0])
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# print(source_depth)
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print(sources[0].shape, source_depths[0].shape)
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show_target(target)
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showpoints(sources, source_depths, poses, model, target, target_depth)
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import numpy as np
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import ctypes as ct
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import cv2
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import sys
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import argparse
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from datasets import ViewDataSet3D
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from completion import CompletionNet
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import torch
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from torchvision import datasets, transforms
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from torch.autograd import Variable
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import time
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from numpy import cos, sin
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import utils
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import torch.nn.functional as F
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mousex,mousey=0.5,0.5
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changed=True
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pitch,yaw,x,y,z = 0,0,0,0,0
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roll = 0
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org_pitch, org_yaw, org_x, org_y, org_z = 0,0,0,0,0
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org_roll = 0
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mousedown = False
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clickstart = (0,0)
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fps = 0
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dll=np.ctypeslib.load_library('render_cuda_f','.')
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def onmouse(*args):
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global mousex,mousey,changed
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global pitch,yaw,x,y,z
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global org_pitch, org_yaw, org_x, org_y, org_z
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global org_roll, roll
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global clickstart
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if args[0] == cv2.EVENT_LBUTTONDOWN:
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org_pitch, org_yaw, org_x, org_y, org_z =\
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pitch,yaw,x,y,z
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clickstart = (mousex, mousey)
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if args[0] == cv2.EVENT_RBUTTONDOWN:
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org_roll = roll
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clickstart = (mousex, mousey)
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if (args[3] & cv2.EVENT_FLAG_LBUTTON):
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pitch = org_pitch + (mousex - clickstart[0])/10
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yaw = org_yaw + (mousey - clickstart[1])
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changed=True
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if (args[3] & cv2.EVENT_FLAG_RBUTTON):
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roll = org_roll + (mousex - clickstart[0])/50
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changed=True
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my=args[1]
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mx=args[2]
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mousex=mx/float(256)
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mousey=my/float(256 * 2)
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def showpoints(imgs, depths, poses, model, target, tdepth):
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global mousex,mousey,changed
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global pitch,yaw,x,y,z,roll
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global fps
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showsz = target.shape[0]
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show=np.zeros((showsz,showsz * 2,3),dtype='uint8')
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target_depth = np.zeros((showsz,showsz * 2)).astype(np.int32)
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target_depth[:] = (tdepth[:,:,0] * 12800).astype(np.int32)
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#from IPython import embed; embed()
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overlay = False
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show_depth = False
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cv2.namedWindow('show3d')
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cv2.moveWindow('show3d',0,0)
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cv2.setMouseCallback('show3d',onmouse)
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imgv = Variable(torch.zeros(1,3, showsz, showsz*2), volatile=True).cuda()
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maskv = Variable(torch.zeros(1,2, showsz, showsz*2), volatile=True).cuda()
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cpose = np.eye(4)
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def render(imgs, depths, pose, model, poses):
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global fps
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t0 = time.time()
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#target_depth[:] = 65535
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show[:] = 0
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before = time.time()
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for i in range(len(imgs)):
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#print(poses[0])
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pose_after = pose.dot(np.linalg.inv(poses[0])).dot(poses[i]).astype(np.float32)
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#from IPython import embed; embed()
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print('after',pose_after)
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dll.render(ct.c_int(imgs[i].shape[0]),
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ct.c_int(imgs[i].shape[1]),
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imgs[i].ctypes.data_as(ct.c_void_p),
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depths[i].ctypes.data_as(ct.c_void_p),
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pose_after.ctypes.data_as(ct.c_void_p),
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show.ctypes.data_as(ct.c_void_p),
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target_depth.ctypes.data_as(ct.c_void_p)
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)
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print('PC render time:', time.time() - before)
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if model:
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tf = transforms.ToTensor()
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before = time.time()
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source = tf(show)
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source_depth = tf(np.expand_dims(target_depth, 2).astype(np.float32)/65536 * 255)
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mean = torch.from_numpy(np.array([0.57441127, 0.54226291, 0.50356019]).astype(np.float32))
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source = source.unsqueeze(0)
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source_depth = source_depth.unsqueeze(0)
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print(source.size(), source_depth.size())
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mask = (torch.sum(source,1)==0).float().unsqueeze(1)
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print(mask.size())
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source += mask.repeat(1,3,1,1) * F.upsample(F.max_pool2d(source, 2), scale_factor=2, mode = 'nearest').data
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mask2 = (torch.sum(source,1)==0).float().unsqueeze(1)
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source += mask2.repeat(1,3,1,1) * F.upsample(F.max_pool2d(source, 4), scale_factor=4, mode = 'nearest').data
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mask3 = (torch.sum(source,1)==0).float().unsqueeze(1)
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source += mask3.repeat(1,3,1,1) * F.upsample(F.max_pool2d(source, 8), scale_factor=8, mode = 'nearest').data
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mask4 = (torch.sum(source,1)==0).float().unsqueeze(1)
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source += mask4.repeat(1,3,1,1) * mean.view(1,3,1,1).repeat(1,1,1024,2048)
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source_depth = torch.cat([source_depth, mask], 0)
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#print(source.size(), source_depth.size())
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imgv.data.copy_(source)
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maskv.data.copy_(source_depth)
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print('Transfer time', time.time() - before)
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before = time.time()
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recon = model(imgv, maskv)
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print('NNtime:', time.time() - before)
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before = time.time()
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show2 = recon.data.cpu().numpy()[0].transpose(1,2,0)
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show[:] = (show2[:] * 255).astype(np.uint8)
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print('Transfer to CPU time:', time.time() - before)
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t1 =time.time()
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t = t1-t0
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fps = 1/t
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cv2.waitKey(5)%256
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while True:
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if changed:
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alpha = yaw
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beta = pitch
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gamma = roll
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cpose = cpose.flatten()
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cpose[0] = cos(alpha) * cos(beta);
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||||
cpose[1] = cos(alpha) * sin(beta) * sin(gamma) - sin(alpha) * cos(gamma);
|
||||
cpose[2] = cos(alpha) * sin(beta) * cos(gamma) + sin(alpha) * sin(gamma);
|
||||
cpose[3] = 0
|
||||
|
||||
cpose[4] = sin(alpha) * cos(beta);
|
||||
cpose[5] = sin(alpha) * sin(beta) * sin(gamma) + cos(alpha) * cos(gamma);
|
||||
cpose[6] = sin(alpha) * sin(beta) * cos(gamma) - cos(alpha) * sin(gamma);
|
||||
cpose[7] = 0
|
||||
|
||||
cpose[8] = -sin(beta);
|
||||
cpose[9] = cos(beta) * sin(gamma);
|
||||
cpose[10] = cos(beta) * cos(gamma);
|
||||
cpose[11] = 0
|
||||
|
||||
cpose[12:16] = 0
|
||||
cpose[15] = 1
|
||||
|
||||
cpose = cpose.reshape((4,4))
|
||||
|
||||
cpose2 = np.eye(4)
|
||||
cpose2[0,3] = x
|
||||
cpose2[1,3] = y
|
||||
cpose2[2,3] = z
|
||||
|
||||
cpose = np.dot(cpose, cpose2)
|
||||
|
||||
print('cpose',cpose)
|
||||
render(imgs, depths, cpose.astype(np.float32), model, poses)
|
||||
changed = False
|
||||
|
||||
if overlay:
|
||||
show_out = (show/2 + target/2).astype(np.uint8)
|
||||
elif show_depth:
|
||||
show_out = (target_depth * 10).astype(np.uint8)
|
||||
else:
|
||||
show_out = show
|
||||
|
||||
#cv2.putText(show,'pitch %.3f yaw %.2f roll %.3f x %.2f y %.2f z %.2f'%(pitch, yaw, roll, x, y, z),(15,showsz-15),0,0.5,cv2.CV_RGB(255,255,255))
|
||||
cv2.putText(show,'pitch %.3f yaw %.2f roll %.3f x %.2f y %.2f z %.2f'%(pitch, yaw, roll, x, y, z),(15,showsz-15),0,0.5,(255,255,255))
|
||||
#cv2.putText(show,'fps %.1f'%(fps),(15,15),0,0.5,cv2.cv.CV_RGB(255,255,255))
|
||||
cv2.putText(show,'fps %.1f'%(fps),(15,15),0,0.5,(255,255,255))
|
||||
|
||||
show_rgb = cv2.cvtColor(show_out, cv2.COLOR_BGR2RGB)
|
||||
cv2.imshow('show3d',show_rgb)
|
||||
|
||||
cmd=cv2.waitKey(5)%256
|
||||
|
||||
if cmd==ord('q'):
|
||||
break
|
||||
elif cmd == ord('w'):
|
||||
x -= 0.05
|
||||
changed = True
|
||||
elif cmd == ord('s'):
|
||||
x += 0.05
|
||||
changed = True
|
||||
elif cmd == ord('a'):
|
||||
y += 0.05
|
||||
changed = True
|
||||
elif cmd == ord('d'):
|
||||
y -= 0.05
|
||||
changed = True
|
||||
elif cmd == ord('z'):
|
||||
z += 0.01
|
||||
changed = True
|
||||
elif cmd == ord('x'):
|
||||
z -= 0.01
|
||||
changed = True
|
||||
|
||||
elif cmd == ord('r'):
|
||||
pitch,yaw,x,y,z = 0,0,0,0,0
|
||||
roll = 0
|
||||
changed = True
|
||||
elif cmd == ord('t'):
|
||||
pose = poses[0]
|
||||
print('pose', pose)
|
||||
RT = pose.reshape((4,4))
|
||||
R = RT[:3,:3]
|
||||
T = RT[:3,-1]
|
||||
|
||||
x,y,z = np.dot(np.linalg.inv(R),T)
|
||||
roll, pitch, yaw = (utils.rotationMatrixToEulerAngles(R))
|
||||
|
||||
changed = True
|
||||
|
||||
|
||||
elif cmd == ord('o'):
|
||||
overlay = not overlay
|
||||
elif cmd == ord('f'):
|
||||
show_depth = not show_depth
|
||||
elif cmd == ord('v'):
|
||||
cv2.imwrite('save.jpg', show_rgb)
|
||||
|
||||
|
||||
def show_target(target_img):
|
||||
cv2.namedWindow('target')
|
||||
cv2.moveWindow('target',0,256 + 50)
|
||||
show_rgb = cv2.cvtColor(target_img, cv2.COLOR_BGR2RGB)
|
||||
cv2.imshow('target', show_rgb)
|
||||
|
||||
if __name__=='__main__':
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--debug' , action='store_true', help='debug mode')
|
||||
parser.add_argument('--dataroot' , required = True, help='dataset path')
|
||||
parser.add_argument('--idx' , type = int, default = 0, help='index of data')
|
||||
parser.add_argument('--model' , type = str, default = '', help='path of model')
|
||||
|
||||
opt = parser.parse_args()
|
||||
d = ViewDataSet3D(root=opt.dataroot, transform = np.array, mist_transform = np.array, seqlen = 5, off_3d = False)
|
||||
idx = opt.idx
|
||||
|
||||
data = d[idx]
|
||||
|
||||
sources = data[0]
|
||||
target = data[1]
|
||||
source_depths = data[2]
|
||||
target_depth = data[3]
|
||||
poses = [item.numpy() for item in data[-1]]
|
||||
print('target', np.max(target_depth[:]))
|
||||
|
||||
model = None
|
||||
if opt.model != '':
|
||||
comp = CompletionNet()
|
||||
comp = torch.nn.DataParallel(comp).cuda()
|
||||
comp.load_state_dict(torch.load(opt.model))
|
||||
model = comp.module
|
||||
model.eval()
|
||||
print(model)
|
||||
print(poses[0])
|
||||
# print(source_depth)
|
||||
print(sources[0].shape, source_depths[0].shape)
|
||||
show_target(target)
|
||||
showpoints(sources, source_depths, poses, model, target, target_depth)
|
||||
Loading…
Reference in New Issue