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import _init_paths
import argparse
import os
from PIL import Image
import numpy as np
import numpy.ma as ma
import torch
import random
import torchvision.transforms as transforms
import yaml
import cv2
from torch.autograd import Variable
from lib.network import PoseNet, PoseRefineNet
from lib.loss import Loss
from lib.loss_refiner import Loss_refine
from lib.knn.__init__ import KNearestNeighbor
from lib.transformations import euler_matrix, quaternion_matrix, quaternion_from_matrix
import copy
import os
from PIL import Image
import matplotlib.pyplot as plt
border_list = [-1, 40, 80, 120, 160, 200, 240, 280, 320, 360, 400, 440, 480, 520, 560, 600, 640, 680]
img_width = 480
img_length = 640
def mask_to_bbox(mask):
mask = mask.astype(np.uint8)
_, contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
x = 0
y = 0
w = 0
h = 0
for contour in contours:
tmp_x, tmp_y, tmp_w, tmp_h = cv2.boundingRect(contour)
if tmp_w * tmp_h > w * h:
x = tmp_x
y = tmp_y
w = tmp_w
h = tmp_h
return [x, y, w, h]
def get_bbox(bbox):
bbx = [bbox[1], bbox[1] + bbox[3], bbox[0], bbox[0] + bbox[2]]
if bbx[0] < 0:
bbx[0] = 0
if bbx[1] >= 480:
bbx[1] = 479
if bbx[2] < 0:
bbx[2] = 0
if bbx[3] >= 640:
bbx[3] = 639
rmin, rmax, cmin, cmax = bbx[0], bbx[1], bbx[2], bbx[3]
r_b = rmax - rmin
for tt in range(len(border_list)):
if r_b > border_list[tt] and r_b < border_list[tt + 1]:
r_b = border_list[tt + 1]
break
c_b = cmax - cmin
for tt in range(len(border_list)):
if c_b > border_list[tt] and c_b < border_list[tt + 1]:
c_b = border_list[tt + 1]
break
center = [int((rmin + rmax) / 2), int((cmin + cmax) / 2)]
rmin = center[0] - int(r_b / 2)
rmax = center[0] + int(r_b / 2)
cmin = center[1] - int(c_b / 2)
cmax = center[1] + int(c_b / 2)
if rmin < 0:
delt = -rmin
rmin = 0
rmax += delt
if cmin < 0:
delt = -cmin
cmin = 0
cmax += delt
if rmax > 480:
delt = rmax - 480
rmax = 480
rmin -= delt
if cmax > 640:
delt = cmax - 640
cmax = 640
cmin -= delt
return rmin, rmax, cmin, cmax
def ply_vtx(path):
f = open(path)
assert f.readline().strip() == "ply"
f.readline()
f.readline()
N = int(f.readline().split()[-1])
while f.readline().strip() != "end_header":
continue
pts = []
for _ in range(N):
pts.append(np.float32(f.readline().split()[:3]))
return np.array(pts)
def get_data(data_root, item, seg_root, obj, model_root, add_noise, noise_trans):
img = Image.open('{0}/rgb/{1}.png'.format(data_root, item))
ori_img = np.array(img)
depth = np.array(Image.open('{0}/depth/{1}.png'.format(data_root, item)))
label = np.array(Image.open('{0}/{1}_label.png'.format(seg_root, item)))
print('{0}/depth/{1}.png'.format(data_root, item))
print('{0}/{1}_label.png'.format(seg_root, item))
print('{0}/gt.yml'.format(data_root))
print('{0}/obj_{1}.ply'.format(model_root, '%02d' % obj))
meta_file = open('{0}/gt.yml'.format(data_root), 'r')
meta = yaml.load(meta_file, Loader=yaml.FullLoader)
pt = ply_vtx('{0}/obj_{1}.ply'.format(model_root, '%02d' % obj))
trancolor = transforms.ColorJitter(0.2, 0.2, 0.2, 0.05)
norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
num = 500
xmap = np.array([[j for i in range(640)] for j in range(480)])
ymap = np.array([[i for i in range(640)] for j in range(480)])
cam_cx = 325.26110
cam_cy = 242.04899
cam_fx = 572.41140
cam_fy = 573.57043
num_pt_mesh_large = 500
num_pt_mesh_small = 500
rank = int(item)
if obj == 2:
for i in range(0, len(meta[obj])):
if meta[rank][i]['obj_id'] == 2:
meta = meta[rank][i]
break
else:
meta = meta[rank][0]
mask_depth = ma.getmaskarray(ma.masked_not_equal(depth, 0))
mask_label = ma.getmaskarray(ma.masked_equal(label, np.array(255)))
mask = mask_label * mask_depth
if add_noise:
img = trancolor(img)
img = np.array(img)[:, :, :3]
img = np.transpose(img, (2, 0, 1))
img_masked = img
rmin, rmax, cmin, cmax = get_bbox(mask_to_bbox(mask_label))
img_masked = img_masked[:, rmin:rmax, cmin:cmax]
target_r = np.resize(np.array(meta['cam_R_m2c']), (3, 3))
target_t = np.array(meta['cam_t_m2c'])
add_t = np.array([random.uniform(-noise_trans, noise_trans) for i in range(3)])
choose = mask[rmin:rmax, cmin:cmax].flatten().nonzero()[0]
if len(choose) == 0:
cc = torch.LongTensor([0])
return(cc, cc, cc, cc, cc, cc)
if len(choose) > num:
c_mask = np.zeros(len(choose), dtype=int)
c_mask[:num] = 1
np.random.shuffle(c_mask)
choose = choose[c_mask.nonzero()]
else:
choose = np.pad(choose, (0, num - len(choose)), 'wrap')
depth_masked = depth[rmin:rmax, cmin:cmax].flatten()[choose][:, np.newaxis].astype(np.float32)
xmap_masked = xmap[rmin:rmax, cmin:cmax].flatten()[choose][:, np.newaxis].astype(np.float32)
ymap_masked = ymap[rmin:rmax, cmin:cmax].flatten()[choose][:, np.newaxis].astype(np.float32)
choose = np.array([choose])
cam_scale = 1.0
pt2 = depth_masked / cam_scale
pt0 = (ymap_masked - cam_cx) * pt2 / cam_fx
pt1 = (xmap_masked - cam_cy) * pt2 / cam_fy
cloud = np.concatenate((pt0, pt1, pt2), axis=1)
cloud = cloud / 1000.0
print(cloud.shape)
if add_noise:
cloud = np.add(cloud, add_t)
model_points = pt/ 1000.0
dellist = [j for j in range(0, len(model_points))]
dellist = random.sample(dellist, len(model_points) - num_pt_mesh_small)
model_points = np.delete(model_points, dellist, axis=0)
target = np.dot(model_points, target_r.T)
if add_noise:
target = np.add(target, target_t / 1000.0 + add_t)
out_t = target_t / 1000.0 + add_t
else:
target = np.add(target, target_t / 1000.0)
out_t = target_t / 1000.0
return torch.from_numpy(cloud.astype(np.float32)), \
torch.LongTensor(choose.astype(np.int32)), \
norm(torch.from_numpy(img_masked.astype(np.float32))), \
torch.from_numpy(target.astype(np.float32)), \
torch.from_numpy(model_points.astype(np.float32)), \
torch.LongTensor([obj])
parser = argparse.ArgumentParser()
parser.add_argument('--data_root', type=str, default = '', help='ycb or linemod')
parser.add_argument('--seg_root', type=str, default = '', help='ycb or linemod')
parser.add_argument('--model_root', type=str, default = '', help='ycb or linemod')
parser.add_argument('--item', type=str, default = '', help='')
parser.add_argument('--obj', type=int, default=1, help='')
parser.add_argument('--model', type=str, default = '', help='resume PoseNet model')
parser.add_argument('--refine_model', type=str, default = '', help='resume PoseRefineNet model')
parser.add_argument('--output', type=str, default = '', help='resume PoseRefineNet model')
opt = parser.parse_args()
estimator = PoseNet(num_points = 500, num_obj = 13)
estimator.cuda()
refiner = PoseRefineNet(num_points = 500, num_obj = 13)
refiner.cuda()
estimator.load_state_dict(torch.load(opt.model))
refiner.load_state_dict(torch.load(opt.refine_model))
bs = 1
num_points = 500
iteration = 4
knn = KNearestNeighbor(1)
for i in range(1):
points, choose, img, target, model_points, idx = get_data(opt.data_root, opt.item, opt.seg_root, opt.obj, opt.model_root, True, 0.0)
if len(points.size()) == 1:
print('No.{0} NOT Pass! Lost detection!'.format(i))
continue
points, choose, img, target, model_points, idx = Variable(points.unsqueeze(0)).cuda(), \
Variable(choose.unsqueeze(0)).cuda(), \
Variable(img.unsqueeze(0)).cuda(), \
Variable(target.unsqueeze(0)).cuda(), \
Variable(model_points.unsqueeze(0)).cuda(), \
Variable(idx.unsqueeze(0)).cuda()
pred_r, pred_t, pred_c, emb = estimator(img, points, choose, idx)
pred_r = pred_r / torch.norm(pred_r, dim=2).view(1, num_points, 1)
pred_c = pred_c.view(bs, num_points)
how_max, which_max = torch.max(pred_c, 1)
pred_t = pred_t.view(bs * num_points, 1, 3)
my_r = pred_r[0][which_max[0]].view(-1).cpu().data.numpy()
my_t = (points.view(bs * num_points, 1, 3) + pred_t)[which_max[0]].view(-1).cpu().data.numpy()
my_pred = np.append(my_r, my_t)
for ite in range(0, iteration):
Tt = Variable(torch.from_numpy(my_t.astype(np.float32))).cuda().view(1, 3).repeat(num_points, 1).contiguous().view(1, num_points, 3)
my_mat = quaternion_matrix(my_r)
R = Variable(torch.from_numpy(my_mat[:3, :3].astype(np.float32))).cuda().view(1, 3, 3)
my_mat[0:3, 3] = my_t
new_points = torch.bmm((points - Tt), R).contiguous()
pred_r, pred_t = refiner(new_points, emb, idx)
pred_r = pred_r.view(1, 1, -1)
pred_r = pred_r / (torch.norm(pred_r, dim=2).view(1, 1, 1))
my_r_2 = pred_r.view(-1).cpu().data.numpy()
my_t_2 = pred_t.view(-1).cpu().data.numpy()
my_mat_2 = quaternion_matrix(my_r_2)
my_mat_2[0:3, 3] = my_t_2
my_mat_final = np.dot(my_mat, my_mat_2)
my_r_final = copy.deepcopy(my_mat_final)
my_r_final[0:3, 3] = 0
my_r_final = quaternion_from_matrix(my_r_final, True)
my_t_final = np.array([my_mat_final[0][3], my_mat_final[1][3], my_mat_final[2][3]])
my_pred = np.append(my_r_final, my_t_final)
my_r = my_r_final
my_t = my_t_final
model_points = model_points[0].cpu().detach().numpy()
my_r = quaternion_matrix(my_r)[:3, :3]
pred = np.dot(model_points, my_r.T) + my_t
target = target[0].cpu().detach().numpy()
pred = pred*1000.0
target = target*1000.0
cam_scale = 1.0
cam_cx = 325.26110
cam_cy = 242.04899
cam_fx = 572.41140
cam_fy = 573.57043
depth_masked = pred[:,2] * cam_scale
ymap_masked_pred = pred[:,0] * cam_fx / pred[:,2] + cam_cx
xmap_masked_pred = pred[:,1] * cam_fy / pred[:,2] + cam_cy
depth_masked = target[:,2] * cam_scale
ymap_masked_target = target[:,0] * cam_fx / target[:,2] + cam_cx
xmap_masked_target = target[:,1] * cam_fy / target[:,2] + cam_cy
image = Image.open('{0}/rgb/{1}.png'.format(opt.data_root, opt.item))
plt.figure(figsize=(8,12))
plt.imshow(image)
plt.scatter(ymap_masked_pred,xmap_masked_pred,marker='.',c='r',alpha=1)
plt.axis('on')
plt.title('image')
plt.show()
plt.savefig('{0}/test_pred.png'.format(opt.output))
plt.figure(figsize=(8,12))
plt.imshow(image)
plt.scatter(ymap_masked_target,xmap_masked_target,marker='.',c='r',alpha=1)
plt.axis('on')
plt.title('image')
plt.show()
plt.savefig('{0}/test_target.png'.format(opt.output))
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