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generate_my_dataset.py
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# coding=utf-8
# Author: Tomas Hodan ([email protected])
# Center for Machine Perception, Czech Technical University in Prague
# Visualizes the object models at the ground truth poses.
# 生成适合Brachmann论文的数据集
# depth_noseg 直接使用
# info TODO 需要生成
# obj TODO 需要生成
# rgb_noseg
# seg TODO 渲染生成 1
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import cv2
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from pysixd import inout, misc, renderer
from params.dataset_params import get_dataset_params
dataset = 'hinterstoisser'
#dataset = 'tless'
# dataset = 'tudlight'
# dataset = 'rutgers'
# dataset = 'tejani'
# dataset = 'doumanoglou'
# dataset = 'toyotalight'
# Dataset parameters
dp = get_dataset_params(dataset)
# Select IDs of scenes, images and GT poses to be processed.
# Empty list [] means that all IDs will be used.
scene_ids = [1]
im_ids = []
gt_ids = []
# Indicates whether to render RGB image
vis_rgb = True
# Indicates whether to resolve visibility in the rendered RGB image (using
# depth renderings). If True, only the part of object surface, which is not
# occluded by any other modeled object, is visible. If False, RGB renderings
# of individual objects are blended together.
vis_rgb_resolve_visib = True
# Indicates whether to render depth image
vis_depth = False
# If to use the original model color
vis_orig_color = False
# Define new object colors (used if vis_orig_colors == False)
colors = inout.load_yaml('../data/colors.yml')
# Path masks for output images
training_dataset_path = '/media/sun/Data1/Brachmann/test/{}/training'
test_dataset_path = '/media/sun/Data1/Brachmann/test/{}/test'
# Output path masks
out_rgb_mpath = training_dataset_path + '/{:02d}/rgb_noseg/color_{:05d}.png'
out_depth_mpath = training_dataset_path + '/{:02d}/depth_noseg/depth_{:05d}.png'
out_seg_mpath = training_dataset_path + '/{:02d}/seg/seg_{:05d}.png'
out_obj_mpath = training_dataset_path + '/{:02d}/obj/obj_{:05d}.png'
out_info_path = training_dataset_path + '/{:02d}/info/info_{:05}.txt'
test_rgb_mpath = test_dataset_path + '/{:02d}/rgb_noseg/color_{:05d}.png'
test_depth_mpath = test_dataset_path + '/{:02d}/depth_noseg/depth_{:05d}.png'
test_seg_mpath = test_dataset_path + '/{:02d}/seg/seg_{:05d}.png'
test_obj_mpath = test_dataset_path + '/{:02d}/obj/obj_{:05d}.png'
test_info_path = test_dataset_path + '/{:02d}/info/info_{:05}.txt'
now_test = False
# out_obj_gt_path = dataset_path+'/{:02d}/gt.yml'
# out_views_vis_mpath = dataset_path+'/views_radius={}.ply'
# base_path = "/media/sun/Data1/Brachmann/test"
# vis_rgb_mpath = base_path + 'output/vis_gt_poses_{}/{:02d}/{:04d}.jpg'
# vis_depth_mpath = base_path + '/output/vis_gt_poses_{}/{:02d}/{:04d}_depth_diff.jpg'
# Whether to consider only the specified subset of images
use_image_subset = False
# Subset of images to be considered
# 使用部分图片集合
if use_image_subset:
im_ids_sets = inout.load_yaml(dp['test_set_fpath'])
else:
im_ids_sets = None
scene_ids_curr = range(1, dp['scene_count'] + 1)
if scene_ids:
scene_ids_curr = set(scene_ids_curr).intersection(scene_ids) # 求交集
for scene_id in scene_ids_curr:
# 创建文件夹
misc.ensure_dir(os.path.dirname(out_rgb_mpath.format(dataset, scene_id, 0)))
misc.ensure_dir(os.path.dirname(out_depth_mpath.format(dataset, scene_id, 0)))
misc.ensure_dir(os.path.dirname(out_seg_mpath.format(dataset, scene_id, 0)))
misc.ensure_dir(os.path.dirname(out_obj_mpath.format(dataset, scene_id, 0)))
misc.ensure_dir(os.path.dirname(out_info_path.format(dataset, scene_id, 0)))
# Load scene info and gt poses
# 加载场景信息，和gt pose
scene_info = inout.load_info(dp['scene_info_mpath'].format(scene_id))
scene_gt = inout.load_gt(dp['scene_gt_mpath'].format(scene_id))
models_info = inout.load_yaml(dp['models_info_path'])
# Load models of objects that appear in the current scene
# 加载模型
obj_ids = set([gt['obj_id'] for gts in scene_gt.values() for gt in gts])
models = {}
for obj_id in obj_ids:
models[obj_id] = inout.load_ply(dp['model_mpath'].format(obj_id))
# Considered subset of images for the current scene
if im_ids_sets is not None:
im_ids_curr = im_ids_sets[scene_id]
else:
im_ids_curr = sorted(scene_info.keys())
if im_ids:
im_ids_curr = set(im_ids_curr).intersection(im_ids)
# 创建数据集
for im_id in im_ids_curr:
print('scene: {}, im: {}'.format(scene_id, im_id))
# Load the images
rgb = inout.load_im(dp['test_rgb_mpath'].format(scene_id, im_id))
depth = inout.load_depth(dp['test_depth_mpath'].format(scene_id, im_id))
depth = depth.astype(np.float32) # [mm]
depth *= dp['cam']['depth_scale'] # to [mm]
# Render the objects at the ground truth poses
im_size = (depth.shape[1], depth.shape[0])
# ren_rgb_info = np.zeros(rgb.shape, np.uint8)
ren_depth = np.zeros(depth.shape, np.float32)
black_out_mask = np.zeros(depth.shape, np.float32)
white_mask = np.ones(depth.shape, np.float32) * 255
img_obj = np.zeros(rgb.shape,np.float32)
gt_ids_curr = range(len(scene_gt[im_id]))
if gt_ids:
gt_ids_curr = set(gt_ids_curr).intersection(gt_ids)
for gt_id in gt_ids_curr:
gt = scene_gt[im_id][gt_id]
obj_id = gt['obj_id']
if vis_orig_color:
color = (1, 1, 1)
else:
# color = tuple(colors[(obj_id - 1) % len(colors)]) # 从颜色表中挑颜色,这里的颜色是渲染表面颜色
color = (1, 1, 1) # 这里我们直接使用白色
color_uint8 = tuple([int(255 * c) for c in color])
model = models[gt['obj_id']]
K = scene_info[im_id]['cam_K']
R = gt['cam_R_m2c']
t = gt['cam_t_m2c']
# Rendering
if vis_rgb:
if vis_orig_color:
m_rgb = renderer.render(model, im_size, K, R, t, mode='rgb')
else:
m_rgb = renderer.render(model, im_size, K, R, t, mode='rgb',
surf_color=color)
if vis_depth or (vis_rgb and vis_rgb_resolve_visib):
m_depth = renderer.render(model, im_size, K, R, t, mode='depth')
# Get mask of the surface parts that are closer than the
# surfaces rendered before
visible_mask = np.logical_or(ren_depth == 0, m_depth < ren_depth)
mask = np.logical_and(m_depth != 0, visible_mask)
# plt.imshow(mask)
ren_depth[mask] = m_depth[mask].astype(ren_depth.dtype)
#################### 计算imgObj ##################
for x in range(im_size[0]):
for y in range(im_size[1]):
depth_value = ren_depth[y,x]
if depth_value <= 0.00000000000001:
img_obj[y,x,0] = 0
img_obj[y,x,1] = 0
img_obj[y,x,2] = 0
continue
pt3d = np.dot(np.linalg.inv(K), np.array([depth_value * x, depth_value * y, depth_value]))
# print pt3d.shape
# print pt3d
pt3d = pt3d - t.squeeze() #
pt3d = np.dot(R.transpose() , pt3d)
img_obj[y, x, 0] = pt3d[0]
img_obj[y, x, 1] = pt3d[1]
img_obj[y, x, 2] = pt3d[2]
#################### 计算imgObj ##################
black_out_mask[mask] = white_mask[mask].astype(black_out_mask.dtype)
#################### save imgs##################
if im_id > 300:
now_test = True
# print "out"
if(not now_test):
inout.save_im(out_rgb_mpath.format(dataset, scene_id, im_id),
rgb.astype(np.uint8))
inout.save_depth(out_depth_mpath.format(dataset,obj_id, im_id), depth)
from numpngw import write_png
write_png(out_obj_mpath.format(dataset, scene_id, im_id), img_obj.astype(np.uint16))
inout.save_im(out_seg_mpath.format(dataset, scene_id, im_id),
black_out_mask.astype(np.uint8))
R_str = [[str(num) for num in item] for item in R.tolist() ]
R_str = [" ".join(item) for item in R_str]
R_str = [item+'\n' for item in R_str]
t_str = [str(item/1000) for item in t.squeeze().tolist()]
t_str = " ".join(t_str)
size_x = models_info[gt['obj_id']]["size_x"]
size_y = models_info[gt['obj_id']]["size_y"]
size_z = models_info[gt['obj_id']]["size_z"]
extend = [size_x, size_y, size_z]
extend_str = [str(item/1000) for item in extend]
extend_str = " ".join(extend_str)
obj_name = "{:02d}"
# def save_info_Barchmann(path, content):
with open(out_info_path.format(dataset, scene_id, im_id), 'w') as f:
f.write("image size \n")
f.write("640 480\n")
f.write(str(obj_name.format(scene_id))+"\n")
f.write("rotation: \n")
f.writelines(R_str)
f.write("center: \n")
f.write(t_str+'\n')
f.write("extent: \n")
f.write(extend_str+'\n')
else:
if im_id > 500:
print "Finish job"
sys.exit(0)
# print "fuck"
misc.ensure_dir(os.path.dirname(test_rgb_mpath.format(dataset, scene_id, 0)))
misc.ensure_dir(os.path.dirname(test_depth_mpath.format(dataset, scene_id, 0)))
misc.ensure_dir(os.path.dirname(test_seg_mpath.format(dataset, scene_id, 0)))
misc.ensure_dir(os.path.dirname(test_obj_mpath.format(dataset, scene_id, 0)))
misc.ensure_dir(os.path.dirname(test_info_path.format(dataset, scene_id, 0)))
inout.save_im(test_rgb_mpath.format(dataset, scene_id, im_id),
rgb.astype(np.uint8))
inout.save_depth(test_depth_mpath.format(dataset, obj_id, im_id), depth)
from numpngw import write_png
write_png(test_obj_mpath.format(dataset, scene_id, im_id), img_obj.astype(np.uint16))
inout.save_im(test_seg_mpath.format(dataset, scene_id, im_id),
black_out_mask.astype(np.uint8))
R_str = [[str(num) for num in item] for item in R.tolist()]
R_str = [" ".join(item) for item in R_str]
R_str = [item + '\n' for item in R_str]
t_str = [str(item / 1000) for item in t.squeeze().tolist()]
t_str = " ".join(t_str)
size_x = models_info[gt['obj_id']]["size_x"]
size_y = models_info[gt['obj_id']]["size_y"]
size_z = models_info[gt['obj_id']]["size_z"]
extend = [size_x, size_y, size_z]
extend_str = [str(item / 1000) for item in extend]
extend_str = " ".join(extend_str)
obj_name = "{:02d}"
# def save_info_Barchmann(path, content):
with open(test_info_path.format(dataset, scene_id, im_id), 'w') as f:
f.write("image size \n")
f.write("640 480\n")
f.write(str(obj_name.format(scene_id)) + "\n")
f.write("rotation: \n")
f.writelines(R_str)
f.write("center: \n")
f.write(t_str + '\n')
f.write("extent: \n")
f.write(extend_str + '\n')
# # Save RGB visualization
# if vis_rgb:
# # vis_im_rgb = 0.5 * rgb.astype(np.float32) +\
# # 0.5 * ren_rgb + \
# # 1.0 * ren_rgb_info
# # vis_im_rgb[vis_im_rgb > 255] = 255
#
# inout.save_im(vis_rgb_mpath.format(dataset, scene_id, im_id),
# black_out_mask.astype(np.uint8))
#
# # Save image of depth differences
# if vis_depth:
# # Calculate the depth difference at pixels where both depth maps
# # are valid
# valid_mask = (depth > 0) * (ren_depth > 0)
# depth_diff = valid_mask * (depth - ren_depth.astype(np.float32))
#
# f, ax = plt.subplots(1, 1)
# cax = ax.matshow(depth_diff)
# ax.axis('off')
# ax.set_title('measured - GT depth [mm]')
# f.colorbar(cax, fraction=0.03, pad=0.01)
# f.tight_layout(pad=0)
# plt.savefig(vis_depth_mpath.format(dataset, scene_id, im_id), pad=0,
# bbox_inches='tight')
# plt.close()