/
render_car_instances.py
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/
render_car_instances.py
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"""
Brief: Demo for render labelled car 3d poses to the image
Author: wangpeng54@baidu.com
Date: 2018/6/10
"""
import argparse
import cv2
import car_models
import numpy as np
import json
import pickle as pkl
import utils.data as data
import utils.utils as uts
import utils.eval_utils as eval_uts
import renderer.render_egl as render
import logging
from collections import OrderedDict
logger = logging.getLogger()
logger.setLevel(logging.INFO)
class CarPoseVisualizer(object):
def __init__(self, args=None, scale=0.4, linewidth=0.):
"""Initializer
Input:
scale: whether resize the image in case image is too large
linewidth: 0 indicates a binary mask, while > 0 indicates
using a frame.
"""
self.dataset = data.ApolloScape(args)
self._data_config = self.dataset.get_3d_car_config()
self.MAX_DEPTH = 1e4
self.MAX_INST_NUM = 100
h, w = self._data_config['image_size']
# must round prop to 4 due to renderer requirements
# this will change the original size a bit, we usually need rescale
# due to large image size
self.image_size = np.uint32(uts.round_prop_to(
np.float32([h * scale, w * scale])))
self.scale = scale
self.linewidth = linewidth
self.colors = np.random.random((self.MAX_INST_NUM, 3)) * 255
def load_car_models(self):
"""Load all the car models
"""
self.car_models = OrderedDict([])
logging.info('loading %d car models' % len(car_models.models))
for model in car_models.models:
car_model = '%s/%s.pkl' % (self._data_config['car_model_dir'],
model.name)
with open(car_model) as f:
self.car_models[model.name] = pkl.load(f)
# fix the inconsistency between obj and pkl
self.car_models[model.name]['vertices'][:, [0, 1]] *= -1
def render_car(self, pose, car_name):
"""Render a car instance given pose and car_name
"""
car = self.car_models[car_name]
scale = np.ones((3, ))
pose = np.array(pose)
vert = uts.project(pose, scale, car['vertices'])
K = self.intrinsic
intrinsic = np.float64([K[0, 0], K[1, 1], K[0, 2], K[1, 2]])
depth, mask = render.renderMesh_py(np.float64(vert),
np.float64(car['faces']),
intrinsic,
self.image_size[0],
self.image_size[1],
np.float64(self.linewidth))
return depth, mask
def compute_reproj_sim(self, car_names, out_file=None):
"""Compute the similarity matrix between every pair of cars.
"""
if out_file is None:
out_file = './sim_mat.txt'
sim_mat = np.eye(len(self.car_model))
for i in range(len(car_names)):
for j in range(i, len(car_names)):
name1 = car_names[i][0]
name2 = car_names[j][0]
ind_i = self.car_model.keys().index(name1)
ind_j = self.car_model.keys().index(name2)
sim_mat[ind_i, ind_j] = self.compute_reproj(name1, name2)
sim_mat[ind_j, ind_i] = sim_mat[ind_i, ind_j]
np.savetxt(out_file, sim_mat, fmt='%1.6f')
def compute_reproj(self, car_name1, car_name2):
"""Compute reprojection error between two cars
"""
sims = np.zeros(10)
for i, rot in enumerate(np.linspace(0, np.pi, num=10)):
pose = np.array([0, rot, 0, 0, 0,5.5])
depth1, mask1 = self.render_car(pose, car_name1)
depth2, mask2 = self.render_car(pose, car_name2)
sims[i] = eval_uts.IOU(mask1, mask2)
return np.mean(sims)
def merge_inst(self,
depth_in,
inst_id,
total_mask,
total_depth):
"""Merge the prediction of each car instance to a full image
"""
render_depth = depth_in.copy()
render_depth[render_depth <= 0] = np.inf
depth_arr = np.concatenate([render_depth[None, :, :],
total_depth[None, :, :]], axis=0)
idx = np.argmin(depth_arr, axis=0)
total_depth = np.amin(depth_arr, axis=0)
total_mask[idx == 0] = inst_id
return total_mask, total_depth
def rescale(self, image, intrinsic):
"""resize the image and intrinsic given a relative scale
"""
intrinsic_out = uts.intrinsic_vec_to_mat(intrinsic,
self.image_size)
hs, ws = self.image_size
image_out = cv2.resize(image.copy(), (ws, hs))
return image_out, intrinsic_out
def showAnn(self, image_name):
"""Show the annotation of a pose file in an image
Input:
image_name: the name of image
Output:
depth: a rendered depth map of each car
masks: an instance mask of the label
image_vis: an image show the overlap of car model and image
"""
car_pose_file = '%s/%s.json' % (
self._data_config['pose_dir'], image_name)
with open(car_pose_file) as f:
car_poses = json.load(f)
image_file = '%s/%s.jpg' % (self._data_config['image_dir'], image_name)
image = cv2.imread(image_file, cv2.IMREAD_UNCHANGED)[:, :, ::-1]
# intrinsic are all used by Camera 5
intrinsic = self.dataset.get_intrinsic(image_name, 'Camera_5')
image, self.intrinsic = self.rescale(image, intrinsic)
self.depth = self.MAX_DEPTH * np.ones(self.image_size)
self.mask = np.zeros(self.depth.shape)
for i, car_pose in enumerate(car_poses):
car_name = car_models.car_id2name[car_pose['car_id']].name
depth, mask = self.render_car(car_pose['pose'], car_name)
self.mask, self.depth = self.merge_inst(
depth, i + 1, self.mask, self.depth)
self.depth[self.depth == self.MAX_DEPTH] = -1.0
image = 0.5 * image
for i in range(len(car_poses)):
frame = np.float32(self.mask == i + 1)
frame = np.tile(frame[:, :, None], (1, 1, 3))
image = image + frame * 0.5 * self.colors[i, :]
uts.plot_images({'image_vis': np.uint8(image),
'depth': self.depth, 'mask': self.mask},
layout=[1, 3])
return image, self.mask, self.depth
class LabelResaver(object):
""" Resave the raw labeled file to the required json format for evaluation
"""
#(TODO Peng) Figure out why running pdb it is correct, but segment fault when
# running
def __init__(self, args):
self.visualizer = CarPoseVisualizer(args, scale=0.5)
self.visualizer.load_car_models()
def strs_to_mat(self, strs):
"""convert str to numpy matrix
"""
assert len(strs) == 4
mat = np.zeros((4, 4))
for i in range(4):
mat[i, :] = np.array([np.float32(str_f) for str_f in strs[i].split(' ')])
return mat
def read_car_pose(self, file_name):
"""load the labelled car pose
"""
cars = []
lines = [line.strip() for line in open(file_name)]
i = 0
while i < len(lines):
car = OrderedDict([])
line = lines[i].strip()
if 'Model Name :' in line:
car_name = line[len('Model Name : '):]
car['car_id'] = car_models.car_name2id[car_name].id
pose = self.strs_to_mat(lines[i + 2: i + 6])
pose[:3, 3] = pose[:3, 3] / 100.0 # convert cm to meter
rot = uts.rotation_matrix_to_euler_angles(
pose[:3, :3], check=False)
trans = pose[:3, 3].flatten()
pose = np.hstack([rot, trans])
car['pose'] = pose
i += 6
cars.append(car)
else:
i += 1
return cars
def convert(self, pose_file_in, pose_file_out):
""" Convert the raw labelled file to required json format
Input:
file_name: str filename
"""
car_poses = self.read_car_pose(pose_file_in)
car_num = len(car_poses)
MAX_DEPTH = self.visualizer.MAX_DEPTH
image_size = self.visualizer.image_size
intrinsic = self.visualizer.dataset.get_intrinsic(
pose_file_in, 'Camera_5')
self.visualizer.intrinsic = uts.intrinsic_vec_to_mat(intrinsic,
image_size)
self.depth = MAX_DEPTH * np.ones(image_size)
self.mask = np.zeros(self.depth.shape)
vis_rate = np.zeros(car_num)
for i, car_pose in enumerate(car_poses):
car_name = car_models.car_id2name[car_pose['car_id']].name
depth, mask = self.visualizer.render_car(car_pose['pose'], car_name)
self.mask, self.depth = self.visualizer.merge_inst(
depth, i + 1, self.mask, self.depth)
vis_rate[i] = np.float32(np.sum(mask == (i + 1))) / (
np.float32(np.sum(mask)) + np.spacing(1))
keep_idx = []
for i, car_pose in enumerate(car_poses):
area = np.round(np.float32(np.sum(
self.mask == (i + 1))) / (self.visualizer.scale ** 2))
if area > 1:
keep_idx.append(i)
car_pose['pose'] = car_pose['pose'].tolist()
car_pose['area'] = int(area)
car_pose['visible_rate'] = float(vis_rate[i])
keep_idx.append(i)
car_poses = [car_poses[idx] for idx in keep_idx]
with open(pose_file_out, 'w') as f:
json.dump(car_poses, f, sort_keys=True, indent=4,
ensure_ascii=False)
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='Render car instance and convert car labelled files.')
parser.add_argument('--image_name', default='180116_053947113_Camera_5',
help='the dir of ground truth')
parser.add_argument('--data_dir', default='../apolloscape/3d_car_instance_sample/',
help='the dir of ground truth')
parser.add_argument('--split', default='sample_data', help='split for visualization')
args = parser.parse_args()
assert args.image_name
if False:
# (TODO) Debugging correct but segment fault when running. Useless for visualization
print('Test converter')
pose_file_in = './test_files/%s.poses' % args.image_name
pose_file_out = './test_files/%s.json' % args.image_name
label_resaver = LabelResaver(args)
label_resaver.convert(pose_file_in, pose_file_out)
print('Test visualizer')
visualizer = CarPoseVisualizer(args)
visualizer.load_car_models()
visualizer.showAnn(args.image_name)