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How to use i_ran method in Testify
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tf_kitti_to_calib.py
Source:tf_kitti_to_calib.py 
1# -*- coding: utf-8 -*-2# @Author: twankim3# @Date: 2017-06-26 16:55:004# @Last Modified by: twankim5# @Last Modified time: 2017-09-27 14:30:056from __future__ import absolute_import7from __future__ import division8from __future__ import print_function9import argparse10import sys11import os12import glob13from skimage.io import (imread,imsave)14import numpy as np15import tensorflow as tf16import tensorflow.contrib.slim as slim17import _init_paths18from datasets.config import cfg19from datasets.dataset_kitti import get_calib_mat20 21from datasets.utils_dataset import *22def main(args):23 if not args.is_cpu:24 os.environ["CUDA_VISIBLE_DEVICES"] = args.gid25 else:26 os.environ["CUDA_VISIBLE_DEVICES"] = ""27 verbose = args.verbose28 path_kitti = args.path_kitti29 assert os.path.exists(path_kitti),\30 "Download KITTI Dataset or enter correct path"31 32 path_out = args.path_out33 if not os.path.exists(path_out):34 os.makedirs(path_out)35 path_cal = os.path.join(path_kitti,cfg._TASK,cfg._DIR_CALIB)36 path_velo = os.path.join(path_kitti,cfg._TASK,cfg._DIR_VELO)37 path_img = os.path.join(path_kitti,cfg._TASK,cfg._DIR_IMAGE)38 if args.is_train:39 image_set = 'training'40 f_tfrec = os.path.join(path_out,cfg._TF_FORMAT_TRAIN.format(41 'kitti',42 image_set.split('ing')[0]))43 else:44 max_theta = args.max_theta45 max_dist = args.max_dist46 image_set = 'testing'47 f_tfrec = os.path.join(path_out,cfg._TF_FORMAT_TEST.format(48 'kitti',49 image_set.split('ing')[0],50 max_theta,51 max_dist))52 imList = glob.glob(os.path.join(path_cal,53 image_set,54 cfg._TYPE_CALIB,55 '*'+cfg._FORMAT_CALIB))56 imList.sort()57 imNames = [os.path.split(d)[1].strip('.txt') for d in imList]58 print("... Writing {} set".format(image_set))59 with tf.python_io.TFRecordWriter(f_tfrec) as tfrecord_writer:60 with tf.Graph().as_default():61 with tf.Session('') as sess:62 for iter, imName in enumerate(imNames):63 # Get original calibration info64 f_calib = os.path.join(path_cal,65 image_set,66 cfg._TYPE_CALIB,67 imName+cfg._FORMAT_CALIB)68 calib_dict = get_calib_mat(f_calib)69 # Read velodyne points70 f_velo = os.path.join(path_velo,71 image_set,72 cfg._TYPE_VELO,73 imName+cfg._FORMAT_VELO)74 points_org = np.fromfile(f_velo,dtype=np.float32)75 # exclude points reflectance76 points = points_org.reshape(-1,4)[:,:3]77 # Read image file78 f_img = os.path.join(path_img,79 image_set,80 cfg._TYPE_IMAGE,81 imName+cfg._FORMAT_IMAGE)82 im = imread(f_img)83 im_height,im_width = np.shape(im)[0:2]84 # For training, generate random decalib while training,85 # For testing, generate fixed random decalib.86 if image_set == 'training':87 if verbose:88 sys.stdout.write(89 '... ({}) Writing file to TfRecord {}/{}\n'.format(90 image_set,iter+1,len(imNames)))91 sys.stdout.flush()92 # Write to tfrecord93 im_placeholder = tf.placeholder(dtype=tf.uint8)94 encoded_image = tf.image.encode_png(im_placeholder)95 png_string = sess.run(encoded_image,96 feed_dict={im_placeholder:im})97 mat_intrinsic = calib_dict[cfg._SET_CALIB[0]].flatten()98 mat_rect = calib_dict[cfg._SET_CALIB[1]].flatten()99 mat_extrinsic = calib_dict[cfg._SET_CALIB[2]].flatten()100 example = calib_to_tfexample_train(101 png_string,102 b'png',103 im_height,104 im_width,105 points_org,106 mat_intrinsic,107 mat_rect,108 mat_extrinsic109 )110 tfrecord_writer.write(example.SerializeToString())111 elif image_set == 'testing':112 # # !!!! For debugging113 # # Project velodyne points to image plane114 # points2D, pointsDist = project_lidar_to_img(calib_dict,115 # points,116 # im_height,117 # im_width)118 # im_depth_ho = points_to_img(points2D,119 # pointsDist,120 # im_height,121 # im_width)122 # imsave('data_ex/ho_{}.png'.format(image_set),im_depth_ho)123 # --- Generate random ratation for decalibration data ---124 # Generate random vectors for decalibration125 param_rands = gen_ran_decalib(max_theta,126 max_dist,127 cfg._NUM_GEN)128 list_im = [im]*cfg._NUM_GEN129 list_im_depth = [None]*cfg._NUM_GEN130 list_crop = [None]*cfg._NUM_GEN131 list_param_decalib = [None]*cfg._NUM_GEN132 for i_ran in xrange(cfg._NUM_GEN):133 param_decalib = gen_decalib(max_theta,134 max_dist,135 param_rands,136 i_ran)137 # Copy intrinsic parameters and rotation matrix 138 # (for reference cam)139 ran_dict = calib_dict.copy()140 # Replace extrinsic parameters to decalibrated ones141 ran_dict[cfg._SET_CALIB[2]] = np.dot(142 ran_dict[cfg._SET_CALIB[2]],143 quat_to_transmat(param_decalib['q_r'],144 param_decalib['t_vec']))145 146 points2D_ran, pointsDist_ran = project_lidar_to_img(147 ran_dict,148 points,149 im_height,150 im_width)151 list_im_depth[i_ran],list_crop[i_ran] = points_to_img(152 points2D_ran,153 pointsDist_ran,154 im_height,155 im_width)156 list_param_decalib[i_ran] = param_decalib157 # !!!! For debugging158 # imsave('data_ex/ho_{}_{}.png'.format(image_set,i_ran),159 # list_im_depth[i_ran])160 # print(' - Angle:{}, nonzero:{}'.format(161 # param_decalib['rot'],162 # sum(sum(list_im_depth[i_ran]>0)))) 163 im_placeholder = tf.placeholder(dtype=tf.uint8)164 im_depth_placeholder = tf.placeholder(dtype=tf.uint8)165 encoded_image = tf.image.encode_png(im_placeholder)166 encoded_image_depth = tf.image.encode_png(im_depth_placeholder)167 if verbose:168 sys.stdout.write(169 '... ({}) Writing file to TfRecord {}/{}\n'.format(170 image_set,iter+1,len(imNames)))171 sys.stdout.flush()172 png_strings = [sess.run([encoded_image,encoded_image_depth],173 feed_dict={im_placeholder:im,174 im_depth_placeholder:im_depth}) \175 for im,im_depth in zip(list_im,list_im_depth)]176 for i_string in xrange(cfg._NUM_GEN):177 example = calib_to_tfexample_test(178 png_strings[i_string][0],179 png_strings[i_string][1],180 b'png',181 im_height,182 im_width,183 list_param_decalib[i_string]['y'],184 list_param_decalib[i_string]['rot'],185 list_param_decalib[i_string]['a_vec'],186 list_crop[i_string]187 )188 tfrecord_writer.write(example.SerializeToString())189def parse_args():190 def str2bool(v):191 return v.lower() in ('true', '1')192 parser = argparse.ArgumentParser(description=193 'Dataset conversion to TF format')194 parser.add_argument('-gid', dest='gid',195 help='CUDA_VISIBLE_DEVICES (Check your machine ID and ex) 0,1',196 default = '0', type = str)197 parser.add_argument('-is_cpu', dest='is_cpu',198 help='Use CPU only. True/False',199 default = False, type = str2bool)200 parser.add_argument('-dir_in', dest='path_kitti',201 help='Path to kitti dataset',202 default = '/data/kitti', type = str)203 parser.add_argument('-dir_out', dest='path_out',204 help='Path to save tfrecord kitti dataset',205 default = '/data/tf/kitti_calib', type = str)206 parser.add_argument('-max_theta', dest='max_theta',207 help='Range of rotation angle in degree [-theta,+theta)',208 default = 20, type=int)209 parser.add_argument('-max_dist', dest='max_dist',210 help='Maximum translation distance in meter',211 default = 1.5, type=float)212 parser.add_argument('-verbose', dest='verbose',213 help='True: Print every data, False: print only train/test',214 default = False, type=str2bool)215 parser.add_argument('-is_train', dest='is_train',216 help='True: Generate training set',217 default = True, type=str2bool)218 args = parser.parse_args()219 return args220if __name__ == '__main__':221 args = parse_args()222 print ("Called with args:")223 print (args)...
deep_calib_kitti.py
Source:deep_calib_kitti.py
1# Copyright 2016 The TensorFlow Authors. All Rights Reserved.2# Modifications copyright (C) 2017 UT Austin/Taewan Kim3#4# Licensed under the Apache License, Version 2.0 (the "License");5# you may not use this file except in compliance with the License.6# You may obtain a copy of the License at7#8# http://www.apache.org/licenses/LICENSE-2.09#10# Unless required by applicable law or agreed to in writing, software11# distributed under the License is distributed on an "AS IS" BASIS,12# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.13# See the License for the specific language governing permissions and14# limitations under the License.15# ==============================================================================16"""Generic evaluation script that evaluates a model using a given dataset."""17# Modified for deep learning based calibration code18# Code for real application on KITTI19# Input: Image, LIDAR, calib file (P2, Rect0), initial guess (H_init)20# Output: Result of calibration (image, H)21from __future__ import absolute_import22from __future__ import division23from __future__ import print_function24from deep_calibrator import *25import os26from skimage.io import (imread,imsave)27import glob28import numpy as np29import tensorflow as tf30import _init_paths31from datasets.config import cfg32from datasets.dataset_kitti import get_calib_mat33from datasets.utils_dataset import *34from nets import factory_nets35tf.app.flags.DEFINE_string(36 'master', '', 'The address of the TensorFlow master to use.')37tf.app.flags.DEFINE_integer(38 'is_rand', True, 'Turn on random decalibration')39tf.app.flags.DEFINE_integer(40 'num_gen', 5, 'Number of random decalibs to generate')41tf.app.flags.DEFINE_string(42 'checkpoint_path', '/data/tf/kitti_calib/checkpoints/vgg_16/weight_1',43 'The directory where the model was written to or an absolute path to a '44 'checkpoint file.')45tf.app.flags.DEFINE_string(46 'dataset_name', 'kitti', 'The name of the dataset to load.')47tf.app.flags.DEFINE_string(48 'dir_image', None, 'The directory where the image files are stored.')49tf.app.flags.DEFINE_string(50 'dir_lidar', None, 'The directory where the lidar files are stored.')51tf.app.flags.DEFINE_string(52 'dir_calib', None, 'The directory where the calibration files are stored.')53tf.app.flags.DEFINE_string(54 'dir_out', None, 'The directory where the output files are stored.')55tf.app.flags.DEFINE_string(56 'format_image', 'png', 'The format of image. default=png')57tf.app.flags.DEFINE_string(58 'format_lidar', 'bin', 'The format of lidar. default=bin')59tf.app.flags.DEFINE_string(60 'format_calib', 'txt', 'The format of calibartion file. default=txt')61tf.app.flags.DEFINE_string(62 'list_param', '20,1.5',63 'List of parameters for the random decalib. max_rotation,max_translation')64tf.app.flags.DEFINE_string(65 'lidar_pool', None,66 'Kernel size for Max-pooling LIDAR Image: height,width. default=None')67tf.app.flags.DEFINE_string(68 'model_name', 'vgg_16', 'The name of the architecture to evaluate.')69tf.app.flags.DEFINE_string(70 'preprocessing_name', None, 'The name of the preprocessing to use. If left '71 'as `None`, then the model_name flag is used.')72tf.app.flags.DEFINE_string(73 'weight_loss', None,74 'The weight to balance predictions. ex) multiplied to the rotation quaternion')75tf.app.flags.DEFINE_integer(76 'eval_image_size', None, 'Eval image size')77tf.app.flags.DEFINE_boolean(78 'is_crop', True, 'Eval image size')79FLAGS = tf.app.flags.FLAGS80def main(_):81 if not FLAGS.dir_image:82 raise ValueError('You must supply the image directory with --dir_image')83 if not FLAGS.dir_lidar:84 raise ValueError('You must supply the lidar directory with --dir_lidar')85 if not FLAGS.dir_calib:86 raise ValueError('You must supply the calibration directory with --dir_calib')87 if not os.path.exists(FLAGS.dir_out):88 os.makedirs(FLAGS.dir_out)89 # Parameters for random generation90 max_theta,max_dist = map(float,FLAGS.list_param.split(','))91 # Get the list of images to process92 imList = glob.glob(os.path.join(FLAGS.dir_image,'*.'+FLAGS.format_image))93 imList.sort()94 imNames = [os.path.split(pp)[1].strip('.{}'.format(FLAGS.format_image)) \95 for pp in imList]96 preprocessing_name = FLAGS.preprocessing_name or FLAGS.model_name97 if tf.gfile.IsDirectory(FLAGS.checkpoint_path):98 checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)99 else:100 checkpoint_path = FLAGS.checkpoint_path101 lidar_pool = [int(l_i) for l_i in FLAGS.lidar_pool.split(',')]102 predictor = Predictor(FLAGS.model_name,103 preprocessing_name,104 checkpoint_path,105 FLAGS.eval_image_size,106 lidar_pool,107 FLAGS.is_crop)108 for iter,imName in enumerate(imNames):109 print('\n-input: {}.png/.bin'.format(imName))110 decalibs_gt = []111 decalibs_pred = []112 decalibs_qr_gt = []113 decalibs_qr_pred = []114 # Get original calibration info115 f_calib = os.path.join(FLAGS.dir_calib,imName+'.'+FLAGS.format_calib)116 temp_dict = get_calib_mat(f_calib)117 # Read lidar points118 f_lidar = os.path.join(FLAGS.dir_lidar,imName+'.'+FLAGS.format_lidar)119 points_org = np.fromfile(f_lidar,dtype=np.float32).reshape(-1,4)120 points = points_org[:,:3] # exclude reflectance121 # Read image file122 f_image = os.path.join(FLAGS.dir_image,imName+'.'+FLAGS.format_image)123 im = imread(f_image)124 im_height,im_width = np.shape(im)[0:2]125 # Project velodyne points to image plane126 points2D, pointsDist = project_lidar_to_img(temp_dict,127 points,128 im_height,129 im_width)130 # Write as one image (Ground truth)131 im_depth,_ = points_to_img(points2D,pointsDist,im_height,im_width)132 f_res_im = os.path.join(FLAGS.dir_out,'{}_gt.{}'.format(133 imName,FLAGS.format_image))134 imlidarwrite(f_res_im,im,points_to_img(points2D,135 pointsDist,136 im_height,137 im_width,138 'standard')[0])139 # Randomly generate dealibration140 param_rands = gen_ran_decalib(max_theta,max_dist,FLAGS.num_gen)141 for i_ran in xrange(FLAGS.num_gen):142 param_decalib = gen_decalib(max_theta,max_dist,param_rands,i_ran)143 ran_dict = temp_dict.copy()144 ran_dict[cfg._SET_CALIB[2]] = np.dot(145 ran_dict[cfg._SET_CALIB[2]],146 quat_to_transmat(param_decalib['q_r'],param_decalib['t_vec']))147 points2D_ran, pointsDist_ran = project_lidar_to_img(ran_dict,148 points,149 im_height,150 im_width)151 # Write before the calibration152 im_depth_ran, params_crop = points_to_img(points2D_ran,153 pointsDist_ran,154 im_height,155 im_width)156 f_res_im_ran = os.path.join(FLAGS.dir_out,'{}_rand{}.{}'.format(157 imName,i_ran,FLAGS.format_image))158 # Save ground truth decalibration159 decalibs_gt.append(param_decalib['y'])160 decalibs_qr_gt.append(param_decalib['q_r'])161 # ---------- Prediction of y (decalibration) ----------162 # For debugging163 # Check actual patches provided to the network164 if FLAGS.is_crop:165 y_preds_val,img_temp,lidar_temp = predictor.predict(im,im_depth_ran,166 params_crop)167 # Normalize quaternion to have unit norm168 q_r_preds = yr_to_qr(y_preds_val[:4],max_theta)169 print(' {}) Norm_previous:{}'.format(i_ran,np.linalg.norm(q_r_preds)))170 q_r_preds = q_r_preds/np.linalg.norm(q_r_preds)171 path_crop = os.path.join(FLAGS.dir_out,'crops')172 if not os.path.exists(path_crop):173 os.makedirs(path_crop)174 if i_ran==0:175 imsave(os.path.join(path_crop,'{}_rgb_org.png'.format(imName)),im)176 crop_name = os.path.join(path_crop,'{}_{}'.format(imName,i_ran))177 imsave(crop_name+'_rgb.png',img_temp)178 imsave(crop_name+'_lidar.png',lidar_temp.astype(np.uint8))179 imsave(crop_name+'_lidar_org.png',np.squeeze(im_depth_ran,axis=2))180 else:181 y_preds_val,q_r_preds = predictor.predict(im,im_depth_ran)182 # Save predicted decalibration183 decalibs_pred.append(y_preds_val)184 decalibs_qr_pred.append(q_r_preds)185 points2D_cal, pointsDist_cal = predictor.calibrate(ran_dict,186 q_r_preds,187 y_preds_val[4:],188 points,189 im_height,190 im_width)191 # Write after the calibration192 im_depth_cal,_ = points_to_img(points2D_cal,193 pointsDist_cal,194 im_height,195 im_width)196 f_res_im_cal = os.path.join(FLAGS.dir_out,'{}_cal{}.{}'.format(197 imName,i_ran,FLAGS.format_image))198 imlidarwrite(f_res_im_ran,im,points_to_img(points2D_ran,199 pointsDist_ran,200 im_height,201 im_width,202 'standard')[0])203 imlidarwrite(f_res_im_cal,im,points_to_img(points2D_cal,204 pointsDist_cal,205 im_height,206 im_width,207 'standard')[0])208 209 # write 7vec, MSE as txt file210 # decalibs_pred, decalibs_gt211 with open(os.path.join(FLAGS.dir_out,imName+'_res.txt'),'w') as f_res:212 for i_ran,(vec_gt,vec_pred) in enumerate(zip(decalibs_gt,decalibs_pred)):213 f_res.write('*{}, gt:{}\n'.format(i_ran,vec_gt))214 f_res.write('*{}, pred:{}\n'.format(i_ran,vec_pred))215 mse_val = ((vec_gt - vec_pred)**2).mean()216 mse_rot = ((vec_gt[:4]-vec_pred[:4])**2).mean()217 mse_tran = ((vec_gt[4:]-vec_pred[4:])**2).mean()218 f_res.write('*{}, MSE:{}, MSE_rot:{}, MSE_trans:{}\n\n'.format(219 i_ran,mse_val,mse_rot,mse_tran))220 with open(os.path.join(FLAGS.dir_out,imName+'_res_qr.txt'),'w') as f_res:221 for i_ran,(qr_gt,qr_pred) in enumerate(zip(decalibs_qr_gt,decalibs_qr_pred)):222 f_res.write('*{}, gt:{}\n'.format(i_ran,qr_gt))223 f_res.write('*{}, pred:{}\n'.format(i_ran,qr_pred))224 mse_qr = ((qr_gt-qr_pred)**2).mean()225 f_res.write('*{}, MSE_qr:{}\n\n'.format(i_ran,mse_qr))226if __name__ == '__main__':...
softmax.py
Source:softmax.py
1import numpy as np2from random import shuffle3from past.builtins import xrange4def softmax_loss_naive(W, X, y, reg):5 dW = np.zeros_like(W)6 #############################################################################7 # TODO: Compute the softmax loss and its gradient using explicit loops. #8 # Store the loss in loss and the gradient in dW. If you are not careful #9 # here, it is easy to run into numeric instability. Don't forget the #10 # regularization! #11 #############################################################################12 num_classes = W.shape[1]13 num_train = X.shape[0]14 loss = 0.015 for i in xrange(len(X)):16 index = y[i]17 scores = X[i].dot(W)18 scores = scores - np.max(scores)19 correct_class_score = scores[index]20 loss -= np.log(np.exp(scores[index])/(np.sum(np.exp(scores))))21 for j in xrange(num_classes):22 wrong = np.exp(scores[j])/np.sum(np.exp(scores)) + np.max(scores)23 right = np.exp(scores[index])/np.sum(np.exp(scores)) + np.max(scores)24 if j==y[i]:25 dW[:,j] = dW[:,j] + (right-1) * X[i]26 else:27 dW[:,j] = dW[:,j] + wrong * X[i]28 loss = (loss/float(len(X))) + reg * np.sum(np.square(W))/2 + loss29 30 dW = dW / len(X) + reg * W 31 #############################################################################32 # END OF YOUR CODE #33 #############################################################################34 return loss, dW35def softmax_loss_vectorized(W, X, y, reg):36 37 38 loss = 0.039 dW = np.zeros_like(W)40 #############################################################################41 # TODO: Compute the softmax loss and its gradient using no explicit loops. #42 # Store the loss in loss and the gradient in dW. If you are not careful #43 # here, it is easy to run into numeric instability. Don't forget the #44 # regularization! #45 #############################################################################46 num_classes = W.shape[0]47 num_train = X.shape[0]48 i_ran = np.arange(len(X))49 50 scores = np.dot(X,W)51 scores-= np.max(scores)52 cor = scores[i_ran,y].reshape(len(X),1)53 54 exp_sum = np.sum(np.exp(scores),axis=1).reshape(num_train,1)55 loss = loss + np.sum(np.log(exp_sum) - cor)56 loss = (loss/float(len(X))) + reg * np.sum(np.square(W))/2 + loss57 58 59 Grad = np.exp(scores)/exp_sum60 Grad[i_ran,y] = Grad[i_ran,y]-1.061 dW = np.dot(X.T,Grad)/num_train + reg*W62 63 #############################################################################64 # END OF YOUR CODE #65 #############################################################################...
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