How to use i_ran method in Testify

Best Python code snippet using Testify_python

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  #############################################################################...

Automation Testing Tutorials

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