Export lion patches to training dataset

In [1]:

    

INPUT_DIR='../../input/kaggle-sea-lion/'
OUTPUT_DIR='../../output/kaggle-sea-lion/02/'

DEBUG = False
FORCE_IMAGES = None
#more than 7 errors
#[66, 186, 187, 235, 292, 342, 418, 426, 516, 529, 585, 593, 643, 803, 816, 857, 871, 912, 920]
#more than 4 errors
#[15, 38, 62, 66, 67, 105, 122, 129, 186, 187, 207, 235, 240, 252, 265, 291, 292, 306, 342, 412, 418, 426, 437, 516, 526, 529, 585, 587, 593, 598, 604, 629, 643, 751, 761, 776, 803, 816, 837, 847, 857, 871, 886, 899, 912, 920, 941]
#more than 2 errors
#[15, 32, 36, 38, 62, 66, 67, 77, 93, 96, 105, 112, 122, 127, 129, 178, 186, 187, 189, 197, 207, 235, 240, 252, 265, 291, 292, 297, 299, 306, 330, 342, 359, 362, 383, 386, 394, 395, 398, 403, 405, 409, 412, 418, 426, 434, 437, 438, 452, 465, 473, 476, 495, 509, 510, 516, 526, 529, 551, 571, 574, 585, 587, 593, 595, 598, 604, 606, 629, 643, 679, 700, 704, 724, 739, 750, 751, 759, 761, 773, 776, 785, 803, 816, 822, 823, 827, 837, 847, 857, 871, 872, 886, 889, 899, 900, 906, 910, 912, 917, 920, 924, 925, 941]
SKIP_IMAGES = [66, 186, 187, 235, 292, 342, 418, 426, 516, 529, 585, 593, 643, 803, 816, 857, 871, 912, 920]

IMAGE_DIMS = (50,50,3)
#IMAGE_DIMS = (84,84,3)
LABEL_DIMS = (6,)

#%prun print('test')
#%pdb


    

In [2]:

    

%matplotlib inline
import cv2
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import h5py
import os

import modules.logging
from modules.logging import logger
import modules.lions as lions
from modules.utils import Timer
import modules.utils as utils


    

    




Using TensorFlow backend.

In [3]:

    

utils.mkdirs(OUTPUT_DIR, recreate=True)
modules.logging.setup_file_logger(OUTPUT_DIR + 'out.log')
logger.info('Dir ' + OUTPUT_DIR + ' created')

x_ds = None
y_ds = None

logger.info('creating dataset')
dataset_path = OUTPUT_DIR + utils.dataset_name('lion-patches', IMAGE_DIMS)
h5file = h5py.File(dataset_path, 'w')
x_ds, y_ds = utils.create_xy_dataset(h5file, IMAGE_DIMS, LABEL_DIMS, x_dtype='u1')
logger.info('done')


    

    




2017-05-17 23:39:06,817 INFO Dir ../../output/kaggle-sea-lion/02/ created
2017-05-17 23:39:06,819 INFO creating dataset
2017-05-17 23:39:06,824 INFO done

In [4]:

    

#from MismatchedTrainImages.txt
MISMATCHED = [3, 7, 9, 21, 30, 34, 71, 81, 89, 97, 151, 184, 215, 234, 242, 268, 290, 311, 331, 344, 380, 384, 406, 421, 469, 475, 490, 499, 507, 530, 531, 605, 607, 614, 621, 638, 644, 687, 712, 721, 767, 779, 781, 794, 800, 811, 839, 840, 869, 882, 901, 903, 905, 909, 913, 927, 946]
MISMATCHED = MISMATCHED + SKIP_IMAGES
logger.info('loading train.csv')
train = pd.read_csv(INPUT_DIR + "Train/train.csv")


    

    




2017-05-17 23:39:06,834 INFO loading train.csv

In [5]:

    

logger.info('process all photos from train file')
count = 0
total_errors = np.zeros(LABEL_DIMS[0]-1)
total_classes = np.zeros(LABEL_DIMS[0])
total_classes_added = np.zeros(LABEL_DIMS[0])
total_too_many_errors = []
for row in train.itertuples():
    #load images
    train_id = row[1]
    
    if(FORCE_IMAGES!=None and train_id not in FORCE_IMAGES):
        continue
    
    image_raw_file = INPUT_DIR + 'Train/'+ str(train_id) +'.jpg'
    image_dotted_file = INPUT_DIR + 'TrainDotted/'+ str(train_id) +'.jpg'

    if(train_id in MISMATCHED):
        logger.warning('skipping mismatched train_id ' + str(train_id))
        continue
    
    if(not os.path.isfile(image_raw_file)):
        logger.warning('file not found. skipping. file=' + image_raw_file)
        continue

    t = Timer('processing photo ' + image_raw_file)
    image_raw = cv2.imread(image_raw_file)
    image_dotted = cv2.imread(image_dotted_file)
    
    classes_count_total,classes_count_added,lion_positions,lion_classes = lions.export_lions(image_raw, image_dotted, x_ds, y_ds, IMAGE_DIMS, debug=DEBUG, min_distance_others=0, non_lion_distance=120)
    
    logger.info('detected classes count')
    logger.info(str(classes_count_total))
    logger.info('-----')
    logger.info('expected classes count')
    logger.info(str(row))
    logger.info('-----')
    logger.info('errors per class')
    error = np.subtract(row[2:], classes_count_total[0:5])
    nr_errors = abs(np.sum(error))
    if(nr_errors>7):
        total_too_many_errors.append(train_id)
        logger.warning(str(nr_errors) + ' ERRORS ON TRAIN ID ' + str(train_id))
    logger.info(str(error))
    logger.info('acum errors per class')
    total_errors = np.add(total_errors, np.absolute(error)[0:5])
    logger.info(str(total_errors))
    logger.info('-----')
    logger.info('acum detected classes count total')
    total_classes = np.add(total_classes, classes_count_total)
    logger.info(str(total_classes))
    logger.info('-----')
    logger.info('acum detected classes count added to dataset')
    total_classes_added = np.add(total_classes_added, classes_count_added)
    logger.info(str(total_classes_added))
    t.stop()
    
    count = count + 1

logger.info('ERRORS PER CLASS')
logger.info(str(total_errors))

logger.info('TRAIN_IDS WITH TOO MANY ERRORS')
logger.info(str(total_too_many_errors))

h5file.close()


    

    




2017-05-17 23:39:06,898 INFO process all photos from train file
2017-05-17 23:39:06,904 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/0.jpg
2017-05-17 23:39:06,906 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/1.jpg
2017-05-17 23:39:06,908 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/2.jpg
2017-05-17 23:39:06,910 WARNING skipping mismatched train_id 3
2017-05-17 23:39:06,912 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/4.jpg
2017-05-17 23:39:06,914 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/5.jpg
2017-05-17 23:39:06,916 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/6.jpg
2017-05-17 23:39:06,917 WARNING skipping mismatched train_id 7
2017-05-17 23:39:06,920 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/8.jpg
2017-05-17 23:39:06,921 WARNING skipping mismatched train_id 9
2017-05-17 23:39:06,924 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/10.jpg
2017-05-17 23:39:06,926 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/11.jpg
2017-05-17 23:39:06,927 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/12.jpg
2017-05-17 23:39:06,929 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/13.jpg
2017-05-17 23:39:06,931 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/14.jpg
2017-05-17 23:39:06,933 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/15.jpg
2017-05-17 23:39:06,935 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/16.jpg
2017-05-17 23:39:06,936 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/17.jpg
2017-05-17 23:39:06,938 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/18.jpg
2017-05-17 23:39:06,939 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/19.jpg
2017-05-17 23:39:06,942 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/20.jpg
2017-05-17 23:39:06,943 WARNING skipping mismatched train_id 21
2017-05-17 23:39:06,945 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/22.jpg
2017-05-17 23:39:06,946 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/23.jpg
2017-05-17 23:39:06,948 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/24.jpg
2017-05-17 23:39:06,950 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/25.jpg
2017-05-17 23:39:06,951 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/26.jpg
2017-05-17 23:39:06,953 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/27.jpg
2017-05-17 23:39:06,954 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/28.jpg
2017-05-17 23:39:06,956 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/29.jpg
2017-05-17 23:39:06,958 WARNING skipping mismatched train_id 30
2017-05-17 23:39:06,960 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/31.jpg
2017-05-17 23:39:06,963 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/32.jpg
2017-05-17 23:39:06,965 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/33.jpg
2017-05-17 23:39:06,966 WARNING skipping mismatched train_id 34
2017-05-17 23:39:06,968 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/35.jpg
2017-05-17 23:39:06,970 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/36.jpg
2017-05-17 23:39:06,971 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/37.jpg
2017-05-17 23:39:06,973 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/38.jpg
2017-05-17 23:39:06,975 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/39.jpg
2017-05-17 23:39:06,977 WARNING file not found. skipping. file=../../input/kaggle-sea-lion/Train/40.jpg
2017-05-17 23:39:06,979 INFO > [started] processing photo ../../input/kaggle-sea-lion/Train/41.jpg...
2017-05-17 23:39:13,966 INFO sea lions found: 177
2017-05-17 23:39:13,967 INFO sea lions added to dataset: 177
2017-05-17 23:39:13,969 INFO non sea lions added to dataset: 106
2017-05-17 23:39:13,971 INFO dataset size: 283
2017-05-17 23:39:13,995 INFO detected classes count
2017-05-17 23:39:13,998 INFO [  15.    0.   85.   18.   59.  106.]
2017-05-17 23:39:13,999 INFO -----
2017-05-17 23:39:14,001 INFO expected classes count
2017-05-17 23:39:14,002 INFO Pandas(Index=41, train_id=41, adult_males=15, subadult_males=0, adult_females=85, juveniles=18, pups=59)
2017-05-17 23:39:14,004 INFO -----
2017-05-17 23:39:14,006 INFO errors per class
2017-05-17 23:39:14,008 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:14,009 INFO acum errors per class
2017-05-17 23:39:14,011 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:14,012 INFO -----
2017-05-17 23:39:14,014 INFO acum detected classes count total
2017-05-17 23:39:14,016 INFO [  15.    0.   85.   18.   59.  106.]
2017-05-17 23:39:14,017 INFO -----
2017-05-17 23:39:14,019 INFO acum detected classes count added to dataset
2017-05-17 23:39:14,020 INFO [  15.    0.   85.   18.   59.  106.]
2017-05-17 23:39:14,022 INFO > [done]    processing photo ../../input/kaggle-sea-lion/Train/41.jpg (7042.276 ms)
2017-05-17 23:39:14,025 INFO > [started] processing photo ../../input/kaggle-sea-lion/Train/42.jpg...
2017-05-17 23:39:15,492 INFO sea lions found: 22
2017-05-17 23:39:15,494 INFO sea lions added to dataset: 22
2017-05-17 23:39:15,496 INFO non sea lions added to dataset: 23
2017-05-17 23:39:15,498 INFO dataset size: 328
2017-05-17 23:39:15,529 INFO detected classes count
2017-05-17 23:39:15,532 INFO [  7.   4.  10.   1.   0.  23.]
2017-05-17 23:39:15,533 INFO -----
2017-05-17 23:39:15,535 INFO expected classes count
2017-05-17 23:39:15,537 INFO Pandas(Index=42, train_id=42, adult_males=7, subadult_males=4, adult_females=10, juveniles=1, pups=0)
2017-05-17 23:39:15,538 INFO -----
2017-05-17 23:39:15,540 INFO errors per class
2017-05-17 23:39:15,542 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:15,544 INFO acum errors per class
2017-05-17 23:39:15,546 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:15,547 INFO -----
2017-05-17 23:39:15,549 INFO acum detected classes count total
2017-05-17 23:39:15,550 INFO [  22.    4.   95.   19.   59.  129.]
2017-05-17 23:39:15,552 INFO -----
2017-05-17 23:39:15,553 INFO acum detected classes count added to dataset
2017-05-17 23:39:15,554 INFO [  22.    4.   95.   19.   59.  129.]
2017-05-17 23:39:15,556 INFO > [done]    processing photo ../../input/kaggle-sea-lion/Train/42.jpg (1531.177 ms)
2017-05-17 23:39:15,559 INFO > [started] processing photo ../../input/kaggle-sea-lion/Train/43.jpg...
2017-05-17 23:39:34,094 INFO sea lions found: 606
2017-05-17 23:39:34,096 INFO sea lions added to dataset: 606
2017-05-17 23:39:34,098 INFO non sea lions added to dataset: 465
2017-05-17 23:39:34,099 INFO dataset size: 1399
2017-05-17 23:39:34,131 INFO detected classes count
2017-05-17 23:39:34,133 INFO [  28.    4.  338.   47.  189.  465.]
2017-05-17 23:39:34,135 INFO -----
2017-05-17 23:39:34,136 INFO expected classes count
2017-05-17 23:39:34,138 INFO Pandas(Index=43, train_id=43, adult_males=28, subadult_males=4, adult_females=338, juveniles=47, pups=189)
2017-05-17 23:39:34,140 INFO -----
2017-05-17 23:39:34,141 INFO errors per class
2017-05-17 23:39:34,143 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:34,145 INFO acum errors per class
2017-05-17 23:39:34,146 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:34,148 INFO -----
2017-05-17 23:39:34,149 INFO acum detected classes count total
2017-05-17 23:39:34,151 INFO [  50.    8.  433.   66.  248.  594.]
2017-05-17 23:39:34,152 INFO -----
2017-05-17 23:39:34,153 INFO acum detected classes count added to dataset
2017-05-17 23:39:34,155 INFO [  50.    8.  433.   66.  248.  594.]
2017-05-17 23:39:34,156 INFO > [done]    processing photo ../../input/kaggle-sea-lion/Train/43.jpg (18597.063 ms)
2017-05-17 23:39:34,159 INFO > [started] processing photo ../../input/kaggle-sea-lion/Train/44.jpg...
2017-05-17 23:39:35,906 INFO sea lions found: 45
2017-05-17 23:39:35,908 INFO sea lions added to dataset: 45
2017-05-17 23:39:35,909 INFO non sea lions added to dataset: 43
2017-05-17 23:39:35,911 INFO dataset size: 1487
2017-05-17 23:39:35,941 INFO detected classes count
2017-05-17 23:39:35,943 INFO [  3.   2.  25.  15.   0.  43.]
2017-05-17 23:39:35,945 INFO -----
2017-05-17 23:39:35,946 INFO expected classes count
2017-05-17 23:39:35,948 INFO Pandas(Index=44, train_id=44, adult_males=3, subadult_males=2, adult_females=25, juveniles=15, pups=0)
2017-05-17 23:39:35,949 INFO -----
2017-05-17 23:39:35,951 INFO errors per class
2017-05-17 23:39:35,952 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:35,954 INFO acum errors per class
2017-05-17 23:39:35,956 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:35,957 INFO -----
2017-05-17 23:39:35,958 INFO acum detected classes count total
2017-05-17 23:39:35,960 INFO [  53.   10.  458.   81.  248.  637.]
2017-05-17 23:39:35,962 INFO -----
2017-05-17 23:39:35,963 INFO acum detected classes count added to dataset
2017-05-17 23:39:35,964 INFO [  53.   10.  458.   81.  248.  637.]
2017-05-17 23:39:35,966 INFO > [done]    processing photo ../../input/kaggle-sea-lion/Train/44.jpg (1806.647 ms)
2017-05-17 23:39:35,969 INFO > [started] processing photo ../../input/kaggle-sea-lion/Train/45.jpg...
2017-05-17 23:39:40,146 INFO sea lions found: 138
2017-05-17 23:39:40,148 INFO sea lions added to dataset: 138
2017-05-17 23:39:40,150 INFO non sea lions added to dataset: 140
2017-05-17 23:39:40,152 INFO dataset size: 1765
2017-05-17 23:39:40,182 INFO detected classes count
2017-05-17 23:39:40,184 INFO [   4.    7.  100.   27.    0.  140.]
2017-05-17 23:39:40,186 INFO -----
2017-05-17 23:39:40,188 INFO expected classes count
2017-05-17 23:39:40,190 INFO Pandas(Index=45, train_id=45, adult_males=4, subadult_males=7, adult_females=100, juveniles=27, pups=0)
2017-05-17 23:39:40,191 INFO -----
2017-05-17 23:39:40,193 INFO errors per class
2017-05-17 23:39:40,195 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:40,196 INFO acum errors per class
2017-05-17 23:39:40,198 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:40,199 INFO -----
2017-05-17 23:39:40,200 INFO acum detected classes count total
2017-05-17 23:39:40,202 INFO [  57.   17.  558.  108.  248.  777.]
2017-05-17 23:39:40,203 INFO -----
2017-05-17 23:39:40,204 INFO acum detected classes count added to dataset
2017-05-17 23:39:40,206 INFO [  57.   17.  558.  108.  248.  777.]
2017-05-17 23:39:40,207 INFO > [done]    processing photo ../../input/kaggle-sea-lion/Train/45.jpg (4237.930 ms)
2017-05-17 23:39:40,209 INFO > [started] processing photo ../../input/kaggle-sea-lion/Train/46.jpg...
2017-05-17 23:39:41,232 INFO sea lions found: 5
2017-05-17 23:39:41,234 INFO sea lions added to dataset: 5
2017-05-17 23:39:41,236 INFO non sea lions added to dataset: 4
2017-05-17 23:39:41,237 INFO dataset size: 1773
2017-05-17 23:39:41,271 INFO detected classes count
2017-05-17 23:39:41,273 INFO [ 1.  4.  0.  0.  0.  4.]
2017-05-17 23:39:41,275 INFO -----
2017-05-17 23:39:41,276 INFO expected classes count
2017-05-17 23:39:41,277 INFO Pandas(Index=46, train_id=46, adult_males=1, subadult_males=4, adult_females=0, juveniles=0, pups=0)
2017-05-17 23:39:41,279 INFO -----
2017-05-17 23:39:41,280 INFO errors per class
2017-05-17 23:39:41,282 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:41,283 INFO acum errors per class
2017-05-17 23:39:41,285 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:41,286 INFO -----
2017-05-17 23:39:41,287 INFO acum detected classes count total
2017-05-17 23:39:41,289 INFO [  58.   21.  558.  108.  248.  781.]
2017-05-17 23:39:41,290 INFO -----
2017-05-17 23:39:41,292 INFO acum detected classes count added to dataset
2017-05-17 23:39:41,293 INFO [  58.   20.  558.  108.  248.  781.]
2017-05-17 23:39:41,295 INFO > [done]    processing photo ../../input/kaggle-sea-lion/Train/46.jpg (1085.045 ms)
2017-05-17 23:39:41,298 INFO > [started] processing photo ../../input/kaggle-sea-lion/Train/47.jpg...
2017-05-17 23:39:44,850 INFO sea lions found: 112
2017-05-17 23:39:44,851 INFO sea lions added to dataset: 112
2017-05-17 23:39:44,852 INFO non sea lions added to dataset: 96
2017-05-17 23:39:44,854 INFO dataset size: 1981
2017-05-17 23:39:44,885 INFO detected classes count
2017-05-17 23:39:44,887 INFO [ 14.  14.  48.   3.  33.  96.]
2017-05-17 23:39:44,888 INFO -----
2017-05-17 23:39:44,890 INFO expected classes count
2017-05-17 23:39:44,891 INFO Pandas(Index=47, train_id=47, adult_males=13, subadult_males=16, adult_females=48, juveniles=3, pups=33)
2017-05-17 23:39:44,893 INFO -----
2017-05-17 23:39:44,894 INFO errors per class
2017-05-17 23:39:44,896 INFO [-1.  2.  0.  0.  0.]
2017-05-17 23:39:44,897 INFO acum errors per class
2017-05-17 23:39:44,899 INFO [ 1.  2.  0.  0.  0.]
2017-05-17 23:39:44,900 INFO -----
2017-05-17 23:39:44,901 INFO acum detected classes count total
2017-05-17 23:39:44,903 INFO [  72.   35.  606.  111.  281.  877.]
2017-05-17 23:39:44,904 INFO -----
2017-05-17 23:39:44,906 INFO acum detected classes count added to dataset
2017-05-17 23:39:44,907 INFO [  72.   34.  606.  111.  281.  877.]
2017-05-17 23:39:44,908 INFO > [done]    processing photo ../../input/kaggle-sea-lion/Train/47.jpg (3610.536 ms)
2017-05-17 23:39:44,911 INFO > [started] processing photo ../../input/kaggle-sea-lion/Train/48.jpg...
2017-05-17 23:39:48,436 INFO sea lions found: 104
2017-05-17 23:39:48,437 INFO sea lions added to dataset: 104
2017-05-17 23:39:48,439 INFO non sea lions added to dataset: 99
2017-05-17 23:39:48,440 INFO dataset size: 2184
2017-05-17 23:39:48,464 INFO detected classes count
2017-05-17 23:39:48,466 INFO [  5.  10.  65.  24.   0.  99.]
2017-05-17 23:39:48,468 INFO -----
2017-05-17 23:39:48,469 INFO expected classes count
2017-05-17 23:39:48,471 INFO Pandas(Index=48, train_id=48, adult_males=5, subadult_males=10, adult_females=66, juveniles=24, pups=0)
2017-05-17 23:39:48,472 INFO -----
2017-05-17 23:39:48,473 INFO errors per class
2017-05-17 23:39:48,475 INFO [ 0.  0.  1.  0.  0.]
2017-05-17 23:39:48,477 INFO acum errors per class
2017-05-17 23:39:48,479 INFO [ 1.  2.  1.  0.  0.]
2017-05-17 23:39:48,480 INFO -----
2017-05-17 23:39:48,481 INFO acum detected classes count total
2017-05-17 23:39:48,483 INFO [  77.   45.  671.  135.  281.  976.]
2017-05-17 23:39:48,484 INFO -----
2017-05-17 23:39:48,486 INFO acum detected classes count added to dataset
2017-05-17 23:39:48,487 INFO [  77.   44.  671.  135.  281.  976.]
2017-05-17 23:39:48,488 INFO > [done]    processing photo ../../input/kaggle-sea-lion/Train/48.jpg (3577.050 ms)
2017-05-17 23:39:48,492 INFO > [started] processing photo ../../input/kaggle-sea-lion/Train/49.jpg...
2017-05-17 23:39:49,879 INFO sea lions found: 19
2017-05-17 23:39:49,881 INFO sea lions added to dataset: 19
2017-05-17 23:39:49,882 INFO non sea lions added to dataset: 17
2017-05-17 23:39:49,884 INFO dataset size: 2220
2017-05-17 23:39:49,907 INFO detected classes count
2017-05-17 23:39:49,909 INFO [  0.   0.   4.  15.   0.  17.]
2017-05-17 23:39:49,911 INFO -----
2017-05-17 23:39:49,912 INFO expected classes count
2017-05-17 23:39:49,914 INFO Pandas(Index=49, train_id=49, adult_males=0, subadult_males=0, adult_females=4, juveniles=15, pups=0)
2017-05-17 23:39:49,916 INFO -----
2017-05-17 23:39:49,917 INFO errors per class
2017-05-17 23:39:49,920 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:49,921 INFO acum errors per class
2017-05-17 23:39:49,923 INFO [ 1.  2.  1.  0.  0.]
2017-05-17 23:39:49,925 INFO -----
2017-05-17 23:39:49,926 INFO acum detected classes count total
2017-05-17 23:39:49,928 INFO [  77.   45.  675.  150.  281.  993.]
2017-05-17 23:39:49,929 INFO -----
2017-05-17 23:39:49,931 INFO acum detected classes count added to dataset
2017-05-17 23:39:49,932 INFO [  77.   44.  675.  150.  281.  993.]
2017-05-17 23:39:49,934 INFO > [done]    processing photo ../../input/kaggle-sea-lion/Train/49.jpg (1441.824 ms)
2017-05-17 23:39:49,938 INFO > [started] processing photo ../../input/kaggle-sea-lion/Train/50.jpg...
2017-05-17 23:39:50,543 INFO sea lions found: 1
2017-05-17 23:39:50,545 INFO sea lions added to dataset: 1
2017-05-17 23:39:50,547 INFO non sea lions added to dataset: 1
2017-05-17 23:39:50,548 INFO dataset size: 2222
2017-05-17 23:39:50,573 INFO detected classes count
2017-05-17 23:39:50,576 INFO [ 1.  0.  0.  0.  0.  1.]
2017-05-17 23:39:50,577 INFO -----
2017-05-17 23:39:50,579 INFO expected classes count
2017-05-17 23:39:50,580 INFO Pandas(Index=50, train_id=50, adult_males=1, subadult_males=0, adult_females=0, juveniles=0, pups=0)
2017-05-17 23:39:50,582 INFO -----
2017-05-17 23:39:50,583 INFO errors per class
2017-05-17 23:39:50,585 INFO [ 0.  0.  0.  0.  0.]
2017-05-17 23:39:50,587 INFO acum errors per class
2017-05-17 23:39:50,589 INFO [ 1.  2.  1.  0.  0.]
2017-05-17 23:39:50,590 INFO -----
2017-05-17 23:39:50,591 INFO acum detected classes count total
2017-05-17 23:39:50,593 INFO [  78.   45.  675.  150.  281.  994.]
2017-05-17 23:39:50,594 INFO -----
2017-05-17 23:39:50,596 INFO acum detected classes count added to dataset
2017-05-17 23:39:50,597 INFO [  78.   44.  675.  150.  281.  994.]
2017-05-17 23:39:50,599 INFO > [done]    processing photo ../../input/kaggle-sea-lion/Train/50.jpg (660.769 ms)
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2017-05-17 23:39:52,234 INFO ERRORS PER CLASS
2017-05-17 23:39:52,235 INFO [ 1.  2.  1.  0.  0.]
2017-05-17 23:39:52,237 INFO TRAIN_IDS WITH TOO MANY ERRORS
2017-05-17 23:39:52,238 INFO []

In [6]:

    

dataset_path = OUTPUT_DIR + utils.dataset_name('lion-patches', IMAGE_DIMS)
with h5py.File(dataset_path, 'r') as h5file:
    utils.show_images(h5file['X'][0:100].astype('uint8'), image_labels=utils.onehot_to_label(h5file['Y'][0:100]), is_bgr=True, cols=10, size=2)
    logger.info('output dataset length ' + str(len(h5file['Y'][()])))
    logger.info('class distribution')
    logger.info(str(utils.class_distribution(h5file['Y'][()])))


    

    




2017-05-17 23:39:52,289 INFO showing 100 images
2017-05-17 23:39:52,293 INFO > [started] generating image patches. rows=11; cols=10...
/notebooks/datascience-snippets/kaggle-sea-lion/modules/utils.py:623: FutureWarning: comparison to `None` will result in an elementwise object comparison in the future.
  if(image_labels!=None):






    













    




2017-05-17 23:40:00,694 INFO > [done]    generating image patches. rows=11; cols=10 (8401.115 ms)
2017-05-17 23:40:00,745 INFO output dataset length 2222
2017-05-17 23:40:00,746 INFO class distribution
2017-05-17 23:40:00,751 INFO [ 78  44 675 150 281 994]

In [1]:

    




    

    




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