"""Sea Lion Prognostication Engine https://www.kaggle.com/c/noaa-fisheries-steller-sea-lion-population-count """ import sys import os from collections import namedtuple import operator import glob import csv from math import sqrt import numpy as np import PIL from PIL import Image, ImageDraw, ImageFilter import skimage import skimage.io import skimage.measure import shapely import shapely.geometry from shapely.geometry import Polygon # Notes # cls -- sea lion class # tid -- train, train dotted, or test image id # _nb -- short for number # x, y -- don't forget image arrays organized row, col, channels # # With contributions from @bitsofbits ... # # ================ Meta ==================== __description__ = 'Sea Lion Prognostication Engine' __version__ = '0.1.0' __license__ = 'MIT' __author__ = 'Gavin Crooks (@threeplusone)' __status__ = "Prototype" __copyright__ = "Copyright 2017" # python -c 'import sealiondata; sealiondata.package_versions()' def package_versions(): print('sealionengine \t', __version__) print('python \t', sys.version[0:5]) print('numpy \t', np.__version__) print('skimage \t', skimage.__version__) print('pillow (PIL) \t', PIL.__version__) print('shapely \t', shapely.__version__) SOURCEDIR = os.path.join('..', 'input') DATADIR = '.' VERBOSITY = namedtuple('VERBOSITY', ['QUITE', 'NORMAL', 'VERBOSE', 'DEBUG'])(0,1,2,3) SeaLionCoord = namedtuple('SeaLionCoord', ['tid', 'cls', 'x', 'y']) class SeaLionData(object): def __init__(self, sourcedir=SOURCEDIR, datadir=DATADIR, verbosity=VERBOSITY.NORMAL): self.sourcedir = sourcedir self.datadir = datadir self.verbosity = verbosity self.cls_nb = 5 self.cls_names = ( 'adult_males', 'subadult_males', 'adult_females', 'juveniles', 'pups', 'NOT_A_SEA_LION') self.cls = namedtuple('ClassIndex', self.cls_names)(*range(0,6)) # backported from @bitsofbits. Average actual color of dot centers. self.cls_colors = ( (243,8,5), # red (244,8,242), # magenta (87,46,10), # brown (25,56,176), # blue (38,174,21), # green ) self.dot_radius = 3 self.train_nb = 947 self.test_nb = 18636 self.paths = { # Source paths 'sample' : os.path.join(sourcedir, 'sample_submission.csv'), 'counts' : os.path.join(sourcedir, 'Train', 'train.csv'), 'train' : os.path.join(sourcedir, 'Train', '{tid}.jpg'), 'dotted' : os.path.join(sourcedir, 'TrainDotted', '{tid}.jpg'), 'test' : os.path.join(sourcedir, 'Test', '{tid}.jpg'), # Data paths 'coords' : os.path.join(datadir, 'coords.csv'), } # From MismatchedTrainImages.txt self.bad_train_ids = ( 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) self._counts = None @property def trainshort_ids(self): return (0,1,2,4,5,6,8,10) # Trainshort1 #return range(41,51) # Trainshort2 @property def train_ids(self): """List of all valid train ids""" tids = range(0, self.train_nb) tids = list(set(tids) - set(self.bad_train_ids) ) # Remove bad ids tids.sort() return tids @property def test_ids(self): return range(0, self.test_nb) def path(self, name, **kwargs): """Return path to various source files""" path = self.paths[name].format(**kwargs) return path @property def counts(self) : """A map from train_id to list of sea lion class counts""" if self._counts is None : counts = {} fn = self.path('counts') with open(fn) as f: f.readline() for line in f: tid_counts = list(map(int, line.split(','))) counts[tid_counts[0]] = tid_counts[1:] self._counts = counts return self._counts def rmse(self, tid_counts) : true_counts = self.counts error = np.zeros(shape=[5] ) for tid in tid_counts: true_counts = self.counts[tid] obs_counts = tid_counts[tid] diff = np.asarray(true_counts) - np.asarray(obs_counts) error += diff*diff #print(error) error /= len(tid_counts) rmse = np.sqrt(error).sum() / 5 return rmse def load_train_image(self, train_id, border=0, mask=False): """Return image as numpy array border -- add a black border of this width around image mask -- If true mask out masked areas from corresponding dotted image """ img = self._load_image('train', train_id, border) if mask : # The masked areas are not uniformly black, presumable due to # jpeg compression artifacts dot_img = self._load_image('dotted', train_id, border).astype(np.uint16).sum(axis=-1) img = np.copy(img) img[dot_img<40] = 0 return img def load_dotted_image(self, train_id, border=0): return self._load_image('dotted', train_id, border) def load_test_image(self, test_id, border=0): return self._load_image('test', test_id, border) def _load_image(self, itype, tid, border=0) : fn = self.path(itype, tid=tid) img = np.asarray(Image.open(fn)) if border : height, width, channels = img.shape bimg = np.zeros( shape=(height+border*2, width+border*2, channels), dtype=np.uint8) bimg[border:-border, border:-border, :] = img img = bimg return img def coords(self, train_id): """Extract coordinates of dotted sealions and return list of SeaLionCoord objects)""" # Empirical constants MIN_DIFFERENCE = 16 MIN_AREA = 9 MAX_AREA = 100 MAX_AVG_DIFF = 50 MAX_COLOR_DIFF = 32 src_img = np.asarray(self.load_train_image(train_id, mask=True), dtype = np.float) dot_img = np.asarray(self.load_dotted_image(train_id), dtype = np.float) img_diff = np.abs(src_img-dot_img) # Detect bad data. If train and dotted images are very different then somethings wrong. avg_diff = img_diff.sum() / (img_diff.shape[0] * img_diff.shape[1]) if avg_diff > MAX_AVG_DIFF: return None img_diff = np.max(img_diff, axis=-1) img_diff[img_diff=MIN_DIFFERENCE] = 255 sealions = [] for cls, color in enumerate(self.cls_colors): # color search backported from @bitsofbits. color_array = np.array(color)[None, None, :] has_color = np.sqrt(np.sum(np.square(dot_img * (img_diff > 0)[:,:,None] - color_array), axis=-1)) < MAX_COLOR_DIFF contours = skimage.measure.find_contours(has_color.astype(float), 0.5) if self.verbosity == VERBOSITY.DEBUG : print() fn = 'diff_{}_{}.png'.format(train_id,cls) print('Saving train/dotted difference: {}'.format(fn)) Image.fromarray((has_color*255).astype(np.uint8)).save(fn) for cnt in contours : p = Polygon(shell=cnt) area = p.area if(area > MIN_AREA and area < MAX_AREA) : y, x= p.centroid.coords[0] # DANGER : skimage and cv2 coordinates transposed? x = int(round(x)) y = int(round(y)) sealions.append( SeaLionCoord(train_id, cls, x, y) ) if self.verbosity >= VERBOSITY.VERBOSE : counts = [0,0,0,0,0] for c in sealions : counts[c.cls] +=1 print() print('train_id','true_counts','counted_dots', 'difference', sep='\t') true_counts = self.counts[train_id] print(train_id, true_counts, counts, np.array(true_counts) - np.array(counts) , sep='\t' ) if self.verbosity == VERBOSITY.DEBUG : img = np.copy(sld.load_dotted_image(train_id)) r = self.dot_radius dy,dx,c = img.shape for tid, cls, cx, cy in sealions : for x in range(cx-r, cx+r+1) : img[cy, x, :] = 255 for y in range(cy-r, cy+r+1) : img[y, cx, :] = 255 fn = 'cross_{}.png'.format(train_id) print('Saving crossed dots: {}'.format(fn)) Image.fromarray(img).save(fn) return sealions def save_coords(self, train_ids=None): if train_ids is None: train_ids = self.train_ids fn = self.path('coords') self._progress('Saving sealion coordinates to {}'.format(fn)) with open(fn, 'w') as csvfile: writer =csv.writer(csvfile) writer.writerow( SeaLionCoord._fields ) for tid in train_ids : self._progress() for coord in self.coords(tid): writer.writerow(coord) self._progress('done') def load_coords(self): fn = self.path('coords') self._progress('Loading sea lion coordinates from {}'.format(fn)) with open(fn) as f: f.readline() return [SeaLionCoord(*[int(n) for n in line.split(',')]) for line in f] def save_sea_lion_chunks(self, coords, chunksize=128): self._progress('Saving image chunks...') self._progress('\n', verbosity=VERBOSITY.VERBOSE) last_tid = -1 for tid, cls, x, y in coords : if tid != last_tid: img = self.load_train_image(tid, border=chunksize//2, mask=True) last_tid = tid fn = 'chunk_{tid}_{cls}_{x}_{y}_{size}.png'.format(size=chunksize, tid=tid, cls=cls, x=x, y=y) self._progress(' Saving '+fn, end='\n', verbosity=VERBOSITY.VERBOSE) Image.fromarray( img[y:y+chunksize, x:x+chunksize, :]).save(fn) self._progress() self._progress('done') def _progress(self, string=None, end=' ', verbosity=VERBOSITY.NORMAL): if self.verbosity < verbosity: return if not string : print('.', end='') elif string == 'done': print(' done') else: print(string, end=end) sys.stdout.flush() # end SeaLionData # Count sea lion dots and compare to truth from train.csv sld = SeaLionData() sld.verbosity = VERBOSITY.VERBOSE for tid in sld.trainshort_ids: coord = sld.coords(tid)