From 5e2823f3f7cfed715f0669e76ce7feceafcf3017 Mon Sep 17 00:00:00 2001 From: Sergey Karayev Date: Thu, 5 Dec 2013 16:38:36 -0800 Subject: [PATCH 1/5] processing images in batch, with option to use selective search window proposals --- python/caffe/imagenet/wrapper2.py | 315 ++++++++++++++++++++++++++++++ 1 file changed, 315 insertions(+) create mode 100644 python/caffe/imagenet/wrapper2.py diff --git a/python/caffe/imagenet/wrapper2.py b/python/caffe/imagenet/wrapper2.py new file mode 100644 index 00000000..e23b48b4 --- /dev/null +++ b/python/caffe/imagenet/wrapper2.py @@ -0,0 +1,315 @@ +""" +Classify a number of images at once, optionally using the selective +search window proposal method. + +This implementation follows + Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik. + Rich feature hierarchies for accurate object detection and semantic + segmentation. + http://arxiv.org/abs/1311.2524 + +The selective_search_ijcv_with_python code is available at + https://github.com/sergeyk/selective_search_ijcv_with_python + +TODO: +- [ ] batch up image filenames as well: don't want to load all of them into memory +""" +import numpy as np +import os +import sys +import gflags +import pandas as pd +import time +import skimage.io +import skimage.transform +import selective_search_ijcv_with_python as selective_search +import caffe + +IMAGE_DIM = 256 +CROPPED_DIM = 227 +IMAGE_CENTER = int((IMAGE_DIM - CROPPED_DIM) / 2) + +CROP_MODES = ['center_only', 'corners', 'selective_search'] + +BATCH_SIZE = 256 + +# Load the imagenet mean file +IMAGENET_MEAN = np.load( + os.path.join(os.path.dirname(__file__), 'ilsvrc_2012_mean.npy')) + + +def load_image(filename): + """ + Input: + filename: string + + Output: + image: an image of size (256 x 256 x 3) of type uint8. + """ + img = skimage.io.imread(filename) + if img.ndim == 2: + img = np.tile(img[:, :, np.newaxis], (1, 1, 3)) + elif img.shape[2] == 4: + img = img[:, :, :3] + return img + + +def format_image(image, window=None, dim=IMAGE_DIM): + """ + Input: + image: (H x W x 3) ndarray + window: (4) ndarray + (y, x, h, w) coordinates + dim: int + Final width of the square image. + + Output: + image: (3 x H x W) ndarray + Resized to (dim, dim). + """ + # Crop a subimage if window is provided. + if window is not None: + image = image[ + window[0]:window[2], + window[1]:window[3] + ] + + # Resize to ImageNet size, convert to BGR, subtract mean. + image = (skimage.transform.resize(image, (IMAGE_DIM, IMAGE_DIM)) * 255)[:, :, ::-1] + image -= IMAGENET_MEAN + + # Resize further if needed. + if not dim == IMAGE_DIM: + image = skimage.transform.resize(image, (dim, dim)) + image = image.swapaxes(1, 2).swapaxes(0, 1) + return image + + +def _assemble_images_center_only(image_fnames): + """ + For each image, square the image and crop its center. + + Input: + image_fnames: list + + Output: + images_df: pandas.DataFrame + With 'image', 'filename' columns. + """ + all_images = [] + for image_filename in image_fnames: + image = format_image(load_image(image_filename)) + all_images.append(np.ascontiguousarray( + image[np.newaxis, :, + IMAGE_CENTER:IMAGE_CENTER + CROPPED_DIM, + IMAGE_CENTER:IMAGE_CENTER + CROPPED_DIM], + dtype=np.float32 + )) + + images_df = pd.DataFrame({ + 'image': all_images, + 'filename': image_fnames + }) + return images_df + + +def _assemble_images_corners(image_fnames): + """ + For each image, square the image and crop its center, four corners, + and mirrored version of the above. + + Input: + image_fnames: list + + Output: + images_df: pandas.DataFrame + With 'image', 'filename' columns. + """ + all_images = [] + for image_filename in image_fnames: + image = format_image(load_image(image_filename)) + indices = [0, IMAGE_DIM - CROPPED_DIM] + + images = np.empty((10, 3, CROPPED_DIM, CROPPED_DIM), dtype=np.float32) + curr = 0 + for i in indices: + for j in indices: + images[curr] = image[:, i:i + CROPPED_DIM, j:j + CROPPED_DIM] + curr += 1 + images[4] = image[ + :, + IMAGE_CENTER:IMAGE_CENTER + CROPPED_DIM, + IMAGE_CENTER:IMAGE_CENTER + CROPPED_DIM + ] + images[5:] = images[:5, :, :, ::-1] # flipped versions + + all_images.append(images) + + images_df = pd.DataFrame({ + 'image': [row for row in images for images in all_images], + 'filename': np.repeat(image_fnames, 10) + }) + return images_df + + +def _assemble_images_selective_search(image_fnames): + """ + Run Selective Search window proposals on all images, then for each + image-window pair, extract a square crop. + + Input: + image_fnames: list + + Output: + images_df: pandas.DataFrame + With 'image', 'filename' columns. + """ + windows_list = selective_search.get_windows(image_fnames) + + data = [] + for image_fname, windows in zip(image_fnames, windows_list): + image = load_image(image_fname) + for window in windows: + data.append({ + 'image': format_image(image, window, CROPPED_DIM)[np.newaxis, :], + 'window': window, + 'filename': image_fname + }) + + images_df = pd.DataFrame(data) + return images_df + + +def assemble_batches(image_fnames, crop_mode='center_only', batch_size=256): + """ + Assemble DataFrame of image crops for feature computation. + + Input: + image_fnames: list of string + mode: string + 'center_only': the CROPPED_DIM middle of the image is taken as is + 'corners': take CROPPED_DIM-sized boxes at 4 corners and center of + the image, as well as their flipped versions: a total of 10. + 'selective_search': run Selective Search region proposal on the + image, and take each enclosing subwindow. + + Output: + df_batches: list of DataFrames, each one of BATCH_SIZE rows. + Each row has 'image', 'filename', and 'window' info. + Column 'image' contains (X x 3 x 227 x 227) ndarrays. + Column 'filename' contains source filenames. + If 'filename' is None, then the row is just for padding. + """ + if crop_mode == 'center_only': + images_df = _assemble_images_center_only(image_fnames) + + elif crop_mode == 'corners': + images_df = _assemble_images_corners(image_fnames) + + elif crop_mode == 'selective_search': + images_df = _assemble_images_selective_search(image_fnames) + + else: + raise Exception("Unknown mode: not in {}".format(CROP_MODES)) + + # Make sure the DataFrame has a multiple of BATCH_SIZE rows: + # just fill the extra rows with NaN filenames and all-zero images. + N = images_df.shape[0] + remainder = N % BATCH_SIZE + if remainder > 0: + zero_image = np.zeros_like(images_df['image'].iloc[0]) + remainder_df = pd.DataFrame([{ + 'filename': None, + 'image': zero_image, + 'window': [0, 0, 0, 0] + }] * (BATCH_SIZE - remainder)) + images_df = images_df.append(remainder_df) + N = images_df.shape[0] + + # Split into batches of BATCH_SIZE. + ind = np.arange(N) / BATCH_SIZE + df_batches = [images_df[ind == i] for i in range(N / BATCH_SIZE)] + return df_batches + + +def compute_feats(images_df, layer='imagenet'): + if layer == 'imagenet': + num_output = 1000 + else: + raise ValueError("Unknown layer requested: {}".format(layer)) + + num = images_df.shape[0] + input_blobs = [np.concatenate(images_df['image'].values)] + output_blobs = [np.empty((num, num_output, 1, 1), dtype=np.float32)] + print(len(input_blobs), len(output_blobs)) + print(input_blobs[0].shape, output_blobs[0].shape) + + #caffenet.Forward(input_blobs, output_blobs) + feats = [output_blobs[0][i].flatten() for i in range(len(output_blobs[0]))] + + # Add the features and delete the images. + del images_df['image'] + images_df['feat'] = feats + return images_df + + +if __name__ == "__main__": + ## Parse cmdline options + gflags.DEFINE_string( + "model_def", "", "The model definition file.") + gflags.DEFINE_string( + "pretrained_model", "", "The pretrained model.") + gflags.DEFINE_boolean( + "gpu", False, "use gpu for computation") + gflags.DEFINE_string( + "crop_mode", "center_only", "Crop mode, from {}".format(CROP_MODES)) + gflags.DEFINE_string( + "images_file", "", "File that contains image filenames.") + gflags.DEFINE_string( + "batch_size", 256, "Number of image crops to let through in one go") + gflags.DEFINE_string( + "output", "", "The output DataFrame HDF5 filename.") + gflags.DEFINE_string( + "layer", "imagenet", "Layer to output.") + FLAGS = gflags.FLAGS + FLAGS(sys.argv) + + ## Load list of image filenames and assemble into batches. + t = time.time() + print('Assembling batches...') + with open(FLAGS.images_file) as f: + image_fnames = [_.strip() for _ in f.readlines()] + image_batches = assemble_batches( + image_fnames, FLAGS.crop_mode, FLAGS.batch_size) + print('{} batches assembled in {:.3f} s'.format( + len(image_batches), time.time() - t)) + + # Initialize network by loading model definition and weights. + t = time.time() + print("Loading Caffe model.") + caffenet = caffe.CaffeNet(FLAGS.model_def, FLAGS.pretrained_model) + caffenet.set_phase_test() + if FLAGS.gpu: + caffenet.set_mode_gpu() + print("Caffe model loaded in {:.3f} s".format(time.time() - t)) + + # Process the batches. + t = time.time() + print 'Processing {} files in {} batches'.format( + len(image_fnames), len(image_batches)) + dfs_with_feats = [] + for i in range(len(image_batches)): + if i % 10 == 0: + print('Batch {}/{}, elapsed time: {:.3f} s'.format( + i, len(image_batches), time.time() - t)) + dfs_with_feats.append(compute_feats(image_batches[i])) + + # Concatenate, droppping the padding rows. + df = pd.concat(dfs_with_feats).dropna(subset=['filename']) + print("Processing complete after {:.3f} s.".format(time.time() - t)) + + # Write our the results. + t = time.time() + df.to_hdf(FLAGS.output, 'df', mode='w') + print("Done. Saving to {} took {:.3f} s.".format( + FLAGS.output, time.time() - t)) From b1aff6dfc3b63112ff483f75b9491be9b30f98df Mon Sep 17 00:00:00 2001 From: Sergey Karayev Date: Thu, 5 Dec 2013 17:21:47 -0800 Subject: [PATCH 2/5] center_only and corners modes work correctly --- python/caffe/imagenet/wrapper2.py | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/python/caffe/imagenet/wrapper2.py b/python/caffe/imagenet/wrapper2.py index e23b48b4..abb8ca1b 100644 --- a/python/caffe/imagenet/wrapper2.py +++ b/python/caffe/imagenet/wrapper2.py @@ -31,7 +31,8 @@ IMAGE_CENTER = int((IMAGE_DIM - CROPPED_DIM) / 2) CROP_MODES = ['center_only', 'corners', 'selective_search'] -BATCH_SIZE = 256 +# NOTE: this must match the setting in the prototxt that is used! +BATCH_SIZE = 245 # Load the imagenet mean file IMAGENET_MEAN = np.load( @@ -146,7 +147,7 @@ def _assemble_images_corners(image_fnames): all_images.append(images) images_df = pd.DataFrame({ - 'image': [row for row in images for images in all_images], + 'image': [row[np.newaxis, :] for row in images for images in all_images], 'filename': np.repeat(image_fnames, 10) }) return images_df @@ -239,12 +240,12 @@ def compute_feats(images_df, layer='imagenet'): raise ValueError("Unknown layer requested: {}".format(layer)) num = images_df.shape[0] - input_blobs = [np.concatenate(images_df['image'].values)] + input_blobs = [np.ascontiguousarray( + np.concatenate(images_df['image'].values), dtype='float32')] output_blobs = [np.empty((num, num_output, 1, 1), dtype=np.float32)] - print(len(input_blobs), len(output_blobs)) print(input_blobs[0].shape, output_blobs[0].shape) - #caffenet.Forward(input_blobs, output_blobs) + caffenet.Forward(input_blobs, output_blobs) feats = [output_blobs[0][i].flatten() for i in range(len(output_blobs[0]))] # Add the features and delete the images. @@ -313,3 +314,5 @@ if __name__ == "__main__": df.to_hdf(FLAGS.output, 'df', mode='w') print("Done. Saving to {} took {:.3f} s.".format( FLAGS.output, time.time() - t)) + + sys.exit() From c850cc06c7d25a0c047768cc6b6d55bed4dae29c Mon Sep 17 00:00:00 2001 From: Sergey Karayev Date: Thu, 5 Dec 2013 19:12:50 -0800 Subject: [PATCH 3/5] fixed bug that renormalized window crops on second resize --- examples/synset_words.txt | 1000 +++++++++++++++++++++++++++++ python/caffe/imagenet/wrapper2.py | 26 +- 2 files changed, 1016 insertions(+), 10 deletions(-) create mode 100644 examples/synset_words.txt diff --git a/examples/synset_words.txt b/examples/synset_words.txt new file mode 100644 index 00000000..9243b005 --- /dev/null +++ b/examples/synset_words.txt @@ -0,0 +1,1000 @@ +n01440764 tench, Tinca tinca +n01443537 goldfish, Carassius auratus +n01484850 great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias +n01491361 tiger shark, Galeocerdo cuvieri +n01494475 hammerhead, hammerhead shark +n01496331 electric ray, crampfish, numbfish, torpedo +n01498041 stingray +n01514668 cock +n01514859 hen +n01518878 ostrich, Struthio camelus +n01530575 brambling, Fringilla montifringilla +n01531178 goldfinch, Carduelis carduelis +n01532829 house finch, linnet, Carpodacus mexicanus +n01534433 junco, snowbird +n01537544 indigo bunting, indigo finch, indigo bird, Passerina cyanea +n01558993 robin, American robin, Turdus migratorius +n01560419 bulbul +n01580077 jay +n01582220 magpie +n01592084 chickadee +n01601694 water ouzel, dipper +n01608432 kite +n01614925 bald eagle, American eagle, Haliaeetus leucocephalus +n01616318 vulture +n01622779 great grey owl, great gray owl, Strix nebulosa +n01629819 European fire salamander, Salamandra salamandra +n01630670 common newt, Triturus vulgaris +n01631663 eft +n01632458 spotted salamander, Ambystoma maculatum +n01632777 axolotl, mud puppy, Ambystoma mexicanum +n01641577 bullfrog, Rana catesbeiana +n01644373 tree frog, tree-frog +n01644900 tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui +n01664065 loggerhead, loggerhead turtle, Caretta caretta +n01665541 leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea +n01667114 mud turtle +n01667778 terrapin +n01669191 box turtle, box tortoise +n01675722 banded gecko +n01677366 common iguana, iguana, Iguana iguana +n01682714 American chameleon, anole, Anolis carolinensis +n01685808 whiptail, whiptail lizard +n01687978 agama +n01688243 frilled lizard, Chlamydosaurus kingi +n01689811 alligator lizard +n01692333 Gila monster, Heloderma suspectum +n01693334 green lizard, Lacerta viridis +n01694178 African chameleon, Chamaeleo chamaeleon +n01695060 Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis +n01697457 African crocodile, Nile crocodile, Crocodylus niloticus +n01698640 American alligator, Alligator mississipiensis +n01704323 triceratops +n01728572 thunder snake, worm snake, Carphophis amoenus +n01728920 ringneck snake, ring-necked snake, ring snake +n01729322 hognose snake, puff adder, sand viper +n01729977 green snake, grass snake +n01734418 king snake, kingsnake +n01735189 garter snake, grass snake +n01737021 water snake +n01739381 vine snake +n01740131 night snake, Hypsiglena torquata +n01742172 boa constrictor, Constrictor constrictor +n01744401 rock python, rock snake, Python sebae +n01748264 Indian cobra, Naja naja +n01749939 green mamba +n01751748 sea snake +n01753488 horned viper, cerastes, sand viper, horned asp, Cerastes cornutus +n01755581 diamondback, diamondback rattlesnake, Crotalus adamanteus +n01756291 sidewinder, horned rattlesnake, Crotalus cerastes +n01768244 trilobite +n01770081 harvestman, daddy longlegs, Phalangium opilio +n01770393 scorpion +n01773157 black and gold garden spider, Argiope aurantia +n01773549 barn spider, Araneus cavaticus +n01773797 garden spider, Aranea diademata +n01774384 black widow, Latrodectus mactans +n01774750 tarantula +n01775062 wolf spider, hunting spider +n01776313 tick +n01784675 centipede +n01795545 black grouse +n01796340 ptarmigan +n01797886 ruffed grouse, partridge, Bonasa umbellus +n01798484 prairie chicken, prairie grouse, prairie fowl +n01806143 peacock +n01806567 quail +n01807496 partridge +n01817953 African grey, African gray, Psittacus erithacus +n01818515 macaw +n01819313 sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita +n01820546 lorikeet +n01824575 coucal +n01828970 bee eater +n01829413 hornbill +n01833805 hummingbird +n01843065 jacamar +n01843383 toucan +n01847000 drake +n01855032 red-breasted merganser, Mergus serrator +n01855672 goose +n01860187 black swan, Cygnus atratus +n01871265 tusker +n01872401 echidna, spiny anteater, anteater +n01873310 platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus +n01877812 wallaby, brush kangaroo +n01882714 koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus +n01883070 wombat +n01910747 jellyfish +n01914609 sea anemone, anemone +n01917289 brain coral +n01924916 flatworm, platyhelminth +n01930112 nematode, nematode worm, roundworm +n01943899 conch +n01944390 snail +n01945685 slug +n01950731 sea slug, nudibranch +n01955084 chiton, coat-of-mail shell, sea cradle, polyplacophore +n01968897 chambered nautilus, pearly nautilus, nautilus +n01978287 Dungeness crab, Cancer magister +n01978455 rock crab, Cancer irroratus +n01980166 fiddler crab +n01981276 king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica +n01983481 American lobster, Northern lobster, Maine lobster, Homarus americanus +n01984695 spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish +n01985128 crayfish, crawfish, crawdad, crawdaddy +n01986214 hermit crab +n01990800 isopod +n02002556 white stork, Ciconia ciconia +n02002724 black stork, Ciconia nigra +n02006656 spoonbill +n02007558 flamingo +n02009229 little blue heron, Egretta caerulea +n02009912 American egret, great white heron, Egretta albus +n02011460 bittern +n02012849 crane +n02013706 limpkin, Aramus pictus +n02017213 European gallinule, Porphyrio porphyrio +n02018207 American coot, marsh hen, mud hen, water hen, Fulica americana +n02018795 bustard +n02025239 ruddy turnstone, Arenaria interpres +n02027492 red-backed sandpiper, dunlin, Erolia alpina +n02028035 redshank, Tringa totanus +n02033041 dowitcher +n02037110 oystercatcher, oyster catcher +n02051845 pelican +n02056570 king penguin, Aptenodytes patagonica +n02058221 albatross, mollymawk +n02066245 grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus +n02071294 killer whale, killer, orca, grampus, sea wolf, Orcinus orca +n02074367 dugong, Dugong dugon +n02077923 sea lion +n02085620 Chihuahua +n02085782 Japanese spaniel +n02085936 Maltese dog, Maltese terrier, Maltese +n02086079 Pekinese, Pekingese, Peke +n02086240 Shih-Tzu +n02086646 Blenheim spaniel +n02086910 papillon +n02087046 toy terrier +n02087394 Rhodesian ridgeback +n02088094 Afghan hound, Afghan +n02088238 basset, basset hound +n02088364 beagle +n02088466 bloodhound, sleuthhound +n02088632 bluetick +n02089078 black-and-tan coonhound +n02089867 Walker hound, Walker foxhound +n02089973 English foxhound +n02090379 redbone +n02090622 borzoi, Russian wolfhound +n02090721 Irish wolfhound +n02091032 Italian greyhound +n02091134 whippet +n02091244 Ibizan hound, Ibizan Podenco +n02091467 Norwegian elkhound, elkhound +n02091635 otterhound, otter hound +n02091831 Saluki, gazelle hound +n02092002 Scottish deerhound, deerhound +n02092339 Weimaraner +n02093256 Staffordshire bullterrier, Staffordshire bull terrier +n02093428 American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier +n02093647 Bedlington terrier +n02093754 Border terrier +n02093859 Kerry blue terrier +n02093991 Irish terrier +n02094114 Norfolk terrier +n02094258 Norwich terrier +n02094433 Yorkshire terrier +n02095314 wire-haired fox terrier +n02095570 Lakeland terrier +n02095889 Sealyham terrier, Sealyham +n02096051 Airedale, Airedale terrier +n02096177 cairn, cairn terrier +n02096294 Australian terrier +n02096437 Dandie Dinmont, Dandie Dinmont terrier +n02096585 Boston bull, Boston terrier +n02097047 miniature schnauzer +n02097130 giant schnauzer +n02097209 standard schnauzer +n02097298 Scotch terrier, Scottish terrier, Scottie +n02097474 Tibetan terrier, chrysanthemum dog +n02097658 silky terrier, Sydney silky +n02098105 soft-coated wheaten terrier +n02098286 West Highland white terrier +n02098413 Lhasa, Lhasa apso +n02099267 flat-coated retriever +n02099429 curly-coated retriever +n02099601 golden retriever +n02099712 Labrador retriever +n02099849 Chesapeake Bay retriever +n02100236 German short-haired pointer +n02100583 vizsla, Hungarian pointer +n02100735 English setter +n02100877 Irish setter, red setter +n02101006 Gordon setter +n02101388 Brittany spaniel +n02101556 clumber, clumber spaniel +n02102040 English springer, English springer spaniel +n02102177 Welsh springer spaniel +n02102318 cocker spaniel, English cocker spaniel, cocker +n02102480 Sussex spaniel +n02102973 Irish water spaniel +n02104029 kuvasz +n02104365 schipperke +n02105056 groenendael +n02105162 malinois +n02105251 briard +n02105412 kelpie +n02105505 komondor +n02105641 Old English sheepdog, bobtail +n02105855 Shetland sheepdog, Shetland sheep dog, Shetland +n02106030 collie +n02106166 Border collie +n02106382 Bouvier des Flandres, Bouviers des Flandres +n02106550 Rottweiler +n02106662 German shepherd, German shepherd dog, German police dog, alsatian +n02107142 Doberman, Doberman pinscher +n02107312 miniature pinscher +n02107574 Greater Swiss Mountain dog +n02107683 Bernese mountain dog +n02107908 Appenzeller +n02108000 EntleBucher +n02108089 boxer +n02108422 bull mastiff +n02108551 Tibetan mastiff +n02108915 French bulldog +n02109047 Great Dane +n02109525 Saint Bernard, St Bernard +n02109961 Eskimo dog, husky +n02110063 malamute, malemute, Alaskan malamute +n02110185 Siberian husky +n02110341 dalmatian, coach dog, carriage dog +n02110627 affenpinscher, monkey pinscher, monkey dog +n02110806 basenji +n02110958 pug, pug-dog +n02111129 Leonberg +n02111277 Newfoundland, Newfoundland dog +n02111500 Great Pyrenees +n02111889 Samoyed, Samoyede +n02112018 Pomeranian +n02112137 chow, chow chow +n02112350 keeshond +n02112706 Brabancon griffon +n02113023 Pembroke, Pembroke Welsh corgi +n02113186 Cardigan, Cardigan Welsh corgi +n02113624 toy poodle +n02113712 miniature poodle +n02113799 standard poodle +n02113978 Mexican hairless +n02114367 timber wolf, grey wolf, gray wolf, Canis lupus +n02114548 white wolf, Arctic wolf, Canis lupus tundrarum +n02114712 red wolf, maned wolf, Canis rufus, Canis niger +n02114855 coyote, prairie wolf, brush wolf, Canis latrans +n02115641 dingo, warrigal, warragal, Canis dingo +n02115913 dhole, Cuon alpinus +n02116738 African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus +n02117135 hyena, hyaena +n02119022 red fox, Vulpes vulpes +n02119789 kit fox, Vulpes macrotis +n02120079 Arctic fox, white fox, Alopex lagopus +n02120505 grey fox, gray fox, Urocyon cinereoargenteus +n02123045 tabby, tabby cat +n02123159 tiger cat +n02123394 Persian cat +n02123597 Siamese cat, Siamese +n02124075 Egyptian cat +n02125311 cougar, puma, catamount, mountain lion, painter, panther, Felis concolor +n02127052 lynx, catamount +n02128385 leopard, Panthera pardus +n02128757 snow leopard, ounce, Panthera uncia +n02128925 jaguar, panther, Panthera onca, Felis onca +n02129165 lion, king of beasts, Panthera leo +n02129604 tiger, Panthera tigris +n02130308 cheetah, chetah, Acinonyx jubatus +n02132136 brown bear, bruin, Ursus arctos +n02133161 American black bear, black bear, Ursus americanus, Euarctos americanus +n02134084 ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus +n02134418 sloth bear, Melursus ursinus, Ursus ursinus +n02137549 mongoose +n02138441 meerkat, mierkat +n02165105 tiger beetle +n02165456 ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle +n02167151 ground beetle, carabid beetle +n02168699 long-horned beetle, longicorn, longicorn beetle +n02169497 leaf beetle, chrysomelid +n02172182 dung beetle +n02174001 rhinoceros beetle +n02177972 weevil +n02190166 fly +n02206856 bee +n02219486 ant, emmet, pismire +n02226429 grasshopper, hopper +n02229544 cricket +n02231487 walking stick, walkingstick, stick insect +n02233338 cockroach, roach +n02236044 mantis, mantid +n02256656 cicada, cicala +n02259212 leafhopper +n02264363 lacewing, lacewing fly +n02268443 dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk +n02268853 damselfly +n02276258 admiral +n02277742 ringlet, ringlet butterfly +n02279972 monarch, monarch butterfly, milkweed butterfly, Danaus plexippus +n02280649 cabbage butterfly +n02281406 sulphur butterfly, sulfur butterfly +n02281787 lycaenid, lycaenid butterfly +n02317335 starfish, sea star +n02319095 sea urchin +n02321529 sea cucumber, holothurian +n02325366 wood rabbit, cottontail, cottontail rabbit +n02326432 hare +n02328150 Angora, Angora rabbit +n02342885 hamster +n02346627 porcupine, hedgehog +n02356798 fox squirrel, eastern fox squirrel, Sciurus niger +n02361337 marmot +n02363005 beaver +n02364673 guinea pig, Cavia cobaya +n02389026 sorrel +n02391049 zebra +n02395406 hog, pig, grunter, squealer, Sus scrofa +n02396427 wild boar, boar, Sus scrofa +n02397096 warthog +n02398521 hippopotamus, hippo, river horse, Hippopotamus amphibius +n02403003 ox +n02408429 water buffalo, water ox, Asiatic buffalo, Bubalus bubalis +n02410509 bison +n02412080 ram, tup +n02415577 bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis +n02417914 ibex, Capra ibex +n02422106 hartebeest +n02422699 impala, Aepyceros melampus +n02423022 gazelle +n02437312 Arabian camel, dromedary, Camelus dromedarius +n02437616 llama +n02441942 weasel +n02442845 mink +n02443114 polecat, fitch, foulmart, foumart, Mustela putorius +n02443484 black-footed ferret, ferret, Mustela nigripes +n02444819 otter +n02445715 skunk, polecat, wood pussy +n02447366 badger +n02454379 armadillo +n02457408 three-toed sloth, ai, Bradypus tridactylus +n02480495 orangutan, orang, orangutang, Pongo pygmaeus +n02480855 gorilla, Gorilla gorilla +n02481823 chimpanzee, chimp, Pan troglodytes +n02483362 gibbon, Hylobates lar +n02483708 siamang, Hylobates syndactylus, Symphalangus syndactylus +n02484975 guenon, guenon monkey +n02486261 patas, hussar monkey, Erythrocebus patas +n02486410 baboon +n02487347 macaque +n02488291 langur +n02488702 colobus, colobus monkey +n02489166 proboscis monkey, Nasalis larvatus +n02490219 marmoset +n02492035 capuchin, ringtail, Cebus capucinus +n02492660 howler monkey, howler +n02493509 titi, titi monkey +n02493793 spider monkey, Ateles geoffroyi +n02494079 squirrel monkey, Saimiri sciureus +n02497673 Madagascar cat, ring-tailed lemur, Lemur catta +n02500267 indri, indris, Indri indri, Indri brevicaudatus +n02504013 Indian elephant, Elephas maximus +n02504458 African elephant, Loxodonta africana +n02509815 lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens +n02510455 giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca +n02514041 barracouta, snoek +n02526121 eel +n02536864 coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch +n02606052 rock beauty, Holocanthus tricolor +n02607072 anemone fish +n02640242 sturgeon +n02641379 gar, garfish, garpike, billfish, Lepisosteus osseus +n02643566 lionfish +n02655020 puffer, pufferfish, blowfish, globefish +n02666196 abacus +n02667093 abaya +n02669723 academic gown, academic robe, judge's robe +n02672831 accordion, piano accordion, squeeze box +n02676566 acoustic guitar +n02687172 aircraft carrier, carrier, flattop, attack aircraft carrier +n02690373 airliner +n02692877 airship, dirigible +n02699494 altar +n02701002 ambulance +n02704792 amphibian, amphibious vehicle +n02708093 analog clock +n02727426 apiary, bee house +n02730930 apron +n02747177 ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin +n02749479 assault rifle, assault gun +n02769748 backpack, back pack, knapsack, packsack, rucksack, haversack +n02776631 bakery, bakeshop, bakehouse +n02777292 balance beam, beam +n02782093 balloon +n02783161 ballpoint, ballpoint pen, ballpen, Biro +n02786058 Band Aid +n02787622 banjo +n02788148 bannister, banister, balustrade, balusters, handrail +n02790996 barbell +n02791124 barber chair +n02791270 barbershop +n02793495 barn +n02794156 barometer +n02795169 barrel, cask +n02797295 barrow, garden cart, lawn cart, wheelbarrow +n02799071 baseball +n02802426 basketball +n02804414 bassinet +n02804610 bassoon +n02807133 bathing cap, swimming cap +n02808304 bath towel +n02808440 bathtub, bathing tub, bath, tub +n02814533 beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon +n02814860 beacon, lighthouse, beacon light, pharos +n02815834 beaker +n02817516 bearskin, busby, shako +n02823428 beer bottle +n02823750 beer glass +n02825657 bell cote, bell cot +n02834397 bib +n02835271 bicycle-built-for-two, tandem bicycle, tandem +n02837789 bikini, two-piece +n02840245 binder, ring-binder +n02841315 binoculars, field glasses, opera glasses +n02843684 birdhouse +n02859443 boathouse +n02860847 bobsled, bobsleigh, bob +n02865351 bolo tie, bolo, bola tie, bola +n02869837 bonnet, poke bonnet +n02870880 bookcase +n02871525 bookshop, bookstore, bookstall +n02877765 bottlecap +n02879718 bow +n02883205 bow tie, bow-tie, bowtie +n02892201 brass, memorial tablet, plaque +n02892767 brassiere, bra, bandeau +n02894605 breakwater, groin, groyne, mole, bulwark, seawall, jetty +n02895154 breastplate, aegis, egis +n02906734 broom +n02909870 bucket, pail +n02910353 buckle +n02916936 bulletproof vest +n02917067 bullet train, bullet +n02927161 butcher shop, meat market +n02930766 cab, hack, taxi, taxicab +n02939185 caldron, cauldron +n02948072 candle, taper, wax light +n02950826 cannon +n02951358 canoe +n02951585 can opener, tin opener +n02963159 cardigan +n02965783 car mirror +n02966193 carousel, carrousel, merry-go-round, roundabout, whirligig +n02966687 carpenter's kit, tool kit +n02971356 carton +n02974003 car wheel +n02977058 cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM +n02978881 cassette +n02979186 cassette player +n02980441 castle +n02981792 catamaran +n02988304 CD player +n02992211 cello, violoncello +n02992529 cellular telephone, cellular phone, cellphone, cell, mobile phone +n02999410 chain +n03000134 chainlink fence +n03000247 chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour +n03000684 chain saw, chainsaw +n03014705 chest +n03016953 chiffonier, commode +n03017168 chime, bell, gong +n03018349 china cabinet, china closet +n03026506 Christmas stocking +n03028079 church, church building +n03032252 cinema, movie theater, movie theatre, movie house, picture palace +n03041632 cleaver, meat cleaver, chopper +n03042490 cliff dwelling +n03045698 cloak +n03047690 clog, geta, patten, sabot +n03062245 cocktail shaker +n03063599 coffee mug +n03063689 coffeepot +n03065424 coil, spiral, volute, whorl, helix +n03075370 combination lock +n03085013 computer keyboard, keypad +n03089624 confectionery, confectionary, candy store +n03095699 container ship, containership, container vessel +n03100240 convertible +n03109150 corkscrew, bottle screw +n03110669 cornet, horn, trumpet, trump +n03124043 cowboy boot +n03124170 cowboy hat, ten-gallon hat +n03125729 cradle +n03126707 crane +n03127747 crash helmet +n03127925 crate +n03131574 crib, cot +n03133878 Crock Pot +n03134739 croquet ball +n03141823 crutch +n03146219 cuirass +n03160309 dam, dike, dyke +n03179701 desk +n03180011 desktop computer +n03187595 dial telephone, dial phone +n03188531 diaper, nappy, napkin +n03196217 digital clock +n03197337 digital watch +n03201208 dining table, board +n03207743 dishrag, dishcloth +n03207941 dishwasher, dish washer, dishwashing machine +n03208938 disk brake, disc brake +n03216828 dock, dockage, docking facility +n03218198 dogsled, dog sled, dog sleigh +n03220513 dome +n03223299 doormat, welcome mat +n03240683 drilling platform, offshore rig +n03249569 drum, membranophone, tympan +n03250847 drumstick +n03255030 dumbbell +n03259280 Dutch oven +n03271574 electric fan, blower +n03272010 electric guitar +n03272562 electric locomotive +n03290653 entertainment center +n03291819 envelope +n03297495 espresso maker +n03314780 face powder +n03325584 feather boa, boa +n03337140 file, file cabinet, filing cabinet +n03344393 fireboat +n03345487 fire engine, fire truck +n03347037 fire screen, fireguard +n03355925 flagpole, flagstaff +n03372029 flute, transverse flute +n03376595 folding chair +n03379051 football helmet +n03384352 forklift +n03388043 fountain +n03388183 fountain pen +n03388549 four-poster +n03393912 freight car +n03394916 French horn, horn +n03400231 frying pan, frypan, skillet +n03404251 fur coat +n03417042 garbage truck, dustcart +n03424325 gasmask, respirator, gas helmet +n03425413 gas pump, gasoline pump, petrol pump, island dispenser +n03443371 goblet +n03444034 go-kart +n03445777 golf ball +n03445924 golfcart, golf cart +n03447447 gondola +n03447721 gong, tam-tam +n03450230 gown +n03452741 grand piano, grand +n03457902 greenhouse, nursery, glasshouse +n03459775 grille, radiator grille +n03461385 grocery store, grocery, food market, market +n03467068 guillotine +n03476684 hair slide +n03476991 hair spray +n03478589 half track +n03481172 hammer +n03482405 hamper +n03483316 hand blower, blow dryer, blow drier, hair dryer, hair drier +n03485407 hand-held computer, hand-held microcomputer +n03485794 handkerchief, hankie, hanky, hankey +n03492542 hard disc, hard disk, fixed disk +n03494278 harmonica, mouth organ, harp, mouth harp +n03495258 harp +n03496892 harvester, reaper +n03498962 hatchet +n03527444 holster +n03529860 home theater, home theatre +n03530642 honeycomb +n03532672 hook, claw +n03534580 hoopskirt, crinoline +n03535780 horizontal bar, high bar +n03538406 horse cart, horse-cart +n03544143 hourglass +n03584254 iPod +n03584829 iron, smoothing iron +n03590841 jack-o'-lantern +n03594734 jean, blue jean, denim +n03594945 jeep, landrover +n03595614 jersey, T-shirt, tee shirt +n03598930 jigsaw puzzle +n03599486 jinrikisha, ricksha, rickshaw +n03602883 joystick +n03617480 kimono +n03623198 knee pad +n03627232 knot +n03630383 lab coat, laboratory coat +n03633091 ladle +n03637318 lampshade, lamp shade +n03642806 laptop, laptop computer +n03649909 lawn mower, mower +n03657121 lens cap, lens cover +n03658185 letter opener, paper knife, paperknife +n03661043 library +n03662601 lifeboat +n03666591 lighter, light, igniter, ignitor +n03670208 limousine, limo +n03673027 liner, ocean liner +n03676483 lipstick, lip rouge +n03680355 Loafer +n03690938 lotion +n03691459 loudspeaker, speaker, speaker unit, loudspeaker system, speaker system +n03692522 loupe, jeweler's loupe +n03697007 lumbermill, sawmill +n03706229 magnetic compass +n03709823 mailbag, postbag +n03710193 mailbox, letter box +n03710637 maillot +n03710721 maillot, tank suit +n03717622 manhole cover +n03720891 maraca +n03721384 marimba, xylophone +n03724870 mask +n03729826 matchstick +n03733131 maypole +n03733281 maze, labyrinth +n03733805 measuring cup +n03742115 medicine chest, medicine cabinet +n03743016 megalith, megalithic structure +n03759954 microphone, mike +n03761084 microwave, microwave oven +n03763968 military uniform +n03764736 milk can +n03769881 minibus +n03770439 miniskirt, mini +n03770679 minivan +n03773504 missile +n03775071 mitten +n03775546 mixing bowl +n03776460 mobile home, manufactured home +n03777568 Model T +n03777754 modem +n03781244 monastery +n03782006 monitor +n03785016 moped +n03786901 mortar +n03787032 mortarboard +n03788195 mosque +n03788365 mosquito net +n03791053 motor scooter, scooter +n03792782 mountain bike, all-terrain bike, off-roader +n03792972 mountain tent +n03793489 mouse, computer mouse +n03794056 mousetrap +n03796401 moving van +n03803284 muzzle +n03804744 nail +n03814639 neck brace +n03814906 necklace +n03825788 nipple +n03832673 notebook, notebook computer +n03837869 obelisk +n03838899 oboe, hautboy, hautbois +n03840681 ocarina, sweet potato +n03841143 odometer, hodometer, mileometer, milometer +n03843555 oil filter +n03854065 organ, pipe organ +n03857828 oscilloscope, scope, cathode-ray oscilloscope, CRO +n03866082 overskirt +n03868242 oxcart +n03868863 oxygen mask +n03871628 packet +n03873416 paddle, boat paddle +n03874293 paddlewheel, paddle wheel +n03874599 padlock +n03876231 paintbrush +n03877472 pajama, pyjama, pj's, jammies +n03877845 palace +n03884397 panpipe, pandean pipe, syrinx +n03887697 paper towel +n03888257 parachute, chute +n03888605 parallel bars, bars +n03891251 park bench +n03891332 parking meter +n03895866 passenger car, coach, carriage +n03899768 patio, terrace +n03902125 pay-phone, pay-station +n03903868 pedestal, plinth, footstall +n03908618 pencil box, pencil case +n03908714 pencil sharpener +n03916031 perfume, essence +n03920288 Petri dish +n03924679 photocopier +n03929660 pick, plectrum, plectron +n03929855 pickelhaube +n03930313 picket fence, paling +n03930630 pickup, pickup truck +n03933933 pier +n03935335 piggy bank, penny bank +n03937543 pill bottle +n03938244 pillow +n03942813 ping-pong ball +n03944341 pinwheel +n03947888 pirate, pirate ship +n03950228 pitcher, ewer +n03954731 plane, carpenter's plane, woodworking plane +n03956157 planetarium +n03958227 plastic bag +n03961711 plate rack +n03967562 plow, plough +n03970156 plunger, plumber's helper +n03976467 Polaroid camera, Polaroid Land camera +n03976657 pole +n03977966 police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria +n03980874 poncho +n03982430 pool table, billiard table, snooker table +n03983396 pop bottle, soda bottle +n03991062 pot, flowerpot +n03992509 potter's wheel +n03995372 power drill +n03998194 prayer rug, prayer mat +n04004767 printer +n04005630 prison, prison house +n04008634 projectile, missile +n04009552 projector +n04019541 puck, hockey puck +n04023962 punching bag, punch bag, punching ball, punchball +n04026417 purse +n04033901 quill, quill pen +n04033995 quilt, comforter, comfort, puff +n04037443 racer, race car, racing car +n04039381 racket, racquet +n04040759 radiator +n04041544 radio, wireless +n04044716 radio telescope, radio reflector +n04049303 rain barrel +n04065272 recreational vehicle, RV, R.V. +n04067472 reel +n04069434 reflex camera +n04070727 refrigerator, icebox +n04074963 remote control, remote +n04081281 restaurant, eating house, eating place, eatery +n04086273 revolver, six-gun, six-shooter +n04090263 rifle +n04099969 rocking chair, rocker +n04111531 rotisserie +n04116512 rubber eraser, rubber, pencil eraser +n04118538 rugby ball +n04118776 rule, ruler +n04120489 running shoe +n04125021 safe +n04127249 safety pin +n04131690 saltshaker, salt shaker +n04133789 sandal +n04136333 sarong +n04141076 sax, saxophone +n04141327 scabbard +n04141975 scale, weighing machine +n04146614 school bus +n04147183 schooner +n04149813 scoreboard +n04152593 screen, CRT screen +n04153751 screw +n04154565 screwdriver +n04162706 seat belt, seatbelt +n04179913 sewing machine +n04192698 shield, buckler +n04200800 shoe shop, shoe-shop, shoe store +n04201297 shoji +n04204238 shopping basket +n04204347 shopping cart +n04208210 shovel +n04209133 shower cap +n04209239 shower curtain +n04228054 ski +n04229816 ski mask +n04235860 sleeping bag +n04238763 slide rule, slipstick +n04239074 sliding door +n04243546 slot, one-armed bandit +n04251144 snorkel +n04252077 snowmobile +n04252225 snowplow, snowplough +n04254120 soap dispenser +n04254680 soccer ball +n04254777 sock +n04258138 solar dish, solar collector, solar furnace +n04259630 sombrero +n04263257 soup bowl +n04264628 space bar +n04265275 space heater +n04266014 space shuttle +n04270147 spatula +n04273569 speedboat +n04275548 spider web, spider's web +n04277352 spindle +n04285008 sports car, sport car +n04286575 spotlight, spot +n04296562 stage +n04310018 steam locomotive +n04311004 steel arch bridge +n04311174 steel drum +n04317175 stethoscope +n04325704 stole +n04326547 stone wall +n04328186 stopwatch, stop watch +n04330267 stove +n04332243 strainer +n04335435 streetcar, tram, tramcar, trolley, trolley car +n04336792 stretcher +n04344873 studio couch, day bed +n04346328 stupa, tope +n04347754 submarine, pigboat, sub, U-boat +n04350905 suit, suit of clothes +n04355338 sundial +n04355933 sunglass +n04356056 sunglasses, dark glasses, shades +n04357314 sunscreen, sunblock, sun blocker +n04366367 suspension bridge +n04367480 swab, swob, mop +n04370456 sweatshirt +n04371430 swimming trunks, bathing trunks +n04371774 swing +n04372370 switch, electric switch, electrical switch +n04376876 syringe +n04380533 table lamp +n04389033 tank, army tank, armored combat vehicle, armoured combat vehicle +n04392985 tape player +n04398044 teapot +n04399382 teddy, teddy bear +n04404412 television, television system +n04409515 tennis ball +n04417672 thatch, thatched roof +n04418357 theater curtain, theatre curtain +n04423845 thimble +n04428191 thresher, thrasher, threshing machine +n04429376 throne +n04435653 tile roof +n04442312 toaster +n04443257 tobacco shop, tobacconist shop, tobacconist +n04447861 toilet seat +n04456115 torch +n04458633 totem pole +n04461696 tow truck, tow car, wrecker +n04462240 toyshop +n04465501 tractor +n04467665 trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi +n04476259 tray +n04479046 trench coat +n04482393 tricycle, trike, velocipede +n04483307 trimaran +n04485082 tripod +n04486054 triumphal arch +n04487081 trolleybus, trolley coach, trackless trolley +n04487394 trombone +n04493381 tub, vat +n04501370 turnstile +n04505470 typewriter keyboard +n04507155 umbrella +n04509417 unicycle, monocycle +n04515003 upright, upright piano +n04517823 vacuum, vacuum cleaner +n04522168 vase +n04523525 vault +n04525038 velvet +n04525305 vending machine +n04532106 vestment +n04532670 viaduct +n04536866 violin, fiddle +n04540053 volleyball +n04542943 waffle iron +n04548280 wall clock +n04548362 wallet, billfold, notecase, pocketbook +n04550184 wardrobe, closet, press +n04552348 warplane, military plane +n04553703 washbasin, handbasin, washbowl, lavabo, wash-hand basin +n04554684 washer, automatic washer, washing machine +n04557648 water bottle +n04560804 water jug +n04562935 water tower +n04579145 whiskey jug +n04579432 whistle +n04584207 wig +n04589890 window screen +n04590129 window shade +n04591157 Windsor tie +n04591713 wine bottle +n04592741 wing +n04596742 wok +n04597913 wooden spoon +n04599235 wool, woolen, woollen +n04604644 worm fence, snake fence, snake-rail fence, Virginia fence +n04606251 wreck +n04612504 yawl +n04613696 yurt +n06359193 web site, website, internet site, site +n06596364 comic book +n06785654 crossword puzzle, crossword +n06794110 street sign +n06874185 traffic light, traffic signal, stoplight +n07248320 book jacket, dust cover, dust jacket, dust wrapper +n07565083 menu +n07579787 plate +n07583066 guacamole +n07584110 consomme +n07590611 hot pot, hotpot +n07613480 trifle +n07614500 ice cream, icecream +n07615774 ice lolly, lolly, lollipop, popsicle +n07684084 French loaf +n07693725 bagel, beigel +n07695742 pretzel +n07697313 cheeseburger +n07697537 hotdog, hot dog, red hot +n07711569 mashed potato +n07714571 head cabbage +n07714990 broccoli +n07715103 cauliflower +n07716358 zucchini, courgette +n07716906 spaghetti squash +n07717410 acorn squash +n07717556 butternut squash +n07718472 cucumber, cuke +n07718747 artichoke, globe artichoke +n07720875 bell pepper +n07730033 cardoon +n07734744 mushroom +n07742313 Granny Smith +n07745940 strawberry +n07747607 orange +n07749582 lemon +n07753113 fig +n07753275 pineapple, ananas +n07753592 banana +n07754684 jackfruit, jak, jack +n07760859 custard apple +n07768694 pomegranate +n07802026 hay +n07831146 carbonara +n07836838 chocolate sauce, chocolate syrup +n07860988 dough +n07871810 meat loaf, meatloaf +n07873807 pizza, pizza pie +n07875152 potpie +n07880968 burrito +n07892512 red wine +n07920052 espresso +n07930864 cup +n07932039 eggnog +n09193705 alp +n09229709 bubble +n09246464 cliff, drop, drop-off +n09256479 coral reef +n09288635 geyser +n09332890 lakeside, lakeshore +n09399592 promontory, headland, head, foreland +n09421951 sandbar, sand bar +n09428293 seashore, coast, seacoast, sea-coast +n09468604 valley, vale +n09472597 volcano +n09835506 ballplayer, baseball player +n10148035 groom, bridegroom +n10565667 scuba diver +n11879895 rapeseed +n11939491 daisy +n12057211 yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum +n12144580 corn +n12267677 acorn +n12620546 hip, rose hip, rosehip +n12768682 buckeye, horse chestnut, conker +n12985857 coral fungus +n12998815 agaric +n13037406 gyromitra +n13040303 stinkhorn, carrion fungus +n13044778 earthstar +n13052670 hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa +n13054560 bolete +n13133613 ear, spike, capitulum +n15075141 toilet tissue, toilet paper, bathroom tissue \ No newline at end of file diff --git a/python/caffe/imagenet/wrapper2.py b/python/caffe/imagenet/wrapper2.py index abb8ca1b..825f398f 100644 --- a/python/caffe/imagenet/wrapper2.py +++ b/python/caffe/imagenet/wrapper2.py @@ -37,6 +37,9 @@ BATCH_SIZE = 245 # Load the imagenet mean file IMAGENET_MEAN = np.load( os.path.join(os.path.dirname(__file__), 'ilsvrc_2012_mean.npy')) +CROPPED_IMAGENET_MEAN = IMAGENET_MEAN[ + IMAGE_CENTER:IMAGE_CENTER + CROPPED_DIM, + IMAGE_CENTER:IMAGE_CENTER + CROPPED_DIM, :] def load_image(filename): @@ -55,18 +58,18 @@ def load_image(filename): return img -def format_image(image, window=None, dim=IMAGE_DIM): +def format_image(image, window=None, cropped_size=False): """ Input: image: (H x W x 3) ndarray window: (4) ndarray (y, x, h, w) coordinates - dim: int - Final width of the square image. + cropped_size: bool + Whether to output cropped size image or full size image. Output: image: (3 x H x W) ndarray - Resized to (dim, dim). + Resized to either IMAGE_DIM or CROPPED_DIM. """ # Crop a subimage if window is provided. if window is not None: @@ -75,13 +78,16 @@ def format_image(image, window=None, dim=IMAGE_DIM): window[1]:window[3] ] - # Resize to ImageNet size, convert to BGR, subtract mean. - image = (skimage.transform.resize(image, (IMAGE_DIM, IMAGE_DIM)) * 255)[:, :, ::-1] - image -= IMAGENET_MEAN - # Resize further if needed. - if not dim == IMAGE_DIM: - image = skimage.transform.resize(image, (dim, dim)) + # Resize to ImageNet size, convert to BGR, subtract mean. + image = image[:, :, ::-1] + if cropped_size: + image = skimage.transform.resize(image, (CROPPED_DIM, CROPPED_DIM)) * 255 + image -= CROPPED_IMAGENET_MEAN + else: + image = skimage.transform.resize(image, (IMAGE_DIM, IMAGE_DIM)) * 255 + image -= IMAGENET_MEAN + image = image.swapaxes(1, 2).swapaxes(0, 1) return image From 5c0693e30a08607094f2a7321cf71926a158483e Mon Sep 17 00:00:00 2001 From: Sergey Karayev Date: Thu, 5 Dec 2013 19:33:13 -0800 Subject: [PATCH 4/5] selective search notebook and renaming to power_wrapper --- .../{wrapper2.py => power_wrapper.py} | 0 .../imagenet/selective_search_demo.ipynb | 220 ++++++++++++++++++ 2 files changed, 220 insertions(+) rename python/caffe/imagenet/{wrapper2.py => power_wrapper.py} (100%) create mode 100644 python/caffe/imagenet/selective_search_demo.ipynb diff --git a/python/caffe/imagenet/wrapper2.py b/python/caffe/imagenet/power_wrapper.py similarity index 100% rename from python/caffe/imagenet/wrapper2.py rename to python/caffe/imagenet/power_wrapper.py diff --git a/python/caffe/imagenet/selective_search_demo.ipynb b/python/caffe/imagenet/selective_search_demo.ipynb new file mode 100644 index 00000000..7db99a89 --- /dev/null +++ b/python/caffe/imagenet/selective_search_demo.ipynb @@ -0,0 +1,220 @@ +{ + "metadata": { + "name": "" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First of all, we'll need a little [Python script](https://github.com/sergeyk/selective_search_ijcv_with_python) to run the Matlab Selective Search code.\n", + "\n", + "Let's run detection on an image of a cat lounging on a couch, which we will download from the web.\n", + "\n", + " wget http://farm2.static.flickr.com/1104/1204206441_bc256e34f2.jpg\n", + " echo \"1204206441_bc256e34f2.jpg\" > image_cat.txt\n", + " python power_wrapper.py --images_file=image_cat.txt --crop_mode=selective_search --model_def=../../../examples/imagenet_deploy.prototxt --pretrained_model=alexnet_train_iter_470000 --output=selective_cat.h5\n", + " \n", + "Now we can load the results to visualize them.\n", + "You'll see we store filenames, windows, and computed ImageNet predictions.\n", + "Of course, we only ran on one image, so the filenames are all the same.\n", + "In general, `power_wrapper` is most efficient when running on a lot of images: it first extracts window proposals for all of them, then batches the windows for efficient GPU processing, and then outputs the results." + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import pandas as pd\n", + "\n", + "df = pd.read_hdf('images_feats_selective_cat.h5', 'df')\n", + "print(df)\n", + "print('')\n", + "print(df.iloc[0])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n", + "Int64Index: 146 entries, 0 to 145\n", + "Data columns (total 3 columns):\n", + "filename 146 non-null values\n", + "window 146 non-null values\n", + "feat 146 non-null values\n", + "dtypes: object(3)\n", + "\n", + "filename /home/sergeyk/work/caffe/1204206441_bc256e34f2...\n", + "window [0, 0, 375, 500]\n", + "feat [2.23269e-08, 2.23006e-09, 7.42724e-07, 3.9558...\n", + "Name: 0, dtype: object\n" + ] + } + ], + "prompt_number": 40 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Load ImageNet class names and make a DataFrame of features.\n", + "\n", + "with open('../../../examples/synset_words.txt') as f:\n", + " labels = [' '.join(l.strip().split(' ')[1:]).split(',')[0] for l in f.readlines()]\n", + "feats_df = pd.DataFrame(np.vstack(df_.feat.values), columns=labels)\n", + "feats_df" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "html": [ + "
\n",
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 146 entries, 0 to 145\n",
+        "Columns: 1000 entries, tench to toilet tissue\n",
+        "dtypes: float32(1000)\n",
+        "
" + ], + "metadata": {}, + "output_type": "pyout", + "prompt_number": 41, + "text": [ + "\n", + "Int64Index: 146 entries, 0 to 145\n", + "Columns: 1000 entries, tench to toilet tissue\n", + "dtypes: float32(1000)" + ] + } + ], + "prompt_number": 41 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Let's look at the activations and plot the top classes,\n", + "# taking max across all windows.\n", + "gray()\n", + "matshow(feats_df.values)\n", + "xlabel('Classes')\n", + "ylabel('Windows')\n", + "\n", + "max_s = feats_df.max(0)\n", + "max_s.sort(ascending=False)\n", + "print(max_s[:10])" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "tabby 0.899196\n", + "window screen 0.816435\n", + "shower curtain 0.738980\n", + "spindle 0.700357\n", + "tiger cat 0.573101\n", + "theater curtain 0.482326\n", + "radiator 0.452591\n", + "Egyptian cat 0.396828\n", + "window shade 0.372198\n", + "handkerchief 0.354255\n", + "dtype: float32\n" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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E1PLYzUc/+6EsE+68VtpFyh8j/FowIIzJGTNmt3ihRKzONdyiN4hX7/G1E5+T\nVVe37zE4OIjBwUEMDAzYDitXO4BEOFFMDxW9VI77osZJHy+ta8xM8PRMyVjyyGw12wxlQs8O6jrY\nbj0XJmslFowGukZ5rLZUcUM8HjfUCz0HVF73H3iGz9p2skwEGXHlyhWmOPXi1sqhDHS91H1WSyzA\nnrmqU5nV8tjNR6MJGyuZnJj82k0vXvpsFE5Q9aFZvWT3nb/+9a/r5g/1JcQg9Cu7hHcUFhamzUhz\nfXZKbVaqnXkN27tr5fVTfnVc2hUgtT5p01htmjw8PIwbb7wRw8PDePXVVwFcMylXrAfUzyrmy1qn\nKqLml3qmuLi4GFeuXLG03lCbvkcikXSajhw5EpcuXTKMJwzO+4hgofwkCIIgRIXMmHMcpdOv5+HR\n7H4/icViaUW0Gzd1rgiRiEQi6dlbu2WNEAPRzJjVZrI862PR6kw7nksnTJiAkydP2g7TD5NMPY+z\nvNNWO3HFM3xWneKh86xHvikEbcYs2jYCp2bMgPkqqN8m8vlmxsyjTyCaLoaNQAa7w8PDuHLlStox\nit/k02CXIPKZjRs34vz583j88ccBAO973/vw17/+NWCpCIIgCIIgCD/wbc/umjVr0NXVhStXrqCx\nsRHTp0/HV7/6VS6RO8Wpe/Vcxu80GTlypK/xaRHxaAuCH//6r/+aHugC8GygSxNobNhJL9Y0LSoq\nAqC/Hy0olHcIk344kVWvHvX7nSdMmOBrfH7Cs50Suc3zQzYljiDrCSfvqWw/8SouFr8CWpRtNU7j\n1gtPi519xlr87l/aOR7IihtuuAHV1dXp7/k2ThG5fgJsDHYPHTqEsrIy7NixA7fddhtaWlrw1FNP\n+SGbIaIegRMU6n18fmG0V9AvyCyE4EEYTKlEwk56saZpb28vAH0HQmawNK6sHQ/lHcKkH05k1atH\nnYSj7bwWFxfbfpbF3NkJimxWgwIeg3ytTvJsp4LQRbtpwvKeTjvFShx26wkvJm3Gjx/P/Ix6iwzg\nLk31dHjGjBnMMqnR0yuneqsXlhPHcE48gLvBqH1gcV526NAhLFy4MP0938YpovfJLXNwaGgIg4OD\n2LFjB5YtW4Z4PB6q2e58QHQlI4gwINLKoqh4WfebdSj04mWp95x2PNysmtghjG2pHY+jRl58vX7f\nRCKRdU2RzWqfMY/BpJdtsVeDXdZy5xYljbzWBbvppchhp/5vaWlxJIt6wOcmH/X0680338z4zjKx\nJ+pknlXYimRVAAAgAElEQVRZ5Y3iOFOLkj52yvUPf/hD7Nq1i6tcBD8sB7uf+MQnMHHiRPT09GDx\n4sVoaWnBiBEj/JDNU4zMN5zgRSdZaYC0MhrJrL7OsxGxCisSiXCJL4xmg4Rz9DqlSplUPGOrG229\n+3nDurKoJhqNZuiuYprLA5HMg7zsHJl1KILqlHldH4VxgsXNioXX+ah1QGVFbW2tR5Jcg6f+eFUP\nmJW7cePGuQ7f6GgcL3WBJd0VOezU/zz7OlboDVr1yp42HVnKp1/9LdHrOR6TVHfddZdu2uebObOo\nMHtjlmUZQ0NDrma8Ozs7sX79ehw8eBCSJGHLli2YMmUK7rrrLpw8eRITJ07E008/jfLy8kxhaSBE\nEIRHBOHRPGx46UXTLOygvHd67UkzLF5J1ai9TrPi9fu6kS1M+OG9mnc8oqKU8TB4zdXTb20eheE9\nRMMszeyWgR07duCOO+6gPgRnfHNQNWnSJPzjP/4jvvvd76YHp25Nux566CHcfvvtOHz4MA4cOID6\n+no0NzejqakJR48exZIlS9Dc3GwrLD8GwF7NqNpZpbUTBs0ceY9Xema1V8QuvOXT6ryT8N3IpF7l\n5WU9YIX6qC9WvJRPL+ygJv687PiahR1Uh9vrTmMYBxIir154MdAVcZKdp94kk0luYdmBp9ULD6xM\nVdWLLlVVVY7j4dGm6+m31upJ/R5WcRqtuIqo824wex+ryQG9sqYX3ooVK5gHumFOZ56OzfzAcmX3\n6tWr2LNnD1566SW89NJLOHr0KBobG7Fjxw5HEV6+fBlz587N8q5aX1+PF198EdXV1Th37hxuvvlm\nvP3225nChlgx3GI1+wr423HKhxlfIvdRN3Sk08GQTCbR39/vaRxWeRtU3odR5/Rk1nYYWVaXlAmt\noFdEvDhDlOcqWxCr1oWFhRn7GVnTSC9vrdLEizLBK8yCggJcvXqV6ZloNIqKigq8++67AOzrxOjR\no3H+/HnL+1jeTRTrJSuZtWd5e11PlpSUoKenx1UYEyZMQHt7e5Z+aMtQruJVHvm2shuLxRCPxxGN\nRhGJRFBVVZXhXpuVEydOoKqqCuvWrcO8efPwwAMP4MqVK2hvb0+HW11djfb2dsdx5CJWqx5+d5jC\n1kEjCD3UnQ7S6WDweqALGOdtEBOFatTxhmUyV281SNt5N+rM6838y7LMrQPuZmWBlw6ow+FpGaAe\n6PqlK9pOOmsa6eWtVZr43WFm8RfCOtAFrllCKANdwL5O6A109SwotO9m9h4iDHQB6/pY66DK6/rZ\nyKEeCy+++KJu+ubDQBcQv/9k2TKUlZXh05/+NK677jps3boVr776Kr73ve85jnBoaAj79+/Hhg0b\nsH//fhQXF2eZLPN0HkUQIuOlns+aNcuzsP0ibPVA2OQNG+r05WE6K1ID7Ycsdh0emuHGU6peR5+n\n6Vu+7FX0S2+9qM+cboOrqalhut+u7FbHjBUWFqb/f+CBB5hkYJXFCj391obNuvLOA6twWPNCZHNY\nIyZOnKhbN1ZWVlK/QEVQaWGpUT/72c+waNEifPvb38bq1avxxS9+Ec8//7zjCOvq6lBXV4f3v//9\nAIA777wT+/fvR01NDc6dOwcAaGtrw+jRox3HwRu3ew8VtAVY2SuTTCYzfotEIul9itr9GFqvy8q9\nXuyZsrNXUJHBbnhU6DPxstNy4MABz8L2Az9MPI08hToNx84KAuEcdfqKskrhBr91QqufPM7UBdx1\nTvNlgCoqZjroRf3r1DMvq7UfL9krKyvT/+/Zs8dRGE4sOIysILSI0K5YpTVrXvhZJ0iSxGXfulEd\nePHiRaEmVYMmsJMV7Hpjfvvtt/HMM8/giSeewPnz5x2ZcygsXrwYP/jBDzB16lT8+7//O3p7ewEA\no0aNwqOPPorm5mZ0dnbqrvgSBEEQBA9E2jMrkixmuNlj7bWnWFH2JHpNWHQlDChpaSdNg053O/u2\ng5bRDWH2nfDWW29h/vz5vmzLySe4eZ23GuzecccdeOONNzBp0iQsXrwYixYtwoIFCzJMO1h58803\nsX79egwMDGDSpEnYsmULhoeHsWrVKpw6dYqOHiKIPOezn/0spk2bho9//OMArnnwVCbFCIIgCIIg\niNzGt8Hua6+9hnnz5glxtAANdsM9a0cQokFnEoYXllU8J15UecFSZ4tWvzstH0bv4YfnbSN4eFy1\ngx/57ZeeeBGPH2WRxcM3i47zaC/slIF4PI7h4eGsuPQmfceNG4fW1lYAwIgRI3D58mXbsohW3wRF\nIpHAwMBA1nWWVf/x48dj7Nix2LNnD3OaUj4Y49tgd2BgAN/5znfwhz/8AQBw880345Of/KTrs3ad\noLdflFdCqDtOTlzjm8mSSCQwODiY9bsSZyKRQCQSSTcAyh7coaEhFBYWZlRu2njUngR5d9q17t+1\n76RUDmb3aaEVOsJrWOoFM31kOepDr1xyq6QZGlzeuDlSxmuog0CIjkjlxQlelLHi4mIu3m/DCtVb\n4jFr1izXPk6M8lXtj4hgx7fB7sc+9jEMDQ1h7dq1kGUZTz31FGKxGH7wgx9wEYAFWtkVB94VNjUA\n+YUo+c1rj180GkUqlRLinfIFFh0ymrkPGlHKAQt20jLIFVwjeJX1MK+qiiDD2LFjcfbsWa5hamGR\nu6qqChcuXPBNHt5pynNCxWrhIlf3xKtXx9Ww6lFHR0fWBLkdi5IgztDmjejn7FoOdvVmPHjMgjjB\nr8GuF4fLu8Go4+BlYyhJEuLxeLpTY6eSs3MPr06QCB0Bgg11J9moAVDK+IgRI3DlyhUMDQ1lrWqq\nPR8r19Vh+60bkiQhFotlmJ0pHRDFQ6NyPR6PIx6Po7e3N+MeK0sSBXUalJSUoLu726vXEh6qA9xD\naZi7UN7yJ1cHe/mOl2WF1bQ812FNa175YnleQCwWwzvvvJP+fvz4cceu48OCF4fLu8FocKjt/PNE\nluWM2Xs7Fbyde3jN9lMjHj7U+mQ00ynLMmRZRmdnZ4bpvzq/lXvU19Vh+60bsixjcHAwY8Cq/J9K\npTKuDw4Ops2m1fdYha9+Z+VaPg90WQnjuY1e4OZMTpZwCwoKuITLEz/8jnipZ6ztPK8Je974uWjB\nm0mTJjl6jpfu6fW9b7nlFsfhiVov+j3GsMofO+lUUFCAVatWZV2ngW4mQfXdLTXqa1/7Gm699VZc\nd911AICWlhZs2bLFc8F4kC8zm7nwjvmSV/mMekVWmSFX53skEtEd3FqFKYLeaFdwRTJLEiWNgsRu\nx9fvtPJbT3i8m56DIW24Rg6IgtyvaTWpxCPvvdyfG8YyzNPE1qsVIZYtDsePH7cdvxov9eLll1/O\n+M6STlq5RFm59rvttBrM2knPe+65Bz/5yU90f7PKExa/N6Iiun8CW+fsXr16FUeOHIEkSZg2bRqX\nA5idQHt2CSI/+MIXvoDS0lI88sgjAICRI0fi0qVLAUtFEARBEARB+IHne3Z/8YtfZHgB1bJy5Uou\nArBAg11x8NpBFa1G5TaizAKG1UEVlQ+2NBB15jyM+WjH74KIDsHIQZUYMvjtEMoKPxxmqSEHVeIx\nfvx4nDp1Kus6S16NHj0aFy9ezEqf0tJSyy1HIlmCOSW0Dqruu+8+SJKE8+fP4+WXX8att94KAPjd\n736HhQsX4te//jUXAVgQ3UGV1okOy3FAirKLMOhTjmNRm6uJ0PASwWOlB3aP4NI6lALeM29W/tfu\nUwWuNezqxl0bjtY02i+0cU6ePBnvvPNO1vWioiIMDQ2Znumnd135m0qldE3A1Wlo1vkx68xEo1FI\nkpTR6IpQ7kWQwQty9b2IYMjVgYjX5Es5zJf3dILXaUNp7xxu6SZb8MEPflA+e/Zs+vvZs2flpqYm\nq8c8AQB9BPlIkhS4DPShD33C/6G6JL8/hYWFgcvA6+OlLnsVNo9wzcLgJbcI9UQikXD0XCQS8ex9\nR4wYIXS+Ofls2rTJ1/gKCgpch1FcXCyPGzcusDTL1Q8vLF2Mtba2oqamJv29urpad7mfhY0bN2LG\njBlobGzE3Xffjf7+fly6dAlNTU2YOnUqli5dis7OTstweHpXjMfj6dWR4uJi03tZV5gVObXPKZvi\nCwsLM7zPRaPR9DOJRMJWHF541TPbm61ebVLuU9LQLH2Kior4CknkHH5uV9B6fXQSd2FhYdY1pTzy\neJcgt29Mnjw547sX9YycxzPeonpDNYO3Pvb19XENL0i81GWvwjYL125em4Wh/c2pznv1/iz6zLoV\nQgnbiZmx3fd14+1XG4feMXdB8Oijj2Z897oN5FEP9/X16Z7VG5SPI7d4leZB9Wcsc/iDH/wgPvSh\nD+FHP/oRtmzZgttvvx1NTU2OI2xpacH3v/997N+/H2+99RaGh4exbds2NDc3o6mpCUePHsWSJUvQ\n3NxsGZaVyQ5rJaYUbitvkayVgCKn9jmlAuzr68swHRweHk4/Y3ffkxKW+p2dKpXyXH9/v2EYSnyy\nLKf3bylpaJY+yrErRnH6cTwEEQyKmSzw3iSOWr8ikYilziqTKer74vF4+nltmFZo98k4aeD7+voQ\nj8czJqYUPeahz3oysbyjm4Zcfewc4K1XUS8hfw/8cKNPuX5sIU9YdTYsOi5rtl4ETWVlpWdhOx0w\nGqWNXtmzuyASZrweeBt5j2dh1qxZunkxMDBg2Q8QpSyoYU1zvUl/HuHywrLV+uY3v4lPfvKTeOON\nN3DgwAF84hOfwDe/+U3HEZaVlSEej6O3txdDQ0Po7e3F2LFjsWvXLqxduxYAsHbtWuzYscMyLCsF\nYklUdUfcaiaGVTGNOuHK9WQymfEukUgk/V3pyNuNQ/3OTpVKeS6RSBiGobeyW1hYCEmSdCtk5f7y\n8nLTOGnPUe4yPDycdS6uWr8UB09WqwTae5TZdvUEjF205ctJB7OoqAiDg4O651LzcDqhV55Y3tHN\nAHXs2LEZ30VslO0g6upxGNPTjT6F3QmLn7DqrKg6rkWvrxIk7777ru17/ZLZKB69ssfTEZyo9ZHX\ncvE4F/zAgQO6eVFYWGjZrxWlLLjBroWOWV4qCx6e5Dc3g2gGvve978klJSVyVVWV/NGPflSWZVku\nLy9P/55KpTK+K0AA+3H60Ic+9KFP8J/Kykrb9+bSvtAwfIz2Nsbj8cBkWrduXeDpQh/IsVjMl3js\n7jktKioy/d3Jflsecul99Oqxuro6z2TNl09xcbHrMEaMGCFPmDDB0bOUb8YfXliu7P7iF7/AlClT\nUFZWhtLSUpSWlqKsrMzqMUOOHz+OJ554Ai0tLTh79ix6enqyDmK2O7Lnud8pmUym46yurja91+nK\nrlZeZfW2rKwsbQKgrIwqM02jRo1iioMnZuY96pVdxTRNmZXR7lFW5yePGTQiN1Hri9U9vNBahzgJ\nX8/CxGifvhNisZh3s50WVFRUZHwXaeafZUVG1H2hIqUnT4xWm4I8/mnLli2BxR1WvNBPv1b3ZZur\nZUZbqxR4b92wK5ceevXY6dOn0/+HdZuJFV7Xk27yROHy5cs4efJk1nU7W5lEzbdcap8sR0iPPPII\ndu3aha6uLnR3d6O7uxtdXV2OI9y3bx8WLlyIUaNGIRaLYeXKlXjllVdQU1ODc+fOAQDa2towevRo\ny7B4Kkh/f39a4dvb203vZS0YipxaeRXThq6urnQlJssyUqlUeg/BxYsXmeLgiVlnUkkDWZbTjZdi\nhqrdoyyrzE557I0gchO1vljdwwuteZGT8PVMlIz26TthaGjI0rzbKzo6OjK+ByGDEUZbIvQQdZJN\npPTkidE+Qrvbcrxg/fr1gcUdVrzQTz/8crBMDlot3ojkRG7EiBFZ1yZMmJD+XyRZeeJ1PcljUFdU\nVIS6urqssOxszRM133KpfbJM4ZqaGkyfPp1bhPX19Xj11VfR19cHWZbx/PPPo6GhAcuWLcPWrVsB\nAFu3bsWKFSu4xUkQBEHkFlaOBNWI2pkQHaedQKPJ1yA7T3/4wx8Ci5t4Dz90QJZl27rL2yGpFXbk\nMrpHzzJi7ty5rmXKd6wWYezkWUlJCaZMmZJTA8RcQpItcuahhx7CuXPnsGLFigwPqitXrnQc6Ve/\n+lVs3boVkUgE8+bNww9+8AN0d3dj1apVOHXqFCZOnIinn346a+bebiURJmWzklf7u9n9Wrfx0Wg0\nvVLslXy8woxGoxgeHkYsFsPQ0BAikUha7kgkEtjKFsEPdZ6OGDECly9fztAFRQeUv3oov6mfq62t\nxZkzZ3Tj8YuCggJEIpG0SZyix17VR0pZpzJhD1HbhSB01S1uZA7j+2phbbOJTERLH6WuDis8y5RZ\n2wuIl3e84KEDpaWl6OnpMTzOKRfTTY1XusErTMvB7n333XftRs1AM4j9L37Zj/PKNCUcpTLSG7hK\nkoTCwkL09fUhlUqlB3bKvdFo1LIQelWYzNIhHo8Huv+KyD205YX1OaPvdp/lFQ5vlLC1skqS5PnA\nwU2aeA1PWYJ6rzAO/qw6xIBYeuIUJ++gbYvDmL9qvMjHZDKZPq5QBKzekUce8u5TehG2Oiw7YbpJ\nF1aZva5PKisrmXxA6BGPx9NbjtQo/ftcx6u6zrfBrkjk0mZpp+RCJ4LIT9S6qzdZwtLQikaudXJz\njbBMzoVB993IWFFRkbUPnCCCpKioyNJJlYJf5dMonkQikeX8jefKdNhXuZ1i1V7byffNmzdj06ZN\nOHv2LHP8diYQ8xVe5c3whPdNmzbh0UcfxYMPPpj1myRJ+MY3vsFFAIINLytabYEOooNoVNmKvMJE\n2COZTGJwcBDDw8MoKChIm6srlbzS4KivWaHsxVQsJ4DgzIX0dJLXrK6eubcbudzcF0bC0pEIQx64\nkY9lnzXBRr4OVPRgKUd+OMxixUh+vf5YWOq2MGNHl77+9a+nTybRYqWP8Xg89Pkoev1jONgdGBjA\n3r17MWvWrAzPiiwb/4lwoS2MQayEGBUWrWyidwiJbNROILq7uwFkNtTK/yyVvno2Nkid0O4pV+Ti\nZb6kDpu1UbSbLrlcpsKyyh4GOfVWl+zidWcoXywq9DrPInc0/YalLuvp6QFgb4DsVx1ppMN6MvI0\naw5ah4Ka7OMR5549ezBu3DhH4efCKSVB644VhoPdzs5OPPzwwzh8+DAaGxtx4403YuHChbjxxhsx\ncuRIP2VkIgwz427w09whiLQUfXaIcI6eGbPenlkzvVOv/iodguLiYscrRrx0PBqNQpKktO4qA4Jc\nr4/Cgoj1ip4lQhj0xU06JhIJTzt2fgx0rQbULG200/zWe8aLgb5bffR6jykPiouLdR0L6eHXZIqR\nDunJqL3XzV7YoCeLePjPcEIymXRdL33gAx/Q9fpvx79GkOnOK22D1h0rLPfs9vf3Y9++fXjllVfw\n8ssv45VXXkF5eTkOHz7sl4xpaEXZ34aCBrsET6LRaPosZmWwq64gg9A3r5wcOXW25QWidS6DQIR8\n0GLUCRIpv3jLEoa90yLqih3stp1+Ot4zG/iztvVeTfSz6KRfZdNo+4uebqrTxe1WnqDLp0h1HyuT\nJk3C0NAQTp48mfUbzwkyUfBrooSXPlgePtjX14euri5cvnwZly9fxtixY/GBD3yAS+QEO35WBEFU\nOmEr8IR9lIEu8F4+B22GzDNOvbDC2nAHgZeTmWHJB2UAbBevzw/mkSfqMGjCOhNterhJn3g8bus+\nP8uCegucHcze3+0Ks1W4dtKeNX+c5ifLNgF1n0mxMHJKGCd5RGF4eBilpaW6v1nlSVjaJzV2thaK\nVN8bmjE/8MADOHToEEpLS7FgwQIsXLgQn/nMZ1BRUeGnfMxoV1cAsRVJNBfsQROLxYSf+Secoazs\nplKptDmj9jxlvSO6rHBTJrxe2SXsk2/pJctyukyor7HgRedUrbs88kQdRhjy2K2Mbsq+m7iNVkmD\ndO7IurfbTC43HWezcJXBop00YS1vTtO5oKBAd2uO0YBCue7WKi5oR0lhbjf7+voMjy/KxcGuFj3H\noiK9l+G08KlTp9Df34+amhrU1taitrYW5eXlfsrmCG3D6kVi85ytYA1LFOXxakWBBrq5y9DQULqz\noOyPUXcelP9ZddyrGX9W9Dr1IpRXEWSwA89VIDf3+omdfXl+e4vlMTg1ah+CXDky2k+n/d9tedEO\nLO3e6xQlDqO2041zR7ftvFa/1ZYLolhxKfIY5RUPywR13WYnDCMfFHrPjhkzJv2/kTdguzg9/5hX\n/WrmmMtLCgsLXYcRjUZRWVmpW2a8tsARAS/8BXANz2zPbiqVwsGDB9P7dd966y2MGjUKH/jAB/D/\n/t//4yqIHUTtsLhB5JVds7jCPANHBINaZ5T9Wupres6njMLQCytIJEnKmNn02kEVlT82RNwTJUkS\notFohu6KkK96ZVKNm7QsLy9HZ2enaxlFpqCgwLazGx56qef/wAxR+hCiwOJd3K/93EY6pBe/U4si\nbZsFBF9PKvL7LYeZDthN0/r6epw4cUJ3wsBKb3LBQZVXZd2XPbuRSASNjY247bbbcNttt+HGG2/E\nO++8g82bN1sGfP/996O6uhqNjY3pa5cuXUJTUxOmTp2KpUuXZjR6GzduxJQpU1BfX49nn33WxSu9\nR64Ojv3CTMlEb8AI8VDrjJ7pmFLZm1X6eiumbga6vGZcZVnOaJwVmbwqJ6wTZIR4GO3xDnrFWq9M\nqnHTCeV1FJfIsKyOOe3gqvNdWdH1Qhfc1o9uVxrVeLU6pj7n3QoeJu52ZbK7Qqi1/LAbh7bNAoCi\noiJbz3qFkr5+D/zMLArt5vn48eNRUlKi+xvPcsAbXn0U0ccEhqV78+bNuOuuuzB+/HjcdNNN+NWv\nfoXp06fjl7/8JS5dumQZ8Lp167B79+6Ma83NzWhqasLRo0exZMkSNDc3AwAOHTqE7du349ChQ9i9\nezc2bNjARdlFT3xAjD1aBOE3opRNr8qTSOVUlLTmjV2HPKz3+oneRA1Lfomat0aduyDLxZw5c3yJ\nx4/803vO7iQES5xu80tvEOF0UO6V7igmxkbhqweYPE3czRgcHNSVR28r4YQJExzFoUd3d7fjZ3ni\n5B3cTPaYTXTYDffw4cO45ZZbdH9zei45wQ/D6YaWlhasWrUKjz/+OMaOHcsc8KJFi9DS0pJxbdeu\nXXjxxRcBAGvXrsXNN9+M5uZm7Ny5E2vWrEE8HsfEiRMxefJk7N27l7w+6xAGsyCCCAtUnsIL67El\nBDtOy4fRwCHIsvaXv/wlsLh548YZHtV5mVitxAcxQWN0dnxvb2/WtXPnzmV8d5O/opgx+43ZO2u3\nTRkxd+5cx4PasB53FiYMB7uPP/4498ja29tRXV0NAKiurkZ7ezsA4OzZsxkD27q6Opw5c8ZRHFSR\nhxsq9PmBKOXU6z21orxnLpILK6BBesq1g54sdupoo9WQXHDUYvX+fuShGydifuqXaPqsR2FhoaFD\nKCAzv3m8j50wjAbgeoMptdWKW9n0POr6SVC6wqPf2dvbiwMHDuj+ZjWJIHoZsYPoZT2wlsfqPEEn\nJgmiJrbVu7C8q5nDKF4kk0luYbHCUuHQXsTwIko5VeubF56ZWcqrlTdQp15B7d4bi8UCHYx4WZ71\nVkR44yTttHt0rcqFCHWenf1nRh07r73tm6WPniWA+n5e59R6Vbdx905qIzyjPYh20UsLxWyY9X14\nWGfohWE20AUy2win9SNr3T00NKTrnV5Ph3k6fLMqn0HVP3a8Jbspd1ZHXtkJ+4UXXsCFCxccPS+a\n80QniNKnM8LXnk11dXXa5KKtrQ2jR48GANTW1qK1tTV93+nTp1FbW8scvqiJzbNxNOokG52/5gSn\n7uf9RtT8Jqzh2WgqHRCrIySs5HCjT5FIJCMsq2N0jJwTmd3L40gYM9RHQwWBl+XZbacd4L/vV/HG\nrL1mhgh1nh0dMRoU8MgHM6w6rWYEcewdj4luJ2HZCQ9wP0mkNzHi9IgnHgMCvTCsBlI89uw6qbv1\nVnH18ljtVMptu2o1kRXUdhCvHduZvZfdNC0rKzPU9zBMYuY6vg52ly9fjq1btwIAtm7dihUrVqSv\nb9u2DQMDAzhx4gSOHTuGBQsWeCqL1coyC144P1EKnxMPgbzOC1SHZfY7j3R0MlAhwoue10dlwBiJ\nRFBYWIhYLJbWB7MOifZ83iDMW6PRaIbu8rSOcLvaKsIAKWh6enocPafuvFgNhlgnCvyaxOCNnUGH\nUVoE6QBHL22VbVVuwzGC5+SF27CctK0jRoxgfkaNcrycGj8m01ne1WogpdZlP8un3gBML371hASr\nfNp0svKD4PTkA9F9Jpi9l916vbOz0/ZZ16y/ExyQPWL16tXymDFj5Hg8LtfV1clPPvmkfPHiRXnJ\nkiXylClT5KamJrmjoyN9/2OPPSZPmjRJnjZtmrx7927dMAHk3CcSiQQug9FHkiSu99GHPqJ9otGo\n8OE6KV/qZyRJSn+3E1ZQddKYMWM8S0u/6ii9eMziLigosLy/uLhYqHd080kkEp6Gz6q7JSUloU5P\n9aeiooJbWMq7u80vs/yIx+OuZOP9jJf1njp+O/WZus5mkTEWi7mSs6ioyHWYXqSj12WRh8yRSCT9\n8SNN8uXDC+n/BpGhgFb9CCI/ePTRRzFhwgRs2LABwDXzR6ercwRBEARBEES44DVEpcFuwLA61YrF\nYhgeHiazByLUiOJMLhaLOTbNIsJDPB4PZE+mFaJ7n3daTo2eC7K8+VXn+BGPm6OHWI6Xcfsues/z\n1im3+N0W2YlvxIgR6OrqyrpPL+8SiUR6f6+yrcxpnVJUVOSLMz8jRK0P7eTZBz/4QbzzzjtZR64C\n1vWeqO8tArzKZvjPAQg5rBk5NDQkxCCBINwgig7TQDe8sOxltuNBOAhE7+DolVM7k85GeRPk3j3F\nIabX+FG3qeNgjY/F0ZPbd3GqP17IYoSVgyreiyx23uPy5cu695WWlmZdmz9/fsZ3N3VKkANdILj6\n0LL6wrcAABnvSURBVKp9sJNnV69eRXl5ua6+WPUzROkP5TI02CUIIm/JRWuRfIGlYxSWox1E00e9\ngYCdjplRelt5KfeS8+fPBxY3b9R6IrrzHy1+deztlqWrV6+a/m5HXt7l1qic6Dl2fPXVV9P/FxYW\nMsmSC+de88CqfbBTxk6fPo2/+Zu/caTflA/eQylMEITviNKppxnV/MCvwa6202Kl59pOVFD6aNTZ\n4uE5Vx221cDCK/w0VXUz2LD7rPpdlEkfu8+68RTP82xc7XFtbsLSw25+i9gGGHkJV47uVKOe9Ovv\n72dKJz0z6SApKCgIJF5Zc965Fjvtx/DwMF577TXd38JwpJxbRB+wh3rPrij7/tyi3mehPoInlUpl\n7dHwcz+Q3j4DZW+Bnhx29h1Y7Z3LlTwlvCGZTGJgYADRaDStm272AbLsX9PDSF+VfVQ89JnnUWJW\n8WjjIB8B3qEcs5VKpUKVvm50Ohf2pvFso0QNK2xhuoGlDeChv3bfP4h0Uu//1UO0vOPFuHHj0Nra\n6ioMRY+0aVRQUID+/n5h0000vyXaMkZ7dhGO2RD1AF07WC8uLgZwrYJR3kWZYTKaadKGF41GEY1G\nuc6qqGWxukfpsAH2zArDundBlJXIfEIvzZVGQ61HvCpq7ZnRdvJc0VftjLjSYeChz7IsB1YuyEeA\nd9ipZ0VEr1zwPFdadHjmlwhhmdVzXuimU1N2r9pgq4FuRUVF+n8e+77t9tXsvq8bfwTaOKz6cGp9\nCHoVmBeSJKGjo8N1OAUFBbqTAQMDA0LX8U77T1rdYbXUMMKrydBQD3bDgJkjiStXrgBA1qyPMtMv\ny3JWRayd8RgeHsbw8LAnCqLXCGjjkWWZKW6RC70ZYZU7zPiR5mod1w4qWeIPy55QM8Km4ywmb0GZ\nx1khut7odV706nutubNRegc5aThjxozA4hYZv8u9U6/orHLyGlR2dnam/9czI2bFTpk38qhcVlaW\nde3OO+90LIs2TVkGPqLXXXaRZRl9fX2m99ipt+LxOObNm8c8gWA3fNHQ6o7oFkpkxmwQj5/7e9Sr\nudrr2iV9M9mMwnAqk1sTTycEESfhD2p9VMzZ1deUvDczq1HMmNWdATdn8PIyq4zFYkilUumwgj7C\nQY0bsznRTJxyCUmS0mbiYTLtzXczZlHfQVS5tIhmCuvnUUxewFOmfK3veby30Ra9RCKBoaEhYctm\nMpnk5puBxztq9ZnMmMF3NoSHd0Mn8igDWvUspNo02Gx2UmtyqVUKp0rC6n1Q+b+0tFRXdvU1K3M3\nGujmLmq9UhoFPUcrZjqgmASpK1WnA10gu6yzlmFJkpBMJrMas6Ac8eihnQCzc59CrpiqiYhiji9q\nJ4gIF17oURhXnFhhMQP2anVZDyfOytxiNeDLVX2Ix+Ouw1D6Ldo0Ki0tFW6CBHhPTqOBbmFhIdP2\nFLvvGJQjK89ivf/++1FdXY3Gxsb0tX/5l3/B9OnTMXv2bKxcuRKXL19O/7Zx40ZMmTIF9fX1ePbZ\nZ23FwbNyV2eU0xkepwqdSqWyzCmV72ayeDH7oZWLJe7u7u6MgYjyV33NagYpVytTInNPhzKIUuc3\ny15xXmjLF2v4sizr6rRIjZteGtuFx4yvXbws+6LWK6Ke/6ugl25udDsXJk+s+h0susZTL/VMXN3C\ny7meHnp7d83u92orgplDJi2s+eU0/eLxuO6zevHzHDxMmDDB9Hc/nCQGEbaVGbMdxo4di8rKyqw0\nunjxomUeBdE+WeVlX1+f7fafZbKAZV84Tzwb7K5btw67d+/OuLZ06VIcPHgQb775JqZOnYqNGzcC\nAA4dOoTt27fj0KFD2L17NzZs2BDK2W5W5zZO8LMTrVZgvxyQOHk/XpW99gw7p42r2qO2kWxqZ0hO\nO0eRSES386jON78rUbP41Hs6lMkcWZYRj8fTHR/l+Xg8npF26vdUBgiKgzbgPWdvSroGMSvOGi6r\nA5KysjJUVVWlv5eXl9vah6iWRe/cVFHwsm4TafJBjdP9i37BO91Ef1+nOJ1Q4pm+XV1d3MLihd77\nKdf0Bplm6eGVtYyX+eU0f43KiV54PPvKJ0+e5BaWE8LcBpw+fRrvvvuu7m9WFouitk92GRwcFP4d\nPBvsLlq0KMOLHQA0NTWlO7A33HADTp8+DQDYuXMn1qxZg3g8jokTJ2Ly5MnYu3evo3jtdCCtZped\nDp6crLRqV7i0f52ew8d6r95z5eXl6WtmMzws6WV1r5OVDl6VvXaPpdPGVb1CaSSb2hmS08ZWaxGg\noG4o/a6AzOKLx+PpgagyuI1EIhgcHEwfJ6S+pk47xaW/sv9FWSUeHh5GPB5PO3tT0tXue/OaDNAO\nsO14HLUro5LHXV1duHDhQno1+vLlyzh48KAt2RTMdFovLXJhJU5UjCarchnR3peXPCJ09JQJP56I\nZG0R1Iq5H+GKgnbCP1/gka9mYYh+Bq0oeFm+AsuBJ598ErfffjsA4OzZs6irq0v/VldXhzNnzjgK\n106jYzXL4ueqsnqFS++vnvdju7jds3v+/Hlb97Okl9W9+egcIV9QZv9kWU7P6msHtFYTBOrnlPtE\nWC3SDrBZTOPshM1y3ew+VhNxkffQs0yMeTEQcIvRZJVIOO2kiWieracDoqe/EVofH8B7pzvwxItB\nvDLB4OUqqeL00w4sJqZmzkHtYtezrx4lJSVZ1/72b/82I2w3AwZRnCqKhp16cMyYMfjIRz5i24M9\na/j5gJeThoGk8GOPPYZEIoG7777b8J4gZ9D8VDzWuPyULYg8EG3mn+CH2jRZMRFX57dizqw2H9ea\nZCum4YoJs17jzqK3vMqT9oy5SZMmcQkX0DfNtjKDV94rGo1mrDKbDUKUcIIaqLDWNywTY14MBHgg\n+kqR086HUd4E2akTVQecoD2CELjmCCcM+DXBYFd3eewhNLpHrz9jJzyj8qOnwy+99FJG2G4GDE7P\nQM517CzonD9/Hjt37nTk6DGM2zbViN6OAQEMdn/0ox/hmWeewU9/+tP0tdraWrS2tqa/nz59GrW1\ntX6LlsZPxWONy09zqSBMs8I6005YozZNVsxp1fmtmDOrTW21JtnKyq/ixE2vcWfRW15lXXvG3PHj\nx7mEC+ibZluZwas9W6tXme04vAvKusJLT6eiDgS07yxap8GprwajCZMgV3z1VsW8wI881PpuAK45\niAwDfk142I2HZWXXKG+Nruv1Z+zIZeRbQc86Qd1XZi1fWrl5WiSFCasya6dMz5gxAzfddJPuvVb9\n2rAv8oiwjcMS2UNOnDghz5w5M/39N7/5jdzQ0CBfuHAh476DBw/Ks2fPlvv7++W//vWv8vve9z45\nlUplhQfA048kSRl//fhIkpT+KNcikYhv8Zt9SkpKbKVVLBazLbdyr1W4dvOKPuH5JBKJ9P+Kbqmv\nlZaWypFIxFRH1PkuSZIcjUZdlReeZU0tW2VlJbdw3ciorVvMyo3eb1VVVb6VtXwr09q8oQ993HyS\nyST3MEXpiwDe1Q/RaDTwd/PzfUWLU4RPYWGhp2nnRdkU6eNlW8YLz6bY1qxZg4ULF+LIkSMYN24c\nnnzySTz44IPo6elBU1MT5s6diw0bNgAAGhoasGrVKjQ0NOC2227Dt7/97UBdcftpaiXrrEwFbdKg\npL16lk+bJrJqVUlZCbIjNy+vdHbvI8REMZdS8lGSJPT19UGWZdPZ6Wg0img0ilgslrHiqfZ8zVJ3\n8Cpr2pnZG264QVeWeDzOPPvuRkZt/cJ6nuDFixd9K2tG8Yi22skLvbo/DORqfoQdL1blgu6LqPGq\nrLCE66fuB1E3hLE+4gEPT99maefnEX5BEIa2TJJFl1AFNbLXBp0iNUAEEWZisRg5RRMESZI8azDj\n8bgvjsxY62c793uZLlYUFha6PoNSLX9BQYFnR8i4RZvOTtOdpU5x2p6rZVP+j0aj3LcBua0f9dLQ\nrz4Ma/7Zud8oPYye1btuJ5/Ky8vR1dWVlU565VFdt8VisfT2Hjto5Qu6fJrlgZdttVXe29GN0tJS\n1NbW4siRI8z1RpB1vOjwShca7BJZJBIJDAwM5M3A2qiicVq55nLFxaNRAN7r8Cj3K2VbrwOn7IfV\nln9ldVfr0VwEiouLdZ2JOClTSueovLwcV65cSXds1GlntSqq/G6m05MnT8aJEycQi8WEmon2ohPv\nJ2GuR3nXZTzTwkrvRaoP/KCoqIi86aqwqwMsusLaJ/Bjks2NroetnHglL89ww1zfiwi3fOESCuEY\n1o3xfgz4FXOoIDxn2n0/nulgVJicziKGqfFgxerdrAbCRvdrzWCU/9Wz1GqzZVmWMTg4aOikyi5e\nlSdloKv1bumkEVQGep2dnRgcHMwawJq9u3aSwEynjx8/juHh4cAGukb1DY+Brl+OkVgdjYh+5ARv\n+VjN6M2w0nuvsKozgspTL5ywednfUHvdt4NXsljJoZwPr/zPgl7dZaeOiMVipl72WR1lGaEtJ07r\nSTtOvNzi5cCcR5mNRCJIJpO6bTyLE7QwopyOofwvImK3tC7hVSEEibZw+zmQCmLQZreyzeUBZa5S\nWFiYrhBLS0shSVLGYFBpEMwaBvVxO9pV4aDLtTp+xfOrF7P6Wt1X0kKvI6Q9EsksjYIeeHlZpv0y\nVdfr3BpNcJiZPooC7xV1Ec7EVqOX1lbpb6WnQa3qeFF+3NYJZmmpt8fY7H7Wgabd9LCa3FPOhwfY\n93bq6YKdMmVkiqyEpzcxzAOnYXklj1EcvOFRZqPRqGH95rZO8QM37Y4kSUJa2KnJ6cGuUaKLlBki\nyaIlCNNB0TpDBD96e3vTOnX58mXIspzR4VEaHLOGR28l2M4Kp1V4PFCH1dPTwz18o8ZISQu9jlAq\nlcoox2byBG0q7OUgj+eKIi+MLBKCXJV0e78VdXV1XMNjwcyyxOpaGLh48SL3MN3WCUYTc4B+PW+W\n9qx9A9ajh+wsjjg9ikuNnZVdo3TQq8fWrFlj+ZxdnOY373qCddXfLTzOFy4pKUFlZaVuWtjxyxA0\nbnQnDGbbOT3YJQiCL35XyiI0AqIQ1k64XbzMa6eTaEHon7Yz7MeWDV73W3HhwgWu4bHgxGlMmAhD\n/ZBIJJi3utj5TQ+76aEM8BYtWmQZDg/rFzcTCHrn7P785z93HB4vax7eutfQ0MA1PCt4yN/R0WHp\nO8PL+AlzyEFVyAibQwGCEBlyJiEOXu/JEjGf860+98srNsEHkcqNCM6JeKSHnfi0fhkU9BxkVVZW\n4t1333Ulk0LQ3piDgoen5wceeABHjhzBH/7wh6zfrPQm39oBFshBVZ5CBYIg+CFKR47wduVN1InS\nfKvPgzSVF1UHRCbM+ulFfjs1Y2Z14GS0xaG2tjbr2rx589L/u92uIcpAVzQLMjvybN++HdXV1ba3\nSxD+QoNdgiAIInSwdCBoUuMaPPam2cFoX2KQ+eBHB5oG1N4hguOj6upqR3Goy4ObMtDe3p51raqq\nKv2/V/ua/cbvcsTDgWFvb6/hCq2bUywIPoip6QRBEARhAkuHSNROnR5edvT0POB6gdEKbpCDQT86\nlLnWafXKbNgJrGXYC107e/aso+fU5cGNXHoryzNmzHActqiTgH6XIx66Eo/HdScjCDHwrAdw//33\no7q6Go2NjVm//dd//RcikQguXbqUvrZx40ZMmTIF9fX1ePbZZ70SiyCIEEErJYQRuTawAMTbu8Xb\ni7VfK8t65FJd4uTIpLDDOjCzW45Y0s3pfnPWMm00sB8zZkzWtd27d2c8lwt6IFIdCNg7ErOgoACJ\nRIL5rHUg98uuCHg22F23bl1GIVRobW3Fc889hwkTJqSvHTp0CNu3b8ehQ4ewe/dubNiwQdgZJ4Ig\n/EO0Ro8IJ0Efq2QX0fSdtzMpvTNN/ero5VKfIsxHJvklp914/E43O/EZ6erbb7+dde1b3/pW+n+j\n83nDRk1Nja/xWQ1m7dSD3d3dOHDggKO2JhfyTHQ8G+wuWrQIFRUVWdc/85nP4Ktf/WrGtZ07d2LN\nmjWIx+OYOHEiJk+ejL1793olGkEQBACaURUJL/OCx/mYfiDail1ZWRnX8PTywYuOHg+zdZFN39Wr\nR4p+iHiWtB48jxdyGo/T+410gkVX3MRzyy23ZF1bsmRJ+v9oNMpdliDw2xzYas9uUVGRZRiRSASl\npaWO6g2R65pcwdcU3rlzJ+rq6jBr1qyM62fPns04bL6urg5nzpzxXB5RC7oZosgctBxFRUVUQYSY\nXMy7oMtE2PFydjvMK7tBzvp3d3dnXbOj50b3+LW6yiMep2Gw1G1O60G1Piv6EZYjncz0maeu2w1L\nmYCxc7/RPSy6YieeaDSqW4Z+97vfZV1THzs0PDzMXZZ84K677jL9vbe31zKMBQsWoK2tLSv97ZTx\nXMgH0fs/vvU4e3t78ZWvfAVf+tKX0tecHi7OizAqmCgyB63Yvb29OWWWlm+IkndBdK4I/wnLYFc0\nnA6+je7RW0EJui3hDUvdJko96BYn+xRFQc+03gi/3nNoaMh2e6LeEsiKqGXvC1/4gq/xvfzyy67D\n2L9/v+4KsJ0yTn0H7/FtsHv8+HG0tLRg9uzZuO6663D69Glcf/31aG9vR21tLVpbW9P3nj59Wvc8\nMQVRC6iX8DgHjCdBN25hMdkixCYf6xI3eJFeitOifMyLXHln7XuwtFeSJOVMOgCZKzk820leacQS\njtbs3OhZs8kkVt2wIkhdcTJpFolEMtLRrvwlJSW27jt58iSzTAraQZZfaWsVz8aNG32RQ6GystJ1\nGOPHj0dHR0f6u/KOdpxbEd7j22C3sbER7e3tOHHiBE6cOIG6ujrs378f1dXVWL58ObZt24aBgQGc\nOHECx44dw4IFCwzDysdZENHO6QraZCro+IncIB/rEjd4kV7KcThe5oWo+SyqXKxo38PovZTr6t8L\nCgpyJh2AzJUcnhYFvNKIJRztqqfRs2aDeru6YZcgdcVJ3KlUKiMd7YQhSRJ6enqyruuZxN5+++3M\nMhmhyOb1BJRVGnz0ox/1LG49jh8/bvq7nbQ4efJkxiKd8o48zvAVnTBMVno22F2zZg0WLlyIo0eP\nYty4cdiyZUvG7+rEaWhowKpVq9DQ0IDbbrsN3/72t00TL+hVRSJ45Q46foIg+KCUZZHKtEiyeIkI\nDrFuuukmX+MLK2HVSd4r0l6ngwjpbGT6qnf997//Pbd41auRvNLByb70Y8eOcYnbLnq+CdTYmaAo\nLy/H1atXeYkUKmRZzspnEcqRGkkO0ZSqaIkXBKKdwxgUlA7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+ "text": [ + "" + ] + } + ], + "prompt_number": 42 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Find, print, and display max detection.\n", + "i, col = np.unravel_index(feats_df.values.argmax(), feats_df.shape)\n", + "\n", + "window = df.iloc[i]['window']\n", + "df.iloc[i]['filename']\n", + "\n", + "# Show top predictions\n", + "f = pd.Series(df['feat'].iloc[i], index=labels)\n", + "print(f.order(ascending=False)[:5])\n", + "\n", + "# Show bounding box in original image\n", + "im = imread('1204206441_bc256e34f2.jpg')\n", + "imshow(im)\n", + "currentAxis = plt.gca()\n", + "coords = (window[1], window[0]), window[3], window[2]\n", + "print('(x, y), w, h: {}'.format(coords))\n", + "currentAxis.add_patch(Rectangle(*coords, fill=False, edgecolor='r', linewidth=5))" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "tabby 0.899196\n", + "tiger cat 0.048648\n", + "Egyptian cat 0.035560\n", + "lynx 0.007948\n", + "coyote 0.005635\n", + "dtype: float32\n", + "(x, y), w, h: ((0, 97), 500, 316)" + ] + }, + { + "output_type": "stream", + "stream": "stdout", + "text": [ + "\n" + ] + }, + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 44, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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Bz3EdeNku1wZdGmgYPQWrrJg6NGVIZyfT1bUSQgqj5sGwjvsOzRWXD0jOpMkg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\n4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WaWyMAUrP5J2n8D6TCUOhvVBc07YX2FM9aNRz/wCVdZBosuifvJZn+y6ZXI\nD2pLtvY35niCuFLKL0j/iu2Vw76D/iCz8JEfSBRSP5Yc0gsmP4P9jRGFKssl6eONbf/zH1LX/umF\n1dqV2X/H6ssqItFhFpQHTZPCO42NVoWRCvIN+o5OpcsHpTwRTEYMSn1h6AOXSi075DuVQlPFciPF\nyaiYrhNZRNfDrAVQtnxioxNyp+h3DjlQ23H5jiSAknYySjLKjoaySSPjhVIvRDbcC0jh7EGGM+eg\nFmOeHU1ZnulZGJGU/afPEcdG5pWc5yqmATE3JL+BwpRzRUXHhS4nIbnmgnmh1A09PhjyxxpVtFyp\nGvFGrWUFveU3xnhwaT8ReUGrLQ2sGh7jc5xhZDZk/B2BdZhREXnCpCJxpdRl+dVchoOJrwTYrGga\nPt6oegGc5IEF4AfrcEvIOylLAuXemapYNpAbzgPJrxz8QcZXxIVp73iCeGesx4KR70i9MeIPxD6Y\nPZEQXAZ3/zfCD0b/YPaK8oylYihpwUxdI5pwsm+o7CscL2C6EtMobDyswjyoGUQJ4A2JYM4PMvUz\n4ucvOL5m4LKTGghXfDqqTkHW90yn1sC7rHj0ElR5pgxoIVSeIQ3VJyQuqGzAkiAWuzDmAFEqQcEp\n9QZZ8OxEXBGg8pVCxwja1gh3ECgVqk1UJqXcV2eKMOM7GQ2zgtWTrVaCCy51RXzrO7U8f+phK5Hr\nXamVNfONScaSLElpnLaiZZiTI+9YWXyKai+EF9xPWrkSEZh+pclf8TBOTrzemCpQr+tmasI5P7jU\nQpbJgWDyzG47ve90MUQ/kLh8KjAKQwLXHyifzjIOLjghQYjTopN0Zm1ML1Tdl1JFDqL8hsRGsQrS\n8Pk/iY4VWE4oLUTeVnSCVs5HMmOCXHCClIY7lHTO+INRrmy2E7E6JjVh+HKWoODcV5xE5nowq5Nd\nVzSJ9LUQE4i4k/bO9Hcm3zlyYyuGj8aWSsTXBXaIsqJzKWRvMG/0MZnxQMtviNTl9LDEMKr8QKLT\n7MoZb9jliSMvnLmtjlE3LGCzDeGxHmL9C4OGF0drp9oBWaj5jPVJc9jzSvGOxyDmJPxtwSd4xpvR\nIynlK+6O45TN8P4DqxD8tlgK4aubtzuSxohGH7cV7lYrPu9YOpE/IJ/oDsFAc3DMg+lK1U4JwRjM\n/gMVJzV4HO/IAHKjlycmF0wUMHp/YNYRGSsCOU46b7gVZjpwssXfqPog4js+dpA7nrIODHl82mEd\n4Scyn8j6hjCwDHb5Bc8N2Xa0TD7G31d374K7fyIujTknohP1r2huCOs5KPXk4e+UHMyZGM+YJ2c3\ntAglZPnYrcB4o6Wi8YB4w/0D0YPwgU8Y2ZddF+jzhu3J/fzBHILIQRTHygnx76h94L627uHHsq8q\nkJBhjNOJfNBj4vmCFWj8hP+HdTO/fy5X39Fy4L4x/B3hgsyN4oaz4rZPCim59NZ5gix5mnDQz45k\np7AT8x2Tg6jJ6U6zgswd0yutXSjlQh9OjxtFv+F5YqWwtf8Vn43IstQOdlt65u4rYDCTyN8w7mwo\nJXeqKOJ3QicqimCM6UuypaA1CX+nlIM5hdRnBj/w+TuFk5KV8yFE/U4LXcwHCszn1RClgG7M8+DS\nkjFuCDf6cdLimRm/cQxhzLoMMf+Azz9dFaB0XE8qHxSpzHFidaJlUERRPnB9IsfJpbwy48HWdM2u\nyo7wgciVjFypjKdCnGj+xLTfMNuI4SutdJP1Ms5B2YVMWzNRv1J1JVM+1c45BLSRNgn/HTLZVBZ8\nJdacdwZUM0Inkk94PygpnHOg7QI0RH2ZH9II+aCWhveVBDr9geoVuKDcCD9BB1Um4QWlMQnSPvAi\nhG5kTojK5JVSTzQm6RtToPDAAPWE9oEDclywOMn9hSQIHI+lzY1QLG1tQzV4Ln/hftxxLlg1OO9U\n+xuab1x34X6sh3yXV4oOQnckb2xFYb7Qh5BS8eZLc2k3vH9gfNB7+dyYPxGxLX5CfSP9id4XSKVk\noFmRBq6Nyt+IcV8kKe0IbXVntuPbKibhSsZOqYOJoQxaneRUIgqtvkIGpo1qF87xByKFYhsjb6j9\n97VJ5pVgMGdDNcDmkhF5XwAbbnhcICcqhnNHy0DzGe/Btsmar8oky0r53be25t8SKIrHwXbdEa+c\nQympZF5ILhBKsw30gz7vXNpOxs6IN8zW1l7iXxjupH3AfCblO3t7xcegyDfc/g75jewJrJ/b40TK\ntgwVGWuB5o7oC1o6mRXJjT4OhCu23whfYxet650qw1FR7vMDDIRJyY0QEDWMK+4D1QszO1VPBn2N\nt+JK9pO2dWZZP8P0SbMXIk5q+7Kcg74xHYKJyVjqBRvsceGck5H7uln6/TOVWECvgDHX0cXTdSfm\n60rwjUK1Z2R+EG5Qv2DjHW2y3i1puB9ctqc1WrNKs43e3wn5n8Qg4JJo7Ax7ZkjDcXwGRZ5wNiYv\nn7Y5Be1LZ5pPqNoqTnlhfOSK0ZVJqcF2XdcdooFf0dB1xZmC6nfq1pij4gz6+A9f+0nR+YlXU+yy\nr3RWVZCNqeuKpfKC8zu1NlzWZjyyoOVKmGF1uaI8WN5mgdk35rggUSh2wSxBD5x34GSeOykbtaxO\ny4pz7x+kTsjlxQ5PihSqLXut+DeML4j4WnDJX1eY4aXS+51WhM4fBBcs3zE6+yU5zknSqCWpuZPl\ngRYl9Aa6ZmdjngidnrfPzulC80SdNXMl0HRMvuLjmUQJHQi+XlwU5UqVAH1anb0InkGTCU3xsbbV\n6Y7ISWhhmjHjv2Oz0zwY2RFp+HhiZl9WTp+82iucGypfUDdiNEQuuKxwyjFjOXPmB9MHyUb4QKVy\n+vfPUL2E+UTjZzQNS4EcxKzMszLHvoqiOUW3BR+pq9O2MDQuiCqBE2nM7ExOVDdAESoyE+GAMDRf\n8HOHHNSWRHEemQTnZyjmG4nQtieORyd8ULWuBY51sryhbZDxROhA65U53lE1gr8zzwuC4/GgbgMI\nyETzDebjc4zxhFQD/W3BcLghqex7I0ahyc9L3ynKHB9ECi6J2I64oRTCJz0L7o56JecAva0ZMfFJ\nOxOO/h3RidjJOSroBdUnzC5McpHd5sdSA9m2orv9CrkRUfBuZDQaT6DJGY+Ffyyd0B8IQhDMOBAp\neGygS87ludN5rKV16ZAdrVfiseFaOPQPWv0rm4Cmo1TGcaNGRY+f/iF17Z9eWMVjnaofv1FZy4La\nJqUUMnM9qCKQuWaAGKL35dHOhJKUC2uDrcp0x0ellG1tKkmGN3QPkh2RFyKeCU5yvhApFH4mKP+p\ns9t3oR9/UkowaTQzdAgeE6+Taj+BLoCER0fUydkwL6iCXQq1/MzIwXADO2n1iZiTtg1SoNRnzjEQ\nNpKTVnfUXkmeEXvCeTDmE6EG8YTWBypCq7aWY/EOOlGWQ6rrKhjDbbEl75NNr5geCwSSG/F45XXb\nyVEZUjhdkXklpTPGBbFn+ijMaDjfeJLC7Dtd7nQJ3IM5v+LS8PzBIf/GtPuaAw/FZcP0aUmpwiBP\nImC7vJBDUa98RKfMoG2D8bit7scnEl8YrlAvBI2DpMj/zsg/SevMccFnBZl8n9DLMj0MnbgFzTqM\nOyo7pVyw6pi8YrYhcmJ5JaJT8gvEwKfgupCVM53pG8MTqSf1MtfzkEmfnXBICuMszHFDY0Oy0Xti\n9oL7OihFVqEMOjNPRAb9Y6eUhe0r+rFs0/68iFpygr4yQ6m5FA9OUNqV8K/MXIsb/LpIaV5R+UD8\nA/EHlk+AErws1xNPJCeVKxlCqZOURkolpdHj/1qL2PgKMhEa/TzwbmhRRKDuc83M49sqStrJPNey\nrUyEF+anIsB0WZ5LKdS6LQVG3pblVb9+HiLPS87EBzN//exuJ1Z0sQZyktkhNmrTlVxrDZMrVh5M\nuaMZXOQrsxvBXzH/F/wcSOgaKdlibWjc8dwwToq/0PIF+k5Tlta8KOJXLvpfeIy/0zPZLl/J3Nku\nv9D1Add/TMf6Tx8FDFHanMj2y5IPxWT2CXJH8rLgKqyHIPLxScO54PFB0cYMRUnAUWvLnjk74Y1S\nCtoeKDD7hVoXjtCjU61hOtH8xtG/0yrMsSERFE2MK9oG7RCOMSk4MRTTAfaCAqVunI8/1rXbPuiz\nU/KKzU7nd7ZLoZ+dUipznkt8HhVSyOjU8szpb9Sycc53qhSEgJzs7ZWIB/gJ+gDfcT3Is2D5jdTk\nnEHRskwLn0i89E7o+FyIOWNeyP2EDJIH3Z2yG5IN13MVw74ROfAYbLvgOZHwNefeT2RsiHSabkj5\nk4grjyOwXPZez7FMAbaTdjJvjwXVNmN3GPEZKa5Xok+OUrFRKBZUawy5Lg3pqTAvEBPTD1w/2PiC\naqXHg4yCSLAxsAStJ7ttfNwdp5C2SFQib5DXNUMfSmudnr9TtGIMSJCtcN4/CHmmFch5srWN0pa1\ntY/vbHtdRoISpCTFFHdZMicVRO5IbFR9whNgFUchiVA8B0+vVzJ9FXUrixQVd0pOSmxkObn7QbWv\nKA98dqRU0kDimT461t7wvIIankKrP3GOB8iJ6UbJyrYVZv4BXBnDUFW8F0wXhc30g2L/lXOc1DrW\n7yIda4+lW63Owx9oPC+tNXeiP1NyFbce31B5Jsqf1DmI8USUjoojciEjyVkxFZxjjTAM3B2TDYml\nLU8CRZZFuXxhzhuVnWInR97RIkgo5/lv7GWn8oKqg76x7xv9/O8rYrx+IVXQ4ngo8AMfQikwzkls\nf2LaFkUsVuQ9PklbDZbJT2xl0ufCkB75O1YuZJ7/kLr2T+9YWymU2tjU0HRKbUwpDKngN5qcxF6h\nzYX6k2AipBawgeZJEafaHdwXjzOvmCqWA+uNDSPiIIBIhXxH5Q3PH5QaaDZifs7qJJllwZiZT2gp\nSBSCQd2CLEvyBR9E3illQiYespZlcoIm121n9rHmSkPw+AEGIw4yZTmj4o2LVVQTzReYHYkH4Qre\nKHZQ5cquXyGuhL+ACiLfqQa1BEVPqg1MhGC5TYxA0sEazy8bMhsShekCshOxk9Kw+rR0hvFOcqcU\nY06HrDT5CtEofVA4MLmQcpLzC3Cg9kJjI8biNaA3gt+RKVjduJ9XStk44iRsee593KAO4v6BZyc1\nGdyYPvBcrp15DoIL6QbulISak00r7ndmQh+KtZ2UymMo5dI4+w8EOH1C/rK6SpZ76X6eZAguyinL\ns6YZtO1K987pHdmMkQdxJm1bB1Y/nJBO5kbJnfT76sjlQXCy11fUHpzxHZH2eYAu7adpReJncjxw\nhzRjSmXmO6JG2Z8ZoqQGT+0LMRNmQ/1K9gtCInpQqpD+TE5bzsQYpHWuz4WpRp83as11qHFZciL7\nYMYDK8ksgeOoTIx3mp7kSPr5QPREALMTlXWIoH9StwPFaGVQygvCN4qybMd5Iu26IDURbPpERF+G\nhvonkYLoxoxkTEO0kPlBZJIieCoeS3KW2dcykZOUHZOyfmfxTCnfmCFILcysjLmUPDNB5YUeg+nJ\nPMBio3sjqjFxdLsg5YkxJ9sFzvhBUEjdKcUxHkj5g4/uRH5H+E6Vjc0GTa//kLr2Ty+skXXNz/RO\nyjvneFDZaQNq+YrnN2wEFsmjPxYpZ56UWFAQiyDyAXVjxrZAHOqkdob8gKpMlK3ujMe5rqn6guZ/\nweRfGROwQLJzHIlIwDA8DjwPejiZDvGVOcDGtjrL+ZVzBJMXRBq1VCY/lnQng+N0IuzziujUtnPO\ngzIr+AOTRuULMx2hUK6DRw7cr0vrWj7Q8oXMApZ4/ona7VND+YR+PtQpGy7JeAwkn6jzwowLXZed\n8bjlws3xK6IPit4ochIj2LSgemGct/XAasHm8sHfxzvUgrMxsuEsN1HoH3hUkMrdH7h8YwYU/8Yu\n/xs8gvDAijPOjbQHGp1mL5i9UvgJ3Z/J0cgMNJ4XiQjhjB+krjHN1EnYzsmFwyenPwhRmixPO3Ng\nXkBPYr6RRegzwU6wQfcfFEsiQPSZzgcxJzoaJis5vndAHByir4nTtFizxQlWrvSzMOZJ9x8ojtlA\n5ImZhWNu9PGK6oKyjLGweMf4HWEg+jsPOWj6vozveUHjCzkHMQdFAvEnfAY5H7gmUQQvT2u+7knE\n9/XnZXxqRgsaQvcbKo7nBT8N6WuBWRk0W9KrMe/E+YJYp8RPxAxECnM0YBLubOUZiW0dRHKBeUH9\nCWzSU3ARRIV+diyUlgU5jYwDq8k564o6kp8hf0I1afbZkFhdkUSzotvvq6i1QWYjuIBMLs2o0hAL\nzn5HYll0e/+d3SD8vg7vDOZ4p1xe6QyKJiXfGTLx8oHlJHvFcFIrpx9sG2g0WvsrOneIwuhJq0nL\nLxhJuiDyL+R+0PPO1Mc/pK790wuruWM+1jw0d2Ju9LjhciFE19XNCyMK1+2vpMPWdmbeUL3isiP8\nwjiFUsH0G6Hg3WlxJU5h5p9LirU9YWpQbmC/4fwgrSB2QaSyXyqtvOB5LrkG61SmNbTdKTWQ/cKZ\nTpYbKh9sxUktTManlW4iJFkGYqAqa+g/71wpDPFP+ZgzdG3QQz+Y585migu4BGVWZv/ABMbxoPCF\n2S8U+8J05TRn5O/4eCyy1zZIgtPukJ1LFg5/Izip7NT8CWLnHN8IrqDfOfuNINiu39jLV87+IPeT\nOfIzX+yD2pStghEQRsYGgI8b27Zj3HiqO0feGONG2QMVRTOp24Ot/EzGxmOeDDnw8UGdge2Klid6\n/kF6Y55B4UKxTpHCpn/jPBqW31lgR+FS25p5X+AxT1wnTiOyrcieolSFOW5spRFxoAyKDZo9U+uF\nwe9MCiMrtf3JXgutbICT43VhTHNDLamls5Vka0LEjuhP9B6UMtnMEP+VVg5iOMGN4AcqO3v95T/n\nslsUZjxTqMz4QdqBtG+cVDyVzH8npRO1fmplB9H/z8/r8krUKFpJgYkzUIZDPwN3YUthciM06WMt\nlo75issFKRUzZcxK1w+sVUYIpZ6ICqLK2/k7j0iwDzxvJEqP7+tmJ5PME5OTasuk0H0jayfkXP9P\nZXExNN8xe4OIT77wRDLIdLKCzH+BeMV4ZXYh43cikmN2nCT9jT1f6d5x/YOqz9yzEFqwfdG8VJ+p\nHwMdDtXo+spGhWGIPaMyQGBLp2blmIGLEP0PtJ3sFyVzo8+F6ZzqmH2htsG8KxYXcv6/R0n9f/38\n0wtr6FjLj5wgQq0Ta5Wog5lOqpE2EU2SP7EyVoe0PzNIMGGMSSudzAcpLdOKwAAAIABJREFUD9pn\nbtWJQTlgXElOUv6gIVhc+Dja0rcdH4ssToHYKNUwa4Ss3KbKg707ZRYsnrF+4O5EgvGF8IPKneKJ\nlBe6bgzfqXIFN4wLplcyn7lpMGRAPi2Pehom50oRkEBC2E0wdj78wEzIvGMKnh+UPZlyQ9oDGcmY\nlWxrmXWZf2GzpGlj9oNbBKLfOPPOZIAtCHe1D7p/MPlfCAOTTmSn5kGznfMR1FwRHbBBVHI6l+vk\nPD+Ahkil1UBnwVoSCuLKkDcmGyoDE8dkknyQZY0+Golqp8s7QlBNMf1KH3fEEk9wuSNlsRQuJfAo\neCo6G2Mqmjd2faGJkfPGRqylVqzre5UrinCe2yJr2YZQaDLYdHKxJ2x+oYjB/MocnRE3aruQ9gZ5\nIxACIyPQTPCKlYbzRq3GmEH45NKuVIOIJ9wX/Fv1AAalNpBO5AvIATYQrez1GzL/4P/h7l1+Lcuy\ner1vjDHnXGvvfR4RmVlZTxDIYCFkjKtxG5ZMjw4NukhFj0cHmtx/AFo0uTYSNg3LDUtINJEblGiB\nZEuosFVlo1uyLWQQ1LsiI+M89t5rzccYbswDsrkYEC7dsryl7GRGnjg6sWPuucb4/b7vxoTRrgwp\n81EbUJkLHMvvzZpqzJSJ0Cm2khdw8tw1RJntJ60Uk1mJ1sO8VWLTZ+ADQ1lyEEPZh5FTZvN5Gy/5\nRNHEwQrDVzRuSLmT80pJN8RQ8A1GQa0RWtE8b+ZJXqNxpm4XrGw4nf16pKQ7zPPkQgDD53iu9nfU\n/oyPznrcUX9vMgu8z9EcN3hxNC1s50ySAwsJfCfqla5CorEfGpYLPc6sC9R4JrtQOONR5hgCR+Uy\nc7dyQHSdhYSXMY+/kK00NjzOIMEh3dCkz5r39+D1/4GD1ZBc5ptPHZEHZChJBuEPDMDtnhSFxW6R\neI0kIekRH/dT/ra8ZdsekZFQ0dmHptDaM24L3ZSkr6avSoXaVlLqNK94Wai+olaRUNq+48OJ6KgI\nvd1Ty06Ud9TxluZ/Gzp+RW0D70LXA92c6/YGjUrKHWSfM8M68XRanOJX7nIhCvh6w+6VkW6R4rgo\nJKNFYsnG63JLbbDHFLyJHonYSKmQ7QO28Z25qKkFdUHKM118tozSB5hvaHTKOr1WjecpGAmbriT5\nFtkL6DIZAIBxoUjDNAFPkDsejZQz1y3I6cTez4g09r0SWejtQuvfJvKAfsdehU6dyD1f8Wp4vaLJ\nufrGEOboI4zenvDohO4vwrkg+jJp/OlK2C2gqCmRErOFOXOXrp06Dpz3K9hbQveJWqx5KkJ4JMzZ\n2oT2aFq5tmDzQSt/Qx2D5sKlvZhYLRGRqC54BGI7PjIYYGeExmiZkEqyTI9G7RfquJLXwORA83cM\nH6hBrTuqd4QMRk/s8QqG4DGIfODsAnyG0WZBxHthYIxaGMA+FJdKrY+43sBwxAs+dnycWcwmv5iV\nFgEWZG5xNbpccBGwfWaf44JFgj4QHRzVkN4YFZATH29/idkgpyujTeSj2TZ1OvaST+63lHRPYiWV\nM71dZl453QIT3n13f+Tq36bFQDVjo1DsZl5gCExXRjyB34DtaIYwIXjNYCDjSHE4rs5yOk1uhibS\nsuLjwLCVg/wHdBds3BBcyWlh050WlaID7xMWM8bOos6+f5saG2bBosF2VvLS2ep3MH2F5TqhSPIE\narTx+ntyrv2TB+sv/uIv8slPfpKf+Imf+Lt/9+u//ut87nOf4/Of/zyf//zn+cM//MO/+2+/+Zu/\nyY/+6I/yYz/2Y/zRH/3RP/kNiCitdcRvZnXO71E3Rjuw5vexPjB2Nr/QwyfxKjbq/kixN6hVYnyA\n6idAM94X3J0RmZynfWCxjkSQxi21O2HPJDUWXSkxw+mZwwzPyzY7zXGDj46tG6Mpvd2R8j3od5Ck\ntPwd1sMyWx7bhjYj5UA0IbHS4kBej3hs9K0S+3RdVUksAqU5SqPWM+YK/UJEndCL5DTtmChZpkI5\nZOAtw0js/S0H+xTdg5EqLRxvBj7YvaF8lxGJHiD1hDRDIxFjfihkFdQP9PFA35+gGFdVatwRUjjH\nA+KF6zUIFba+gxa6CLkIGp1s93ifYrqxHyjtniIDyY9I3NBc2PtA1QkbCE7RBJ4ZXriMR3pkIibd\nSXXDB8yB58doX4jxhIgT3Sk2H7kTAf2K+cKyZI7LAY07BMFKJ6zi+sRpOb70zleu52mrFWngB8zv\nWVQpy87tzWt6vc7WkRs5n4jRXmqPMfm5Irh3llIgCj4aJemM9ugNdd9IOc+MspymITclxjiT2UAK\niSc89smL6A7jHZI/JieZizwGJk+QGtQg6QX191B5jx47Icro46V6ueKS6H5hj/neIC40fyZ4ZPjf\ngrhfYavOLLc2SBe6dwaDIEH6mGTKsfwgPoS6zUxxhNL7M6PvtFYY8kxnZ+tvGPYA4w7RQZNBtieS\nZiQyMRQdh2lC0DOuj0S8Q7mQ7TizrnKitifwlRhXsp+QJkgMQp/YXlqWfXs3v1cf9F3I6Qmpgsr/\nTrYyTQjjAzzuMRZyOiDLiVDFxDnpaxpOXu65S0fGmEu85Qhbu1LKB4g2Wj/OxI6u5Lqg/74MAr/w\nC7/AF7/4xb93GAq/9mu/xpe//GW+/OUv8zM/8zMAfPWrX+X3f//3+epXv8oXv/hFfvVXf3XOKP+R\nlw1nSSumisYJyYUoTsg+56jLjo7OfbnFe0PdyZMbh+kt4QdsaZg5qi9PsFRSaYTtM3ic4LK/oYdg\nKiTVFx1JxbKDBm08gw66lxfZ3xXCWeRI9D6p6WEMvcFkkEeh90ZPTjUhLOFtdubDA2qwtzMkpXF9\n+cvpJFFGXBnimL0PMRiS6EzGZPZB3Sq9CyqF7sJSmDle2RECiwOQWPKCqRImVPL0GsVG0jvWohxV\nWJcJ6k46HfDgCAPSFSvvkcotY+xIKDEeUdlRS6RFOS2OdCOh+H4mq5HiDo/EchI6nR5l8j3zhicQ\njOMC/SqIXVGOLDI77RbBkiayMLphEi+Z5AW8kPOYH6r9hOapKxFxUl7pnujS2H1nKe/RhckEDaGs\nifBbvJUXfKARdplhfkvEGIxoKAvLuqO24JKwSGzbBnKkLIWUGjamUiWlyWUF8D5vziMaI57IpdCu\nidEfab2x5BtkzBhRznNh01onsSJ2h+VKUuVY7nBArVHsgIyB2JWkjXBoBOY3aIrJDsgbaQ3C58Eu\naT4J5HSLceCQVpAxF4JuWBREhGMpJDF6c/r2hKTCNjrZP4npiub8otbJRL+yOBhGKYk2Oih4FNB3\nWH7GbMXHLH2IH3ANQhMSgsQNIzpIprYdlzJRkX6aOVUWcr5DyTAUUyNpIeQJQhD22QJzRa2TQqeU\nsiyYzvx6KoXdjW7QRsL1cSZxxnUS75JTh4LPJWHo3CyECMETIyaqM0jEKGQ9ETzMyNsw4IRHnUSv\n8e+JbvVTP/VTvH79716P/yE17B/8wR/whS98gZwzP/RDP8SP/MiP8KUvfekf/fphhZCKys5ApuCr\nJUwnUk1ixcy5jguul7n08SdSDnrvWEAfO3nJ9NrIUhE5YnKDhpGy46NwWD5DH09ThGdCdwEpIBeI\nhvmBXhdCNra4svcPybLS/SOKJvq24f4xYzwz8okuj4QPUtyT4gRxmV83PkbtwiEJQsIyZLulpDMq\nhX1s+HgF4ay5s5TARmPNczGxiWCHDLm+cFgNmXJ31A/UekZ1ovo8nOhnVr0Q21tCjOYHxJTWCpVE\n7Yboia12bHF6VCBhvMYssW0XWs2sdkvIAdcj6rNLnkZC6z6D76nSY6NTXzxLlfA8mQQ+xxrYFZed\nzTMjVfA8Wz8xM7uDPqM1vpBNCNGZKbRE9pWD3jACXBJ7mzdI05XeK+t6wvwWc+Pd/teIQqvfAHbG\naLPe2seLIgXoC61faX4h1Bg4wyte5YVVMIhcaD2B7FwvT3gdVDMGTNKZPrHnyu7fQnRCVTJHYhie\nrpAElSN17JAuLEvB+4XWN3JRmp+p/UqME4Ixeqf1A3XkOYs+dPD3aXQkX0leaOMdMY6EPoIYbX8x\nHYwgjU8AndaDi38Nkw8xg973+biu+xRS2kYqRhtfw3Wlj7csqZFso/dn3AdR5/iips6eJyy6jQuH\nk1O3NOEm7ROonGj9LTc3HbgQPJAVTBzH8Z7xkWgvOnUTwezdtE5wz/DC3jphDcs7KgckBcSYs3DP\nNIxSMr7PKOPWjHH2WRIJoFdWW+bPwC7Y+BRRMylV0trZ9j6FnrJjZLQXLNcJWqm3jBCi3xPjBk1X\n3BOtZzTdknIFHifYxpzNt3/61PxnvP7FM9bf/u3f5id/8if5pV/6Jd69ewfAN77xDT73uc/93a/5\n3Oc+x9e//vV/9OvMW8tKxzA60sZ87BVHJBgx6CHACZXP4JGwdKDtN6Qs034VymgDyQmPw2wPt20C\nputKG0GXb7GUO4RErRUJx3ylXo9EFLoaeQ0WPWBDEPuIGhsj7tHlhB0U09fklNB2JsUnpptenhF7\nxHSllATjDm83VComDesJtY9xvWckZ+xCtndAY/c8l0NqSDqgJFI/ES1g3JBsRXXgZMQSgx0k0/vN\nDMyn+bNDbonlSNv6PHDaO1JpDBzR65xrxU67dkxPoMLeHmj9HbkYluF8/ibOI8MN8zs0ChvKvii1\nB4kEI6E4vXZsZHIEJhXTAZ7xemRtitQzRQ6Il4myiwP7vhOxQs+zOSQJ6QND2PqZak881TOkgdgz\ni84aZXQnJWW7dIJ3qBhL/iHwgfkdwkrfM6GPWLZJQCNRh5Ftji1UlWwnRKccMAaM3hDfOJQFlWAp\nN8AN6jsJpZ5h5UPKuOGQjozWQA74C8DE5EiWD8g2pjGgHxl9RpyK3NNbn4JLaYiOWd/UTuKBRENj\nENcV4gGT9+jdkeiUbAx/wvtCr2/JqdH3Shdhi7cEM8KX9AM2/z+IAe5zoRsxkPQ++9YYNVgPn0G9\ng5+o/cRAWLiZ2VKHPga5r+TLxzRvrMs9bVsn+rIXDqsh7iz6Plt1+jDE36PVjo8TOb0ilQcUwaNB\nrMyw+AGThFkQ+jFwYLRXIIXwbRYHeJ/dC8n6LBlExpYb0BuyNEZWigiSj5CUoZfJ3x1HnI/IZZs2\nET+yrK/pA0KOhAWRJlNWWBCdHF9LG1YcSzcIILFy2c6Ts5FOdH/GTMmW/6VH4v/t9S86WH/lV36F\nv/zLv+QrX/kKn/70p/nX//pf/z/+2skO/ce+g5XedzwMBdaSsDRpSN4VK3WGi31H7UUsKLekw/7y\neDL75JETYo0UZ/Iy3+zRFrDpV8/5ZtLX1Tmsd2z+SNWPQRyJxmIXUhfEbNpeI5PTe/NxUDYsMiaN\nQznStskqnbj/BhwZQ2ZmMo15APZEd2GY0b1jPtMzIcF1v51/QcZ5er5ssNVHiAvId2dYXB/ooxJS\nZ2TFG8p78zFXN5IHOgwZO9GUiMbhoJgkItZpQA0jvENXbtZXHHIhZBpRTRM56eyTa2NZV26XzzFa\npbLN0UR3Dg7ZDqT8SYQ+4cf4bKjYjmDk1Mj5CeTCRRd6mssnkX2OB0Rp9XG2iqRwPCwgbcJeOKLj\nDrqzlo54RlipscOhUBlsY+DpmZEyqLPYE8uyUL3T/IxlR+QI6pg2xBzvk5OrsrLIwGNHNcBvpn+K\niowDEoLpXNiUkzNqp/oj5XAiXlB8SZ2UC9Xfcb3Om5vZBvo0/Vis9H6ls5PSa6TMllXS1yRVRjzj\nOoCK2i2aEmkZNPbJMOiBpKDbYIwLZXEkHPHX+NhRm/PpNWVKLFi7xeIF4hO3JD2SVEnlRNs6IQvd\nE6OdCTmSTFnzA310hg1kBL44mqd26Lp8GiFzve70plgC0THlh93Y4y3uE5bjnFlWxeMyZ/Q+dfDZ\nEs4ToYPuFdVO2xsa9y8G2weGn0Ab3p0hD4gFs60Ge73QdcwPHCDpiwWkXbCXJI/3ZzR2QOluaApE\nzvT+EXihtRXXHZIzgimhlDM1xgSpx0a7DiRdQXdSykQs9LaTsxJ9tr6+F69/0Vf58MMPXxBswi//\n8i//3eP+Zz/7Wf7mb/7m737d1772NT772c/+g1/j11/++a/+6/+GL335f55eJhMoDTwmjzEujD2h\nfsTMpyxMlKCS8y1t7ORyi9Qj2p8nrJYz3gwZd4j4xL9lZ9vf4dRZSaxO5kjye8QTiBJxT7Izrq8Y\nMjew5/ExEgUfmZAJ06i7kG9XRrzCpdOuh8kvsM5enxnRMJswzHCHCkv+kFYaaKPYkdtbweXMuRpB\nh5ooegI9Mew1TQcpf4BzIdoNOTJFCiU/ka1xWDI7iS4HNJ8gVqQEDUVlYz0u7H1lsNGGozlo40xK\nmVIWenP2/YzXhSQZb5epBeYNKSp7PNHiDaITShKx0/qZfBw0B6zgtiM6s5KEkuVzXPYrixhaMwfK\nfPRNgumgudFy4sKVLYLoMMYVzYOn/et0WbjW+ymVjI3sGVoQ7ZE8dqwL0o0RgW8LOjJ7NIgbhjq1\nP2L9gLaFNZwWD/g44RzokkE2Bu/Q1Ni2CyMGQ+fc1mPBYz6qq4DoVKQkvQF1LpuQ8olRb8h6wiQx\nxkLQUZ3etVY7YhNcM4agGsgY5PyKel1IfII6DnjsWCSiwVKMa8xF02FbsG5QPnhpCTlNnugIoje0\nEJobngdtfcd53NJ6JelOyEdYEvAn8popOWHJEXmfLI8og9o/5NoHrT4gRUgXYHRGhpO+xUPIqVDK\nwLtjxYnlzBhP5PjwBTU4yLbS26DYDdoLqQSWO0nmPDvlOxwFThxvC4Pvzqcic3oEaoL7zuonjmlh\n7wu6TDNF8YbmIGIhR8ZLR8uBq7/DSZT1hsFCyolkd/Qe4EdynFCEkm4odqReBjlXml+wtJDNENto\n/WOWfEDcUAx8wWKQLfja3zzxp3/6Z/yPf/Y/8Ov/kkPx770k/qFh6d97/dVf/RU/+7M/y5//+Z8D\n8M1vfpNPf/rTAPzWb/0Wf/Znf8bv/d7v8dWvfpWf//mf50tf+hJf//rX+emf/mn+4i/+4t+5tYoI\nf/ub/k9//N+RGPQxLZbCPZYf6XUw/MDQTsLpOElvEIU+rvQmQCdFor08niWmSqTXZ/axk2xhxCPO\nQu9CSaAs7G1DrIAMVJXt+sxpOREDWjgSAt4ph0LrjX07Y8lAZ+DafJls7FDqvpOtUFslr8rogkqg\nOUHA9XphWeYjUmegYWDbvGX3aXw1m0kGUSBNFuZi7/F4+TbZVsyVoftc3NjtXI4NGCOQAr1fWSS9\nzEKNVp1Rj5DOiCb2+sRhOYI7HtM7VXtgJdH2znLYsChz2eFgvr4YKx9I5RUxHgg1rjVAxnzz9guK\ncsgZb0LziWJTXXF9R/SZyXSfAfC6CYRSSqH1jZLThG/EgFjooyMStF45Hk44gx4X2h6o3cxZrRwo\nqRExCyW1XkkmhIC7vTz22cxfjkmFGiGIFMQ2kswKaOtQMrNaOYJjOdL6Mx4LQ+eGGjbCF8wgYidG\nma6zpdKagu6YOOHrNASHMNwYbIQ0kq3IgN4KYo3uDbEgpwkSgj7/3/EwRwmeyOkGtBPdqU0o5UAd\nbzFzTG7wEFy3CWUJYXDFxzLjZKooE7vpMfv7HhVLGWHgLfC4zqKDgMdOSopIYm8NYlpWPQS1QcgD\nEbf4eEZ5xRg7mmdLMAv4SHNeHnkugpl/fuFKG43E7ZQukhnjiaR3mDmXayelE1AxcXCdF5/2QE6v\n6HFF/QOwj1ApRIfmE1Tu1VlzTF1QBY+KaiK4MnqwLkda6wQHwp8xK2x1pxTBu+CqqG6M7QDq5EVm\n4y52qjumCW/Of/E7/yXCP7xH+ue+/kkIyxe+8AX+5E/+hDdv3vADP/AD/MZv/AZ//Md/zFe+8hVE\nhB/+4R/md3/3dwH48R//cX7u536OH//xHyelxO/8zu/8k6OAsMDtPfp1I8aVpVyJHtjLlnsqpK/I\nvrwg2h6m755lPvJ5JefCtvmkxI9HugiBoBaI3xCy0Z4F1hs8NcwUuqN5I3EPdqTtjiUwTfS+kfM0\nAKSU2FkgoFihbtPIOh9ZjbQsXC8by5KpfXafR1SKr6hv9BiUHgwdMBQXJfmKEpx3R+yAM5UcIQvq\nDZMDW39E9YbuDTdoNXEoE1FYpRH+TFqYN0i5Y/fAtNNbp9VMyte5XXVH4jBdnaHgCYZyOF7xNr1R\nY18h7YR0Qoy8Ope6k8sdl/odDukVPoxCndnJGOAL1YVdNzQJ3jq2gPDA4ndsXmckpk3GfU5tcnYl\nTduDHNnDGQKEU5aXmiS39NZnqcKDgiG2w1CSDXprZLuhRyOl0zTeDseszw9bBayDTRZu1p2IePng\nTqR0YIoohciNUXUmQuyID1A2NDnxcov1ccFGQ22hJSOis5rPWX4Y1Qeixt7fsCx3k7263yLekTQw\neyZcOdhx1qyrU7LSxzJhJXKPmkO0aUblhs4ZTR1o5FRQybReJ8/XJ1lXs2Ge0eT0PhCpuGccpayB\nd4Ox4PVKSmBJGF2xFawlntsbwj9AZVZn1SZcW9yo7cJS7oFC9EboC3jbbwivdNlnaUOPCOskrUlD\nSROM4pmp9w6IxmKvXjK5NnGb44JopstgXU/01kn2Go+p7OljY7GV2p+JOLyMcAZqnTbACER0ci26\nolZm5nwHS0aLC3tPrBKksSBxQfI6iz37cVZZ/ULbDliC4bMUMFxJ+XvDpfpn3Vi/16//6431y//9\nf4v2HyC0cb1eyDkhujPG/FS1vKIy7Y3n56AsAuKz90xBzPDYcM8TJizvsY+3RAgiV6K+R1qNp+0t\nqplkiegG0hATRhgiwXbZWK0w6JgKJd0SYfS4UutOeMLSBY0TeZ1u+xj3bP4GlwMqGzaDmNOq6Rsu\niehBG1DslhoPrDlNvF+bb1pNiZTyZEtez6Ryg0fmsX3EzbLMrrV3ms/bkWp/yS2ueGtYDhzh+fyG\nNX1IOTh73Rl9o6SV3gcRBfRM9BvKcqC3MymvXP0NR/tBHt5+i8Ox0MwxEl2EEoGQeXd+wjSTD3C9\nvGOxA2qD3g1hZdsv3N0vbE9GzsrQv0baLV0DZ8FiofdncJ1xuLRhes9l/8ZEw8Vn2P1rHMprst5R\n+8PL4hGa7OhYwRprOlH9gboLS5lBcEJQfgCXj3AXtu2ZnF8j2gl5wvzIkldGVy7+dYrdMcYO6qgv\nlHLAOxPCYkEdTyz63kTXpc4YwfUM63JH03dIVNb0Hr1fsHC2eMOqn56LOQ1KyfSuBI3eQaIzqOR0\nh8gzPibIZvTGWu5R+y7XayLkQF47Yw96NdYTtA1UKiZzrtykIzX9nQU3Qpl6eJ0s11aQvLPFhvkt\naz7hEWyXC1rOtD1NIHcYooraxr4dGfGWtdxzuT6y5ve5tgvZCoyBMGa4Xt9D7bsIt0TdCM/4ekT7\nGdFn1Cd02rJT45HwTzB6oywH3IO2XZHk5LLSWsMoqK1c65u58O355VCeOwVYGfWKlfnh0GrF8hXR\nE91jcpOls1cj5yB6x8cN6TQYvWH9E2z1Hcvxwr4PxihkW9l9n000KYR+l+dz4nAwAqXvRyKEdGj8\n57/1b/5f31i/782rFO8T+ULYA5ryZFDKiRAQeR+V+S2OLVFseoHoCxGKyIJEmgrsmjFeIWzzTeeg\n/j62NC61o4mXeanTfc5hY2wUCuPqrGkuPSwdqC3T/ZFI356gZJ2321xup6+qrnScKt9CIpGiknWB\ndIv7LRKVFgsugiCc1oUlP3JcT+z7BeKA5B1dQEsAV5LsNC3UUMxWTpEYVV5cQ4KqsbWPURFkGPhb\nyiq4GEKmxCtcr4w2I2o5HyGEdb2Z3E8/kIpz7u+mHHBUlnJi376NHjq7Gr4ro28sfaq5z+2bpCRY\n7MRWKSOhNEYrSCyInxEb1B1av9DGA2n/QYIC+4HSHY/nl0ykkIuS+CR7e0PyDxnjyJCPWfUOH080\nf8PwSoQzvCM9Y62TBUbb8G1aHnpb6N3YaxD6Bqczxow4jZhjpNheoSmo/hE9HhE/0vuFxCt6F5Id\n6QHb2EASPhJZ32Orj5g4FsboM2e7ta8T7RlrK/jEAlYXtJ3mZjl89v1dcX2g7uMFCP5AeKf7ziDj\norNVZs5WH7hc42UTXYieCZ8HRd1vGWF0jOqOu9LHkSGFfcDWjWFntFzp8ZbRHdJGq0bylcKR3p8R\neRkvRZ6JiSoogoTQd0MiMHnF5XlWVPvY59OcQo0L48XdJTGbXIqyRaanFes7IYGkV4z0mr0I76oz\n/INpWtCOO4SDLT5xoN1pbAy90v0j1qLs9RktY2pmZCBSZhSOjYgNFQiekRd6WyC0ZvP9nBIxlBTv\nz9n5JdGb0vlLLD3iLogcKCUT0TmkIMaFziP4pzgdjuAJ50rJV5b1EY3/n1Rah1R0JOp1wdJADEZs\nRMQUz0WfHE7A9Am1K8mM0TMjNnp0wgvoMyJ1tlP0MGd2aVDHBJ7QjTUpxTI53eKyASvV36Gpo3KP\n6Qp0VHdkLPTtk6g4tGXCUtxZj8HerkQ/TrJ+vkVYMBGyOyUN9qEcF6c4L4P8RNeJguueppF2HOlu\nsxESSidNqrsHzsew5PlG805tMts+BtfrRziN1l9T6xMWieYPsCjNN8YIRnRGa6is9N4hBiGd2naW\ncK5VCRvoJdOiof6a7D7jLK0zbEGHUuRmfkAVodOISIQI5MGIRyIy2QwVI6/QAjpv2dlo7AyPF2bm\nldoava2zdTPu0eTcLEoJ2OoVp9DaztjqFECWhaXc0pl/vnVseEBKkM3JtgALgw4yb/24UFKeBol4\nJrAZwwkjW8zNdezgC82v9F7xuCLyjM0W/qyr+jPb/owPsFxYyx1F79nlwk6je0aj4awcj4VSVkbo\nrOf2hZSdVW9hPaH2ARGgHvh+QSIQDhzWVwh3+JgNs3kKybSZcsZ0x0JYbOI0tV/J5nNDrgLtbnJI\nY8HkFWiwrIb0xHhJrLT9HTAwjogGqhdGBGNcaC0x7M30ry0XQj7aH+9XAAAgAElEQVSesScVwoU1\n3aGR6bEwCGTcsPcrQqD6TIsnIFjGQMYDqd6yaiZHwUdnu16AbZ6s0Uh6Q4RgUUh6wOJAa4mcPkXb\nZ4143zviTCOE3oHf0wYTksQBiwXxJzQZ7tBbMDpI2mf1WvpcHPNZ9pGnPim94Ci9U3ubRpAQSA+A\nzxGXfICmBv0GjfV7cq593w9WiZXanVLmX34rA1sSKR/Y24XaThAZMUfkFeo3qHXWY5/m09ZwD0KP\ndGnoQdF8JpWFvSolnYgxOKTXs/W0N8y+Az478e6FPi6IbUR/wthQu+HSByrviKGY7UicJ2FuXEhS\nGH5m1Vdcr+f5hu1nAth7QlLheYuX2+eCmpNd2MaGFqXH3fQmycC8UiTYni+kaKh+BxFD2pXRN8Km\n5wq/xdIneHV4H0lCt4+xcmLnY0YccN25O96ypDPHpbJ7UOUZTU8IJ7LdcjoJJa+ICwxDUqDccLAL\nQsfyE2k1avdZv/SFLAOVhBncvEr07RVjTySRGeDPg8FHlHFA+9wKH/I9pQSlCDmCpCtmwdC3WBp4\nfBPR+aEoYiw5MfpG8h1dDc2C7h3fz0hONC4Id2Sdqu4hK5YSJV1gnLDYGe3Cmu8AZbSd9ZBp+4ro\nPVWEQWKowzLIhzv2biyaKGk+RuoyqMMoZtTtgGiZt3UMZ2fwxCG/xuuOa6OqonbHpXaG7BzKDbU+\no5bo/cDuHRsnkLcQO7UP1tsd9SC5zr5/2TF7JDyRTVEZFFvRpKgdcJTejWGK2IKTEBuzqTWcS30k\nykJOGxKZEYKtR1yCNp7QXFhMaHrmToLMa2ooap+cIf32aZLcg4DJ+/TxFosxmQSxzcRHNnI+UGPH\n5e6lXLGRljukFL51vqVnYfhfU8ZA4h25HCjrq9kYiwvIbHPlLCz5QK2DHh9RrCDREX3CrKJyeFkI\nPhOyoiakcqKUW/beGSEs+bP0fkU0U7KiaZmIS7/HZSNaQnxnzUbRA94TrU9wy2IzYZFTR+odbiuy\nPtLkWwRHul/Q7xGE5ftuENjrxrEcuTr4ELKsIMzmhoDkC8FCaxmxd1Mm5zu9ncjq1HBMOgmFWNHh\n1B5zDkai9WeEwdY/Ykm35GWltgW0cr1UljUY4zUeQFpInBn1AUnBkCNDKjBVE+EvMjKriKxs8Q5L\nnRhH8qJc2wbGjI7lBaey7Q1dGuyC6i3Zdix1YKW70bsgkVhvYL8M1vQJJDop31PHM9JX5ADjqWO6\n8+yQ/MQx33J9/g7rckeMNu0AbWVssC4nVHdidIJ7kI7LMxE3VBZSlrlwu1lxlK0PPBW0vcea4Kk9\nMsoyb2DYfAyLhe0qBA+IDVp0ND2Q9xOpBK3suHeuG6hN2EuVNDmiqRJ0RjtMqMf4JDESmmDITvUd\nYqWVhaiFmt5QlhsWzbSnA5rO5OWJ/cJ01I8LlVuCO6R1mt6AVPq4TJBMylyrE3ah+Qa1k8oB4haz\nzPPzW5LC8zC83UzbwTBMN2pz0jJJ8605SU8ot4Q+08Y7UjqybQ9TmZIeWCiM4Vz3J4iV3vrMeqYy\nVScp02rGQ7heBbEnsp1wndyG1md/fqtXwo2QuVx1r2ga9CpId0IXPMZksKKQziy8QrcrnhPhSgqf\nOU6MnGaaJGJjceVZGprfQZ3LzzGUZFeGXBF8Xiz8DqfgfDQXhX6DsNPqyyIwbfNDvs68aMpwSDvS\nBmqfZlhFdJZ1YJpuczrg4zhh7t4ZXaEJlFsGb1AvhN6gkiB16u7k5YZeN4YP1iLUfkZ86m+u25lV\n35ueqpiLSeMVapUxJhhcRPBxz3V/YC2ZMgzVMzWOJBEuLZNLRZvg8ppkGUkdk4Vrrd+Tc+37frCq\nKVfaPLDUUev0nvCeKalAdHpcGKNRZGHQXloVFYqyqNF9p22wpuPMOfYTSQyxZ0xWVGDfAk9K8wu1\n76QsrOWIysBHZZBIsXOJBClxEJm9/HTDub0hiRIyDQMl5dlDJ2PpyPXyhIyVlI701v4WOkdIpqXG\n3g6sC4ztEbG5LJMwigrnURk253m2HOmyYb5O6nsxrvXMGu8ReiXihOkzwZk2lGRHap80/sVegShp\nKdQYpJEZMahMjJ3EDLErj/QGSzrQ65WgoaVQODMk8dwLtmQ8EqJB36bbqFii1oFmow0h2S2j7sjS\nEV9hKKEDLQLtgPMWS51MZm/Mpcz6cnsone7nqZtxwfTACCPGFRL0bSWvC+f9kRAjsNmQYyfkysFO\njPFAHYbbzkELW4M9DqSysveA6NjYSWrEkrluzvHU2bYrxU70MTikxEiDNna6TqdaUPEO4oJyw17f\nseZbujs+DjR5xNLC9elKyQfu7gtjUxSZ9gpJpHyk1WcidXpfp0YoTe25x2va3jhIpcZOaKL2juXA\nsjLqMtt2Q4mmpJKJLkR/BClIOhByYcRGsUyP9JKbbUgC6gE4IDLYx/nFphGoT6uqqE8lUTZGF+gT\nsOEjIZJxdywZtQqH42XWRVkYDq1P8Z7lNNMdo1OWitdJ1Ao/kHOAVALBRWgOEZ0is/4Kipa3DArS\nb4i4IsYs6kiiq845tnXUEnU8z+SIJQbOsrzG64XwWTDxOnC54L6gfGLaDMLBpiS0dsPHwLxgVGQ9\nYr1Au0BSugfuG7m/xvuZZO17cq593w/WkhYkHrmaU0Nxv3vxkU9o7ZoyjFfUpbJtG1bSZE/GRpAR\nb9zKyrviPPvOoT6j5WYuI6y8EMLnbMgHiKyoXLHxKTze4RzRDNf9kbB7crxEO8rCFt9i5cSiH1Lr\nG5blZiYFOmTN5IBYHqdPqsn81NOdNiZzwDgiNFQTe71ieqT7RpiiqfF86S8RlkLEWySM0IxoZ1wc\nMUWT0uy7lF2pApEzui/00SipTCpSrFzbd7i1D2cHehH2OljXRuLIvitjOMnK1L5khXSiyMK1bZyv\nhWV5YXx6Jds9IRVRn00dWTifHxBJHJYTqewYZ84+wA8QG6GFhQMjbxCV7O9xfX7gmA0rTl42AihF\nEBOuFzCHkguX/fxiETiweKVqYh8XVj2wh0AKek0sxen7DXJc6bVCJPKS6D5nru3pEdET0jOVt9yv\nn6DxlhYFT87e+1x0SkeWzrWfOa539HMBbySt7GMaGixdCA+GOtv4mMU+hfsDx+U1//ZP/4hv/sU3\nuGzv+LH/+F/xH/4n/xE1AsFQ3dEyEFfCB8cjXLf8omd3RBuRhKtsJFPSKGjsM1c7Km4XaoOlHOk9\ncM6YBYkj58tOYkdSJ5UbRoeSG+c9UVT5+r/93/hf/tevcr5u/MBnfpB/9Z/+Z0g6UNszPR8wghQd\n7+dpch0nwideEHaUFZVO6zsp37NfAtWEh7IclVrrBOi0l5FdGENuWNIOouySCJsze/iYJb/CUp0M\njngCy7TxDo17kgppMVotpJ4gV8SVnAdBo4iw1xfmQm1Ylrmp54xLzJ9z3CAxnwwcJ9yxGCzm1NYp\n3NJtI+WVfbtwXIT9+R1lXdhHhuq4JNCK6JkkiT7e/56ca9/3Gevwxo6wlteYr9AGWQrGARkL3gz3\nMeG2I7BYp9VTT6gqmHENQ2vl4JmebqhdiDjT+k6kQYsFPa6MWAgGIveYPWCsoG2+keSARmPEIyML\ne1xZ5VN0H4RfUH/NtjU05g1OZNBzZ7+uWDrAsjJ0YfSExY77kT7+T+7e7deyLLvT+saYl7XW3vtc\nIjKyMrMK39vGlo0QIMQDQsKyyg/wwP/hf8T1ZsktSzy1BA8goxZSowaM+wF1NwI3LYwa22Xstl3t\nrq7KyktEnMvee601L2PwME+5eTIIFaRESPmSiow8oYgz9pxz/H7fB5RXeHOmeECkAYrKQinjRORp\nJ+ZKjge615cFl8JiuDpTvGfWW8Kc8DJR94AlBqGKR3I+gHaSvqb0Rhen7ZWUVjBlrx3NUO1FbxLG\nJzR+Ru3CosIhCvu+IrFgoVDLu4FnFIcc2NuVaTqQ4jIyj3ag9kgMQvMroh/T+8jv6n43FN1hgGSK\nNmod38ASKkJCuiJMdM8078QUcHfEOiVM1DhTu1EJlF6gQIoNmOip8XD9EsIyoDBtWDyxK5BpsuLp\nTOSWjUc6mSkemXwi9oXgSkLREojeqPURCRvX1cetRU64XvB+IMgNYq9Q++ClpumU+oSebpjvX/HR\nz/w0f/Zn3+Hv/d3/mk///I/JWcATvd3idqD1mVL9BdHXEb8jInh9wfq1TG9f4Fpofh3YTD8ScmBv\nwwI8yS1igR6caT6B17EULcsLgMh5/Is/5O/+5/8J//Af/2Oet4CnwhdfvuPv/M7fotadjR3ZHLEr\nPQSWsODrB+NEWCvWAyK3iPcB3I6vaGYDkiMFo7Jtj0O1HhPoEbMTjc6+Fy5+pUon6BO6V8wfUC30\nsmJbJeqXgFPaOzSAcSaIUcuZGJ1GoZvT2nusPo/GomRmfUXbD8xzJ5ggdXwPaITaMlvpL9zYQg59\nJHtcqOqQ6rBABGHyzDIzhvjhjtpg0gD5yBJP9BLpPdCoaHz4kcy1r3ywmsOstwNawQ5+we1FNEYF\nqXQe2dYnlnwcfivptH4ZkasO1s5IFExfnFah0nsgxxtqBRzqeianjRQ7JhtbP9CSUD1h1slhBJxT\nuMd7RcRpNCQeXq7LjxzmW1I4sl4LJkZpG04lysQkZ+bgEGaMEyGN02ScH5hixttY4nh39vJMYELD\nZWg3vL0g9Q6DG+qjsxw8Yq28OKbGE4LK2JwGb3Sf6UXIOgOBVgMSLmOIMYGfBvKtRYIfyfEI3XAD\n+kb1yG7Cpp+xTAFvSvRI04jYcVQei6F9wtuMC1T/ApdC84o2JdcDvf8Ak8uo86aNblDqeRDiMRo/\nwJ3xa8qZUobPvnujWaEUcDVMnWYr3c9AZW8GdhnD0rZBfto25pbY9kcQkO0AHqk9Y6GMpxiLuIwo\nTlCnFaNTuLYrLRSurdMYRQPrUIoxz0qthWqG+C3glPqXICuwMajQC60KP/0TP0XjkciZ16+OxDbz\nv/4vv8//9Pf/G/atEN3x3tCotO2ClOuLWutK2xZgRbqw90fcX4N2grzBPGE0vB5RwHajtcTqiVaE\nSkFkoVii+Jds7T3/23//3/H3/9E/QUPifnrDB4fAQY88bU/M9ee4PH5nqIHSxl461s9YT2g+05qx\nHAIpHoY5oa9IT8jL30fzC2ILTiPpEXXHmtH8C8yfcDfmMA5C7pFalS7zQPTZAdIZD43WMta34TXo\nCeGEyUpIgrHSXTAE4mtEJ1TDOHDZu2GW2N7gzcmhkMIjiKNB0RfDBO0wYDQKXWeq3VDNaTyCObvv\nWA8QCn1/DxiVgtXPEN3HEkzCSCD18COZa1/5YI0ibPWJahdU78eRX22caNgpVocw1zpx2ggxEkPC\ne8S9Uzyg+QazTJKZYwgcdUFxtjquKOKNOUbUE15PnA639PqOtj1yyINpCQsyDVByDBOldiorza6Y\nzQQ/UstbSnmH+RO9VaJ+OOhNslGY6eHKnB7p/UL37wH1pWSw4ZJQARUdimDf6TUT/TA221Gp9RnT\nDYkTbpmuZzQru51Z7TpMny1Qe8XqDLESY0VrpXMmJKGXG9AdZ7RlSnlm3y6jkCQbwcML4Su99L83\ncr/DZcFjHiYBCeDPQEJDIGYnzu9Bdno/oarEGCkKfQ5IDoQ4IzbR2w4miHZS7KQ4McevsaQPcK9Y\nOxBkBl+ZD4YEiFFo2/SSXR5LmnlaWHIl5ttB1Zdh6ZRppEfmyQjTLZsm9hJHV96d0/GOkCpxvlJq\npexpwDqIHOcTU4zkAPQJ10rUV4TI0CunA/My0fqKBkM4onIgxpkUjy8wnCt5/oCf/ql/i7/8Z5VD\nOHG4feYnvv5vcPm88p/+rb/JP/mDf0DMV5QdCwny3dgN9ETX9xBA4oWc7khTpW037PU8qFLeRwQq\nKIQL615HskQyp3DE0wNhhg+S8j//vX/In3zvgbv8ajBhpx0vmXWPBDvwvL0j6i0pTbg70zGistD0\nHduW0Pye3oVuY3Mfpzs29cFQFhuLJ3kkh1fUpuCZFDPelSndkvRIMXCZRgHEEuKfE+J4n+/VUR95\natFIiuN7IEikt6EEh04MgujOSAgvRL3F6vj/dR6JIZLTHVtRNHzM3ss47FgexhB5oHlgraMmPbWZ\nWZXOCasZpWIdzCeQe1yUsi0c0itaK7gre1tpvKYw/Ujm2lc+WJsqEpQUb2l+GTk6GiFVWhe83+F+\nguC0xiDSu5GyUkofTaHSEImYVnqEWl/AKmQEofeIiYyrbSiU/Qp+Ar1l61eQON4+q4+lU6tMfiK3\nCS8zIRSCBHKcCNFZ8mtazWz13QvKL6AUtN2w1oTJAtuboSw2pXVFcudS3iMRrM2oz6jWwW+Vnec1\novEVWW+hG1PqeDviLRJNSSxMIXLKlSydfBTqlmk94mHGOYHNzHNkSgu1Xmh+JYU7NPq46rRADxGN\nBSall51DuMOCk9JKZkWnCatK7UN62OxEtzw2uz4hOrHvEXEHF5I2DrwhUUhBhhnUhW4bho/nGMrL\nSXZ8w7rPiNxQtwXpkWXJ4Ia0jpX95bpsuClJFggNrxHaDVqEJkaUO0L7PtK/xMNKKzvBRsqk2YT3\nhRAWmhW2XulyoPYw/i5QcXH2baL2HWGi7XfU/ohXUBLl2tEwUfpAPBYqIUCYTjy3Jz74G5+Qj5l0\nf0Na7rD9wvGDEz/xyd/g9/6H/53/+Lf+M/75X/wlt4dA4h22N3a7UM3oOL1m1ALeDQ0XWh3v7UEC\nitF9osqMpEeoD6Dvqf5MrBO9Gv/t3/ltvvODd0wpcA3PyBzoUXg7J7atY945r89sGVqfmBQoClpp\n9X60B/vN+KCXmegZ8TP71lArIylAJ3BHsU8J6cpWhkI+yoTVCuLMUent6cUYsOH+EdoPCDspHNjr\nMzEnqt1hkpEoaADvC0p+yY0Wggcy46aylYfRcjQh6hs0CIUznmc2aUzh9HJA2JFwIU8HgiSmUMGf\nqTxCMLI4bg3rkRA2YtiRJESdWaLTyBAOiEIKzhy+IMtfD+b/v/vjK19eUQWSQNtIElBPiI03tySO\n2zq4p+aEGMY1V4zrupHSTGNFNWHtSvADjYppwVhx20EPuFRqE6ZjpvRKawmNV3BF+ox6HWg4nWje\naQKa3o9TXBOKJWJ2vB8oCFkqOQc8JNwD1p2ktzT5ckCvtTIdhNLGNSjHGyjvUX2FMYjnUcF7YitD\nMRynNmhFaUI8gGdCHAi2ECeKN6zKyP4FBwkEB6dSUfD68uEzaq8qJ8pWmdOGMuHpAZdBlu/6hm19\nxzTDWq8ECbjHkUSoV7oLkInNmcMV64bEiWQLxZ7o/T1BphHNqbDb95FwQ7UGdkv1C7yI/DSsiCjr\nfqVWAzKw0VyZo9B7YbtEqvUh5BtEFaY0I1SKPeKuoO9Hhl6V5J3eK66vaH5m8iude3ZZmX0iaB8/\nt2+IQ/RI4UuQgNeFoIHaV1JaQAS3yjRFvH1I1S9pdUKp1G2E+c0iOdqoqbZIZgZmfvpnfpIpRsTe\n8C6/G9nYw84vfHLiLz77nP/ib/+XTMfMr/x7/z5vPsik5ZaUZ5oEsjpvH7/P/d03sHYAKu8f/4If\nfP+7fPmDL/l3/u1/dyjT68bztpJw3j42Pvwk8+1/9If8+Vvh/ubEkmf2vpKmI0Hu4OmJB90wz2gU\n8m7o9D1iOtDdCCXiEVpZQAyVhqKIwuWsxEURP3ItZ6I4KVeCTJRNITq1V5QN6yMBYTTMDkgH7zMW\n3uNhQn2m1A104rw/D0hNnxnxhDNNC97vSXEbdgeZCXpD6xfoAyRjUYl+xZqQVSl9xfqFoAHpkZyd\nYiCqbPVMzkdE7kFXrL8CWSmyMwXDX06qycdpt4VGLQUNRxChWiOmCfxHc9b8ygerh4pKZLVKjgm3\ngr302jXUwQzpx3EilTIcUPtKnu5Gn7sZzk5zRUWxZjidLB8SljPehuKh+ZXNNrwZoRkaFTwQNKBy\nZG/QXYkqJNnxPiOpEOMMJQN95PRaQ0MHKUxyhKlwvTrX/cIyz5B2MMVaJoeI9Mp+/YJ0iNBXghk5\nzDQrSJjoUslSiX6D4yRTmjWyKK1cqL2yZMF3SPNM60aMC6V9jugNlDZo/DgaO9YvGBNYRyzSPdOl\n4g5ZJkJU9uf3LOE1pZxJUrG+YOGK+jLiNaIjk4rjfaivvVy4FsP0QogTnUhwYfdKTEfMIi6d4J0Y\nbuluXNa33B4/pvbnl829k6QjYeG6nZnyCQkz1jciyiF/QrVnrvv7MWCdl/xmwMKES8PqikUlIOO/\n03tqK2iAIAudPticbQfpzNMtaqO+2Zsi6UotQownTCHpiK7VdScdG301NFZCPAIXqJXeKionkKGR\ncYGYhG/85E/xJ3/0Dzi9+nHu/Q3vti9I6UQ8THxSZlr7Pt97eOC/+t3fgQ53N3e8uf+YH//6a/7F\nD77k4en7/OSP/TSfvf0ehMzT83u2tRHjDb//x3+bGCfq/o5jnshHIfaZ29PXefvlZ3zy9YT44FJ8\n9OE3aPuZXr6k55kmmUkda5XzXLjlp9hapdqKxG28y+eBll/XwHxbscuBebmy7TNy3DgQqH0bb7/l\nCv6eFD+m20qXA64b2p0SQFMnaKL2C+oH9mZEhjmj9CeW7FQ70bpxVOVaCimcyALWbzDOuMNWnrEU\nkXgcOM/+KaHdoNMNxZzanzhMH9J7Z+eKyAIKtT+C3o3EC2dSPzEl5bmP2dKvTlqM/cUGLBrB7onh\nMmJ2VcgpIBZo4f8j59X/2z9iKLgH5imy7xWnoP4ak4pxMyp7cWgXzGf2qqOZg0GTQaKXxLwIpVyJ\nOSNEJFxo7cVxnq5c6064dGY9UKMiHglBEK2IJLAVRDEdhKqgkGQiikD4nO36gFokyIzYaTiwzNnW\nhvqAISNHtvUG5zAiU73Q+jZyf32Blri6UtqKunKtF9QnrDvmI+C+i5M0UXqjBZimRNcDYblj3Qsh\nOPSA9zvMEiShidBeoMRgwwWkjbTkQYESJ4weD9teCGnB5Im8OJLSKDz01/SWB8REInOKHPLCNMlI\nRkhknjOTHphDZE4TOeykZuQamG1nsomohsrGFGeWdMLq6Kwn/QCVCdVIrY3jYWbb37KWdxgVYqf5\nI0pmSSfW7RExQzpEfU+yKwetTHOklYEGlDbj/o6U4rjezQesDWJ8DIHAid6N3QspG1jDy5GoM2aN\no54xy0whDRLY+XPmOGO9j1O+L6i+xlqi1A13w7WRU0Ja5xuf/AT9srB9tnJtK0v9MZw8yPxZOd3e\n8NGbW7QFRODxuvLpw2f8j3/wB3z+9CnnLfMH//Q7fPr5E9fLgsprpvmIaiNY4/zwFm+dGBeszOTZ\n+Oztn6LJeXgHj7thh8j33xa+uHae2sTliy/I8Uh3o7TIB8vXCFwxu0LfyRI5BmUKb+g6cXN34vr8\niKUre2vEfKXXsbzVmNjqOzQZMXwCtpFCRoGyCxoiczuwWCK0Z5a5EXLAqmGpYHYlyYHe71A3lJ3q\nE9O0EIjDtRYqOU6UspOCMaGD/6ufEfMHtBAxbSP5km5o1jDtow22VWiNWV6B/YBpElK6pfgD1R/x\nPhqDy00Dqczplhxe0VsihJ2oxiQLITllb+OG2/9/8saqMhFf1L2q0zhVeB+KBzaEBOJ4aPRtQ8Vx\nO9LcaQE0RCDSqyAeaG2j1TrUxej4px85xNeUsLBxJrSCeRu/Vnf2shFCGhriCs0rRqBTaR7wfkNO\nX3uh3TS6GS4NdB0kJQaurtkVk7fg73EXWg8YM46CbEgC6gohsntj0hllJ4eFZk+oLnjrbG0dim+J\nEALRlfxCsncSlWfQQl4MUUEixClTq9HaRO8LqoeRo5wzOY3T39YiU55o9rJRd0PU8Whs/RGPRrGH\nYVDAhgDO4uiVo1y2K92N3mZK32kKuwQ2uVCis4UVy4EumUalS31RpRwG+IZK605KE2IjZD7P48Mz\nMTPAhCtNHLVlDCnb6TZjaeLiR7CB2VOmwSDVgIqAbLh3cKXVhhAwuxI1jI18GdE+kyswZJXXysvP\nA8zQeM9a95GksA2xNvKscgUXektUu9B7o7ny7vrA4aOPWO4WXn3tyPHDjsot71qHlLm7Tfg+cXN6\nhbnS24W27qgF6h7YywO17EgUnq8PPJ2fKeVALRN7vbAsiuqJ4ivRA2VTtk2pGLJklvlENOHV/c7x\nuHA43tNvX2PeaXXibppIKjgbGkaO1t3ZbWevX6DurNeNGO7wFoh6w8DOJaoXeosvrcdI9fcgCW8F\nUZhSphWn6JnVjB5mej9grWCyIfsJ98HVxVbEMyoHulXcwqiRpwayge8DAxgSmz/i/cIiaWAuw8a+\nbePgYyvChYiPnUbc6CoU20n6dcwaZjsh3LP3C5o6jrPXATMy2biWd2gcJRf3gDNYtGFOtB5/ZE8B\nX/lglX6L98Jazy8P2wdKU8RPiCWQx8Fx8AUL+9DbhhnquJ4ihd6vWEukkAmS0Gy0Xgk6UeszpT7T\nkjHFnaMteDqxd6ebjT/4kBAiSzwxzy8AZEk07/Q+bJamT4S4klKmlIDZhPU8ZIc6DSGeCvN0oO1H\ntuuAr3Qbj/ymigZGCcAdlenlWg8qg5k6TYG+CZqUUh7INFSEPayUOHit++ovfvnIvjdqTXQbCwHr\nI18bkw2NiglbccwyWEIN1msd+DdLRL9BXNhrIcQje73STFA9YGrDutkrOUNvRogzxIYEofUrvXSi\nBsq+YcWxUnHTYc7smdBvwJ11fUJ1wlFEnb2dKf4Oo1O3sWBo/kTrFxZVpCvkwmYXzAI5QqjGIpXa\nnohhsEX3tlHqTHPFPNL7maQztRV69/E8YTtBCp0zhlCtU1vC2hGPd2BlVEVRznvACFTbCVEG/7MM\nPquGH/ITInVz8Gf2S+Rf/blfGE8gQbG9gDvHuqPtLfTE3T3c3AdOpyNZb6hW0ZBY+5mPPvoZTssd\npzxxdwP3NzM5Xbh/lTgub+gOhxuh7pXD/cQX7x5prOSsWGPVQJIAACAASURBVD2j6uxyJh2+QUy3\nnM9f0J++y7ouhL7SgmAo5pl120bDymYq88uQrJi/I0QfJl6v1PoAONZH1VoIiBZyfAMOJkBXulVS\nWphcUHtGeqVboTRlCq8obAg7VvfBHGCn9ZXeL9SiLx+yDeweq0ckwr5fyO1DNLxilXskOaXd0zRS\ngW5HahPU1/GkJ0ecwN4TIf9L9nCIO8gt3h0rkRAnQCnNiHIPrCA71TvINHCWXhBNaNh/JHPtqx+s\nQKsgciVZYZLGFNpYPoVtbA0bYIVJAymNXjxSQBb6fkSZxzY1rHgXUhrb+14g6S2Bmbl1hMDFxxly\nWAIOuCY8biBKtULtkWlOIw/ZAqpXkjpzmAieR2RpMrZyRYKyrReEOhJbdUJFqdqQ6TwiLDoR5wON\ngLuiOb7IAq/UsA8+pgSCGvs1oFOjr1diuKfbLcEPJD/R9x0VG66hlxOzhEYMAj2yX5UQFsRnrAn7\n/kyKhSiN5huVjbzEUWXUQvOOyz668ZZJIszpliQn6rrT7ETjJdbSj+QYSHn/K1WLl0iSIy4Ly/HH\nIDk5Hbluia6dqg/09EDIkcPhHpcVe+FBgBLDHaqRw2FimTK9LOMppRpgZJRlcqap0PsFk8JaNlIM\nmDlpPnM4LhASvRdqfybIHc3fM+VIzDuSnbUJ9SVXmeKRHG8JsRLiM3Z5R7EGVjESt3kkG4I6pV7Y\na8blFrOMSCKFyBRPeFjpHgl54/Fp5Xl94qY/k+ZHevuCtXUeamItj6h0zufnwRH2Qo6J5s/caEb0\nUxrKeW2sW+OyPqOayRHMVkKIqAfuj3f88+9+FzPDbGLfnd4PeG08vH3i8x98yr59l/U6ceU1S7ig\n85F3D++pfcN95TidmFNDQuAYRyTOCERdkCIgA6Q954nYAslHAzJFReyE6oVq/aXs0AjBEdYBuA4n\nGsN+kPVMs40sioczxGG7MKlEi0hUwhyY4mvQjPEIWuiekPmAzJUQlQhDARPHYnmKd6TUCKY0VVzG\n2/yUCnPcBkEsRppsGDeIbigzh+PMvq9gNwMoziNiT0zhiFsboKPeSXYzcqztR7N2+soH68r7F0/6\nyLh16WiCKQbqFugyj0iEKyInaAHvTpru6dbpWvF4xbUCM5I2ep8wk+G4CoUWlT47jUFSKqURZCx8\nemc47okkC2RptLrhYaOqUrpiEugmL16kE907MZ/YqpPmBVA0RrqstLUR3TELVGs02fHSyDhSjeRG\n00CPgbQeWYk0KXRrwyDqmelwJKiy+pUqFRiqj9aU6TCPNknOtGI4jtsFbQ9MQUgaCFkhGNYWrDfA\niXEamdVQSDLjFGq9kmFcc+2CEgh6hcgg58uE+YyF6+iZW2SKwraekUnYOeO8x3kLIpAb+JeIhZe+\n/TQ+wPw6qPkG3Z0YGlghScJbp7Qr8SBs5RnV0e1GEyLjOhniPeetoBG2eh5eel9orXE87LS2AnDd\nz6jeoGrY7kw6Y7UhISNi4AVBR7U0JIrKuAb7kRgczQdQRcPMXkb/vtrDGObsECa6Gp0Jese6czpG\n1vPG03WlnTekNoTIQQwxHYmCdKKsRj4dSDkw60zVG9zu8XYhp5EDrbtRysqnn70dt6kGpcD750fM\nMiEMlczz+Ynz+sQP3n6Be+Ph7XexFri7SZwCeG5s5cy6O0FPBJ9QGs7NePoi0PqEa8F8wWQFTeMN\n2b/B2Xdq2BEbbjJPG7UHYm8Er5hVrDsuhuqCaqT1NrTSpoQAGiDphzh3VFdEAh6V6EKUgvszai98\nWmHAc9oBcejlCmUnyUTvO0l01KbFKbGPbG/agEwtTohHQkqsayOw4+UdZkdCclq7kPMNpb5nYkFS\noumJDmg6UM1oouw0CIKmHw33/ysfrF4jSMQ9YkSs3dJ7pjVFQqMXBVmxuNNRmu+Yn9nWxwHldVC/\no+4bQa7EvuBtZ+yNJ7wKwVZsDYQ0TI3NLuM5ITIcSW2lyEbJkaspLkrgSKASRAZsmB++2XaiKipn\n5rmCZ2o/0CxhMtGmhufIpAeWlMmyYCb0ymjYaCZHJwDzIUF7pvROnF8RemaOBjaANEuGsj2wb41q\neXi66oQHQZlxO1BqJeTT0LdIw/QZQYj6AZf9GY3pxTwQ6A7NDoRYSW3GS6LoMLqGdsC6jKHPM7U6\nzRq9V0QFs8beztQyZHvigWAZ0cOAb9SA7PKCu6uITi/pgesAUduILnVvbPuZ2uK4wpszpxtqKSNn\n6B2JM/LCTjV5S6kXvKeXheOoYXqDqInr84ZjiCp5SkAfCo6gXOsDMZyx8kjZIyB0/xz6aBBF39m2\n93Qt7NbpMngJZTWSLpQyQcu0EgmeEBPafkW8UeWKFcCV4IW3/+yJd9955vufPRPVCIcjtR/GN7A9\nEfM02Lk7TEvC/MLz85lujXW7sBZDwqDYr9tQv8dkoDuljjdvCULKQo6ZrRWqJx7OnTdv/hU++/I9\n57pyxSiXQg6vWI6w2zOuldL2wZXID0g/glygv6J6QcIdzS+47lT5HkFeDZOvTLR2Rawj/YJrY+s3\n1HZErVOqjjtNvxCloBIwzVSrdG+YbUS/kn0ZyvQw0XuitYaGme4VvA2Ogt8g1qntSsgTxTO7Q9XG\nbhtmO7FP5B65kTtan9m00+WAudO7M6cT2JEuI2rn5mNQ1zOSnM2FfTOS3tDlggQjhgPYjoZ9OOPs\n/COZa1/5YJVUaLaOULnvhFxp/oTrE10uhGmi94a0A9IdfMH1BpdMThNLSIgPRUkXYW9KUKVrxeID\nIUam9JrNDK+3FJtfKFFD5pejc0jLGBQtEE3JEhC/ksMtc8wcQnxZigjBEpEw4Bl1ZkoN5YLLFddn\ncnnNwF0MgHWcMtu+joYW0KXSrYMoTS6IH5Eu9LLS5InaM06gYzgjMaGToKyIX0g5E0zoso8rbYKt\nXOkSx+DsYLsPqHA8UtqZbmesKb33URGtjk8bFid6W1mrUWLD9IHUFqIkYB3SO8/0XkEb5gGJGWsV\n953GePNEGntoVL0OQr0tuF/Z1gfUbon9gJUd1bcEXQjhQ4Qnan+iC1zKTggnktyBK86V2hrYjJWv\nISjLsiDakD6cWBKeqGVnihMpnnDXkdToRu+Bsl0IRIhvCPmONBeu2wNz+oTWr6P+yw3zPAhn3jpq\nEesbOY3T2DJv5OmAyzYIW+0ZPKPhhkO6JS2dPEfu3vw488lYj5nT6Z50+hjbKm9eTcTYmO/eoLpx\ne5/52tdvQCp1G1yI4ykzzZm9VDQmcj4NN1WYaKXQm3B//5rleKCXC1/72htCTth6ZL9e0brz7t17\nHh/e8d0//xPK8w8osWOhcJw+Ivsrgh7pCGaB/TqQmTkdQN6OrbjtiN3RSiNyA3w2oDDaqQFcFAsZ\n1w/IaSfHyzjdMXYGqhGCEOTEQW+R/sM6dgI5QbwSNWFehnWASGv6kr6IaNoBZ9cHJMyUvTDnKzPG\nwjyONH5DlSstdNqcEHVu0onYjeAFulH1HZIvqIJNkU4dddnYsD6y43mZ6GVn8unlUHZGwy0mCe+V\n2D/+kcy1r3ywBlWyDDxa66NVFfyE+IcE/ZikV6aQ0NjoegVxJslMUbAuA94RBNcTaxF02qm2oxyJ\ncgNSMK1M0caVxAWPjc5Gio61RswzrV4p/kSzTu8jyO5daR7osbG3RzQ2Vl/x2HEJwPBCtV4GbNsm\nenoayYY+vFe9KHNWrD1DC1RLwIw44Ashylj4eAHJIBWTykgzzBgOPRL6NMoKreBS6RUimdgDk9+S\n7AavG4FpAElsJQYlkEkh0duOkjFT+ksNMU6FPCnTEvF+BJ8wHcP+MN0SgqNpxT2C3XKYj7SyEeeJ\nlO5Y8oxWJfrQaEcGaSjElRjguLyi9gtFK6ozWW4JZkQm0vyG3iaUmSkN4dvoeu0kHWkE821ozwmD\n0VsnNBrTPFFapotjOvrmUQNuIzoXozIvJ0gHsAsiKzGcyPmW82V/eTO9QOiD1+uZaQlc15F6MBT3\nFbObQb3KE1sdH/4xr6g8UvedJgubFT74+EM+fT9BnJg+jHxwu/LBzR2XraF2B12Zww37Bl9+/p7z\nszNPGZUTqkeEwDI5OVa2fWeeE6UU5uUO68LT01sw4/b1Gy7nMx+/ec0v/Osf82M//po3n7xmWVY+\n+tqP0+MR5R5B2K4X/sP/6D+g+6e4XMl5orTvvcgXfSwj+QANAhpp7ZkYlVrXwYvtNgDn3pEKEwHv\nZ8THv4/xhFOQWlCPdPshfvEJTYnSHNECfgHvhBCBTpADtVdER5sR8QHoDo5vglUnpxOtKl02YlJU\nbtj7F2gMuK/0diYJwFs87BQXkAuhK1pvwBMzw/AKK6krk7RRbLGESaP4hlTFNZGlMDUBn1jt+iOZ\na1/5YI2ex9uXz0Q90NoGjKC+9hGXqO5Df1ID2hPNR/4N2/AABKHb6Mf33lE/veTmHOsdqwUno3LG\n3IgW8BeJoGrmabuQ0ox5QMOoUpoVVBgNkM1IstBKJJIoNq4onhrbWklhprb2ohcxdtlp+khxo/hG\nJ6DxRIqC1ReWpFToQ12RFiXED0hhojdD3Kl1aGmO8z3BnXhsaFhQKsWMJp2mVyyALithEUp3umSY\nFjqjRRaWA8SxNCsIHgLOAeWlTCHh5ZTUaNUQvQepuA2/fIp3dGvsdcArPHSi2jgZ0pFD5t11qED2\n0uheMZ/GRl53ukXcBPeJJkYLDYsgrTKlQGmF0spI11kiJh1/NuhQHJsDRts7GivdnyllpzfBulPr\nGQ1C7StKpvXKtZ1pJeElYLq8pDcMZ6LwPN77eqCZgZ/o3qjtDpOPQBbKbohOuJ8xT7QKZjt4Zz8L\n1iY8HFj8QK/GNN3z6dPn3Bzv+bFT5OHtl/zg/J6LFZa7RqobyyGR48tiLk2EYEyzsV4LKQp16/Sq\nlPbAtDTMBxd0WgI3pwWJkHXG2w2fv33m4e1KfTZu0pVvfPQxbd+J/sjuj+zXxM//a/8my1zQN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4cEUIDikntER8MB2+FCac8Wi6Z7JKtQa1K8lYvN8R+dHn1T/29R/tb5VS+MpXvsKv//qv86u/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TMLTbVgd3BowTSPRlO3LX2vMKZnTImiE7PuAVrDfHVErRec3euYLxe0jURFh60MWViic8Rc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+ "text": [ + "" + ] + } + ], + "prompt_number": 44 + } + ], + "metadata": {} + } + ] +} From 837633b0d8422678571a2a2e532f1cab8f0a146a Mon Sep 17 00:00:00 2001 From: Sergey Karayev Date: Thu, 5 Dec 2013 19:35:32 -0800 Subject: [PATCH 5/5] minor edit --- python/caffe/imagenet/selective_search_demo.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/caffe/imagenet/selective_search_demo.ipynb b/python/caffe/imagenet/selective_search_demo.ipynb index 7db99a89..b8c69f60 100644 --- a/python/caffe/imagenet/selective_search_demo.ipynb +++ b/python/caffe/imagenet/selective_search_demo.ipynb @@ -13,7 +13,7 @@ "source": [ "First of all, we'll need a little [Python script](https://github.com/sergeyk/selective_search_ijcv_with_python) to run the Matlab Selective Search code.\n", "\n", - "Let's run detection on an image of a cat lounging on a couch, which we will download from the web.\n", + "Let's run detection on an image of a cat lounging on a couch (one of the ImageNet pictures), which we will download from the web.\n", "\n", " wget http://farm2.static.flickr.com/1104/1204206441_bc256e34f2.jpg\n", " echo \"1204206441_bc256e34f2.jpg\" > image_cat.txt\n",