From 1767e807ea7c2d2af646518bb39d1fd275fb5939 Mon Sep 17 00:00:00 2001 From: James Ketrenos Date: Tue, 10 Jan 2023 16:29:11 -0800 Subject: [PATCH] Its working pretty well now Signed-off-by: James Ketrenos --- Dockerfile | 3 +- docker-compose.yml | 4 +- ketrface/cluster.py | 150 +- ketrface/detect.py | 33 +- ketrface/headers.py | 7 +- ketrface/identities.html | 6405 ++++++++--------- .../ketrface/__pycache__/db.cpython-310.pyc | Bin 2064 -> 2087 bytes .../__pycache__/dbscan.cpython-310.pyc | Bin 1429 -> 1439 bytes .../ketrface/__pycache__/util.cpython-310.pyc | Bin 2416 -> 2431 bytes ketrface/ketrface/db.py | 5 +- ketrface/ketrface/dbscan.py | 7 +- ketrface/ketrface/util.py | 1 + server/db/photos.js | 6 +- 13 files changed, 3074 insertions(+), 3547 deletions(-) diff --git a/Dockerfile b/Dockerfile index 607e6a5..7c25e58 100644 --- a/Dockerfile +++ b/Dockerfile @@ -20,7 +20,8 @@ RUN wget -qO- https://deb.nodesource.com/setup_18.x | bash - RUN DEBIAN_FRONTEND=NONINTERACTIVE apt-get install -y \ python2 \ jhead \ - nodejs + nodejs \ + jq # Install the latest npm and npx RUN npm install --global npm@latest diff --git a/docker-compose.yml b/docker-compose.yml index 084578a..44fc29f 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -9,9 +9,9 @@ services: # - db restart: always ports: - - 8134:8123 + - 8135:8123 volumes: - - /multimedia/Dad:/pictures + - /home/jketreno/.pic-chalkwharf-bk:/pictures - ${PWD}/db:/db - ${PWD}:/website - ${PWD}/models:/root/.deepface diff --git a/ketrface/cluster.py b/ketrface/cluster.py index bb566b5..1925d86 100644 --- a/ketrface/cluster.py +++ b/ketrface/cluster.py @@ -6,8 +6,7 @@ import sqlite3 from sqlite3 import Error from PIL import Image import numpy as np -from deepface import DeepFace -from deepface.detectors import FaceDetector + import functools from ketrface.util import * @@ -41,6 +40,7 @@ def gen_html(identities): photoId = face['photoId'] distance = "{:0.4f}".format(face['distance']) confidence = "{:0.3f}".format(face['confidence']) + focus = int(face['focus']) label = face['cluster'] if type(label) != str: label = f'Cluster ({face["cluster"]["id"]})' @@ -49,25 +49,40 @@ def gen_html(identities): path = f'{html_base}/faces/{"{:02d}".format(faceId % 10)}' print(f'') print(f'
{label}: {distance}
') - print(f'
{faceId} {photoId} {confidence}
') + print(f'
{faceId} {photoId} {confidence} {focus}
') print('') print('') print('') +def update_cluster_averages(identities): + for identity in identities: + average = [] + for face in identity['faces']: + if len(average) == 0: + average = face['descriptors'] + else: + average = np.add(average, face['descriptors']) + average = np.divide(average, len(identity['faces'])) + identity['descriptors'] = average + return identities def load_faces(db_path = db_path): + print(f'Connecting to database: {db_path}') conn = create_connection(db_path) faces = [] with conn: + print('Querying faces') cur = conn.cursor() res = cur.execute(''' - SELECT faces.id,facedescriptors.descriptors,faces.faceConfidence,faces.photoId + SELECT faces.id,facedescriptors.descriptors,faces.faceConfidence,faces.photoId,faces.focus FROM faces JOIN facedescriptors ON (faces.descriptorId=facedescriptors.id) WHERE faces.identityId IS null AND faces.faceConfidence>0.99 ''') for row in res.fetchall(): - id, descriptors, confidence, photoId = row + id, descriptors, confidence, photoId, focus = row + if focus is None: + focus = 100 # Assume full focus if focus not set face = { 'id': id, 'type': 'face', @@ -75,12 +90,38 @@ def load_faces(db_path = db_path): 'distance': 0, 'photoId': photoId, 'descriptors': np.frombuffer(descriptors), - 'cluster': Undefined + 'cluster': Undefined, + 'focus': focus } face['faces'] = [ face ] faces.append(face) return faces +def update_distances(identities, prune = False): + removed = 0 + for identity in identities: + for face in identity['faces']: + average = identity['descriptors'] + distance = findCosineDistance(average, face['descriptors']) + if prune and distance > MAX_EPOCH_DISTANCE: + average = np.dot(average, len(identity['faces'])) + average = np.subtract(average, face['descriptors']) + + face['cluster'] = Undefined + face['distance'] = 0 + identity['faces'].remove(face) + + identity['descriptors'] = np.divide(average, len(identity['faces'])) + removed += 1 + else: + face['distance'] = distance + return removed + +def sort_identities(identities): + identities.sort(reverse = True, key = lambda x: len(x['faces'])) + for identity in identities: + identity['faces'].sort(reverse = False, key = lambda x: x['distance']) + def cluster_sort(A, B): diff = A['cluster'] - B['cluster'] if diff > 0: @@ -101,23 +142,11 @@ print('Scanning for clusters') identities = DBSCAN(faces) # process_faces(faces) print(f'{len(identities)} clusters grouped') - +MAX_CLUSTER_DISTANCE = 0.15 # Used to merge clusters +MAX_EPOCH_DISTANCE = 0.14 # Used to prune outliers # Compute average center for all clusters -sum = 0 -for identity in identities: - sum += len(identity['faces']) - print(f'{identity["id"]} has {len(identity["faces"])} faces') - average = [] - - for face in identity['faces']: - if len(average) == 0: - average = face['descriptors'] - else: - average = np.add(average, face['descriptors']) - - average = np.divide(average, len(identity['faces'])) - identity['descriptors'] = average +identities = update_cluster_averages(identities) removed = -1 epoch = 1 @@ -126,33 +155,68 @@ epoch = 1 while removed != 0: print(f'Epoch {epoch}...') epoch += 1 - removed = 0 - for identity in identities: - for face in identity['faces']: - average = identity['descriptors'] - distance = findCosineDistance(average, face['descriptors']) - if distance > 0.14: - average = np.dot(average, len(identity['faces'])) - average = np.subtract(average, face['descriptors']) - - face['cluster'] = Undefined - face['distance'] = 0 - identity['faces'].remove(face) - - identity['descriptors'] = np.divide(average, len(identity['faces'])) - removed += 1 - else: - face['distance'] = distance + removed = update_distances(identities, prune = True) if removed > 0: print(f'Excluded {removed} faces this epoch') - -identities.sort(reverse = True, key = lambda x: len(x['faces'])) -for identity in identities: - identity['faces'].sort(reverse = False, key = lambda x: x['distance']) + + print(f'{len(identities)} identities seeded.') +# Cluster the clusters... +print('Reducing clusters via DBSCAN') +reduced = DBSCAN(identities, eps = MAX_CLUSTER_DISTANCE, minPts = 2) +# For each cluster, merge the lists of faces referenced in the cluster's +# "faces" field, which is pointing to clusters (and not actual faces) +for cluster in reduced: + merged = [] + for identity in cluster['faces']: + merged = merged + identity['faces'] + cluster['faces'] = merged + +# Creating a set containing those faces which have not been bound +# to an identity to recluster them in isolation from the rest of +# the faces +noise = [] +undefined = [] +clustered = [] +for face in faces: + if face['cluster'] == Noise: + noise.append(face) + elif face['cluster'] == Undefined: + undefined.append(face) + +print(f'Stats: Noise = {len(noise)}, Undefined = {len(undefined)}') + +straglers = DBSCAN(noise + undefined) +reduced = update_cluster_averages(reduced + straglers) + +# Give all merged identity lists a unique ID +for id, identity in enumerate(reduced): + identity['id'] = id + for face in identity['faces']: + face['cluster'] = identity + +update_distances(reduced) + +sort_identities(reduced) + +# This generates a set of differences between clusters and makes +# a recommendation to merge clusters (outside of DBSCAN) +# +# Worth testing on larger data set +for i, A in enumerate(reduced): + for k, B in enumerate(reduced): + if k < i: + continue + if A == B: + continue + distance = findCosineDistance(A['descriptors'], B['descriptors']) + if distance < MAX_CLUSTER_DISTANCE: + distance = "{:0.4f}".format(distance) + print(f'{A["id"]} to {B["id"]} = {distance}: MERGE') + print('Writing to "identities.html"') redirect_on('identities.html') -gen_html(identities) +gen_html(reduced) redirect_off() diff --git a/ketrface/detect.py b/ketrface/detect.py index f05d78d..cb66678 100644 --- a/ketrface/detect.py +++ b/ketrface/detect.py @@ -1,5 +1,4 @@ import sys -import zlib import json import os import piexif @@ -13,6 +12,7 @@ import cv2 from ketrface.util import * from ketrface.db import * +face_base = '../' model_name = 'VGG-Face' # 'ArcFace' detector_backend = 'mtcnn' # 'retinaface' model = DeepFace.build_model(model_name) @@ -92,18 +92,22 @@ def extract_faces(img, threshold=0.95, allow_upscaling = True, focus_threshold = 'right_eye': list(face['keypoints']['right_eye']), }, 'score': face['confidence'], + } + to_drop = [] + # Re-implementation of 'extract_faces' with the addition of keeping a # copy of the face image for caching on disk for k, key in enumerate(faces): print(f'Processing face {k+1}/{len(faces)}') identity = faces[key] + identity['focus'] = 100 # Until laplacian variance is executed + facial_area = identity["facial_area"] landmarks = identity["landmarks"] left_eye = landmarks["left_eye"] right_eye = landmarks["right_eye"] - # markup = True markup = False if markup == True: # Draw the face rectangle and eyes @@ -142,16 +146,12 @@ def extract_faces(img, threshold=0.95, allow_upscaling = True, focus_threshold = facial_img = img[top: bottom, left: right] - image = Image.fromarray(facial_img) - - gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + gray = cv2.cvtColor(facial_img, cv2.COLOR_BGR2GRAY) focus = variance_of_laplacian(gray) - if focus < focus_threshold: - print(f'Dropping {ke+1} due to focus {focus}.') - faces.pop(key) identity['focus'] = focus # Eye order is reversed as the routine does them backwards + image = Image.fromarray(facial_img) image = alignment_procedure(image, right_eye, left_eye) image = image.resize(size = input_shape, resample = Image.LANCZOS) resized = np.asarray(image) @@ -174,6 +174,9 @@ def extract_faces(img, threshold=0.95, allow_upscaling = True, focus_threshold = identity['image'] = Image.fromarray(resized) +# for key in to_drop: +# faces.pop(key) + return faces @@ -192,9 +195,14 @@ with conn: photoId, photoFaces, albumPath, photoFilename = row img_path = f'{base}{albumPath}{photoFilename}' print(f'Processing {i+1}/{count}: {img_path}') - img = Image.open(img_path) - img = ImageOps.exif_transpose(img) # auto-rotate if needed - img = img.convert() + try: + img = Image.open(img_path) + img = ImageOps.exif_transpose(img) # auto-rotate if needed + img = img.convert("RGB") # Catch "RGBA" and convert to 3-channel + except: + print(f'Unable to load / process {img_path}. Skipping.') + continue + img = np.asarray(img) faces = extract_faces(img) if faces is None: @@ -221,6 +229,7 @@ with conn: 'photoId': photoId, 'scanVersion': face['version'], 'faceConfidence': face['score'], + 'focus': face['focus'], 'top': face['face']['top'], 'left': face['face']['left'], 'bottom': face['face']['bottom'], @@ -228,7 +237,7 @@ with conn: 'descriptorId': faceDescriptorId, }) - path = f'faces/{"{:02d}".format(faceId % 10)}' + path = f'{face_base}faces/{"{:02d}".format(faceId % 10)}' try: os.mkdir(path) except FileExistsError: diff --git a/ketrface/headers.py b/ketrface/headers.py index 2f9eac9..2beeb0c 100644 --- a/ketrface/headers.py +++ b/ketrface/headers.py @@ -8,13 +8,14 @@ from io import BytesIO from ketrface.util import * +face_base = "../" faceId = int(sys.argv[1]) -path = f'faces/{"{:02d}".format(faceId % 10)}' +path = f'{face_base}faces/{"{:02d}".format(faceId % 10)}' img = Image.open(f'{path}/{faceId}.jpg') exif_dict = piexif.load(img.info["exif"]) compressed_str = exif_dict["Exif"][piexif.ExifIFD.UserComment] str = zlib_uudecode(compressed_str) -json = json.loads(str) -print(json) +parsed = json.loads(str) +print(json.dumps(parsed, indent=2)) \ No newline at end of file diff --git a/ketrface/identities.html b/ketrface/identities.html index f2fd3be..51024e8 100644 --- a/ketrface/identities.html +++ b/ketrface/identities.html @@ -1,2975 +1,3195 @@
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Cluster (1): 0.1125
-
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-
-
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Cluster (1): 0.1131
-
4646 73 1.000
-
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Cluster (1): 0.1137
-
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-
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Cluster (1): 0.1151
-
5208 220 0.995
-
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Cluster (1): 0.1153
-
4538 50 0.998
-
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Cluster (1): 0.1165
-
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-
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Cluster (1): 0.1176
-
5220 225 0.999
-
-
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Cluster (1): 0.1177
-
4932 141 1.000
-
-
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Cluster (1): 0.1197
-
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-
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Cluster (1): 0.1202
-
5972 429 0.995
-
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Cluster (1): 0.1208
-
5164 210 0.995
-
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Cluster (1): 0.1223
-
5054 180 1.000
-
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Cluster (1): 0.1229
-
5804 389 0.999
-
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Cluster (1): 0.1234
-
5336 278 0.995
-
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Cluster (1): 0.1258
-
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-
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Cluster (1): 0.1291
-
5340 280 0.994
-
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Cluster (1): 0.1296
-
5597 341 0.997
-
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Cluster (1): 0.1299
-
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-
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Cluster (1): 0.1310
-
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-
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Cluster (1): 0.1315
-
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-
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Cluster (1): 0.1337
-
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-
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Cluster (1): 0.1344
-
5534 326 0.996
-
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Cluster (1): 0.1348
-
4699 83 0.999
-
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Cluster (1): 0.1348
-
4934 142 1.000
-
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Cluster (1): 0.1352
-
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-
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Cluster (1): 0.1374
-
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-
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Cluster (1): 0.1392
-
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-
-
- -
Cluster (1): 0.1398
-
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-
-
-
-
-
Identity 8 has 101
-
-
- -
Cluster (8): 0.0356
-
5896 411 0.998
-
-
- -
Cluster (8): 0.0384
-
5081 188 1.000
-
-
- -
Cluster (8): 0.0405
-
5866 402 0.999
-
-
- -
Cluster (8): 0.0451
-
5111 198 0.998
-
-
- -
Cluster (8): 0.0453
-
5105 196 1.000
-
-
- -
Cluster (8): 0.0457
-
5049 177 0.995
-
-
- -
Cluster (8): 0.0467
-
5222 226 0.999
-
-
- -
Cluster (8): 0.0473
-
5214 223 1.000
-
-
- -
Cluster (8): 0.0474
-
5051 179 0.999
-
-
- -
Cluster (8): 0.0476
-
5811 391 1.000
-
-
- -
Cluster (8): 0.0500
-
5655 350 0.999
-
-
- -
Cluster (8): 0.0501
-
5498 319 0.998
-
-
- -
Cluster (8): 0.0511
-
5899 414 0.997
-
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Cluster (8): 0.0515
-
5427 304 1.000
-
-
- -
Cluster (8): 0.0517
-
5848 399 0.999
-
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Cluster (8): 0.0537
-
5833 396 0.998
-
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Cluster (8): 0.0547
-
5046 175 0.998
-
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Cluster (8): 0.0561
-
5071 186 0.999
-
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- -
Cluster (8): 0.0568
-
5219 225 1.000
-
-
- -
Cluster (8): 0.0578
-
5805 389 0.999
-
-
- -
Cluster (8): 0.0608
-
5096 193 0.999
-
-
- -
Cluster (8): 0.0610
-
5033 173 1.000
-
-
- -
Cluster (8): 0.0615
-
5753 378 1.000
-
-
- -
Cluster (8): 0.0622
-
5205 219 0.996
-
-
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Cluster (8): 0.0639
-
5079 187 0.997
-
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Cluster (8): 0.0640
-
5140 207 1.000
-
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Cluster (8): 0.0646
-
5246 234 0.998
-
-
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Cluster (8): 0.0649
-
5326 270 0.994
-
-
- -
Cluster (8): 0.0667
-
5276 243 1.000
-
-
- -
Cluster (8): 0.0679
-
4996 156 0.998
-
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Cluster (8): 0.0682
-
5053 180 1.000
-
-
- -
Cluster (8): 0.0686
-
5709 361 1.000
-
-
- -
Cluster (8): 0.0699
-
5176 216 0.998
-
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Cluster (8): 0.0700
-
4898 132 0.997
-
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Cluster (8): 0.0708
-
5599 342 0.999
-
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Cluster (8): 0.0708
-
4865 126 1.000
-
-
- -
Cluster (8): 0.0712
-
5064 184 0.998
-
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- -
Cluster (8): 0.0719
-
5093 192 1.000
-
-
- -
Cluster (8): 0.0722
-
5579 333 1.000
-
-
- -
Cluster (8): 0.0723
-
4832 120 0.999
-
-
- -
Cluster (8): 0.0738
-
5009 164 0.999
-
-
- -
Cluster (8): 0.0752
-
5743 373 1.000
-
-
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Cluster (8): 0.0761
-
4874 130 0.999
-
-
- -
Cluster (8): 0.0766
-
4790 106 1.000
-
-
- -
Cluster (8): 0.0775
-
5500 320 1.000
-
-
- -
Cluster (8): 0.0792
-
5519 326 1.000
-
-
- -
Cluster (8): 0.0794
-
5716 363 0.997
-
-
- -
Cluster (8): 0.0798
-
5154 208 0.998
-
-
- -
Cluster (8): 0.0800
-
5059 182 1.000
-
-
- -
Cluster (8): 0.0803
-
5884 406 0.998
-
-
- -
Cluster (8): 0.0805
-
4830 119 0.999
-
-
- -
Cluster (8): 0.0806
-
4951 150 1.000
-
-
- -
Cluster (8): 0.0808
-
4821 117 1.000
-
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Cluster (8): 0.0812
-
5158 209 0.999
-
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Cluster (8): 0.0814
-
5091 191 1.000
-
-
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Cluster (8): 0.0815
-
5099 194 1.000
-
-
- -
Cluster (8): 0.0817
-
4792 108 1.000
-
-
- -
Cluster (8): 0.0822
-
4845 122 1.000
-
-
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Cluster (8): 0.0824
-
4862 125 0.999
-
-
- -
Cluster (8): 0.0826
-
4718 87 1.000
-
-
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Cluster (8): 0.0849
-
4440 9 1.000
-
-
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Cluster (8): 0.0851
-
5841 398 0.999
-
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Cluster (8): 0.0857
-
4705 84 1.000
-
-
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Cluster (8): 0.0859
-
4412 6 0.997
-
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Cluster (8): 0.0865
-
5688 358 1.000
-
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Cluster (8): 0.0870
-
5606 344 1.000
-
-
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Cluster (8): 0.0870
-
4797 109 0.999
-
-
- -
Cluster (8): 0.0875
-
5491 318 0.997
-
-
- -
Cluster (8): 0.0903
-
4912 136 1.000
-
-
- -
Cluster (8): 0.0917
-
4437 8 1.000
-
-
- -
Cluster (8): 0.0919
-
5913 415 0.999
-
-
- -
Cluster (8): 0.0925
-
5171 213 0.998
-
-
- -
Cluster (8): 0.0929
-
5361 288 1.000
-
-
- -
Cluster (8): 0.0958
-
4883 131 1.000
-
-
- -
Cluster (8): 0.0960
-
5048 176 0.999
-
-
- -
Cluster (8): 0.0961
-
4592 65 0.999
-
-
- -
Cluster (8): 0.0966
-
5012 166 1.000
-
-
- -
Cluster (8): 0.0977
-
4537 50 1.000
-
-
- -
Cluster (8): 0.0999
-
5990 434 0.993
-
-
- -
Cluster (8): 0.1013
-
5283 245 1.000
-
-
- -
Cluster (8): 0.1035
-
5704 360 1.000
-
-
- -
Cluster (8): 0.1042
-
5879 405 1.000
-
-
- -
Cluster (8): 0.1044
-
5697 359 1.000
-
-
- -
Cluster (8): 0.1049
-
5135 204 1.000
-
-
- -
Cluster (8): 0.1057
-
5066 185 1.000
-
-
- -
Cluster (8): 0.1059
-
5210 221 0.999
-
-
- -
Cluster (8): 0.1072
-
4630 70 1.000
-
-
- -
Cluster (8): 0.1086
-
5792 386 1.000
-
-
- -
Cluster (8): 0.1126
-
5120 201 1.000
-
-
- -
Cluster (8): 0.1156
-
4525 43 0.999
-
-
- -
Cluster (8): 0.1158
-
4657 76 0.994
-
-
- -
Cluster (8): 0.1169
-
4528 44 0.998
-
-
- -
Cluster (8): 0.1183
-
5456 314 0.999
-
-
- -
Cluster (8): 0.1195
-
5837 397 1.000
-
-
- -
Cluster (8): 0.1201
-
5766 380 0.999
-
-
- -
Cluster (8): 0.1202
-
5015 167 1.000
-
-
- -
Cluster (8): 0.1206
-
5862 401 0.998
-
-
- -
Cluster (8): 0.1227
-
4523 42 1.000
-
-
- -
Cluster (8): 0.1260
-
4533 48 0.998
-
-
- -
Cluster (8): 0.1305
-
4421 7 0.999
-
-
- -
Cluster (8): 0.1328
-
4637 71 0.999
-
-
-
-
-
Identity 12 has 42
-
-
- -
Cluster (12): 0.0610
-
4549 53 0.994
-
-
- -
Cluster (12): 0.0683
-
4454 20 0.999
-
-
- -
Cluster (12): 0.0795
-
4654 76 1.000
-
-
- -
Cluster (12): 0.0812
-
4547 53 0.998
-
-
- -
Cluster (12): 0.0862
-
4518 40 0.995
-
-
- -
Cluster (12): 0.0867
-
4753 90 0.992
-
-
- -
Cluster (12): 0.0876
-
4841 122 1.000
-
-
- -
Cluster (12): 0.0880
-
4512 39 1.000
-
-
- -
Cluster (12): 0.0899
-
4464 21 0.996
-
-
- -
Cluster (12): 0.0902
-
5383 297 1.000
-
-
- -
Cluster (12): 0.0928
-
4544 53 1.000
-
-
- -
Cluster (12): 0.0937
-
4647 74 1.000
-
-
- -
Cluster (12): 0.0945
-
4752 90 0.995
-
-
- -
Cluster (12): 0.0946
-
4510 38 0.998
-
-
- -
Cluster (12): 0.0984
-
4837 122 1.000
-
-
- -
Cluster (12): 0.0996
-
5387 297 0.999
-
-
- -
Cluster (12): 0.1004
-
5298 259 1.000
-
-
- -
Cluster (12): 0.1005
-
5852 400 0.991
-
-
- -
Cluster (12): 0.1015
-
4860 122 0.993
-
-
- -
Cluster (12): 0.1054
-
4691 80 0.999
-
-
- -
Cluster (12): 0.1061
-
5254 236 0.998
-
-
- -
Cluster (12): 0.1097
-
4672 79 0.999
-
-
- -
Cluster (12): 0.1099
-
4764 94 0.996
-
-
- -
Cluster (12): 0.1100
-
4851 122 1.000
-
-
- -
Cluster (12): 0.1123
-
4673 79 0.999
-
-
- -
Cluster (12): 0.1134
-
4854 122 1.000
-
-
- -
Cluster (12): 0.1144
-
4548 53 0.994
-
-
- -
Cluster (12): 0.1156
-
4855 122 0.999
-
-
- -
Cluster (12): 0.1174
-
5812 392 1.000
-
-
- -
Cluster (12): 0.1174
-
4553 54 0.995
-
-
- -
Cluster (12): 0.1175
-
4846 122 1.000
-
-
- -
Cluster (12): 0.1201
-
4839 122 1.000
-
-
- -
Cluster (12): 0.1201
-
4677 79 0.998
-
-
- -
Cluster (12): 0.1212
-
4670 79 1.000
-
-
- -
Cluster (12): 0.1216
-
5395 297 0.995
-
-
- -
Cluster (12): 0.1216
-
4539 52 0.997
-
-
- -
Cluster (12): 0.1280
-
5390 297 0.998
-
-
- -
Cluster (12): 0.1291
-
4551 54 0.998
-
-
- -
Cluster (12): 0.1320
-
5590 337 0.998
-
-
- -
Cluster (12): 0.1357
-
5963 425 0.997
-
-
- -
Cluster (12): 0.1361
-
5817 392 0.994
-
-
- -
Cluster (12): 0.1376
-
5227 228 0.995
-
-
-
-
-
Identity 5 has 19
-
-
- -
Cluster (5): 0.0644
-
5643 350 0.999
-
-
- -
Cluster (5): 0.0668
-
4441 9 0.995
-
-
- -
Cluster (5): 0.0684
-
4401 4 1.000
-
-
- -
Cluster (5): 0.0725
-
4438 8 0.995
-
-
- -
Cluster (5): 0.0754
-
4443 10 0.997
-
-
- -
Cluster (5): 0.0758
-
4709 84 0.998
-
-
- -
Cluster (5): 0.0802
-
5906 415 1.000
-
-
- -
Cluster (5): 0.0819
-
5560 331 1.000
-
-
- -
Cluster (5): 0.0841
-
5508 324 1.000
-
-
- -
Cluster (5): 0.0842
-
5689 358 1.000
-
-
- -
Cluster (5): 0.0852
-
4696 82 1.000
-
-
- -
Cluster (5): 0.0920
-
4933 142 1.000
-
-
- -
Cluster (5): 0.0929
-
5973 430 1.000
-
-
- -
Cluster (5): 0.1020
-
5870 403 1.000
-
-
- -
Cluster (5): 0.1088
-
5699 359 0.998
-
-
- -
Cluster (5): 0.1100
-
4825 119 1.000
-
-
- -
Cluster (5): 0.1114
-
4717 87 1.000
-
-
- -
Cluster (5): 0.1333
-
5518 326 1.000
-
-
- -
Cluster (5): 0.1390
-
5991 434 0.990
-
-
-
-
-
Identity 6 has 17
-
-
- -
Cluster (6): 0.0613
-
5641 350 1.000
-
-
- -
Cluster (6): 0.0624
-
5117 200 0.999
-
-
- -
Cluster (6): 0.0645
-
4702 84 1.000
-
-
- -
Cluster (6): 0.0672
-
5070 186 0.999
-
-
- -
Cluster (6): 0.0759
-
4402 4 0.999
-
-
- -
Cluster (6): 0.0832
-
5211 222 1.000
-
-
- -
Cluster (6): 0.0897
-
5909 415 0.999
-
-
- -
Cluster (6): 0.0927
-
5713 363 1.000
-
-
- -
Cluster (6): 0.0934
-
5690 358 0.999
-
-
- -
Cluster (6): 0.1004
-
5515 326 1.000
-
-
- -
Cluster (6): 0.1079
-
5989 434 0.994
-
-
- -
Cluster (6): 0.1094
-
5721 365 1.000
-
-
- -
Cluster (6): 0.1106
-
5975 430 0.996
-
-
- -
Cluster (6): 0.1168
-
5213 223 1.000
-
-
- -
Cluster (6): 0.1179
-
5781 384 0.998
-
-
- -
Cluster (6): 0.1289
-
5318 270 1.000
-
-
- -
Cluster (6): 0.1386
-
4634 70 0.997
-
-
-
-
-
Identity 9 has 17
-
-
- -
Cluster (9): 0.0634
-
5056 181 0.990
-
-
- -
Cluster (9): 0.0690
-
5562 331 0.999
-
-
- -
Cluster (9): 0.0698
-
4816 116 1.000
-
-
- -
Cluster (9): 0.0710
-
4948 149 0.999
-
-
- -
Cluster (9): 0.0800
-
5212 222 0.996
-
-
- -
Cluster (9): 0.0829
-
4997 156 0.997
-
-
- -
Cluster (9): 0.0836
-
5077 187 0.999
-
-
- -
Cluster (9): 0.0876
-
4849 122 1.000
-
-
- -
Cluster (9): 0.0921
-
4769 96 0.999
-
-
- -
Cluster (9): 0.0922
-
4892 131 0.999
-
-
- -
Cluster (9): 0.0943
-
5999 436 1.000
-
-
- -
Cluster (9): 0.0948
-
4706 84 0.999
-
-
- -
Cluster (9): 0.1028
-
5914 415 0.998
-
-
- -
Cluster (9): 0.1089
-
4427 7 0.998
-
-
- -
Cluster (9): 0.1156
-
4734 88 0.999
-
-
- -
Cluster (9): 0.1197
-
5327 270 0.992
-
-
- -
Cluster (9): 0.1264
-
5800 388 0.993
-
-
-
-
-
Identity 11 has 17
-
-
- -
Cluster (11): 0.0766
-
4576 59 1.000
-
-
- -
Cluster (11): 0.0790
-
5396 297 0.995
-
-
- -
Cluster (11): 0.0821
-
4455 20 0.997
-
-
- -
Cluster (11): 0.0829
-
4602 67 0.992
-
-
- -
Cluster (11): 0.0887
-
5388 297 0.999
-
-
- -
Cluster (11): 0.0977
-
4559 56 0.999
-
-
- -
Cluster (11): 0.0988
-
4462 21 0.998
-
-
- -
Cluster (11): 0.1028
-
4856 122 0.999
-
-
- -
Cluster (11): 0.1068
-
4578 59 0.997
-
-
- -
Cluster (11): 0.1078
-
5286 246 0.994
-
-
- -
Cluster (11): 0.1129
-
4448 16 0.993
-
-
- -
Cluster (11): 0.1147
-
5818 392 0.992
-
-
- -
Cluster (11): 0.1178
-
4751 90 0.999
-
-
- -
Cluster (11): 0.1241
-
4552 54 0.995
-
-
- -
Cluster (11): 0.1289
-
5389 297 0.998
-
-
- -
Cluster (11): 0.1336
-
4561 56 0.993
-
-
- -
Cluster (11): 0.1395
-
5358 287 0.992
-
-
-
-
-
Identity 40 has 17
-
-
- -
Cluster (40): 0.0917
-
4707 84 0.998
-
-
- -
Cluster (40): 0.0933
-
4979 152 1.000
-
-
- -
Cluster (40): 0.0979
-
5525 326 0.999
-
-
- -
Cluster (40): 0.1022
-
5647 350 0.999
-
-
- -
Cluster (40): 0.1032
-
5610 344 0.999
-
-
- -
Cluster (40): 0.1108
-
5782 384 0.996
-
-
- -
Cluster (40): 0.1160
-
5113 199 0.999
-
-
- -
Cluster (40): 0.1175
-
5443 310 1.000
-
-
- -
Cluster (40): 0.1193
-
5693 358 0.994
-
-
- -
Cluster (40): 0.1215
-
5455 314 1.000
-
-
- -
Cluster (40): 0.1225
-
5587 336 1.000
-
-
- -
Cluster (40): 0.1227
-
5615 345 0.998
-
-
- -
Cluster (40): 0.1236
-
6008 436 0.999
-
-
- -
Cluster (40): 0.1245
-
5039 173 0.997
-
-
- -
Cluster (40): 0.1287
-
5887 409 1.000
-
-
- -
Cluster (40): 0.1371
-
5486 318 0.999
-
-
- -
Cluster (40): 0.1383
-
5479 318 1.000
-
-
-
-
-
Identity 0 has 16
-
-
- -
Cluster (0): 0.0634
-
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7 455 1.000 42
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- -
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- -
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225 792 1.000 94
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264 894 1.000 22
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663 1772 1.000 38
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314 1020 1.000 197
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399 1245 1.000 1754
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352 1129 0.992 21
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328 1067 1.000 80
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695 1842 1.000 344
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409 1263 1.000 192
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255 875 1.000 33
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384 1208 1.000 212
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433 1334 0.997 313
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150 631 1.000 1414
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424 1309 0.997 92
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294 979 1.000 1204
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Identity 50 has 14
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- -
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5139 207 1.000
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19 468 0.996 59
- -
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1178 2565 0.997 3812
- -
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4843 122 1.000
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1148 2516 0.992 66
- -
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5355 286 0.997
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322 1041 0.999 50
- -
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4891 131 0.999
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898 2181 0.998 73
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5189 219 1.000
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5062 184 1.000
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373 1168 0.995 1742
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635 1740 0.998 425
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5129 202 1.000
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880 2143 0.998 41
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5316 270 1.000
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1210 2592 0.996 3574
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5188 219 1.000
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863 2122 0.999 36
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5041 173 0.994
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1194 2573 0.998 1430
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292 970 0.999 76
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Cluster (15): 0.1613
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1007 2440 0.999 21
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Cluster (15): 0.1846
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734 1915 0.999 18
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- -
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4918 136 0.999
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571 1642 0.998 571
- -
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5532 326 0.998
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670 1784 1.000 30
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5638 350 1.000
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- -
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93 548 1.000 656
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28 478 0.991 324
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- -
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162 658 0.992 23
- -
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5720 365 1.000
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527 1554 0.999 48
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197 723 0.998 120
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151 631 0.992 37
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4741 88 0.998
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135 614 0.999 37
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@@ -2977,669 +3197,159 @@
Identity 17 has 5
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@@ -3647,524 +3357,259 @@
Identity 29 has 3
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