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updated tutorials to modular_metrics PR
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Lines changed: 58 additions & 44 deletions

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‎panoptica/example_spine_matched_instance.ipynb‎

Lines changed: 27 additions & 25 deletions
Original file line numberDiff line numberDiff line change
@@ -62,7 +62,8 @@
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"import numpy as np\n",
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"from auxiliary.nifti.io import read_nifti\n",
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"from rich import print as pprint\n",
65-
"from panoptica import MatchedInstancePair, Panoptic_Evaluator"
65+
"from panoptica import MatchedInstancePair, Panoptic_Evaluator\n",
66+
"from panoptica.metrics import Metrics"
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]
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},
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{
@@ -192,7 +193,7 @@
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"text": [
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"Panoptic: Start Evaluation\n",
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"-- Got MatchedInstancePair, will evaluate instances\n",
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"-- evaluate took 1.7750227451324463 seconds to execute.\n"
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"-- evaluate took 1.4501242637634277 seconds to execute.\n"
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]
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}
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],
@@ -201,7 +202,8 @@
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"\n",
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"evaluator = Panoptic_Evaluator(\n",
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" expected_input=MatchedInstancePair,\n",
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" iou_threshold=0.5,\n",
205+
" decision_metric=Metrics.IOU,\n",
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" decision_threshold=0.5,\n",
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")\n",
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"\n",
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"result, debug_data = evaluator.evaluate(sample)"
@@ -226,15 +228,15 @@
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"text": [
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"Number of instances in prediction: 22\n",
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"Number of instances in reference: 22\n",
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"True Positives (tp): 19\n",
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"False Positives (fp): 3\n",
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"False Negatives (fn): 3\n",
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"Recognition Quality / F1 Score (RQ): 0.8636363636363636\n",
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"Segmentation Quality (SQ): 0.8328184295330796 ± 0.15186064004517466\n",
231+
"True Positives (tp): 22\n",
232+
"False Positives (fp): 0\n",
233+
"False Negatives (fn): 0\n",
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"Recognition Quality / F1 Score (RQ): 1.0\n",
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"Segmentation Quality (SQ): 0.719252280051296 ± 0.15186064004517466\n",
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"Panoptic Quality (PQ): 0.719252280051296\n",
235-
"DSC-based Segmentation Quality (DQ_DSC): 0.900292616009954 ± 0.10253566174957332\n",
237+
"DSC-based Segmentation Quality (DQ_DSC): 0.7775254410995057 ± 0.10253566174957332\n",
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"DSC-based Panoptic Quality (PQ_DSC): 0.7775254410995057\n",
237-
"Average symmetric surface distance (ASSD): 0.250331887879225 ± 0.07696680402317076\n",
239+
"Average symmetric surface distance (ASSD): 0.2161957213502398 ± 0.07696680402317076\n",
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"ASSD-based Panoptic Quality (PQ_ASSD): 0.2161957213502398\n"
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]
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}
@@ -280,17 +282,17 @@
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"<span style=\"font-weight: bold\">{</span>\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'num_pred_instances'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'num_ref_instances'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>,\n",
283-
" <span style=\"color: #008000; text-decoration-color: #008000\">'tp'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">19</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'fp'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'fn'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">3</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'rq'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8636363636363636</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'sq'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.8328184295330796</span>,\n",
285+
" <span style=\"color: #008000; text-decoration-color: #008000\">'tp'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">22</span>,\n",
286+
" <span style=\"color: #008000; text-decoration-color: #008000\">'fp'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>,\n",
287+
" <span style=\"color: #008000; text-decoration-color: #008000\">'fn'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0</span>,\n",
288+
" <span style=\"color: #008000; text-decoration-color: #008000\">'rq'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">1.0</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'sq'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.719252280051296</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_sd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.15186064004517466</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'pq'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.719252280051296</span>,\n",
290-
" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_dsc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.900292616009954</span>,\n",
292+
" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_dsc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7775254410995057</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_dsc_sd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.10253566174957332</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'pq_dsc'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.7775254410995057</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_assd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.250331887879225</span>,\n",
295+
" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_assd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.2161957213502398</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'sq_assd_sd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.07696680402317076</span>,\n",
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" <span style=\"color: #008000; text-decoration-color: #008000\">'pq_assd'</span>: <span style=\"color: #008080; text-decoration-color: #008080; font-weight: bold\">0.2161957213502398</span>\n",
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"<span style=\"font-weight: bold\">}</span>\n",
@@ -301,17 +303,17 @@
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"\u001b[1m{\u001b[0m\n",
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" \u001b[32m'num_pred_instances'\u001b[0m: \u001b[1;36m22\u001b[0m,\n",
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" \u001b[32m'num_ref_instances'\u001b[0m: \u001b[1;36m22\u001b[0m,\n",
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" \u001b[32m'tp'\u001b[0m: \u001b[1;36m19\u001b[0m,\n",
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" \u001b[32m'fp'\u001b[0m: \u001b[1;36m3\u001b[0m,\n",
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" \u001b[32m'fn'\u001b[0m: \u001b[1;36m3\u001b[0m,\n",
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" \u001b[32m'rq'\u001b[0m: \u001b[1;36m0.8636363636363636\u001b[0m,\n",
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" \u001b[32m'sq'\u001b[0m: \u001b[1;36m0.8328184295330796\u001b[0m,\n",
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" \u001b[32m'tp'\u001b[0m: \u001b[1;36m22\u001b[0m,\n",
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" \u001b[32m'fp'\u001b[0m: \u001b[1;36m0\u001b[0m,\n",
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" \u001b[32m'fn'\u001b[0m: \u001b[1;36m0\u001b[0m,\n",
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" \u001b[32m'rq'\u001b[0m: \u001b[1;36m1.0\u001b[0m,\n",
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" \u001b[32m'sq'\u001b[0m: \u001b[1;36m0.719252280051296\u001b[0m,\n",
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" \u001b[32m'sq_sd'\u001b[0m: \u001b[1;36m0.15186064004517466\u001b[0m,\n",
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" \u001b[32m'pq'\u001b[0m: \u001b[1;36m0.719252280051296\u001b[0m,\n",
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" \u001b[32m'sq_dsc'\u001b[0m: \u001b[1;36m0.900292616009954\u001b[0m,\n",
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" \u001b[32m'sq_dsc'\u001b[0m: \u001b[1;36m0.7775254410995057\u001b[0m,\n",
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" \u001b[32m'sq_dsc_sd'\u001b[0m: \u001b[1;36m0.10253566174957332\u001b[0m,\n",
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" \u001b[32m'pq_dsc'\u001b[0m: \u001b[1;36m0.7775254410995057\u001b[0m,\n",
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" \u001b[32m'sq_assd'\u001b[0m: \u001b[1;36m0.250331887879225\u001b[0m,\n",
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" \u001b[32m'sq_assd'\u001b[0m: \u001b[1;36m0.2161957213502398\u001b[0m,\n",
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" \u001b[32m'sq_assd_sd'\u001b[0m: \u001b[1;36m0.07696680402317076\u001b[0m,\n",
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" \u001b[32m'pq_assd'\u001b[0m: \u001b[1;36m0.2161957213502398\u001b[0m\n",
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"\u001b[1m}\u001b[0m\n"
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.13"
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"version": "3.11.6"
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}
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},
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"nbformat": 4,

‎panoptica/example_spine_semantic.ipynb‎

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@@ -55,7 +55,7 @@
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},
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{
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"cell_type": "code",
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"source": [
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},
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{
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"cell_type": "code",
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"metadata": {},
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{
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"-- Got SemanticPair, will approximate instances\n",
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"-- Got UnmatchedInstancePair, will match instances\n",
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"-- Got MatchedInstancePair, will evaluate instances\n",
184-
"-- evaluate took 4.660651922225952 seconds to execute.\n"
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"-- evaluate took 3.744478940963745 seconds to execute.\n"
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]
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}
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],
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" expected_input=SemanticPair,\n",
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" instance_approximator=ConnectedComponentsInstanceApproximator(),\n",
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" instance_matcher=NaiveThresholdMatching(),\n",
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" iou_threshold=0.5,\n",
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")\n",
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"\n",
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"result, debug_data = evaluator.evaluate(sample)"
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},
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{
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{
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"# get dict for further processing, e.g. for pandas\n",
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"pprint(\"results dict: \", result.to_dict())"
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]
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{
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.6"
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"nbformat": 4,

‎panoptica/example_spine_unmatched_instance.ipynb‎

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{
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" 107, 108, 109, 203, 204, 205, 206, 207, 208, 209], dtype=uint8))"
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{
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"Panoptic: Start Evaluation\n",
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"-- Got UnmatchedInstancePair, will match instances\n",
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"-- Got MatchedInstancePair, will evaluate instances\n",
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"-- evaluate took 2.6646389961242676 seconds to execute.\n"
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"-- evaluate took 2.4123013019561768 seconds to execute.\n"
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]
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}
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],
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"evaluator = Panoptic_Evaluator(\n",
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" expected_input=UnmatchedInstancePair,\n",
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" instance_matcher=NaiveThresholdMatching(),\n",
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" iou_threshold=0.5,\n",
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")\n",
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"\n",
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"result, debug_data = evaluator.evaluate(sample)"
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"# get dict for further processing, e.g. for pandas\n",
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"pprint(\"results dict: \", result.to_dict())"
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"version": "3.11.6"
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}
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},
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"nbformat": 4,

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