292 lines
10 KiB
Python
292 lines
10 KiB
Python
from contextlib import ExitStack as DoesNotRaise
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from typing import Optional, Tuple
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import numpy as np
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import pytest
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from supervision.detection.vlm import from_florence_2
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@pytest.mark.parametrize(
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"florence_result, resolution_wh, expected_results, exception",
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[
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( # Object detection: empty
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{"<OD>": {"bboxes": [], "labels": []}},
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(10, 10),
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(np.array([], dtype=np.float32), np.array([]), None, None),
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DoesNotRaise(),
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),
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( # Object detection: two detections
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{
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"<OD>": {
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"bboxes": [[4, 4, 6, 6], [5, 5, 7, 7]],
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"labels": ["car", "door"],
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}
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},
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(10, 10),
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(
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np.array([[4, 4, 6, 6], [5, 5, 7, 7]], dtype=np.float32),
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np.array(["car", "door"]),
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None,
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None,
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),
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DoesNotRaise(),
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),
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( # Caption: unsupported
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{"<CAPTION>": "A green car parked in front of a yellow building."},
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(10, 10),
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None,
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pytest.raises(ValueError),
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),
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( # Detailed Caption: unsupported
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{
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"<DETAILED_CAPTION>": "The image shows a blue Volkswagen Beetle parked "
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"in front of a yellow building with two brown doors, surrounded by "
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"trees and a clear blue sky."
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},
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(10, 10),
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None,
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pytest.raises(ValueError),
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),
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( # More Detailed Caption: unsupported
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{
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"<MORE_DETAILED_CAPTION>": "The image shows a vintage Volkswagen "
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"Beetle car parked on a "
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"cobblestone street in front of a yellow building with two wooden "
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"doors. The car is painted in a bright turquoise color and has a "
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"white stripe running along the side. It has two doors on either side "
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"of the car, one on top of the other, and a small window on the "
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"front. The building appears to be old and dilapidated, with peeling "
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"paint and crumbling walls. The sky is blue and there are trees in "
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"the background."
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},
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(10, 10),
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None,
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pytest.raises(ValueError),
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),
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( # Caption to Phrase Grounding: empty
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{"<CAPTION_TO_PHRASE_GROUNDING>": {"bboxes": [], "labels": []}},
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(10, 10),
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(np.array([], dtype=np.float32), np.array([]), None, None),
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DoesNotRaise(),
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),
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( # Caption to Phrase Grounding: two detections
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{
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"<CAPTION_TO_PHRASE_GROUNDING>": {
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"bboxes": [[4, 4, 6, 6], [5, 5, 7, 7]],
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"labels": ["a green car", "a yellow building"],
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}
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},
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(10, 10),
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(
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np.array([[4, 4, 6, 6], [5, 5, 7, 7]], dtype=np.float32),
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np.array(["a green car", "a yellow building"]),
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None,
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None,
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),
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DoesNotRaise(),
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),
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( # Dense Region caption: empty
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{"<DENSE_REGION_CAPTION>": {"bboxes": [], "labels": []}},
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(10, 10),
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(np.array([], dtype=np.float32), np.array([]), None, None),
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DoesNotRaise(),
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),
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( # Caption to Phrase Grounding: two detections
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{
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"<DENSE_REGION_CAPTION>": {
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"bboxes": [[4, 4, 6, 6], [5, 5, 7, 7]],
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"labels": ["a green car", "a yellow building"],
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}
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},
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(10, 10),
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(
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np.array([[4, 4, 6, 6], [5, 5, 7, 7]], dtype=np.float32),
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np.array(["a green car", "a yellow building"]),
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None,
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None,
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),
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DoesNotRaise(),
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),
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( # Region proposal
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{
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"<REGION_PROPOSAL>": {
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"bboxes": [[4, 4, 6, 6], [5, 5, 7, 7]],
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"labels": ["", ""],
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}
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},
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(10, 10),
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(
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np.array([[4, 4, 6, 6], [5, 5, 7, 7]], dtype=np.float32),
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None,
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None,
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None,
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),
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DoesNotRaise(),
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),
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( # Referring Expression Segmentation
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{
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"<REFERRING_EXPRESSION_SEGMENTATION>": {
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"polygons": [[[1, 1, 2, 1, 2, 2, 1, 2]]],
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"labels": [""],
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}
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},
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(10, 10),
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(
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np.array([[1.0, 1.0, 2.0, 2.0]], dtype=np.float32),
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None,
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np.array(
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[
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[
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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]
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],
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dtype=bool,
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),
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None,
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),
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DoesNotRaise(),
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),
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( # Referring Expression Segmentation
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{
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"<REFERRING_EXPRESSION_SEGMENTATION>": {
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"polygons": [[[1, 1, 2, 1, 2, 2, 1, 2]]],
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"labels": [""],
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}
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},
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(10, 10),
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(
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np.array([[1.0, 1.0, 2.0, 2.0]], dtype=np.float32),
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None,
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np.array(
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[
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[
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 0, 0, 0, 0, 0, 0, 0],
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[0, 1, 1, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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]
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],
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dtype=bool,
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),
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None,
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),
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DoesNotRaise(),
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),
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( # OCR: unsupported
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{"<OCR>": "A"},
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(10, 10),
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None,
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pytest.raises(ValueError),
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),
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( # OCR with Region: obb boxes
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{
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"<OCR_WITH_REGION>": {
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"quad_boxes": [[2, 2, 6, 4, 5, 6, 1, 5], [4, 4, 5, 5, 4, 6, 3, 5]],
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"labels": ["some text", "other text"],
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}
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},
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(10, 10),
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(
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np.array([[1, 2, 6, 6], [3, 4, 5, 6]], dtype=np.float32),
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np.array(["some text", "other text"]),
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None,
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np.array(
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[[[2, 2], [6, 4], [5, 6], [1, 5]], [[4, 4], [5, 5], [4, 6], [3, 5]]]
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),
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),
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DoesNotRaise(),
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),
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( # Open Vocabulary Detection
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{
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"<OPEN_VOCABULARY_DETECTION>": {
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"bboxes": [[4, 4, 6, 6], [5, 5, 7, 7]],
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"bboxes_labels": ["cat", "cat"],
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"polygon": [],
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"polygons_labels": [],
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}
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},
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(10, 10),
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(
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np.array([[4, 4, 6, 6], [5, 5, 7, 7]], dtype=np.float32),
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np.array(["cat", "cat"]),
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None,
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None,
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),
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DoesNotRaise(),
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),
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( # Region to Category: empty
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{"<REGION_TO_CATEGORY>": "No object detected."},
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(10, 10),
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(np.empty((0, 4), dtype=np.float32), np.array([]), None, None),
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DoesNotRaise(),
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),
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( # Region to Category: detected
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{"<REGION_TO_CATEGORY>": "some object<loc_300><loc_400><loc_500><loc_600>"},
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(10, 10),
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(
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np.array([[3, 4, 5, 6]], dtype=np.float32),
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np.array(["some object"]),
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None,
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None,
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),
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DoesNotRaise(),
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),
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( # Region to Description: empty
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{"<REGION_TO_DESCRIPTION>": "No object detected."},
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(10, 10),
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(np.empty((0, 4), dtype=np.float32), np.array([]), None, None),
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DoesNotRaise(),
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),
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( # Region to Description: detected
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{"<REGION_TO_DESCRIPTION>": "descr<loc_300><loc_400><loc_500><loc_600>"},
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(10, 10),
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(
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np.array([[3, 4, 5, 6]], dtype=np.float32),
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np.array(["descr"]),
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None,
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None,
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),
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DoesNotRaise(),
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),
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],
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)
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def test_florence_2(
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florence_result: dict,
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resolution_wh: Tuple[int, int],
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expected_results: Tuple[
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np.ndarray, Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]
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],
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exception: Exception,
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) -> None:
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with exception:
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result = from_florence_2(florence_result, resolution_wh)
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np.testing.assert_array_equal(result[0], expected_results[0])
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if expected_results[1] is None:
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assert result[1] is None
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else:
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np.testing.assert_array_equal(result[1], expected_results[1])
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if expected_results[2] is None:
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assert result[2] is None
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else:
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np.testing.assert_array_equal(result[2], expected_results[2])
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if expected_results[3] is None:
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assert result[3] is None
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else:
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np.testing.assert_array_equal(result[3], expected_results[3])
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