494 lines
18 KiB
Python
494 lines
18 KiB
Python
# Copyright 2022 The MediaPipe Authors.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Tests for object detector."""
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import enum
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import os
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from unittest import mock
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from absl.testing import absltest
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from absl.testing import parameterized
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import numpy as np
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.tasks.python.components.containers import bounding_box as bounding_box_module
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from mediapipe.tasks.python.components.containers import category as category_module
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from mediapipe.tasks.python.components.containers import detections as detections_module
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from mediapipe.tasks.python.core import base_options as base_options_module
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from mediapipe.tasks.python.test import test_utils
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from mediapipe.tasks.python.vision import object_detector
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from mediapipe.tasks.python.vision.core import image_processing_options as image_processing_options_module
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from mediapipe.tasks.python.vision.core import vision_task_running_mode as running_mode_module
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_BaseOptions = base_options_module.BaseOptions
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_Category = category_module.Category
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_BoundingBox = bounding_box_module.BoundingBox
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_Detection = detections_module.Detection
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_DetectionResult = detections_module.DetectionResult
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_Image = image_module.Image
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_ImageProcessingOptions = image_processing_options_module.ImageProcessingOptions
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_ObjectDetector = object_detector.ObjectDetector
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_ObjectDetectorOptions = object_detector.ObjectDetectorOptions
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_RUNNING_MODE = running_mode_module.VisionTaskRunningMode
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_MODEL_FILE = 'coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.tflite'
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_NO_NMS_MODEL_FILE = 'efficientdet_lite0_fp16_no_nms.tflite'
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_IMAGE_FILE = 'cats_and_dogs.jpg'
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_EXPECTED_DETECTION_RESULT = _DetectionResult(
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detections=[
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_Detection(
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bounding_box=_BoundingBox(
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origin_x=608,
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origin_y=164,
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width=381,
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height=432,
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),
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categories=[
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_Category(
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index=None,
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score=0.69921875,
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display_name=None,
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category_name='cat',
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)
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],
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),
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_Detection(
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bounding_box=_BoundingBox(
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origin_x=57,
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origin_y=398,
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width=386,
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height=196,
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),
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categories=[
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_Category(
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index=None,
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score=0.65625,
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display_name=None,
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category_name='cat',
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)
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],
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),
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_Detection(
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bounding_box=_BoundingBox(
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origin_x=256,
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origin_y=394,
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width=173,
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height=202,
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),
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categories=[
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_Category(
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index=None,
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score=0.51171875,
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display_name=None,
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category_name='cat',
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)
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],
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),
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_Detection(
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bounding_box=_BoundingBox(
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origin_x=360,
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origin_y=195,
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width=330,
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height=412,
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),
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categories=[
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_Category(
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index=None,
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score=0.48828125,
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display_name=None,
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category_name='cat',
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)
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],
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),
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]
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)
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_ALLOW_LIST = ['cat', 'dog']
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_DENY_LIST = ['cat']
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_SCORE_THRESHOLD = 0.3
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_MAX_RESULTS = 3
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_TEST_DATA_DIR = 'mediapipe/tasks/testdata/vision'
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class ModelFileType(enum.Enum):
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FILE_CONTENT = 1
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FILE_NAME = 2
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class ObjectDetectorTest(parameterized.TestCase):
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def setUp(self):
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super().setUp()
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self.test_image = _Image.create_from_file(
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test_utils.get_test_data_path(os.path.join(_TEST_DATA_DIR, _IMAGE_FILE))
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)
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self.model_path = test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, _MODEL_FILE)
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)
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def test_create_from_file_succeeds_with_valid_model_path(self):
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# Creates with default option and valid model file successfully.
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with _ObjectDetector.create_from_model_path(self.model_path) as detector:
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self.assertIsInstance(detector, _ObjectDetector)
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def test_create_from_options_succeeds_with_valid_model_path(self):
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# Creates with options containing model file successfully.
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base_options = _BaseOptions(model_asset_path=self.model_path)
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options = _ObjectDetectorOptions(base_options=base_options)
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with _ObjectDetector.create_from_options(options) as detector:
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self.assertIsInstance(detector, _ObjectDetector)
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def test_create_from_options_fails_with_invalid_model_path(self):
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with self.assertRaisesRegex(
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RuntimeError, 'Unable to open file at /path/to/invalid/model.tflite'
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):
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base_options = _BaseOptions(
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model_asset_path='/path/to/invalid/model.tflite'
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)
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options = _ObjectDetectorOptions(base_options=base_options)
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_ObjectDetector.create_from_options(options)
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def test_create_from_options_succeeds_with_valid_model_content(self):
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# Creates with options containing model content successfully.
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with open(self.model_path, 'rb') as f:
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base_options = _BaseOptions(model_asset_buffer=f.read())
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options = _ObjectDetectorOptions(base_options=base_options)
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detector = _ObjectDetector.create_from_options(options)
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self.assertIsInstance(detector, _ObjectDetector)
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, 4, _EXPECTED_DETECTION_RESULT),
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(ModelFileType.FILE_CONTENT, 4, _EXPECTED_DETECTION_RESULT),
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)
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def test_detect(
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self, model_file_type, max_results, expected_detection_result
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):
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# Creates detector.
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=self.model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.model_path, 'rb') as f:
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model_content = f.read()
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base_options = _BaseOptions(model_asset_buffer=model_content)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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options = _ObjectDetectorOptions(
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base_options=base_options, max_results=max_results
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)
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detector = _ObjectDetector.create_from_options(options)
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# Performs object detection on the input.
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detection_result = detector.detect(self.test_image)
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# Comparing results.
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self.assertEqual(detection_result, expected_detection_result)
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# Closes the detector explicitly when the detector is not used in
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# a context.
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detector.close()
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@parameterized.parameters(
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(ModelFileType.FILE_NAME, 4, _EXPECTED_DETECTION_RESULT),
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(ModelFileType.FILE_CONTENT, 4, _EXPECTED_DETECTION_RESULT),
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)
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def test_detect_in_context(
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self, model_file_type, max_results, expected_detection_result
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):
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(model_asset_path=self.model_path)
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elif model_file_type is ModelFileType.FILE_CONTENT:
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with open(self.model_path, 'rb') as f:
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model_contents = f.read()
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base_options = _BaseOptions(model_asset_buffer=model_contents)
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else:
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# Should never happen
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raise ValueError('model_file_type is invalid.')
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options = _ObjectDetectorOptions(
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base_options=base_options, max_results=max_results
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)
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with _ObjectDetector.create_from_options(options) as detector:
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# Performs object detection on the input.
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detection_result = detector.detect(self.test_image)
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# Comparing results.
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self.assertEqual(detection_result, expected_detection_result)
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def test_score_threshold_option(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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score_threshold=_SCORE_THRESHOLD,
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)
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with _ObjectDetector.create_from_options(options) as detector:
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# Performs object detection on the input.
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detection_result = detector.detect(self.test_image)
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detections = detection_result.detections
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for detection in detections:
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score = detection.categories[0].score
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self.assertGreaterEqual(
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score,
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_SCORE_THRESHOLD,
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f'Detection with score lower than threshold found. {detection}',
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)
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def test_max_results_option(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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max_results=_MAX_RESULTS,
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)
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with _ObjectDetector.create_from_options(options) as detector:
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# Performs object detection on the input.
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detection_result = detector.detect(self.test_image)
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detections = detection_result.detections
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self.assertLessEqual(
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len(detections), _MAX_RESULTS, 'Too many results returned.'
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)
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def test_allow_list_option(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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category_allowlist=_ALLOW_LIST,
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)
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with _ObjectDetector.create_from_options(options) as detector:
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# Performs object detection on the input.
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detection_result = detector.detect(self.test_image)
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detections = detection_result.detections
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for detection in detections:
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label = detection.categories[0].category_name
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self.assertIn(
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label,
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_ALLOW_LIST,
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f'Label {label} found but not in label allow list',
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)
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def test_deny_list_option(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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category_denylist=_DENY_LIST,
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)
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with _ObjectDetector.create_from_options(options) as detector:
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# Performs object detection on the input.
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detection_result = detector.detect(self.test_image)
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detections = detection_result.detections
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for detection in detections:
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label = detection.categories[0].category_name
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self.assertNotIn(
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label, _DENY_LIST, f'Label {label} found but in deny list.'
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)
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def test_combined_allowlist_and_denylist(self):
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# Fails with combined allowlist and denylist
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with self.assertRaisesRegex(
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ValueError,
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r'`category_allowlist` and `category_denylist` are mutually '
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r'exclusive options.',
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):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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category_allowlist=['foo'],
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category_denylist=['bar'],
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)
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with _ObjectDetector.create_from_options(options) as unused_detector:
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pass
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def test_empty_detection_outputs_with_in_model_nms(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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score_threshold=1,
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)
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with _ObjectDetector.create_from_options(options) as detector:
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# Performs object detection on the input.
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detection_result = detector.detect(self.test_image)
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self.assertEmpty(detection_result.detections)
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def test_empty_detection_outputs_without_in_model_nms(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(
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model_asset_path=test_utils.get_test_data_path(
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os.path.join(_TEST_DATA_DIR, _NO_NMS_MODEL_FILE))),
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score_threshold=1,
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)
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with _ObjectDetector.create_from_options(options) as detector:
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# Performs object detection on the input.
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detection_result = detector.detect(self.test_image)
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self.assertEmpty(detection_result.detections)
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def test_missing_result_callback(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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)
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with self.assertRaisesRegex(
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ValueError, r'result callback must be provided'
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):
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with _ObjectDetector.create_from_options(options) as unused_detector:
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pass
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@parameterized.parameters((_RUNNING_MODE.IMAGE), (_RUNNING_MODE.VIDEO))
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def test_illegal_result_callback(self, running_mode):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=running_mode,
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result_callback=mock.MagicMock(),
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)
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with self.assertRaisesRegex(
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ValueError, r'result callback should not be provided'
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):
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with _ObjectDetector.create_from_options(options) as unused_detector:
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pass
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def test_calling_detect_for_video_in_image_mode(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.IMAGE,
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)
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with _ObjectDetector.create_from_options(options) as detector:
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the video mode'
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):
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detector.detect_for_video(self.test_image, 0)
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def test_calling_detect_async_in_image_mode(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.IMAGE,
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)
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with _ObjectDetector.create_from_options(options) as detector:
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the live stream mode'
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):
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detector.detect_async(self.test_image, 0)
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def test_calling_detect_in_video_mode(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO,
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)
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with _ObjectDetector.create_from_options(options) as detector:
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the image mode'
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):
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detector.detect(self.test_image)
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def test_calling_detect_async_in_video_mode(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO,
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)
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with _ObjectDetector.create_from_options(options) as detector:
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the live stream mode'
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):
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detector.detect_async(self.test_image, 0)
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def test_detect_for_video_with_out_of_order_timestamp(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO,
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)
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with _ObjectDetector.create_from_options(options) as detector:
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unused_result = detector.detect_for_video(self.test_image, 1)
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with self.assertRaisesRegex(
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ValueError, r'Input timestamp must be monotonically increasing'
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):
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detector.detect_for_video(self.test_image, 0)
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# TODO: Tests how `detect_for_video` handles the temporal data
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# with a real video.
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def test_detect_for_video(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.VIDEO,
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max_results=4,
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)
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with _ObjectDetector.create_from_options(options) as detector:
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for timestamp in range(0, 300, 30):
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detection_result = detector.detect_for_video(self.test_image, timestamp)
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self.assertEqual(detection_result, _EXPECTED_DETECTION_RESULT)
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def test_calling_detect_in_live_stream_mode(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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result_callback=mock.MagicMock(),
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)
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with _ObjectDetector.create_from_options(options) as detector:
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the image mode'
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):
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detector.detect(self.test_image)
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def test_calling_detect_for_video_in_live_stream_mode(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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result_callback=mock.MagicMock(),
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)
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with _ObjectDetector.create_from_options(options) as detector:
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with self.assertRaisesRegex(
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ValueError, r'not initialized with the video mode'
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):
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detector.detect_for_video(self.test_image, 0)
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def test_detect_async_calls_with_illegal_timestamp(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM,
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max_results=4,
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result_callback=mock.MagicMock(),
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)
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with _ObjectDetector.create_from_options(options) as detector:
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detector.detect_async(self.test_image, 100)
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with self.assertRaisesRegex(
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ValueError, r'Input timestamp must be monotonically increasing'
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):
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detector.detect_async(self.test_image, 0)
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@parameterized.parameters(
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(0, _EXPECTED_DETECTION_RESULT), (1, _DetectionResult(detections=[]))
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)
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def test_detect_async_calls(self, threshold, expected_result):
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observed_timestamp_ms = -1
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def check_result(
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result: _DetectionResult, output_image: _Image, timestamp_ms: int
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):
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self.assertEqual(result, expected_result)
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self.assertTrue(
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np.array_equal(
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output_image.numpy_view(), self.test_image.numpy_view()
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)
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)
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self.assertLess(observed_timestamp_ms, timestamp_ms)
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self.observed_timestamp_ms = timestamp_ms
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|
|
options = _ObjectDetectorOptions(
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base_options=_BaseOptions(model_asset_path=self.model_path),
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|
running_mode=_RUNNING_MODE.LIVE_STREAM,
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|
max_results=4,
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|
score_threshold=threshold,
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|
result_callback=check_result,
|
|
)
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|
detector = _ObjectDetector.create_from_options(options)
|
|
for timestamp in range(0, 300, 30):
|
|
detector.detect_async(self.test_image, timestamp)
|
|
detector.close()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
absltest.main()
|