Project import generated by Copybara.
GitOrigin-RevId: 1e13be30e2c6838d4a2ff768a39c414bc80534bb
This commit is contained in:
MediaPipe Team
committed by
Sebastian Schmidt
Sebastian Schmidt
parent
63e679d99c
commit
4dc4b19ddb
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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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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# Placeholder for internal Python strict library compatibility macro.
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package(default_visibility = ["//mediapipe/tasks:internal"])
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licenses(["notice"])
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py_library(
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name = "test_util",
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testonly = 1,
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srcs = ["test_util.py"],
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srcs_version = "PY3",
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deps = [
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"//mediapipe/python:_framework_bindings",
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],
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)
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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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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@@ -0,0 +1,55 @@
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# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
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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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"""Test util for MediaPipe Tasks."""
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import os
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from absl import flags
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import cv2
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from mediapipe.python._framework_bindings import image as image_module
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from mediapipe.python._framework_bindings import image_frame as image_frame_module
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FLAGS = flags.FLAGS
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_Image = image_module.Image
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_ImageFormat = image_frame_module.ImageFormat
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_RGB_CHANNELS = 3
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def test_srcdir():
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"""Returns the path where to look for test data files."""
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if "test_srcdir" in flags.FLAGS:
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return flags.FLAGS["test_srcdir"].value
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elif "TEST_SRCDIR" in os.environ:
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return os.environ["TEST_SRCDIR"]
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else:
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raise RuntimeError("Missing TEST_SRCDIR environment.")
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def get_test_data_path(file_or_dirname: str) -> str:
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"""Returns full test data path."""
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for (directory, subdirs, files) in os.walk(test_srcdir()):
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for f in subdirs + files:
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if f.endswith(file_or_dirname):
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return os.path.join(directory, f)
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raise ValueError("No %s in test directory" % file_or_dirname)
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# TODO: Implement image util module to read image data from file.
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def read_test_image(image_file: str) -> _Image:
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"""Reads a MediaPipe Image from the image file."""
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image_data = cv2.imread(image_file)
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if image_data.shape[2] != _RGB_CHANNELS:
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raise ValueError("Input image must contain three channel rgb data.")
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return _Image(_ImageFormat.SRGB, cv2.cvtColor(image_data, cv2.COLOR_BGR2RGB))
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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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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# Placeholder for internal Python strict test compatibility macro.
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package(default_visibility = ["//mediapipe/tasks:internal"])
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licenses(["notice"])
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# TODO: This test fails in OSS
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@@ -0,0 +1,13 @@
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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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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@@ -0,0 +1,329 @@
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# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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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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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_util
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from mediapipe.tasks.python.vision import object_detector
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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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_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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_IMAGE_FILE = 'cats_and_dogs.jpg'
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_EXPECTED_DETECTION_RESULT = _DetectionResult(detections=[
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_Detection(
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bounding_box=_BoundingBox(
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origin_x=608, origin_y=164, width=381, height=432),
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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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_Detection(
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bounding_box=_BoundingBox(
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origin_x=60, origin_y=398, width=386, height=196),
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categories=[
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_Category(
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index=None,
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score=0.64453125,
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display_name=None,
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category_name='cat')
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]),
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_Detection(
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bounding_box=_BoundingBox(
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origin_x=257, origin_y=394, width=173, height=202),
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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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_Detection(
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bounding_box=_BoundingBox(
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origin_x=362, origin_y=195, width=325, height=412),
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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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_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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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 = test_util.read_test_image(
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test_util.get_test_data_path(_IMAGE_FILE))
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self.model_path = test_util.get_test_data_path(_MODEL_FILE)
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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(file_name=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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# Invalid empty model path.
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with self.assertRaisesRegex(
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ValueError,
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r"ExternalFile must specify at least one of 'file_content', "
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r"'file_name' or 'file_descriptor_meta'."):
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base_options = _BaseOptions(file_name='')
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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(file_content=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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def test_detect(self, model_file_type, max_results,
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expected_detection_result):
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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(file_name=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(file_content=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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detector = _ObjectDetector.create_from_options(options)
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# Performs object detection on the input.
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image_result = detector.detect(self.test_image)
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# Comparing results.
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self.assertEqual(image_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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def test_detect_in_context(self, model_file_type, max_results,
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expected_detection_result):
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if model_file_type is ModelFileType.FILE_NAME:
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base_options = _BaseOptions(file_name=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(file_content=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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with _ObjectDetector.create_from_options(options) as detector:
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# Performs object detection on the input.
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image_result = detector.detect(self.test_image)
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# Comparing results.
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self.assertEqual(image_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(file_name=self.model_path),
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score_threshold=_SCORE_THRESHOLD)
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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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image_result = detector.detect(self.test_image)
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detections = image_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, _SCORE_THRESHOLD,
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f'Detection with score lower than threshold found. {detection}')
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def test_max_results_option(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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max_results=_MAX_RESULTS)
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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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image_result = detector.detect(self.test_image)
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detections = image_result.detections
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self.assertLessEqual(
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len(detections), _MAX_RESULTS, 'Too many results returned.')
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def test_allow_list_option(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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category_allowlist=_ALLOW_LIST)
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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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image_result = detector.detect(self.test_image)
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detections = image_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(label, _ALLOW_LIST,
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f'Label {label} found but not in label allow list')
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def test_deny_list_option(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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category_denylist=_DENY_LIST)
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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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image_result = detector.detect(self.test_image)
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detections = image_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(label, _DENY_LIST,
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f'Label {label} found but in deny list.')
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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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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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category_allowlist=['foo'],
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category_denylist=['bar'])
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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(self):
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(file_name=self.model_path), score_threshold=1)
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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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image_result = detector.detect(self.test_image)
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self.assertEmpty(image_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(file_name=self.model_path),
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running_mode=_RUNNING_MODE.LIVE_STREAM)
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with self.assertRaisesRegex(ValueError,
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r'result callback must be provided'):
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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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def pass_through(unused_result: _DetectionResult,
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unused_output_image: _Image, unused_timestamp_ms: int):
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pass
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(file_name=self.model_path),
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running_mode=running_mode,
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result_callback=pass_through)
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with self.assertRaisesRegex(ValueError,
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r'result callback should not be provided'):
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with _ObjectDetector.create_from_options(options) as unused_detector:
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pass
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def test_detect_async_calls_with_illegal_timestamp(self):
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def pass_through(unused_result: _DetectionResult,
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unused_output_image: _Image, unused_timestamp_ms: int):
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pass
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options = _ObjectDetectorOptions(
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base_options=_BaseOptions(file_name=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=pass_through)
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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'):
|
||||
detector.detect_async(self.test_image, 0)
|
||||
|
||||
@parameterized.parameters((0, _EXPECTED_DETECTION_RESULT),
|
||||
(1, _DetectionResult(detections=[])))
|
||||
def test_detect_async_calls(self, threshold, expected_result):
|
||||
observed_timestamp_ms = -1
|
||||
|
||||
def check_result(result: _DetectionResult, output_image: _Image,
|
||||
timestamp_ms: int):
|
||||
self.assertEqual(result, expected_result)
|
||||
self.assertTrue(
|
||||
np.array_equal(output_image.numpy_view(),
|
||||
self.test_image.numpy_view()))
|
||||
self.assertLess(observed_timestamp_ms, timestamp_ms)
|
||||
self.observed_timestamp_ms = timestamp_ms
|
||||
|
||||
options = _ObjectDetectorOptions(
|
||||
base_options=_BaseOptions(file_name=self.model_path),
|
||||
running_mode=_RUNNING_MODE.LIVE_STREAM,
|
||||
max_results=4,
|
||||
score_threshold=threshold,
|
||||
result_callback=check_result)
|
||||
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()
|
||||
Reference in New Issue
Block a user