Merge branch 'master' into image-embedder-python
This commit is contained in:
Kinar R
@@ -31,13 +31,13 @@ namespace core {
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|
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// Base options for MediaPipe C++ Tasks.
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struct BaseOptions {
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// The model asset file contents as as string.
|
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// The model asset file contents as a string.
|
||||
std::unique_ptr<std::string> model_asset_buffer;
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|
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// The path to the model asset to open and mmap in memory.
|
||||
std::string model_asset_path = "";
|
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|
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// The delegate to run MediaPipe. If the delegate is not set, default
|
||||
// The delegate to run MediaPipe. If the delegate is not set, the default
|
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// delegate CPU is used.
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enum Delegate {
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CPU = 0,
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@@ -273,11 +273,12 @@ class GestureRecognizerGraph : public core::ModelTaskGraph {
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hand_gesture_subgraph[Output<std::vector<ClassificationList>>(
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kHandGesturesTag)];
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return {{.gesture = hand_gestures,
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.handedness = handedness,
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.hand_landmarks = hand_landmarks,
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.hand_world_landmarks = hand_world_landmarks,
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.image = hand_landmarker_graph[Output<Image>(kImageTag)]}};
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return GestureRecognizerOutputs{
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/*gesture=*/hand_gestures,
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/*handedness=*/handedness,
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/*hand_landmarks=*/hand_landmarks,
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/*hand_world_landmarks=*/hand_world_landmarks,
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/*image=*/hand_landmarker_graph[Output<Image>(kImageTag)]};
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}
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};
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+4
-4
@@ -144,10 +144,10 @@ Rect CalculateBound(const NormalizedLandmarkList& list) {
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}
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// Populate normalized non rotated face bounding box
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return {.left = bounding_box_left,
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.top = bounding_box_top,
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.right = bounding_box_right,
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.bottom = bounding_box_bottom};
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return Rect{/*left=*/bounding_box_left,
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/*top=*/bounding_box_top,
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/*right=*/bounding_box_right,
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/*bottom=*/bounding_box_bottom};
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}
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// Uses IoU and distance of some corresponding hand landmarks to detect
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@@ -48,7 +48,7 @@ public abstract class BaseOptions {
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public abstract Builder setModelAssetBuffer(ByteBuffer value);
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/**
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* Sets device Delegate to run the MediaPipe pipeline. If the delegate is not set, default
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* Sets device delegate to run the MediaPipe pipeline. If the delegate is not set, the default
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* delegate CPU is used.
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*/
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public abstract Builder setDelegate(Delegate delegate);
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@@ -68,7 +68,11 @@ class AudioData(object):
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ValueError: If the input array has an incorrect shape or if
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`offset` + `size` exceeds the length of the `src` array.
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"""
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if src.shape[1] != self._audio_format.num_channels:
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if len(src.shape) == 1:
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if self._audio_format.num_channels != 1:
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raise ValueError(f"Input audio is mono, but the audio data is expected "
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f"to have {self._audio_format.num_channels} channels.")
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elif src.shape[1] != self._audio_format.num_channels:
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raise ValueError(f"Input audio contains an invalid number of channels. "
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f"Expect {self._audio_format.num_channels}.")
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@@ -93,6 +97,28 @@ class AudioData(object):
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self._buffer = np.roll(self._buffer, -shift, axis=0)
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self._buffer[-shift:, :] = src[offset:offset + size].copy()
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@classmethod
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def create_from_array(cls,
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src: np.ndarray,
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sample_rate: Optional[float] = None) -> "AudioData":
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"""Creates an `AudioData` object from a NumPy array.
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Args:
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src: A NumPy source array contains the input audio.
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sample_rate: the optional audio sample rate.
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Returns:
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An `AudioData` object that contains a copy of the NumPy source array as
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the data.
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"""
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obj = cls(
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buffer_length=src.shape[0],
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audio_format=AudioFormat(
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num_channels=1 if len(src.shape) == 1 else src.shape[1],
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sample_rate=sample_rate))
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obj.load_from_array(src)
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return obj
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@property
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def audio_format(self) -> AudioFormat:
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"""Gets the audio format of the audio."""
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@@ -157,7 +157,7 @@ def _normalize_number_fields(pb):
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descriptor.FieldDescriptor.TYPE_ENUM):
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normalized_values = [int(x) for x in values]
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elif desc.type == descriptor.FieldDescriptor.TYPE_FLOAT:
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normalized_values = [round(x, 5) for x in values]
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normalized_values = [round(x, 4) for x in values]
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elif desc.type == descriptor.FieldDescriptor.TYPE_DOUBLE:
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normalized_values = [round(float(x), 6) for x in values]
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|
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|
||||
@@ -0,0 +1,36 @@
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||||
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
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||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Placeholder for internal Python strict test compatibility macro.
|
||||
|
||||
package(default_visibility = ["//mediapipe/tasks:internal"])
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licenses(["notice"])
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py_test(
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name = "text_classifier_test",
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srcs = ["text_classifier_test.py"],
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data = [
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"//mediapipe/tasks/testdata/text:bert_text_classifier_models",
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"//mediapipe/tasks/testdata/text:text_classifier_models",
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],
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deps = [
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"//mediapipe/tasks/python/components/containers:category",
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"//mediapipe/tasks/python/components/containers:classifications",
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"//mediapipe/tasks/python/components/processors:classifier_options",
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"//mediapipe/tasks/python/core:base_options",
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"//mediapipe/tasks/python/test:test_utils",
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"//mediapipe/tasks/python/text:text_classifier",
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||||
],
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)
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@@ -0,0 +1,13 @@
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||||
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
@@ -0,0 +1,244 @@
|
||||
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""Tests for text classifier."""
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||||
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||||
import enum
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import os
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from absl.testing import absltest
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from absl.testing import parameterized
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from mediapipe.tasks.python.components.containers import category
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from mediapipe.tasks.python.components.containers import classifications as classifications_module
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from mediapipe.tasks.python.components.processors import classifier_options
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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.text import text_classifier
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_BaseOptions = base_options_module.BaseOptions
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_ClassifierOptions = classifier_options.ClassifierOptions
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_Category = category.Category
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_ClassificationEntry = classifications_module.ClassificationEntry
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_Classifications = classifications_module.Classifications
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_TextClassifierResult = classifications_module.ClassificationResult
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_TextClassifier = text_classifier.TextClassifier
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_TextClassifierOptions = text_classifier.TextClassifierOptions
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_BERT_MODEL_FILE = 'bert_text_classifier.tflite'
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_REGEX_MODEL_FILE = 'test_model_text_classifier_with_regex_tokenizer.tflite'
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_TEST_DATA_DIR = 'mediapipe/tasks/testdata/text'
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_NEGATIVE_TEXT = 'What a waste of my time.'
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_POSITIVE_TEXT = ('This is the best movie I’ve seen in recent years.'
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'Strongly recommend it!')
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_BERT_NEGATIVE_RESULTS = _TextClassifierResult(classifications=[
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_Classifications(
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entries=[
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_ClassificationEntry(
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categories=[
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_Category(
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index=0,
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score=0.999479,
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display_name='',
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||||
category_name='negative'),
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_Category(
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index=1,
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score=0.00052154,
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||||
display_name='',
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category_name='positive')
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||||
],
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||||
timestamp_ms=0)
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||||
],
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head_index=0,
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head_name='probability')
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||||
])
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_BERT_POSITIVE_RESULTS = _TextClassifierResult(classifications=[
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_Classifications(
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entries=[
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||||
_ClassificationEntry(
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||||
categories=[
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||||
_Category(
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||||
index=1,
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||||
score=0.999466,
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||||
display_name='',
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||||
category_name='positive'),
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||||
_Category(
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||||
index=0,
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||||
score=0.000533596,
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||||
display_name='',
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||||
category_name='negative')
|
||||
],
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||||
timestamp_ms=0)
|
||||
],
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||||
head_index=0,
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||||
head_name='probability')
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||||
])
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||||
_REGEX_NEGATIVE_RESULTS = _TextClassifierResult(classifications=[
|
||||
_Classifications(
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||||
entries=[
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||||
_ClassificationEntry(
|
||||
categories=[
|
||||
_Category(
|
||||
index=0,
|
||||
score=0.81313,
|
||||
display_name='',
|
||||
category_name='Negative'),
|
||||
_Category(
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||||
index=1,
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||||
score=0.1868704,
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||||
display_name='',
|
||||
category_name='Positive')
|
||||
],
|
||||
timestamp_ms=0)
|
||||
],
|
||||
head_index=0,
|
||||
head_name='probability')
|
||||
])
|
||||
_REGEX_POSITIVE_RESULTS = _TextClassifierResult(classifications=[
|
||||
_Classifications(
|
||||
entries=[
|
||||
_ClassificationEntry(
|
||||
categories=[
|
||||
_Category(
|
||||
index=1,
|
||||
score=0.5134273,
|
||||
display_name='',
|
||||
category_name='Positive'),
|
||||
_Category(
|
||||
index=0,
|
||||
score=0.486573,
|
||||
display_name='',
|
||||
category_name='Negative')
|
||||
],
|
||||
timestamp_ms=0)
|
||||
],
|
||||
head_index=0,
|
||||
head_name='probability')
|
||||
])
|
||||
|
||||
|
||||
class ModelFileType(enum.Enum):
|
||||
FILE_CONTENT = 1
|
||||
FILE_NAME = 2
|
||||
|
||||
|
||||
class ImageClassifierTest(parameterized.TestCase):
|
||||
|
||||
def setUp(self):
|
||||
super().setUp()
|
||||
self.model_path = test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, _BERT_MODEL_FILE))
|
||||
|
||||
def test_create_from_file_succeeds_with_valid_model_path(self):
|
||||
# Creates with default option and valid model file successfully.
|
||||
with _TextClassifier.create_from_model_path(self.model_path) as classifier:
|
||||
self.assertIsInstance(classifier, _TextClassifier)
|
||||
|
||||
def test_create_from_options_succeeds_with_valid_model_path(self):
|
||||
# Creates with options containing model file successfully.
|
||||
base_options = _BaseOptions(model_asset_path=self.model_path)
|
||||
options = _TextClassifierOptions(base_options=base_options)
|
||||
with _TextClassifier.create_from_options(options) as classifier:
|
||||
self.assertIsInstance(classifier, _TextClassifier)
|
||||
|
||||
def test_create_from_options_fails_with_invalid_model_path(self):
|
||||
# Invalid empty model path.
|
||||
with self.assertRaisesRegex(
|
||||
ValueError,
|
||||
r"ExternalFile must specify at least one of 'file_content', "
|
||||
r"'file_name', 'file_pointer_meta' or 'file_descriptor_meta'."):
|
||||
base_options = _BaseOptions(model_asset_path='')
|
||||
options = _TextClassifierOptions(base_options=base_options)
|
||||
_TextClassifier.create_from_options(options)
|
||||
|
||||
def test_create_from_options_succeeds_with_valid_model_content(self):
|
||||
# Creates with options containing model content successfully.
|
||||
with open(self.model_path, 'rb') as f:
|
||||
base_options = _BaseOptions(model_asset_buffer=f.read())
|
||||
options = _TextClassifierOptions(base_options=base_options)
|
||||
classifier = _TextClassifier.create_from_options(options)
|
||||
self.assertIsInstance(classifier, _TextClassifier)
|
||||
|
||||
@parameterized.parameters(
|
||||
(ModelFileType.FILE_NAME, _BERT_MODEL_FILE, _NEGATIVE_TEXT,
|
||||
_BERT_NEGATIVE_RESULTS), (ModelFileType.FILE_CONTENT, _BERT_MODEL_FILE,
|
||||
_NEGATIVE_TEXT, _BERT_NEGATIVE_RESULTS),
|
||||
(ModelFileType.FILE_NAME, _BERT_MODEL_FILE, _POSITIVE_TEXT,
|
||||
_BERT_POSITIVE_RESULTS), (ModelFileType.FILE_CONTENT, _BERT_MODEL_FILE,
|
||||
_POSITIVE_TEXT, _BERT_POSITIVE_RESULTS),
|
||||
(ModelFileType.FILE_NAME, _REGEX_MODEL_FILE, _NEGATIVE_TEXT,
|
||||
_REGEX_NEGATIVE_RESULTS), (ModelFileType.FILE_CONTENT, _REGEX_MODEL_FILE,
|
||||
_NEGATIVE_TEXT, _REGEX_NEGATIVE_RESULTS),
|
||||
(ModelFileType.FILE_NAME, _REGEX_MODEL_FILE, _POSITIVE_TEXT,
|
||||
_REGEX_POSITIVE_RESULTS), (ModelFileType.FILE_CONTENT, _REGEX_MODEL_FILE,
|
||||
_POSITIVE_TEXT, _REGEX_POSITIVE_RESULTS))
|
||||
def test_classify(self, model_file_type, model_name, text,
|
||||
expected_classification_result):
|
||||
# Creates classifier.
|
||||
model_path = test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, model_name))
|
||||
if model_file_type is ModelFileType.FILE_NAME:
|
||||
base_options = _BaseOptions(model_asset_path=model_path)
|
||||
elif model_file_type is ModelFileType.FILE_CONTENT:
|
||||
with open(model_path, 'rb') as f:
|
||||
model_content = f.read()
|
||||
base_options = _BaseOptions(model_asset_buffer=model_content)
|
||||
else:
|
||||
# Should never happen
|
||||
raise ValueError('model_file_type is invalid.')
|
||||
|
||||
options = _TextClassifierOptions(base_options=base_options)
|
||||
classifier = _TextClassifier.create_from_options(options)
|
||||
|
||||
# Performs text classification on the input.
|
||||
text_result = classifier.classify(text)
|
||||
# Comparing results.
|
||||
test_utils.assert_proto_equals(self, text_result.to_pb2(),
|
||||
expected_classification_result.to_pb2())
|
||||
# Closes the classifier explicitly when the classifier is not used in
|
||||
# a context.
|
||||
classifier.close()
|
||||
|
||||
@parameterized.parameters((ModelFileType.FILE_NAME, _BERT_MODEL_FILE,
|
||||
_NEGATIVE_TEXT, _BERT_NEGATIVE_RESULTS),
|
||||
(ModelFileType.FILE_CONTENT, _BERT_MODEL_FILE,
|
||||
_NEGATIVE_TEXT, _BERT_NEGATIVE_RESULTS))
|
||||
def test_classify_in_context(self, model_file_type, model_name, text,
|
||||
expected_classification_result):
|
||||
# Creates classifier.
|
||||
model_path = test_utils.get_test_data_path(
|
||||
os.path.join(_TEST_DATA_DIR, model_name))
|
||||
if model_file_type is ModelFileType.FILE_NAME:
|
||||
base_options = _BaseOptions(model_asset_path=model_path)
|
||||
elif model_file_type is ModelFileType.FILE_CONTENT:
|
||||
with open(model_path, 'rb') as f:
|
||||
model_content = f.read()
|
||||
base_options = _BaseOptions(model_asset_buffer=model_content)
|
||||
else:
|
||||
# Should never happen
|
||||
raise ValueError('model_file_type is invalid.')
|
||||
|
||||
options = _TextClassifierOptions(base_options=base_options)
|
||||
|
||||
with _TextClassifier.create_from_options(options) as classifier:
|
||||
# Performs text classification on the input.
|
||||
text_result = classifier.classify(text)
|
||||
# Comparing results.
|
||||
test_utils.assert_proto_equals(self, text_result.to_pb2(),
|
||||
expected_classification_result.to_pb2())
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
absltest.main()
|
||||
@@ -0,0 +1,38 @@
|
||||
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Placeholder for internal Python strict library and test compatibility macro.
|
||||
|
||||
package(default_visibility = ["//mediapipe/tasks:internal"])
|
||||
|
||||
licenses(["notice"])
|
||||
|
||||
py_library(
|
||||
name = "text_classifier",
|
||||
srcs = [
|
||||
"text_classifier.py",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/python:packet_creator",
|
||||
"//mediapipe/python:packet_getter",
|
||||
"//mediapipe/tasks/cc/components/containers/proto:classifications_py_pb2",
|
||||
"//mediapipe/tasks/cc/text/text_classifier/proto:text_classifier_graph_options_py_pb2",
|
||||
"//mediapipe/tasks/python/components/containers:classifications",
|
||||
"//mediapipe/tasks/python/components/processors:classifier_options",
|
||||
"//mediapipe/tasks/python/core:base_options",
|
||||
"//mediapipe/tasks/python/core:optional_dependencies",
|
||||
"//mediapipe/tasks/python/core:task_info",
|
||||
"//mediapipe/tasks/python/text/core:base_text_task_api",
|
||||
],
|
||||
)
|
||||
@@ -0,0 +1,13 @@
|
||||
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
@@ -0,0 +1,31 @@
|
||||
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
# Placeholder for internal Python strict library and test compatibility macro.
|
||||
|
||||
package(default_visibility = ["//mediapipe/tasks:internal"])
|
||||
|
||||
licenses(["notice"])
|
||||
|
||||
py_library(
|
||||
name = "base_text_task_api",
|
||||
srcs = [
|
||||
"base_text_task_api.py",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/framework:calculator_py_pb2",
|
||||
"//mediapipe/python:_framework_bindings",
|
||||
"//mediapipe/tasks/python/core:optional_dependencies",
|
||||
],
|
||||
)
|
||||
@@ -0,0 +1,16 @@
|
||||
"""Copyright 2022 The MediaPipe Authors.
|
||||
|
||||
All Rights Reserved.
|
||||
|
||||
Licensed under the Apache License, Version 2.0 (the "License");
|
||||
you may not use this file except in compliance with the License.
|
||||
You may obtain a copy of the License at
|
||||
|
||||
http://www.apache.org/licenses/LICENSE-2.0
|
||||
|
||||
Unless required by applicable law or agreed to in writing, software
|
||||
distributed under the License is distributed on an "AS IS" BASIS,
|
||||
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
See the License for the specific language governing permissions and
|
||||
limitations under the License.
|
||||
"""
|
||||
@@ -0,0 +1,55 @@
|
||||
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""MediaPipe text task base api."""
|
||||
|
||||
from mediapipe.framework import calculator_pb2
|
||||
from mediapipe.python._framework_bindings import task_runner
|
||||
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
|
||||
|
||||
_TaskRunner = task_runner.TaskRunner
|
||||
|
||||
|
||||
class BaseTextTaskApi(object):
|
||||
"""The base class of the user-facing mediapipe text task api classes."""
|
||||
|
||||
def __init__(self,
|
||||
graph_config: calculator_pb2.CalculatorGraphConfig) -> None:
|
||||
"""Initializes the `BaseVisionTaskApi` object.
|
||||
|
||||
Args:
|
||||
graph_config: The mediapipe text task graph config proto.
|
||||
"""
|
||||
self._runner = _TaskRunner.create(graph_config)
|
||||
|
||||
def close(self) -> None:
|
||||
"""Shuts down the mediapipe text task instance.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If the mediapipe text task failed to close.
|
||||
"""
|
||||
self._runner.close()
|
||||
|
||||
@doc_controls.do_not_generate_docs
|
||||
def __enter__(self):
|
||||
"""Returns `self` upon entering the runtime context."""
|
||||
return self
|
||||
|
||||
@doc_controls.do_not_generate_docs
|
||||
def __exit__(self, unused_exc_type, unused_exc_value, unused_traceback):
|
||||
"""Shuts down the mediapipe text task instance on exit of the context manager.
|
||||
|
||||
Raises:
|
||||
RuntimeError: If the mediapipe text task failed to close.
|
||||
"""
|
||||
self.close()
|
||||
@@ -0,0 +1,140 @@
|
||||
# Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
"""MediaPipe text classifier task."""
|
||||
|
||||
import dataclasses
|
||||
|
||||
from mediapipe.python import packet_creator
|
||||
from mediapipe.python import packet_getter
|
||||
from mediapipe.tasks.cc.components.containers.proto import classifications_pb2
|
||||
from mediapipe.tasks.cc.text.text_classifier.proto import text_classifier_graph_options_pb2
|
||||
from mediapipe.tasks.python.components.containers import classifications
|
||||
from mediapipe.tasks.python.components.processors import classifier_options
|
||||
from mediapipe.tasks.python.core import base_options as base_options_module
|
||||
from mediapipe.tasks.python.core import task_info as task_info_module
|
||||
from mediapipe.tasks.python.core.optional_dependencies import doc_controls
|
||||
from mediapipe.tasks.python.text.core import base_text_task_api
|
||||
|
||||
TextClassifierResult = classifications.ClassificationResult
|
||||
_BaseOptions = base_options_module.BaseOptions
|
||||
_TextClassifierGraphOptionsProto = text_classifier_graph_options_pb2.TextClassifierGraphOptions
|
||||
_ClassifierOptions = classifier_options.ClassifierOptions
|
||||
_TaskInfo = task_info_module.TaskInfo
|
||||
|
||||
_CLASSIFICATION_RESULT_OUT_STREAM_NAME = 'classification_result_out'
|
||||
_CLASSIFICATION_RESULT_TAG = 'CLASSIFICATION_RESULT'
|
||||
_TEXT_IN_STREAM_NAME = 'text_in'
|
||||
_TEXT_TAG = 'TEXT'
|
||||
_TASK_GRAPH_NAME = 'mediapipe.tasks.text.text_classifier.TextClassifierGraph'
|
||||
|
||||
|
||||
@dataclasses.dataclass
|
||||
class TextClassifierOptions:
|
||||
"""Options for the text classifier task.
|
||||
|
||||
Attributes:
|
||||
base_options: Base options for the text classifier task.
|
||||
classifier_options: Options for the text classification task.
|
||||
"""
|
||||
base_options: _BaseOptions
|
||||
classifier_options: _ClassifierOptions = _ClassifierOptions()
|
||||
|
||||
@doc_controls.do_not_generate_docs
|
||||
def to_pb2(self) -> _TextClassifierGraphOptionsProto:
|
||||
"""Generates an TextClassifierOptions protobuf object."""
|
||||
base_options_proto = self.base_options.to_pb2()
|
||||
classifier_options_proto = self.classifier_options.to_pb2()
|
||||
|
||||
return _TextClassifierGraphOptionsProto(
|
||||
base_options=base_options_proto,
|
||||
classifier_options=classifier_options_proto)
|
||||
|
||||
|
||||
class TextClassifier(base_text_task_api.BaseTextTaskApi):
|
||||
"""Class that performs classification on text."""
|
||||
|
||||
@classmethod
|
||||
def create_from_model_path(cls, model_path: str) -> 'TextClassifier':
|
||||
"""Creates an `TextClassifier` object from a TensorFlow Lite model and the default `TextClassifierOptions`.
|
||||
|
||||
Args:
|
||||
model_path: Path to the model.
|
||||
|
||||
Returns:
|
||||
`TextClassifier` object that's created from the model file and the
|
||||
default `TextClassifierOptions`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `TextClassifier` object from the provided
|
||||
file such as invalid file path.
|
||||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
base_options = _BaseOptions(model_asset_path=model_path)
|
||||
options = TextClassifierOptions(base_options=base_options)
|
||||
return cls.create_from_options(options)
|
||||
|
||||
@classmethod
|
||||
def create_from_options(cls,
|
||||
options: TextClassifierOptions) -> 'TextClassifier':
|
||||
"""Creates the `TextClassifier` object from text classifier options.
|
||||
|
||||
Args:
|
||||
options: Options for the text classifier task.
|
||||
|
||||
Returns:
|
||||
`TextClassifier` object that's created from `options`.
|
||||
|
||||
Raises:
|
||||
ValueError: If failed to create `TextClassifier` object from
|
||||
`TextClassifierOptions` such as missing the model.
|
||||
RuntimeError: If other types of error occurred.
|
||||
"""
|
||||
task_info = _TaskInfo(
|
||||
task_graph=_TASK_GRAPH_NAME,
|
||||
input_streams=[':'.join([_TEXT_TAG, _TEXT_IN_STREAM_NAME])],
|
||||
output_streams=[
|
||||
':'.join([
|
||||
_CLASSIFICATION_RESULT_TAG,
|
||||
_CLASSIFICATION_RESULT_OUT_STREAM_NAME
|
||||
])
|
||||
],
|
||||
task_options=options)
|
||||
return cls(task_info.generate_graph_config())
|
||||
|
||||
def classify(self, text: str) -> TextClassifierResult:
|
||||
"""Performs classification on the input `text`.
|
||||
|
||||
Args:
|
||||
text: The input text.
|
||||
|
||||
Returns:
|
||||
A `TextClassifierResult` object that contains a list of text
|
||||
classifications.
|
||||
|
||||
Raises:
|
||||
ValueError: If any of the input arguments is invalid.
|
||||
RuntimeError: If text classification failed to run.
|
||||
"""
|
||||
output_packets = self._runner.process(
|
||||
{_TEXT_IN_STREAM_NAME: packet_creator.create_string(text)})
|
||||
|
||||
classification_result_proto = classifications_pb2.ClassificationResult()
|
||||
classification_result_proto.CopyFrom(
|
||||
packet_getter.get_proto(
|
||||
output_packets[_CLASSIFICATION_RESULT_OUT_STREAM_NAME]))
|
||||
|
||||
return TextClassifierResult([
|
||||
classifications.Classifications.create_from_pb2(classification)
|
||||
for classification in classification_result_proto.classifications
|
||||
])
|
||||
@@ -0,0 +1,33 @@
|
||||
# This contains the MediaPipe Audio Classifier Task.
|
||||
#
|
||||
# This task takes audio data and outputs the classification result.
|
||||
|
||||
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
|
||||
|
||||
package(default_visibility = ["//mediapipe/tasks:internal"])
|
||||
|
||||
licenses(["notice"])
|
||||
|
||||
mediapipe_ts_library(
|
||||
name = "audio_classifier",
|
||||
srcs = [
|
||||
"audio_classifier.ts",
|
||||
"audio_classifier_options.ts",
|
||||
"audio_classifier_result.ts",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/framework:calculator_jspb_proto",
|
||||
"//mediapipe/framework:calculator_options_jspb_proto",
|
||||
"//mediapipe/tasks/cc/audio/audio_classifier/proto:audio_classifier_graph_options_jspb_proto",
|
||||
"//mediapipe/tasks/cc/components/containers/proto:classifications_jspb_proto",
|
||||
"//mediapipe/tasks/web/components/containers:category",
|
||||
"//mediapipe/tasks/web/components/containers:classifications",
|
||||
"//mediapipe/tasks/web/components/processors:base_options",
|
||||
"//mediapipe/tasks/web/components/processors:classifier_options",
|
||||
"//mediapipe/tasks/web/components/processors:classifier_result",
|
||||
"//mediapipe/tasks/web/core",
|
||||
"//mediapipe/tasks/web/core:classifier_options",
|
||||
"//mediapipe/tasks/web/core:task_runner",
|
||||
"//mediapipe/web/graph_runner:wasm_mediapipe_lib_ts",
|
||||
],
|
||||
)
|
||||
@@ -0,0 +1,215 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
import {CalculatorGraphConfig} from '../../../../framework/calculator_pb';
|
||||
import {CalculatorOptions} from '../../../../framework/calculator_options_pb';
|
||||
import {AudioClassifierGraphOptions} from '../../../../tasks/cc/audio/audio_classifier/proto/audio_classifier_graph_options_pb';
|
||||
import {ClassificationResult} from '../../../../tasks/cc/components/containers/proto/classifications_pb';
|
||||
import {convertBaseOptionsToProto} from '../../../../tasks/web/components/processors/base_options';
|
||||
import {convertClassifierOptionsToProto} from '../../../../tasks/web/components/processors/classifier_options';
|
||||
import {convertFromClassificationResultProto} from '../../../../tasks/web/components/processors/classifier_result';
|
||||
import {TaskRunner} from '../../../../tasks/web/core/task_runner';
|
||||
import {WasmLoaderOptions} from '../../../../tasks/web/core/wasm_loader_options';
|
||||
import {createMediaPipeLib, FileLocator} from '../../../../web/graph_runner/wasm_mediapipe_lib';
|
||||
// Placeholder for internal dependency on trusted resource url
|
||||
|
||||
import {AudioClassifierOptions} from './audio_classifier_options';
|
||||
import {Classifications} from './audio_classifier_result';
|
||||
|
||||
const MEDIAPIPE_GRAPH =
|
||||
'mediapipe.tasks.audio.audio_classifier.AudioClassifierGraph';
|
||||
|
||||
// Note: `input_audio` is hardcoded in 'gl_graph_runner_internal_audio' and
|
||||
// cannot be changed
|
||||
// TODO: Change this to `audio_in` to match the name in the CC
|
||||
// implementation
|
||||
const AUDIO_STREAM = 'input_audio';
|
||||
const SAMPLE_RATE_STREAM = 'sample_rate';
|
||||
const CLASSIFICATION_RESULT_STREAM = 'classification_result';
|
||||
|
||||
// The OSS JS API does not support the builder pattern.
|
||||
// tslint:disable:jspb-use-builder-pattern
|
||||
|
||||
/** Performs audio classification. */
|
||||
export class AudioClassifier extends TaskRunner {
|
||||
private classifications: Classifications[] = [];
|
||||
private defaultSampleRate = 48000;
|
||||
private readonly options = new AudioClassifierGraphOptions();
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new audio classifier from the
|
||||
* provided options.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param audioClassifierOptions The options for the audio classifier. Note
|
||||
* that either a path to the model asset or a model buffer needs to be
|
||||
* provided (via `baseOptions`).
|
||||
*/
|
||||
static async createFromOptions(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
audioClassifierOptions: AudioClassifierOptions):
|
||||
Promise<AudioClassifier> {
|
||||
// Create a file locator based on the loader options
|
||||
const fileLocator: FileLocator = {
|
||||
locateFile() {
|
||||
// The only file loaded with this mechanism is the Wasm binary
|
||||
return wasmLoaderOptions.wasmBinaryPath.toString();
|
||||
}
|
||||
};
|
||||
|
||||
const classifier = await createMediaPipeLib(
|
||||
AudioClassifier, wasmLoaderOptions.wasmLoaderPath,
|
||||
/* assetLoaderScript= */ undefined,
|
||||
/* glCanvas= */ undefined, fileLocator);
|
||||
await classifier.setOptions(audioClassifierOptions);
|
||||
return classifier;
|
||||
}
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new audio classifier based on
|
||||
* the provided model asset buffer.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param modelAssetBuffer A binary representation of the model.
|
||||
*/
|
||||
static createFromModelBuffer(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
modelAssetBuffer: Uint8Array): Promise<AudioClassifier> {
|
||||
return AudioClassifier.createFromOptions(
|
||||
wasmLoaderOptions, {baseOptions: {modelAssetBuffer}});
|
||||
}
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new audio classifier based on
|
||||
* the path to the model asset.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param modelAssetPath The path to the model asset.
|
||||
*/
|
||||
static async createFromModelPath(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
modelAssetPath: string): Promise<AudioClassifier> {
|
||||
const response = await fetch(modelAssetPath.toString());
|
||||
const graphData = await response.arrayBuffer();
|
||||
return AudioClassifier.createFromModelBuffer(
|
||||
wasmLoaderOptions, new Uint8Array(graphData));
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets new options for the audio classifier.
|
||||
*
|
||||
* Calling `setOptions()` with a subset of options only affects those options.
|
||||
* You can reset an option back to its default value by explicitly setting it
|
||||
* to `undefined`.
|
||||
*
|
||||
* @param options The options for the audio classifier.
|
||||
*/
|
||||
async setOptions(options: AudioClassifierOptions): Promise<void> {
|
||||
if (options.baseOptions) {
|
||||
const baseOptionsProto =
|
||||
await convertBaseOptionsToProto(options.baseOptions);
|
||||
this.options.setBaseOptions(baseOptionsProto);
|
||||
}
|
||||
|
||||
this.options.setClassifierOptions(convertClassifierOptionsToProto(
|
||||
options, this.options.getClassifierOptions()));
|
||||
this.refreshGraph();
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets the sample rate for all calls to `classify()` that omit an explicit
|
||||
* sample rate. `48000` is used as a default if this method is not called.
|
||||
*
|
||||
* @param sampleRate A sample rate (e.g. `44100`).
|
||||
*/
|
||||
setDefaultSampleRate(sampleRate: number) {
|
||||
this.defaultSampleRate = sampleRate;
|
||||
}
|
||||
|
||||
/**
|
||||
* Performs audio classification on the provided audio data and waits
|
||||
* synchronously for the response.
|
||||
*
|
||||
* @param audioData An array of raw audio capture data, like
|
||||
* from a call to getChannelData on an AudioBuffer.
|
||||
* @param sampleRate The sample rate in Hz of the provided audio data. If not
|
||||
* set, defaults to the sample rate set via `setDefaultSampleRate()` or
|
||||
* `48000` if no custom default was set.
|
||||
* @return The classification result of the audio datas
|
||||
*/
|
||||
classify(audioData: Float32Array, sampleRate?: number): Classifications[] {
|
||||
sampleRate = sampleRate ?? this.defaultSampleRate;
|
||||
|
||||
// Configures the number of samples in the WASM layer. We re-configure the
|
||||
// number of samples and the sample rate for every frame, but ignore other
|
||||
// side effects of this function (such as sending the input side packet and
|
||||
// the input stream header).
|
||||
this.configureAudio(
|
||||
/* numChannels= */ 1, /* numSamples= */ audioData.length, sampleRate);
|
||||
|
||||
const timestamp = performance.now();
|
||||
this.addDoubleToStream(sampleRate, SAMPLE_RATE_STREAM, timestamp);
|
||||
this.addAudioToStream(audioData, timestamp);
|
||||
|
||||
this.classifications = [];
|
||||
this.finishProcessing();
|
||||
return [...this.classifications];
|
||||
}
|
||||
|
||||
/**
|
||||
* Internal function for converting raw data into a classification, and
|
||||
* adding it to our classfications list.
|
||||
**/
|
||||
private addJsAudioClassification(binaryProto: Uint8Array): void {
|
||||
const classificationResult =
|
||||
ClassificationResult.deserializeBinary(binaryProto);
|
||||
this.classifications.push(
|
||||
...convertFromClassificationResultProto(classificationResult));
|
||||
}
|
||||
|
||||
/** Updates the MediaPipe graph configuration. */
|
||||
private refreshGraph(): void {
|
||||
const graphConfig = new CalculatorGraphConfig();
|
||||
graphConfig.addInputStream(AUDIO_STREAM);
|
||||
graphConfig.addInputStream(SAMPLE_RATE_STREAM);
|
||||
graphConfig.addOutputStream(CLASSIFICATION_RESULT_STREAM);
|
||||
|
||||
const calculatorOptions = new CalculatorOptions();
|
||||
calculatorOptions.setExtension(
|
||||
AudioClassifierGraphOptions.ext, this.options);
|
||||
|
||||
// Perform audio classification. Pre-processing and results post-processing
|
||||
// are built-in.
|
||||
const classifierNode = new CalculatorGraphConfig.Node();
|
||||
classifierNode.setCalculator(MEDIAPIPE_GRAPH);
|
||||
classifierNode.addInputStream('AUDIO:' + AUDIO_STREAM);
|
||||
classifierNode.addInputStream('SAMPLE_RATE:' + SAMPLE_RATE_STREAM);
|
||||
classifierNode.addOutputStream(
|
||||
'CLASSIFICATION_RESULT:' + CLASSIFICATION_RESULT_STREAM);
|
||||
classifierNode.setOptions(calculatorOptions);
|
||||
|
||||
graphConfig.addNode(classifierNode);
|
||||
|
||||
this.attachProtoListener(CLASSIFICATION_RESULT_STREAM, binaryProto => {
|
||||
this.addJsAudioClassification(binaryProto);
|
||||
});
|
||||
|
||||
const binaryGraph = graphConfig.serializeBinary();
|
||||
this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -0,0 +1,17 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
export {ClassifierOptions as AudioClassifierOptions} from '../../../../tasks/web/core/classifier_options';
|
||||
@@ -0,0 +1,18 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
export {Category} from '../../../../tasks/web/components/containers/category';
|
||||
export {ClassificationEntry, Classifications} from '../../../../tasks/web/components/containers/classifications';
|
||||
@@ -29,31 +29,31 @@ export function convertClassifierOptionsToProto(
|
||||
baseOptions?: ClassifierOptionsProto): ClassifierOptionsProto {
|
||||
const classifierOptions =
|
||||
baseOptions ? baseOptions.clone() : new ClassifierOptionsProto();
|
||||
if (options.displayNamesLocale) {
|
||||
if (options.displayNamesLocale !== undefined) {
|
||||
classifierOptions.setDisplayNamesLocale(options.displayNamesLocale);
|
||||
} else if (options.displayNamesLocale === undefined) {
|
||||
classifierOptions.clearDisplayNamesLocale();
|
||||
}
|
||||
|
||||
if (options.maxResults) {
|
||||
if (options.maxResults !== undefined) {
|
||||
classifierOptions.setMaxResults(options.maxResults);
|
||||
} else if ('maxResults' in options) { // Check for undefined
|
||||
classifierOptions.clearMaxResults();
|
||||
}
|
||||
|
||||
if (options.scoreThreshold) {
|
||||
if (options.scoreThreshold !== undefined) {
|
||||
classifierOptions.setScoreThreshold(options.scoreThreshold);
|
||||
} else if ('scoreThreshold' in options) { // Check for undefined
|
||||
classifierOptions.clearScoreThreshold();
|
||||
}
|
||||
|
||||
if (options.categoryAllowlist) {
|
||||
if (options.categoryAllowlist !== undefined) {
|
||||
classifierOptions.setCategoryAllowlistList(options.categoryAllowlist);
|
||||
} else if ('categoryAllowlist' in options) { // Check for undefined
|
||||
classifierOptions.clearCategoryAllowlistList();
|
||||
}
|
||||
|
||||
if (options.categoryDenylist) {
|
||||
if (options.categoryDenylist !== undefined) {
|
||||
classifierOptions.setCategoryDenylistList(options.categoryDenylist);
|
||||
} else if ('categoryDenylist' in options) { // Check for undefined
|
||||
classifierOptions.clearCategoryDenylistList();
|
||||
|
||||
@@ -12,6 +12,18 @@ mediapipe_ts_library(
|
||||
],
|
||||
)
|
||||
|
||||
mediapipe_ts_library(
|
||||
name = "task_runner",
|
||||
srcs = [
|
||||
"task_runner.ts",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/web/graph_runner:register_model_resources_graph_service_ts",
|
||||
"//mediapipe/web/graph_runner:wasm_mediapipe_image_lib_ts",
|
||||
"//mediapipe/web/graph_runner:wasm_mediapipe_lib_ts",
|
||||
],
|
||||
)
|
||||
|
||||
mediapipe_ts_library(
|
||||
name = "classifier_options",
|
||||
srcs = [
|
||||
|
||||
@@ -0,0 +1,83 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
import {SupportModelResourcesGraphService} from '../../../web/graph_runner/register_model_resources_graph_service';
|
||||
import {SupportImage} from '../../../web/graph_runner/wasm_mediapipe_image_lib';
|
||||
import {WasmMediaPipeLib, WasmModule} from '../../../web/graph_runner/wasm_mediapipe_lib';
|
||||
|
||||
// tslint:disable-next-line:enforce-name-casing
|
||||
const WasmMediaPipeImageLib =
|
||||
SupportModelResourcesGraphService(SupportImage(WasmMediaPipeLib));
|
||||
|
||||
/** Base class for all MediaPipe Tasks. */
|
||||
export abstract class TaskRunner extends WasmMediaPipeImageLib {
|
||||
private processingErrors: Error[] = [];
|
||||
|
||||
constructor(wasmModule: WasmModule) {
|
||||
super(wasmModule);
|
||||
|
||||
// Disables the automatic render-to-screen code, which allows for pure
|
||||
// CPU processing.
|
||||
this.setAutoRenderToScreen(false);
|
||||
|
||||
// Enables use of our model resource caching graph service.
|
||||
this.registerModelResourcesGraphService();
|
||||
}
|
||||
|
||||
/**
|
||||
* Takes the raw data from a MediaPipe graph, and passes it to C++ to be run
|
||||
* over the video stream. Will replace the previously running MediaPipe graph,
|
||||
* if there is one.
|
||||
* @param graphData The raw MediaPipe graph data, either in binary
|
||||
* protobuffer format (.binarypb), or else in raw text format (.pbtxt or
|
||||
* .textproto).
|
||||
* @param isBinary This should be set to true if the graph is in
|
||||
* binary format, and false if it is in human-readable text format.
|
||||
*/
|
||||
override setGraph(graphData: Uint8Array, isBinary: boolean): void {
|
||||
this.attachErrorListener((code, message) => {
|
||||
this.processingErrors.push(new Error(message));
|
||||
});
|
||||
super.setGraph(graphData, isBinary);
|
||||
this.handleErrors();
|
||||
}
|
||||
|
||||
/**
|
||||
* Forces all queued-up packets to be pushed through the MediaPipe graph as
|
||||
* far as possible, performing all processing until no more processing can be
|
||||
* done.
|
||||
*/
|
||||
override finishProcessing(): void {
|
||||
super.finishProcessing();
|
||||
this.handleErrors();
|
||||
}
|
||||
|
||||
/** Throws the error from the error listener if an error was raised. */
|
||||
private handleErrors() {
|
||||
const errorCount = this.processingErrors.length;
|
||||
if (errorCount === 1) {
|
||||
// Re-throw error to get a more meaningful stacktrace
|
||||
throw new Error(this.processingErrors[0].message);
|
||||
} else if (errorCount > 1) {
|
||||
throw new Error(
|
||||
'Encountered multiple errors: ' +
|
||||
this.processingErrors.map(e => e.message).join(', '));
|
||||
}
|
||||
this.processingErrors = [];
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -0,0 +1,34 @@
|
||||
# This contains the MediaPipe Text Classifier Task.
|
||||
#
|
||||
# This task takes text input performs Natural Language classification (including
|
||||
# BERT-based text classification).
|
||||
|
||||
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
|
||||
|
||||
package(default_visibility = ["//mediapipe/tasks:internal"])
|
||||
|
||||
licenses(["notice"])
|
||||
|
||||
mediapipe_ts_library(
|
||||
name = "text_classifier",
|
||||
srcs = [
|
||||
"text_classifier.ts",
|
||||
"text_classifier_options.d.ts",
|
||||
"text_classifier_result.d.ts",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/framework:calculator_jspb_proto",
|
||||
"//mediapipe/framework:calculator_options_jspb_proto",
|
||||
"//mediapipe/tasks/cc/components/containers/proto:classifications_jspb_proto",
|
||||
"//mediapipe/tasks/cc/text/text_classifier/proto:text_classifier_graph_options_jspb_proto",
|
||||
"//mediapipe/tasks/web/components/containers:category",
|
||||
"//mediapipe/tasks/web/components/containers:classifications",
|
||||
"//mediapipe/tasks/web/components/processors:base_options",
|
||||
"//mediapipe/tasks/web/components/processors:classifier_options",
|
||||
"//mediapipe/tasks/web/components/processors:classifier_result",
|
||||
"//mediapipe/tasks/web/core",
|
||||
"//mediapipe/tasks/web/core:classifier_options",
|
||||
"//mediapipe/tasks/web/core:task_runner",
|
||||
"//mediapipe/web/graph_runner:wasm_mediapipe_lib_ts",
|
||||
],
|
||||
)
|
||||
@@ -0,0 +1,180 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
import {CalculatorGraphConfig} from '../../../../framework/calculator_pb';
|
||||
import {CalculatorOptions} from '../../../../framework/calculator_options_pb';
|
||||
import {ClassificationResult} from '../../../../tasks/cc/components/containers/proto/classifications_pb';
|
||||
import {TextClassifierGraphOptions} from '../../../../tasks/cc/text/text_classifier/proto/text_classifier_graph_options_pb';
|
||||
import {convertBaseOptionsToProto} from '../../../../tasks/web/components/processors/base_options';
|
||||
import {convertClassifierOptionsToProto} from '../../../../tasks/web/components/processors/classifier_options';
|
||||
import {convertFromClassificationResultProto} from '../../../../tasks/web/components/processors/classifier_result';
|
||||
import {TaskRunner} from '../../../../tasks/web/core/task_runner';
|
||||
import {WasmLoaderOptions} from '../../../../tasks/web/core/wasm_loader_options';
|
||||
import {createMediaPipeLib, FileLocator} from '../../../../web/graph_runner/wasm_mediapipe_lib';
|
||||
// Placeholder for internal dependency on trusted resource url
|
||||
|
||||
import {TextClassifierOptions} from './text_classifier_options';
|
||||
import {Classifications} from './text_classifier_result';
|
||||
|
||||
const INPUT_STREAM = 'text_in';
|
||||
const CLASSIFICATION_RESULT_STREAM = 'classification_result_out';
|
||||
const TEXT_CLASSIFIER_GRAPH =
|
||||
'mediapipe.tasks.text.text_classifier.TextClassifierGraph';
|
||||
|
||||
// The OSS JS API does not support the builder pattern.
|
||||
// tslint:disable:jspb-use-builder-pattern
|
||||
|
||||
/** Performs Natural Language classification. */
|
||||
export class TextClassifier extends TaskRunner {
|
||||
private classifications: Classifications[] = [];
|
||||
private readonly options = new TextClassifierGraphOptions();
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new text classifier from the
|
||||
* provided options.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param textClassifierOptions The options for the text classifier. Note that
|
||||
* either a path to the TFLite model or the model itself needs to be
|
||||
* provided (via `baseOptions`).
|
||||
*/
|
||||
static async createFromOptions(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
textClassifierOptions: TextClassifierOptions): Promise<TextClassifier> {
|
||||
// Create a file locator based on the loader options
|
||||
const fileLocator: FileLocator = {
|
||||
locateFile() {
|
||||
// The only file we load is the Wasm binary
|
||||
return wasmLoaderOptions.wasmBinaryPath.toString();
|
||||
}
|
||||
};
|
||||
|
||||
const classifier = await createMediaPipeLib(
|
||||
TextClassifier, wasmLoaderOptions.wasmLoaderPath,
|
||||
/* assetLoaderScript= */ undefined,
|
||||
/* glCanvas= */ undefined, fileLocator);
|
||||
await classifier.setOptions(textClassifierOptions);
|
||||
return classifier;
|
||||
}
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new text classifier based on the
|
||||
* provided model asset buffer.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param modelAssetBuffer A binary representation of the model.
|
||||
*/
|
||||
static createFromModelBuffer(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
modelAssetBuffer: Uint8Array): Promise<TextClassifier> {
|
||||
return TextClassifier.createFromOptions(
|
||||
wasmLoaderOptions, {baseOptions: {modelAssetBuffer}});
|
||||
}
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new text classifier based on the
|
||||
* path to the model asset.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param modelAssetPath The path to the model asset.
|
||||
*/
|
||||
static async createFromModelPath(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
modelAssetPath: string): Promise<TextClassifier> {
|
||||
const response = await fetch(modelAssetPath.toString());
|
||||
const graphData = await response.arrayBuffer();
|
||||
return TextClassifier.createFromModelBuffer(
|
||||
wasmLoaderOptions, new Uint8Array(graphData));
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets new options for the text classifier.
|
||||
*
|
||||
* Calling `setOptions()` with a subset of options only affects those options.
|
||||
* You can reset an option back to its default value by explicitly setting it
|
||||
* to `undefined`.
|
||||
*
|
||||
* @param options The options for the text classifier.
|
||||
*/
|
||||
async setOptions(options: TextClassifierOptions): Promise<void> {
|
||||
if (options.baseOptions) {
|
||||
const baseOptionsProto =
|
||||
await convertBaseOptionsToProto(options.baseOptions);
|
||||
this.options.setBaseOptions(baseOptionsProto);
|
||||
}
|
||||
|
||||
this.options.setClassifierOptions(convertClassifierOptionsToProto(
|
||||
options, this.options.getClassifierOptions()));
|
||||
this.refreshGraph();
|
||||
}
|
||||
|
||||
|
||||
/**
|
||||
* Performs Natural Language classification on the provided text and waits
|
||||
* synchronously for the response.
|
||||
*
|
||||
* @param text The text to process.
|
||||
* @return The classification result of the text
|
||||
*/
|
||||
classify(text: string): Classifications[] {
|
||||
// Get classification classes by running our MediaPipe graph.
|
||||
this.classifications = [];
|
||||
this.addStringToStream(
|
||||
text, INPUT_STREAM, /* timestamp= */ performance.now());
|
||||
this.finishProcessing();
|
||||
return [...this.classifications];
|
||||
}
|
||||
|
||||
// Internal function for converting raw data into a classification, and
|
||||
// adding it to our classifications list.
|
||||
private addJsTextClassification(binaryProto: Uint8Array): void {
|
||||
const classificationResult =
|
||||
ClassificationResult.deserializeBinary(binaryProto);
|
||||
console.log(classificationResult.toObject());
|
||||
this.classifications.push(
|
||||
...convertFromClassificationResultProto(classificationResult));
|
||||
}
|
||||
|
||||
/** Updates the MediaPipe graph configuration. */
|
||||
private refreshGraph(): void {
|
||||
const graphConfig = new CalculatorGraphConfig();
|
||||
graphConfig.addInputStream(INPUT_STREAM);
|
||||
graphConfig.addOutputStream(CLASSIFICATION_RESULT_STREAM);
|
||||
|
||||
const calculatorOptions = new CalculatorOptions();
|
||||
calculatorOptions.setExtension(
|
||||
TextClassifierGraphOptions.ext, this.options);
|
||||
|
||||
const classifierNode = new CalculatorGraphConfig.Node();
|
||||
classifierNode.setCalculator(TEXT_CLASSIFIER_GRAPH);
|
||||
classifierNode.addInputStream('TEXT:' + INPUT_STREAM);
|
||||
classifierNode.addOutputStream(
|
||||
'CLASSIFICATION_RESULT:' + CLASSIFICATION_RESULT_STREAM);
|
||||
classifierNode.setOptions(calculatorOptions);
|
||||
|
||||
graphConfig.addNode(classifierNode);
|
||||
|
||||
this.attachProtoListener(CLASSIFICATION_RESULT_STREAM, binaryProto => {
|
||||
this.addJsTextClassification(binaryProto);
|
||||
});
|
||||
|
||||
const binaryGraph = graphConfig.serializeBinary();
|
||||
this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,17 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
export {ClassifierOptions as TextClassifierOptions} from '../../../../tasks/web/core/classifier_options';
|
||||
@@ -0,0 +1,18 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
export {Category} from '../../../../tasks/web/components/containers/category';
|
||||
export {ClassificationEntry, Classifications} from '../../../../tasks/web/components/containers/classifications';
|
||||
@@ -0,0 +1,40 @@
|
||||
# This contains the MediaPipe Gesture Recognizer Task.
|
||||
#
|
||||
# This task takes video frames and outputs synchronized frames along with
|
||||
# the detection results for one or more gesture categories, using Gesture Recognizer.
|
||||
|
||||
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
|
||||
|
||||
package(default_visibility = ["//mediapipe/tasks:internal"])
|
||||
|
||||
licenses(["notice"])
|
||||
|
||||
mediapipe_ts_library(
|
||||
name = "gesture_recognizer",
|
||||
srcs = [
|
||||
"gesture_recognizer.ts",
|
||||
"gesture_recognizer_options.d.ts",
|
||||
"gesture_recognizer_result.d.ts",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/framework:calculator_jspb_proto",
|
||||
"//mediapipe/framework:calculator_options_jspb_proto",
|
||||
"//mediapipe/framework/formats:classification_jspb_proto",
|
||||
"//mediapipe/framework/formats:landmark_jspb_proto",
|
||||
"//mediapipe/framework/formats:rect_jspb_proto",
|
||||
"//mediapipe/tasks/cc/vision/gesture_recognizer/proto:gesture_classifier_graph_options_jspb_proto",
|
||||
"//mediapipe/tasks/cc/vision/gesture_recognizer/proto:gesture_recognizer_graph_options_jspb_proto",
|
||||
"//mediapipe/tasks/cc/vision/gesture_recognizer/proto:hand_gesture_recognizer_graph_options_jspb_proto",
|
||||
"//mediapipe/tasks/cc/vision/hand_detector/proto:hand_detector_graph_options_jspb_proto",
|
||||
"//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarker_graph_options_jspb_proto",
|
||||
"//mediapipe/tasks/cc/vision/hand_landmarker/proto:hand_landmarks_detector_graph_options_jspb_proto",
|
||||
"//mediapipe/tasks/web/components/containers:category",
|
||||
"//mediapipe/tasks/web/components/containers:landmark",
|
||||
"//mediapipe/tasks/web/components/processors:base_options",
|
||||
"//mediapipe/tasks/web/components/processors:classifier_options",
|
||||
"//mediapipe/tasks/web/core",
|
||||
"//mediapipe/tasks/web/core:classifier_options",
|
||||
"//mediapipe/tasks/web/core:task_runner",
|
||||
"//mediapipe/web/graph_runner:wasm_mediapipe_lib_ts",
|
||||
],
|
||||
)
|
||||
@@ -0,0 +1,374 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
import {CalculatorGraphConfig} from '../../../../framework/calculator_pb';
|
||||
import {CalculatorOptions} from '../../../../framework/calculator_options_pb';
|
||||
import {ClassificationList} from '../../../../framework/formats/classification_pb';
|
||||
import {LandmarkList, NormalizedLandmarkList} from '../../../../framework/formats/landmark_pb';
|
||||
import {NormalizedRect} from '../../../../framework/formats/rect_pb';
|
||||
import {GestureClassifierGraphOptions} from '../../../../tasks/cc/vision/gesture_recognizer/proto/gesture_classifier_graph_options_pb';
|
||||
import {GestureRecognizerGraphOptions} from '../../../../tasks/cc/vision/gesture_recognizer/proto/gesture_recognizer_graph_options_pb';
|
||||
import {HandGestureRecognizerGraphOptions} from '../../../../tasks/cc/vision/gesture_recognizer/proto/hand_gesture_recognizer_graph_options_pb';
|
||||
import {HandDetectorGraphOptions} from '../../../../tasks/cc/vision/hand_detector/proto/hand_detector_graph_options_pb';
|
||||
import {HandLandmarkerGraphOptions} from '../../../../tasks/cc/vision/hand_landmarker/proto/hand_landmarker_graph_options_pb';
|
||||
import {HandLandmarksDetectorGraphOptions} from '../../../../tasks/cc/vision/hand_landmarker/proto/hand_landmarks_detector_graph_options_pb';
|
||||
import {Category} from '../../../../tasks/web/components/containers/category';
|
||||
import {Landmark} from '../../../../tasks/web/components/containers/landmark';
|
||||
import {convertBaseOptionsToProto} from '../../../../tasks/web/components/processors/base_options';
|
||||
import {convertClassifierOptionsToProto} from '../../../../tasks/web/components/processors/classifier_options';
|
||||
import {TaskRunner} from '../../../../tasks/web/core/task_runner';
|
||||
import {WasmLoaderOptions} from '../../../../tasks/web/core/wasm_loader_options';
|
||||
import {createMediaPipeLib, FileLocator, ImageSource, WasmModule} from '../../../../web/graph_runner/wasm_mediapipe_lib';
|
||||
// Placeholder for internal dependency on trusted resource url
|
||||
|
||||
import {GestureRecognizerOptions} from './gesture_recognizer_options';
|
||||
import {GestureRecognitionResult} from './gesture_recognizer_result';
|
||||
|
||||
export {ImageSource};
|
||||
|
||||
// The OSS JS API does not support the builder pattern.
|
||||
// tslint:disable:jspb-use-builder-pattern
|
||||
|
||||
const IMAGE_STREAM = 'image_in';
|
||||
const NORM_RECT_STREAM = 'norm_rect';
|
||||
const HAND_GESTURES_STREAM = 'hand_gestures';
|
||||
const LANDMARKS_STREAM = 'hand_landmarks';
|
||||
const WORLD_LANDMARKS_STREAM = 'world_hand_landmarks';
|
||||
const HANDEDNESS_STREAM = 'handedness';
|
||||
const GESTURE_RECOGNIZER_GRAPH =
|
||||
'mediapipe.tasks.vision.gesture_recognizer.GestureRecognizerGraph';
|
||||
|
||||
const DEFAULT_NUM_HANDS = 1;
|
||||
const DEFAULT_SCORE_THRESHOLD = 0.5;
|
||||
const DEFAULT_CATEGORY_INDEX = -1;
|
||||
|
||||
const FULL_IMAGE_RECT = new NormalizedRect();
|
||||
FULL_IMAGE_RECT.setXCenter(0.5);
|
||||
FULL_IMAGE_RECT.setYCenter(0.5);
|
||||
FULL_IMAGE_RECT.setWidth(1);
|
||||
FULL_IMAGE_RECT.setHeight(1);
|
||||
|
||||
/** Performs hand gesture recognition on images. */
|
||||
export class GestureRecognizer extends TaskRunner {
|
||||
private gestures: Category[][] = [];
|
||||
private landmarks: Landmark[][] = [];
|
||||
private worldLandmarks: Landmark[][] = [];
|
||||
private handednesses: Category[][] = [];
|
||||
|
||||
private readonly options: GestureRecognizerGraphOptions;
|
||||
private readonly handLandmarkerGraphOptions: HandLandmarkerGraphOptions;
|
||||
private readonly handLandmarksDetectorGraphOptions:
|
||||
HandLandmarksDetectorGraphOptions;
|
||||
private readonly handDetectorGraphOptions: HandDetectorGraphOptions;
|
||||
private readonly handGestureRecognizerGraphOptions:
|
||||
HandGestureRecognizerGraphOptions;
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new gesture recognizer from the
|
||||
* provided options.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param gestureRecognizerOptions The options for the gesture recognizer.
|
||||
* Note that either a path to the model asset or a model buffer needs to
|
||||
* be provided (via `baseOptions`).
|
||||
*/
|
||||
static async createFromOptions(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
gestureRecognizerOptions: GestureRecognizerOptions):
|
||||
Promise<GestureRecognizer> {
|
||||
// Create a file locator based on the loader options
|
||||
const fileLocator: FileLocator = {
|
||||
locateFile() {
|
||||
// The only file we load via this mechanism is the Wasm binary
|
||||
return wasmLoaderOptions.wasmBinaryPath.toString();
|
||||
}
|
||||
};
|
||||
|
||||
const recognizer = await createMediaPipeLib(
|
||||
GestureRecognizer, wasmLoaderOptions.wasmLoaderPath,
|
||||
/* assetLoaderScript= */ undefined,
|
||||
/* glCanvas= */ undefined, fileLocator);
|
||||
await recognizer.setOptions(gestureRecognizerOptions);
|
||||
return recognizer;
|
||||
}
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new gesture recognizer based on
|
||||
* the provided model asset buffer.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param modelAssetBuffer A binary representation of the model.
|
||||
*/
|
||||
static createFromModelBuffer(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
modelAssetBuffer: Uint8Array): Promise<GestureRecognizer> {
|
||||
return GestureRecognizer.createFromOptions(
|
||||
wasmLoaderOptions, {baseOptions: {modelAssetBuffer}});
|
||||
}
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new gesture recognizer based on
|
||||
* the path to the model asset.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param modelAssetPath The path to the model asset.
|
||||
*/
|
||||
static async createFromModelPath(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
modelAssetPath: string): Promise<GestureRecognizer> {
|
||||
const response = await fetch(modelAssetPath.toString());
|
||||
const graphData = await response.arrayBuffer();
|
||||
return GestureRecognizer.createFromModelBuffer(
|
||||
wasmLoaderOptions, new Uint8Array(graphData));
|
||||
}
|
||||
|
||||
constructor(wasmModule: WasmModule) {
|
||||
super(wasmModule);
|
||||
|
||||
this.options = new GestureRecognizerGraphOptions();
|
||||
this.handLandmarkerGraphOptions = new HandLandmarkerGraphOptions();
|
||||
this.options.setHandLandmarkerGraphOptions(this.handLandmarkerGraphOptions);
|
||||
this.handLandmarksDetectorGraphOptions =
|
||||
new HandLandmarksDetectorGraphOptions();
|
||||
this.handLandmarkerGraphOptions.setHandLandmarksDetectorGraphOptions(
|
||||
this.handLandmarksDetectorGraphOptions);
|
||||
this.handDetectorGraphOptions = new HandDetectorGraphOptions();
|
||||
this.handLandmarkerGraphOptions.setHandDetectorGraphOptions(
|
||||
this.handDetectorGraphOptions);
|
||||
this.handGestureRecognizerGraphOptions =
|
||||
new HandGestureRecognizerGraphOptions();
|
||||
this.options.setHandGestureRecognizerGraphOptions(
|
||||
this.handGestureRecognizerGraphOptions);
|
||||
|
||||
this.initDefaults();
|
||||
|
||||
// Disables the automatic render-to-screen code, which allows for pure
|
||||
// CPU processing.
|
||||
this.setAutoRenderToScreen(false);
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets new options for the gesture recognizer.
|
||||
*
|
||||
* Calling `setOptions()` with a subset of options only affects those options.
|
||||
* You can reset an option back to its default value by explicitly setting it
|
||||
* to `undefined`.
|
||||
*
|
||||
* @param options The options for the gesture recognizer.
|
||||
*/
|
||||
async setOptions(options: GestureRecognizerOptions): Promise<void> {
|
||||
if (options.baseOptions) {
|
||||
const baseOptionsProto =
|
||||
await convertBaseOptionsToProto(options.baseOptions);
|
||||
this.options.setBaseOptions(baseOptionsProto);
|
||||
}
|
||||
|
||||
if ('numHands' in options) {
|
||||
this.handDetectorGraphOptions.setNumHands(
|
||||
options.numHands ?? DEFAULT_NUM_HANDS);
|
||||
}
|
||||
if ('minHandDetectionConfidence' in options) {
|
||||
this.handDetectorGraphOptions.setMinDetectionConfidence(
|
||||
options.minHandDetectionConfidence ?? DEFAULT_SCORE_THRESHOLD);
|
||||
}
|
||||
if ('minHandPresenceConfidence' in options) {
|
||||
this.handLandmarksDetectorGraphOptions.setMinDetectionConfidence(
|
||||
options.minHandPresenceConfidence ?? DEFAULT_SCORE_THRESHOLD);
|
||||
}
|
||||
if ('minTrackingConfidence' in options) {
|
||||
this.handLandmarkerGraphOptions.setMinTrackingConfidence(
|
||||
options.minTrackingConfidence ?? DEFAULT_SCORE_THRESHOLD);
|
||||
}
|
||||
|
||||
if (options.cannedGesturesClassifierOptions) {
|
||||
// Note that we have to support both JSPB and ProtobufJS and cannot
|
||||
// use JSPB's getMutableX() APIs.
|
||||
const graphOptions = new GestureClassifierGraphOptions();
|
||||
graphOptions.setClassifierOptions(convertClassifierOptionsToProto(
|
||||
options.cannedGesturesClassifierOptions,
|
||||
this.handGestureRecognizerGraphOptions
|
||||
.getCannedGestureClassifierGraphOptions()
|
||||
?.getClassifierOptions()));
|
||||
this.handGestureRecognizerGraphOptions
|
||||
.setCannedGestureClassifierGraphOptions(graphOptions);
|
||||
} else if (options.cannedGesturesClassifierOptions === undefined) {
|
||||
this.handGestureRecognizerGraphOptions
|
||||
.getCannedGestureClassifierGraphOptions()
|
||||
?.clearClassifierOptions();
|
||||
}
|
||||
|
||||
if (options.customGesturesClassifierOptions) {
|
||||
const graphOptions = new GestureClassifierGraphOptions();
|
||||
graphOptions.setClassifierOptions(convertClassifierOptionsToProto(
|
||||
options.customGesturesClassifierOptions,
|
||||
this.handGestureRecognizerGraphOptions
|
||||
.getCustomGestureClassifierGraphOptions()
|
||||
?.getClassifierOptions()));
|
||||
this.handGestureRecognizerGraphOptions
|
||||
.setCustomGestureClassifierGraphOptions(graphOptions);
|
||||
} else if (options.customGesturesClassifierOptions === undefined) {
|
||||
this.handGestureRecognizerGraphOptions
|
||||
.getCustomGestureClassifierGraphOptions()
|
||||
?.clearClassifierOptions();
|
||||
}
|
||||
|
||||
this.refreshGraph();
|
||||
}
|
||||
|
||||
/**
|
||||
* Performs gesture recognition on the provided single image and waits
|
||||
* synchronously for the response.
|
||||
* @param imageSource An image source to process.
|
||||
* @param timestamp The timestamp of the current frame, in ms. If not
|
||||
* provided, defaults to `performance.now()`.
|
||||
* @return The detected gestures.
|
||||
*/
|
||||
recognize(imageSource: ImageSource, timestamp: number = performance.now()):
|
||||
GestureRecognitionResult {
|
||||
this.gestures = [];
|
||||
this.landmarks = [];
|
||||
this.worldLandmarks = [];
|
||||
this.handednesses = [];
|
||||
|
||||
this.addGpuBufferAsImageToStream(imageSource, IMAGE_STREAM, timestamp);
|
||||
this.addProtoToStream(
|
||||
FULL_IMAGE_RECT.serializeBinary(), 'mediapipe.NormalizedRect',
|
||||
NORM_RECT_STREAM, timestamp);
|
||||
this.finishProcessing();
|
||||
|
||||
return {
|
||||
gestures: this.gestures,
|
||||
landmarks: this.landmarks,
|
||||
worldLandmarks: this.worldLandmarks,
|
||||
handednesses: this.handednesses
|
||||
};
|
||||
}
|
||||
|
||||
/** Sets the default values for the graph. */
|
||||
private initDefaults(): void {
|
||||
this.handDetectorGraphOptions.setNumHands(DEFAULT_NUM_HANDS);
|
||||
this.handDetectorGraphOptions.setMinDetectionConfidence(
|
||||
DEFAULT_SCORE_THRESHOLD);
|
||||
this.handLandmarksDetectorGraphOptions.setMinDetectionConfidence(
|
||||
DEFAULT_SCORE_THRESHOLD);
|
||||
this.handLandmarkerGraphOptions.setMinTrackingConfidence(
|
||||
DEFAULT_SCORE_THRESHOLD);
|
||||
}
|
||||
|
||||
/** Converts the proto data to a Category[][] structure. */
|
||||
private toJsCategories(data: Uint8Array[]): Category[][] {
|
||||
const result: Category[][] = [];
|
||||
for (const binaryProto of data) {
|
||||
const inputList = ClassificationList.deserializeBinary(binaryProto);
|
||||
const outputList: Category[] = [];
|
||||
for (const classification of inputList.getClassificationList()) {
|
||||
outputList.push({
|
||||
score: classification.getScore() ?? 0,
|
||||
index: classification.getIndex() ?? DEFAULT_CATEGORY_INDEX,
|
||||
categoryName: classification.getLabel() ?? '',
|
||||
displayName: classification.getDisplayName() ?? '',
|
||||
});
|
||||
}
|
||||
result.push(outputList);
|
||||
}
|
||||
return result;
|
||||
}
|
||||
|
||||
/** Converts raw data into a landmark, and adds it to our landmarks list. */
|
||||
private addJsLandmarks(data: Uint8Array[]): void {
|
||||
for (const binaryProto of data) {
|
||||
const handLandmarksProto =
|
||||
NormalizedLandmarkList.deserializeBinary(binaryProto);
|
||||
const landmarks: Landmark[] = [];
|
||||
for (const handLandmarkProto of handLandmarksProto.getLandmarkList()) {
|
||||
landmarks.push({
|
||||
x: handLandmarkProto.getX() ?? 0,
|
||||
y: handLandmarkProto.getY() ?? 0,
|
||||
z: handLandmarkProto.getZ() ?? 0,
|
||||
normalized: true
|
||||
});
|
||||
}
|
||||
this.landmarks.push(landmarks);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Converts raw data into a landmark, and adds it to our worldLandmarks
|
||||
* list.
|
||||
*/
|
||||
private adddJsWorldLandmarks(data: Uint8Array[]): void {
|
||||
for (const binaryProto of data) {
|
||||
const handWorldLandmarksProto =
|
||||
LandmarkList.deserializeBinary(binaryProto);
|
||||
const worldLandmarks: Landmark[] = [];
|
||||
for (const handWorldLandmarkProto of
|
||||
handWorldLandmarksProto.getLandmarkList()) {
|
||||
worldLandmarks.push({
|
||||
x: handWorldLandmarkProto.getX() ?? 0,
|
||||
y: handWorldLandmarkProto.getY() ?? 0,
|
||||
z: handWorldLandmarkProto.getZ() ?? 0,
|
||||
normalized: false
|
||||
});
|
||||
}
|
||||
this.worldLandmarks.push(worldLandmarks);
|
||||
}
|
||||
}
|
||||
|
||||
/** Updates the MediaPipe graph configuration. */
|
||||
private refreshGraph(): void {
|
||||
const graphConfig = new CalculatorGraphConfig();
|
||||
graphConfig.addInputStream(IMAGE_STREAM);
|
||||
graphConfig.addInputStream(NORM_RECT_STREAM);
|
||||
graphConfig.addOutputStream(HAND_GESTURES_STREAM);
|
||||
graphConfig.addOutputStream(LANDMARKS_STREAM);
|
||||
graphConfig.addOutputStream(WORLD_LANDMARKS_STREAM);
|
||||
graphConfig.addOutputStream(HANDEDNESS_STREAM);
|
||||
|
||||
const calculatorOptions = new CalculatorOptions();
|
||||
calculatorOptions.setExtension(
|
||||
GestureRecognizerGraphOptions.ext, this.options);
|
||||
|
||||
const recognizerNode = new CalculatorGraphConfig.Node();
|
||||
recognizerNode.setCalculator(GESTURE_RECOGNIZER_GRAPH);
|
||||
recognizerNode.addInputStream('IMAGE:' + IMAGE_STREAM);
|
||||
recognizerNode.addInputStream('NORM_RECT:' + NORM_RECT_STREAM);
|
||||
recognizerNode.addOutputStream('HAND_GESTURES:' + HAND_GESTURES_STREAM);
|
||||
recognizerNode.addOutputStream('LANDMARKS:' + LANDMARKS_STREAM);
|
||||
recognizerNode.addOutputStream('WORLD_LANDMARKS:' + WORLD_LANDMARKS_STREAM);
|
||||
recognizerNode.addOutputStream('HANDEDNESS:' + HANDEDNESS_STREAM);
|
||||
recognizerNode.setOptions(calculatorOptions);
|
||||
|
||||
graphConfig.addNode(recognizerNode);
|
||||
|
||||
this.attachProtoVectorListener(LANDMARKS_STREAM, binaryProto => {
|
||||
this.addJsLandmarks(binaryProto);
|
||||
});
|
||||
this.attachProtoVectorListener(WORLD_LANDMARKS_STREAM, binaryProto => {
|
||||
this.adddJsWorldLandmarks(binaryProto);
|
||||
});
|
||||
this.attachProtoVectorListener(HAND_GESTURES_STREAM, binaryProto => {
|
||||
this.gestures.push(...this.toJsCategories(binaryProto));
|
||||
});
|
||||
this.attachProtoVectorListener(HANDEDNESS_STREAM, binaryProto => {
|
||||
this.handednesses.push(...this.toJsCategories(binaryProto));
|
||||
});
|
||||
|
||||
const binaryGraph = graphConfig.serializeBinary();
|
||||
this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
+65
@@ -0,0 +1,65 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
import {BaseOptions} from '../../../../tasks/web/core/base_options';
|
||||
import {ClassifierOptions} from '../../../../tasks/web/core/classifier_options';
|
||||
|
||||
/** Options to configure the MediaPipe Gesture Recognizer Task */
|
||||
export interface GestureRecognizerOptions {
|
||||
/** Options to configure the loading of the model assets. */
|
||||
baseOptions?: BaseOptions;
|
||||
|
||||
/**
|
||||
* The maximum number of hands can be detected by the GestureRecognizer.
|
||||
* Defaults to 1.
|
||||
*/
|
||||
numHands?: number|undefined;
|
||||
|
||||
/**
|
||||
* The minimum confidence score for the hand detection to be considered
|
||||
* successful. Defaults to 0.5.
|
||||
*/
|
||||
minHandDetectionConfidence?: number|undefined;
|
||||
|
||||
/**
|
||||
* The minimum confidence score of hand presence score in the hand landmark
|
||||
* detection. Defaults to 0.5.
|
||||
*/
|
||||
minHandPresenceConfidence?: number|undefined;
|
||||
|
||||
/**
|
||||
* The minimum confidence score for the hand tracking to be considered
|
||||
* successful. Defaults to 0.5.
|
||||
*/
|
||||
minTrackingConfidence?: number|undefined;
|
||||
|
||||
/**
|
||||
* Sets the optional `ClassifierOptions` controling the canned gestures
|
||||
* classifier, such as score threshold, allow list and deny list of gestures.
|
||||
* The categories for canned gesture
|
||||
* classifiers are: ["None", "Closed_Fist", "Open_Palm", "Pointing_Up",
|
||||
* "Thumb_Down", "Thumb_Up", "Victory", "ILoveYou"]
|
||||
*/
|
||||
// TODO: Note this option is subject to change
|
||||
cannedGesturesClassifierOptions?: ClassifierOptions|undefined;
|
||||
|
||||
/**
|
||||
* Options for configuring the custom gestures classifier, such as score
|
||||
* threshold, allow list and deny list of gestures.
|
||||
*/
|
||||
// TODO b/251816640): Note this option is subject to change.
|
||||
customGesturesClassifierOptions?: ClassifierOptions|undefined;
|
||||
}
|
||||
@@ -0,0 +1,35 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
import {Category} from '../../../../tasks/web/components/containers/category';
|
||||
import {Landmark} from '../../../../tasks/web/components/containers/landmark';
|
||||
|
||||
/**
|
||||
* Represents the gesture recognition results generated by `GestureRecognizer`.
|
||||
*/
|
||||
export interface GestureRecognitionResult {
|
||||
/** Hand landmarks of detected hands. */
|
||||
landmarks: Landmark[][];
|
||||
|
||||
/** Hand landmarks in world coordniates of detected hands. */
|
||||
worldLandmarks: Landmark[][];
|
||||
|
||||
/** Handedness of detected hands. */
|
||||
handednesses: Category[][];
|
||||
|
||||
/** Recognized hand gestures of detected hands */
|
||||
gestures: Category[][];
|
||||
}
|
||||
@@ -0,0 +1,33 @@
|
||||
# This contains the MediaPipe Image Classifier Task.
|
||||
#
|
||||
# This task takes video or image frames and outputs the classification result.
|
||||
|
||||
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
|
||||
|
||||
package(default_visibility = ["//mediapipe/tasks:internal"])
|
||||
|
||||
licenses(["notice"])
|
||||
|
||||
mediapipe_ts_library(
|
||||
name = "image_classifier",
|
||||
srcs = [
|
||||
"image_classifier.ts",
|
||||
"image_classifier_options.ts",
|
||||
"image_classifier_result.ts",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/framework:calculator_jspb_proto",
|
||||
"//mediapipe/framework:calculator_options_jspb_proto",
|
||||
"//mediapipe/tasks/cc/components/containers/proto:classifications_jspb_proto",
|
||||
"//mediapipe/tasks/cc/vision/image_classifier/proto:image_classifier_graph_options_jspb_proto",
|
||||
"//mediapipe/tasks/web/components/containers:category",
|
||||
"//mediapipe/tasks/web/components/containers:classifications",
|
||||
"//mediapipe/tasks/web/components/processors:base_options",
|
||||
"//mediapipe/tasks/web/components/processors:classifier_options",
|
||||
"//mediapipe/tasks/web/components/processors:classifier_result",
|
||||
"//mediapipe/tasks/web/core",
|
||||
"//mediapipe/tasks/web/core:classifier_options",
|
||||
"//mediapipe/tasks/web/core:task_runner",
|
||||
"//mediapipe/web/graph_runner:wasm_mediapipe_lib_ts",
|
||||
],
|
||||
)
|
||||
@@ -0,0 +1,186 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
import {CalculatorGraphConfig} from '../../../../framework/calculator_pb';
|
||||
import {CalculatorOptions} from '../../../../framework/calculator_options_pb';
|
||||
import {ClassificationResult} from '../../../../tasks/cc/components/containers/proto/classifications_pb';
|
||||
import {ImageClassifierGraphOptions} from '../../../../tasks/cc/vision/image_classifier/proto/image_classifier_graph_options_pb';
|
||||
import {convertBaseOptionsToProto} from '../../../../tasks/web/components/processors/base_options';
|
||||
import {convertClassifierOptionsToProto} from '../../../../tasks/web/components/processors/classifier_options';
|
||||
import {convertFromClassificationResultProto} from '../../../../tasks/web/components/processors/classifier_result';
|
||||
import {TaskRunner} from '../../../../tasks/web/core/task_runner';
|
||||
import {WasmLoaderOptions} from '../../../../tasks/web/core/wasm_loader_options';
|
||||
import {createMediaPipeLib, FileLocator, ImageSource} from '../../../../web/graph_runner/wasm_mediapipe_lib';
|
||||
// Placeholder for internal dependency on trusted resource url
|
||||
|
||||
import {ImageClassifierOptions} from './image_classifier_options';
|
||||
import {Classifications} from './image_classifier_result';
|
||||
|
||||
const IMAGE_CLASSIFIER_GRAPH =
|
||||
'mediapipe.tasks.vision.image_classifier.ImageClassifierGraph';
|
||||
const INPUT_STREAM = 'input_image';
|
||||
const CLASSIFICATION_RESULT_STREAM = 'classification_result';
|
||||
|
||||
export {ImageSource}; // Used in the public API
|
||||
|
||||
// The OSS JS API does not support the builder pattern.
|
||||
// tslint:disable:jspb-use-builder-pattern
|
||||
|
||||
/** Performs classification on images. */
|
||||
export class ImageClassifier extends TaskRunner {
|
||||
private classifications: Classifications[] = [];
|
||||
private readonly options = new ImageClassifierGraphOptions();
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new image classifier from the
|
||||
* provided options.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param imageClassifierOptions The options for the image classifier. Note
|
||||
* that either a path to the model asset or a model buffer needs to be
|
||||
* provided (via `baseOptions`).
|
||||
*/
|
||||
static async createFromOptions(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
imageClassifierOptions: ImageClassifierOptions):
|
||||
Promise<ImageClassifier> {
|
||||
// Create a file locator based on the loader options
|
||||
const fileLocator: FileLocator = {
|
||||
locateFile() {
|
||||
// The only file we load is the Wasm binary
|
||||
return wasmLoaderOptions.wasmBinaryPath.toString();
|
||||
}
|
||||
};
|
||||
|
||||
const classifier = await createMediaPipeLib(
|
||||
ImageClassifier, wasmLoaderOptions.wasmLoaderPath,
|
||||
/* assetLoaderScript= */ undefined,
|
||||
/* glCanvas= */ undefined, fileLocator);
|
||||
await classifier.setOptions(imageClassifierOptions);
|
||||
return classifier;
|
||||
}
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new image classifier based on
|
||||
* the provided model asset buffer.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param modelAssetBuffer A binary representation of the model.
|
||||
*/
|
||||
static createFromModelBuffer(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
modelAssetBuffer: Uint8Array): Promise<ImageClassifier> {
|
||||
return ImageClassifier.createFromOptions(
|
||||
wasmLoaderOptions, {baseOptions: {modelAssetBuffer}});
|
||||
}
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new image classifier based on
|
||||
* the path to the model asset.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param modelAssetPath The path to the model asset.
|
||||
*/
|
||||
static async createFromModelPath(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
modelAssetPath: string): Promise<ImageClassifier> {
|
||||
const response = await fetch(modelAssetPath.toString());
|
||||
const graphData = await response.arrayBuffer();
|
||||
return ImageClassifier.createFromModelBuffer(
|
||||
wasmLoaderOptions, new Uint8Array(graphData));
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets new options for the image classifier.
|
||||
*
|
||||
* Calling `setOptions()` with a subset of options only affects those options.
|
||||
* You can reset an option back to its default value by explicitly setting it
|
||||
* to `undefined`.
|
||||
*
|
||||
* @param options The options for the image classifier.
|
||||
*/
|
||||
async setOptions(options: ImageClassifierOptions): Promise<void> {
|
||||
if (options.baseOptions) {
|
||||
const baseOptionsProto =
|
||||
await convertBaseOptionsToProto(options.baseOptions);
|
||||
this.options.setBaseOptions(baseOptionsProto);
|
||||
}
|
||||
|
||||
this.options.setClassifierOptions(convertClassifierOptionsToProto(
|
||||
options, this.options.getClassifierOptions()));
|
||||
this.refreshGraph();
|
||||
}
|
||||
|
||||
/**
|
||||
* Performs image classification on the provided image and waits synchronously
|
||||
* for the response.
|
||||
*
|
||||
* @param imageSource An image source to process.
|
||||
* @param timestamp The timestamp of the current frame, in ms. If not
|
||||
* provided, defaults to `performance.now()`.
|
||||
* @return The classification result of the image
|
||||
*/
|
||||
classify(imageSource: ImageSource, timestamp?: number): Classifications[] {
|
||||
// Get classification classes by running our MediaPipe graph.
|
||||
this.classifications = [];
|
||||
this.addGpuBufferAsImageToStream(
|
||||
imageSource, INPUT_STREAM, timestamp ?? performance.now());
|
||||
this.finishProcessing();
|
||||
return [...this.classifications];
|
||||
}
|
||||
|
||||
/**
|
||||
* Internal function for converting raw data into a classification, and
|
||||
* adding it to our classfications list.
|
||||
**/
|
||||
private addJsImageClassification(binaryProto: Uint8Array): void {
|
||||
const classificationResult =
|
||||
ClassificationResult.deserializeBinary(binaryProto);
|
||||
this.classifications.push(
|
||||
...convertFromClassificationResultProto(classificationResult));
|
||||
}
|
||||
|
||||
/** Updates the MediaPipe graph configuration. */
|
||||
private refreshGraph(): void {
|
||||
const graphConfig = new CalculatorGraphConfig();
|
||||
graphConfig.addInputStream(INPUT_STREAM);
|
||||
graphConfig.addOutputStream(CLASSIFICATION_RESULT_STREAM);
|
||||
|
||||
const calculatorOptions = new CalculatorOptions();
|
||||
calculatorOptions.setExtension(
|
||||
ImageClassifierGraphOptions.ext, this.options);
|
||||
|
||||
// Perform image classification. Pre-processing and results post-processing
|
||||
// are built-in.
|
||||
const classifierNode = new CalculatorGraphConfig.Node();
|
||||
classifierNode.setCalculator(IMAGE_CLASSIFIER_GRAPH);
|
||||
classifierNode.addInputStream('IMAGE:' + INPUT_STREAM);
|
||||
classifierNode.addOutputStream(
|
||||
'CLASSIFICATION_RESULT:' + CLASSIFICATION_RESULT_STREAM);
|
||||
classifierNode.setOptions(calculatorOptions);
|
||||
|
||||
graphConfig.addNode(classifierNode);
|
||||
|
||||
this.attachProtoListener(CLASSIFICATION_RESULT_STREAM, binaryProto => {
|
||||
this.addJsImageClassification(binaryProto);
|
||||
});
|
||||
|
||||
const binaryGraph = graphConfig.serializeBinary();
|
||||
this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -0,0 +1,17 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
export {ClassifierOptions as ImageClassifierOptions} from '../../../../tasks/web/core/classifier_options';
|
||||
@@ -0,0 +1,18 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
export {Category} from '../../../../tasks/web/components/containers/category';
|
||||
export {ClassificationEntry, Classifications} from '../../../../tasks/web/components/containers/classifications';
|
||||
@@ -0,0 +1,30 @@
|
||||
# This contains the MediaPipe Object Detector Task.
|
||||
#
|
||||
# This task takes video frames and outputs synchronized frames along with
|
||||
# the detection results for one or more object categories, using Object Detector.
|
||||
|
||||
load("//mediapipe/framework/port:build_config.bzl", "mediapipe_ts_library")
|
||||
|
||||
package(default_visibility = ["//mediapipe/tasks:internal"])
|
||||
|
||||
licenses(["notice"])
|
||||
|
||||
mediapipe_ts_library(
|
||||
name = "object_detector",
|
||||
srcs = [
|
||||
"object_detector.ts",
|
||||
"object_detector_options.d.ts",
|
||||
"object_detector_result.d.ts",
|
||||
],
|
||||
deps = [
|
||||
"//mediapipe/framework:calculator_jspb_proto",
|
||||
"//mediapipe/framework:calculator_options_jspb_proto",
|
||||
"//mediapipe/framework/formats:detection_jspb_proto",
|
||||
"//mediapipe/tasks/cc/vision/object_detector/proto:object_detector_options_jspb_proto",
|
||||
"//mediapipe/tasks/web/components/containers:category",
|
||||
"//mediapipe/tasks/web/components/processors:base_options",
|
||||
"//mediapipe/tasks/web/core",
|
||||
"//mediapipe/tasks/web/core:task_runner",
|
||||
"//mediapipe/web/graph_runner:wasm_mediapipe_lib_ts",
|
||||
],
|
||||
)
|
||||
@@ -0,0 +1,233 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
import {CalculatorGraphConfig} from '../../../../framework/calculator_pb';
|
||||
import {CalculatorOptions} from '../../../../framework/calculator_options_pb';
|
||||
import {Detection as DetectionProto} from '../../../../framework/formats/detection_pb';
|
||||
import {ObjectDetectorOptions as ObjectDetectorOptionsProto} from '../../../../tasks/cc/vision/object_detector/proto/object_detector_options_pb';
|
||||
import {convertBaseOptionsToProto} from '../../../../tasks/web/components/processors/base_options';
|
||||
import {TaskRunner} from '../../../../tasks/web/core/task_runner';
|
||||
import {WasmLoaderOptions} from '../../../../tasks/web/core/wasm_loader_options';
|
||||
import {createMediaPipeLib, FileLocator, ImageSource} from '../../../../web/graph_runner/wasm_mediapipe_lib';
|
||||
// Placeholder for internal dependency on trusted resource url
|
||||
|
||||
import {ObjectDetectorOptions} from './object_detector_options';
|
||||
import {Detection} from './object_detector_result';
|
||||
|
||||
const INPUT_STREAM = 'input_frame_gpu';
|
||||
const DETECTIONS_STREAM = 'detections';
|
||||
const OBJECT_DETECTOR_GRAPH = 'mediapipe.tasks.vision.ObjectDetectorGraph';
|
||||
|
||||
const DEFAULT_CATEGORY_INDEX = -1;
|
||||
|
||||
export {ImageSource}; // Used in the public API
|
||||
|
||||
// The OSS JS API does not support the builder pattern.
|
||||
// tslint:disable:jspb-use-builder-pattern
|
||||
|
||||
/** Performs object detection on images. */
|
||||
export class ObjectDetector extends TaskRunner {
|
||||
private detections: Detection[] = [];
|
||||
private readonly options = new ObjectDetectorOptionsProto();
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new object detector from the
|
||||
* provided options.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param objectDetectorOptions The options for the Object Detector. Note that
|
||||
* either a path to the model asset or a model buffer needs to be
|
||||
* provided (via `baseOptions`).
|
||||
*/
|
||||
static async createFromOptions(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
objectDetectorOptions: ObjectDetectorOptions): Promise<ObjectDetector> {
|
||||
// Create a file locator based on the loader options
|
||||
const fileLocator: FileLocator = {
|
||||
locateFile() {
|
||||
// The only file we load is the Wasm binary
|
||||
return wasmLoaderOptions.wasmBinaryPath.toString();
|
||||
}
|
||||
};
|
||||
|
||||
const detector = await createMediaPipeLib(
|
||||
ObjectDetector, wasmLoaderOptions.wasmLoaderPath,
|
||||
/* assetLoaderScript= */ undefined,
|
||||
/* glCanvas= */ undefined, fileLocator);
|
||||
await detector.setOptions(objectDetectorOptions);
|
||||
return detector;
|
||||
}
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new object detector based on the
|
||||
* provided model asset buffer.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param modelAssetBuffer A binary representation of the model.
|
||||
*/
|
||||
static createFromModelBuffer(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
modelAssetBuffer: Uint8Array): Promise<ObjectDetector> {
|
||||
return ObjectDetector.createFromOptions(
|
||||
wasmLoaderOptions, {baseOptions: {modelAssetBuffer}});
|
||||
}
|
||||
|
||||
/**
|
||||
* Initializes the Wasm runtime and creates a new object detector based on the
|
||||
* path to the model asset.
|
||||
* @param wasmLoaderOptions A configuration object that provides the location
|
||||
* of the Wasm binary and its loader.
|
||||
* @param modelAssetPath The path to the model asset.
|
||||
*/
|
||||
static async createFromModelPath(
|
||||
wasmLoaderOptions: WasmLoaderOptions,
|
||||
modelAssetPath: string): Promise<ObjectDetector> {
|
||||
const response = await fetch(modelAssetPath.toString());
|
||||
const graphData = await response.arrayBuffer();
|
||||
return ObjectDetector.createFromModelBuffer(
|
||||
wasmLoaderOptions, new Uint8Array(graphData));
|
||||
}
|
||||
|
||||
/**
|
||||
* Sets new options for the object detector.
|
||||
*
|
||||
* Calling `setOptions()` with a subset of options only affects those options.
|
||||
* You can reset an option back to its default value by explicitly setting it
|
||||
* to `undefined`.
|
||||
*
|
||||
* @param options The options for the object detector.
|
||||
*/
|
||||
async setOptions(options: ObjectDetectorOptions): Promise<void> {
|
||||
if (options.baseOptions) {
|
||||
const baseOptionsProto =
|
||||
await convertBaseOptionsToProto(options.baseOptions);
|
||||
this.options.setBaseOptions(baseOptionsProto);
|
||||
}
|
||||
|
||||
// Note that we have to support both JSPB and ProtobufJS, hence we
|
||||
// have to expliclity clear the values instead of setting them to
|
||||
// `undefined`.
|
||||
if (options.displayNamesLocale !== undefined) {
|
||||
this.options.setDisplayNamesLocale(options.displayNamesLocale);
|
||||
} else if ('displayNamesLocale' in options) { // Check for undefined
|
||||
this.options.clearDisplayNamesLocale();
|
||||
}
|
||||
|
||||
if (options.maxResults !== undefined) {
|
||||
this.options.setMaxResults(options.maxResults);
|
||||
} else if ('maxResults' in options) { // Check for undefined
|
||||
this.options.clearMaxResults();
|
||||
}
|
||||
|
||||
if (options.scoreThreshold !== undefined) {
|
||||
this.options.setScoreThreshold(options.scoreThreshold);
|
||||
} else if ('scoreThreshold' in options) { // Check for undefined
|
||||
this.options.clearScoreThreshold();
|
||||
}
|
||||
|
||||
if (options.categoryAllowlist !== undefined) {
|
||||
this.options.setCategoryAllowlistList(options.categoryAllowlist);
|
||||
} else if ('categoryAllowlist' in options) { // Check for undefined
|
||||
this.options.clearCategoryAllowlistList();
|
||||
}
|
||||
|
||||
if (options.categoryDenylist !== undefined) {
|
||||
this.options.setCategoryDenylistList(options.categoryDenylist);
|
||||
} else if ('categoryDenylist' in options) { // Check for undefined
|
||||
this.options.clearCategoryDenylistList();
|
||||
}
|
||||
|
||||
this.refreshGraph();
|
||||
}
|
||||
|
||||
/**
|
||||
* Performs object detection on the provided single image and waits
|
||||
* synchronously for the response.
|
||||
* @param imageSource An image source to process.
|
||||
* @param timestamp The timestamp of the current frame, in ms. If not
|
||||
* provided, defaults to `performance.now()`.
|
||||
* @return The list of detected objects
|
||||
*/
|
||||
detect(imageSource: ImageSource, timestamp?: number): Detection[] {
|
||||
// Get detections by running our MediaPipe graph.
|
||||
this.detections = [];
|
||||
this.addGpuBufferAsImageToStream(
|
||||
imageSource, INPUT_STREAM, timestamp ?? performance.now());
|
||||
this.finishProcessing();
|
||||
return [...this.detections];
|
||||
}
|
||||
|
||||
/** Converts raw data into a Detection, and adds it to our detection list. */
|
||||
private addJsObjectDetections(data: Uint8Array[]): void {
|
||||
for (const binaryProto of data) {
|
||||
const detectionProto = DetectionProto.deserializeBinary(binaryProto);
|
||||
const scores = detectionProto.getScoreList();
|
||||
const indexes = detectionProto.getLabelIdList();
|
||||
const labels = detectionProto.getLabelList();
|
||||
const displayNames = detectionProto.getDisplayNameList();
|
||||
|
||||
const detection: Detection = {categories: []};
|
||||
for (let i = 0; i < scores.length; i++) {
|
||||
detection.categories.push({
|
||||
score: scores[i],
|
||||
index: indexes[i] ?? DEFAULT_CATEGORY_INDEX,
|
||||
categoryName: labels[i] ?? '',
|
||||
displayName: displayNames[i] ?? '',
|
||||
});
|
||||
}
|
||||
|
||||
const boundingBox = detectionProto.getLocationData()?.getBoundingBox();
|
||||
if (boundingBox) {
|
||||
detection.boundingBox = {
|
||||
originX: boundingBox.getXmin() ?? 0,
|
||||
originY: boundingBox.getYmin() ?? 0,
|
||||
width: boundingBox.getWidth() ?? 0,
|
||||
height: boundingBox.getHeight() ?? 0
|
||||
};
|
||||
}
|
||||
|
||||
this.detections.push(detection);
|
||||
}
|
||||
}
|
||||
|
||||
/** Updates the MediaPipe graph configuration. */
|
||||
private refreshGraph(): void {
|
||||
const graphConfig = new CalculatorGraphConfig();
|
||||
graphConfig.addInputStream(INPUT_STREAM);
|
||||
graphConfig.addOutputStream(DETECTIONS_STREAM);
|
||||
|
||||
const calculatorOptions = new CalculatorOptions();
|
||||
calculatorOptions.setExtension(
|
||||
ObjectDetectorOptionsProto.ext, this.options);
|
||||
|
||||
const detectorNode = new CalculatorGraphConfig.Node();
|
||||
detectorNode.setCalculator(OBJECT_DETECTOR_GRAPH);
|
||||
detectorNode.addInputStream('IMAGE:' + INPUT_STREAM);
|
||||
detectorNode.addOutputStream('DETECTIONS:' + DETECTIONS_STREAM);
|
||||
detectorNode.setOptions(calculatorOptions);
|
||||
|
||||
graphConfig.addNode(detectorNode);
|
||||
|
||||
this.attachProtoVectorListener(DETECTIONS_STREAM, binaryProto => {
|
||||
this.addJsObjectDetections(binaryProto);
|
||||
});
|
||||
|
||||
const binaryGraph = graphConfig.serializeBinary();
|
||||
this.setGraph(new Uint8Array(binaryGraph), /* isBinary= */ true);
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -0,0 +1,52 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
import {BaseOptions} from '../../../../tasks/web/core/base_options';
|
||||
|
||||
/** Options to configure the MediaPipe Object Detector Task */
|
||||
export interface ObjectDetectorOptions {
|
||||
/** Options to configure the loading of the model assets. */
|
||||
baseOptions?: BaseOptions;
|
||||
|
||||
/**
|
||||
* The locale to use for display names specified through the TFLite Model
|
||||
* Metadata, if any. Defaults to English.
|
||||
*/
|
||||
displayNamesLocale?: string|undefined;
|
||||
|
||||
/** The maximum number of top-scored detection results to return. */
|
||||
maxResults?: number|undefined;
|
||||
|
||||
/**
|
||||
* Overrides the value provided in the model metadata. Results below this
|
||||
* value are rejected.
|
||||
*/
|
||||
scoreThreshold?: number|undefined;
|
||||
|
||||
/**
|
||||
* Allowlist of category names. If non-empty, detection results whose category
|
||||
* name is not in this set will be filtered out. Duplicate or unknown category
|
||||
* names are ignored. Mutually exclusive with `categoryDenylist`.
|
||||
*/
|
||||
categoryAllowlist?: string[]|undefined;
|
||||
|
||||
/**
|
||||
* Denylist of category names. If non-empty, detection results whose category
|
||||
* name is in this set will be filtered out. Duplicate or unknown category
|
||||
* names are ignored. Mutually exclusive with `categoryAllowlist`.
|
||||
*/
|
||||
categoryDenylist?: string[]|undefined;
|
||||
}
|
||||
@@ -0,0 +1,38 @@
|
||||
/**
|
||||
* Copyright 2022 The MediaPipe Authors. All Rights Reserved.
|
||||
*
|
||||
* Licensed under the Apache License, Version 2.0 (the "License");
|
||||
* you may not use this file except in compliance with the License.
|
||||
* You may obtain a copy of the License at
|
||||
*
|
||||
* http://www.apache.org/licenses/LICENSE-2.0
|
||||
*
|
||||
* Unless required by applicable law or agreed to in writing, software
|
||||
* distributed under the License is distributed on an "AS IS" BASIS,
|
||||
* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
* See the License for the specific language governing permissions and
|
||||
* limitations under the License.
|
||||
*/
|
||||
|
||||
import {Category} from '../../../../tasks/web/components/containers/category';
|
||||
|
||||
/** An integer bounding box, axis aligned. */
|
||||
export interface BoundingBox {
|
||||
/** The X coordinate of the top-left corner, in pixels. */
|
||||
originX: number;
|
||||
/** The Y coordinate of the top-left corner, in pixels. */
|
||||
originY: number;
|
||||
/** The width of the bounding box, in pixels. */
|
||||
width: number;
|
||||
/** The height of the bounding box, in pixels. */
|
||||
height: number;
|
||||
}
|
||||
|
||||
/** Represents one object detected by the `ObjectDetector`. */
|
||||
export interface Detection {
|
||||
/** A list of `Category` objects. */
|
||||
categories: Category[];
|
||||
|
||||
/** The bounding box of the detected objects. */
|
||||
boundingBox?: BoundingBox;
|
||||
}
|
||||
Reference in New Issue
Block a user