M1 MacでMediaPipe(Text & Audio & Vision)

MediapipeのTextタスクとAudioタスクとVisionタスクを試したので、メモ程度にまとめました。

1. 環境構築

$ sw_vers
ProductName:		macOS
ProductVersion:		13.2.1

$ python -V
Python 3.9.9

$ pip install mediapipe==0.10.14

2. テキスト分類

モデルはこちら(BERT分類器)から

TextClassification.py

from mediapipe.tasks import python
from mediapipe.tasks.python import text

MODEL_PATH = "./model/classifier.tflite"

TEXT_LIST = [
    "I'm going to work today.",      #今日は仕事に行きます。
    "you're no good",                #君はダメだ。
    "It is sunny today",             #今日は晴れです。
    "It's cloudy today"              #今日は曇りです。
]

def main():
    base_options = python.BaseOptions(model_asset_path=MODEL_PATH)
    options = text.TextClassifierOptions(base_options=base_options)
    classifier = text.TextClassifier.create_from_options(options)

    for input_text in TEXT_LIST:
        classification_result = classifier.classify(input_text)
        top_category = classification_result.classifications[0].categories[0]
        
        print("word：[", input_text, "]")
        print(top_category)

if __name__ == "__main__":
    main()

2-1. 実行してみる

$ python TextClassification.py

word：[ I'm going to work today. ]
Category(index=0, score=0.6713167428970337, display_name='', category_name='negative')

word：[ you're no good ]
Category(index=0, score=0.9954270124435425, display_name='', category_name='negative')

word：[ It is sunny today ]
Category(index=1, score=0.996543824672699, display_name='', category_name='positive')

word：[ It's cloudy today ]
Category(index=0, score=0.8927320241928101, display_name='', category_name='negative')

3. テキスト埋め込み

モデルはこちら(平均単語埋め込み)から

TextEmbedded.py

import mediapipe as mp

MODEL_PATH = './model/universal_sentence_encoder.tflite'

INPUT_TEXT = "The input text to be embedded."

def main():
    BaseOptions = mp.tasks.BaseOptions
    TextEmbedder = mp.tasks.text.TextEmbedder
    TextEmbedderOptions = mp.tasks.text.TextEmbedderOptions

    options = TextEmbedderOptions(
        base_options=BaseOptions(model_asset_path=MODEL_PATH),
        quantize=True)
    text_embedder = TextEmbedder.create_from_options(options)

    embedding_result = text_embedder.embed(INPUT_TEXT)
    
    print("word：[", INPUT_TEXT, "]：")
    print(embedding_result.embeddings)

if __name__ == "__main__":
    main()

3-1. 実行してみる

$ python TextEmbedded.py

word：[ The input text to be embedded. ]：
[Embedding(embedding=array([127, 233, 159, 127,  46,  32, 128, 128, 127, 204, 128, 162, 127,
       128,  33,  78, 220, 196, 173, 128, 142, 127, 123, 210, 249, 127,
       184, 128,  77, 127,   7, 181, 180,  86,  16,  19, 202, 181, 127,
        92, 162, 127,  96, 127, 134, 127, 128, 113, 127,   1, 128, 127,
       178,  85, 127, 125,  30,  53,  17, 127, 128, 128, 127, 211,  32,
       127,  91, 248, 128, 127, 183, 187, 204, 237, 227, 128, 127,  40,
       211, 206, 127,  91, 127, 245,   1,  24, 127, 172, 128,  80, 128,
       243, 128, 128, 128, 223, 127, 115,  45, 147], dtype=uint8), head_index=1, head_name='response_encoding')]

4. 言語の検出

モデルはこちら(言語検出)から

LanguageDetection.py

from mediapipe.tasks import python
from mediapipe.tasks.python import text

model_path = './model/language_detector.tflite'
langauge_list = [
    "ハロー世界",            #日本語
    "hello world",          #英語
    "Bonjour le monde",     #フランス語
    "안녕 세계",              #韓国語
    "Привет, мир",          #ロシア語
    "你好世界"               #中国語
]

def main():
    base_options = python.BaseOptions(model_asset_path=model_path)

    base_options = python.BaseOptions(model_asset_path=model_path)
    options = text.LanguageDetectorOptions(base_options=base_options)

    for input_text in langauge_list:
        with python.text.LanguageDetector.create_from_options(options) as detector:
            detection_result = detector.detect(input_text)
            print("[", input_text, "]：")
            print(detection_result.detections[0])
            
if __name__ == "__main__":
    main()

4-1. 実行してみる

$ python LanguageDetection.py

[ ハロー世界 ]：
LanguageDetectorResult.Detection(language_code='ja', probability=0.9841582775115967)

[ hello world ]：
LanguageDetectorResult.Detection(language_code='en', probability=0.8353540897369385)

[ Bonjour le monde ]：
LanguageDetectorResult.Detection(language_code='fr', probability=0.997443437576294)

[ 안녕 세계 ]：
LanguageDetectorResult.Detection(language_code='ko', probability=0.9999277591705322)

[ Привет, мир ]：
LanguageDetectorResult.Detection(language_code='ru', probability=0.7444538474082947)

[ 你好世界 ]：
LanguageDetectorResult.Detection(language_code='zh', probability=0.9943158030509949)

5. 音声分類(会話の音声)

モデルはこちら(YamNet)から

スピーチ用の音声はこちらを使用

AudioClassifcation_speech.py

import urllib.request
import os

import numpy as np

from mediapipe.tasks import python
from mediapipe.tasks.python.components import containers
from mediapipe.tasks.python import audio
from scipy.io import wavfile

AUDIO_PATH = './audio/speech_16000_hz_mono.wav'
MODEL_PATH = './model/lite-model_yamnet_classification_tflite_1.tflite'

def main():
    if not os.path.isfile(AUDIO_PATH):
        url = 'https://storage.googleapis.com/mediapipe-assets/speech_16000_hz_mono.wav'
        urllib.request.urlretrieve(url, AUDIO_PATH)

    base_options = python.BaseOptions(model_asset_path=MODEL_PATH)
    options = audio.AudioClassifierOptions(base_options=base_options, max_results=4)

    with audio.AudioClassifier.create_from_options(options) as classifier:
        sample_rate, wav_data = wavfile.read(AUDIO_PATH)
        audio_clip = containers.AudioData.create_from_array(
            wav_data.astype(float) / np.iinfo(np.int16).max, sample_rate)
        classification_result_list = classifier.classify(audio_clip)

    print("[", AUDIO_PATH, "]：")
    for idx, _ in enumerate([0, 975, 1950, 2925]):
        classification_result = classification_result_list[idx]
        top_category = classification_result.classifications[0].categories[0]
        print(top_category)

if __name__ == "__main__":
    main()

5-1. 実行してみる

$ python AudioClassifcation_speech.py

[ ./audio/speech_16000_hz_mono.wav ]
Category(index=0, score=0.91796875, display_name='', category_name='Speech')
Category(index=0, score=0.98828125, display_name='', category_name='Speech')
Category(index=0, score=0.984375, display_name='', category_name='Speech')
Category(index=0, score=0.99609375, display_name='', category_name='Speech')

6. 音声分類(音楽の音声)

音楽用の音声はこちらを使用

AudioClassifcation_music.py

import urllib.request
import os

import numpy as np

from mediapipe.tasks import python
from mediapipe.tasks.python.components import containers
from mediapipe.tasks.python import audio
from scipy.io import wavfile

AUDIO_PATH = './audio/loop1.wav'
MODEL_PATH = './model/lite-model_yamnet_classification_tflite_1.tflite'

def main():
    if not os.path.isfile(AUDIO_PATH):
        url = 'https://www.ne.jp/asahi/music/myuu/wave/loop1.wav'
        urllib.request.urlretrieve(url, AUDIO_PATH)
    
    base_options = python.BaseOptions(model_asset_path=MODEL_PATH)
    options = audio.AudioClassifierOptions(
        base_options=base_options, max_results=4)

    with audio.AudioClassifier.create_from_options(options) as classifier:
        sample_rate, wav_data = wavfile.read(AUDIO_PATH)
        audio_clip = containers.AudioData.create_from_array(
            wav_data.astype(float) / np.iinfo(np.int16).max, sample_rate)
        classification_result_list = classifier.classify(audio_clip)

    print("[", AUDIO_PATH, "]：")
    for idx, _ in enumerate([0, 975, 1950, 2925]):
        classification_result = classification_result_list[idx]
        top_category = classification_result.classifications[0].categories[0]
        print(top_category)

if __name__ == "__main__":
    main()

6-1. 実行してみる

$ python AudioClassifcation_music.py

[ ./audio/loop1.wav ]：
Category(index=132, score=0.984375, display_name='', category_name='Music')
Category(index=132, score=0.984375, display_name='', category_name='Music')
Category(index=132, score=0.98828125, display_name='', category_name='Music')
Category(index=132, score=0.96875, display_name='', category_name='Music')

7. オブジェクト検出

モデルはこちら(EfficientNet-Lite0 (int8))から

使用する画像

cat.jpeg

dog.jpg

ObjectDetection.py

import numpy as np
import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision

import cv2

# 「image」フォルダに格納してください。
IMAGE_LIST = [
    'cat.jpeg',
    'dog.jpg'
]
model_path = './model/efficientdet_lite0_int8.tflite'

MARGIN = 10  # pixels
ROW_SIZE = 10  # pixels
FONT_SIZE = 2
FONT_THICKNESS = 2
TEXT_COLOR = (0, 0, 255)  # red

def visualize(image, detection_result) -> np.ndarray:
    """Draws bounding boxes on the input image and return it.
    Args:
        image: The input RGB image.
        detection_result: The list of all "Detection" entities to be visualize.
    Returns:
        Image with bounding boxes.
    """
    for detection in detection_result.detections:
        # Draw bounding_box
        bbox = detection.bounding_box
        start_point = bbox.origin_x, bbox.origin_y
        end_point = bbox.origin_x + bbox.width, bbox.origin_y + bbox.height
        cv2.rectangle(image, start_point, end_point, TEXT_COLOR, 3)

        # Draw label and score
        category = detection.categories[0]
        category_name = category.category_name
        probability = round(category.score, 100)
        result_text = category_name + ' (' + str(probability) + ')'
        text_location = (MARGIN + bbox.origin_x, MARGIN + ROW_SIZE + bbox.origin_y)
        cv2.putText(image, result_text, text_location, cv2.FONT_HERSHEY_PLAIN, FONT_SIZE, TEXT_COLOR, FONT_THICKNESS)

    return image

def main():
    base_options = python.BaseOptions(model_asset_path=model_path)
    options = vision.ObjectDetectorOptions(base_options=base_options, score_threshold=0.5)
    detector = vision.ObjectDetector.create_from_options(options)
    
    for input_image in IMAGE_LIST:
        save_image = f'./image/detection_result_{input_image}'
        
        input_image = "./image/" + input_image

        image = mp.Image.create_from_file(input_image)

        detection_result = detector.detect(image)

        image_copy = np.copy(image.numpy_view())
        annotated_image = visualize(image_copy, detection_result)
        rgb_annotated_image = cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB)
        cv2.imwrite(save_image, rgb_annotated_image)
        print("image：[", input_image, "]")
        print("result：[", detection_result.detections[0].categories[0], "]")
    
if __name__ == "__main__":
    main()

7-1. 実行してみる

$ python ObjectDetection.py

image：[ ./image/cat.jpeg ]
result：[ Category(index=None, score=0.80078125, display_name=None, category_name='cat') ]
image：[ ./image/dog.jpg ]
result：[ Category(index=None, score=0.88671875, display_name=None, category_name='dog') ]

detection_result_cat.jpeg

detection_result_dog.jpg

他のモデルと比較

ObjectDetection.py

import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python import vision

# 「image」フォルダに格納してください。
INPUT_IMAGE = './image/cat.jpeg'
MODEL_LIST = [
    './model/efficientdet_lite0_int8.tflite',      #EfficientDet-Lite0 (int8)
    './model/efficientdet_lite0_float32.tflite',   #EfficientDet-Lite0（float 32）
    './model/efficientdet_lite2_int8.tflite',      #EfficientDet-Lite2 (int8）
    './model/efficientdet_lite2_float16.tflite',   #EfficientDet-Lite2 (float16）
    './model/efficientdet_lite2_float32.tflite',   #EfficientDet-Lite2 (float32）
    './model/ssd_mobilenet_v2_int8.tflite',        #SSDMobileNet-V2 (int8)
    './model/ssd_mobilenet_v2_float32.tflite'      #SSDMobileNet-V2 (float32)
]

def main():
    for model_path in MODEL_LIST :
        base_options = python.BaseOptions(model_asset_path=model_path)
        options = vision.ObjectDetectorOptions(base_options=base_options, score_threshold=0.5)
        detector = vision.ObjectDetector.create_from_options(options)

        image = mp.Image.create_from_file(INPUT_IMAGE)

        detection_result = detector.detect(image)
        print("model：[", model_path, "]")
        print(detection_result.detections[0].categories)
    
if __name__ == "__main__":
    main()

model：[ ./model/efficientdet_lite0_int8.tflite ]
[Category(index=None, score=0.80078125, display_name=None, category_name='cat')]

model：[ ./model/efficientdet_lite0_float32.tflite ]
[Category(index=None, score=0.7745001316070557, display_name=None, category_name='cat')]

model：[ ./model/efficientdet_lite2_int8.tflite ]
[Category(index=None, score=0.7109375, display_name=None, category_name='cat')]

model：[ ./model/efficientdet_lite2_float16.tflite ]
[Category(index=None, score=0.7353924512863159, display_name=None, category_name='cat')]

model：[ ./model/efficientdet_lite2_float32.tflite ]
[Category(index=None, score=0.7346012592315674, display_name=None, category_name='cat')]

model：[ ./model/ssd_mobilenet_v2_int8.tflite ]
[Category(index=None, score=0.6182375550270081, display_name=None, category_name='teddy bear')]

model：[ ./model/ssd_mobilenet_v2_float32.tflite ]
[Category(index=None, score=0.6182098984718323, display_name=None, category_name='teddy bear')]

8. 画像分類

モデルはこちら(EfficientNet-Lite0（float 32)から

ImageClassification.py

import mediapipe as mp
from mediapipe.tasks import python
from mediapipe.tasks.python.components import processors
from mediapipe.tasks.python import vision

IMAGE_FILENAMES = [
    './image/cat.jpeg',
    './image/dog.jpg'
]
model_path = './model/efficientdet_lite0_float16.tflite'

def main():
    base_options = python.BaseOptions(model_asset_path=model_path)
    options = vision.ImageClassifierOptions(base_options=base_options, max_results=4)
    classifier = vision.ImageClassifier.create_from_options(options)

    for image_name in IMAGE_FILENAMES:
        image = mp.Image.create_from_file(image_name)

        classification_result = classifier.classify(image)
        
        print("[", image_name, "]：")
        for result in classification_result.classifications[0].categories:
            print("result：", result)

if __name__ == "__main__":
    main()

8-1. 実行してみる

$ python ImageClassification.py

[ ./image/cat.jpeg ]：
result： Category(index=258, score=0.09676501899957657, display_name='', category_name='Samoyed')
result： Category(index=850, score=0.08314952254295349, display_name='', category_name='teddy')
result： Category(index=265, score=0.05580028146505356, display_name='', category_name='toy poodle')
result： Category(index=283, score=0.0362594872713089, display_name='', category_name='Persian cat')
[ ./image/dog.jpg ]：
result： Category(index=207, score=0.27572956681251526, display_name='', category_name='golden retriever')
result： Category(index=208, score=0.20771914720535278, display_name='', category_name='Labrador retriever')
result： Category(index=222, score=0.05555836856365204, display_name='', category_name='kuvasz')
result： Category(index=267, score=0.03965079411864281, display_name='', category_name='standard poodle')