Panduan Lengkap MediaPipe: Computer Vision Made Easy

MediaPipe adalah framework open-source dari Google untuk membangun pipeline machine learning multimodal. MediaPipe menyediakan solusi siap pakai untuk berbagai task computer vision seperti face detection, hand tracking, pose estimation, dan banyak lagi.

Dalam tutorial ini, kita akan mempelajari MediaPipe dari dasar hingga implementasi praktis untuk berbagai aplikasi real-time.

Mengapa MediaPipe?

Keunggulan MediaPipe:

Cross-Platform: Berjalan di Android, iOS, Web, Desktop (Python/C++)

Real-Time Performance: Dioptimalkan untuk inferensi real-time

Pre-trained Models: Model siap pakai dengan akurasi tinggi

Easy to Use: API yang sederhana dan intuitif

Production Ready: Digunakan oleh produk Google (Google Meet, etc)

Open Source: Gratis dan dapat dimodifikasi

Solusi yang Tersedia:

| Solusi | Deskripsi |

|--------|-----------|

| Face Detection | Deteksi wajah dalam gambar/video |

| Face Mesh | 468 landmark wajah 3D |

| Hand Tracking | 21 landmark tangan |

| Pose Estimation | 33 landmark tubuh |

| Holistic | Kombinasi face, hands, dan pose |

| Object Detection | Deteksi objek umum |

| Image Segmentation | Segmentasi selfie/background |

| Gesture Recognition | Pengenalan gesture tangan |

Instalasi

Install MediaPipe

# Install dengan pip
pip install mediapipe

Install dengan OpenCV (biasanya sudah termasuk)
pip install opencv-python

Untuk versi GPU (opsional)
pip install mediapipe-gpu

Versi spesifik
pip install mediapipe==0.10.9

Verifikasi Instalasi

import mediapipe as mp
import cv2

print(f"MediaPipe version: {mp.version}")
print(f"OpenCV version: {cv2.version}")

Struktur MediaPipe

import mediapipe as mp

Solutions - Pre-built ML pipelines
mp.solutions.facedetection

mp.solutions.facemesh
mp.solutions.hands
mp.solutions.pose
mp.solutions.holistic
mp.solutions.objectron
mp.solutions.selfiesegmentation


Drawing utilities
mp.solutions.drawingutils
mp.solutions.drawingstyles

Face Detection

Basic Face Detection

import cv2
import mediapipe as mp

Initialize
mpfacedetection = mp.solutions.facedetection
mpdrawing = mp.solutions.drawingutils

def detectfacesimage(imagepath):

    """Detect faces in a static image."""
    # Read image
    image = cv2.imread(imagepath)
    imagergb = cv2.cvtColor(image, cv2.COLORBGR2RGB)

    # Initialize face detection
    with mpfacedetection.FaceDetection(
        modelselection=1,  # 0: short-range (2m), 1: full-range (5m)

        mindetectionconfidence=0.5

    ) as facedetection:

        # Process image
        results = facedetection.process(imagergb)

        # Draw detections
        if results.detections:
            for detection in results.detections:
                mpdrawing.drawdetection(image, detection)

                # Get bounding box
                bbox = detection.locationdata.relativeboundingbox

                h, w,  = image.shape
                x = int(bbox.xmin  w)

                y = int(bbox.ymin  h)
                width = int(bbox.width  w)

                height = int(bbox.height  h)

                # Get confidence score
                score = detection.score[0]
                print(f"Face detected: confidence={score:.2f}, bbox=({x}, {y}, {width}, {height})")

    cv2.imshow('Face Detection', image)
    cv2.waitKey(0)
    cv2.destroyAllWindows()

Run
detectfacesimage('photo.jpg')

Real-Time Face Detection (Webcam)

import cv2
import mediapipe as mp

def realtimefacedetection():
    """Real-time face detection from webcam."""
    mpfacedetection = mp.solutions.facedetection

    mpdrawing = mp.solutions.drawingutils