【Raspberry Pi】電子工作入門⑥～機械学習による物体認識～【Python】

# task2_face_numbering.py
# 複数の顔を検出して番号をつけて表示する

from picamera2 import Picamera2
import cv2
import time

# Haar Cascadeの読み込み
cascade_path = "/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml"
face_cascade = cv2.CascadeClassifier(cascade_path)

# カメラ初期化
picam2 = Picamera2()
picam2.configure(picam2.create_preview_configuration(main={"size": (640, 360)}))
picam2.start()
time.sleep(0.2)

while True:
    # カメラ画像の取得
    rgb = picam2.capture_array()
    bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
    gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)

    # 顔検出
    faces = face_cascade.detectMultiScale(
        gray,
        scaleFactor=1.2,
        minNeighbors=5,
        minSize=(30, 30)
    )

    # 検出した顔に番号をつけて描画
    for i, (x, y, w, h) in enumerate(faces):
        # 顔の矩形
        cv2.rectangle(bgr, (x, y), (x+w, y+h), (0, 255, 0), 2)
        # 番号ラベル（Face 1, Face 2,...）
        label = f"Face {i+1}"
        cv2.putText(bgr, label, (x, y - 10),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (255, 0, 0), 2)

    cv2.putText(bgr, f"Detected: {len(faces)} faces",
                (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2)

    cv2.imshow("Face Numbering", bgr)

    key = cv2.waitKey(1) & 0xFF
    if key == ord('q'):
        break

cv2.destroyAllWindows()
picam2.stop()

from picamera2 import Picamera2
import cv2
import time

# モデル読み込み
face_path = "/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml"
eye_path  = "/usr/share/opencv4/haarcascades/haarcascade_eye.xml"

face_cascade = cv2.CascadeClassifier(face_path)
eye_cascade  = cv2.CascadeClassifier(eye_path)

# カメラ初期化
picam2 = Picamera2()
picam2.configure(picam2.create_preview_configuration(main={"size": (640, 360)}))
picam2.start()
time.sleep(0.2)

while True:
    rgb = picam2.capture_array()
    bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
    gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)

    # 顔検出
    faces = face_cascade.detectMultiScale(gray, 1.2, 5, minSize=(30, 30))

    for (x, y, w, h) in faces:
        # 顔領域を描画
        cv2.rectangle(bgr, (x, y), (x+w, y+h), (0, 255, 0), 2)

        # 顔領域だけ切り出して目を検出（誤検出を減らす）
        roi_gray  = gray[y:y+h, x:x+w]
        roi_color = bgr[y:y+h, x:x+w]

        eyes = eye_cascade.detectMultiScale(roi_gray, 1.2, 10, minSize=(15, 15))
        for (ex, ey, ew, eh) in eyes:
            cv2.rectangle(roi_color, (ex, ey), (ex+ew, ey+eh), (255, 0, 0), 2)

    cv2.imshow("Face + Eyes", bgr)
    key = cv2.waitKey(1) & 0xFF
    if key == ord('q'):
        break

cv2.destroyAllWindows()
picam2.stop()

from picamera2 import Picamera2
import cv2
import time

face_path  = "/usr/share/opencv4/haarcascades/haarcascade_frontalface_default.xml"
smile_path = "/usr/share/opencv4/haarcascades/haarcascade_smile.xml"

face_cascade  = cv2.CascadeClassifier(face_path)
smile_cascade = cv2.CascadeClassifier(smile_path)

picam2 = Picamera2()
picam2.configure(picam2.create_preview_configuration(main={"size": (640, 360)}))
picam2.start()
time.sleep(0.2)

while True:
    rgb = picam2.capture_array()
    bgr = cv2.cvtColor(rgb, cv2.COLOR_RGB2BGR)
    gray = cv2.cvtColor(bgr, cv2.COLOR_BGR2GRAY)

    faces = face_cascade.detectMultiScale(gray, 1.2, 5, minSize=(30, 30))
    for (x, y, w, h) in faces:
        cv2.rectangle(bgr, (x, y), (x+w, y+h), (0, 255, 0), 2)

        roi_gray  = gray[y:y+h, x:x+w]
        roi_color = bgr[y:y+h, x:x+w]

        smiles = smile_cascade.detectMultiScale(
            roi_gray,
            scaleFactor=1.8,   # 笑顔検出はスケールを大きめにすると安定
            minNeighbors=20,   # 高めにすると誤検出が減る
            minSize=(25, 25)
        )
        for (sx, sy, sw, sh) in smiles:
            cv2.rectangle(roi_color, (sx, sy), (sx+sw, sy+sh), (0, 0, 255), 2)

    cv2.imshow("Smile Detection", bgr)
    key = cv2.waitKey(1) & 0xFF
    if key == ord('q'):
        break

cv2.destroyAllWindows()
picam2.stop()