opencv中自帶了haar人臉特征分類器借宵。

1.1 image如果為彩色圖：image.shape[0] [1] [2] (水平壤玫、垂直像素、通道數(shù))

1.2將圖片變?yōu)榛叶葓D

1.3它可以檢測(cè)出圖片中所有的人臉楚里，并將人臉用vector保存各個(gè)人臉的坐標(biāo)猎贴、大小（用矩形表示）

1.4調(diào)整scaleFactor參數(shù)的大小达址，可以增加識(shí)別的靈敏度沉唠，推薦1.1

1.5CASC_PATH = 你的haarcascade_frontalface_alt2.xml文件地址

def format_image(image):
    # image如果為彩色圖：image.shape[0][1][2](水平苛败、垂直像素、通道數(shù))
    if len(image.shape) > 2 and image.shape[2] == 3:
        # 將圖片變?yōu)榛叶葓D
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        # 它可以檢測(cè)出圖片中所有的人臉嘀韧，并將人臉用vector保存各個(gè)人臉的坐標(biāo)乳蛾、大斜杀摇（用矩形表示）
        # 調(diào)整scaleFactor參數(shù)的大小，可以增加識(shí)別的靈敏度因惭，推薦1.1
        #CASC_PATH = 你的haarcascade_frontalface_alt2.xml文件地址
        CASC_PATH = 'E:/miniconda/envs/lstm/Lib/site-packages/cv2/data/haarcascade_frontalface_alt2.xml' #
        cascade_classifier = cv2.CascadeClassifier(CASC_PATH)
        faces = cascade_classifier.detectMultiScale(image, scaleFactor=1.1, minNeighbors=5)
    # 如果圖片中沒有檢測(cè)到人臉蹦魔，則返回None
    if not len(faces) > 0:
        return None, None
    # max_are_face包含了人臉的坐標(biāo)咳燕，大小
    max_are_face = faces[0]
    # 在所有人臉中選一張最大的臉
    for face in faces:
        if face[2] * face[3] > max_are_face[2] * max_are_face[3]:
            max_are_face = face

    # 這兩步可有可無
    face_coor = max_are_face
    image = image[face_coor[1]:(face_coor[1] + face_coor[2]), face_coor[0]:(face_coor[0] + face_coor[3])]
    # 調(diào)整圖片大小招盲，變?yōu)?8*48
    try:
        image = cv2.resize(image, (48, 48), interpolation=cv2.INTER_CUBIC)
    except Exception:
        print("problem during resize")
        return None, None

    return image, face_coor

對(duì)接到視頻中，使用cv2.VideoCapture(0)

capture = cv2.VideoCapture(0)
    fps = 0.0
    while (True):
        t1 = time.time()
        # 讀取某一幀
        ref, frame = capture.read()
        # 格式轉(zhuǎn)變咆繁，BGRtoRGB
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        # 轉(zhuǎn)變成Image
        # 進(jìn)行檢測(cè)
        (p_image, face_coor) = format_image(frame)
        if face_coor is not None:
            # 獲取人臉的坐標(biāo),并用矩形框出
            [x, y, w, h] = face_coor
            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 1)
        # time.sleep(0.2)
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
        # fps = int(round(capture.get(cv2.CAP_PROP_FPS)))
        fps = (fps + (1. / (time.time() - t1)))/2
        frame = cv2.putText(frame, "fps= %.2f" % (fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.imshow("video", frame)
        c = cv2.waitKey(1) & 0xff
        if c == 27:
            capture.release()
            break
    capture.release()
    cv2.destroyAllWindows()

整體代碼：

import time
import cv2

def format_image(image):
    # image如果為彩色圖：image.shape[0][1][2](水平玩般、垂直像素坏为、通道數(shù))
    if len(image.shape) > 2 and image.shape[2] == 3:
        # 將圖片變?yōu)榛叶葓D
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        # 它可以檢測(cè)出圖片中所有的人臉久脯，并將人臉用vector保存各個(gè)人臉的坐標(biāo)镰吆、大小（用矩形表示）
        # 調(diào)整scaleFactor參數(shù)的大小摧找，可以增加識(shí)別的靈敏度牢硅，推薦1.1
        #CASC_PATH = 你的haarcascade_frontalface_alt2.xml文件地址
        CASC_PATH = 'E:/miniconda/envs/lstm/Lib/site-packages/cv2/data/haarcascade_frontalface_alt2.xml' #
        cascade_classifier = cv2.CascadeClassifier(CASC_PATH)
        faces = cascade_classifier.detectMultiScale(image, scaleFactor=1.1, minNeighbors=5)
    # 如果圖片中沒有檢測(cè)到人臉减余，則返回None
    if not len(faces) > 0:
        return None, None
    # max_are_face包含了人臉的坐標(biāo)，大小
    max_are_face = faces[0]
    # 在所有人臉中選一張最大的臉
    for face in faces:
        if face[2] * face[3] > max_are_face[2] * max_are_face[3]:
            max_are_face = face

    # 這兩步可有可無
    face_coor = max_are_face
    image = image[face_coor[1]:(face_coor[1] + face_coor[2]), face_coor[0]:(face_coor[0] + face_coor[3])]
    # 調(diào)整圖片大小堡牡，變?yōu)?8*48
    try:
        image = cv2.resize(image, (48, 48), interpolation=cv2.INTER_CUBIC)
    except Exception:
        print("problem during resize")
        return None, None

    return image, face_coor


if __name__ == "__main__":
    capture = cv2.VideoCapture(0)
    fps = 0.0
    while (True):
        t1 = time.time()
        # 讀取某一幀
        ref, frame = capture.read()
        # 格式轉(zhuǎn)變晤柄，BGRtoRGB
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        # 轉(zhuǎn)變成Image
        # 進(jìn)行檢測(cè)
        (p_image, face_coor) = format_image(frame)
        if face_coor is not None:
            # 獲取人臉的坐標(biāo),并用矩形框出
            [x, y, w, h] = face_coor
            cv2.rectangle(frame, (x, y), (x + w, y + h), (255, 0, 0), 1)
        # time.sleep(0.2)
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
        # fps = int(round(capture.get(cv2.CAP_PROP_FPS)))
        fps = (fps + (1. / (time.time() - t1)))/2
        frame = cv2.putText(frame, "fps= %.2f" % (fps), (0, 40), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2)
        cv2.imshow("video", frame)
        c = cv2.waitKey(1) & 0xff
        if c == 27:
            capture.release()
            break
    capture.release()
    cv2.destroyAllWindows()