超詳細(xì)注釋之OpenCV Haar級(jí)聯(lián)檢測(cè)器進(jìn)行面部檢測(cè)

detector = cv2.CascadeClassifier(path)

 results = detector.detectMultiScale(
		   gray, scaleFactor=1.05, minNeighbors=5,
		   minSize=(30, 30), flags=cv2.CASCADE_SCALE_IMAGE)

# USAGE
# python opencv_haar_cascades_images.py --cascades cascades --image ml.jpg

# 導(dǎo)入必要的包
import argparse
import os  # 不同系統(tǒng)路徑分隔符

import cv2  # opencv綁定
import imutils

# 構(gòu)建命令行參數(shù)及解析
# --cascades 級(jí)聯(lián)檢測(cè)器的路徑
ap = argparse.ArgumentParser()
ap.add_argument("-c", "--cascades", type=str, default="cascades",
                help="path to input directory containing haar cascades")
ap.add_argument("-i", "--image", type=str, default="ml2.jpg",
                help="path to input image")
args = vars(ap.parse_args())

# 初始化字典，并保存Haar級(jí)聯(lián)檢測(cè)器名稱及文件路徑
detectorPaths = {
    "face": "haarcascade_frontalface_default.xml",
    "eyes": "haarcascade_eye.xml",
    "smile": "haarcascade_smile.xml",
}

# 初始化字典以保存多個(gè)Haar級(jí)聯(lián)檢測(cè)器
print("[INFO] loading haar cascades...")
detectors = {}

# 遍歷檢測(cè)器路徑
for (name, path) in detectorPaths.items():
    # 加載Haar級(jí)聯(lián)檢測(cè)器并保存到map
    path = os.path.sep.join([args["cascades"], path])
    detectors[name] = cv2.CascadeClassifier(path)

# 從磁盤讀取圖像，縮放，并轉(zhuǎn)換灰度圖
print(args['image'])
image = cv2.imread(args["image"])
image = imutils.resize(image, width=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 使用合適的Haar檢測(cè)器執(zhí)行面部檢測(cè)
faceRects = detectors["face"].detectMultiScale(
    gray, scaleFactor=1.05, minNeighbors=5, minSize=(30, 30),
    flags=cv2.CASCADE_SCALE_IMAGE)

# 遍歷檢測(cè)到的所有面部
for (fX, fY, fW, fH) in faceRects:
    # 提取面部ROI
    faceROI = gray[fY:fY + fH, fX:fX + fW]

    # 在面部ROI應(yīng)用左右眼級(jí)聯(lián)檢測(cè)器
    eyeRects = detectors["eyes"].detectMultiScale(
        faceROI, scaleFactor=1.1, minNeighbors=10,
        minSize=(15, 15), flags=cv2.CASCADE_SCALE_IMAGE)

    # 在面部ROI應(yīng)用嘴部檢測(cè)
    smileRects = detectors["smile"].detectMultiScale(
        faceROI, scaleFactor=1.1, minNeighbors=10,
        minSize=(15, 15), flags=cv2.CASCADE_SCALE_IMAGE)

    # 遍歷眼睛邊界框
    for (eX, eY, eW, eH) in eyeRects:
        # 繪制眼睛邊界框（紅色）
        ptA = (fX + eX, fY + eY)
        ptB = (fX + eX + eW, fY + eY + eH)
        cv2.rectangle(image, ptA, ptB, (0, 0, 255), 2)

    # 遍歷嘴部邊界框
    for (sX, sY, sW, sH) in smileRects:
        # 繪制嘴邊界框（藍(lán)色）
        ptA = (fX + sX, fY + sY)
        ptB = (fX + sX + sW, fY + sY + sH)
        cv2.rectangle(image, ptA, ptB, (255, 0, 0), 2)

    # 繪制面部邊界框（綠色）
    cv2.rectangle(image, (fX, fY), (fX + fW, fY + fH),
                  (0, 255, 0), 2)

# 展示輸出幀
cv2.imshow("image", image)
cv2.waitKey(0)
# 清理工作
cv2.destroyAllWindows()

# USAGE
# python opencv_haar_cascades.py --cascades cascades

# 導(dǎo)入必要的包
import argparse
import os  # 不同系統(tǒng)路徑分隔符
import time  # sleep 2秒

import cv2  # opencv綁定
import imutils
from imutils.video import VideoStream  # 訪問(wèn)網(wǎng)絡(luò)攝像頭

# 構(gòu)建命令行參數(shù)及解析
# --cascades 級(jí)聯(lián)檢測(cè)器的路徑
ap = argparse.ArgumentParser()
ap.add_argument("-c", "--cascades", type=str, default="cascades",
                help="path to input directory containing haar cascades")
args = vars(ap.parse_args())

# 初始化字典，并保存Haar級(jí)聯(lián)檢測(cè)器名稱及文件路徑
detectorPaths = {
    "face": "haarcascade_frontalface_default.xml",
    "eyes": "haarcascade_eye.xml",
    "smile": "haarcascade_smile.xml",
}

# 初始化字典以保存多個(gè)Haar級(jí)聯(lián)檢測(cè)器
print("[INFO] loading haar cascades...")
detectors = {}

# 遍歷檢測(cè)器路徑
for (name, path) in detectorPaths.items():
    # 加載Haar級(jí)聯(lián)檢測(cè)器并保存到map
    path = os.path.sep.join([args["cascades"], path])
    detectors[name] = cv2.CascadeClassifier(path)

# 初始化視頻流，允許攝像頭預(yù)熱2s
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)

# 遍歷視頻流的每一幀
while True:
    # 獲取視頻流的每一幀，縮放，并轉(zhuǎn)換灰度圖
    frame = vs.read()
    frame = imutils.resize(frame, width=500)
    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)

    # 使用合適的Haar檢測(cè)器執(zhí)行面部檢測(cè)
    faceRects = detectors["face"].detectMultiScale(
        gray, scaleFactor=1.05, minNeighbors=5, minSize=(30, 30),
        flags=cv2.CASCADE_SCALE_IMAGE)

    # 遍歷檢測(cè)到的所有面部
    for (fX, fY, fW, fH) in faceRects:
        # 提取面部ROI
        faceROI = gray[fY:fY + fH, fX:fX + fW]

        # 在面部ROI應(yīng)用左右眼級(jí)聯(lián)檢測(cè)器
        eyeRects = detectors["eyes"].detectMultiScale(
            faceROI, scaleFactor=1.1, minNeighbors=10,
            minSize=(15, 15), flags=cv2.CASCADE_SCALE_IMAGE)

        # 在面部ROI應(yīng)用嘴部檢測(cè)
        smileRects = detectors["smile"].detectMultiScale(
            faceROI, scaleFactor=1.1, minNeighbors=10,
            minSize=(15, 15), flags=cv2.CASCADE_SCALE_IMAGE)

        # 遍歷眼睛邊界框
        for (eX, eY, eW, eH) in eyeRects:
            # 繪制眼睛邊界框（紅色）
            ptA = (fX + eX, fY + eY)
            ptB = (fX + eX + eW, fY + eY + eH)
            cv2.rectangle(frame, ptA, ptB, (0, 0, 255), 2)

        # 遍歷嘴部邊界框
        for (sX, sY, sW, sH) in smileRects:
            # 繪制嘴邊界框（藍(lán)色）
            ptA = (fX + sX, fY + sY)
            ptB = (fX + sX + sW, fY + sY + sH)
            cv2.rectangle(frame, ptA, ptB, (255, 0, 0), 2)

        # 繪制面部邊界框（綠色）
        cv2.rectangle(frame, (fX, fY), (fX + fW, fY + fH),
                      (0, 255, 0), 2)

    # 展示輸出幀
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1)  0xFF
    # 按下‘q'鍵，退出循環(huán)
    if key == ord("q"):
        break

# 清理工作
cv2.destroyAllWindows()
vs.stop()