超詳細註釋之OpenCV實現視頻實時人臉模糊和人臉馬賽克

# USAGE
# python blur_face.py --image examples/we.jpg --face face_detector
# python blur_face.py --image examples/we.jpg --face face_detector --method pixelated

# 使用OpenCV實現圖像中的人臉模糊
# 導入必要的包
import argparse
import os

import cv2
import imutils
import numpy as np
from pyimagesearch.face_blurring import anonymize_face_pixelate
from pyimagesearch.face_blurring import anonymize_face_simple

# 構建命令行參數及解析
# --image 輸入人臉圖像
# --face 人臉檢測模型的目錄
# --method 使用簡單高斯模糊、像素模糊
# --blocks 面部分塊數，默認20
# --confidence 面部檢測置信度，過濾弱檢測的值，默認50%
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True,
                help="path to input image")
ap.add_argument("-f", "--face", required=True,
                help="path to face detector model directory")
ap.add_argument("-m", "--method", type=str, default="simple",
                choices=["simple", "pixelated"],
                help="face blurring/anonymizing method")
ap.add_argument("-b", "--blocks", type=int, default=20,
                help="# of blocks for the pixelated blurring method")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
                help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

# 加載基於Caffe的人臉檢測模型
# 從磁盤加載序列化的面部檢測模型及標簽文件
print("[INFO] loading face detector model...")
prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
weightsPath = os.path.sep.join([args["face"],
                                "res10_300x300_ssd_iter_140000.caffemodel"])
net = cv2.dnn.readNet(prototxtPath, weightsPath)

# 從此盤加載輸入圖像，獲取圖像維度
image = cv2.imread(args["image"])
image = imutils.resize(image, width=600)
orig = image.copy()
(h, w) = image.shape[:2]

# 預處理圖像，構建圖像blob
blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),
                             (104.0, 177.0, 123.0))

# 傳遞blob到網絡，並獲取面部檢測結果
print("[INFO] computing face detections...")
net.setInput(blob)
detections = net.forward()

# 遍歷人臉檢測結果
for i in range(0, detections.shape[2]):
    # 提取檢測的置信度，即可能性
    confidence = detections[0, 0, i, 2]

    # 過濾弱檢測結果，確保均高於最小置信度
    if confidence > args["confidence"]:
        # 計算人臉的邊界框（x,y）
        box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
        (startX, startY, endX, endY) = box.astype("int")

        # 提取面部ROI
        face = image[startY:endY, startX:endX]

        # 檢查是使用簡單高斯模糊 還是 像素模糊方法
        if args["method"] == "simple":
            face = anonymize_face_simple(face, factor=3.0)
        # 否則應用像素匿名模糊方法
        else:
            face = anonymize_face_pixelate(face,
                                           blocks=args["blocks"])

        # 用模糊的匿名面部覆蓋圖像中的原始人臉ROI
        image[startY:endY, startX:endX] = face

# 原始圖像和匿名圖像並排顯示
output = np.hstack([orig, image])
cv2.imshow("Origin VS " + str(args['method']), output)
cv2.waitKey(0)

# USAGE
# python blur_face_video.py --face face_detector
# python blur_face_video.py --face face_detector --method pixelated

# 導入必要的包
import argparse
import os
import time

import cv2
import imutils
import numpy as np
from imutils.video import VideoStream
from pyimagesearch.face_blurring import anonymize_face_pixelate
from pyimagesearch.face_blurring import anonymize_face_simple

# 構建命令行參數及解析
# --face 人臉檢測模型的目錄
# --method 使用簡單高斯模糊、像素模糊
# --blocks 面部分塊數，默認20
# --confidence 面部檢測置信度，過濾弱檢測的值，默認50%
ap = argparse.ArgumentParser()
ap.add_argument("-f", "--face", required=True,
                help="path to face detector model directory")
ap.add_argument("-m", "--method", type=str, default="simple",
                choices=["simple", "pixelated"],
                help="face blurring/anonymizing method")
ap.add_argument("-b", "--blocks", type=int, default=20,
                help="# of blocks for the pixelated blurring method")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
                help="minimum probability to filter weak detections")
args = vars(ap.parse_args())

# 從磁盤加載訓練好的人臉檢測器Caffe模型
print("[INFO] loading face detector model...")
prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
weightsPath = os.path.sep.join([args["face"],
                                "res10_300x300_ssd_iter_140000.caffemodel"])
net = cv2.dnn.readNet(prototxtPath, weightsPath)

# 初始化視頻流，預熱傳感器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:
    # 從線程化的視頻流獲取一幀，保持寬高比的縮放寬度為400px
    frame = vs.read()
    frame = imutils.resize(frame, width=400)

    # 獲取幀的維度，預處理幀（構建blob）
    (h, w) = frame.shape[:2]
    blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
                                 (104.0, 177.0, 123.0))

    # 傳遞blob到網絡並獲取面部檢測結果
    net.setInput(blob)
    detections = net.forward()

    # 遍歷人臉檢測結果
    for i in range(0, detections.shape[2]):
        # 提取檢測的置信度，即可能性
        confidence = detections[0, 0, i, 2]

        # 過濾弱檢測結果，確保均高於最小置信度
        if confidence > args["confidence"]:
            # 計算人臉的邊界框（x,y）
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # 提取面部ROI
            face = frame[startY:endY, startX:endX]

            # 檢查是使用簡單高斯模糊 還是 像素模糊方法
            if args["method"] == "simple":
                face = anonymize_face_simple(face, factor=3.0)
            # 否則應用像素匿名模糊方法
            else:
                face = anonymize_face_pixelate(face,
                                               blocks=args["blocks"])

            # 用模糊的匿名面部ROI覆蓋圖像中的原始人臉ROI
            frame[startY:endY, startX:endX] = face

    # 展示輸出幀
    cv2.imshow("Frame", frame)
    key = cv2.waitKey(1) & 0xFF

    # 按下‘q'鍵，退出循環
    if key == ord("q"):
        break

# 做一些清理工作
# 關閉所有窗口，釋放視頻流指針
cv2.destroyAllWindows()
vs.stop()