【Mind+Python】基于深度学习和计算机视觉技术进行人脸模糊处理及匿名化 简单

#导入NumPy和OpenCV
import numpy as np
import cv2

#定义anonymize_face_simple函数
def anonymize_face_simple(image, factor=3.0):

    #在输入图像的空间维度上确定基于模糊核的大小
	(h, w) = image.shape[:2]
	kW = int(w / factor)
	kH = int(h / factor)
        
    #确保内核的宽度是奇数
	if kW % 2 == 0:
		kW -= 1

    #确保内核宽度是奇数
	if kH % 2 == 0:
		kH -= 1

	return cv2.GaussianBlur(image, (kW, kH), 0)

#定义anonymize_face_pixelate函数
def anonymize_face_pixelate(image, blocks=3):

    #将输入图像分成N*N个块
	(h, w) = image.shape[:2]
	xSteps = np.linspace(0, w, blocks + 1, dtype="int")
	ySteps = np.linspace(0, h, blocks + 1, dtype="int")

    #在x和y方向循环遍历块
	for i in range(1, len(ySteps)):
		for j in range(1, len(xSteps)):

            #对于当前块计算开始和结束的坐标(x, y) 
			startX = xSteps[j - 1]
			startY = ySteps[i - 1]
			endX = xSteps[j]
			endY = ySteps[i]

            #使用NumPy数组切片提取ROI，计算ROI的平均值，然后用原始图像中ROI上的平均RGB值绘制一个矩形
			roi = image[startY:endY, startX:endX]
			(B, G, R) = [int(x) for x in cv2.mean(roi)[:3]]
			cv2.rectangle(image, (startX, startY), (endX, endY),
				(B, G, R), -1)

    #返回像素化的模糊图像
	return image

#导入需要用到的软件包
from zy.face_blurring import anonymize_face_pixelate
from zy.face_blurring import anonymize_face_simple
import numpy as np
import argparse
import cv2
import os

#构造参数parse并解析参数
ap = argparse.ArgumentParser()
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())

#从电脑磁盘中加载人脸检测器模型
print("[INFO] loading face detector model...")
prototxtPath = os.path.sep.join([r'D:\opencv-face-blurring\face_detector', "deploy.prototxt"])
weightsPath = os.path.sep.join([r'D:\opencv-face-blurring\face_detector',
	"res10_300x300_ssd_iter_140000.caffemodel"])
net = cv2.dnn.readNet(prototxtPath, weightsPath)

#从电脑磁盘中读取并加载输入图像，进行复制，然后提取图像在空间维度的尺寸
image = cv2.imread(r'D:\opencv-face-blurring\examples\zy.png')
orig = image.copy()
(h, w) = image.shape[:2]

#构建blob
blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300),
	(104.0, 177.0, 123.0))

#通过网络传递blob并获得人脸检测
print("[INFO] computing face detections...")
net.setInput(blob)
detections = net.forward()

#循环检测
for i in range(0, detections.shape[2]):

    #提取与检测相关的置信度（即概率）
	confidence = detections[0, 0, i, 2]
	if confidence > args["confidence"]:
		box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
		(startX, startY, endX, endY) = box.astype("int")

        #提取人脸ROI
		face = image[startY:endY, startX:endX]

        #判断是否应用“simple”面部模糊处理方法
		if args["method"] == "simple":
			face = anonymize_face_simple(face, factor=3.0)

        #否则，必须应用“blocks”面部模糊处理方法
		else:
			face = anonymize_face_pixelate(face,
				blocks=args["blocks"])

        #将模糊的人脸图像存储在输出图像中
		image[startY:endY, startX:endX] = face

#并排显示原始图像和带有模糊处理的输出图像
output = np.hstack([orig, image])
cv2.imshow("Output", output)
cv2.waitKey(0)

#导入需要用到的软件包
from zy.face_blurring import anonymize_face_pixelate
from zy.face_blurring import anonymize_face_simple
from imutils.video import VideoStream
import numpy as np
import argparse
import imutils
import time
import cv2
import os

#构造参数parse并解析参数
ap = argparse.ArgumentParser()
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())

#从电脑磁盘加载人脸检测器模型
print("[INFO] loading face detector model...")
prototxtPath = os.path.sep.join([r'D:\opencv-face-blurring\face_detector', "deploy.prototxt"])
weightsPath = os.path.sep.join([r'D:\opencv-face-blurring\face_detector',
	"res10_300x300_ssd_iter_140000.caffemodel"])
net = cv2.dnn.readNet(prototxtPath, weightsPath)

#初始化视频流
print("[INFO] starting video stream...")
vs = VideoStream(src=0).start()
time.sleep(2.0)

#循环视频流中的帧
while True:

    #从线程视频流中抓取帧并调整其大小，最大宽度为400像素
	frame = vs.read()
	frame = imutils.resize(frame, width=400)

    #获取框架的尺寸，然后构造一个blob
	(h, w) = frame.shape[:2]
	blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300),
		(104.0, 177.0, 123.0))

    #通过网络传递blob并获得人脸检测
	net.setInput(blob)
	detections = net.forward()

    #循环检测
	for i in range(0, detections.shape[2]):

        #提取与检测相关的置信度（即概率）
		confidence = detections[0, 0, i, 2]

        #通过确保置信度大于最小置信度来过滤掉弱检测
		if confidence > args["confidence"]:

            #计算对象边界框的坐标(x, y) 
			box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
			(startX, startY, endX, endY) = box.astype("int")

            #提取人脸ROI
			face = frame[startY:endY, startX:endX]

            #检查是否应用了“simple”的面部模糊处理方法
			if args["method"] == "simple":
				face = anonymize_face_simple(face, factor=3.0)

            #否则，必须应用“blocks”面部模糊处理方法
			else:
				face = anonymize_face_pixelate(face,
					blocks=args["blocks"])

            #将模糊的人脸图像存储在输出图像中
			frame[startY:endY, startX:endX] = face

    #显示输出帧
	cv2.imshow("Frame", frame)
	key = cv2.waitKey(1) & 0xFF

    #如果按下‘q’键，则中断循环
	if key == ord("q"):
		break

#做一些清理工作
cv2.destroyAllWindows()
vs.stop()