如何使用opencv(python)模糊面孔? [英] How to use opencv (python) to blur faces?

问题描述

我想知道是否有一种方法可以模糊由ar级联人脸分类器自动识别的人脸.

I would like to know is there is a way to blur the faces that have been automatically identify by the haarcascade face classifier.

使用下面的代码，我能够检测到面孔，在该面孔周围裁剪图像或在其上绘制一个矩形.

using the code below, I'm able to detect the faces, crop the image around this face or draw a rectangle on it.

image = cv2.imread(imagepath)

# Specify the trained cascade classifier
face_cascade_name = "./haarcascade_frontalface_alt.xml"

# Create a cascade classifier
face_cascade = cv2.CascadeClassifier()

# Load the specified classifier
face_cascade.load(face_cascade_name)

#Preprocess the image
grayimg = cv2.cvtColor(image, cv2.cv.CV_BGR2GRAY)
grayimg = cv2.equalizeHist(grayimg)

#Run the classifiers
faces = face_cascade.detectMultiScale(grayimg, 1.1, 2, 0|cv2.cv.CV_HAAR_SCALE_IMAGE, (30, 30))

print "Faces detected"

if len(faces) != 0:            # If there are faces in the images
    for f in faces:         # For each face in the image

        # Get the origin co-ordinates and the length and width till where the face extends
        x, y, w, h = [ v for v in f ]

        # Draw rectangles around all the faces
        cv2.rectangle(image, (x,y), (x+w,y+h), (255,255,255))
        sub_face = image[y:y+h, x:x+w]
        for i in xrange(1,31,2):
            cv2.blur(sub_face, (i,i))
        face_file_name = "./face_" + str(y) + ".jpg"
        cv2.imwrite(face_file_name, sub_face)

但是我想模糊人们的面孔，以使他们无法被认识.

But I would like to blur the face of the people so they can't be recognized.

您对此有想法吗?

感谢您的帮助

Arnaud

推荐答案

我终于成功完成了我想做的事情. 为此，请按照Hammer的建议使用高斯模糊. 代码是:

I finally succeeded to do what I want. To do that apply a gaussianblur as Hammer has suggested. The code is :

image = cv2.imread(imagepath)
result_image = image.copy()

# Specify the trained cascade classifier
face_cascade_name = "./haarcascade_frontalface_alt.xml"

# Create a cascade classifier
face_cascade = cv2.CascadeClassifier()

# Load the specified classifier
face_cascade.load(face_cascade_name)

#Preprocess the image
grayimg = cv2.cvtColor(image, cv2.cv.CV_BGR2GRAY)
grayimg = cv2.equalizeHist(grayimg)

#Run the classifiers
faces = face_cascade.detectMultiScale(grayimg, 1.1, 2, 0|cv2.cv.CV_HAAR_SCALE_IMAGE, (30, 30))

print "Faces detected"

if len(faces) != 0:         # If there are faces in the images
    for f in faces:         # For each face in the image

        # Get the origin co-ordinates and the length and width till where the face extends
        x, y, w, h = [ v for v in f ]

        # get the rectangle img around all the faces
        cv2.rectangle(image, (x,y), (x+w,y+h), (255,255,0), 5)
        sub_face = image[y:y+h, x:x+w]
        # apply a gaussian blur on this new recangle image
        sub_face = cv2.GaussianBlur(sub_face,(23, 23), 30)
        # merge this blurry rectangle to our final image
        result_image[y:y+sub_face.shape[0], x:x+sub_face.shape[1]] = sub_face
        face_file_name = "./face_" + str(y) + ".jpg"
        cv2.imwrite(face_file_name, sub_face)

# cv2.imshow("Detected face", result_image)
cv2.imwrite("./result.png", result_image)

Arnaud

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