如何使用opencv(python)模糊面孔？

Question

我想知道是否有一种模糊haarcascade face分类器自动识别的面部的方法.

使用下面的代码,我能够检测到面部,裁剪这个面部周围的图像或在其上绘制一个矩形.

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)

但我想模糊人们的面貌,使他们无法被认出来.

你对如何做到了吗？

谢谢你的帮助

阿尔诺

Answer 1

我终于成功地做了我想做的事.要做到这一点,应用Hammer建议的高斯布鲁尔.代码是:

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)

阿尔诺

Answer 2

代码的整个结尾可以替换为：

img[startX:endX, startY:endY] = cv2.blur(img[startX:endX, startY:endY], (23, 23))

代替 ：

    # 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

特别是因为您不要求使用圆形面罩，所以（对我而言）更容易阅读。

PS：很抱歉没有发表评论，没有足够的声誉来做到这一点。即使帖子已有 5 年的历史，我想这可能是值得的，因为在这个特定问题中找到了它..