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保存下巴仅作为图像与dlib面部标志检测，其余是透明的

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我已经有一个面部标志检测器，可以使用opencv和dlib保存图像，代码如下：

# import the necessary packages
from imutils import face_utils
import numpy as np
import argparse
import imutils
import dlib
import cv2


# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--shape-predictor", required=True, help="Path to facial landmark predictor")
ap.add_argument("-i", "--image", required=True, help="Path to input image")
args = vars(ap.parse_args())

# initialize dlib's face detector (HOG-based) and then create the facial landmark predictor
detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor(args["shape_predictor"])

# load the input image, resize it, and convert it to grayscale
image = cv2.imread(args["image"])
image = imutils.resize(image, width=500)
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# detect faces in the grayscale image
rects = detector(gray, 1)

for (i, rect) in enumerate(rects):
    # determine the facial landmarks for the face region, then
    # convert the landmark (x, y)-coordinates to a NumPy array
    shape = predictor(gray, rect)
    shape = face_utils.shape_to_np(shape)

    # loop over the face parts individually
    print(face_utils.FACIAL_LANDMARKS_IDXS.items())
    for (name, (i, j)) in face_utils.FACIAL_LANDMARKS_IDXS.items():
        print(" i = ", i, " j = ", j)
        # clone the original image so we can draw on it, then 
        # display the name of the face part of the image
        clone = image.copy()
        cv2.putText(clone, name, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)

        # loop over the subset of facial landmarks, drawing the 
        # specific face part using a red dots
        for (x, y) in shape[i:j]:
            cv2.circle(clone, (x, y), 1, (0, 0, 255), -1)

        # extract the ROI of the face region as a separate image
        (x, y, w, h) = cv2.boundingRect(np.array([shape[i:j]]))
        roi = image[y:y+h,x:x+w]
        roi = imutils.resize(roi, width=250, inter=cv2.INTER_CUBIC)

        # show the particular face part
        cv2.imshow("ROI", roi)
        cv2.imwrite(name + '.jpg', roi)
        cv2.imshow("Image", clone)
        cv2.waitKey(0)

    # visualize all facial landmarks with a transparent overly
    output = face_utils.visualize_facial_landmarks(image, shape)
    cv2.waitKey(0)

我有阿诺德的脸，我用 opencv imwrite 保存了他的一部分脸 .

Arnold

我想要达到的目的只是获得下颌的形象，我不想挽救颈部 . 见下图：

Arnold_2

有没有人知道如何除去 dlib 检测到的下巴以外的其他部分 .

这样的东西是预期的输出：
Arnold_3

python python-3.x opencv dlib

1 回答

  • 7

    Original image

    The generated mask

    Transparent version

    目前还不是很清楚你要掩盖多少原始图像 . 假设您正在使用 shape_predictor_68_face_landmarks.dat ，DLib的标记0到16定义了下颚线，因此您可以制作一个扩展这些下划线的遮罩以覆盖框架的下半部分 .

    请原谅我粗略的蟒蛇技能，但该代码将掩盖在下颚线下方，并将图像切割到感兴趣的区域以匹配您问题中的预期输出 .

    Cropped masked image

    # import the necessary packages
from imutils import face_utils
import numpy as np
import imutils
import dlib
import cv2
import os

detector = dlib.get_frontal_face_detector()
predictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')

# load image
img = cv2.imread('thegovernator.png')
h, w, ch = img.shape
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# add an alpha channel to image
b,g,r = cv2.split(img);
a = np.ones((h,w,1), np.uint8) * 255
img = cv2.merge((b, g, r, a))
# detect face
rects = detector(gray,1)
roi = rects[0] # region of interest
shape = predictor(gray, roi)
shape = face_utils.shape_to_np(shape)
# extract jawline
jawline = shape[0:17]
top = min(jawline[:,1])
bottom = max(jawline[:,1])
# extend contour for masking
jawline = np.append(jawline, [ w-1, jawline[-1][1] ]).reshape(-1, 2)
jawline = np.append(jawline, [ w-1, h-1 ]).reshape(-1, 2)
jawline = np.append(jawline, [ 0, h-1 ]).reshape(-1, 2)
jawline = np.append(jawline, [ 0, jawline[0][1] ]).reshape(-1, 2)
contours = [ jawline ]
# generate mask
mask = np.ones((h,w,1), np.uint8) * 255 # times 255 to make mask 'showable'
cv2.drawContours(mask, contours, -1, 0, -1) # remove below jawline
# apply to image
result = cv2.bitwise_and(img, img, mask = mask)
result = result[top:bottom, roi.left():roi.left()+roi.width()] # crop ROI
cv2.imwrite('result.png', result); 
cv2.imshow('masked image', result)
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