我已经有一个面部标志检测器，并且已经可以使用 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，我使用 opencv imwrite 保存了他的部分脸。

我想要实现的是只获取下巴的图像，我不想保存颈部。见下图:

除了 dlib 检测到的下颚外，有没有人知道如何移除其他部分。

预期的输出是这样的:

最佳答案

+ =

目前还不清楚您要屏蔽掉多少原始图像。假设您使用的是 shape_predictor_68_face_landmarks.dat，DLib 的地标 0 到 16 定义了下巴线，因此您可以制作一个 mask ，将它们延伸到框架的下半部分。

请原谅我粗糙的 python 技能，但该代码将掩盖下巴线以下并将图像剪切到感兴趣的区域以匹配您问题中的预期输出。

# 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)

关于python - 使用 dlib 面部标志检测仅将下巴保存为图像，其余部分为透明，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/49163935/