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

Save jaw only as image with dlib facial landmark detection and the rest to be transparent

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,我用 opencv imwrite 保存了他的一部分脸。

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,有谁知道我该如何移除其他部分?

预期的输出是这样的:

+ =

不太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遮罩,将它们延伸到框架的下半部分。

请原谅我粗鲁的 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)