{"id":73994,"date":"2026-02-08T22:10:03","date_gmt":"2026-02-08T14:10:03","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/73994.html"},"modified":"2026-02-08T22:10:03","modified_gmt":"2026-02-08T14:10:03","slug":"%e5%9f%ba%e4%ba%8e-opencv-%e4%b8%8e-dlib-%e7%9a%84%e4%ba%ba%e8%84%b8%e6%9b%bf%e6%8d%a2","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/73994.html","title":{"rendered":"\u57fa\u4e8e OpenCV \u4e0e Dlib \u7684\u4eba\u8138\u66ff\u6362"},"content":{"rendered":"

\u4e00\u3001\u6838\u5fc3\u539f\u7406<\/h3>\n

1.\u68c0\u6d4b\u4e24\u5f20\u4eba\u8138\u56fe\u7247\u7684 68 \u4e2a\u7279\u5f81\u5173\u952e\u70b9；<\/p>\n

2.\u6839\u636e\u5173\u952e\u70b9\u8ba1\u7b97\u4eba\u8138\u95f4\u7684\u4eff\u5c04\u53d8\u6362\u77e9\u9635，\u5b9e\u73b0\u4eba\u8138\u4f4d\u7f6e \/ \u89d2\u5ea6\u7684\u5bf9\u9f50；<\/p>\n

3.\u751f\u6210\u4eba\u8138\u63a9\u819c，\u533a\u5206\u4eba\u8138\u533a\u57df\u4e0e\u80cc\u666f\u533a\u57df；<\/p>\n

4.\u5bf9\u66ff\u6362\u4eba\u8138\u8fdb\u884c\u989c\u8272\u5f52\u4e00\u5316，\u5339\u914d\u76ee\u6807\u4eba\u8138\u7684\u8272\u5f69\u98ce\u683c；<\/p>\n

5.\u878d\u5408\u63a9\u819c\u533a\u57df，\u5b8c\u6210\u6700\u7ec8\u7684\u6362\u8138\u6548\u679c\u3002<\/p>\n

\u4e8c\u3001\u73af\u5883\u51c6\u5907<\/h3>\n

1.\u4e0b\u8f7d\u4eba\u8138\u5173\u952e\u70b9\u6a21\u578b<\/p>\n

\u9700\u4e0b\u8f7d Dlib \u5b98\u65b9\u7684 68 \u4e2a\u4eba\u8138\u5173\u952e\u70b9\u9884\u6d4b\u6a21\u578b：shape_predictor_68_face_landmarks.dat<\/p>\n

\u4e09\u3001\u4ee3\u7801\u89e3\u6790<\/h3>\n

1. \u4eba\u8138\u5173\u952e\u70b9\u5206\u533a<\/h4>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u4eba\u8138\u7684 68 \u4e2a\u5173\u952e\u70b9\u53ef\u5206\u4e3a\u4e0d\u540c\u533a\u57df（\u5982\u4e0b\u5df4\u3001\u7709\u6bdb\u3001\u773c\u775b\u3001\u9f3b\u5b50\u3001\u5634\u5df4\u7b49），\u6211\u4eec\u53ea\u9700\u5173\u6ce8\u6838\u5fc3\u9762\u90e8\u533a\u57df（\u6392\u9664\u4e0b\u5df4），\u7528\u4e8e\u540e\u7eed\u63a9\u819c\u751f\u6210\u548c\u53d8\u6362\u8ba1\u7b97<\/p>\n

import cv2
\nimport dlib
\nimport numpy as np<\/p>\n

# \u4eba\u813868\u5173\u952e\u70b9\u5206\u533a\u5b9a\u4e49
\nJAW_POINTS = list(range(0,17)) # \u4e0b\u5df4\u5173\u952e\u70b9
\nRIGHT_BROW_POINTS = list(range(17,22)) # \u53f3\u7709\u6bdb
\nLEFT_BROW_POINTS = list(range(22,27)) # \u5de6\u7709\u6bdb
\nNOSE_POINTS = list(range(27,35)) # \u9f3b\u5b50
\nRIGHT_EYE_POINTS = list(range(36,42)) # \u53f3\u773c
\nLEFT_EYE_POINTS = list(range(42,48)) # \u5de6\u773c
\nMOUTH_POINTS = list(range(48,61)) # \u5634\u5df4<\/p>\n

# \u6838\u5fc3\u9762\u90e8\u5173\u952e\u70b9（\u6392\u9664\u4e0b\u5df4，\u4ec5\u4fdd\u7559\u4e94\u5b98\u533a\u57df）
\nFACE_POINTS = list(range(17,68))
\n# \u7ec4\u5408\u5173\u952e\u533a\u57df，\u7528\u4e8e\u540e\u7eed\u53d8\u6362\u8ba1\u7b97
\nPOINTS = [LEFT_BROW_POINTS+RIGHT_EYE_POINTS+LEFT_EYE_POINTS +RIGHT_BROW_POINTS + NOSE_POINTS + MOUTH_POINTS]
\nPOINTStuple = tuple(POINTS) # \u8f6c\u6362\u4e3a\u5143\u7ec4，\u65b9\u4fbf\u540e\u7eed\u7d22\u5f15<\/p>\n

2. \u51fd\u6570 ：\u751f\u6210\u4eba\u8138\u63a9\u819c<\/h4>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u63a9\u819c（Mask）\u7684\u4f5c\u7528\u662f\u6807\u8bb0\u51fa\u56fe\u7247\u4e2d\u7684\u4eba\u8138\u533a\u57df，\u540e\u7eed\u4ec5\u5bf9\u8be5\u533a\u57df\u8fdb\u884c\u66ff\u6362\u64cd\u4f5c，\u907f\u514d\u5f71\u54cd\u80cc\u666f\u3002\u901a\u8fc7\u5173\u952e\u70b9\u7684\u51f8\u5305（convexHull）\u751f\u6210\u4eba\u8138\u8f6e\u5ed3，\u518d\u586b\u5145\u5e76\u9ad8\u65af\u6a21\u7cca\u4f7f\u8fb9\u7f18\u66f4\u81ea\u7136：<\/p>\n

def getFaceMask(im, keyPoints):
\n """
\n \u6839\u636e\u4eba\u8138\u5173\u952e\u70b9\u751f\u6210\u4eba\u8138\u63a9\u819c
\n :param im: \u8f93\u5165\u56fe\u50cf
\n :param keyPoints: \u4eba\u813868\u5173\u952e\u70b9\u77e9\u9635
\n :return: \u5f52\u4e00\u5316\u4e14\u9ad8\u65af\u6a21\u7cca\u540e\u7684\u4eba\u8138\u63a9\u819c（0-1\u4e4b\u95f4）
\n """
\n # \u521b\u5efa\u4e0e\u539f\u56fe\u540c\u5c3a\u5bf8\u7684\u51680\u77e9\u9635（\u5355\u901a\u9053）
\n im_mask = np.zeros(im.shape[:2], dtype=np.float64)
\n for p in POINTS:
\n # \u6839\u636e\u5173\u952e\u70b9\u751f\u6210\u51f8\u5305（\u4eba\u8138\u8f6e\u5ed3）
\n hull = cv2.convexHull(keyPoints[p])
\n # \u586b\u5145\u51f8\u5305\u533a\u57df\u4e3a1（\u6807\u8bb0\u4eba\u8138\u533a\u57df）
\n cv2.fillConvexPoly(im_mask, hull, color=1)<\/p>\n

# \u5c06\u5355\u901a\u9053\u63a9\u819c\u8f6c\u6362\u4e3a3\u901a\u9053（\u5339\u914dRGB\u56fe\u50cf）
\n im_mask = np.array([im_mask, im_mask, im_mask]).transpose((1,2,0))
\n # \u9ad8\u65af\u6a21\u7cca：45×45\u6838\u4f7f\u63a9\u819c\u8fb9\u7f18\u8fc7\u6e21\u66f4\u81ea\u7136，\u907f\u514d\u66ff\u6362\u540e\u8fb9\u7f18\u751f\u786c
\n im_mask = cv2.GaussianBlur(im_mask, (45,45), 0)
\n return im_mask<\/p>\n

3. \u51fd\u6570 ：\u8ba1\u7b97\u4eff\u5c04\u53d8\u6362\u77e9\u9635<\/h4>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u8981\u5c06 B \u4eba\u8138\u5bf9\u9f50\u5230 A \u4eba\u8138\u7684\u4f4d\u7f6e \/ \u89d2\u5ea6，\u9700\u8ba1\u7b97\u4e24\u8005\u7684\u4eff\u5c04\u53d8\u6362\u77e9\u9635 M\u3002\u6838\u5fc3\u6b65\u9aa4\u662f\u5f52\u4e00\u5316\u5173\u952e\u70b9 + \u5947\u5f02\u503c\u5206\u89e3（SVD） \u6c42\u89e3\u65cb\u8f6c\u77e9\u9635，\u6700\u7ec8\u5f97\u5230\u5b8c\u6574\u7684\u4eff\u5c04\u53d8\u6362\u77e9\u9635<\/p>\n

def getM(points1, points2):
\n """
\n \u8ba1\u7b97\u4ecepoints1\u5230points2\u7684\u4eff\u5c04\u53d8\u6362\u77e9\u9635
\n :param points1: \u76ee\u6807\u4eba\u8138\u5173\u952e\u70b9（A\u56fe）
\n :param points2: \u6e90\u4eba\u8138\u5173\u952e\u70b9（B\u56fe）
\n :return: \u4eff\u5c04\u53d8\u6362\u77e9\u9635M
\n """
\n # \u8f6c\u6362\u4e3a\u6d6e\u70b9\u578b，\u907f\u514d\u6574\u6570\u8fd0\u7b97\u8bef\u5dee
\n points1 = points1.astype(np.float64)
\n points2 = points2.astype(np.float64)<\/p>\n

# \u8ba1\u7b97\u5173\u952e\u70b9\u7684\u5747\u503c（\u4e2d\u5fc3\u4f4d\u7f6e）
\n c1 = np.mean(points1, axis=0)
\n c2 = np.mean(points2, axis=0)<\/p>\n

# \u5f52\u4e00\u5316：\u51cf\u53bb\u5747\u503c（\u6d88\u9664\u5e73\u79fb\u5dee\u5f02）
\n points1 -= c1
\n points2 -= c2<\/p>\n

# \u8ba1\u7b97\u6807\u51c6\u5dee（\u6d88\u9664\u5c3a\u5ea6\u5dee\u5f02）
\n s1 = np.std(points1)
\n s2 = np.std(points2)<\/p>\n

# \u5f52\u4e00\u5316：\u9664\u4ee5\u6807\u51c6\u5dee
\n points1 \/= s1
\n points2 \/= s2<\/p>\n

# \u5947\u5f02\u503c\u5206\u89e3（SVD）\u6c42\u89e3\u65cb\u8f6c\u77e9\u9635
\n U, S, Vt = np.linalg.svd(points1.T @ points2)
\n R = (U @ Vt).T # \u65cb\u8f6c\u77e9\u9635<\/p>\n

# \u7ec4\u5408\u4eff\u5c04\u53d8\u6362\u77e9\u9635：\u5c3a\u5ea6*\u65cb\u8f6c + \u5e73\u79fb
\n M = np.hstack(((s2\/s1)*R, c2.T – (s2\/s1)*R @ c1.T))
\n return M<\/p>\n

4. \u51fd\u6570 ：\u4eba\u8138\u5173\u952e\u70b9\u68c0\u6d4b<\/h4>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u57fa\u4e8e Dlib \u7684\u68c0\u6d4b\u5668\u548c\u5173\u952e\u70b9\u9884\u6d4b\u6a21\u578b，\u63d0\u53d6\u56fe\u7247\u4e2d\u7684 68 \u4e2a\u4eba\u8138\u5173\u952e\u70b9：<\/p>\n

def getKeyPoints(im):
\n """
\n \u68c0\u6d4b\u56fe\u50cf\u4e2d\u7684\u4eba\u8138\u5173\u952e\u70b9
\n :param im: \u8f93\u5165\u56fe\u50cf
\n :return: 68\u4e2a\u5173\u952e\u70b9\u7684\u77e9\u9635（68×2）
\n """
\n # \u68c0\u6d4b\u4eba\u8138\u4f4d\u7f6e（\u77e9\u5f62\u6846）
\n detector = dlib.get_frontal_face_detector()
\n rects = detector(im, 1)
\n # \u9884\u6d4b\u5173\u952e\u70b9（\u53d6\u7b2c\u4e00\u4e2a\u68c0\u6d4b\u5230\u7684\u4eba\u8138）
\n predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
\n shape = predictor(im, rects[0])
\n # \u8f6c\u6362\u4e3a\u77e9\u9635\u683c\u5f0f（\u65b9\u4fbf\u540e\u7eed\u8ba1\u7b97）
\n key_points = np.matrix([[p.x, p.y] for p in shape.parts()])
\n return key_points<\/p>\n

5. \u51fd\u6570 ：\u989c\u8272\u5f52\u4e00\u5316<\/h4>\n

\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u76f4\u63a5\u66ff\u6362\u4eba\u8138\u4f1a\u51fa\u73b0\u8272\u5f69\u4e0d\u5339\u914d\u7684\u95ee\u9898，\u901a\u8fc7\u9ad8\u65af\u6ee4\u6ce2\u8ba1\u7b97\u8272\u5f69\u6743\u91cd，\u5c06 B \u4eba\u8138\u7684\u8272\u5f69\u98ce\u683c\u5339\u914d\u5230 A \u4eba\u8138：<\/p>\n

def normalcolor(a, b):
\n """
\n \u5c06B\u56fe\u7684\u8272\u5f69\u98ce\u683c\u5339\u914d\u5230A\u56fe
\n :param a: \u76ee\u6807\u56fe\u50cf（A\u56fe）
\n :param b: \u6e90\u56fe\u50cf（\u53d8\u6362\u540e\u7684B\u56fe）
\n :return: \u8272\u5f69\u5f52\u4e00\u5316\u540e\u7684B\u56fe
\n """
\n # \u5927\u6838\u9ad8\u65af\u6ee4\u6ce2：\u63d0\u53d6\u56fe\u50cf\u7684\u6574\u4f53\u8272\u5f69\u98ce\u683c（\u5ffd\u7565\u7ec6\u8282）
\n ksize = (111, 111)
\n aGauss = cv2.GaussianBlur(a, ksize, 0)
\n bGauss = cv2.GaussianBlur(b, ksize, 0)<\/p>\n

# \u8ba1\u7b97\u8272\u5f69\u6743\u91cd（\u5904\u7406\u96640\u65e0\u7a77\u5927\u7684\u60c5\u51b5）
\n weight = aGauss \/ bGauss
\n weight[np.isinf(weight)] = 0 # \u65e0\u7a77\u5927\u503c\u7f6e0<\/p>\n

# \u5e94\u7528\u6743\u91cd，\u5339\u914d\u8272\u5f69
\n return b * weight<\/p>\n

6. \u6574\u5408\u6240\u6709\u6b65\u9aa4\u5b8c\u6210\u6362\u8138<\/h4>\n

if __name__ == "__main__":
\n # 1. \u8bfb\u53d6\u56fe\u7247
\n a = cv2.imread("0.png") # \u76ee\u6807\u56fe\u7247（\u4fdd\u7559\u80cc\u666f，\u66ff\u6362\u4eba\u8138）
\n b = cv2.imread("7.png") # \u6e90\u56fe\u7247（\u63d0\u4f9b\u8981\u66ff\u6362\u7684\u4eba\u8138）<\/p>\n

# 2. \u521d\u59cb\u5316\u68c0\u6d4b\u5668\u548c\u9884\u6d4b\u5668
\n detector = dlib.get_frontal_face_detector()
\n predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")<\/p>\n

# 3. \u68c0\u6d4b\u4eba\u8138\u5173\u952e\u70b9
\n aKeyPoints = getKeyPoints(a)
\n bKeyPoints = getKeyPoints(b)<\/p>\n

# 4. \u751f\u6210\u4eba\u8138\u63a9\u819c
\n aMask = getFaceMask(a, aKeyPoints)
\n bMask = getFaceMask(b, bKeyPoints)<\/p>\n

# 5. \u8ba1\u7b97\u4eff\u5c04\u53d8\u6362\u77e9\u9635（B\u5bf9\u9f50\u5230A）
\n M = getM(aKeyPoints[POINTStuple], bKeyPoints[POINTStuple])<\/p>\n

# 6. \u53d8\u6362B\u7684\u63a9\u819c\u548c\u4eba\u8138\u56fe\u50cf
\n dsize = a.shape[:2][::-1] # \u76ee\u6807\u5c3a\u5bf8（\u5bbd, \u9ad8）
\n # \u53d8\u6362B\u7684\u63a9\u819c
\n bMaskWarp = cv2.warpAffine(bMask, M, dsize, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.WARP_INVERSE_MAP)
\n # \u53d8\u6362B\u7684\u4eba\u8138\u56fe\u50cf
\n bWrap = cv2.warpAffine(b, M, dsize, borderMode=cv2.BORDER_TRANSPARENT, flags=cv2.WARP_INVERSE_MAP)<\/p>\n

# 7. \u878d\u5408\u63a9\u819c（\u53d6A\u548c\u53d8\u6362\u540eB\u63a9\u819c\u7684\u6700\u5927\u503c）
\n mask = np.max([aMask, bMaskWarp], axis=0)<\/p>\n

# 8. \u989c\u8272\u5f52\u4e00\u5316
\n bcolor = normalcolor(a, bWrap)<\/p>\n

# 9. \u878d\u5408\u56fe\u50cf：\u63a9\u819c\u533a\u57df\u7528B\u7684\u4eba\u8138，\u975e\u63a9\u819c\u533a\u57df\u7528A\u7684\u80cc\u666f
\n out = a * (1.0 – mask) + bcolor * mask<\/p>\n

# 10. \u663e\u793a\u7ed3\u679c
\n cv2.imshow("\u539f\u59cb\u56feA", a)
\n cv2.imshow("\u539f\u59cb\u56feB", b)
\n cv2.imshow("\u6362\u8138\u7ed3\u679c", out \/ 255) # \u5f52\u4e00\u5316\u52300-1\u663e\u793a
\n cv2.waitKey(0)
\n cv2.destroyAllWindows()<\/p>\n

\u56db\u3001\u5173\u952e\u53c2\u6570\u8c03\u6574\u6280\u5de7<\/h3>\n
  • \n

    \u9ad8\u65af\u6a21\u7cca\u6838\u5927\u5c0f：getFaceMask\u4e2d\u7684(45,45)\u548cnormalcolor\u4e2d\u7684(111,111)\u662f\u6838\u5fc3\u53c2\u6570\u3002\u6838\u8d8a\u5927，\u8fb9\u7f18\u8d8a\u6a21\u7cca\u81ea\u7136，\u4f46\u8fc7\u5927\u4f1a\u5bfc\u81f4\u4eba\u8138\u7ec6\u8282\u4e22\u5931；\u6838\u8d8a\u5c0f，\u8fb9\u7f18\u8d8a\u6e05\u6670，\u4f46\u53ef\u80fd\u51fa\u73b0\u751f\u786c\u7684\u62fc\u63a5\u75d5\u8ff9，\u5efa\u8bae\u6839\u636e\u56fe\u7247\u5206\u8fa8\u7387\u8c03\u6574（\u5206\u8fa8\u7387\u9ad8\u5219\u6838\u5927，\u53cd\u4e4b\u5219\u5c0f）\u3002<\/p>\n<\/li>\n

  • \n

    \u5173\u952e\u70b9\u533a\u57df\u9009\u62e9：\u672c\u6587\u6392\u9664\u4e86\u4e0b\u5df4\u5173\u952e\u70b9（\u4ec5\u7528 17-68），\u82e5\u9700\u66ff\u6362\u5b8c\u6574\u4eba\u8138（\u5305\u62ec\u4e0b\u5df4），\u53ef\u5c06POINTS\u6539\u4e3aFACE_POINTS\u3002<\/p>\n<\/li>\n","protected":false},"excerpt":{"rendered":"

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