1、图片效果

2、原代码

  1. # !/usr/bin/python
  2. # -*- coding: utf-8 -*-
  3. # Copyright (c) 2015 Matthew Earl
  4. #
  5. # Permission is hereby granted, free of charge, to any person obtaining a copy
  6. # of this software and associated documentation files (the "Software"), to deal
  7. # in the Software without restriction, including without limitation the rights
  8. # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  9. # copies of the Software, and to permit persons to whom the Software is
  10. # furnished to do so, subject to the following conditions:
  11. #
  12. #     The above copyright notice and this permission notice shall be included
  13. #     in all copies or substantial portions of the Software.
  14. #
  15. #     THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
  16. #     OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
  17. #     MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
  18. #     NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
  19. #     DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
  20. #     OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
  21. #     USE OR OTHER DEALINGS IN THE SOFTWARE.
  22.  
  23. """
  24. This is the code behind the Switching Eds blog post:
  25.     http://matthewearl.github.io/2015/07/28/switching-eds-with-python/
  26. See the above for an explanation of the code below.
  27. To run the script you'll need to install dlib (http://dlib.net) including its
  28. Python bindings, and OpenCV. You'll also need to obtain the trained model from
  29. sourceforge:
  30.     http://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2
  31. Unzip with `bunzip2` and change `PREDICTOR_PATH` to refer to this file. The
  32. script is run like so:
  33.     ./faceswap.py <head image> <face image>
  34. If successful, a file `output.jpg` will be produced with the facial features
  35. from `<head image>` replaced with the facial features from `<face image>`.
  36. """
  37.  
  38. import cv2
  39. import dlib
  40. import numpy
  41.  
  42. import sys
  43. output = 'out3' # 输出图像名称
  44. sys.argv = ["isWap_faces.py", "./facesImage/head1.jpg", "./facesImage/head.jpg"]
  45. # PREDICTOR_PATH = "/home/matt/dlib-18.16/shape_predictor_68_face_landmarks.dat"
  46. PREDICTOR_PATH = "./model/shape_predictor_68_face_landmarks.dat"
  47. SCALE_FACTOR = 1
  48. FEATHER_AMOUNT = 11
  49.  
  50. FACE_POINTS = list(range(17, 68))
  51. MOUTH_POINTS = list(range(48, 61))
  52. RIGHT_BROW_POINTS = list(range(17, 22))
  53. LEFT_BROW_POINTS = list(range(22, 27))
  54. RIGHT_EYE_POINTS = list(range(36, 42))
  55. LEFT_EYE_POINTS = list(range(42, 48))
  56. NOSE_POINTS = list(range(27, 35))
  57. JAW_POINTS = list(range(0, 17))
  58.  
  59. # Points used to line up the images.
  60. ALIGN_POINTS = (LEFT_BROW_POINTS + RIGHT_EYE_POINTS + LEFT_EYE_POINTS +
  61.                 RIGHT_BROW_POINTS + NOSE_POINTS + MOUTH_POINTS)
  62.  
  63. # Points from the second image to overlay on the first. The convex hull of each
  64. # element will be overlaid.
  65. OVERLAY_POINTS = [
  66.     LEFT_EYE_POINTS + RIGHT_EYE_POINTS + LEFT_BROW_POINTS + RIGHT_BROW_POINTS,
  67.     NOSE_POINTS + MOUTH_POINTS,
  68. ]
  69.  
  70. # Amount of blur to use during colour correction, as a fraction of the
  71. # pupillary distance.
  72. COLOUR_CORRECT_BLUR_FRAC = 0.4
  73.  
  74. detector = dlib.get_frontal_face_detector()
  75. predictor = dlib.shape_predictor(PREDICTOR_PATH)
  76.  
  77. class TooManyFaces(Exception):
  78.     pass
  79.  
  80. class NoFaces(Exception):
  81.     pass
  82.  
  83. def get_landmarks(im):
  84.     rects = detector(im, 1)
  85.  
  86.     if len(rects) > 1:
  87.         raise TooManyFaces
  88.     if len(rects) == 0:
  89.         raise NoFaces
  90.  
  91.     return numpy.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])
  92.  
  93. def annotate_landmarks(im, landmarks):
  94.     im = im.copy()
  95.     for idx, point in enumerate(landmarks):
  96.         pos = (point[0, 0], point[0, 1])
  97.         cv2.putText(im, str(idx), pos,
  98.                     fontFace=cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,
  99.                     fontScale=0.4,
  100.                     color=(0, 0, 255))
  101.         cv2.circle(im, pos, 3, color=(0, 255, 255))
  102.     return im
  103.  
  104. def draw_convex_hull(im, points, color):
  105.     points = cv2.convexHull(points)
  106.     cv2.fillConvexPoly(im, points, color=color)
  107.  
  108. def get_face_mask(im, landmarks):
  109.     im = numpy.zeros(im.shape[:2], dtype=numpy.float64)
  110.  
  111.     for group in OVERLAY_POINTS:
  112.         draw_convex_hull(im,
  113.                          landmarks[group],
  114.                          color=1)
  115.  
  116.     im = numpy.array([im, im, im]).transpose((1, 2, 0))
  117.  
  118.     im = (cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0) > 0) * 1.0
  119.     im = cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0)
  120.  
  121.     return im
  122.  
  123. def transformation_from_points(points1, points2):
  124.     """
  125.     Return an affine transformation [s * R | T] such that:
  126.         sum ||s*R*p1,i + T - p2,i||^2
  127.     is minimized.
  128.     """
  129.     # Solve the procrustes problem by subtracting centroids, scaling by the
  130.     # standard deviation, and then using the SVD to calculate the rotation. See
  131.     # the following for more details:
  132.     #   https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem
  133.  
  134.     points1 = points1.astype(numpy.float64)
  135.     points2 = points2.astype(numpy.float64)
  136.  
  137.     c1 = numpy.mean(points1, axis=0)
  138.     c2 = numpy.mean(points2, axis=0)
  139.     points1 -= c1
  140.     points2 -= c2
  141.  
  142.     s1 = numpy.std(points1)
  143.     s2 = numpy.std(points2)
  144.     points1 /= s1
  145.     points2 /= s2
  146.  
  147.     U, S, Vt = numpy.linalg.svd(points1.T * points2)
  148.  
  149.     # The R we seek is in fact the transpose of the one given by U * Vt. This
  150.     # is because the above formulation assumes the matrix goes on the right
  151.     # (with row vectors) where as our solution requires the matrix to be on the
  152.     # left (with column vectors).
  153.     R = (U * Vt).T
  154.  
  155.     return numpy.vstack([numpy.hstack(((s2 / s1) * R,
  156.                                        c2.T - (s2 / s1) * R * c1.T)),
  157.                          numpy.matrix([0., 0., 1.])])
  158.  
  159. def read_im_and_landmarks(fname):
  160.     im = cv2.imread(fname, cv2.IMREAD_COLOR)
  161.     im = cv2.resize(im, (im.shape[1] * SCALE_FACTOR,
  162.                          im.shape[0] * SCALE_FACTOR))
  163.     s = get_landmarks(im)
  164.  
  165.     return im, s
  166.  
  167. def warp_im(im, M, dshape):
  168.     output_im = numpy.zeros(dshape, dtype=im.dtype)
  169.     cv2.warpAffine(im,
  170.                    M[:2],
  171.                    (dshape[1], dshape[0]),
  172.                    dst=output_im,
  173.                    borderMode=cv2.BORDER_TRANSPARENT,
  174.                    flags=cv2.WARP_INVERSE_MAP)
  175.     return output_im
  176.  
  177. def correct_colours(im1, im2, landmarks1):
  178.     blur_amount = COLOUR_CORRECT_BLUR_FRAC * numpy.linalg.norm(
  179.         numpy.mean(landmarks1[LEFT_EYE_POINTS], axis=0) -
  180.         numpy.mean(landmarks1[RIGHT_EYE_POINTS], axis=0))
  181.     blur_amount = int(blur_amount)
  182.     if blur_amount % 2 == 0:
  183.         blur_amount += 1
  184.     im1_blur = cv2.GaussianBlur(im1, (blur_amount, blur_amount), 0)
  185.     im2_blur = cv2.GaussianBlur(im2, (blur_amount, blur_amount), 0)
  186.  
  187.     # Avoid divide-by-zero errors.
  188.     im2_blur += (128 * (im2_blur <= 1.0)).astype(im2_blur.dtype)
  189.  
  190.     return (im2.astype(numpy.float64) * im1_blur.astype(numpy.float64) /
  191.             im2_blur.astype(numpy.float64))
  192.  
  193. im1, landmarks1 = read_im_and_landmarks(sys.argv[1])
  194. im2, landmarks2 = read_im_and_landmarks(sys.argv[2])
  195.  
  196. M = transformation_from_points(landmarks1[ALIGN_POINTS],
  197.                                landmarks2[ALIGN_POINTS])
  198.  
  199. mask = get_face_mask(im2, landmarks2)
  200. warped_mask = warp_im(mask, M, im1.shape)
  201. combined_mask = numpy.max([get_face_mask(im1, landmarks1), warped_mask],
  202.                           axis=0)
  203.  
  204. warped_im2 = warp_im(im2, M, im1.shape)
  205. warped_corrected_im2 = correct_colours(im1, warped_im2, landmarks1)
  206.  
  207. output_im = im1 * (1.0 - combined_mask) + warped_corrected_im2 * combined_mask
  208.  
  209. cv2.imwrite('./outImage/{}.jpg'.format(output), output_im)

　　

3、目录结构

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