添加换脸功能

faceai/faceswap.py

@@ -0,0 +1,160 @@
+#coding=utf-8
+import cv2
+import numpy
+import dlib
+
+modelPath = "C:\Python36\Lib\site-packages\dlib-data\shape_predictor_68_face_landmarks.dat"
+SCALE_FACTOR = 1
+FEATHER_AMOUNT = 11
+
+FACE_POINTS = list(range(17, 68))
+MOUTH_POINTS = list(range(48, 61))
+RIGHT_BROW_POINTS = list(range(17, 22))
+LEFT_BROW_POINTS = list(range(22, 27))
+RIGHT_EYE_POINTS = list(range(36, 42))
+LEFT_EYE_POINTS = list(range(42, 48))
+NOSE_POINTS = list(range(27, 35))
+JAW_POINTS = list(range(0, 17))
+
+ALIGN_POINTS = (LEFT_BROW_POINTS + RIGHT_EYE_POINTS + LEFT_EYE_POINTS +
+                RIGHT_BROW_POINTS + NOSE_POINTS + MOUTH_POINTS)
+
+OVERLAY_POINTS = [
+    LEFT_EYE_POINTS + RIGHT_EYE_POINTS + LEFT_BROW_POINTS + RIGHT_BROW_POINTS,
+    NOSE_POINTS + MOUTH_POINTS,
+]
+
+COLOUR_CORRECT_BLUR_FRAC = 0.6
+
+detector = dlib.get_frontal_face_detector()
+predictor = dlib.shape_predictor(modelPath)
+
+
+class TooManyFaces(Exception):
+    pass
+
+
+class NoFaces(Exception):
+    pass
+
+
+def get_landmarks(im):
+    rects = detector(im, 1)
+
+    if len(rects) > 1:
+        raise TooManyFaces
+    if len(rects) == 0:
+        raise NoFaces
+
+    return numpy.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])
+
+
+def annotate_landmarks(im, landmarks):
+    im = im.copy()
+    for idx, point in enumerate(landmarks):
+        pos = (point[0, 0], point[0, 1])
+        cv2.putText(
+            im,
+            str(idx),
+            pos,
+            fontFace=cv2.FONT_HERSHEY_SCRIPT_SIMPLEX,
+            fontScale=0.4,
+            color=(0, 0, 255))
+        cv2.circle(im, pos, 3, color=(0, 255, 255))
+    return im
+
+
+def draw_convex_hull(im, points, color):
+    points = cv2.convexHull(points)
+    cv2.fillConvexPoly(im, points, color=color)
+
+
+def get_face_mask(im, landmarks):
+    im = numpy.zeros(im.shape[:2], dtype=numpy.float64)
+
+    for group in OVERLAY_POINTS:
+        draw_convex_hull(im, landmarks[group], color=1)
+
+    im = numpy.array([im, im, im]).transpose((1, 2, 0))
+
+    im = (cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0) > 0) * 1.0
+    im = cv2.GaussianBlur(im, (FEATHER_AMOUNT, FEATHER_AMOUNT), 0)
+
+    return im
+
+
+def transformation_from_points(points1, points2):
+    points1 = points1.astype(numpy.float64)
+    points2 = points2.astype(numpy.float64)
+    c1 = numpy.mean(points1, axis=0)
+    c2 = numpy.mean(points2, axis=0)
+    points1 -= c1
+    points2 -= c2
+    s1 = numpy.std(points1)
+    s2 = numpy.std(points2)
+    points1 /= s1
+    points2 /= s2
+    U, S, Vt = numpy.linalg.svd(points1.T * points2)
+    R = (U * Vt).T
+    return numpy.vstack([
+        numpy.hstack(((s2 / s1) * R, c2.T - (s2 / s1) * R * c1.T)),
+        numpy.matrix([0., 0., 1.])
+    ])
+
+
+def read_im_and_landmarks(fname):
+    im = cv2.imread(fname, cv2.IMREAD_COLOR)
+    im = cv2.resize(im,
+                    (im.shape[1] * SCALE_FACTOR, im.shape[0] * SCALE_FACTOR))
+    s = get_landmarks(im)
+
+    return im, s
+
+
+def warp_im(im, M, dshape):
+    output_im = numpy.zeros(dshape, dtype=im.dtype)
+    cv2.warpAffine(
+        im,
+        M[:2], (dshape[1], dshape[0]),
+        dst=output_im,
+        borderMode=cv2.BORDER_TRANSPARENT,
+        flags=cv2.WARP_INVERSE_MAP)
+    return output_im
+
+
+def correct_colours(im1, im2, landmarks1):
+    blur_amount = COLOUR_CORRECT_BLUR_FRAC * numpy.linalg.norm(
+        numpy.mean(landmarks1[LEFT_EYE_POINTS], axis=0) -
+        numpy.mean(landmarks1[RIGHT_EYE_POINTS], axis=0))
+    blur_amount = int(blur_amount)
+    if blur_amount % 2 == 0:
+        blur_amount += 1
+    im1_blur = cv2.GaussianBlur(im1, (blur_amount, blur_amount), 0)
+    im2_blur = cv2.GaussianBlur(im2, (blur_amount, blur_amount), 0)
+
+    im2_blur += (128 * (im2_blur <= 1.0)).astype(im2_blur.dtype)
+
+    return (im2.astype(numpy.float64) * im1_blur.astype(numpy.float64) /
+            im2_blur.astype(numpy.float64))
+
+
+im1, landmarks1 = read_im_and_landmarks("img/ag-2.png")
+im2, landmarks2 = read_im_and_landmarks("img/ag.png")
+
+M = transformation_from_points(landmarks1[ALIGN_POINTS],
+                               landmarks2[ALIGN_POINTS])
+
+mask = get_face_mask(im2, landmarks2)
+warped_mask = warp_im(mask, M, im1.shape)
+combined_mask = numpy.max(
+    [get_face_mask(im1, landmarks1), warped_mask], axis=0)
+
+warped_im2 = warp_im(im2, M, im1.shape)
+warped_corrected_im2 = correct_colours(im1, warped_im2, landmarks1)
+
+output_im = im1 * (1.0 - combined_mask) + warped_corrected_im2 * combined_mask
+
+cv2.imwrite("img/faceswap.png", output_im)
+
+# cv2.waitKey(0)
+# cv2.destroyAllWindows()