import cv2 import dlib import numpy from time import sleep import sys
## Our pretrained model that predicts the rectangles that correspond to the facial features of a face PREDICTOR_PATH = "D://study material//[FreeTutorials.Us] master-computer-vision-with-opencv-in-python//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))
# Points used to line up the images. ALIGN_POINTS = (LEFT_BROW_POINTS + RIGHT_EYE_POINTS + LEFT_EYE_POINTS +
RIGHT_BROW_POINTS + NOSE_POINTS + MOUTH_POINTS)
# Points from the second image to overlay on the first. The convex hull of each
# element will be overlaid. OVERLAY_POINTS = [
LEFT_EYE_POINTS + RIGHT_EYE_POINTS + LEFT_BROW_POINTS + RIGHT_BROW_POINTS,
NOSE_POINTS + MOUTH_POINTS, ]
# Amount of blur to use during colour correction, as a fraction of the
# pupillary distance. COLOUR_CORRECT_BLUR_FRAC = 0.6 cascade_path='D://study material//Python//OpenCv//Haar cascade//haarcascade_frontalcatface.xml' cascade = cv2.CascadeClassifier(cascade_path) detector = dlib.get_frontal_face_detector() predictor = dlib.shape_predictor(PREDICTOR_PATH)
def get_landmarks(im, dlibOn):
if (dlibOn == True):
rects = detector(im, 1)
if len(rects) > 1:
return "error"
if len(rects) == 0:
return "error"
return numpy.matrix([[p.x, p.y] for p in predictor(im, rects[0]).parts()])
else:
rects = cascade.detectMultiScale(im, 1.3,5)
if len(rects) > 1:
return "error"
if len(rects) == 0:
return "error"
x,y,w,h =rects[0]
rect=dlib.rectangle(x,y,x+w,y+h)
return numpy.matrix([[p.x, p.y] for p in predictor(im, rect).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):
"""
Return an affine transformation [s * R | T] such that:
sum ||s*R*p1,i + T - p2,i||^2
is minimized.
"""
# Solve the procrustes problem by subtracting centroids, scaling by the
# standard deviation, and then using the SVD to calculate the rotation. See
# the following for more details:
# https://en.wikipedia.org/wiki/Orthogonal_Procrustes_problem
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)
# The R we seek is in fact the transpose of the one given by U * Vt. This
# is because the above formulation assumes the matrix goes on the right
# (with row vectors) where as our solution requires the matrix to be on the
# left (with column vectors).
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,None,fx=0.35, fy=0.35, interpolation = cv2.INTER_LINEAR)
im = cv2.resize(im, (im.shape[1] * SCALE_FACTOR,
im.shape[0] * SCALE_FACTOR))
s = get_landmarks(im,dlibOn)
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)
# Avoid divide-by-zero errors.
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))
def face_swap(img,name):
s = get_landmarks(img,True)
if (s == "error"):
print ("No or too many faces")
return img
im1, landmarks1 = img, s
im2, landmarks2 = read_im_and_landmarks(name)
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
#output_im is no longer in the expected OpenCV format so we use openCV
#to write the image to diks and then reload it
cv2.imwrite('output.jpg', output_im)
image = cv2.imread('output.jpg')
frame = cv2.resize(image,None,fx=1.5, fy=1.5, interpolation = cv2.INTER_LINEAR)
return image
cap = cv2.VideoCapture(0)
# Name is the image we want to swap onto ours
# dlibOn controls if use dlib's facial landmark detector (better)
# or use HAAR Cascade Classifiers (faster)
filter_image = "D://study material//[FreeTutorials.Us] master-computer-vision-with-opencv-in-python//OpenCV-with-Python-master//images//Trump.jpg"
### Put your image here! dlibOn = False
if cap.isOpened():
ret,frame = cap.read() else:
ret = False
while ret:
ret, frame = cap.read()
#Reduce image size by 75% to reduce processing time and improve framerates
frame = cv2.resize(frame, None, fx=0.75, fy=0.75, interpolation = cv2.INTER_LINEAR)
# flip image so that it's more mirror like
frame = cv2.flip(frame, 1)
cv2.imshow('Our Amazing Face Swapper', face_swap(frame, filter_image))
if cv2.waitKey(1) == 13: #13 is the Enter Key
break
cap.release() cv2.destroyAllWindows()
It gives the error: runfile('D:/study material/Python/OpenCv/Programs/swapface.py', wdir='D:/study material/Python/OpenCv/Programs') D:/study material/Python/OpenCv/Programs/swapface.py:379: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison if (s == "error"): Traceback (most recent call last):
File "<ipython-input-1-e5644068d857>", line 1, in <module> runfile('D:/study material/Python/OpenCv/Programs/swapface.py', wdir='D:/study material/Python/OpenCv/Programs')
File "C:\Users\Raunak\AppData\Local\conda\conda\envs\py35\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 668, in runfile execfile(filename, namespace)
File "C:\Users\Raunak\AppData\Local\conda\conda\envs\py35\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 108, in execfile exec(compile(f.read(), filename, 'exec'), namespace)
File "D:/study material/Python/OpenCv/Programs/swapface.py", line 435, in <module> cv2.imshow('Our Amazing Face Swapper', face_swap(frame, filter_image))
File "D:/study material/Python/OpenCv/Programs/swapface.py", line 387, in face_swap landmarks2[ALIGN_POINTS])
TypeError: string indices must be integers
How to solve this error? Please help it is quite urgent...