def tran_src(src_img, src_points, dst_points, face_area=None):
# print(1111111)
print(src_img.shape)
jaw = core.JAW_END
dst_list = dst_points \
+ core.matrix_rectangle(face_area[0], face_area[1], face_area[2], face_area[3]) \
+ core.matrix_rectangle(0, 0, src_img.shape[1], src_img.shape[0])
src_list = src_points \
+ core.matrix_rectangle(face_area[0], face_area[1], face_area[2], face_area[3]) \
+ core.matrix_rectangle(0, 0, src_img.shape[1], src_img.shape[0])
jaw_points = []
for i in range(0, jaw):
# print(i)
jaw_points.append(dst_list[i])
jaw_points.append(src_list[i])
warp_jaw = cv2.convexHull(np.array(jaw_points), returnPoints=False)
warp_jaw = warp_jaw.tolist()
for i in range(0, len(warp_jaw)):
warp_jaw[i] = warp_jaw[i][0]
warp_jaw.sort()
if len(warp_jaw) <= jaw:
dst_list = dst_list[jaw - len(warp_jaw):]
src_list = src_list[jaw - len(warp_jaw):]
for i in range(0, len(warp_jaw)):
dst_list[i] = jaw_points[int(warp_jaw[i])]
src_list[i] = jaw_points[int(warp_jaw[i])]
else:
for i in range(0, jaw):
if len(warp_jaw) > jaw and warp_jaw[i] == 2 * i and warp_jaw[
i + 1] == 2 * i + 1:
warp_jaw.remove(2 * i)
dst_list[i] = jaw_points[int(warp_jaw[i])]
dt = core.measure_triangle(src_img, dst_list, src_points, dst_points)
res_img = np.zeros(src_img.shape, dtype=src_img.dtype)
for i in range(0, len(dt)):
t_src = []
t_dst = []
for j in range(0, 3):
t_src.append(src_list[dt[i][j]])
t_dst.append(dst_list[dt[i][j]])
if (checkLine(t_src) or checkLine(t_dst)):
# print("not checked")
continue
else:
core.affine_triangle(src_img, res_img, t_src, t_dst)
return res_img
def tran_src(src_img, src_points, dst_points, face_area=None):
jaw = core.JAW_END
newface = get_auto_facearae(src_points, src_img)
dst_list = dst_points \
+ core.matrix_rectangle(newface[0], newface[1], newface[2], newface[3]) \
+ core.matrix_rectangle(0, 0, src_img.shape[1], src_img.shape[0])
src_list = src_points \
+ core.matrix_rectangle(newface[0], newface[1], newface[2], newface[3]) \
+ core.matrix_rectangle(0, 0, src_img.shape[1], src_img.shape[0])
jaw_points = []
for i in range(0, jaw):
jaw_points.append(dst_list[i])
jaw_points.append(src_list[i])
warp_jaw = cv2.convexHull(np.array(jaw_points), returnPoints=False)
warp_jaw = warp_jaw.tolist()
for i in range(0, len(warp_jaw)):
warp_jaw[i] = warp_jaw[i][0]
warp_jaw.sort()
if len(warp_jaw) <= jaw:
dst_list = dst_list[jaw - len(warp_jaw):]
src_list = src_list[jaw - len(warp_jaw):]
for i in range(0, len(warp_jaw)):
dst_list[i] = jaw_points[int(warp_jaw[i])]
src_list[i] = jaw_points[int(warp_jaw[i])]
else:
for i in range(0, jaw):
if len(warp_jaw) > jaw and warp_jaw[i] == 2 * i and warp_jaw[
i + 1] == 2 * i + 1:
warp_jaw.remove(2 * i)
dst_list[i] = jaw_points[int(warp_jaw[i])]
dt = core.measure_triangle(src_img, dst_list)
res_img = np.zeros(src_img.shape, dtype=src_img.dtype)
for i in range(0, len(dt)):
t_src = []
t_dst = []
for j in range(0, 3):
t_src.append(src_list[dt[i][j]])
t_dst.append(dst_list[dt[i][j]])
core.affine_triangle(src_img, res_img, t_src, t_dst)
return res_img
def tran_src(src_img, src_points, dst_points, face_area=None):
# jaw = core.JAW_END
dst_list = dst_points \
+ core.matrix_rectangle(face_area[0], face_area[1], face_area[2], face_area[3]) \
+ core.matrix_rectangle(0, 0, src_img.shape[1], src_img.shape[0])
src_list = src_points \
+ core.matrix_rectangle(face_area[0], face_area[1], face_area[2], face_area[3]) \
+ core.matrix_rectangle(0, 0, src_img.shape[1], src_img.shape[0])
# jaw_points = []
# for i in range(0, jaw):
# jaw_points.append(dst_list[i])
# jaw_points.append(src_list[i])
# warp_jaw = cv2.convexHull(np.array(jaw_points), returnPoints=False)
# warp_jaw = warp_jaw.tolist()
# for i in range(0, len(warp_jaw)):
# warp_jaw[i] = warp_jaw[i][0]
# warp_jaw.sort()
# if len(warp_jaw) <= jaw:
# dst_list = dst_list[jaw - len(warp_jaw):]
# src_list = src_list[jaw - len(warp_jaw):]
# for i in range(0, len(warp_jaw)):
# dst_list[i] = jaw_points[int(warp_jaw[i])]
# src_list[i] = jaw_points[int(warp_jaw[i])]
# else:
# for i in range(0, jaw):
# if len(warp_jaw) > jaw and warp_jaw[i] == 2 * i and warp_jaw[i + 1] == 2 * i + 1:
# warp_jaw.remove(2 * i)
# dst_list[i] = jaw_points[int(warp_jaw[i])]
dt = core.measure_triangle(src_img.shape, dst_list)
res_img = np.zeros(src_img.shape, dtype=src_img.dtype)
for i in range(0, len(dt)):
t_src = []
t_dst = []
for j in range(0, 3):
t_src.append(src_list[dt[i][j]])
t_dst.append(dst_list[dt[i][j]])
core.affine_triangle(src_img, res_img, t_src, t_dst)
return res_img
