def morph_img(src_img, src_points, dst_img, dst_points, alpha=0.5):
morph_points = []
src_img = src_img.astype(np.float32)
dst_img = dst_img.astype(np.float32)
res_img = np.zeros(src_img.shape, src_img.dtype)
for i in range(0, len(src_points)):
x = (1 - alpha) * src_points[i][0] + alpha * dst_points[i][0]
y = (1 - alpha) * src_points[i][1] + alpha * dst_points[i][1]
morph_points.append((x, y))
dt = core.measure_triangle(src_img, morph_points)
for i in range(0, len(dt)):
t1 = []
t2 = []
t = []
for j in range(0, 3):
t1.append(src_points[dt[i][j]])
t2.append(dst_points[dt[i][j]])
t.append(morph_points[dt[i][j]])
core.morph_triangle(src_img, dst_img, res_img, t1, t2, t, alpha)
return res_img
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 morph_img(src_img, src_points, dst_img, dst_points, alpha=0.5):
morph_points = []
src_img = src_img.astype(np.float32)
dst_img = dst_img.astype(np.float32)
res_img = np.zeros(src_img.shape, src_img.dtype)
# for i in src_points:
# print(i)
# 这一步的目的是调整脸型，将原图关键点和目标图关键点之间取中间点，根据alpha值来取
for i in range(0, len(src_points)):
x = (1 - alpha) * src_points[i][0] + alpha * dst_points[i][0]
y = (1 - alpha) * src_points[i][1] + alpha * dst_points[i][1]
morph_points.append((x, y))
dt = core.measure_triangle(src_img, morph_points, src_points, dst_points)
for i in range(0, len(dt)):
t1 = []
t2 = []
t = []
for j in range(0, 3):
t1.append(src_points[dt[i][j]])
t2.append(dst_points[dt[i][j]])
t.append(morph_points[dt[i][j]])
if (checkLine(t) or checkLine(t1) or checkLine(t2)):
continue
core.morph_triangle(src_img, dst_img, res_img, t1, t2, t, alpha, i)
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
def morph_img(src_img, src_points, dst_img, dst_points, alpha=0.5,show_bg=None):
# test time
start = time.time()
src_img = src_img.astype(np.float32)
dst_img = dst_img.astype(np.float32)
morph_points = []
if show_bg:
res_img = src_img.copy()
else:
res_img = np.zeros(src_img.shape, src_img.dtype)
for i in range(0, len(src_points)):
x = (1 - alpha) * src_points[i][0] + alpha * dst_points[i][0]
y = (1 - alpha) * src_points[i][1] + alpha * dst_points[i][1]
morph_points.append((x, y))
# dt = core.measure_triangle(src_img, morph_points)
dt = core.measure_triangle(src_img, src_points)
for i in range(0, len(dt)):
t1 = []
t2 = []
t = []
for j in range(0, 3):
t1.append(src_points[dt[i][j]])
t2.append(dst_points[dt[i][j]])
t.append(morph_points[dt[i][j]])
core.morph_triangle(src_img, dst_img, res_img, t1, t2, t, alpha)
stop = time.time()
print("morph_img用时"+str(stop-start)+"秒")
return res_img
