def face_merge(dst_img,
src_img,
out_img,
face_area,
alpha=0.75,
skin_buff=0,
skin_detail=0,
skin_p=0,
blur_detail_x=None,
blur_detail_y=None,
mat_multiple=None):
src_matrix, src_points = core.face_points(src_img)
dst_matrix, dst_points = core.face_points(dst_img)
src_img = cv2.imread(src_img, cv2.IMREAD_COLOR)
dst_img = cv2.imread(dst_img, cv2.IMREAD_COLOR)
dst_img = transformation_points(src_img, src_matrix[core.FACE_POINTS],
dst_img, dst_matrix[core.FACE_POINTS])
_, dst_points = core.face_points(dst_img)
dst_img = morph_img(src_img, src_points, dst_img, dst_points, alpha)
dst_matrix, dst_points = core.face_points(dst_img.astype('uint8'))
src_img = tran_src(src_img, src_points, dst_points, face_area)
dst_img = merge_img(src_img, dst_img, dst_matrix, dst_points,
blur_detail_x, blur_detail_y, mat_multiple)
cv2.imwrite(out_img, dst_img)
def face_merge(src_img, dst_img, out_img,
face_area, alpha=0.75,
k_size=None, mat_multiple=None):
src_matrix, src_points, src_img = core.face_points(src_img)
dst_matrix, dst_points, dst_img = core.face_points(dst_img)
dst_img = transformation_points(src_img=src_img, src_points=src_matrix[core.FACE_POINTS],
dst_img=dst_img, dst_points=dst_matrix[core.FACE_POINTS])
# 转换
trans_file = 'images/' + str(int(time.time() * 1000)) + '.jpg'
cv2.imwrite(trans_file, dst_img)
_, dst_points, _ = core.face_points(trans_file)
dst_img = morph_img(src_img, src_points, dst_img, dst_points, alpha)
# 融合
morph_file = 'images/' + str(int(time.time() * 1000)) + '.jpg'
cv2.imwrite(morph_file, dst_img)
dst_matrix, dst_points, _ = core.face_points(morph_file)
src_img = tran_src(src_img, src_points, dst_points, face_area)
cv2.imwrite("images/1.jpg", src_img)
dst_img = merge_img(src_img, dst_img, dst_matrix, dst_points, k_size, mat_multiple)
# 在最后矫正颜色就可以去掉黑色块块
cv2.imwrite("images/2.jpg", dst_img)
dst_img = correct_color(src_img, dst_img, src_matrix[core.FACE_POINTS])
# 删除掉临时生成的文件
os.remove(trans_file)
os.remove(morph_file)
cv2.imwrite(out_img, dst_img)
return None
def face_merge(src_img,
dst_img,
out_img,
alpha=0.75,
k_size=(10, 5),
mat_multiple=0.5):
src_matrix, src_points, src_faces, err = core.face_points(src_img)
##直接将第一次寻找目标人物读取的人脸数据作为参数传过来，减少查询人脸识别API次数
dst_matrix, dst_points, dst_faces, err = core.face_points(dst_img)
if not (isinstance(src_img, np.ndarray)
and isinstance(dst_img, np.ndarray)):
src_img = cv2.imread(src_img, cv2.IMREAD_COLOR)
dst_img = cv2.imread(dst_img, cv2.IMREAD_COLOR)
dst_img = transformation_points(src_img=src_img,
src_points=src_matrix[core.FACE_POINTS],
dst_img=dst_img,
dst_points=dst_matrix[core.FACE_POINTS])
# 转换
trans_file = 'images/' + "trans" + '.jpg'
cv2.imwrite(trans_file, dst_img)
_, dst_points, trans_faces, err = core.face_points(dst_img)
dst_img = morph_img(src_img, src_points, dst_img, dst_points, alpha)
# 融合
# morph_file = 'images/' + "merge" + '.jpg'
# cv2.imwrite(morph_file, dst_img)
dst_matrix, dst_points, morph_faces, err = core.face_points(dst_img)
if isinstance(src_faces, dict):
src_img = tran_src(src_img, src_points, dst_points, [
int(src_faces['x']),
int(src_faces['y']),
int(src_faces['width']),
int(src_faces['height'])
])
else:
src_img = tran_src(src_img, src_points, dst_points, [
int(src_faces[-1][0]),
int(src_faces[-1][1]),
int(src_faces[-1][2]),
int(src_faces[-1][3])
])
# cv2.imwrite('images/' + "tran_src" + '.jpg',src_img)
dst_img = merge_img(src_img, dst_img, dst_matrix, dst_points, k_size,
mat_multiple)
# 删除掉临时生成的文件
# os.remove(trans_file)
# os.remove(morph_file)
cv2.imwrite(out_img, dst_img)
return err
def face_merge(src_img,
dst_img,
out_img,
face_area,
alpha=0.75,
k_size=None,
mat_multiple=None,
dst_matrix=[],
dst_points=[]):
src_img_name = str(src_img)
dst_img_name = str(dst_img)
src_matrix, src_points, src_faces, err = core.face_points(src_img)
##直接将第一次寻找目标人物读取的人脸数据作为参数传过来，减少查询人脸识别API次数
# dst_matrix, dst_points, dst_faces,err = core.face_points(dst_img)
src_img = cv2.imread(src_img, cv2.IMREAD_COLOR)
dst_img = cv2.imread(dst_img, cv2.IMREAD_COLOR)
dst_img = transformation_points(src_img=src_img,
src_points=src_matrix[core.FACE_POINTS],
dst_img=dst_img,
dst_points=dst_matrix[core.FACE_POINTS])
# 转换
trans_file = 'images/' + str(int(time.time() * 1000)) + '.jpg'
cv2.imwrite(trans_file, dst_img)
_, dst_points, trans_faces, err = core.face_points(trans_file)
dst_img = morph_img(src_img, src_points, dst_img, dst_points, alpha)
# 融合
morph_file = 'images/' + str(int(time.time() * 1000)) + '.jpg'
cv2.imwrite(morph_file, dst_img)
dst_matrix, dst_points, morph_faces, err = core.face_points(morph_file)
if isinstance(src_faces, dict):
src_img = tran_src(src_img, src_points, dst_points, face_area)
else:
rTemp = src_faces.replace("u'", "\"")
rTemp = rTemp.replace("'", "\"")
src_faces = json.loads(rTemp)
src_img = tran_src(src_img, src_points, dst_points, face_area)
dst_img = merge_img(src_img, dst_img, dst_matrix, dst_points, k_size,
mat_multiple)
# 在最后矫正颜色就可以去掉黑色块块
dst_img = correct_color(src_img, dst_img, src_matrix[core.FACE_POINTS])
# 删除掉临时生成的文件
os.remove(trans_file)
os.remove(morph_file)
cv2.imwrite(out_img, dst_img)
return err
def face_merge(dst_img,
dst_points,
src_img,
src_points,
out_img,
face_area,
alpha=0.75,
k_size=None,
mat_multiple=None,
is_peach=None):
src_img = cv2.imread(src_img, cv2.IMREAD_COLOR)
dst_img = cv2.imread(dst_img, cv2.IMREAD_COLOR)
dst_img = tran_similarity(src_img, [
src_points[core.LEFT_EYE_CENTER], src_points[core.RIGHT_EYE_CENTER],
src_points[core.NOSE_TOP]
], dst_img, [
dst_points[core.LEFT_EYE_CENTER], dst_points[core.RIGHT_EYE_CENTER],
dst_points[core.NOSE_TOP]
])
trans_file = 'images/' + str(int(time.time() * 1000)) + '.jpg'
cv2.imwrite(trans_file, dst_img)
dst_points, err = core.face_points(trans_file)
dst_img, dst_points = morph_img(src_img,
src_points,
dst_img,
dst_points,
alpha,
show_bg=True)
morph_file = 'images/' + str(int(time.time() * 1000)) + '.jpg'
cv2.imwrite(morph_file, dst_img)
dst_points, err = core.face_points(morph_file)
start = time.clock()
src_img = tran_src(src_img, src_points, dst_points, face_area)
print('模特图变形耗时：', time.clock() - start)
start = time.clock()
dst_img = merge_img(src_img, dst_img, src_points, k_size, mat_multiple,
is_peach)
print('人脸替换耗时：', time.clock() - start)
os.remove(trans_file)
os.remove(morph_file)
cv2.imwrite(out_img, dst_img)
return err
def getTriangleData(dst_img):
# print("getTriangleData")
src_matrix, src_points, src_faces, err = core.face_points(dst_img)
# 计算人脸三角形面积(双眼球和下巴最中间点)，保证同一张脸多次访问结果一致
result = 0
# print(src_faces)
return src_matrix,src_points
if isinstance(src_faces, dict):
x1 = src_faces['face_shape']['pupil'][0]['x'] - src_faces['face_shape']['pupil'][1]['x']
x2 = src_faces['face_shape']['pupil'][0]['x'] - src_faces['face_shape']['face_profile'][10]['x']
x3 = src_faces['face_shape']['pupil'][1]['x'] - src_faces['face_shape']['face_profile'][10]['x']
y1 = src_faces['face_shape']['pupil'][0]['y'] - src_faces['face_shape']['pupil'][1]['y']
y2 = src_faces['face_shape']['pupil'][0]['y'] - src_faces['face_shape']['face_profile'][10]['y']
y3 = src_faces['face_shape']['pupil'][1]['y'] - src_faces['face_shape']['face_profile'][10]['y']
result= (x1*x1 + x2*x2 + x3*x3 + y1*y1 + y2*y2 + y3*y3)/100
else:
rTemp = src_faces.replace("u'", "\"")
rTemp = rTemp.replace("'", "\"")
src_faces = json.loads(rTemp)
x1 = src_faces['face_shape']['pupil'][0]['x'] - src_faces['face_shape']['pupil'][1]['x']
x2 = src_faces['face_shape']['pupil'][0]['x'] - src_faces['face_shape']['face_profile'][10]['x']
x3 = src_faces['face_shape']['pupil'][1]['x'] - src_faces['face_shape']['face_profile'][10]['x']
y1 = src_faces['face_shape']['pupil'][0]['y'] - src_faces['face_shape']['pupil'][1]['y']
y2 = src_faces['face_shape']['pupil'][0]['y'] - src_faces['face_shape']['face_profile'][10]['y']
y3 = src_faces['face_shape']['pupil'][1]['y'] - src_faces['face_shape']['face_profile'][10]['y']
result = (x1 * x1 + x2 * x2 + x3 * x3 + y1 * y1 + y2 * y2 + y3 * y3)/100
return result,src_matrix, src_points
def get(self):
nowTime = time.time()
url = self.get_argument("url")
cityIndex = int(self.get_argument("cityIndex"))
locationIndex = int(self.get_argument("locationIndex"))
gender = int(self.get_argument("gender"))
print(
'======Begin Face Merge ======: ',
str(cityIndex) + '===' + url + '===' + str(locationIndex) + "===" +
str(gender))
# 要融合的图片下载一次就够了——用户上传的图片
dst_img = downloadImageFromUrl(url)
dst_matrix, dst_points, dst_faces, err = core.face_points(dst_img)
# 取得model的图片,下载一次就够了
src_img = getModelImage(cityIndex, locationIndex, gender)
# 生成不同融合度的图片
# images.append(url)
output_image = merge_one(src_img, dst_img, 1, dst_matrix, dst_points)
self.write({"imageUrl": output_image})
# tempImage = str(dst_img)
# if os.path.exists(tempImage):
# # 删除文件
# os.remove(tempImage)
endTime = time.time()
print('Total Cost: ', (endTime - nowTime))
def face_merge(dst_img,
src_img,
out_img,
face_area,
alpha=0.75,
skin_buff=0,
skin_detail=0,
skin_p=0,
blur_detail_x=None,
blur_detail_y=None,
mat_multiple=None):
src_matrix, src_points, err = core.face_points(src_img)
dst_matrix, dst_points, err = core.face_points(dst_img)
src_img = cv2.imread(src_img, cv2.IMREAD_COLOR)
dst_img = cv2.imread(dst_img, cv2.IMREAD_COLOR)
if skin_buff != 0 or skin_detail != 0:
dst_img = core.skin_buffing(dst_img, skin_buff, skin_detail, skin_p)
dst_img = transformation_points(src_img, src_matrix[core.FACE_POINTS],
dst_img, dst_matrix[core.FACE_POINTS])
trans_file = 'images/' + str(int(time.time() * 1000)) + '.jpg'
cv2.imwrite(trans_file, dst_img)
_, dst_points, err = core.face_points(trans_file)
dst_img = morph_img(src_img, src_points, dst_img, dst_points, alpha)
morph_file = 'images/' + str(int(time.time() * 1000)) + '.jpg'
cv2.imwrite(morph_file, dst_img)
dst_matrix, dst_points, err = core.face_points(morph_file)
src_img = tran_src(src_img, src_points, dst_points, face_area)
dst_img = merge_img(src_img, dst_img, dst_matrix, dst_points,
blur_detail_x, blur_detail_y, mat_multiple)
os.remove(trans_file)
os.remove(trans_file + '.txt')
os.remove(morph_file)
os.remove(morph_file + '.txt')
cv2.imwrite(out_img, dst_img)
return err
def face_merge(dst_img,
src_img,
out_img,
face_area,
alpha=0.75,
k_size=None,
mat_multiple=None):
src_matrix, src_points, err = core.face_points(src_img)
dst_matrix, dst_points, err = core.face_points(dst_img)
src_img = cv2.imread(src_img, cv2.IMREAD_COLOR)
dst_img = cv2.imread(dst_img, cv2.IMREAD_COLOR)
dst_img = transformation_points(src_img=src_img,
src_points=src_matrix[core.FACE_POINTS],
dst_img=dst_img,
dst_points=dst_matrix[core.FACE_POINTS])
trans_file = 'images/trans_' + str(int(time.time() * 1000)) + '.jpg'
cv2.imwrite(trans_file, dst_img)
_, dst_points, err = core.face_points(trans_file)
# 再次取点后融合脸部
dst_img = morph_img(src_img, src_points, dst_img, dst_points, alpha)
morph_file = 'images/morph_' + str(int(time.time() * 1000)) + '.jpg'
cv2.imwrite(morph_file, dst_img)
dst_matrix, dst_points, err = core.face_points(morph_file)
# 处理加工模特图片
src_img = tran_src(src_img, src_points, dst_points, face_area)
dst_img = merge_img(src_img, dst_img, dst_matrix, dst_points, k_size,
mat_multiple)
#os.remove(trans_file)
#os.remove(trans_file + '.json')
#os.remove(morph_file)
#os.remove(morph_file + '.json')
cv2.imwrite(out_img, dst_img)
return err
# -*- coding: utf-8 -*-
import core # 引入这个包下的所有方法
if __name__ == '__main__':
src = 'images/zhang.jpg'
dst = 'core/huge_newsize.jpg'
output = 'D:\\WWW\\bihuang\\Public\\upload\\face_ouput\\huge_output.jpg'
src_points, _ = core.face_points(src)
dst_points, _ = core.face_points(dst)
core.face_merge(src_img='images/zhang.jpg', # 源图像.jpg
src_points=src_points, # 源图像人脸关键点数组坐标
dst_img='core/huge_newsize.jpg', # 输入带融合的目标图像
out_img='D:\\WWW\\bihuang\\Public\\upload\\face_ouput\\huge_output.jpg', # 输出到指定的路径
dst_points=dst_points, # 目标图像人脸关键点数组坐标
face_area=[100, 100, 100, 100], # 模板图中人脸融合的位置左上角横坐标（left），左上角纵坐标（top），人脸框宽度（width），人脸框高度（height）
alpha=0.65, # [0~1]融合比，比例越大目标图像的特征就越多
k_size=(300, 250), # 滤波窗口尺寸-图像均值平滑滤波模板
mat_multiple=1.2) # 缩放获取到的人脸心型区域-图像缩放因子
def face_merge(dst_img, src_img, out_img,
face_area, alpha=0.75,
k_size=None, mat_multiple=None):
# 获取两张图片的人脸关键点（矩阵格式与数组格式）
# src_matrix, src_points = core.face_points(src_img)
# dst_matrix, dst_points = core.face_points(dst_img)
#
#
# print(test)
print(src_img)
dst_name = dst_img.split('.')[1].split('/')[-1]
# print(src_name)
start = time.time()
src_matrix,src_points = core.face_points(src_img)
dst_matrix,dst_points = core.face_points(dst_img)
# opencv读取图片
start_read = time.time()
src_img = cv2.imread(src_img, cv2.IMREAD_COLOR)
dst_img = cv2.imread(dst_img, cv2.IMREAD_COLOR)
# stop_read = time.time()
# print("读两张图片用时"+str(stop_read-start_read)+"秒")
src_img = cv2.resize(src_img,(src_img.shape[1] * SCALE_FACTOR,
src_img.shape[0] * SCALE_FACTOR))
dst_img = cv2.resize(dst_img,(dst_img.shape[1] * SCALE_FACTOR,
dst_img.shape[0] * SCALE_FACTOR))
# # print(src_img1)
print("********************")
# 获取两张图片的人脸关键点（矩阵格式与数组格式）
# src_matrix,src_points = core.face_points(src_img)
# dst_matrix, dst_points = core.face_points(dst_img)
dst_img = transformation_points(src_img=src_img, src_points=src_matrix[core.FACE_POINTS],
dst_img=dst_img, dst_points=dst_matrix[core.FACE_POINTS])
# print(dst_img)
trans_file = 'temp/' + dst_name + str(time.time()) + '_trans.jpg'
if ((os.path.isfile(trans_file))== False):
cv2.imwrite(trans_file, dst_img)
# print(trans_file)
# trans_img = cv2.imread(trans_file,cv2.IMREAD_COLOR)
# test del
dst_matrix,dst_points = core.face_points(trans_file)
# print(dst_points)
# print(len(dst_points))
# 再次取点后融合脸部
start_morph = time.time()
dst_img = morph_img(src_img, src_points, dst_img, dst_points, alpha)
stop_morph = time.time()
print("morph用时"+str(stop_morph-start_morph)+"秒")
# morph_file = 'temp/' + dst_name +str(int(time.time() * 1000))+ '_morph.jpg'
morph_file = 'temp/' + dst_name +str(time.time()) + '_morph.jpg'
if((os.path.isfile(morph_file))==False):
cv2.imwrite(morph_file, dst_img)
# morph_img1 = cv2.imread(morph_file,cv2.IMREAD_COLOR)
# print(morph_file)
#test del
# dst_matrix,dst_points = core.face_points(morph_file)
# 再次对上一部的结果进行取点，然后运用三角放射将模特图片脸部轮廓、关键点变形成上一部得到的关键点
# src_img = tran_src(src_img, src_points, dst_points, face_area)
# 将融合后的新图片脸部区域用泊松融合算法贴合到模特图上
start_merge = time.time()
dst_img = merge_img(src_img, dst_img, dst_matrix, dst_points, k_size, mat_multiple)
stop_merge = time.time()
print("merge用时"+str(stop_merge-start_merge)+"秒")
# os.remove(trans_file)
# os.remove(trans_file + '.txt')
# os.remove(morph_file)
# os.remove(morph_file + '.txt')
cv2.imwrite(out_img, dst_img)
stop =time.time()
print("这张用时"+str(stop-start)+"秒")