def movie_swap(temp_movie, dst_movie):
import glob, os
# gen frames for temp and dst
start = time()
gen_frames(temp_movie, 'A2')
gen_frames(dst_movie, 'B2')
if not os.path.exists('out'):
os.mkdir('out')
# swap every face
framesA = sorted(glob.glob('A2/*.png'), key=lambda x:int(os.path.basename(x).split('.')[0].split('-')[-1]))
framesB = sorted(glob.glob('B2/*.png'), key=lambda x:int(os.path.basename(x).split('.')[0].split('-')[-1]))
i = 0
for f_a, f_b in zip(framesA, framesB):
core.face_merge(src_img=f_a,
dst_img=f_b,
out_img='out/frame-{0}.png'.format(i),
face_area=[50, 30, 500, 485],
alpha=0.95,
k_size=(15, 10),
mat_multiple=0.95)
i = i + 1
print("已处理",i,"张图片")
stop = time()
print("总共用时"+str(stop-start)+"秒")
# composite movie
os.system('ffmpeg -i out/frame-%0d.png -c:v libx264 -vf "fps=25,format=yuv420p" out/out.mp4')
def merge_one(dst_img, deltaData, gender, alpha, dst_matrix, dst_points):
nowTime = time.time()
# 参数
src_img, src_url, modelName, modelShortName, modelDescription = getModelImage(
gender, deltaData)
out_img = 'result/output' + str(int(time.time() * 1000)) + '.jpg'
face_area = [100, 50, 500, 500]
# src_img —— 模特图片
# dst_img —— 待融合的图片
# out_img —— 结果图片输出路径
# face_area —— 指定模板图中进行人脸融合的人脸框位置。四个正整数数组，依次代表人脸框左上角纵坐标（top），左上角横坐标（left），人脸框宽度（width），人脸框高度（height），通过设定改参数可以减少结果的大范围变形，把变形风险控制在人脸框区域
# alpha —— 融合比例，范围 [0,1]。数字越大融合结果包含越多融合图 (dst_img) 特征。
# blur_size—— 模糊核大小，用于模糊人脸融合边缘，减少融合后的违和感
# mat_multiple —— 缩放获取到的人脸心型区域
# 头像融合
core.face_merge(src_img=src_img,
dst_img=dst_img,
out_img=out_img,
face_area=face_area,
alpha=alpha,
k_size=(10, 5),
mat_multiple=0.95,
dst_matrix=dst_matrix,
dst_points=dst_points)
final_img = uploadFileToOSS(out_img)
print('Face Merge Success: ', final_img)
endTime = time.time()
print('Time Cost: ', (endTime - nowTime))
return final_img, src_url, modelName, modelShortName, modelDescription
def main(src_img_path, dst_img_path, out_img_path, alpha, mat_multiple):
core.face_merge(src_img=src_img_path,
dst_img=dst_img_path,
out_img=out_img_path,
face_area=[50, 30, 500, 485],
alpha=alpha,
blur_detail_x=15,
blur_detail_y=10,
mat_multiple=mat_multiple)
def main():
start = time()
core.face_merge(src_img='./target/sen-00015.png',
dst_img='./shiyuan/shiyuan-6.png',
out_img='output/1.png',
face_area=[50, 30, 500, 485],
alpha=0.75,
k_size=(15, 10),
mat_multiple=0.95)
stop = time()
print(str(stop - start) + "秒")
def merge_one(src_img, dst_img, alpha):
out_img = 'images/out.jpg'
face_area = [50, 50, 500, 485]
# 头像融合
core.face_merge(src_img=src_img,
dst_img=dst_img,
out_img=out_img,
face_area=face_area,
alpha=alpha,
k_size=(10, 5),
mat_multiple=0.95)
return out_img
def main():
# os.environ['CUDA_VISIBLE_DEVICES'] = 0
ap = argparse.ArgumentParser(description='batch face swap routine')
ap.add_argument('-s',
'--source_dir',
type=str,
help='input source directory')
ap.add_argument('-d',
'--dest_dir',
type=str,
help='input destination directory')
ap.add_argument('-o', '--output_dir', type=str, help='output directory')
args = ap.parse_args()
sd = args.source_dir
dd = args.dest_dir
od = args.output_dir
i = 0
source_path_list = []
target_path_list = []
if not os.path.exists(od):
os.mkdir(od)
types = ('*.jpg', '*.png')
for files in types:
source_path_list.extend(glob.glob(os.path.join((sd), files)))
# target_path_list.extend(glob.glob(os.path.join((dd),files)))
# source_path_list = glob.glob(sd)
# target_path_list = glob.glob(dd)
# source_path_list = glob.glob(os.path.join(sd,'*.png'))
# target_path_list = glob.glob(os.path.join(dd,'*.png'))
source_path_list.sort(key=lambda x: int(x.split('.')[1].split('-')[-1]))
# target_path_list.sort(key=lambda x: int(x.split('.')[1].split('-')[-1]))
# print(source_path_list)
d = './C/jin-02.png'
for s in source_path_list:
core.face_merge(src_img=s,
dst_img=d,
out_img=os.path.join(od, 'output-{0}.jpg'.format(i)),
face_area=[50, 30, 500, 485],
alpha=0.75,
k_size=(15, 10),
mat_multiple=0.95)
i = i + 1
print("已处理", i, "张图片")
def merge_one(src_img, dst_img, alpha, dst_matrix, dst_points):
nowTime = time.time()
# 参数
out_img = 'result/output' + str(int(time.time() * 1000)) + '.png'
# face_area = [100, 50, 500, 500]
face_area = [50, 50, 500, 500]
# 头像融合
core.face_merge(src_img=src_img,
dst_img=dst_img,
out_img=out_img,
face_area=face_area,
alpha=alpha,
k_size=(10, 5),
mat_multiple=0.9,
dst_matrix=dst_matrix,
dst_points=dst_points)
final_img = uploadFileToOSS(out_img)
print('Face Merge Success: ', final_img)
endTime = time.time()
print('Time Cost: ', (endTime - nowTime))
return final_img
def main():
ap = argparse.ArgumentParser(description='batch face swap routine')
ap.add_argument('-s',
'--source_dir',
type=str,
help='input source directory')
ap.add_argument('-d',
'--dest_dir',
type=str,
help='input destination directory')
ap.add_argument('-o', '--output_dir', type=str, help='output directory')
args = ap.parse_args()
sd = args.source_dir
dd = args.dest_dir
od = args.output_dir
i = 0
source_path_list = []
target_path_list = []
if not os.path.exists(od):
os.mkdir(od)
# types = ('*.jpg','*.png')
source_path_list = glob.glob(os.path.join(sd, '*.png'))
target_path_list = glob.glob(os.path.join(dd, '*.png'))
source_path_list.sort(key=lambda x: int(x.split('.')[0].split('-')[-1]))
target_path_list.sort(key=lambda x: int(x.split('.')[0].split('-')[-1]))
for s, d in zip((glob.iglob(os.path.join(sd, '*.png'))),
glob.iglob(os.path.join(dd, '*.png'))):
core.face_merge(src_img=s,
dst_img=d,
out_img=os.path.join(od, 'output-{0}.jpg'.format(i)),
face_area=[50, 30, 500, 485],
alpha=0.75,
k_size=(15, 10),
mat_multiple=0.95)
i = i + 1
# -*- coding: utf-8 -*-
# @Time : 2017/9/2 13:40
# @Author : 郑梓斌
import core
if __name__ == '__main__':
core.face_merge(src_img='images/model.jpg',
dst_img='images/20171030175254.jpg',
out_img='images/output.jpg',
face_area=[50, 30, 500, 485],
alpha=0.75,
blur_detail_x=15,
blur_detail_y=10,
mat_multiple=0.95)
# -*- coding: utf-8 -*-
# @Time : 2017/9/2 13:40
# @Author : 郑梓斌
import core
if __name__ == '__main__':
core.face_merge(src_img='6.jpg',
dst_img='33.jpg',
out_img='output.jpg',
face_area=[50, 30, 500, 485],
alpha=0.65,
blur_detail_x=15,
blur_detail_y=10,
mat_multiple=0.90)
def reg():
token = request.form.get("token")
is_exp = request.form.get("is_exp")
is_exp = int(is_exp)
account_body_id = request.form.get("account_body_id")
a = ''.join(token)
source_image1_beauty = "/home/scy/face_fusion/pugongyin/" + a + "a.jpg"
source_image1_draw = "/home/scy/face_fusion/pugongyin/" + a + "aceshi.jpg"
model_image_nv = "/home/scy/face_fusion/yry-master/yry-master/images/nvgai1.png"
face_image_output = "/home/scy/face_fusion/pugongyin/" + a + "bceshi.jpg"
model_image_nan = "/home/scy/face_fusion/yry-master/yry-master/images/nan.png"
if is_exp == 1:
conn = pymysql.connect(host='192.168.1.228',
port=3306,
user='******',
password='******',
db='pgy')
cursor = conn.cursor()
cursor.execute("select sex from md_exp_body where account_body_id=%s",
[
account_body_id,
])
reslut = cursor.fetchone()
sex = reslut[0]
if account_body_id:
if sex == 2:
img_400x320 = cv2.imread(source_image1_beauty)
bt = cv2.resize(img_400x320,
None,
fx=1.1,
fy=1,
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(source_image1_draw, bt)
src_points = [
(701, 276), (701, 316), (707, 355), (710, 393), (714, 431),
(720, 469), (726, 508), (736, 546), (749, 582), (768, 616),
(794, 646), (826, 669), (862, 688), (900, 653), (941, 663),
(983, 670), (1028, 674), (1072, 674), (1111, 668),
(1147, 657), (1181, 691), (1214, 672), (1245, 646),
(1270, 617), (1292, 583), (1306, 547), (1314, 509),
(1320, 470), (1324, 431), (1326, 393), (1327, 354),
(1325, 315), (1324, 276), (840, 308), (865, 284), (897,
277),
(930, 286), (953, 311), (926, 316), (894, 319), (864, 313),
(907, 297), (905, 295), (769, 155), (818, 147), (865, 171),
(930, 177), (973, 191), (841, 248), (884, 247), (927, 251),
(970, 260), (1000, 540), (961, 551), (937, 565), (975,
569),
(1050, 541), (1087, 554), (1108, 569), (1072, 571),
(1025, 547), (1025, 573), (922, 564), (1123, 568),
(1073, 571), (1096, 590), (1064, 605), (976, 567),
(951, 586), (983, 603), (1025, 571), (1024, 610),
(1023, 292), (1024, 340), (1024, 388), (1024, 436),
(983, 312), (961, 412), (944, 456), (969, 470), (997, 475),
(1027, 485), (1066, 312), (1088, 412), (1107, 456),
(1082, 470), (1055, 475), (1097, 319), (1121, 287),
(1155, 278), (1188, 284), (1216, 301), (1189, 312),
(1158, 318), (1126, 316), (1151, 297), (1149, 295),
(1085, 185), (1127, 175), (1170, 173), (1213, 148),
(1248, 154), (1087, 253), (1128, 247), (1169, 245),
(1209, 248)
]
dst_points = core.face_points3(source_image1_draw)
core.face_merge1(
src_img=model_image_nv,
src_points=src_points,
dst_img=source_image1_draw,
out_img=face_image_output,
dst_points=dst_points,
face_area=[705, 157, 629, 629],
alpha=0.9,
k_size=(1, 240),
mat_multiple=1.3,
)
with open(face_image_output, "rb") as f:
base64_data1 = base64.b64encode(f.read())
face_output = 'data:image/jpeg;base64,' + (str(
base64_data1, 'utf-8'))
cursor.execute(
"update md_exp_body set model_img=%s where account_body_id=%s",
[face_output, account_body_id])
conn.commit()
cursor.close()
conn.close()
reslut = {}
reslut["status"] = "200"
reslut["result"] = "successful"
json_str = json.dumps(reslut)
rst = make_response(json_str)
return rst
else:
img_400x320 = cv2.imread(source_image1_beauty)
bt = cv2.resize(img_400x320,
None,
fx=1.3,
fy=1,
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(source_image1_draw, bt)
src_points = [
(735, 281), (731, 319), (736, 355), (738, 391), (741, 426),
(745, 461), (751, 498), (758, 533), (768, 569), (782, 602),
(803, 631), (832, 654), (867, 671), (904, 633), (944, 640),
(985, 644), (1027, 645), (1068, 645), (1104, 641),
(1137, 632), (1168, 669), (1199, 651), (1226, 626),
(1246, 597), (1263, 565), (1274, 530), (1280, 495),
(1284, 459), (1287, 424), (1288, 388), (1287, 353),
(1283, 318), (1279, 283), (846, 314), (867, 289), (896,
282),
(927, 289), (951, 309), (925, 315), (896, 318), (868, 315),
(898, 300), (896, 298), (782, 158), (831, 152), (876, 176),
(940, 180), (980, 192), (854, 253), (896, 251), (937, 254),
(977, 261), (1005, 506), (970, 521), (951, 540), (983,
536),
(1051, 506), (1086, 522), (1105, 541), (1072, 537),
(1028, 512), (1028, 536), (938, 541), (1119, 542),
(1072, 536), (1091, 555), (1061, 563), (985, 535),
(964, 554), (993, 563), (1028, 534), (1027, 566),
(1028, 294), (1028, 335), (1028, 376), (1027, 417),
(988, 312), (968, 404), (953, 440), (977, 450),
(1002, 454), (1028, 461), (1070, 312), (1088, 405),
(1102, 441), (1078, 451), (1053, 455), (1106, 317),
(1128, 289), (1157, 282), (1186, 289), (1208, 306),
(1185, 315), (1159, 318), (1131, 314), (1159, 301),
(1157, 299), (1075, 192), (1112, 181), (1155, 177),
(1199, 152), (1238, 155), (1078, 260), (1116, 253),
(1156, 250), (1196, 252)
]
dst_points = core.face_points2(source_image1_draw)
core.face_merge(src_img=model_image_nan,
src_points=src_points,
dst_img=source_image1_draw,
out_img=face_image_output,
dst_points=dst_points,
face_area=[736, 559, 181, 559],
alpha=0.9,
k_size=(1, 260),
mat_multiple=1.3)
with open(face_image_output, "rb") as f:
base64_data1 = base64.b64encode(f.read())
face_output = 'data:image/jpeg;base64,' + (str(
base64_data1, 'utf-8'))
cursor.execute(
"update md_exp_body set model_img=%s where account_body_id=%s",
[face_output, account_body_id])
conn.commit()
cursor.close()
conn.close()
reslut = {}
reslut["status"] = "200"
reslut["result"] = "successful"
json_str = json.dumps(reslut)
rst = make_response(json_str)
return rst
else:
reslut = {}
reslut["status"] = "400"
reslut["result"] = "failed"
json_str = json.dumps(reslut)
rst = make_response(json_str)
return rst
elif is_exp == 0:
conn = pymysql.connect(host='192.168.1.228',
port=3306,
user='******',
password='******',
db='pgy')
cursor = conn.cursor()
cursor.execute(
"select sex from md_account_body where account_body_id=%s", [
account_body_id,
])
reslut = cursor.fetchone()
sex = reslut[0]
if account_body_id:
if sex == 2:
img_400x320 = cv2.imread(source_image1_beauty)
bt = cv2.resize(img_400x320,
None,
fx=1.1,
fy=1,
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(source_image1_draw, bt)
src_points = [
(701, 276), (701, 316), (707, 355), (710, 393), (714, 431),
(720, 469), (726, 508), (736, 546), (749, 582), (768, 616),
(794, 646), (826, 669), (862, 688), (900, 653), (941, 663),
(983, 670), (1028, 674), (1072, 674), (1111, 668),
(1147, 657), (1181, 691), (1214, 672), (1245, 646),
(1270, 617), (1292, 583), (1306, 547), (1314, 509),
(1320, 470), (1324, 431), (1326, 393), (1327, 354),
(1325, 315), (1324, 276), (840, 308), (865, 284), (897,
277),
(930, 286), (953, 311), (926, 316), (894, 319), (864, 313),
(907, 297), (905, 295), (769, 155), (818, 147), (865, 171),
(930, 177), (973, 191), (841, 248), (884, 247), (927, 251),
(970, 260), (1000, 540), (961, 551), (937, 565), (975,
569),
(1050, 541), (1087, 554), (1108, 569), (1072, 571),
(1025, 547), (1025, 573), (922, 564), (1123, 568),
(1073, 571), (1096, 590), (1064, 605), (976, 567),
(951, 586), (983, 603), (1025, 571), (1024, 610),
(1023, 292), (1024, 340), (1024, 388), (1024, 436),
(983, 312), (961, 412), (944, 456), (969, 470), (997, 475),
(1027, 485), (1066, 312), (1088, 412), (1107, 456),
(1082, 470), (1055, 475), (1097, 319), (1121, 287),
(1155, 278), (1188, 284), (1216, 301), (1189, 312),
(1158, 318), (1126, 316), (1151, 297), (1149, 295),
(1085, 185), (1127, 175), (1170, 173), (1213, 148),
(1248, 154), (1087, 253), (1128, 247), (1169, 245),
(1209, 248)
]
dst_points = core.face_points3(source_image1_draw)
core.face_merge1(
src_img=model_image_nv,
src_points=src_points,
dst_img=source_image1_draw,
out_img=face_image_output,
dst_points=dst_points,
face_area=[705, 157, 629, 629],
alpha=0.9,
k_size=(1, 240),
mat_multiple=1.3,
)
with open(face_image_output, "rb") as f:
base64_data1 = base64.b64encode(f.read())
face_output = 'data:image/jpeg;base64,' + (str(
base64_data1, 'utf-8'))
cursor.execute(
"update md_account_body set model_img=%s where account_body_id=%s",
[face_output, account_body_id])
conn.commit()
cursor.close()
conn.close()
reslut = {}
reslut["status"] = "200"
reslut["result"] = "successful"
json_str = json.dumps(reslut)
rst = make_response(json_str)
return rst
else:
img_400x320 = cv2.imread(source_image1_beauty)
bt = cv2.resize(img_400x320,
None,
fx=1.3,
fy=1,
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(source_image1_draw, bt)
src_points = [
(735, 281), (731, 319), (736, 355), (738, 391), (741, 426),
(745, 461), (751, 498), (758, 533), (768, 569), (782, 602),
(803, 631), (832, 654), (867, 671), (904, 633), (944, 640),
(985, 644), (1027, 645), (1068, 645), (1104, 641),
(1137, 632), (1168, 669), (1199, 651), (1226, 626),
(1246, 597), (1263, 565), (1274, 530), (1280, 495),
(1284, 459), (1287, 424), (1288, 388), (1287, 353),
(1283, 318), (1279, 283), (846, 314), (867, 289), (896,
282),
(927, 289), (951, 309), (925, 315), (896, 318), (868, 315),
(898, 300), (896, 298), (782, 158), (831, 152), (876, 176),
(940, 180), (980, 192), (854, 253), (896, 251), (937, 254),
(977, 261), (1005, 506), (970, 521), (951, 540), (983,
536),
(1051, 506), (1086, 522), (1105, 541), (1072, 537),
(1028, 512), (1028, 536), (938, 541), (1119, 542),
(1072, 536), (1091, 555), (1061, 563), (985, 535),
(964, 554), (993, 563), (1028, 534), (1027, 566),
(1028, 294), (1028, 335), (1028, 376), (1027, 417),
(988, 312), (968, 404), (953, 440), (977, 450),
(1002, 454), (1028, 461), (1070, 312), (1088, 405),
(1102, 441), (1078, 451), (1053, 455), (1106, 317),
(1128, 289), (1157, 282), (1186, 289), (1208, 306),
(1185, 315), (1159, 318), (1131, 314), (1159, 301),
(1157, 299), (1075, 192), (1112, 181), (1155, 177),
(1199, 152), (1238, 155), (1078, 260), (1116, 253),
(1156, 250), (1196, 252)
]
dst_points = core.face_points2(source_image1_draw)
core.face_merge(src_img=model_image_nan,
src_points=src_points,
dst_img=source_image1_draw,
out_img=face_image_output,
dst_points=dst_points,
face_area=[736, 559, 181, 559],
alpha=0.9,
k_size=(1, 260),
mat_multiple=1.3)
with open(face_image_output, "rb") as f:
base64_data1 = base64.b64encode(f.read())
face_output = 'data:image/jpeg;base64,' + (str(
base64_data1, 'utf-8'))
cursor.execute(
"update md_account_body set model_img=%s where account_body_id=%s",
[face_output, account_body_id])
conn.commit()
cursor.close()
conn.close()
reslut = {}
reslut["status"] = "200"
reslut["result"] = "successful"
json_str = json.dumps(reslut)
rst = make_response(json_str)
return rst
else:
reslut = {}
reslut["status"] = "400"
reslut["result"] = "failed"
json_str = json.dumps(reslut)
rst = make_response(json_str)
return rst
else:
reslut = {}
reslut["status"] = "400"
reslut["result"] = "failed"
json_str = json.dumps(reslut)
rst = make_response(json_str)
return rst
# -*- 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 reg():
exp_id = request.form.get("exp_id")
exp_id = int(exp_id)
face_data = request.form.get("face_data")
sex = request.form.get("sex")
sex = int(sex)
source_image1_beauty = "/home/scy/face_fusion/pugongyin/" + str(
exp_id) + "a.jpg"
source_image1_draw = "/home/scy/face_fusion/pugongyin/" + str(
exp_id) + "aceshi.jpg"
model_image_nv = "/home/scy/face_fusion/yry-master/yry-master/images/nvgai1.png"
face_image_output = "/home/scy/face_fusion/pugongyin/" + str(
exp_id) + "bceshi.jpg"
model_image_nan = "/home/scy/face_fusion/yry-master/yry-master/images/fenglei.jpg"
if sex == 2:
img_400x320 = cv2.imread(source_image1_beauty)
bt = cv2.resize(img_400x320,
None,
fx=1.1,
fy=1,
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(source_image1_draw, bt)
src_points = [
(701, 276), (701, 316), (707, 355), (710, 393), (714, 431),
(720, 469), (726, 508), (736, 546), (749, 582), (768, 616),
(794, 646), (826, 669), (862, 688), (900, 653), (941, 663),
(983, 670), (1028, 674), (1072, 674), (1111, 668), (1147, 657),
(1181, 691), (1214, 672), (1245, 646), (1270, 617), (1292, 583),
(1306, 547), (1314, 509), (1320, 470), (1324, 431), (1326, 393),
(1327, 354), (1325, 315), (1324, 276), (840, 308), (865, 284),
(897, 277), (930, 286), (953, 311), (926, 316), (894, 319),
(864, 313), (907, 297), (905, 295), (769, 155), (818, 147),
(865, 171), (930, 177), (973, 191), (841, 248), (884, 247),
(927, 251), (970, 260), (1000, 540), (961, 551), (937, 565),
(975, 569), (1050, 541), (1087, 554), (1108, 569), (1072, 571),
(1025, 547), (1025, 573), (922, 564), (1123, 568), (1073, 571),
(1096, 590), (1064, 605), (976, 567), (951, 586), (983, 603),
(1025, 571), (1024, 610), (1023, 292), (1024, 340), (1024, 388),
(1024, 436), (983, 312), (961, 412), (944, 456), (969, 470),
(997, 475), (1027, 485), (1066, 312), (1088, 412), (1107, 456),
(1082, 470), (1055, 475), (1097, 319), (1121, 287), (1155, 278),
(1188, 284), (1216, 301), (1189, 312), (1158, 318), (1126, 316),
(1151, 297), (1149, 295), (1085, 185), (1127, 175), (1170, 173),
(1213, 148), (1248, 154), (1087, 253), (1128, 247), (1169, 245),
(1209, 248)
]
dst_points = core.face_points3(source_image1_draw)
core.face_merge1(
src_img=model_image_nv,
src_points=src_points,
dst_img=source_image1_draw,
out_img=face_image_output,
dst_points=dst_points,
face_area=[705, 157, 629, 629],
alpha=0.9,
k_size=(1, 240),
mat_multiple=1.3,
)
with open(face_image_output, "rb") as f:
base64_data1 = base64.b64encode(f.read())
face_output = 'data:image/jpeg;base64,' + (str(base64_data1, 'utf-8'))
conn = pymysql.connect(host='192.168.1.228',
port=3306,
user='******',
password='******',
db='fenglei')
cursor = conn.cursor()
cursor.execute("update fl_exp set faceTexture=%s where exp_id=%s",
[face_output, exp_id])
conn.commit()
cursor.close()
conn.close()
reslut = {}
reslut["status"] = "200"
reslut["result"] = "successful"
json_str = json.dumps(reslut)
rst = make_response(json_str)
return rst
elif sex == 1:
img_400x320 = cv2.imread(source_image1_beauty)
bt = cv2.resize(img_400x320,
None,
fx=1,
fy=1,
interpolation=cv2.INTER_CUBIC)
cv2.imwrite(source_image1_draw, bt)
src_points = [
(762, 305), (762, 344), (767, 381), (769, 417), (772, 453),
(775, 490), (780, 527), (786, 564), (796, 600), (811, 634),
(831, 664), (857, 690), (885, 712), (916, 683), (951, 699),
(988, 711), (1031, 715), (1077, 712), (1116, 702), (1153, 687),
(1187, 717), (1222, 696), (1255, 670), (1281, 640), (1304, 606),
(1318, 569), (1324, 529), (1328, 488), (1331, 448), (1332, 409),
(1333, 369), (1330, 329), (1327, 287), (867, 335), (887, 312),
(914, 306), (944, 314), (966, 335), (941, 340), (913, 344),
(887, 339), (917, 322), (915, 320), (776, 194), (821, 176),
(871, 194), (940, 202), (981, 222), (848, 279), (891, 272),
(934, 279), (976, 293), (1003, 559), (972, 573), (959, 593),
(987, 591), (1047, 558), (1079, 571), (1095, 590), (1066, 589),
(1025, 564), (1026, 592), (947, 593), (1108, 589), (1066, 592),
(1087, 611), (1061, 627), (987, 594), (967, 614), (992, 629),
(1026, 595), (1027, 635), (1021, 316), (1020, 363), (1019, 410),
(1019, 457), (987, 335), (973, 442), (959, 488), (978, 497),
(999, 501), (1021, 505), (1056, 335), (1070, 441), (1086, 486),
(1065, 496), (1044, 500), (1080, 343), (1105, 313), (1135, 306),
(1164, 311), (1187, 325), (1165, 335), (1137, 341), (1108, 339),
(1136, 322), (1134, 320), (1069, 227), (1113, 205), (1164, 196),
(1210, 182), (1237, 204), (1073, 295), (1118, 279), (1161, 272),
(1201, 283)
]
dst_points = core.face_points4(source_image1_draw)
core.face_merge(src_img=model_image_nan,
src_points=src_points,
dst_img=source_image1_draw,
out_img=face_image_output,
dst_points=dst_points,
face_area=[767, 217, 573, 573],
alpha=0.9,
k_size=(1, 260),
mat_multiple=1.3)
with open(face_image_output, "rb") as f:
base64_data1 = base64.b64encode(f.read())
face_output = 'data:image/jpeg;base64,' + (str(base64_data1, 'utf-8'))
conn = pymysql.connect(host='192.168.1.228',
port=3306,
user='******',
password='******',
db='fenglei')
cursor = conn.cursor()
cursor.execute("update fl_exp set faceTexture=%s where exp_id=%s",
[face_output, exp_id])
conn.commit()
cursor.close()
conn.close()
reslut = {}
reslut["status"] = "200"
reslut["result"] = "successful"
json_str = json.dumps(reslut)
rst = make_response(json_str)
return rst
else:
reslut = {}
reslut["status"] = "400"
reslut["result"] = "failed"
json_str = json.dumps(reslut)
rst = make_response(json_str)
return rst
# -*- coding: utf-8 -*-
# @Time : 2017/9/2 13:40
# @Author : 郑梓斌
import core
if __name__ == '__main__':
core.face_merge(src_img='images/model.jpg',
dst_img='images/20171030175254.jpg',
#dst_img='images/test_multi.jpg',
#dst_img='images/test_single.jpg',
out_img='images/output.jpg',
face_area=[50, 30, 500, 485],
alpha=0.75,
k_size=(15, 10),
mat_multiple=0.95)
# -*- coding: utf-8 -*-
# @Time : 2017/9/2 13:40
# @Author : 郑梓斌
import core
if __name__ == '__main__':
src = 'images/model.jpg'
dst = 'images/20171030175254.jpg'
output = 'images/output.jpg'
src_points, _ = core.face_points(src)
dst_points, _ = core.face_points(dst)
core.face_merge(src_img='images/model.jpg',
src_points=src_points,
dst_img='images/20171030175254.jpg',
out_img='images/output.jpg',
dst_points=dst_points,
face_area=[250, 150, 270, 250],
alpha=0.75,
k_size=(15, 10),
mat_multiple=0.95)
# -*- coding: utf-8 -*-
# @Time : 2017/9/2 13:40
# @Author : 郑梓斌
import core
from time import time
# src-img ---- 模特图片
# dst_img ---- 带融合图片
# out_img ---- 结果图片输出路径
# face_area ---- 指定模板图中进行人脸融合的人脸框位置。四个正整数数组，依次代表人脸框左上角纵坐标（top），左上角横坐标（left），
# 人脸框宽度（width），人脸框高度（height），通过设定改参数可以减少结果的大范围变形，把变形风险控制在人脸框区域
# alpha —— 融合比例，范围 [0,1]。数字越大融合结果包含越多融合图 (dst_img) 特征。
# blur_size—— 模糊核大小，用于模糊人脸融合边缘，减少融合后的违和感
# mat_multiple —— 缩放获取到的人脸心型区域
if __name__ == '__main__':
start = time()
core.face_merge(src_img='./images/b.jpg',
dst_img='./images/aa.jpg',
out_img='output/output_test_source.png',
face_area=[50, 30, 500, 485],
alpha=0.75,
k_size=(15, 10),
mat_multiple=0.95)
stop = time()
print(str(stop - start) + "秒")
import core
core.face_merge("images/temp_item.jpg", "images/model_zbll.jpg",
"result/output_" + "test1.jpg")