Spaces:
Runtime error
Runtime error
Harisreedhar
commited on
Commit
β’
0b756df
1
Parent(s):
7cb2f8d
add
Browse files- .gitignore +2 -0
- app.py +871 -0
- assets/images/logo.png +0 -0
- assets/pretrained_models/79999_iter.pth +3 -0
- assets/pretrained_models/GFPGANv1.4.pth +3 -0
- assets/pretrained_models/inswapper_128.onnx +3 -0
- assets/pretrained_models/readme.md +4 -0
- face_analyser.py +32 -0
- face_parsing/__init__.py +1 -0
- face_parsing/model.py +283 -0
- face_parsing/resnet.py +109 -0
- face_parsing/swap.py +91 -0
- gfpgan/weights/detection_Resnet50_Final.pth +3 -0
- gfpgan/weights/parsing_parsenet.pth +3 -0
- requirements.txt +10 -0
- swapper.py +106 -0
- utils.py +112 -0
.gitignore
ADDED
@@ -0,0 +1,2 @@
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*.pyc
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app.py
ADDED
@@ -0,0 +1,871 @@
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1 |
+
import os
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2 |
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import cv2
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3 |
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import glob
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4 |
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import time
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5 |
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import torch
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import shutil
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7 |
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import gfpgan
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8 |
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import argparse
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9 |
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import platform
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import datetime
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import subprocess
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import insightface
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import onnxruntime
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import numpy as np
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import gradio as gr
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from moviepy.editor import VideoFileClip, ImageSequenceClip
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17 |
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from face_analyser import detect_conditions, analyse_face
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from utils import trim_video, StreamerThread, ProcessBar, open_directory
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from face_parsing import init_parser, swap_regions, mask_regions, mask_regions_to_list
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from swapper import (
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swap_face,
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swap_face_with_condition,
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swap_specific,
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swap_options_list,
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)
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## ------------------------------ USER ARGS ------------------------------
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parser = argparse.ArgumentParser(description="Swap-Mukham Face Swapper")
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parser.add_argument("--out_dir", help="Default Output directory", default=os.getcwd())
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parser.add_argument("--cuda", action="store_true", help="Enable cuda", default=False)
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parser.add_argument(
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"--colab", action="store_true", help="Enable colab mode", default=False
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)
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user_args = parser.parse_args()
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## ------------------------------ DEFAULTS ------------------------------
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USE_COLAB = user_args.colab
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USE_CUDA = user_args.cuda
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DEF_OUTPUT_PATH = user_args.out_dir
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WORKSPACE = None
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OUTPUT_FILE = None
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CURRENT_FRAME = None
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STREAMER = None
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DETECT_CONDITION = "left most"
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DETECT_SIZE = 640
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DETECT_THRESH = 0.6
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NUM_OF_SRC_SPECIFIC = 10
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MASK_INCLUDE = [
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"Skin",
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"R-Eyebrow",
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"L-Eyebrow",
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"L-Eye",
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"R-Eye",
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"Nose",
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"Mouth",
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"L-Lip",
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"U-Lip"
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]
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MASK_EXCLUDE = ["R-Ear", "L-Ear", "Hair", "Hat"]
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MASK_BLUR = 25
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FACE_SWAPPER = None
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FACE_ANALYSER = None
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FACE_ENHANCER = None
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FACE_PARSER = None
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+
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70 |
+
## ------------------------------ SET EXECUTION PROVIDER ------------------------------
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71 |
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# Note: For AMD,MAC or non CUDA users, change settings here
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72 |
+
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PROVIDER = ["CPUExecutionProvider"]
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+
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75 |
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if USE_CUDA:
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available_providers = onnxruntime.get_available_providers()
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77 |
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if "CUDAExecutionProvider" in available_providers:
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print("\n********** Running on CUDA **********\n")
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PROVIDER = ["CUDAExecutionProvider", "CPUExecutionProvider"]
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else:
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USE_CUDA = False
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82 |
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print("\n********** CUDA unavailable running on CPU **********\n")
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else:
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USE_CUDA = False
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print("\n********** Running on CPU **********\n")
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87 |
+
|
88 |
+
## ------------------------------ LOAD MODELS ------------------------------
|
89 |
+
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90 |
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def load_face_analyser_model(name="buffalo_l"):
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91 |
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global FACE_ANALYSER
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92 |
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if FACE_ANALYSER is None:
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93 |
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FACE_ANALYSER = insightface.app.FaceAnalysis(name=name, providers=PROVIDER)
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FACE_ANALYSER.prepare(
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ctx_id=0, det_size=(DETECT_SIZE, DETECT_SIZE), det_thresh=DETECT_THRESH
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96 |
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)
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97 |
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+
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99 |
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def load_face_swapper_model(name="./assets/pretrained_models/inswapper_128.onnx"):
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100 |
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global FACE_SWAPPER
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101 |
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path = os.path.join(os.path.abspath(os.path.dirname(__file__)), name)
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102 |
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if FACE_SWAPPER is None:
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103 |
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FACE_SWAPPER = insightface.model_zoo.get_model(path, providers=PROVIDER)
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104 |
+
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105 |
+
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106 |
+
def load_face_enhancer_model(name="./assets/pretrained_models/GFPGANv1.4.pth"):
|
107 |
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global FACE_ENHANCER
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108 |
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path = os.path.join(os.path.abspath(os.path.dirname(__file__)), name)
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109 |
+
if FACE_ENHANCER is None:
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110 |
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FACE_ENHANCER = gfpgan.GFPGANer(model_path=path, upscale=1)
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111 |
+
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112 |
+
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113 |
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def load_face_parser_model(name="./assets/pretrained_models/79999_iter.pth"):
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114 |
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global FACE_PARSER
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115 |
+
path = os.path.join(os.path.abspath(os.path.dirname(__file__)), name)
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116 |
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if FACE_PARSER is None:
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117 |
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FACE_PARSER = init_parser(name, use_cuda=USE_CUDA)
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118 |
+
|
119 |
+
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120 |
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load_face_analyser_model()
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121 |
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load_face_swapper_model()
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122 |
+
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123 |
+
## ------------------------------ MAIN PROCESS ------------------------------
|
124 |
+
|
125 |
+
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126 |
+
def process(
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127 |
+
input_type,
|
128 |
+
image_path,
|
129 |
+
video_path,
|
130 |
+
directory_path,
|
131 |
+
source_path,
|
132 |
+
output_path,
|
133 |
+
output_name,
|
134 |
+
keep_output_sequence,
|
135 |
+
condition,
|
136 |
+
age,
|
137 |
+
distance,
|
138 |
+
face_enhance,
|
139 |
+
enable_face_parser,
|
140 |
+
mask_include,
|
141 |
+
mask_exclude,
|
142 |
+
mask_blur,
|
143 |
+
*specifics,
|
144 |
+
):
|
145 |
+
global WORKSPACE
|
146 |
+
global OUTPUT_FILE
|
147 |
+
global PREVIEW
|
148 |
+
WORKSPACE, OUTPUT_FILE, PREVIEW = None, None, None
|
149 |
+
|
150 |
+
## ------------------------------ GUI UPDATE FUNC ------------------------------
|
151 |
+
|
152 |
+
def ui_before():
|
153 |
+
return (
|
154 |
+
gr.update(visible=True, value=PREVIEW),
|
155 |
+
gr.update(interactive=False),
|
156 |
+
gr.update(interactive=False),
|
157 |
+
gr.update(visible=False),
|
158 |
+
)
|
159 |
+
|
160 |
+
def ui_after():
|
161 |
+
return (
|
162 |
+
gr.update(visible=True, value=PREVIEW),
|
163 |
+
gr.update(interactive=True),
|
164 |
+
gr.update(interactive=True),
|
165 |
+
gr.update(visible=False),
|
166 |
+
)
|
167 |
+
|
168 |
+
def ui_after_vid():
|
169 |
+
return (
|
170 |
+
gr.update(visible=False),
|
171 |
+
gr.update(interactive=True),
|
172 |
+
gr.update(interactive=True),
|
173 |
+
gr.update(value=OUTPUT_FILE, visible=True),
|
174 |
+
)
|
175 |
+
|
176 |
+
## ------------------------------ LOAD PENDING MODELS ------------------------------
|
177 |
+
start_time = time.time()
|
178 |
+
specifics = list(specifics)
|
179 |
+
half = len(specifics) // 2
|
180 |
+
sources = specifics[:half]
|
181 |
+
specifics = specifics[half:]
|
182 |
+
|
183 |
+
yield "### \n β Loading face analyser model...", *ui_before()
|
184 |
+
load_face_analyser_model()
|
185 |
+
|
186 |
+
yield "### \n β Loading face swapper model...", *ui_before()
|
187 |
+
load_face_swapper_model()
|
188 |
+
|
189 |
+
if face_enhance:
|
190 |
+
yield "### \n β Loading face enhancer model...", *ui_before()
|
191 |
+
load_face_enhancer_model()
|
192 |
+
|
193 |
+
if enable_face_parser:
|
194 |
+
yield "### \n β Loading face parsing model...", *ui_before()
|
195 |
+
load_face_parser_model()
|
196 |
+
|
197 |
+
yield "### \n β Analysing Face...", *ui_before()
|
198 |
+
|
199 |
+
mi = mask_regions_to_list(mask_include)
|
200 |
+
me = mask_regions_to_list(mask_exclude)
|
201 |
+
models = {
|
202 |
+
"swap": FACE_SWAPPER,
|
203 |
+
"enhance": FACE_ENHANCER,
|
204 |
+
"enhance_sett": face_enhance,
|
205 |
+
"face_parser": FACE_PARSER,
|
206 |
+
"face_parser_sett": (enable_face_parser, mi, me, int(mask_blur)),
|
207 |
+
}
|
208 |
+
|
209 |
+
## ------------------------------ ANALYSE SOURCE & SPECIFIC ------------------------------
|
210 |
+
|
211 |
+
analysed_source_specific = []
|
212 |
+
if condition == "Specific Face":
|
213 |
+
for source, specific in zip(sources, specifics):
|
214 |
+
if source is None or specific is None:
|
215 |
+
continue
|
216 |
+
analysed_source = analyse_face(
|
217 |
+
source,
|
218 |
+
FACE_ANALYSER,
|
219 |
+
return_single_face=True,
|
220 |
+
detect_condition=DETECT_CONDITION,
|
221 |
+
)
|
222 |
+
analysed_specific = analyse_face(
|
223 |
+
specific,
|
224 |
+
FACE_ANALYSER,
|
225 |
+
return_single_face=True,
|
226 |
+
detect_condition=DETECT_CONDITION,
|
227 |
+
)
|
228 |
+
analysed_source_specific.append([analysed_source, analysed_specific])
|
229 |
+
else:
|
230 |
+
source = cv2.imread(source_path)
|
231 |
+
analysed_source = analyse_face(
|
232 |
+
source,
|
233 |
+
FACE_ANALYSER,
|
234 |
+
return_single_face=True,
|
235 |
+
detect_condition=DETECT_CONDITION,
|
236 |
+
)
|
237 |
+
|
238 |
+
## ------------------------------ IMAGE ------------------------------
|
239 |
+
|
240 |
+
if input_type == "Image":
|
241 |
+
target = cv2.imread(image_path)
|
242 |
+
analysed_target = analyse_face(target, FACE_ANALYSER, return_single_face=False)
|
243 |
+
if condition == "Specific Face":
|
244 |
+
swapped = swap_specific(
|
245 |
+
analysed_source_specific,
|
246 |
+
analysed_target,
|
247 |
+
target,
|
248 |
+
models,
|
249 |
+
threshold=distance,
|
250 |
+
)
|
251 |
+
else:
|
252 |
+
swapped = swap_face_with_condition(
|
253 |
+
target, analysed_target, analysed_source, condition, age, models
|
254 |
+
)
|
255 |
+
|
256 |
+
filename = os.path.join(output_path, output_name + ".png")
|
257 |
+
cv2.imwrite(filename, swapped)
|
258 |
+
OUTPUT_FILE = filename
|
259 |
+
WORKSPACE = output_path
|
260 |
+
PREVIEW = swapped[:, :, ::-1]
|
261 |
+
|
262 |
+
tot_exec_time = time.time() - start_time
|
263 |
+
_min, _sec = divmod(tot_exec_time, 60)
|
264 |
+
|
265 |
+
yield f"Completed in {int(_min)} min {int(_sec)} sec.", *ui_after()
|
266 |
+
|
267 |
+
## ------------------------------ VIDEO ------------------------------
|
268 |
+
|
269 |
+
elif input_type == "Video":
|
270 |
+
temp_path = os.path.join(output_path, output_name, "sequence")
|
271 |
+
os.makedirs(temp_path, exist_ok=True)
|
272 |
+
|
273 |
+
video_clip = VideoFileClip(video_path)
|
274 |
+
duration = video_clip.duration
|
275 |
+
fps = video_clip.fps
|
276 |
+
total_frames = video_clip.reader.nframes
|
277 |
+
|
278 |
+
analysed_targets = []
|
279 |
+
process_bar = ProcessBar(30, total_frames)
|
280 |
+
yield "### \n β Analysing...", *ui_before()
|
281 |
+
for i, frame in enumerate(video_clip.iter_frames()):
|
282 |
+
analysed_targets.append(
|
283 |
+
analyse_face(frame, FACE_ANALYSER, return_single_face=False)
|
284 |
+
)
|
285 |
+
info_text = "Analysing Faces || "
|
286 |
+
info_text += process_bar.get(i)
|
287 |
+
print("\033[1A\033[K", end="", flush=True)
|
288 |
+
print(info_text)
|
289 |
+
if i % 10 == 0:
|
290 |
+
yield "### \n" + info_text, *ui_before()
|
291 |
+
video_clip.close()
|
292 |
+
|
293 |
+
image_sequence = []
|
294 |
+
video_clip = VideoFileClip(video_path)
|
295 |
+
audio_clip = video_clip.audio if video_clip.audio is not None else None
|
296 |
+
process_bar = ProcessBar(30, total_frames)
|
297 |
+
yield "### \n β Swapping...", *ui_before()
|
298 |
+
for i, frame in enumerate(video_clip.iter_frames()):
|
299 |
+
swapped = frame
|
300 |
+
analysed_target = analysed_targets[i]
|
301 |
+
|
302 |
+
if condition == "Specific Face":
|
303 |
+
swapped = swap_specific(
|
304 |
+
frame,
|
305 |
+
analysed_target,
|
306 |
+
analysed_source_specific,
|
307 |
+
models,
|
308 |
+
threshold=distance,
|
309 |
+
)
|
310 |
+
else:
|
311 |
+
swapped = swap_face_with_condition(
|
312 |
+
frame, analysed_target, analysed_source, condition, age, models
|
313 |
+
)
|
314 |
+
|
315 |
+
image_path = os.path.join(temp_path, f"frame_{i}.png")
|
316 |
+
cv2.imwrite(image_path, swapped[:, :, ::-1])
|
317 |
+
image_sequence.append(image_path)
|
318 |
+
|
319 |
+
info_text = "Swapping Faces || "
|
320 |
+
info_text += process_bar.get(i)
|
321 |
+
print("\033[1A\033[K", end="", flush=True)
|
322 |
+
print(info_text)
|
323 |
+
if i % 6 == 0:
|
324 |
+
PREVIEW = swapped
|
325 |
+
yield "### \n" + info_text, *ui_before()
|
326 |
+
|
327 |
+
yield "### \n β Merging...", *ui_before()
|
328 |
+
edited_video_clip = ImageSequenceClip(image_sequence, fps=fps)
|
329 |
+
|
330 |
+
if audio_clip is not None:
|
331 |
+
edited_video_clip = edited_video_clip.set_audio(audio_clip)
|
332 |
+
|
333 |
+
output_video_path = os.path.join(output_path, output_name + ".mp4")
|
334 |
+
edited_video_clip.set_duration(duration).write_videofile(
|
335 |
+
output_video_path, codec="libx264"
|
336 |
+
)
|
337 |
+
edited_video_clip.close()
|
338 |
+
video_clip.close()
|
339 |
+
|
340 |
+
if os.path.exists(temp_path) and not keep_output_sequence:
|
341 |
+
yield "### \n β Removing temporary files...", *ui_before()
|
342 |
+
shutil.rmtree(temp_path)
|
343 |
+
|
344 |
+
WORKSPACE = output_path
|
345 |
+
OUTPUT_FILE = output_video_path
|
346 |
+
|
347 |
+
tot_exec_time = time.time() - start_time
|
348 |
+
_min, _sec = divmod(tot_exec_time, 60)
|
349 |
+
|
350 |
+
yield f"βοΈ Completed in {int(_min)} min {int(_sec)} sec.", *ui_after_vid()
|
351 |
+
|
352 |
+
## ------------------------------ DIRECTORY ------------------------------
|
353 |
+
|
354 |
+
elif input_type == "Directory":
|
355 |
+
source = cv2.imread(source_path)
|
356 |
+
source = analyse_face(
|
357 |
+
source,
|
358 |
+
FACE_ANALYSER,
|
359 |
+
return_single_face=True,
|
360 |
+
detect_condition=DETECT_CONDITION,
|
361 |
+
)
|
362 |
+
extensions = ["jpg", "jpeg", "png", "bmp", "tiff", "ico", "webp"]
|
363 |
+
temp_path = os.path.join(output_path, output_name)
|
364 |
+
if os.path.exists(temp_path):
|
365 |
+
shutil.rmtree(temp_path)
|
366 |
+
os.mkdir(temp_path)
|
367 |
+
swapped = None
|
368 |
+
|
369 |
+
files = []
|
370 |
+
for file_path in glob.glob(os.path.join(directory_path, "*")):
|
371 |
+
if any(file_path.lower().endswith(ext) for ext in extensions):
|
372 |
+
files.append(file_path)
|
373 |
+
|
374 |
+
files_length = len(files)
|
375 |
+
filename = None
|
376 |
+
for i, file_path in enumerate(files):
|
377 |
+
target = cv2.imread(file_path)
|
378 |
+
analysed_target = analyse_face(
|
379 |
+
target, FACE_ANALYSER, return_single_face=False
|
380 |
+
)
|
381 |
+
|
382 |
+
if condition == "Specific Face":
|
383 |
+
swapped = swap_specific(
|
384 |
+
target,
|
385 |
+
analysed_target,
|
386 |
+
analysed_source_specific,
|
387 |
+
models,
|
388 |
+
threshold=distance,
|
389 |
+
)
|
390 |
+
else:
|
391 |
+
swapped = swap_face_with_condition(
|
392 |
+
target, analysed_target, analysed_source, condition, age, models
|
393 |
+
)
|
394 |
+
|
395 |
+
filename = os.path.join(temp_path, os.path.basename(file_path))
|
396 |
+
cv2.imwrite(filename, swapped)
|
397 |
+
info_text = f"### \n β Processing file {i+1} of {files_length}"
|
398 |
+
PREVIEW = swapped[:, :, ::-1]
|
399 |
+
yield info_text, *ui_before()
|
400 |
+
|
401 |
+
WORKSPACE = temp_path
|
402 |
+
OUTPUT_FILE = filename
|
403 |
+
|
404 |
+
tot_exec_time = time.time() - start_time
|
405 |
+
_min, _sec = divmod(tot_exec_time, 60)
|
406 |
+
|
407 |
+
yield f"βοΈ Completed in {int(_min)} min {int(_sec)} sec.", *ui_after()
|
408 |
+
|
409 |
+
## ------------------------------ STREAM ------------------------------
|
410 |
+
|
411 |
+
elif input_type == "Stream":
|
412 |
+
yield "### \n β Starting...", *ui_before()
|
413 |
+
global STREAMER
|
414 |
+
STREAMER = StreamerThread(src=directory_path)
|
415 |
+
STREAMER.start()
|
416 |
+
|
417 |
+
while True:
|
418 |
+
try:
|
419 |
+
target = STREAMER.frame
|
420 |
+
analysed_target = analyse_face(
|
421 |
+
target, FACE_ANALYSER, return_single_face=False
|
422 |
+
)
|
423 |
+
if condition == "Specific Face":
|
424 |
+
swapped = swap_specific(
|
425 |
+
target,
|
426 |
+
analysed_target,
|
427 |
+
analysed_source_specific,
|
428 |
+
models,
|
429 |
+
threshold=distance,
|
430 |
+
)
|
431 |
+
else:
|
432 |
+
swapped = swap_face_with_condition(
|
433 |
+
target, analysed_target, analysed_source, condition, age, models
|
434 |
+
)
|
435 |
+
PREVIEW = swapped[:, :, ::-1]
|
436 |
+
yield f"Streaming...", *ui_before()
|
437 |
+
except AttributeError:
|
438 |
+
yield "Streaming...", *ui_before()
|
439 |
+
STREAMER.stop()
|
440 |
+
|
441 |
+
|
442 |
+
## ------------------------------ GRADIO FUNC ------------------------------
|
443 |
+
|
444 |
+
|
445 |
+
def update_radio(value):
|
446 |
+
if value == "Image":
|
447 |
+
return (
|
448 |
+
gr.update(visible=True),
|
449 |
+
gr.update(visible=False),
|
450 |
+
gr.update(visible=False),
|
451 |
+
)
|
452 |
+
elif value == "Video":
|
453 |
+
return (
|
454 |
+
gr.update(visible=False),
|
455 |
+
gr.update(visible=True),
|
456 |
+
gr.update(visible=False),
|
457 |
+
)
|
458 |
+
elif value == "Directory":
|
459 |
+
return (
|
460 |
+
gr.update(visible=False),
|
461 |
+
gr.update(visible=False),
|
462 |
+
gr.update(visible=True),
|
463 |
+
)
|
464 |
+
elif value == "Stream":
|
465 |
+
return (
|
466 |
+
gr.update(visible=False),
|
467 |
+
gr.update(visible=False),
|
468 |
+
gr.update(visible=True),
|
469 |
+
)
|
470 |
+
|
471 |
+
|
472 |
+
def swap_option_changed(value):
|
473 |
+
if value == swap_options_list[1] or value == swap_options_list[2]:
|
474 |
+
return (
|
475 |
+
gr.update(visible=True),
|
476 |
+
gr.update(visible=False),
|
477 |
+
gr.update(visible=True),
|
478 |
+
)
|
479 |
+
elif value == swap_options_list[5]:
|
480 |
+
return (
|
481 |
+
gr.update(visible=False),
|
482 |
+
gr.update(visible=True),
|
483 |
+
gr.update(visible=False),
|
484 |
+
)
|
485 |
+
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
|
486 |
+
|
487 |
+
|
488 |
+
def video_changed(video_path):
|
489 |
+
sliders_update = gr.Slider.update
|
490 |
+
button_update = gr.Button.update
|
491 |
+
number_update = gr.Number.update
|
492 |
+
|
493 |
+
if video_path is None:
|
494 |
+
return (
|
495 |
+
sliders_update(minimum=0, maximum=0, value=0),
|
496 |
+
sliders_update(minimum=1, maximum=1, value=1),
|
497 |
+
number_update(value=1),
|
498 |
+
)
|
499 |
+
try:
|
500 |
+
clip = VideoFileClip(video_path)
|
501 |
+
fps = clip.fps
|
502 |
+
total_frames = clip.reader.nframes
|
503 |
+
clip.close()
|
504 |
+
return (
|
505 |
+
sliders_update(minimum=0, maximum=total_frames, value=0, interactive=True),
|
506 |
+
sliders_update(
|
507 |
+
minimum=0, maximum=total_frames, value=total_frames, interactive=True
|
508 |
+
),
|
509 |
+
number_update(value=fps),
|
510 |
+
)
|
511 |
+
except:
|
512 |
+
return (
|
513 |
+
sliders_update(value=0),
|
514 |
+
sliders_update(value=0),
|
515 |
+
number_update(value=1),
|
516 |
+
)
|
517 |
+
|
518 |
+
|
519 |
+
def analyse_settings_changed(detect_condition, detection_size, detection_threshold):
|
520 |
+
yield "### \n β Applying new values..."
|
521 |
+
global FACE_ANALYSER
|
522 |
+
global DETECT_CONDITION
|
523 |
+
DETECT_CONDITION = detect_condition
|
524 |
+
FACE_ANALYSER = insightface.app.FaceAnalysis(name="buffalo_l", providers=PROVIDER)
|
525 |
+
FACE_ANALYSER.prepare(
|
526 |
+
ctx_id=0,
|
527 |
+
det_size=(int(detection_size), int(detection_size)),
|
528 |
+
det_thresh=float(detection_threshold),
|
529 |
+
)
|
530 |
+
yield f"### \n βοΈ Applied detect condition:{detect_condition}, detection size: {detection_size}, detection threshold: {detection_threshold}"
|
531 |
+
|
532 |
+
|
533 |
+
def stop_running():
|
534 |
+
global STREAMER
|
535 |
+
if hasattr(STREAMER, "stop"):
|
536 |
+
STREAMER.stop()
|
537 |
+
STREAMER = None
|
538 |
+
return "Cancelled"
|
539 |
+
|
540 |
+
|
541 |
+
def slider_changed(show_frame, video_path, frame_index):
|
542 |
+
if not show_frame:
|
543 |
+
return None, None
|
544 |
+
if video_path is None:
|
545 |
+
return None, None
|
546 |
+
clip = VideoFileClip(video_path)
|
547 |
+
frame = clip.get_frame(frame_index / clip.fps)
|
548 |
+
frame_array = np.array(frame)
|
549 |
+
clip.close()
|
550 |
+
return gr.Image.update(value=frame_array, visible=True), gr.Video.update(
|
551 |
+
visible=False
|
552 |
+
)
|
553 |
+
|
554 |
+
|
555 |
+
def trim_and_reload(video_path, output_path, output_name, start_frame, stop_frame):
|
556 |
+
yield video_path, f"### \n β Trimming video frame {start_frame} to {stop_frame}..."
|
557 |
+
try:
|
558 |
+
output_path = os.path.join(output_path, output_name)
|
559 |
+
trimmed_video = trim_video(video_path, output_path, start_frame, stop_frame)
|
560 |
+
yield trimmed_video, "### \n βοΈ Video trimmed and reloaded."
|
561 |
+
except Exception as e:
|
562 |
+
print(e)
|
563 |
+
yield video_path, "### \n β Video trimming failed. See console for more info."
|
564 |
+
|
565 |
+
|
566 |
+
## ------------------------------ GRADIO GUI ------------------------------
|
567 |
+
|
568 |
+
css = """
|
569 |
+
footer{display:none !important}
|
570 |
+
"""
|
571 |
+
|
572 |
+
with gr.Blocks(css=css) as interface:
|
573 |
+
gr.Markdown("# πΏ Swap Mukham")
|
574 |
+
gr.Markdown("### Face swap app based on insightface inswapper.")
|
575 |
+
with gr.Row():
|
576 |
+
with gr.Row():
|
577 |
+
with gr.Column(scale=0.4):
|
578 |
+
with gr.Tab("π Swap Condition"):
|
579 |
+
swap_option = gr.Radio(
|
580 |
+
swap_options_list,
|
581 |
+
show_label=False,
|
582 |
+
value=swap_options_list[0],
|
583 |
+
interactive=True,
|
584 |
+
)
|
585 |
+
age = gr.Number(
|
586 |
+
value=25, label="Value", interactive=True, visible=False
|
587 |
+
)
|
588 |
+
|
589 |
+
with gr.Tab("ποΈ Detection Settings"):
|
590 |
+
detect_condition_dropdown = gr.Dropdown(
|
591 |
+
detect_conditions,
|
592 |
+
label="Condition",
|
593 |
+
value=DETECT_CONDITION,
|
594 |
+
interactive=True,
|
595 |
+
info="This condition is only used when multiple faces are detected on source or specific image.",
|
596 |
+
)
|
597 |
+
detection_size = gr.Number(
|
598 |
+
label="Detection Size", value=DETECT_SIZE, interactive=True
|
599 |
+
)
|
600 |
+
detection_threshold = gr.Number(
|
601 |
+
label="Detection Threshold",
|
602 |
+
value=DETECT_THRESH,
|
603 |
+
interactive=True,
|
604 |
+
)
|
605 |
+
apply_detection_settings = gr.Button("Apply settings")
|
606 |
+
|
607 |
+
with gr.Tab("π€ Output Settings"):
|
608 |
+
output_directory = gr.Text(
|
609 |
+
label="Output Directory",
|
610 |
+
value=DEF_OUTPUT_PATH,
|
611 |
+
interactive=True,
|
612 |
+
)
|
613 |
+
output_name = gr.Text(
|
614 |
+
label="Output Name", value="Result", interactive=True
|
615 |
+
)
|
616 |
+
keep_output_sequence = gr.Checkbox(
|
617 |
+
label="Keep output sequence", value=False, interactive=True
|
618 |
+
)
|
619 |
+
|
620 |
+
with gr.Tab("πͺ Other Settings"):
|
621 |
+
with gr.Accordion("Enhance Face", open=True):
|
622 |
+
enable_face_enhance = gr.Checkbox(
|
623 |
+
label="Enable GFPGAN", value=False, interactive=True
|
624 |
+
)
|
625 |
+
with gr.Accordion("Advanced Mask", open=False):
|
626 |
+
enable_face_parser_mask = gr.Checkbox(
|
627 |
+
label="Enable Face Parsing",
|
628 |
+
value=False,
|
629 |
+
interactive=True,
|
630 |
+
)
|
631 |
+
|
632 |
+
mask_include = gr.Dropdown(
|
633 |
+
mask_regions.keys(),
|
634 |
+
value=MASK_INCLUDE,
|
635 |
+
multiselect=True,
|
636 |
+
label="Include",
|
637 |
+
interactive=True,
|
638 |
+
)
|
639 |
+
mask_exclude = gr.Dropdown(
|
640 |
+
mask_regions.keys(),
|
641 |
+
value=MASK_EXCLUDE,
|
642 |
+
multiselect=True,
|
643 |
+
label="Exclude",
|
644 |
+
interactive=True,
|
645 |
+
)
|
646 |
+
mask_blur = gr.Number(
|
647 |
+
label="Blur Mask",
|
648 |
+
value=MASK_BLUR,
|
649 |
+
minimum=0,
|
650 |
+
interactive=True,
|
651 |
+
)
|
652 |
+
|
653 |
+
source_image_input = gr.Image(
|
654 |
+
label="Source face", type="filepath", interactive=True
|
655 |
+
)
|
656 |
+
|
657 |
+
with gr.Box(visible=False) as specific_face:
|
658 |
+
for i in range(NUM_OF_SRC_SPECIFIC):
|
659 |
+
idx = i + 1
|
660 |
+
code = "\n"
|
661 |
+
code += f"with gr.Tab(label='({idx})'):"
|
662 |
+
code += "\n\twith gr.Row():"
|
663 |
+
code += f"\n\t\tsrc{idx} = gr.Image(interactive=True, type='numpy', label='Source Face {idx}')"
|
664 |
+
code += f"\n\t\ttrg{idx} = gr.Image(interactive=True, type='numpy', label='Specific Face {idx}')"
|
665 |
+
exec(code)
|
666 |
+
|
667 |
+
distance_slider = gr.Slider(
|
668 |
+
minimum=0,
|
669 |
+
maximum=2,
|
670 |
+
value=0.6,
|
671 |
+
interactive=True,
|
672 |
+
label="Distance",
|
673 |
+
info="Lower distance is more similar and higher distance is less similar to the target face.",
|
674 |
+
)
|
675 |
+
|
676 |
+
with gr.Group():
|
677 |
+
input_type = gr.Radio(
|
678 |
+
["Image", "Video", "Directory", "Stream"],
|
679 |
+
label="Target Type",
|
680 |
+
value="Video",
|
681 |
+
)
|
682 |
+
|
683 |
+
with gr.Box(visible=False) as input_image_group:
|
684 |
+
image_input = gr.Image(
|
685 |
+
label="Target Image", interactive=True, type="filepath"
|
686 |
+
)
|
687 |
+
|
688 |
+
with gr.Box(visible=True) as input_video_group:
|
689 |
+
vid_widget = gr.Video if USE_COLAB else gr.Text
|
690 |
+
video_input = vid_widget(
|
691 |
+
label="Target Video Path", interactive=True
|
692 |
+
)
|
693 |
+
with gr.Accordion("βοΈ Trim video", open=False):
|
694 |
+
with gr.Column():
|
695 |
+
with gr.Row():
|
696 |
+
set_slider_range_btn = gr.Button(
|
697 |
+
"Set frame range", interactive=True
|
698 |
+
)
|
699 |
+
show_trim_preview_btn = gr.Checkbox(
|
700 |
+
label="Show frame when slider change",
|
701 |
+
value=True,
|
702 |
+
interactive=True,
|
703 |
+
)
|
704 |
+
|
705 |
+
video_fps = gr.Number(
|
706 |
+
value=30,
|
707 |
+
interactive=False,
|
708 |
+
label="Fps",
|
709 |
+
visible=False,
|
710 |
+
)
|
711 |
+
start_frame = gr.Slider(
|
712 |
+
minimum=0,
|
713 |
+
maximum=1,
|
714 |
+
value=0,
|
715 |
+
step=1,
|
716 |
+
interactive=True,
|
717 |
+
label="Start Frame",
|
718 |
+
info="",
|
719 |
+
)
|
720 |
+
end_frame = gr.Slider(
|
721 |
+
minimum=0,
|
722 |
+
maximum=1,
|
723 |
+
value=1,
|
724 |
+
step=1,
|
725 |
+
interactive=True,
|
726 |
+
label="End Frame",
|
727 |
+
info="",
|
728 |
+
)
|
729 |
+
trim_and_reload_btn = gr.Button(
|
730 |
+
"Trim and Reload", interactive=True
|
731 |
+
)
|
732 |
+
|
733 |
+
with gr.Box(visible=False) as input_directory_group:
|
734 |
+
direc_input = gr.Text(label="Path", interactive=True)
|
735 |
+
|
736 |
+
with gr.Column(scale=0.6):
|
737 |
+
info = gr.Markdown(value="...")
|
738 |
+
|
739 |
+
with gr.Row():
|
740 |
+
swap_button = gr.Button("β¨ Swap", variant="primary")
|
741 |
+
cancel_button = gr.Button("β Cancel")
|
742 |
+
|
743 |
+
preview_image = gr.Image(label="Output", interactive=False)
|
744 |
+
preview_video = gr.Video(
|
745 |
+
label="Output", interactive=False, visible=False
|
746 |
+
)
|
747 |
+
|
748 |
+
with gr.Row():
|
749 |
+
output_directory_button = gr.Button(
|
750 |
+
"π", interactive=False, visible=not USE_COLAB
|
751 |
+
)
|
752 |
+
output_video_button = gr.Button(
|
753 |
+
"π¬", interactive=False, visible=not USE_COLAB
|
754 |
+
)
|
755 |
+
|
756 |
+
with gr.Column():
|
757 |
+
gr.Markdown(
|
758 |
+
'[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/harisreedhar)'
|
759 |
+
)
|
760 |
+
gr.Markdown(
|
761 |
+
"### [Source code](https://github.com/harisreedhar/Swap-Mukham) . [Disclaimer](https://github.com/harisreedhar/Swap-Mukham#disclaimer) . [Gradio](https://gradio.app/)"
|
762 |
+
)
|
763 |
+
|
764 |
+
## ------------------------------ GRADIO EVENTS ------------------------------
|
765 |
+
|
766 |
+
set_slider_range_event = set_slider_range_btn.click(
|
767 |
+
video_changed,
|
768 |
+
inputs=[video_input],
|
769 |
+
outputs=[start_frame, end_frame, video_fps],
|
770 |
+
)
|
771 |
+
|
772 |
+
trim_and_reload_event = trim_and_reload_btn.click(
|
773 |
+
fn=trim_and_reload,
|
774 |
+
inputs=[video_input, output_directory, output_name, start_frame, end_frame],
|
775 |
+
outputs=[video_input, info],
|
776 |
+
)
|
777 |
+
|
778 |
+
start_frame_event = start_frame.release(
|
779 |
+
fn=slider_changed,
|
780 |
+
inputs=[show_trim_preview_btn, video_input, start_frame],
|
781 |
+
outputs=[preview_image, preview_video],
|
782 |
+
show_progress=False,
|
783 |
+
)
|
784 |
+
|
785 |
+
end_frame_event = end_frame.release(
|
786 |
+
fn=slider_changed,
|
787 |
+
inputs=[show_trim_preview_btn, video_input, end_frame],
|
788 |
+
outputs=[preview_image, preview_video],
|
789 |
+
show_progress=False,
|
790 |
+
)
|
791 |
+
|
792 |
+
input_type.change(
|
793 |
+
update_radio,
|
794 |
+
inputs=[input_type],
|
795 |
+
outputs=[input_image_group, input_video_group, input_directory_group],
|
796 |
+
)
|
797 |
+
swap_option.change(
|
798 |
+
swap_option_changed,
|
799 |
+
inputs=[swap_option],
|
800 |
+
outputs=[age, specific_face, source_image_input],
|
801 |
+
)
|
802 |
+
|
803 |
+
apply_detection_settings.click(
|
804 |
+
analyse_settings_changed,
|
805 |
+
inputs=[detect_condition_dropdown, detection_size, detection_threshold],
|
806 |
+
outputs=[info],
|
807 |
+
)
|
808 |
+
|
809 |
+
src_specific_inputs = []
|
810 |
+
gen_variable_txt = ",".join(
|
811 |
+
[f"src{i+1}" for i in range(NUM_OF_SRC_SPECIFIC)]
|
812 |
+
+ [f"trg{i+1}" for i in range(NUM_OF_SRC_SPECIFIC)]
|
813 |
+
)
|
814 |
+
exec(f"src_specific_inputs = ({gen_variable_txt})")
|
815 |
+
swap_inputs = [
|
816 |
+
input_type,
|
817 |
+
image_input,
|
818 |
+
video_input,
|
819 |
+
direc_input,
|
820 |
+
source_image_input,
|
821 |
+
output_directory,
|
822 |
+
output_name,
|
823 |
+
keep_output_sequence,
|
824 |
+
swap_option,
|
825 |
+
age,
|
826 |
+
distance_slider,
|
827 |
+
enable_face_enhance,
|
828 |
+
enable_face_parser_mask,
|
829 |
+
mask_include,
|
830 |
+
mask_exclude,
|
831 |
+
mask_blur,
|
832 |
+
*src_specific_inputs,
|
833 |
+
]
|
834 |
+
|
835 |
+
swap_outputs = [
|
836 |
+
info,
|
837 |
+
preview_image,
|
838 |
+
output_directory_button,
|
839 |
+
output_video_button,
|
840 |
+
preview_video,
|
841 |
+
]
|
842 |
+
|
843 |
+
swap_event = swap_button.click(
|
844 |
+
fn=process, inputs=swap_inputs, outputs=swap_outputs, show_progress=False
|
845 |
+
)
|
846 |
+
|
847 |
+
cancel_button.click(
|
848 |
+
fn=stop_running,
|
849 |
+
inputs=None,
|
850 |
+
outputs=[info],
|
851 |
+
cancels=[
|
852 |
+
swap_event,
|
853 |
+
trim_and_reload_event,
|
854 |
+
set_slider_range_event,
|
855 |
+
start_frame_event,
|
856 |
+
end_frame_event,
|
857 |
+
],
|
858 |
+
show_progress=False,
|
859 |
+
)
|
860 |
+
output_directory_button.click(
|
861 |
+
lambda: open_directory(path=WORKSPACE), inputs=None, outputs=None
|
862 |
+
)
|
863 |
+
output_video_button.click(
|
864 |
+
lambda: open_directory(path=OUTPUT_FILE), inputs=None, outputs=None
|
865 |
+
)
|
866 |
+
|
867 |
+
if __name__ == "__main__":
|
868 |
+
if USE_COLAB:
|
869 |
+
print("Running in colab mode")
|
870 |
+
|
871 |
+
interface.queue(concurrency_count=2, max_size=20).launch(share=USE_COLAB)
|
assets/images/logo.png
ADDED
![]() |
assets/pretrained_models/79999_iter.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:468e13ca13a9b43cc0881a9f99083a430e9c0a38abd935431d1c28ee94b26567
|
3 |
+
size 53289463
|
assets/pretrained_models/GFPGANv1.4.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e2cd4703ab14f4d01fd1383a8a8b266f9a5833dacee8e6a79d3bf21a1b6be5ad
|
3 |
+
size 348632874
|
assets/pretrained_models/inswapper_128.onnx
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:e4a3f08c753cb72d04e10aa0f7dbe3deebbf39567d4ead6dce08e98aa49e16af
|
3 |
+
size 554253681
|
assets/pretrained_models/readme.md
ADDED
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
1 |
+
## Downolad these models here
|
2 |
+
- [inswapper_128.onnx](https://huggingface.co/deepinsight/inswapper/resolve/main/inswapper_128.onnx)
|
3 |
+
- [GFPGANv1.4.pth](https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth)
|
4 |
+
- [79999_iter.pth](https://drive.google.com/open?id=154JgKpzCPW82qINcVieuPH3fZ2e0P812)
|
face_analyser.py
ADDED
@@ -0,0 +1,32 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
detect_conditions = [
|
2 |
+
"left most",
|
3 |
+
"right most",
|
4 |
+
"top most",
|
5 |
+
"bottom most",
|
6 |
+
"most width",
|
7 |
+
"most height",
|
8 |
+
]
|
9 |
+
|
10 |
+
|
11 |
+
def analyse_face(image, model, return_single_face=True, detect_condition="left most"):
|
12 |
+
faces = model.get(image)
|
13 |
+
if not return_single_face:
|
14 |
+
return faces
|
15 |
+
|
16 |
+
total_faces = len(faces)
|
17 |
+
if total_faces == 1:
|
18 |
+
return faces[0]
|
19 |
+
|
20 |
+
print(f"{total_faces} face detected. Using {detect_condition} face.")
|
21 |
+
if detect_condition == "left most":
|
22 |
+
return sorted(faces, key=lambda face: face["bbox"][0])[0]
|
23 |
+
elif detect_condition == "right most":
|
24 |
+
return sorted(faces, key=lambda face: face["bbox"][0])[-1]
|
25 |
+
elif detect_condition == "top most":
|
26 |
+
return sorted(faces, key=lambda face: face["bbox"][1])[0]
|
27 |
+
elif detect_condition == "bottom most":
|
28 |
+
return sorted(faces, key=lambda face: face["bbox"][1])[-1]
|
29 |
+
elif detect_condition == "most width":
|
30 |
+
return sorted(faces, key=lambda face: face["bbox"][2])[-1]
|
31 |
+
elif detect_condition == "most height":
|
32 |
+
return sorted(faces, key=lambda face: face["bbox"][3])[-1]
|
face_parsing/__init__.py
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
from .swap import init_parser, swap_regions, mask_regions, mask_regions_to_list
|
face_parsing/model.py
ADDED
@@ -0,0 +1,283 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
#!/usr/bin/python
|
2 |
+
# -*- encoding: utf-8 -*-
|
3 |
+
|
4 |
+
|
5 |
+
import torch
|
6 |
+
import torch.nn as nn
|
7 |
+
import torch.nn.functional as F
|
8 |
+
import torchvision
|
9 |
+
|
10 |
+
from .resnet import Resnet18
|
11 |
+
# from modules.bn import InPlaceABNSync as BatchNorm2d
|
12 |
+
|
13 |
+
|
14 |
+
class ConvBNReLU(nn.Module):
|
15 |
+
def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
|
16 |
+
super(ConvBNReLU, self).__init__()
|
17 |
+
self.conv = nn.Conv2d(in_chan,
|
18 |
+
out_chan,
|
19 |
+
kernel_size = ks,
|
20 |
+
stride = stride,
|
21 |
+
padding = padding,
|
22 |
+
bias = False)
|
23 |
+
self.bn = nn.BatchNorm2d(out_chan)
|
24 |
+
self.init_weight()
|
25 |
+
|
26 |
+
def forward(self, x):
|
27 |
+
x = self.conv(x)
|
28 |
+
x = F.relu(self.bn(x))
|
29 |
+
return x
|
30 |
+
|
31 |
+
def init_weight(self):
|
32 |
+
for ly in self.children():
|
33 |
+
if isinstance(ly, nn.Conv2d):
|
34 |
+
nn.init.kaiming_normal_(ly.weight, a=1)
|
35 |
+
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
36 |
+
|
37 |
+
class BiSeNetOutput(nn.Module):
|
38 |
+
def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
|
39 |
+
super(BiSeNetOutput, self).__init__()
|
40 |
+
self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
|
41 |
+
self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
|
42 |
+
self.init_weight()
|
43 |
+
|
44 |
+
def forward(self, x):
|
45 |
+
x = self.conv(x)
|
46 |
+
x = self.conv_out(x)
|
47 |
+
return x
|
48 |
+
|
49 |
+
def init_weight(self):
|
50 |
+
for ly in self.children():
|
51 |
+
if isinstance(ly, nn.Conv2d):
|
52 |
+
nn.init.kaiming_normal_(ly.weight, a=1)
|
53 |
+
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
54 |
+
|
55 |
+
def get_params(self):
|
56 |
+
wd_params, nowd_params = [], []
|
57 |
+
for name, module in self.named_modules():
|
58 |
+
if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
|
59 |
+
wd_params.append(module.weight)
|
60 |
+
if not module.bias is None:
|
61 |
+
nowd_params.append(module.bias)
|
62 |
+
elif isinstance(module, nn.BatchNorm2d):
|
63 |
+
nowd_params += list(module.parameters())
|
64 |
+
return wd_params, nowd_params
|
65 |
+
|
66 |
+
|
67 |
+
class AttentionRefinementModule(nn.Module):
|
68 |
+
def __init__(self, in_chan, out_chan, *args, **kwargs):
|
69 |
+
super(AttentionRefinementModule, self).__init__()
|
70 |
+
self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
|
71 |
+
self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size= 1, bias=False)
|
72 |
+
self.bn_atten = nn.BatchNorm2d(out_chan)
|
73 |
+
self.sigmoid_atten = nn.Sigmoid()
|
74 |
+
self.init_weight()
|
75 |
+
|
76 |
+
def forward(self, x):
|
77 |
+
feat = self.conv(x)
|
78 |
+
atten = F.avg_pool2d(feat, feat.size()[2:])
|
79 |
+
atten = self.conv_atten(atten)
|
80 |
+
atten = self.bn_atten(atten)
|
81 |
+
atten = self.sigmoid_atten(atten)
|
82 |
+
out = torch.mul(feat, atten)
|
83 |
+
return out
|
84 |
+
|
85 |
+
def init_weight(self):
|
86 |
+
for ly in self.children():
|
87 |
+
if isinstance(ly, nn.Conv2d):
|
88 |
+
nn.init.kaiming_normal_(ly.weight, a=1)
|
89 |
+
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
90 |
+
|
91 |
+
|
92 |
+
class ContextPath(nn.Module):
|
93 |
+
def __init__(self, *args, **kwargs):
|
94 |
+
super(ContextPath, self).__init__()
|
95 |
+
self.resnet = Resnet18()
|
96 |
+
self.arm16 = AttentionRefinementModule(256, 128)
|
97 |
+
self.arm32 = AttentionRefinementModule(512, 128)
|
98 |
+
self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
|
99 |
+
self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
|
100 |
+
self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
|
101 |
+
|
102 |
+
self.init_weight()
|
103 |
+
|
104 |
+
def forward(self, x):
|
105 |
+
H0, W0 = x.size()[2:]
|
106 |
+
feat8, feat16, feat32 = self.resnet(x)
|
107 |
+
H8, W8 = feat8.size()[2:]
|
108 |
+
H16, W16 = feat16.size()[2:]
|
109 |
+
H32, W32 = feat32.size()[2:]
|
110 |
+
|
111 |
+
avg = F.avg_pool2d(feat32, feat32.size()[2:])
|
112 |
+
avg = self.conv_avg(avg)
|
113 |
+
avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
|
114 |
+
|
115 |
+
feat32_arm = self.arm32(feat32)
|
116 |
+
feat32_sum = feat32_arm + avg_up
|
117 |
+
feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
|
118 |
+
feat32_up = self.conv_head32(feat32_up)
|
119 |
+
|
120 |
+
feat16_arm = self.arm16(feat16)
|
121 |
+
feat16_sum = feat16_arm + feat32_up
|
122 |
+
feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
|
123 |
+
feat16_up = self.conv_head16(feat16_up)
|
124 |
+
|
125 |
+
return feat8, feat16_up, feat32_up # x8, x8, x16
|
126 |
+
|
127 |
+
def init_weight(self):
|
128 |
+
for ly in self.children():
|
129 |
+
if isinstance(ly, nn.Conv2d):
|
130 |
+
nn.init.kaiming_normal_(ly.weight, a=1)
|
131 |
+
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
132 |
+
|
133 |
+
def get_params(self):
|
134 |
+
wd_params, nowd_params = [], []
|
135 |
+
for name, module in self.named_modules():
|
136 |
+
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
137 |
+
wd_params.append(module.weight)
|
138 |
+
if not module.bias is None:
|
139 |
+
nowd_params.append(module.bias)
|
140 |
+
elif isinstance(module, nn.BatchNorm2d):
|
141 |
+
nowd_params += list(module.parameters())
|
142 |
+
return wd_params, nowd_params
|
143 |
+
|
144 |
+
|
145 |
+
### This is not used, since I replace this with the resnet feature with the same size
|
146 |
+
class SpatialPath(nn.Module):
|
147 |
+
def __init__(self, *args, **kwargs):
|
148 |
+
super(SpatialPath, self).__init__()
|
149 |
+
self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
|
150 |
+
self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
|
151 |
+
self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
|
152 |
+
self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
|
153 |
+
self.init_weight()
|
154 |
+
|
155 |
+
def forward(self, x):
|
156 |
+
feat = self.conv1(x)
|
157 |
+
feat = self.conv2(feat)
|
158 |
+
feat = self.conv3(feat)
|
159 |
+
feat = self.conv_out(feat)
|
160 |
+
return feat
|
161 |
+
|
162 |
+
def init_weight(self):
|
163 |
+
for ly in self.children():
|
164 |
+
if isinstance(ly, nn.Conv2d):
|
165 |
+
nn.init.kaiming_normal_(ly.weight, a=1)
|
166 |
+
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
167 |
+
|
168 |
+
def get_params(self):
|
169 |
+
wd_params, nowd_params = [], []
|
170 |
+
for name, module in self.named_modules():
|
171 |
+
if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
|
172 |
+
wd_params.append(module.weight)
|
173 |
+
if not module.bias is None:
|
174 |
+
nowd_params.append(module.bias)
|
175 |
+
elif isinstance(module, nn.BatchNorm2d):
|
176 |
+
nowd_params += list(module.parameters())
|
177 |
+
return wd_params, nowd_params
|
178 |
+
|
179 |
+
|
180 |
+
class FeatureFusionModule(nn.Module):
|
181 |
+
def __init__(self, in_chan, out_chan, *args, **kwargs):
|
182 |
+
super(FeatureFusionModule, self).__init__()
|
183 |
+
self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
|
184 |
+
self.conv1 = nn.Conv2d(out_chan,
|
185 |
+
out_chan//4,
|
186 |
+
kernel_size = 1,
|
187 |
+
stride = 1,
|
188 |
+
padding = 0,
|
189 |
+
bias = False)
|
190 |
+
self.conv2 = nn.Conv2d(out_chan//4,
|
191 |
+
out_chan,
|
192 |
+
kernel_size = 1,
|
193 |
+
stride = 1,
|
194 |
+
padding = 0,
|
195 |
+
bias = False)
|
196 |
+
self.relu = nn.ReLU(inplace=True)
|
197 |
+
self.sigmoid = nn.Sigmoid()
|
198 |
+
self.init_weight()
|
199 |
+
|
200 |
+
def forward(self, fsp, fcp):
|
201 |
+
fcat = torch.cat([fsp, fcp], dim=1)
|
202 |
+
feat = self.convblk(fcat)
|
203 |
+
atten = F.avg_pool2d(feat, feat.size()[2:])
|
204 |
+
atten = self.conv1(atten)
|
205 |
+
atten = self.relu(atten)
|
206 |
+
atten = self.conv2(atten)
|
207 |
+
atten = self.sigmoid(atten)
|
208 |
+
feat_atten = torch.mul(feat, atten)
|
209 |
+
feat_out = feat_atten + feat
|
210 |
+
return feat_out
|
211 |
+
|
212 |
+
def init_weight(self):
|
213 |
+
for ly in self.children():
|
214 |
+
if isinstance(ly, nn.Conv2d):
|
215 |
+
nn.init.kaiming_normal_(ly.weight, a=1)
|
216 |
+
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
217 |
+
|
218 |
+
def get_params(self):
|
219 |
+
wd_params, nowd_params = [], []
|
220 |
+
for name, module in self.named_modules():
|
221 |
+
if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
|
222 |
+
wd_params.append(module.weight)
|
223 |
+
if not module.bias is None:
|
224 |
+
nowd_params.append(module.bias)
|
225 |
+
elif isinstance(module, nn.BatchNorm2d):
|
226 |
+
nowd_params += list(module.parameters())
|
227 |
+
return wd_params, nowd_params
|
228 |
+
|
229 |
+
|
230 |
+
class BiSeNet(nn.Module):
|
231 |
+
def __init__(self, n_classes, *args, **kwargs):
|
232 |
+
super(BiSeNet, self).__init__()
|
233 |
+
self.cp = ContextPath()
|
234 |
+
## here self.sp is deleted
|
235 |
+
self.ffm = FeatureFusionModule(256, 256)
|
236 |
+
self.conv_out = BiSeNetOutput(256, 256, n_classes)
|
237 |
+
self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
|
238 |
+
self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
|
239 |
+
self.init_weight()
|
240 |
+
|
241 |
+
def forward(self, x):
|
242 |
+
H, W = x.size()[2:]
|
243 |
+
feat_res8, feat_cp8, feat_cp16 = self.cp(x) # here return res3b1 feature
|
244 |
+
feat_sp = feat_res8 # use res3b1 feature to replace spatial path feature
|
245 |
+
feat_fuse = self.ffm(feat_sp, feat_cp8)
|
246 |
+
|
247 |
+
feat_out = self.conv_out(feat_fuse)
|
248 |
+
feat_out16 = self.conv_out16(feat_cp8)
|
249 |
+
feat_out32 = self.conv_out32(feat_cp16)
|
250 |
+
|
251 |
+
feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
|
252 |
+
feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
|
253 |
+
feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
|
254 |
+
return feat_out, feat_out16, feat_out32
|
255 |
+
|
256 |
+
def init_weight(self):
|
257 |
+
for ly in self.children():
|
258 |
+
if isinstance(ly, nn.Conv2d):
|
259 |
+
nn.init.kaiming_normal_(ly.weight, a=1)
|
260 |
+
if not ly.bias is None: nn.init.constant_(ly.bias, 0)
|
261 |
+
|
262 |
+
def get_params(self):
|
263 |
+
wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
|
264 |
+
for name, child in self.named_children():
|
265 |
+
child_wd_params, child_nowd_params = child.get_params()
|
266 |
+
if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
|
267 |
+
lr_mul_wd_params += child_wd_params
|
268 |
+
lr_mul_nowd_params += child_nowd_params
|
269 |
+
else:
|
270 |
+
wd_params += child_wd_params
|
271 |
+
nowd_params += child_nowd_params
|
272 |
+
return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
|
273 |
+
|
274 |
+
|
275 |
+
if __name__ == "__main__":
|
276 |
+
net = BiSeNet(19)
|
277 |
+
net.cuda()
|
278 |
+
net.eval()
|
279 |
+
in_ten = torch.randn(16, 3, 640, 480).cuda()
|
280 |
+
out, out16, out32 = net(in_ten)
|
281 |
+
print(out.shape)
|
282 |
+
|
283 |
+
net.get_params()
|
face_parsing/resnet.py
ADDED
@@ -0,0 +1,109 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
1 |
+
#!/usr/bin/python
|
2 |
+
# -*- encoding: utf-8 -*-
|
3 |
+
|
4 |
+
import torch
|
5 |
+
import torch.nn as nn
|
6 |
+
import torch.nn.functional as F
|
7 |
+
import torch.utils.model_zoo as modelzoo
|
8 |
+
|
9 |
+
# from modules.bn import InPlaceABNSync as BatchNorm2d
|
10 |
+
|
11 |
+
resnet18_url = 'https://download.pytorch.org/models/resnet18-5c106cde.pth'
|
12 |
+
|
13 |
+
|
14 |
+
def conv3x3(in_planes, out_planes, stride=1):
|
15 |
+
"""3x3 convolution with padding"""
|
16 |
+
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
|
17 |
+
padding=1, bias=False)
|
18 |
+
|
19 |
+
|
20 |
+
class BasicBlock(nn.Module):
|
21 |
+
def __init__(self, in_chan, out_chan, stride=1):
|
22 |
+
super(BasicBlock, self).__init__()
|
23 |
+
self.conv1 = conv3x3(in_chan, out_chan, stride)
|
24 |
+
self.bn1 = nn.BatchNorm2d(out_chan)
|
25 |
+
self.conv2 = conv3x3(out_chan, out_chan)
|
26 |
+
self.bn2 = nn.BatchNorm2d(out_chan)
|
27 |
+
self.relu = nn.ReLU(inplace=True)
|
28 |
+
self.downsample = None
|
29 |
+
if in_chan != out_chan or stride != 1:
|
30 |
+
self.downsample = nn.Sequential(
|
31 |
+
nn.Conv2d(in_chan, out_chan,
|
32 |
+
kernel_size=1, stride=stride, bias=False),
|
33 |
+
nn.BatchNorm2d(out_chan),
|
34 |
+
)
|
35 |
+
|
36 |
+
def forward(self, x):
|
37 |
+
residual = self.conv1(x)
|
38 |
+
residual = F.relu(self.bn1(residual))
|
39 |
+
residual = self.conv2(residual)
|
40 |
+
residual = self.bn2(residual)
|
41 |
+
|
42 |
+
shortcut = x
|
43 |
+
if self.downsample is not None:
|
44 |
+
shortcut = self.downsample(x)
|
45 |
+
|
46 |
+
out = shortcut + residual
|
47 |
+
out = self.relu(out)
|
48 |
+
return out
|
49 |
+
|
50 |
+
|
51 |
+
def create_layer_basic(in_chan, out_chan, bnum, stride=1):
|
52 |
+
layers = [BasicBlock(in_chan, out_chan, stride=stride)]
|
53 |
+
for i in range(bnum-1):
|
54 |
+
layers.append(BasicBlock(out_chan, out_chan, stride=1))
|
55 |
+
return nn.Sequential(*layers)
|
56 |
+
|
57 |
+
|
58 |
+
class Resnet18(nn.Module):
|
59 |
+
def __init__(self):
|
60 |
+
super(Resnet18, self).__init__()
|
61 |
+
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
|
62 |
+
bias=False)
|
63 |
+
self.bn1 = nn.BatchNorm2d(64)
|
64 |
+
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
65 |
+
self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
|
66 |
+
self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
|
67 |
+
self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
|
68 |
+
self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
|
69 |
+
self.init_weight()
|
70 |
+
|
71 |
+
def forward(self, x):
|
72 |
+
x = self.conv1(x)
|
73 |
+
x = F.relu(self.bn1(x))
|
74 |
+
x = self.maxpool(x)
|
75 |
+
|
76 |
+
x = self.layer1(x)
|
77 |
+
feat8 = self.layer2(x) # 1/8
|
78 |
+
feat16 = self.layer3(feat8) # 1/16
|
79 |
+
feat32 = self.layer4(feat16) # 1/32
|
80 |
+
return feat8, feat16, feat32
|
81 |
+
|
82 |
+
def init_weight(self):
|
83 |
+
state_dict = modelzoo.load_url(resnet18_url)
|
84 |
+
self_state_dict = self.state_dict()
|
85 |
+
for k, v in state_dict.items():
|
86 |
+
if 'fc' in k: continue
|
87 |
+
self_state_dict.update({k: v})
|
88 |
+
self.load_state_dict(self_state_dict)
|
89 |
+
|
90 |
+
def get_params(self):
|
91 |
+
wd_params, nowd_params = [], []
|
92 |
+
for name, module in self.named_modules():
|
93 |
+
if isinstance(module, (nn.Linear, nn.Conv2d)):
|
94 |
+
wd_params.append(module.weight)
|
95 |
+
if not module.bias is None:
|
96 |
+
nowd_params.append(module.bias)
|
97 |
+
elif isinstance(module, nn.BatchNorm2d):
|
98 |
+
nowd_params += list(module.parameters())
|
99 |
+
return wd_params, nowd_params
|
100 |
+
|
101 |
+
|
102 |
+
if __name__ == "__main__":
|
103 |
+
net = Resnet18()
|
104 |
+
x = torch.randn(16, 3, 224, 224)
|
105 |
+
out = net(x)
|
106 |
+
print(out[0].size())
|
107 |
+
print(out[1].size())
|
108 |
+
print(out[2].size())
|
109 |
+
net.get_params()
|
face_parsing/swap.py
ADDED
@@ -0,0 +1,91 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torchvision.transforms as transforms
|
3 |
+
import cv2
|
4 |
+
import numpy as np
|
5 |
+
|
6 |
+
from .model import BiSeNet
|
7 |
+
|
8 |
+
mask_regions = {
|
9 |
+
"Background":0,
|
10 |
+
"Skin":1,
|
11 |
+
"L-Eyebrow":2,
|
12 |
+
"R-Eyebrow":3,
|
13 |
+
"L-Eye":4,
|
14 |
+
"R-Eye":5,
|
15 |
+
"Eye-G":6,
|
16 |
+
"L-Ear":7,
|
17 |
+
"R-Ear":8,
|
18 |
+
"Ear-R":9,
|
19 |
+
"Nose":10,
|
20 |
+
"Mouth":11,
|
21 |
+
"U-Lip":12,
|
22 |
+
"L-Lip":13,
|
23 |
+
"Neck":14,
|
24 |
+
"Neck-L":15,
|
25 |
+
"Cloth":16,
|
26 |
+
"Hair":17,
|
27 |
+
"Hat":18
|
28 |
+
}
|
29 |
+
|
30 |
+
run_with_cuda = False
|
31 |
+
|
32 |
+
def init_parser(pth_path, use_cuda=False):
|
33 |
+
global run_with_cuda
|
34 |
+
run_with_cuda = use_cuda
|
35 |
+
|
36 |
+
n_classes = 19
|
37 |
+
net = BiSeNet(n_classes=n_classes)
|
38 |
+
if run_with_cuda:
|
39 |
+
net.cuda()
|
40 |
+
net.load_state_dict(torch.load(pth_path))
|
41 |
+
else:
|
42 |
+
net.load_state_dict(torch.load(pth_path, map_location=torch.device('cpu')))
|
43 |
+
net.eval()
|
44 |
+
return net
|
45 |
+
|
46 |
+
|
47 |
+
def image_to_parsing(img, net):
|
48 |
+
img = cv2.resize(img, (512, 512))
|
49 |
+
img = img[:,:,::-1]
|
50 |
+
transform = transforms.Compose([
|
51 |
+
transforms.ToTensor(),
|
52 |
+
transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
|
53 |
+
])
|
54 |
+
img = transform(img.copy())
|
55 |
+
img = torch.unsqueeze(img, 0)
|
56 |
+
|
57 |
+
with torch.no_grad():
|
58 |
+
if run_with_cuda:
|
59 |
+
img = img.cuda()
|
60 |
+
out = net(img)[0]
|
61 |
+
parsing = out.squeeze(0).cpu().numpy().argmax(0)
|
62 |
+
return parsing
|
63 |
+
|
64 |
+
|
65 |
+
def get_mask(parsing, classes):
|
66 |
+
res = parsing == classes[0]
|
67 |
+
for val in classes[1:]:
|
68 |
+
res += parsing == val
|
69 |
+
return res
|
70 |
+
|
71 |
+
def swap_regions(source, target, net, includes=[1,2,3,4,5,10,11,12,13], excludes=[7,8], blur_size=25):
|
72 |
+
parsing = image_to_parsing(source, net)
|
73 |
+
if len(includes) == 0:
|
74 |
+
return source, np.zeros_like(source)
|
75 |
+
include_mask = get_mask(parsing, includes)
|
76 |
+
include_mask = np.repeat(np.expand_dims(include_mask.astype('float32'), axis=2), 3, 2)
|
77 |
+
if len(excludes) > 0:
|
78 |
+
exclude_mask = get_mask(parsing, excludes)
|
79 |
+
exclude_mask = np.repeat(np.expand_dims(exclude_mask.astype('float32'), axis=2), 3, 2)
|
80 |
+
include_mask -= exclude_mask
|
81 |
+
mask = 1 - cv2.GaussianBlur(include_mask.clip(0,1), (0, 0), blur_size)
|
82 |
+
result = (1 - mask) * cv2.resize(source, (512, 512)) + mask * cv2.resize(target, (512, 512))
|
83 |
+
result = cv2.resize(result.astype("float32"), (source.shape[1], source.shape[0]))
|
84 |
+
return result, mask.astype('float32')
|
85 |
+
|
86 |
+
def mask_regions_to_list(values):
|
87 |
+
out_ids = []
|
88 |
+
for value in values:
|
89 |
+
if value in mask_regions.keys():
|
90 |
+
out_ids.append(mask_regions.get(value))
|
91 |
+
return out_ids
|
gfpgan/weights/detection_Resnet50_Final.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:6d1de9c2944f2ccddca5f5e010ea5ae64a39845a86311af6fdf30841b0a5a16d
|
3 |
+
size 109497761
|
gfpgan/weights/parsing_parsenet.pth
ADDED
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
1 |
+
version https://git-lfs.github.com/spec/v1
|
2 |
+
oid sha256:3d558d8d0e42c20224f13cf5a29c79eba2d59913419f945545d8cf7b72920de2
|
3 |
+
size 85331193
|
requirements.txt
ADDED
@@ -0,0 +1,10 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
|
2 |
+
gradio>=3.33.1
|
3 |
+
insightface==0.7.3
|
4 |
+
moviepy>=1.0.3
|
5 |
+
numpy
|
6 |
+
opencv-python>=4.7.0.72
|
7 |
+
opencv-python-headless>=4.7.0.72
|
8 |
+
onnx==1.14.0
|
9 |
+
onnxruntime==1.15.0
|
10 |
+
gfpgan==1.3.8
|
swapper.py
ADDED
@@ -0,0 +1,106 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import cv2
|
2 |
+
import numpy as np
|
3 |
+
from insightface.utils import face_align
|
4 |
+
from face_parsing.swap import swap_regions
|
5 |
+
from utils import add_logo_to_image
|
6 |
+
|
7 |
+
swap_options_list = [
|
8 |
+
"All face",
|
9 |
+
"Age less than",
|
10 |
+
"Age greater than",
|
11 |
+
"All Male",
|
12 |
+
"All Female",
|
13 |
+
"Specific Face",
|
14 |
+
]
|
15 |
+
|
16 |
+
|
17 |
+
def swap_face(whole_img, target_face, source_face, models):
|
18 |
+
inswapper = models.get("swap")
|
19 |
+
face_enhancer = models.get("enhance", None)
|
20 |
+
face_parser = models.get("face_parser", None)
|
21 |
+
fe_enable = models.get("enhance_sett", False)
|
22 |
+
|
23 |
+
bgr_fake, M = inswapper.get(whole_img, target_face, source_face, paste_back=False)
|
24 |
+
image_size = 128 if not fe_enable else 512
|
25 |
+
aimg, _ = face_align.norm_crop2(whole_img, target_face.kps, image_size=image_size)
|
26 |
+
|
27 |
+
if face_parser is not None:
|
28 |
+
fp_enable, mi, me, mb = models.get("face_parser_sett")
|
29 |
+
if fp_enable:
|
30 |
+
bgr_fake, parsed_mask = swap_regions(
|
31 |
+
bgr_fake, aimg, face_parser, includes=mi, excludes=me, blur_size=mb
|
32 |
+
)
|
33 |
+
|
34 |
+
if fe_enable:
|
35 |
+
_, bgr_fake, _ = face_enhancer.enhance(
|
36 |
+
bgr_fake, paste_back=True, has_aligned=True
|
37 |
+
)
|
38 |
+
bgr_fake = bgr_fake[0]
|
39 |
+
M /= 0.25
|
40 |
+
|
41 |
+
IM = cv2.invertAffineTransform(M)
|
42 |
+
|
43 |
+
img_white = np.full((aimg.shape[0], aimg.shape[1]), 255, dtype=np.float32)
|
44 |
+
bgr_fake = cv2.warpAffine(
|
45 |
+
bgr_fake, IM, (whole_img.shape[1], whole_img.shape[0]), borderValue=0.0
|
46 |
+
)
|
47 |
+
img_white = cv2.warpAffine(
|
48 |
+
img_white, IM, (whole_img.shape[1], whole_img.shape[0]), borderValue=0.0
|
49 |
+
)
|
50 |
+
img_white[img_white > 20] = 255
|
51 |
+
img_mask = img_white
|
52 |
+
mask_h_inds, mask_w_inds = np.where(img_mask == 255)
|
53 |
+
mask_h = np.max(mask_h_inds) - np.min(mask_h_inds)
|
54 |
+
mask_w = np.max(mask_w_inds) - np.min(mask_w_inds)
|
55 |
+
mask_size = int(np.sqrt(mask_h * mask_w))
|
56 |
+
|
57 |
+
k = max(mask_size // 10, 10)
|
58 |
+
img_mask = cv2.erode(img_mask, np.ones((k, k), np.uint8), iterations=1)
|
59 |
+
|
60 |
+
k = max(mask_size // 20, 5)
|
61 |
+
kernel_size = (k, k)
|
62 |
+
blur_size = tuple(2 * i + 1 for i in kernel_size)
|
63 |
+
img_mask = cv2.GaussianBlur(img_mask, blur_size, 0) / 255
|
64 |
+
|
65 |
+
img_mask = np.reshape(img_mask, [img_mask.shape[0], img_mask.shape[1], 1])
|
66 |
+
fake_merged = img_mask * bgr_fake + (1 - img_mask) * whole_img.astype(np.float32)
|
67 |
+
fake_merged = add_logo_to_image(fake_merged.astype("uint8"))
|
68 |
+
return fake_merged
|
69 |
+
|
70 |
+
|
71 |
+
def swap_face_with_condition(
|
72 |
+
whole_img, target_faces, source_face, condition, age, models
|
73 |
+
):
|
74 |
+
swapped = whole_img.copy()
|
75 |
+
|
76 |
+
for target_face in target_faces:
|
77 |
+
if condition == "All face":
|
78 |
+
swapped = swap_face(swapped, target_face, source_face, models)
|
79 |
+
elif condition == "Age less than" and target_face["age"] < age:
|
80 |
+
swapped = swap_face(swapped, target_face, source_face, models)
|
81 |
+
elif condition == "Age greater than" and target_face["age"] > age:
|
82 |
+
swapped = swap_face(swapped, target_face, source_face, models)
|
83 |
+
elif condition == "All Male" and target_face["gender"] == 1:
|
84 |
+
swapped = swap_face(swapped, target_face, source_face, models)
|
85 |
+
elif condition == "All Female" and target_face["gender"] == 0:
|
86 |
+
swapped = swap_face(swapped, target_face, source_face, models)
|
87 |
+
|
88 |
+
return swapped
|
89 |
+
|
90 |
+
|
91 |
+
def swap_specific(source_specifics, target_faces, whole_img, models, threshold=0.6):
|
92 |
+
swapped = whole_img.copy()
|
93 |
+
|
94 |
+
for source_face, specific_face in source_specifics:
|
95 |
+
specific_embed = specific_face["embedding"]
|
96 |
+
specific_embed /= np.linalg.norm(specific_embed)
|
97 |
+
|
98 |
+
for target_face in target_faces:
|
99 |
+
target_embed = target_face["embedding"]
|
100 |
+
target_embed /= np.linalg.norm(target_embed)
|
101 |
+
cosine_distance = 1 - np.dot(specific_embed, target_embed)
|
102 |
+
if cosine_distance > threshold:
|
103 |
+
continue
|
104 |
+
swapped = swap_face(swapped, target_face, source_face, models)
|
105 |
+
|
106 |
+
return swapped
|
utils.py
ADDED
@@ -0,0 +1,112 @@
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|
|
1 |
+
import os
|
2 |
+
import cv2
|
3 |
+
import time
|
4 |
+
import glob
|
5 |
+
import shutil
|
6 |
+
import platform
|
7 |
+
import datetime
|
8 |
+
import subprocess
|
9 |
+
from threading import Thread
|
10 |
+
from moviepy.editor import VideoFileClip, ImageSequenceClip
|
11 |
+
from moviepy.video.io.ffmpeg_tools import ffmpeg_extract_subclip
|
12 |
+
|
13 |
+
|
14 |
+
def trim_video(video_path, output_path, start_frame, stop_frame):
|
15 |
+
video_name, _ = os.path.splitext(os.path.basename(video_path))
|
16 |
+
trimmed_video_filename = video_name + "_trimmed" + ".mp4"
|
17 |
+
temp_path = os.path.join(output_path, "trim")
|
18 |
+
os.makedirs(temp_path, exist_ok=True)
|
19 |
+
trimmed_video_file_path = os.path.join(temp_path, trimmed_video_filename)
|
20 |
+
|
21 |
+
video = VideoFileClip(video_path)
|
22 |
+
fps = video.fps
|
23 |
+
start_time = start_frame / fps
|
24 |
+
duration = (stop_frame - start_frame) / fps
|
25 |
+
|
26 |
+
trimmed_video = video.subclip(start_time, start_time + duration)
|
27 |
+
trimmed_video.write_videofile(
|
28 |
+
trimmed_video_file_path, codec="libx264", audio_codec="aac"
|
29 |
+
)
|
30 |
+
trimmed_video.close()
|
31 |
+
video.close()
|
32 |
+
|
33 |
+
return trimmed_video_file_path
|
34 |
+
|
35 |
+
|
36 |
+
def open_directory(path=None):
|
37 |
+
if path is None:
|
38 |
+
return
|
39 |
+
try:
|
40 |
+
os.startfile(path)
|
41 |
+
except:
|
42 |
+
subprocess.Popen(["xdg-open", path])
|
43 |
+
|
44 |
+
|
45 |
+
class StreamerThread(object):
|
46 |
+
def __init__(self, src=0):
|
47 |
+
self.capture = cv2.VideoCapture(src)
|
48 |
+
self.capture.set(cv2.CAP_PROP_BUFFERSIZE, 2)
|
49 |
+
self.FPS = 1 / 30
|
50 |
+
self.FPS_MS = int(self.FPS * 1000)
|
51 |
+
self.thread = None
|
52 |
+
self.stopped = False
|
53 |
+
self.frame = None
|
54 |
+
|
55 |
+
def start(self):
|
56 |
+
self.thread = Thread(target=self.update, args=())
|
57 |
+
self.thread.daemon = True
|
58 |
+
self.thread.start()
|
59 |
+
|
60 |
+
def stop(self):
|
61 |
+
self.stopped = True
|
62 |
+
self.thread.join()
|
63 |
+
print("stopped")
|
64 |
+
|
65 |
+
def update(self):
|
66 |
+
while not self.stopped:
|
67 |
+
if self.capture.isOpened():
|
68 |
+
(self.status, self.frame) = self.capture.read()
|
69 |
+
time.sleep(self.FPS)
|
70 |
+
|
71 |
+
|
72 |
+
class ProcessBar:
|
73 |
+
def __init__(self, bar_length, total, before="β¬", after="π¨"):
|
74 |
+
self.bar_length = bar_length
|
75 |
+
self.total = total
|
76 |
+
self.before = before
|
77 |
+
self.after = after
|
78 |
+
self.bar = [self.before] * bar_length
|
79 |
+
self.start_time = time.time()
|
80 |
+
|
81 |
+
def get(self, index):
|
82 |
+
total = self.total
|
83 |
+
elapsed_time = time.time() - self.start_time
|
84 |
+
average_time_per_iteration = elapsed_time / (index + 1)
|
85 |
+
remaining_iterations = total - (index + 1)
|
86 |
+
estimated_remaining_time = remaining_iterations * average_time_per_iteration
|
87 |
+
|
88 |
+
self.bar[int(index / total * self.bar_length)] = self.after
|
89 |
+
info_text = f"({index+1}/{total}) {''.join(self.bar)} "
|
90 |
+
info_text += f"(ETR: {int(estimated_remaining_time // 60)} min {int(estimated_remaining_time % 60)} sec)"
|
91 |
+
return info_text
|
92 |
+
|
93 |
+
|
94 |
+
logo_image = cv2.imread("./assets/images/logo.png", cv2.IMREAD_UNCHANGED)
|
95 |
+
|
96 |
+
|
97 |
+
def add_logo_to_image(img, logo=logo_image):
|
98 |
+
logo_size = int(img.shape[1] * 0.1)
|
99 |
+
logo = cv2.resize(logo, (logo_size, logo_size))
|
100 |
+
if logo.shape[2] == 4:
|
101 |
+
alpha = logo[:, :, 3]
|
102 |
+
else:
|
103 |
+
alpha = np.ones_like(logo[:, :, 0]) * 255
|
104 |
+
padding = int(logo_size * 0.1)
|
105 |
+
roi = img.shape[0] - logo_size - padding, img.shape[1] - logo_size - padding
|
106 |
+
for c in range(0, 3):
|
107 |
+
img[roi[0] : roi[0] + logo_size, roi[1] : roi[1] + logo_size, c] = (
|
108 |
+
alpha / 255.0
|
109 |
+
) * logo[:, :, c] + (1 - alpha / 255.0) * img[
|
110 |
+
roi[0] : roi[0] + logo_size, roi[1] : roi[1] + logo_size, c
|
111 |
+
]
|
112 |
+
return img
|