DeepSwapFace / app.py
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import os
import cv2
import glob
import time
import torch
import shutil
import gfpgan
import argparse
import platform
import datetime
import subprocess
import insightface
import onnxruntime
import numpy as np
import gradio as gr
from moviepy.editor import VideoFileClip, ImageSequenceClip
from face_analyser import detect_conditions, analyse_face
from utils import trim_video, StreamerThread, ProcessBar, open_directory
from face_parsing import init_parser, swap_regions, mask_regions, mask_regions_to_list
from swapper import (
swap_face,
swap_face_with_condition,
swap_specific,
swap_options_list,
)
## ------------------------------ USER ARGS ------------------------------
parser = argparse.ArgumentParser(description="Swap-Mukham Face Swapper")
parser.add_argument("--out_dir", help="Default Output directory", default=os.getcwd())
parser.add_argument("--cuda", action="store_true", help="Enable cuda", default=False)
parser.add_argument(
"--colab", action="store_true", help="Enable colab mode", default=False
)
user_args = parser.parse_args()
## ------------------------------ DEFAULTS ------------------------------
USE_COLAB = user_args.colab
USE_CUDA = user_args.cuda
DEF_OUTPUT_PATH = user_args.out_dir
WORKSPACE = None
OUTPUT_FILE = None
CURRENT_FRAME = None
STREAMER = None
DETECT_CONDITION = "left most"
DETECT_SIZE = 640
DETECT_THRESH = 0.6
NUM_OF_SRC_SPECIFIC = 10
MASK_INCLUDE = [
"Skin",
"R-Eyebrow",
"L-Eyebrow",
"L-Eye",
"R-Eye",
"Nose",
"Mouth",
"L-Lip",
"U-Lip"
]
MASK_EXCLUDE = ["R-Ear", "L-Ear", "Hair", "Hat"]
MASK_BLUR = 25
FACE_SWAPPER = None
FACE_ANALYSER = None
FACE_ENHANCER = None
FACE_PARSER = None
## ------------------------------ SET EXECUTION PROVIDER ------------------------------
# Note: For AMD,MAC or non CUDA users, change settings here
PROVIDER = ["CPUExecutionProvider"]
if USE_CUDA:
available_providers = onnxruntime.get_available_providers()
if "CUDAExecutionProvider" in available_providers:
print("\n********** Running on CUDA **********\n")
PROVIDER = ["CUDAExecutionProvider", "CPUExecutionProvider"]
else:
USE_CUDA = False
print("\n********** CUDA unavailable running on CPU **********\n")
else:
USE_CUDA = False
print("\n********** Running on CPU **********\n")
## ------------------------------ LOAD MODELS ------------------------------
def load_face_analyser_model(name="buffalo_l"):
global FACE_ANALYSER
if FACE_ANALYSER is None:
FACE_ANALYSER = insightface.app.FaceAnalysis(name=name, providers=PROVIDER)
FACE_ANALYSER.prepare(
ctx_id=0, det_size=(DETECT_SIZE, DETECT_SIZE), det_thresh=DETECT_THRESH
)
def load_face_swapper_model(name="./assets/pretrained_models/inswapper_128.onnx"):
global FACE_SWAPPER
path = os.path.join(os.path.abspath(os.path.dirname(__file__)), name)
if FACE_SWAPPER is None:
FACE_SWAPPER = insightface.model_zoo.get_model(path, providers=PROVIDER)
def load_face_enhancer_model(name="./assets/pretrained_models/GFPGANv1.4.pth"):
global FACE_ENHANCER
path = os.path.join(os.path.abspath(os.path.dirname(__file__)), name)
if FACE_ENHANCER is None:
FACE_ENHANCER = gfpgan.GFPGANer(model_path=path, upscale=1)
def load_face_parser_model(name="./assets/pretrained_models/79999_iter.pth"):
global FACE_PARSER
path = os.path.join(os.path.abspath(os.path.dirname(__file__)), name)
if FACE_PARSER is None:
FACE_PARSER = init_parser(name, use_cuda=USE_CUDA)
load_face_analyser_model()
load_face_swapper_model()
## ------------------------------ MAIN PROCESS ------------------------------
def process(
input_type,
image_path,
video_path,
directory_path,
source_path,
output_path,
output_name,
keep_output_sequence,
condition,
age,
distance,
face_enhance,
enable_face_parser,
mask_include,
mask_exclude,
mask_blur,
*specifics,
):
global WORKSPACE
global OUTPUT_FILE
global PREVIEW
WORKSPACE, OUTPUT_FILE, PREVIEW = None, None, None
## ------------------------------ GUI UPDATE FUNC ------------------------------
def ui_before():
return (
gr.update(visible=True, value=PREVIEW),
gr.update(interactive=False),
gr.update(interactive=False),
gr.update(visible=False),
)
def ui_after():
return (
gr.update(visible=True, value=PREVIEW),
gr.update(interactive=True),
gr.update(interactive=True),
gr.update(visible=False),
)
def ui_after_vid():
return (
gr.update(visible=False),
gr.update(interactive=True),
gr.update(interactive=True),
gr.update(value=OUTPUT_FILE, visible=True),
)
## ------------------------------ LOAD PENDING MODELS ------------------------------
start_time = time.time()
specifics = list(specifics)
half = len(specifics) // 2
sources = specifics[:half]
specifics = specifics[half:]
yield "### \n βŒ› Loading face analyser model...", *ui_before()
load_face_analyser_model()
yield "### \n βŒ› Loading face swapper model...", *ui_before()
load_face_swapper_model()
if face_enhance:
yield "### \n βŒ› Loading face enhancer model...", *ui_before()
load_face_enhancer_model()
if enable_face_parser:
yield "### \n βŒ› Loading face parsing model...", *ui_before()
load_face_parser_model()
yield "### \n βŒ› Analysing Face...", *ui_before()
mi = mask_regions_to_list(mask_include)
me = mask_regions_to_list(mask_exclude)
models = {
"swap": FACE_SWAPPER,
"enhance": FACE_ENHANCER,
"enhance_sett": face_enhance,
"face_parser": FACE_PARSER,
"face_parser_sett": (enable_face_parser, mi, me, int(mask_blur)),
}
## ------------------------------ ANALYSE SOURCE & SPECIFIC ------------------------------
analysed_source_specific = []
if condition == "Specific Face":
for source, specific in zip(sources, specifics):
if source is None or specific is None:
continue
analysed_source = analyse_face(
source,
FACE_ANALYSER,
return_single_face=True,
detect_condition=DETECT_CONDITION,
)
analysed_specific = analyse_face(
specific,
FACE_ANALYSER,
return_single_face=True,
detect_condition=DETECT_CONDITION,
)
analysed_source_specific.append([analysed_source, analysed_specific])
else:
source = cv2.imread(source_path)
analysed_source = analyse_face(
source,
FACE_ANALYSER,
return_single_face=True,
detect_condition=DETECT_CONDITION,
)
## ------------------------------ IMAGE ------------------------------
if input_type == "Image":
target = cv2.imread(image_path)
analysed_target = analyse_face(target, FACE_ANALYSER, return_single_face=False)
if condition == "Specific Face":
swapped = swap_specific(
analysed_source_specific,
analysed_target,
target,
models,
threshold=distance,
)
else:
swapped = swap_face_with_condition(
target, analysed_target, analysed_source, condition, age, models
)
filename = os.path.join(output_path, output_name + ".png")
cv2.imwrite(filename, swapped)
OUTPUT_FILE = filename
WORKSPACE = output_path
PREVIEW = swapped[:, :, ::-1]
tot_exec_time = time.time() - start_time
_min, _sec = divmod(tot_exec_time, 60)
yield f"Completed in {int(_min)} min {int(_sec)} sec.", *ui_after()
## ------------------------------ VIDEO ------------------------------
elif input_type == "Video":
temp_path = os.path.join(output_path, output_name, "sequence")
os.makedirs(temp_path, exist_ok=True)
video_clip = VideoFileClip(video_path)
duration = video_clip.duration
fps = video_clip.fps
total_frames = video_clip.reader.nframes
analysed_targets = []
process_bar = ProcessBar(30, total_frames)
yield "### \n βŒ› Analysing...", *ui_before()
for i, frame in enumerate(video_clip.iter_frames()):
analysed_targets.append(
analyse_face(frame, FACE_ANALYSER, return_single_face=False)
)
info_text = "Analysing Faces || "
info_text += process_bar.get(i)
print("\033[1A\033[K", end="", flush=True)
print(info_text)
if i % 10 == 0:
yield "### \n" + info_text, *ui_before()
video_clip.close()
image_sequence = []
video_clip = VideoFileClip(video_path)
audio_clip = video_clip.audio if video_clip.audio is not None else None
process_bar = ProcessBar(30, total_frames)
yield "### \n βŒ› Swapping...", *ui_before()
for i, frame in enumerate(video_clip.iter_frames()):
swapped = frame
analysed_target = analysed_targets[i]
if condition == "Specific Face":
swapped = swap_specific(
frame,
analysed_target,
analysed_source_specific,
models,
threshold=distance,
)
else:
swapped = swap_face_with_condition(
frame, analysed_target, analysed_source, condition, age, models
)
image_path = os.path.join(temp_path, f"frame_{i}.png")
cv2.imwrite(image_path, swapped[:, :, ::-1])
image_sequence.append(image_path)
info_text = "Swapping Faces || "
info_text += process_bar.get(i)
print("\033[1A\033[K", end="", flush=True)
print(info_text)
if i % 6 == 0:
PREVIEW = swapped
yield "### \n" + info_text, *ui_before()
yield "### \n βŒ› Merging...", *ui_before()
edited_video_clip = ImageSequenceClip(image_sequence, fps=fps)
if audio_clip is not None:
edited_video_clip = edited_video_clip.set_audio(audio_clip)
output_video_path = os.path.join(output_path, output_name + ".mp4")
edited_video_clip.set_duration(duration).write_videofile(
output_video_path, codec="libx264"
)
edited_video_clip.close()
video_clip.close()
if os.path.exists(temp_path) and not keep_output_sequence:
yield "### \n βŒ› Removing temporary files...", *ui_before()
shutil.rmtree(temp_path)
WORKSPACE = output_path
OUTPUT_FILE = output_video_path
tot_exec_time = time.time() - start_time
_min, _sec = divmod(tot_exec_time, 60)
yield f"βœ”οΈ Completed in {int(_min)} min {int(_sec)} sec.", *ui_after_vid()
## ------------------------------ DIRECTORY ------------------------------
elif input_type == "Directory":
source = cv2.imread(source_path)
source = analyse_face(
source,
FACE_ANALYSER,
return_single_face=True,
detect_condition=DETECT_CONDITION,
)
extensions = ["jpg", "jpeg", "png", "bmp", "tiff", "ico", "webp"]
temp_path = os.path.join(output_path, output_name)
if os.path.exists(temp_path):
shutil.rmtree(temp_path)
os.mkdir(temp_path)
swapped = None
files = []
for file_path in glob.glob(os.path.join(directory_path, "*")):
if any(file_path.lower().endswith(ext) for ext in extensions):
files.append(file_path)
files_length = len(files)
filename = None
for i, file_path in enumerate(files):
target = cv2.imread(file_path)
analysed_target = analyse_face(
target, FACE_ANALYSER, return_single_face=False
)
if condition == "Specific Face":
swapped = swap_specific(
target,
analysed_target,
analysed_source_specific,
models,
threshold=distance,
)
else:
swapped = swap_face_with_condition(
target, analysed_target, analysed_source, condition, age, models
)
filename = os.path.join(temp_path, os.path.basename(file_path))
cv2.imwrite(filename, swapped)
info_text = f"### \n βŒ› Processing file {i+1} of {files_length}"
PREVIEW = swapped[:, :, ::-1]
yield info_text, *ui_before()
WORKSPACE = temp_path
OUTPUT_FILE = filename
tot_exec_time = time.time() - start_time
_min, _sec = divmod(tot_exec_time, 60)
yield f"βœ”οΈ Completed in {int(_min)} min {int(_sec)} sec.", *ui_after()
## ------------------------------ STREAM ------------------------------
elif input_type == "Stream":
yield "### \n βŒ› Starting...", *ui_before()
global STREAMER
STREAMER = StreamerThread(src=directory_path)
STREAMER.start()
while True:
try:
target = STREAMER.frame
analysed_target = analyse_face(
target, FACE_ANALYSER, return_single_face=False
)
if condition == "Specific Face":
swapped = swap_specific(
target,
analysed_target,
analysed_source_specific,
models,
threshold=distance,
)
else:
swapped = swap_face_with_condition(
target, analysed_target, analysed_source, condition, age, models
)
PREVIEW = swapped[:, :, ::-1]
yield f"Streaming...", *ui_before()
except AttributeError:
yield "Streaming...", *ui_before()
STREAMER.stop()
## ------------------------------ GRADIO FUNC ------------------------------
def update_radio(value):
if value == "Image":
return (
gr.update(visible=True),
gr.update(visible=False),
gr.update(visible=False),
)
elif value == "Video":
return (
gr.update(visible=False),
gr.update(visible=True),
gr.update(visible=False),
)
elif value == "Directory":
return (
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=True),
)
elif value == "Stream":
return (
gr.update(visible=False),
gr.update(visible=False),
gr.update(visible=True),
)
def swap_option_changed(value):
if value == swap_options_list[1] or value == swap_options_list[2]:
return (
gr.update(visible=True),
gr.update(visible=False),
gr.update(visible=True),
)
elif value == swap_options_list[5]:
return (
gr.update(visible=False),
gr.update(visible=True),
gr.update(visible=False),
)
return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)
def video_changed(video_path):
sliders_update = gr.Slider.update
button_update = gr.Button.update
number_update = gr.Number.update
if video_path is None:
return (
sliders_update(minimum=0, maximum=0, value=0),
sliders_update(minimum=1, maximum=1, value=1),
number_update(value=1),
)
try:
clip = VideoFileClip(video_path)
fps = clip.fps
total_frames = clip.reader.nframes
clip.close()
return (
sliders_update(minimum=0, maximum=total_frames, value=0, interactive=True),
sliders_update(
minimum=0, maximum=total_frames, value=total_frames, interactive=True
),
number_update(value=fps),
)
except:
return (
sliders_update(value=0),
sliders_update(value=0),
number_update(value=1),
)
def analyse_settings_changed(detect_condition, detection_size, detection_threshold):
yield "### \n βŒ› Applying new values..."
global FACE_ANALYSER
global DETECT_CONDITION
DETECT_CONDITION = detect_condition
FACE_ANALYSER = insightface.app.FaceAnalysis(name="buffalo_l", providers=PROVIDER)
FACE_ANALYSER.prepare(
ctx_id=0,
det_size=(int(detection_size), int(detection_size)),
det_thresh=float(detection_threshold),
)
yield f"### \n βœ”οΈ Applied detect condition:{detect_condition}, detection size: {detection_size}, detection threshold: {detection_threshold}"
def stop_running():
global STREAMER
if hasattr(STREAMER, "stop"):
STREAMER.stop()
STREAMER = None
return "Cancelled"
def slider_changed(show_frame, video_path, frame_index):
if not show_frame:
return None, None
if video_path is None:
return None, None
clip = VideoFileClip(video_path)
frame = clip.get_frame(frame_index / clip.fps)
frame_array = np.array(frame)
clip.close()
return gr.Image.update(value=frame_array, visible=True), gr.Video.update(
visible=False
)
def trim_and_reload(video_path, output_path, output_name, start_frame, stop_frame):
yield video_path, f"### \n βŒ› Trimming video frame {start_frame} to {stop_frame}..."
try:
output_path = os.path.join(output_path, output_name)
trimmed_video = trim_video(video_path, output_path, start_frame, stop_frame)
yield trimmed_video, "### \n βœ”οΈ Video trimmed and reloaded."
except Exception as e:
print(e)
yield video_path, "### \n ❌ Video trimming failed. See console for more info."
## ------------------------------ GRADIO GUI ------------------------------
css = """
footer{display:none !important}
"""
with gr.Blocks(css=css) as interface:
gr.Markdown("# πŸ—Ώ Swap Mukham")
gr.Markdown("### Face swap app based on insightface inswapper.")
with gr.Row():
with gr.Row():
with gr.Column(scale=0.4):
with gr.Tab("πŸ“„ Swap Condition"):
swap_option = gr.Radio(
swap_options_list,
show_label=False,
value=swap_options_list[0],
interactive=True,
)
age = gr.Number(
value=25, label="Value", interactive=True, visible=False
)
with gr.Tab("🎚️ Detection Settings"):
detect_condition_dropdown = gr.Dropdown(
detect_conditions,
label="Condition",
value=DETECT_CONDITION,
interactive=True,
info="This condition is only used when multiple faces are detected on source or specific image.",
)
detection_size = gr.Number(
label="Detection Size", value=DETECT_SIZE, interactive=True
)
detection_threshold = gr.Number(
label="Detection Threshold",
value=DETECT_THRESH,
interactive=True,
)
apply_detection_settings = gr.Button("Apply settings")
with gr.Tab("πŸ“€ Output Settings"):
output_directory = gr.Text(
label="Output Directory",
value=DEF_OUTPUT_PATH,
interactive=True,
)
output_name = gr.Text(
label="Output Name", value="Result", interactive=True
)
keep_output_sequence = gr.Checkbox(
label="Keep output sequence", value=False, interactive=True
)
with gr.Tab("πŸͺ„ Other Settings"):
with gr.Accordion("Enhance Face", open=True):
enable_face_enhance = gr.Checkbox(
label="Enable GFPGAN", value=False, interactive=True
)
with gr.Accordion("Advanced Mask", open=False):
enable_face_parser_mask = gr.Checkbox(
label="Enable Face Parsing",
value=False,
interactive=True,
)
mask_include = gr.Dropdown(
mask_regions.keys(),
value=MASK_INCLUDE,
multiselect=True,
label="Include",
interactive=True,
)
mask_exclude = gr.Dropdown(
mask_regions.keys(),
value=MASK_EXCLUDE,
multiselect=True,
label="Exclude",
interactive=True,
)
mask_blur = gr.Number(
label="Blur Mask",
value=MASK_BLUR,
minimum=0,
interactive=True,
)
source_image_input = gr.Image(
label="Source face", type="filepath", interactive=True
)
with gr.Box(visible=False) as specific_face:
for i in range(NUM_OF_SRC_SPECIFIC):
idx = i + 1
code = "\n"
code += f"with gr.Tab(label='({idx})'):"
code += "\n\twith gr.Row():"
code += f"\n\t\tsrc{idx} = gr.Image(interactive=True, type='numpy', label='Source Face {idx}')"
code += f"\n\t\ttrg{idx} = gr.Image(interactive=True, type='numpy', label='Specific Face {idx}')"
exec(code)
distance_slider = gr.Slider(
minimum=0,
maximum=2,
value=0.6,
interactive=True,
label="Distance",
info="Lower distance is more similar and higher distance is less similar to the target face.",
)
with gr.Group():
input_type = gr.Radio(
["Image", "Video", "Directory", "Stream"],
label="Target Type",
value="Video",
)
with gr.Box(visible=False) as input_image_group:
image_input = gr.Image(
label="Target Image", interactive=True, type="filepath"
)
with gr.Box(visible=True) as input_video_group:
vid_widget = gr.Video #gr.Video if USE_COLAB else gr.Text
video_input = vid_widget(
label="Target Video Path", interactive=True
)
with gr.Accordion("βœ‚οΈ Trim video", open=False):
with gr.Column():
with gr.Row():
set_slider_range_btn = gr.Button(
"Set frame range", interactive=True
)
show_trim_preview_btn = gr.Checkbox(
label="Show frame when slider change",
value=True,
interactive=True,
)
video_fps = gr.Number(
value=30,
interactive=False,
label="Fps",
visible=False,
)
start_frame = gr.Slider(
minimum=0,
maximum=1,
value=0,
step=1,
interactive=True,
label="Start Frame",
info="",
)
end_frame = gr.Slider(
minimum=0,
maximum=1,
value=1,
step=1,
interactive=True,
label="End Frame",
info="",
)
trim_and_reload_btn = gr.Button(
"Trim and Reload", interactive=True
)
with gr.Box(visible=False) as input_directory_group:
direc_input = gr.Text(label="Path", interactive=True)
with gr.Column(scale=0.6):
info = gr.Markdown(value="...")
with gr.Row():
swap_button = gr.Button("✨ Swap", variant="primary")
cancel_button = gr.Button("β›” Cancel")
preview_image = gr.Image(label="Output", interactive=False)
preview_video = gr.Video(
label="Output", interactive=False, visible=False
)
with gr.Row():
output_directory_button = gr.Button(
"πŸ“‚", interactive=False, visible=False
)
output_video_button = gr.Button(
"🎬", interactive=False, visible=False
)
with gr.Column():
gr.Markdown(
'[!["Buy Me A Coffee"](https://www.buymeacoffee.com/assets/img/custom_images/orange_img.png)](https://www.buymeacoffee.com/harisreedhar)'
)
gr.Markdown(
"### [Source code](https://github.com/harisreedhar/Swap-Mukham) . [Disclaimer](https://github.com/harisreedhar/Swap-Mukham#disclaimer) . [Gradio](https://gradio.app/)"
)
## ------------------------------ GRADIO EVENTS ------------------------------
set_slider_range_event = set_slider_range_btn.click(
video_changed,
inputs=[video_input],
outputs=[start_frame, end_frame, video_fps],
)
trim_and_reload_event = trim_and_reload_btn.click(
fn=trim_and_reload,
inputs=[video_input, output_directory, output_name, start_frame, end_frame],
outputs=[video_input, info],
)
start_frame_event = start_frame.release(
fn=slider_changed,
inputs=[show_trim_preview_btn, video_input, start_frame],
outputs=[preview_image, preview_video],
show_progress=False,
)
end_frame_event = end_frame.release(
fn=slider_changed,
inputs=[show_trim_preview_btn, video_input, end_frame],
outputs=[preview_image, preview_video],
show_progress=False,
)
input_type.change(
update_radio,
inputs=[input_type],
outputs=[input_image_group, input_video_group, input_directory_group],
)
swap_option.change(
swap_option_changed,
inputs=[swap_option],
outputs=[age, specific_face, source_image_input],
)
apply_detection_settings.click(
analyse_settings_changed,
inputs=[detect_condition_dropdown, detection_size, detection_threshold],
outputs=[info],
)
src_specific_inputs = []
gen_variable_txt = ",".join(
[f"src{i+1}" for i in range(NUM_OF_SRC_SPECIFIC)]
+ [f"trg{i+1}" for i in range(NUM_OF_SRC_SPECIFIC)]
)
exec(f"src_specific_inputs = ({gen_variable_txt})")
swap_inputs = [
input_type,
image_input,
video_input,
direc_input,
source_image_input,
output_directory,
output_name,
keep_output_sequence,
swap_option,
age,
distance_slider,
enable_face_enhance,
enable_face_parser_mask,
mask_include,
mask_exclude,
mask_blur,
*src_specific_inputs,
]
swap_outputs = [
info,
preview_image,
output_directory_button,
output_video_button,
preview_video,
]
swap_event = swap_button.click(
fn=process, inputs=swap_inputs, outputs=swap_outputs, show_progress=False
)
cancel_button.click(
fn=stop_running,
inputs=None,
outputs=[info],
cancels=[
swap_event,
trim_and_reload_event,
set_slider_range_event,
start_frame_event,
end_frame_event,
],
show_progress=False,
)
output_directory_button.click(
lambda: open_directory(path=WORKSPACE), inputs=None, outputs=None
)
output_video_button.click(
lambda: open_directory(path=OUTPUT_FILE), inputs=None, outputs=None
)
if __name__ == "__main__":
if USE_COLAB:
print("Running in colab mode")
interface.queue(concurrency_count=2, max_size=20).launch(share=USE_COLAB)