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import gradio as gr
import cv2
import multiprocessing
import os
import requests
from refacer import Refacer
# Hugging Face URL to download the model
model_url = "https://huggingface.co/ofter/4x-UltraSharp/resolve/main/inswapper_128.onnx"
model_path = "./inswapper_128.onnx"
# Function to download the model
def download_model():
if not os.path.exists(model_path):
print("Downloading inswapper_128.onnx...")
response = requests.get(model_url)
if response.status_code == 200:
with open(model_path, 'wb') as f:
f.write(response.content)
print("Model downloaded successfully!")
else:
raise Exception(f"Failed to download the model. Status code: {response.status_code}")
else:
print("Model already exists.")
# Download the model when the script runs
download_model()
# Initialize Refacer class (force CPU mode)
refacer = Refacer(force_cpu=True)
# Dummy function to simulate frame-level processing
def process_frame(frame, origin_face, destination_face, threshold):
# Simulate face swapping or any processing needed
result_frame = refacer.reface(frame, [{
'origin': origin_face,
'destination': destination_face,
'threshold': threshold
}])
return result_frame
# Function to process the video in parallel using multiprocessing
def process_video(video_path, origins, destinations, thresholds, max_processes=2):
cap = cv2.VideoCapture(video_path)
frames = []
# Read all frames from the video
while cap.isOpened():
ret, frame = cap.read()
if not ret:
break
frames.append(frame)
cap.release()
# Parallel processing of frames with limited processes (for CPU optimization)
with multiprocessing.Pool(processes=max_processes) as pool:
processed_frames = pool.starmap(process_frame, [
(frame, origins[min(i, len(origins) - 1)], destinations[min(i, len(destinations) - 1)], thresholds[min(i, len(thresholds) - 1)])
for i, frame in enumerate(frames)
])
# Saving the processed frames back into a video
output_video_path = "processed_video.mp4"
fourcc = cv2.VideoWriter_fourcc(*'mp4v') # Compression using mp4 codec
out = cv2.VideoWriter(output_video_path, fourcc, 30.0, (640, 360)) # Reduce resolution to speed up processing
for frame in processed_frames:
out.write(frame)
out.release()
return output_video_path
# Gradio Interface function
def run(video_path, *vars):
# Split the inputs into origins, destinations, and thresholds based on num_faces
num_faces = 5 # You can adjust this based on your UI
origins = vars[:num_faces]
destinations = vars[num_faces:2*num_faces]
thresholds = vars[2*num_faces:]
# Ensure there are no index errors by limiting the number of inputs
if len(origins) != num_faces or len(destinations) != num_faces or len(thresholds) != num_faces:
return "Please provide input for all faces."
refaced_video_path = process_video(video_path, origins, destinations, thresholds)
print(f"Refaced video can be found at {refaced_video_path}")
return refaced_video_path
# Prepare Gradio components
origin = []
destination = []
thresholds = []
with gr.Blocks() as demo:
with gr.Row():
gr.Markdown("# Refacer")
with gr.Row():
video_input = gr.Video(label="Original video", format="mp4")
video_output = gr.Video(label="Refaced video", interactive=False, format="mp4")
for i in range(5): # Set max faces to 5
with gr.Tab(f"Face #{i+1}"):
with gr.Row():
origin.append(gr.Image(label="Face to replace"))
destination.append(gr.Image(label="Destination face"))
with gr.Row():
thresholds.append(gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.2))
with gr.Row():
button = gr.Button("Reface", variant="primary")
button.click(fn=run, inputs=[video_input] + origin + destination + thresholds, outputs=[video_output])
# Launch the Gradio app
demo.launch(show_error=True, server_name="0.0.0.0", server_port=7860)
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