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Update app.py

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  1. app.py +53 -55
app.py CHANGED
@@ -1,68 +1,68 @@
1
  import gradio as gr
2
  from refacer import Refacer
3
  import argparse
4
- import ngrok
 
5
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
6
  parser = argparse.ArgumentParser(description='Refacer')
7
  parser.add_argument("--max_num_faces", type=int, help="Max number of faces on UI", default=5)
8
  parser.add_argument("--force_cpu", help="Force CPU mode", default=False, action="store_true")
9
  parser.add_argument("--share_gradio", help="Share Gradio", default=False, action="store_true")
10
  parser.add_argument("--server_name", type=str, help="Server IP address", default="127.0.0.1")
11
  parser.add_argument("--server_port", type=int, help="Server port", default=7860)
12
- parser.add_argument("--colab_performance", help="Use in colab for better performance", default=False,action="store_true")
13
- parser.add_argument("--ngrok", type=str, help="Use ngrok", default=None)
14
- parser.add_argument("--ngrok_region", type=str, help="ngrok region", default="us")
15
  args = parser.parse_args()
16
 
17
- refacer = Refacer(force_cpu=args.force_cpu,colab_performance=args.colab_performance)
18
-
19
- num_faces=args.max_num_faces
20
-
21
- # Connect to ngrok for ingress
22
- def connect(token, port, options):
23
- account = None
24
- if token is None:
25
- token = 'None'
26
- else:
27
- if ':' in token:
28
- # token = authtoken:username:password
29
- token, username, password = token.split(':', 2)
30
- account = f"{username}:{password}"
31
-
32
- # For all options see: https://github.com/ngrok/ngrok-py/blob/main/examples/ngrok-connect-full.py
33
- if not options.get('authtoken_from_env'):
34
- options['authtoken'] = token
35
- if account:
36
- options['basic_auth'] = account
37
-
38
-
39
- try:
40
- public_url = ngrok.connect(f"127.0.0.1:{port}", **options).url()
41
- except Exception as e:
42
- print(f'Invalid ngrok authtoken? ngrok connection aborted due to: {e}\n'
43
- f'Your token: {token}, get the right one on https://dashboard.ngrok.com/get-started/your-authtoken')
44
- else:
45
- print(f'ngrok connected to localhost:{port}! URL: {public_url}\n'
46
- 'You can use this link after the launch is complete.')
47
 
 
48
 
 
49
  def run(*vars):
50
- video_path=vars[0]
51
- origins=vars[1:(num_faces+1)]
52
- destinations=vars[(num_faces+1):(num_faces*2)+1]
53
- thresholds=vars[(num_faces*2)+1:]
54
 
55
  faces = []
56
- for k in range(0,num_faces):
57
  if origins[k] is not None and destinations[k] is not None:
58
  faces.append({
59
- 'origin':origins[k],
60
- 'destination':destinations[k],
61
- 'threshold':thresholds[k]
62
  })
63
 
64
- return refacer.reface(video_path,faces)
 
 
 
 
65
 
 
66
  origin = []
67
  destination = []
68
  thresholds = []
@@ -71,23 +71,21 @@ with gr.Blocks() as demo:
71
  with gr.Row():
72
  gr.Markdown("# Refacer")
73
  with gr.Row():
74
- video=gr.Video(label="Original video",format="mp4")
75
- video2=gr.Video(label="Refaced video",interactive=False,format="mp4")
76
 
77
- for i in range(0,num_faces):
78
  with gr.Tab(f"Face #{i+1}"):
79
  with gr.Row():
80
  origin.append(gr.Image(label="Face to replace"))
81
  destination.append(gr.Image(label="Destination face"))
82
  with gr.Row():
83
- thresholds.append(gr.Slider(label="Threshold",minimum=0.0,maximum=1.0,value=0.2))
 
84
  with gr.Row():
85
- button=gr.Button("Reface", variant="primary")
86
 
87
- button.click(fn=run,inputs=[video]+origin+destination+thresholds,outputs=[video2])
88
-
89
- if args.ngrok is not None:
90
- connect(args.ngrok, args.server_port, {'region': args.ngrok_region, 'authtoken_from_env': False})
91
-
92
- #demo.launch(share=True,server_name="0.0.0.0", show_error=True)
93
- demo.queue().launch(show_error=True,share=args.share_gradio,server_name=args.server_name,server_port=args.server_port)
 
1
  import gradio as gr
2
  from refacer import Refacer
3
  import argparse
4
+ import os
5
+ import requests
6
 
7
+ # Hugging Face URL to download the model
8
+ model_url = "https://huggingface.co/ofter/4x-UltraSharp/resolve/main/inswapper_128.onnx"
9
+ model_path = "./inswapper_128.onnx"
10
+
11
+ # Function to download the model
12
+ def download_model():
13
+ if not os.path.exists(model_path):
14
+ print("Downloading inswapper_128.onnx...")
15
+ response = requests.get(model_url)
16
+ if response.status_code == 200:
17
+ with open(model_path, 'wb') as f:
18
+ f.write(response.content)
19
+ print("Model downloaded successfully!")
20
+ else:
21
+ raise Exception(f"Failed to download the model. Status code: {response.status_code}")
22
+ else:
23
+ print("Model already exists.")
24
+
25
+ # Download the model when the script runs
26
+ download_model()
27
+
28
+ # Argument parser
29
  parser = argparse.ArgumentParser(description='Refacer')
30
  parser.add_argument("--max_num_faces", type=int, help="Max number of faces on UI", default=5)
31
  parser.add_argument("--force_cpu", help="Force CPU mode", default=False, action="store_true")
32
  parser.add_argument("--share_gradio", help="Share Gradio", default=False, action="store_true")
33
  parser.add_argument("--server_name", type=str, help="Server IP address", default="127.0.0.1")
34
  parser.add_argument("--server_port", type=int, help="Server port", default=7860)
35
+ parser.add_argument("--colab_performance", help="Use in colab for better performance", default=False, action="store_true")
 
 
36
  args = parser.parse_args()
37
 
38
+ # Initialize the Refacer class
39
+ refacer = Refacer(force_cpu=args.force_cpu, colab_performance=args.colab_performance)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
40
 
41
+ num_faces = args.max_num_faces
42
 
43
+ # Run function for refacing video
44
  def run(*vars):
45
+ video_path = vars[0]
46
+ origins = vars[1:(num_faces+1)]
47
+ destinations = vars[(num_faces+1):(num_faces*2)+1]
48
+ thresholds = vars[(num_faces*2)+1:]
49
 
50
  faces = []
51
+ for k in range(0, num_faces):
52
  if origins[k] is not None and destinations[k] is not None:
53
  faces.append({
54
+ 'origin': origins[k],
55
+ 'destination': destinations[k],
56
+ 'threshold': thresholds[k]
57
  })
58
 
59
+ # Call refacer to process video and get file path
60
+ refaced_video_path = refacer.reface(video_path, faces) # refaced video path
61
+ print(f"Refaced video can be found at {refaced_video_path}")
62
+
63
+ return refaced_video_path # Return the file path to show in Gradio output
64
 
65
+ # Prepare Gradio components
66
  origin = []
67
  destination = []
68
  thresholds = []
 
71
  with gr.Row():
72
  gr.Markdown("# Refacer")
73
  with gr.Row():
74
+ video = gr.Video(label="Original video", format="mp4")
75
+ video2 = gr.Video(label="Refaced video", interactive=False, format="mp4")
76
 
77
+ for i in range(0, num_faces):
78
  with gr.Tab(f"Face #{i+1}"):
79
  with gr.Row():
80
  origin.append(gr.Image(label="Face to replace"))
81
  destination.append(gr.Image(label="Destination face"))
82
  with gr.Row():
83
+ thresholds.append(gr.Slider(label="Threshold", minimum=0.0, maximum=1.0, value=0.2))
84
+
85
  with gr.Row():
86
+ button = gr.Button("Reface", variant="primary")
87
 
88
+ button.click(fn=run, inputs=[video] + origin + destination + thresholds, outputs=[video2])
89
+
90
+ # Launch the Gradio app
91
+ demo.queue().launch(show_error=True, share=args.share_gradio, server_name="0.0.0.0", server_port=args.server_port)