added instructions on how to use Windows + OBS

README.md

@@ -1,5 +1,5 @@
 # faceit_live3
-This is an update to http://github.com/faceit_live using [first order model](https://github.com/AliaksandrSiarohin/first-order-model) by Aliaksandr Siarohin to generate the images. This model only requires a single image, so no training is needed and things are much easier.
+This is an update to http://github.com/faceit_live using [first order model](https://github.com/AliaksandrSiarohin/first-order-model) by Aliaksandr Siarohin to generate the images. This model only requires a single image, so no training is needed and things are much easier. I've included instructions on how to set it up under **Windows 10** and **Linux**.
 
 # Demo
 
@@ -11,17 +11,76 @@ Here is a video of the program running. It uses a single page I took from partne
 # Setup
 
 ## Requirements
-This has been tested on **Ubuntu 18.04 with a Titan RTX/X GPU**.
+This has only been tested on **Ubuntu 18.04 and Win 10 with a Titan RTX/X GPU**.
 You will need the following to make it work:
 
-    Linux host OS
+    Linux host OS / Win 10
     NVidia fast GPU (GTX 1080, GTX 1080i, Titan, etc ...)
     Fast Desktop CPU (Quad Core or more)
-    NVidia CUDA 10 and cuDNN 7 libraries installed
+
     Webcam
+    Anaconda Environment (https://www.anaconda.com/distribution/)
 
 
-## Setup Host System
+# Clone this repository
+Don't forget to use the *--recurse-submodules* parameter to checkout all dependencies. In Windows you might need to install a  [Git Client](https://git-scm.com/download/win).
+
+    $ git clone --recurse-submodules https://github.com/alew3/faceit_live3.git
+
+## Download 'vox-adv-cpk.pth.tar' to /model folder
+
+You can find it at: [google-drive](https://drive.google.com/open?id=1PyQJmkdCsAkOYwUyaj_l-l0as-iLDgeH) or [yandex-disk](https://yadi.sk/d/lEw8uRm140L_eQ).
+
+# Install Nvidia Deep Learning Drivers / Libs
+Install the latest Nvidia video driver then the Deep Learning infrastructure:
+
+* NVidia [CUDA 10.1 driver](https://developer.nvidia.com/cuda-downloads) - 2.6GB Download!
+* [cuDNN](https://developer.nvidia.com/cudnn) version for CUDA 10.1 - you will need to register to download it.
+
+Other versions might work, but I haven't tested them.
+
+
+## Usage
+
+Put in the `/media` directory the images in jpg/png you want to play with. Squared images that have just a face filling most of the space will work better.
+
+
+# Setup Windows Version
+
+## Create an Anaconda environment and install requirements
+```
+$ conda create -n faceit_live3 python=3.8
+$ conda activate faceit_live3
+$ conda install pytorch=1.4 torchvision=0.5 cudatoolkit=10.1 -c pytorch
+$ pip install -r requirements.txt
+```
+
+## Setup Virtual Camera for streaming
+
+Download [OBS Studio for Win](https://obsproject.com/download) and install it, afterwards install the [OBS Virtual CAM plugin](https://github.com/CatxFish/obs-virtual-cam/releases) by following instructions on the page.
+
+After you install Virtual CAM.
+- Create a Scene
+- Add a Window Capture item to Sources and select the "Stream Window"
+- Add a Filter to the Window Capture by right clicking and selecting Filters, then "+" and choose Virtual CAM
+- Start the Virtual CAM from the Tools Menu
+
+[![Select the OBSCAM](https://raw.githubusercontent.com/alew3/faceit_live3/master/docsobs.png)]
+
+Open Firefox and joing Google Hangout to test it, don't forget to choose the OBS CAM from the camera options under settings.
+
+[![Select the OBSCAM](https://raw.githubusercontent.com/alew3/faceit_live3/master/docs/obscam.png)]
+
+# Setup Linux Version
+
+## Create an Anaconda environment and install requirements
+```
+$ conda create -n faceit_live3 python=3.8
+$ source activate faceit_live3
+$ conda install pytorch=1.4 torchvision=0.5 cudatoolkit=10.1 -c pytorch
+$ pip install -r requirements.txt
+```
+
 To use the fake webcam feature to enter conferences with our stream we need to insert the **v4l2loopback** kernel module in order to create */dev/video1*. Follow the install instructions at  (https://github.com/umlaeute/v4l2loopback), then let's setup our fake webcam:
 
 ```
@@ -65,61 +124,50 @@ $ v4l2-ctl --list-formats -d /dev/video1
 ```
 
 
-If you have more than one GPU, you might need to set some environment variables:
-```
-# specify which display to use for rendering
-$ export DISPLAY=:1
-
-# which CUDA DEVICE to use (run nvidia-smi to discover the ID)
-$ export CUDA_VISIBLE_DEVICES=0
-
-```
-
-## Clone this repository
-Don't forget to use the *--recurse-submodules* parameter to checkout all dependencies.
-
-    $ git clone --recurse-submodules https://github.com/alew3/faceit_live3.git /local_path/
-
-## Create an Anaconda environment and install requirements
-```
-$ conda create -n faceit_live3 python=3.8
-$ source activate faceit_live3
-$ conda install pytorch=1.4 torchvision=0.5 cudatoolkit=10.1 -c pytorch
-$ pip install -r requirements.txt
-
-```
-
-## Download 'vox-adv-cpk.pth.tar' to /model folder
-
-You can find it at: [google-drive](https://drive.google.com/open?id=1PyQJmkdCsAkOYwUyaj_l-l0as-iLDgeH) or [yandex-disk](https://yadi.sk/d/lEw8uRm140L_eQ).
-
-
-# Usage
-
-Put in the `./media/` directory the images in jpg/png you want to play with.
-
-
-# Run the program
+# Run the program 
 
 ```
 $ python faceit_live.py
 ```
 
 ## Parameters
+    --system # win or linux (default is win)
     --webcam_id # the videoid of the Webcam e.g. 0 if /dev/video0 (default is 0)
-    --stream_id # the /dev/video number to stream to (default is 1)
+    --stream_id # only used in Linux. Set the /dev/video number to stream to (default is 1)
+    --gpu_id # for multiple GPU setups, select which GPU to use (default is 0)
 
 ## Example
 ```
 $ python faceit_live.py --webcam_id 0 --stream_id 1
 ```
 
-## Shortcuts when running
+## Key Shortcuts when running
 ```
 N - cycle next image in media folder
 C - recenter webcam and create a new base image
 T - option to alter between 'Relative' and 'Absolute' transformations mode
+Q - to quit and close all Windows
 ```
 
 # Tip
-For better results, look into the webcam when starting the program or when pressing C, as this will create a base image from your face that is used for the transformation. Move away and closer to the webcam to find the ideal distance for better results.
+For better results, look into the webcam when starting the program or when pressing C, as this will create a base image from your face that is used for the transformation. Move away and closer to the webcam to find the ideal distance for better results.
+
+## Troubleshooting
+
+### Slow
+If it is running slow, check that it is running on the GPU by looking at the TASK MANAGER under Windows and NVidia Control Panel for Linux.
+
+
+### Multiple GPU
+
+If you have more than one GPU, you might need to set some environment variables:
+```
+# specify which display to use for rendering (Linux)
+$ export DISPLAY=:1
+
+# which CUDA DEVICE to use (run nvidia-smi to discover the ID)
+$ export CUDA_VISIBLE_DEVICES=0 (LINUX)
+or
+$ SET CUDA_VISIBLE_DEVICES=0,1 (WIN)
+
+```

faceit_live.py

@@ -10,13 +10,13 @@
 import PIL.ImageFilter
 import io
 from io import BytesIO
-import pyfakewebcam
 import pyautogui
 import os
 import glob
 from argparse import Namespace
 import argparse
 import timeit
+import torch
 warnings.filterwarnings("ignore")
 
 ############## setup ####
@@ -25,16 +25,24 @@
 model_path = 'model/'
 
 parser = argparse.ArgumentParser()
-parser.add_argument('--webcam_id', type = int, default = 2)
+parser.add_argument('--webcam_id', type = int, default = 0)
 parser.add_argument('--stream_id', type = int, default = 1)
-args = parser.parse_args()
-
+parser.add_argument('--gpu_id', type = int, default = 0)
+parser.add_argument('--system', type = str, default = "win")
 
+args = parser.parse_args()
 webcam_id = args.webcam_id
+gpu_id = args.gpu_id
+
 webcam_height = 480
 webcam_width = 640
 screen_width, screen_height = pyautogui.size()
 
+system = args.system
+if system=="linux":
+    import pyfakewebcam
+
+
 stream_id = args.stream_id
 first_order_path = 'first-order-model/'
 sys.path.insert(0,first_order_path)
@@ -47,13 +55,25 @@
 # prevent tqdm from outputting to console
 demo.tqdm = lambda *i, **kwargs: i[0]
 
+print("CUDA is available: ",torch.cuda.is_available())
+if (torch.cuda.is_available()):
+    torch.cuda.device("cuda:" + str(gpu_id))
+    print("Device Name:",torch.cuda.get_device_name(gpu_id))
+    print("Device Count:",torch.cuda.device_count())
+    print("CUDA: ",torch.version.cuda)
+    print("cuDNN",torch.backends.cudnn.version())
+    print("Device",torch.cuda.current_device())
+
+
+
 img_list = []
+print("Scanning /media folder for images to use...")
 for filename in os.listdir(media_path):
     if filename.endswith(".jpg") or filename.endswith(".jpeg") or filename.endswith(".png"):
         img_list.append(os.path.join(media_path, filename))
         print(os.path.join(media_path, filename))
 
-print(img_list, len(img_list))
+#print(img_list, len(img_list))
 
 
 
@@ -66,9 +86,10 @@ def main():
     source_image =  readnextimage(0)
 
     # start streaming
-    camera = pyfakewebcam.FakeWebcam(f'/dev/video{stream_id}', webcam_width, webcam_height)
-    camera.print_capabilities()
-    print(f"Fake webcam created on /dev/video{stream_id}. Use Firefox and join a Google Meeting to test.")
+    if system=="linux":
+        camera = pyfakewebcam.FakeWebcam(f'/dev/video{stream_id}', webcam_width, webcam_height)
+        camera.print_capabilities()
+        print(f"Fake webcam created on /dev/video{stream_id}. Use Firefox and join a Google Meeting to test.")
 
     # capture webcam
     video_capture = cv2.VideoCapture(webcam_id)
@@ -135,7 +156,8 @@ def main():
         stream_v = (stream_v*255).astype(np.uint8)
 
         # stream to fakewebcam
-        camera.schedule_frame(stream_v)
+        if system=="linux":
+            camera.schedule_frame(stream_v)
 
 
         k = cv2.waitKey(1) 
@@ -160,8 +182,9 @@ def main():
 
 # transform face with first-order-model
 def process_image(source_image,base,current,net, generator,kp_detector,relative):
-    predictions = make_animation(source_image, [base,current], generator, kp_detector, relative=relative, adapt_movement_scale=False)
-
+    predictions = make_animation(source_image, [base,current], generator, kp_detector, relative=relative, adapt_movement_scale=False, cpu=False)
+    #print("Device",torch.cuda.current_device())
+    #print("Device Name:",torch.cuda.get_device_name(gpu_id))
     # predictions = [1]# predictions[..., ::-1]
     # predictions = (np.clip(predictions, 0, 1) * 255).astype(np.uint8)
     return predictions[1]