Initial clean project import

python/MODNet-master/demo/video_matting/webcam/README.md

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+## MODNet - WebCam-Based Portrait Video Matting Demo
+This is a MODNet portrait video matting demo based on WebCam. It will call your local WebCam and display the matting results in real time. The demo can run under CPU or GPU.
+
+### 1. Requirements
+The basic requirements for this demo are:
+- Ubuntu System
+- WebCam
+- Python 3+
+
+**NOTE**: If your device does not satisfy the above conditions, please try our [online Colab demo](https://colab.research.google.com/drive/1Pt3KDSc2q7WxFvekCnCLD8P0gBEbxm6J?usp=sharing).
+
+
+### 2. Introduction
+We use ~400 unlabeled video clips (divided into ~50,000 frames) downloaded from the internet to perform SOC to adapt MODNet to the video domain. **Nonetheless, due to insufficient labeled training data (~3k labeled foregrounds), our model may still make errors in portrait semantics estimation under challenging scenes.** Besides, this demo does not currently support the OFD trick, which will be provided soon.
+
+For a better experience, please:
+
+*   make sure the portrait and background are distinguishable, <i>i.e.</i>, are not similar
+*   run in soft and bright ambient lighting
+*   do not be too close or too far from the WebCam
+*   do not move too fast
+
+### 3. Run Demo
+We recommend creating a new conda virtual environment to run this demo, as follow:
+
+1. Clone the MODNet repository:
+    ```
+    git clone https://github.com/ZHKKKe/MODNet.git
+    cd MODNet
+    ```
+
+2. Download the pre-trained model from this [link](https://drive.google.com/file/d/1Nf1ZxeJZJL8Qx9KadcYYyEmmlKhTADxX/view?usp=sharing) and put it into the folder `MODNet/pretrained/`.
+
+
+3. Create a conda virtual environment named `modnet` (if it doesn't exist) and activate it. Here we use `python=3.6` as an example:
+     ```
+    conda create -n modnet python=3.6
+    source activate modnet
+    ```
+
+4. Install the required python dependencies (please make sure your CUDA version is supported by the PyTorch version installed):
+    ```
+    pip install -r demo/video_matting/webcam/requirements.txt
+    ```
+
+5. Execute the main code:
+    ```
+    python -m demo.video_matting.webcam.run
+    ```
+
+### 4. Acknowledgement
+We thank [@tkianai](https://github.com/tkianai) and [@mazhar004](https://github.com/mazhar004) for their contributions to making this demo available for CPU use.

python/MODNet-master/demo/video_matting/webcam/requirements.txt

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+numpy
+Pillow
+opencv-python
+torch >= 1.0.0
+torchvision

python/MODNet-master/demo/video_matting/webcam/run.py

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+import cv2
+import numpy as np
+from PIL import Image
+
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+
+from src.models.modnet import MODNet
+
+
+torch_transforms = transforms.Compose(
+    [
+        transforms.ToTensor(),
+        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),
+    ]
+)
+
+print('Load pre-trained MODNet...')
+pretrained_ckpt = './pretrained/modnet_webcam_portrait_matting.ckpt'
+modnet = MODNet(backbone_pretrained=False)
+modnet = nn.DataParallel(modnet)
+
+GPU = True if torch.cuda.device_count() > 0 else False
+if GPU:
+    print('Use GPU...')
+    modnet = modnet.cuda()
+    modnet.load_state_dict(torch.load(pretrained_ckpt))
+else:
+    print('Use CPU...')
+    modnet.load_state_dict(torch.load(pretrained_ckpt, map_location=torch.device('cpu')))
+
+modnet.eval()
+
+print('Init WebCam...')
+cap = cv2.VideoCapture(0)
+cap.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)
+cap.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)
+
+print('Start matting...')
+while(True):
+    _, frame_np = cap.read()
+    frame_np = cv2.cvtColor(frame_np, cv2.COLOR_BGR2RGB)
+    frame_np = cv2.resize(frame_np, (910, 512), cv2.INTER_AREA)
+    frame_np = frame_np[:, 120:792, :]
+    frame_np = cv2.flip(frame_np, 1)
+
+    frame_PIL = Image.fromarray(frame_np)
+    frame_tensor = torch_transforms(frame_PIL)
+    frame_tensor = frame_tensor[None, :, :, :]
+    if GPU:
+        frame_tensor = frame_tensor.cuda()
+    
+    with torch.no_grad():
+        _, _, matte_tensor = modnet(frame_tensor, True)
+
+    matte_tensor = matte_tensor.repeat(1, 3, 1, 1)
+    matte_np = matte_tensor[0].data.cpu().numpy().transpose(1, 2, 0)
+    fg_np = matte_np * frame_np + (1 - matte_np) * np.full(frame_np.shape, 255.0)
+    view_np = np.uint8(np.concatenate((frame_np, fg_np), axis=1))
+    view_np = cv2.cvtColor(view_np, cv2.COLOR_RGB2BGR)
+
+    cv2.imshow('MODNet - WebCam [Press \'Q\' To Exit]', view_np)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+print('Exit...')