main.py

from __future__ import annotations
from fastapi import FastAPI, File, UploadFile, Form
from fastapi.responses import StreamingResponse
from fastapi.staticfiles import StaticFiles
import torch
import shutil
import cv2
import numpy as np
import dlib
from torchvision import transforms
import torch.nn.functional as F

import gradio as gr
import pathlib
import sys
sys.path.insert(0, 'vtoonify')
from vtoonify_model import Model
from util import load_psp_standalone, get_video_crop_parameter, tensor2cv2
import torch
import torch.nn as nn
import numpy as np
import dlib
import cv2
from model.vtoonify import VToonify
from model.bisenet.model import BiSeNet
import torch.nn.functional as F
from torchvision import transforms
from model.encoder.align_all_parallel import align_face
import gc
import huggingface_hub
import os
from io import BytesIO

app = FastAPI()

MODEL_REPO = 'PKUWilliamYang/VToonify'

class Model:
    def __init__(self, device):
        super().__init__()
        
        self.device = device
        self.style_types = {
            'cartoon1': ['vtoonify_d_cartoon/vtoonify_s026_d0.5.pt', 26],
        
        }
        
        self.landmarkpredictor = self._create_dlib_landmark_model()
        self.parsingpredictor = self._create_parsing_model()
        self.pspencoder = self._load_encoder()    
        self.transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
        ])
        
        self.vtoonify, self.exstyle = self._load_default_model()
        self.color_transfer = False
        self.style_name = 'cartoon1'
        self.video_limit_cpu = 100
        self.video_limit_gpu = 300

    def _create_dlib_landmark_model(self):
        return dlib.shape_predictor(huggingface_hub.hf_hub_download(MODEL_REPO, 'models/shape_predictor_68_face_landmarks.dat'))
    
    def _create_parsing_model(self):
        parsingpredictor = BiSeNet(n_classes=19)
        parsingpredictor.load_state_dict(torch.load(huggingface_hub.hf_hub_download(MODEL_REPO, 'models/faceparsing.pth'),
                                                    map_location=lambda storage, loc: storage))
        parsingpredictor.to(self.device).eval()
        return parsingpredictor
    
    def _load_encoder(self) -> nn.Module:
        style_encoder_path = huggingface_hub.hf_hub_download(MODEL_REPO, 'models/encoder.pt')
        return load_psp_standalone(style_encoder_path, self.device)
    
    def _load_default_model(self) -> tuple[torch.Tensor, str]:
        vtoonify = VToonify(backbone='dualstylegan')
        vtoonify.load_state_dict(torch.load(huggingface_hub.hf_hub_download(MODEL_REPO,
                                            'models/vtoonify_d_cartoon/vtoonify_s026_d0.5.pt'), 
                                            map_location=lambda storage, loc: storage)['g_ema'])
        vtoonify.to(self.device)
        tmp = np.load(huggingface_hub.hf_hub_download(MODEL_REPO, 'models/vtoonify_d_cartoon/exstyle_code.npy'), allow_pickle=True).item()
        exstyle = torch.tensor(tmp[list(tmp.keys())[26]]).to(self.device)
        with torch.no_grad():  
            exstyle = vtoonify.zplus2wplus(exstyle)
        return vtoonify, exstyle
    
    def load_model(self, style_type: str) -> tuple[torch.Tensor, str]:
        if 'illustration' in style_type:
            self.color_transfer = True
        else:
            self.color_transfer = False
        if style_type not in self.style_types.keys():
            return None, 'Oops, wrong Style Type. Please select a valid model.'
        self.style_name = style_type
        model_path, ind = self.style_types[style_type]
        style_path = os.path.join('models', os.path.dirname(model_path), 'exstyle_code.npy')
        self.vtoonify.load_state_dict(torch.load(huggingface_hub.hf_hub_download(MODEL_REPO, 'models/' + model_path), 
                                            map_location=lambda storage, loc: storage)['g_ema'])
        tmp = np.load(huggingface_hub.hf_hub_download(MODEL_REPO, style_path), allow_pickle=True).item()
        exstyle = torch.tensor(tmp[list(tmp.keys())[ind]]).to(self.device)
        with torch.no_grad():  
            exstyle = self.vtoonify.zplus2wplus(exstyle)
        return exstyle, 'Model of %s loaded.' % (style_type)
    
    def detect_and_align(self, frame, top, bottom, left, right, return_para=False):
        message = 'Error: no face detected! Please retry or change the photo.'
        paras = get_video_crop_parameter(frame, self.landmarkpredictor, [left, right, top, bottom])
        instyle = None
        h, w, scale = 0, 0, 0
        if paras is not None:
            h, w, top, bottom, left, right, scale = paras
            H, W = int(bottom-top), int(right-left)
            # for HR image, we apply gaussian blur to it to avoid over-sharp stylization results
            kernel_1d = np.array([[0.125],[0.375],[0.375],[0.125]])
            if scale <= 0.75:
                frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
            if scale <= 0.375:
                frame = cv2.sepFilter2D(frame, -1, kernel_1d, kernel_1d)
            frame = cv2.resize(frame, (w, h))[top:bottom, left:right]
            with torch.no_grad():
                I = align_face(frame, self.landmarkpredictor)
                if I is not None:
                    I = self.transform(I).unsqueeze(dim=0).to(self.device)
                    instyle = self.pspencoder(I)
                    instyle = self.vtoonify.zplus2wplus(instyle)
                    message = 'Successfully rescale the frame to (%d, %d)' % (bottom-top, right-left)
                else:
                    frame = np.zeros((256, 256, 3), np.uint8)
        else:
            frame = np.zeros((256, 256, 3), np.uint8)
        if return_para:
            return frame, instyle, message, w, h, top, bottom, left, right, scale
        return frame, instyle, message
    
    #@torch.inference_mode()
    def detect_and_align_image(self, image: str, top: int, bottom: int, left: int, right: int
                              ) -> tuple[np.ndarray, torch.Tensor, str]:
        if image is None:
            return np.zeros((256, 256, 3), np.uint8), None, 'Error: fail to load empty file.'
        frame = cv2.imread(image)
        if frame is None:
            return np.zeros((256, 256, 3), np.uint8), None, 'Error: fail to load the image.'       
        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
        return self.detect_and_align(frame, top, bottom, left, right)
    
    def detect_and_align_video(self, video: str, top: int, bottom: int, left: int, right: int
                              ) -> tuple[np.ndarray, torch.Tensor, str]:
        if video is None:
            return np.zeros((256, 256, 3), np.uint8), None, 'Error: fail to load empty file.'
        video_cap = cv2.VideoCapture(video)
        if video_cap.get(7) == 0:
            video_cap.release()
            return np.zeros((256, 256, 3), np.uint8), torch.zeros(1, 18, 512).to(self.device), 'Error: fail to load the video.'
        success, frame = video_cap.read()
        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        video_cap.release()
        return self.detect_and_align(frame, top, bottom, left, right)
    
  
def image_toonify(self, aligned_face: np.ndarray, instyle: torch.Tensor, exstyle: torch.Tensor, style_degree: float, style_type: str) -> tuple[np.ndarray, str]:
    if instyle is None or aligned_face is None:
        return np.zeros((256, 256, 3), np.uint8), 'Opps, something wrong with the input. Please go to Step 2 and Rescale Image/First Frame again.'
    if self.style_name != style_type:
        exstyle, _ = self.load_model(style_type)
    if exstyle is None:
        return np.zeros((256, 256, 3), np.uint8), 'Opps, something wrong with the style type. Please go to Step 1 and load model again.'
    with torch.no_grad():
        s_w = instyle.clone()
        s_w[:, :7] = exstyle[:, :7]

        x = self.transform(aligned_face).unsqueeze(dim=0).to(self.device)
        x_p = F.interpolate(self.parsingpredictor(2 * (F.interpolate(x, scale_factor=2, mode='bilinear', align_corners=False)))[0],
                            scale_factor=0.5, recompute_scale_factor=False).detach()
        inputs = torch.cat((x, x_p / 16.), dim=1)
        y_tilde = self.vtoonify(inputs, s_w.repeat(inputs.size(0), 1, 1), d_s=style_degree)
        y_tilde = torch.clamp(y_tilde, -1, 1)
    print('*** Toonify %dx%d image with style of %s' % (y_tilde.shape[2], y_tilde.shape[3], style_type))
    return ((y_tilde[0].cpu().numpy().transpose(1, 2, 0) + 1.0) * 127.5).astype(np.uint8), 'Successfully toonify the image with style of %s' % (self.style_name)

model = Model(device='cuda' if torch.cuda.is_available() else 'cpu')


@app.post("/upload/")
async def process_image(file: UploadFile = File(...), top: int = Form(...), bottom: int = Form(...), left: int = Form(...), right: int = Form(...)):
    if model is None:
        return {"error": "Model not loaded."}

    # Save the uploaded image locally
    with open("uploaded_image.jpg", "wb") as buffer:
        shutil.copyfileobj(file.file, buffer)

    # Process the uploaded image
    aligned_face, instyle, message = model.detect_and_align_image("uploaded_image.jpg", top, bottom, left, right)
    processed_image, message = model.image_toonify(aligned_face, instyle, model.exstyle, style_degree=0.5, style_type='cartoon1')

    # Convert processed image to bytes
    image_bytes = cv2.imencode('.jpg', processed_image)[1].tobytes()

    # Return the processed image as a streaming response
    return StreamingResponse(BytesIO(image_bytes), media_type="image/jpeg")


app.mount("/", StaticFiles(directory="AB", html=True), name="static")


@app.get("/")
def index() -> FileResponse:
    return FileResponse(path="/app/AB/index.html", media_type="text/html")