app.py

import sys
import os

# 依存関係のインストール
os.system("git clone https://github.com/sczhou/CodeFormer.git")
os.system("cd CodeFormer && pip install -r requirements.txt")
os.system("cd CodeFormer && python basicsr/setup.py develop")
sys.path.append(os.path.abspath('CodeFormer'))
sys.path.append(os.path.abspath('CodeFormer/CodeFormer'))
# ウェイトファイルをダウンロード（毎回消えるので毎回必ず実行。）
if not os.path.exists('realesr-general-x4v3.pth'):
    os.system("wget https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth -P .")
if not os.path.exists('GFPGANv1.2.pth'):
    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth -P .")
if not os.path.exists('GFPGANv1.3.pth'):
    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth -P .")
if not os.path.exists('GFPGANv1.4.pth'):
    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth -P .")
if not os.path.exists('RestoreFormer.pth'):
    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth -P .")
if not os.path.exists('CodeFormer.pth'):
    os.system("wget https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth -P .")

import cv2
import torch
from flask import Flask, request, jsonify, send_file
from basicsr.archs.srvgg_arch import SRVGGNetCompact
from gfpgan.utils import GFPGANer
from realesrgan.utils import RealESRGANer
import uuid
import tempfile
from torchvision.transforms.functional import normalize
from torchvision import transforms
from PIL import Image
from basicsr.utils import img2tensor, tensor2img
from facexlib.utils.face_restoration_helper import FaceRestoreHelper
from codeformer.archs.codeformer_arch import CodeFormer

app = Flask(__name__)

# モデルの初期化
model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
model_path = 'realesr-general-x4v3.pth'
half = True if torch.cuda.is_available() else False
upsampler = RealESRGANer(scale=4, model_path=model_path, model=model, tile=0, tile_pad=10, pre_pad=0, half=half)

os.makedirs('output', exist_ok=True)

def restore_with_codeformer(img, scale=2, weight=0.5):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    net = CodeFormer(dim_embd=512, codebook_size=1024, n_head=8, n_layers=9, connect_list=['32', '64', '128', '256']).to(device)
    net.load_state_dict(torch.load('CodeFormer.pth')['params_ema'])
    net.eval()

    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    img = Image.fromarray(img)
    
    face_helper = FaceRestoreHelper(
        upscale_factor=scale, face_size=512, crop_ratio=(1, 1), use_parse=True,
        device=device)
    
    face_helper.clean_all()
    face_helper.read_image(img)
    face_helper.get_face_landmarks_5(only_center_face=False, resize=640)
    face_helper.align_warp_face()

    for idx, cropped_face in enumerate(face_helper.cropped_faces):
        cropped_face_t = img2tensor(cropped_face / 255.0, bgr2rgb=False, float32=True)
        normalize(cropped_face_t, [0.5], [0.5], inplace=True)
        cropped_face_t = cropped_face_t.unsqueeze(0).to(device)
        with torch.no_grad():
            output = net(cropped_face_t, w=weight, adain=True)[0]
        restored_face = tensor2img(output, rgb2bgr=True, min_max=(-1, 1))
        face_helper.add_restored_face(restored_face)
    
    restored_img = face_helper.paste_faces_to_input_image()
    return cv2.cvtColor(restored_img, cv2.COLOR_RGB2BGR)

@app.route('/api/restore', methods=['POST'])
def restore_image():
    try:
        # リクエストからパラメータを取得
        if 'file' not in request.files:
            return jsonify({'error': 'No file uploaded'}), 400
        
        file = request.files['file']
        version = request.form.get('version', 'v1.4')
        scale = float(request.form.get('scale', 2))
        weight = float(request.form.get('weight', 0.5))  # CodeFormer用のweightパラメータ
        
        # 一時ファイルに保存
        temp_dir = tempfile.mkdtemp()
        input_path = os.path.join(temp_dir, file.filename)
        file.save(input_path)
        
        # 画像処理
        extension = os.path.splitext(os.path.basename(str(input_path)))[1]
        img = cv2.imread(input_path, cv2.IMREAD_UNCHANGED)
        
        if len(img.shape) == 3 and img.shape[2] == 4:
            img_mode = 'RGBA'
        elif len(img.shape) == 2:
            img_mode = None
            img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
        else:
            img_mode = None

        h, w = img.shape[0:2]
        if h < 300:
            img = cv2.resize(img, (w * 2, h * 2), interpolation=cv2.INTER_LANCZOS4)

        # バージョンに応じてモデルを選択
        if version == 'v1.2':
            face_enhancer = GFPGANer(
                model_path='GFPGANv1.2.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
        elif version == 'v1.3':
            face_enhancer = GFPGANer(
                model_path='GFPGANv1.3.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
        elif version == 'v1.4':
            face_enhancer = GFPGANer(
                model_path='GFPGANv1.4.pth', upscale=2, arch='clean', channel_multiplier=2, bg_upsampler=upsampler)
            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
        elif version == 'RestoreFormer':
            face_enhancer = GFPGANer(
                model_path='RestoreFormer.pth', upscale=2, arch='RestoreFormer', channel_multiplier=2, bg_upsampler=upsampler)
            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)
        elif version == 'CodeFormer':
            output = restore_with_codeformer(img, scale=scale, weight=weight)
        elif version == 'RealESR-General-x4v3':
            face_enhancer = GFPGANer(
                model_path='realesr-general-x4v3.pth', upscale=2, arch='realesr-general', channel_multiplier=2, bg_upsampler=upsampler,  map_location=torch.device('cpu'))
            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)

        # スケール調整
        if scale != 2:
            interpolation = cv2.INTER_AREA if scale < 2 else cv2.INTER_LANCZOS4
            h, w = img.shape[0:2]
            output = cv2.resize(output, (int(w * scale / 2), int(h * scale / 2)), interpolation=interpolation)
        
        # 出力ファイルを保存
        output_filename = f'output_{uuid.uuid4().hex}'
        if img_mode == 'RGBA':
            output_path = os.path.join('output', f'{output_filename}.png')
            cv2.imwrite(output_path, output)
            mimetype = 'image/png'
        else:
            output_path = os.path.join('output', f'{output_filename}.jpg')
            cv2.imwrite(output_path, output)
            mimetype = 'image/jpeg'
        
        # 結果を返す
        return send_file(output_path, mimetype=mimetype, as_attachment=True, download_name=os.path.basename(output_path))
    
    except Exception as e:
        return jsonify({'error': str(e)}), 500

@app.route('/')
def index():
    return """
<!DOCTYPE html>
<html>
<head>
    <title>Image Upscaling & Restoration API</title>
    <style>
        body { font-family: Arial, sans-serif; max-width: 800px; margin: 0 auto; padding: 20px; }
        .container { border: 1px solid #ddd; padding: 20px; border-radius: 5px; }
        .form-group { margin-bottom: 15px; }
        label { display: block; margin-bottom: 5px; }
        input, select { width: 100%; padding: 8px; box-sizing: border-box; }
        button { background-color: #4CAF50; color: white; padding: 10px 15px; border: none; border-radius: 4px; cursor: pointer; }
        button:hover { background-color: #45a049; }
        #result { margin-top: 20px; }
        #preview { max-width: 100%; margin-top: 10px; }
        #apiUsage { background-color: #f5f5f5; padding: 15px; border-radius: 5px; margin-top: 20px; font-family: monospace; white-space: pre-wrap; }
        #apiUsage h3 { margin-top: 0; }
        #formDataPreview { max-height: 200px; overflow-y: auto; margin-bottom: 10px; }
        .code-block { background-color: #f8f8f8; padding: 10px; border-radius: 4px; border-left: 3px solid #4CAF50; }
        .comment { color: #666; font-style: italic; }
        
        .loader {
            width: 48px;
            height: 48px;
            border: 5px solid #4CAF50;
            border-bottom-color: transparent;
            border-radius: 50%;
            display: inline-block;
            box-sizing: border-box;
            animation: rotation 1s linear infinite;
            margin: 20px auto;
            display: none; /* 初期状態では非表示 */
        }

        @keyframes rotation {
            0% {
                transform: rotate(0deg);
            }
            100% {
                transform: rotate(360deg);
            }
        }
    </style>
</head>
<body>
    <h1>Image Upscaling & Restoration API</h1>
    <div class="container">
        <form id="uploadForm" enctype="multipart/form-data">
            <div class="form-group">
                <label for="file">Upload Image:</label>
                <input type="file" id="file" name="file" required>
            </div>
            <div class="form-group">
                <label for="version">Version:</label>
                <select id="version" name="version">
                    <option value="v1.2">GFPGANv1.2</option>
                    <option value="v1.3">GFPGANv1.3</option>
                    <option value="v1.4" selected>GFPGANv1.4</option>
                    <option value="RestoreFormer">RestoreFormer</option>
                    <option value="CodeFormer">CodeFormer</option>
                    <option value="RealESR-General-x4v3">RealESR-General-x4v3</option>
                </select>
            </div>
            <div class="form-group">
                <label for="scale">Rescaling factor:</label>
                <input type="number" id="scale" name="scale" value="2" step="0.1" min="1" max="4" required>
            </div>
            <div class="form-group" id="weightGroup" style="display: none;">
                <label for="weight">CodeFormer Weight (0-1):</label>
                <input type="number" id="weight" name="weight" value="0.5" step="0.1" min="0" max="1">
            </div>
            <button type="submit" id="submitButton">Process Image</button>
        </form>
        <div id="loading" class="loader"></div>
        <div id="result">
            <h3>Result:</h3>
            <div id="outputContainer" style="display: none;">
                <img id="preview" src="" alt="Processed Image">
                <a id="downloadLink" href="#" download>Download Image</a>
            </div>
        </div>
        <div id="apiUsage">
            <h3>API Usage:</h3>
            <div id="fetchCode" class="code-block">
                // JavaScript fetch code will appear here
            </div>
        </div>
    </div>
    
    <script>
        // CodeFormerが選択された時にweightパラメータを表示
        document.getElementById('version').addEventListener('change', function() {
            const weightGroup = document.getElementById('weightGroup');
            if (this.value === 'CodeFormer') {
                weightGroup.style.display = 'block';
            } else {
                weightGroup.style.display = 'none';
            }
            updateApiUsage();
        });

        // フォームの変更を監視してAPI使用例を更新
        function updateApiUsage() {
            const fileInput = document.getElementById('file');
            const version = document.getElementById('version').value;
            const scale = document.getElementById('scale').value;
            const weight = document.getElementById('weight').value;
            
            // 現在のURLからベースURLを取得（パス、パラメータ、ハッシュを含めない）
            const baseUrl = window.location.origin;
            const apiUrl = baseUrl + '/api/restore';
            
            // ファイルのプレビュー用文字列を準備
            let filePreview = '"img-dataURL"';
            if (fileInput.files.length > 0) {
                const file = fileInput.files[0];
                const reader = new FileReader();
                reader.onload = function(e) {
                    const dataURL = e.target.result;
                    if (dataURL.length > 40) {
                        filePreview = "${dataURL.substring(0, 20)}...${dataURL.substring(dataURL.length - 20)}";
                    } else {
                        filePreview = "${dataURL}";
                    }
                    updateFetchCode(apiUrl, version, scale, weight, filePreview);
                };
                reader.readAsDataURL(file);
            } else {
                updateFetchCode(apiUrl, version, scale, weight, filePreview);
            }
        }
        
        function updateFetchCode(apiUrl, version, scale, weight, filePreview) {
            const fetchCodeDiv = document.getElementById('fetchCode');
            let code = `// JavaScript fetch example:
const formData = new FormData();
formData.append('file', ${filePreview});
formData.append('version', '${version}');
formData.append('scale', ${scale});`;

            if (version === 'CodeFormer') {
                code += `
formData.append('weight', ${weight});`;
            }

            code += `
fetch('${apiUrl}', {
    method: 'POST',
    body: formData
})
.then(response => {
    if (!response.ok) {
        return response.json().then(err => { throw new Error(err.error || 'Unknown error'); });
    }
    return response.blob();
})
.then(blob => {
    // Process the returned image blob
    const url = URL.createObjectURL(blob);
    console.log('Image processed successfully', url);
    // Example: document.getElementById('resultImage').src = url;
})
.catch(error => {
    console.error('Error:', error.message);
});`;

            fetchCodeDiv.innerHTML = code;
        }
        
        // フォーム要素の変更を監視
        document.getElementById('file').addEventListener('change', updateApiUsage);
        document.getElementById('version').addEventListener('change', updateApiUsage);
        document.getElementById('scale').addEventListener('input', updateApiUsage);
        document.getElementById('weight').addEventListener('input', updateApiUsage);
        
        // 初期表示
        updateApiUsage();
        
        document.getElementById('uploadForm').addEventListener('submit', function(e) {
            e.preventDefault();
            
            // ボタンを無効化し、ローディングを表示
            const submitButton = document.getElementById('submitButton');
            const loadingElement = document.getElementById('loading');
            
            submitButton.disabled = true;
            loadingElement.style.display = 'block';
            
            const formData = new FormData();
            formData.append('file', document.getElementById('file').files[0]);
            formData.append('version', document.getElementById('version').value);
            formData.append('scale', document.getElementById('scale').value);
            
            // CodeFormerが選択されている場合はweightも追加
            if (document.getElementById('version').value === 'CodeFormer') {
                formData.append('weight', document.getElementById('weight').value);
            }
            
            // 現在のURLからベースURLを取得（パス、パラメータ、ハッシュを含めない）
            const baseUrl = window.location.origin;
            const apiUrl = baseUrl + '/api/restore';
            
            fetch(apiUrl, {
                method: 'POST',
                body: formData
            })
            .then(response => {
                if (!response.ok) {
                    return response.json().then(err => { throw new Error(err.error || 'Unknown error'); });
                }
                return response.blob();
            })
            .then(blob => {
                const url = URL.createObjectURL(blob);
                const preview = document.getElementById('preview');
                const downloadLink = document.getElementById('downloadLink');
                const outputContainer = document.getElementById('outputContainer');
                
                preview.src = url;
                downloadLink.href = url;
                downloadLink.download = 'restored_' + document.getElementById('file').files[0].name;
                outputContainer.style.display = 'block';
            })
            .catch(error => {
                alert('Error: ' + error.message);
            })
            .finally(() => {
                // 処理が終わったらローディングを非表示にし、ボタンを再有効化
                loadingElement.style.display = 'none';
                submitButton.disabled = false;
            });
        });
    </script>
</body>
</html>
    """

if __name__ == '__main__':
    app.run(host='0.0.0.0', port=7860, debug=True)