app.py

import sys
import os

# 依存関係のインストール（basicsrを明示的にインストール）
os.system("git clone https://github.com/sczhou/CodeFormer.git")
os.system("pip install basicsr facexlib gfpgan")  # 主要な依存関係を先にインストール
os.system("cd CodeFormer && pip install -r requirements.txt")
os.system("pip install -e ./CodeFormer")
os.system("pip install -e ./CodeFormer/basicsr")
# パスを追加（CodeFormerとそのサブディレクトリ）
sys.path.insert(0, os.path.abspath('.'))
sys.path.insert(0, os.path.abspath('CodeFormer'))
sys.path.insert(0, os.path.abspath('CodeFormer/CodeFormer'))
sys.path.insert(0, os.path.abspath('CodeFormer/basicsr'))
os.makedirs("/home/user/app/CodeFormer/basicsr", exist_ok=True)

with open("/home/user/app/CodeFormer/basicsr/version.py", "w") as f:
    f.write("__version__ = '1.0.0'\n__gitsha__ = 'unknown'\n")
# ウェイトファイルをダウンロード
weights = {
    'realesr-general-x4v3.pth': 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth',
    'GFPGANv1.2.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.2.pth',
    'GFPGANv1.3.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.3.pth',
    'GFPGANv1.4.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth',
    'RestoreFormer.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/RestoreFormer.pth',
    'CodeFormer.pth': 'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.4/CodeFormer.pth'
}

for filename, url in weights.items():
    if not os.path.exists(filename):
        os.system(f"wget {url} -O {filename}")

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.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:
        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))
        
        temp_dir = tempfile.mkdtemp()
        input_path = os.path.join(temp_dir, file.filename)
        file.save(input_path)
        
        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)

        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)
            _, _, output = face_enhancer.enhance(img, has_aligned=False, only_center_face=False, paste_back=True)

        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)
        
        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'
        
        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;
        }

        @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>
        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();
        });

        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;
            
            const baseUrl = window.location.origin;
            const apiUrl = baseUrl + '/api/restore';
            
            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 => {
    const url = URL.createObjectURL(blob);
    console.log('Image processed successfully', url);
})
.catch(error => {
    console.error('Error:', error.message);
});`;

            fetchCodeDiv.innerHTML = code;
        }
        
        document.getElementById('file').addEventListener('change', updateApiUsage);
        document.getElementById('version').addEventListener('change', updateApiUsage);
        document.getElementById('scale').addEventListener('input', updateApiUsage);
        document.getElementById('weight').addEventListener('input', updateApiUsage);
        
        updateApiUsage();
        
        document.getElementById('uploadForm').addEventListener('submit', function(e) {
            e.preventDefault();
            
            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);
            
            if (document.getElementById('version').value === 'CodeFormer') {
                formData.append('weight', document.getElementById('weight').value);
            }
            
            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(() => {
                loadingElement.style.display = 'none';
                submitButton.disabled = false;
            });
        });
    </script>
</body>
</html>
    """

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