app.py

import gradio as gr
import cv2
import numpy as np
from PIL import Image
import requests
from io import BytesIO
import base64

def download_image(url):
    """从URL下载图像"""
    response = requests.get(url)
    response.raise_for_status()
    return Image.open(BytesIO(response.content)).convert("RGB")

def downsample_image(image, target_size=(1024, 576), sharpen=True):
    """
    将高分辨率图像降采样至目标大小
    
    参数:
        image: PIL图像对象
        target_size: 目标分辨率元组 (宽度, 高度)
        sharpen: 是否应用锐化
    
    返回:
        降采样后的PIL图像对象
    """
    # 将PIL图像转换为cv2格式
    cv2_image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR)
    
    # 获取原始图像尺寸
    original_height, original_width = cv2_image.shape[:2]
    print(f"原始图像尺寸: {original_width}x{original_height}")
    
    # 计算目标宽高比
    target_width, target_height = target_size
    target_ratio = target_width / target_height
    
    # 计算原始图像尺寸，保持宽高比
    original_ratio = original_width / original_height
    
    if original_ratio > target_ratio:
        # 原始图像更宽，按宽度缩放
        new_width = target_width
        new_height = int(new_width / original_ratio)
    else:
        # 原始图像更高，按高度缩放
        new_height = target_height
        new_width = int(new_height * original_ratio)
    
    print(f"调整后尺寸: {new_width}x{new_height}")
    
    # 应用高斯模糊减少锯齿
    blurred = cv2.GaussianBlur(cv2_image, (3, 3), 0)
    
    # 使用双三次插值降采样
    downsampled = cv2.resize(blurred, (new_width, new_height), interpolation=cv2.INTER_CUBIC)
    
    # 如果需要，应用锐化增强
    if sharpen:
        kernel = np.array([[-1, -1, -1], 
                           [-1, 9, -1], 
                           [-1, -1, -1]])
        downsampled = cv2.filter2D(downsampled, -1, kernel)
    
    # 将cv2图像转换回PIL格式
    result_image = Image.fromarray(cv2.cvtColor(downsampled, cv2.COLOR_BGR2RGB))
    
    return result_image

def process_image(image, sharpen=True):
    """处理上传的图像，返回降采样结果"""
    if image is None:
        return None, None, "请提供图像"
    
    try:
        # 获取原始图像信息
        original_width, original_height = image.size
        original_size = f"{original_width}x{original_height}"
        
        # 降采样图像
        downsampled_image = downsample_image(image, sharpen=sharpen)
        
        # 获取降采样后图像信息
        downsampled_width, downsampled_height = downsampled_image.size
        downsampled_size = f"{downsampled_width}x{downsampled_height}"
        
        # 计算压缩率
        original_pixels = original_width * original_height
        downsampled_pixels = downsampled_width * downsampled_height
        compression_ratio = original_pixels / downsampled_pixels
        
        status = f"原始尺寸: {original_size} → 降采样后: {downsampled_size} (压缩率: {compression_ratio:.1f}x)"
        
        return image, downsampled_image, status
    
    except Exception as e:
        return None, None, f"处理失败: {str(e)}"

def process_image_url(url, sharpen=True):
    """处理图像URL"""
    if not url:
        return None, None, "请提供图像URL"
    
    try:
        # 下载图像
        image = download_image(url)
        
        # 调用处理函数
        return process_image(image, sharpen=sharpen)
    
    except Exception as e:
        return None, None, f"处理失败: {str(e)}"

def create_comparison_image(original, downsampled):
    """创建对比图像，并排显示原始图像和降采样图像"""
    if original is None or downsampled is None:
        return None
    
    # 获取图像尺寸
    original_width, original_height = original.size
    downsampled_width, downsampled_height = downsampled.size
    
    # 计算目标高度，取较小值
    target_height = min(original_height, downsampled_height)
    
    # 按比例缩放图像
    original_scaled = original.resize((int(original_width * target_height / original_height), target_height))
    downsampled_scaled = downsampled.resize((int(downsampled_width * target_height / downsampled_height), target_height))
    
    # 创建新图像，并排显示
    comparison_width = original_scaled.width + downsampled_scaled.width
    comparison_height = target_height
    
    comparison = Image.new('RGB', (comparison_width, comparison_height))
    comparison.paste(original_scaled, (0, 0))
    comparison.paste(downsampled_scaled, (original_scaled.width, 0))
    
    return comparison

def main():
    # 示例图像URL
    example_images = [
        "https://p26-doubao-search-sign.byteimg.com/labis/37b5a464e50688032736bf0c132848ce~tplv-be4g95zd3a-image.jpeg?rk3s=542c0f93&x-expires=1764660945&x-signature=e6oLB%2F8eHQeohzwBZRZSsTtJw8Y%3D",
        "https://p11-doubao-search-sign.byteimg.com/tos-cn-i-qvj2lq49k0/d0aa7a30b66a4c55a783d440e15cdb52~tplv-be4g95zd3a-image.jpeg?rk3s=542c0f93&x-expires=1764660945&x-signature=EB1%2F4QQ1nJr1U1CwV7Qw%2FoJDzwY%3D",
        "https://p11-doubao-search-sign.byteimg.com/labis/4ff8314a923313d6f18fbf0355993f21~tplv-be4g95zd3a-image.jpeg?rk3s=542c0f93&x-expires=1764660945&x-signature=a%2FihmjuTBzmmRTMsBZLaTQfhvtA%3D",
        "https://p26-doubao-search-sign.byteimg.com/labis/4279cfae8ecb40a269dccce03ae30c48~tplv-be4g95zd3a-image.jpeg?rk3s=542c0f93&x-expires=1764660945&x-signature=kVB4Wif5J7a3eMW8VyQn7dV4arE%3D"
    ]
    
    # 创建Gradio应用
    with gr.Blocks(title="Resample Pro - 高质量图像降采样", 
                   theme=gr.themes.Soft(
                       primary_hue="green",
                       secondary_hue="blue",
                       neutral_hue="slate",
                       font=["Inter", "system-ui", "sans-serif"],
                       spacing_size="md",
                       radius_size="md"
                   )) as demo:
        
        # 自定义CSS
        gr.Markdown("""
        <style>
            .gradio-container {
                background-color: #121212;
                color: #ffffff;
            }
            .gradio-title {
                font-weight: 700;
                margin-bottom: 0.5rem;
            }
            .gradio-subtitle {
                font-weight: 400;
                color: #9ca3af;
                margin-bottom: 1rem;
            }
            .image-container {
                position: relative;
                overflow: hidden;
                border-radius: 0.5rem;
                box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1), 0 2px 4px -1px rgba(0, 0, 0, 0.06);
            }
            .image-container img {
                transition: transform 0.3s ease;
            }
            .image-container:hover img {
                transform: scale(1.02);
            }
        </style>
        """)
        
        # 标题和说明
        with gr.Row():
            with gr.Column(scale=1):
                gr.Image("https://p3-flow-imagex-sign.byteimg.com/tos-cn-i-a9rns2rl98/rc/pc/super_tool/60c651b800994851bbdc482bc7745d82~tplv-a9rns2rl98-image.image?rcl=2025100315350927166B93AC1D320B5E7B&rk3s=8e244e95&rrcfp=f06b921b&x-expires=1762068959&x-signature=3nOCcuHdW5SKib3%2FE00MV7voRdc%3D", 
                         width=100, height=100, show_label=False)
            with gr.Column(scale=3):
                gr.Markdown("<h1 class='gradio-title'>Resample Pro</h1>")
                gr.Markdown("<p class='gradio-subtitle'>高质量图像降采样工具 - 将4K图像高效转换为1K，同时保留细节和质量</p>")
        
        gr.Markdown("---")
        
        # 输入区域
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("<h3>上传图像</h3>")
                image_input = gr.Image(type="pil", label="拖放或点击上传", height=300)
                
                gr.Markdown("<h3>或输入图像URL</h3>")
                url_input = gr.Textbox(label="图像URL")
                
                with gr.Row():
                    sharpen_toggle = gr.Checkbox(label="启用智能锐化", value=True)
                    process_button = gr.Button("处理图像", variant="primary")
            
            with gr.Column(scale=1):
                gr.Markdown("<h3>示例图像</h3>")
                example_gallery = gr.Gallery(
                    value=example_images,
                    label="点击使用示例图像",
                    columns=2,
                    rows=2,
                    height=300,
                    object_fit="cover"
                )
        
        gr.Markdown("---")
        
        # 结果区域
        with gr.Row():
            with gr.Column(scale=1):
                gr.Markdown("<h3>原始图像</h3>")
                original_output = gr.Image(label="原始图像", height=300, interactive=False)
                
            with gr.Column(scale=1):
                gr.Markdown("<h3>降采样结果</h3>")
                result_output = gr.Image(label="降采样后图像", height=300, interactive=False)
        
        # 状态和下载
        with gr.Row():
            status_output = gr.Textbox(label="处理状态", interactive=False)
            download_button = gr.Button("下载结果", variant="secondary")
        
        # 细节对比
        gr.Markdown("<h3>细节对比</h3>")
        comparison_output = gr.Image(label="对比视图", height=400, interactive=False)
        
        # 事件处理
        def handle_example_click(image_url):
            return image_url, "", None, None, "准备处理示例图像..."
        
        example_gallery.select(
            fn=handle_example_click,
            inputs=[example_gallery],
            outputs=[url_input, image_input, original_output, result_output, status_output]
        )
        
        def handle_process(image, url, sharpen):
            if image is not None:
                return process_image(image, sharpen)
            elif url:
                return process_image_url(url, sharpen)
            else:
                return None, None, "请上传图像或提供URL"
        
        process_button.click(
            fn=handle_process,
            inputs=[image_input, url_input, sharpen_toggle],
            outputs=[original_output, result_output, status_output]
        ).then(
            fn=create_comparison_image,
            inputs=[original_output, result_output],
            outputs=[comparison_output]
        )
        
        def handle_download(result_image):
            if result_image is None:
                return None, "没有可下载的图像"
            
            # 将图像转换为BytesIO
            buffered = BytesIO()
            result_image.save(buffered, format="PNG")
            buffered.seek(0)
            
            # 返回图像数据和文件名
            return buffered, "图像已准备好下载"
        
        download_button.click(
            fn=handle_download,
            inputs=[result_output],
            outputs=[gr.File(label="下载图像", type="binary"), status_output]
        )
        
        # 页面加载时的欢迎信息
        gr.Markdown("<p class='gradio-subtitle'>欢迎使用Resample Pro！上传一张高分辨率图像或使用示例图像开始体验。</p>")
    
    # 启动应用
    demo.launch(share=True, server_name="0.0.0.0")

if __name__ == "__main__":
    main()