debug_colorize.py

#!/usr/bin/env python3
"""
调试上色效果差异的脚本
"""

import sys
import os
import cv2
import numpy as np

# 添加当前目录到路径
sys.path.insert(0, os.path.dirname(__file__))

from ddcolor_colorizer import DDColorColorizer
from gfpgan_restorer import GFPGANRestorer
import logging

# 设置日志
logging.basicConfig(level=logging.INFO, format='[%(levelname)s] %(message)s')

def simulate_api_processing(image_path):
    """
    模拟API接口的完整处理流程
    """
    print("\n=== 模拟API接口处理流程 ===")
    
    # 初始化组件
    print("初始化GFPGAN修复器...")
    try:
        gfpgan_restorer = GFPGANRestorer()
        if not gfpgan_restorer.is_available():
            print("❌ GFPGAN不可用")
            return None
        print("✅ GFPGAN初始化成功")
    except Exception as e:
        print(f"❌ GFPGAN初始化失败: {e}")
        return None
    
    print("初始化DDColor上色器...")
    try:
        ddcolor_colorizer = DDColorColorizer()
        if not ddcolor_colorizer.is_available():
            print("❌ DDColor不可用")
            return None
        print("✅ DDColor初始化成功")
    except Exception as e:
        print(f"❌ DDColor初始化失败: {e}")
        return None
    
    # 读取图像
    print(f"读取图像: {image_path}")
    image = cv2.imread(image_path)
    if image is None:
        print(f"❌ 无法读取图像: {image_path}")
        return None
    
    print(f"原图尺寸: {image.shape}")
    
    # 保存原图
    ddcolor_colorizer.save_debug_image(image, "api_original")
    
    # 检查原图灰度状态
    original_is_grayscale = ddcolor_colorizer.is_grayscale(image)
    print(f"原图灰度检测: {original_is_grayscale}")
    
    # 新的处理流程：先上色再修复
    # 步骤1: 上色处理
    print("\n步骤1: 上色处理...")
    if original_is_grayscale:
        print("策略: 对原图进行上色")
        colorized_image = ddcolor_colorizer.colorize_image_direct(image)
        ddcolor_colorizer.save_debug_image(colorized_image, "api_colorized")
        strategy = "先上色"
        current_image = colorized_image
    else:
        print("策略: 图像已经是彩色的，跳过上色")
        strategy = "跳过上色"
        current_image = image
    
    # 步骤2: GFPGAN修复
    print("\n步骤2: GFPGAN修复...")
    final_image = gfpgan_restorer.restore_image(current_image)
    print(f"修复后图像尺寸: {final_image.shape}")
    
    # 保存最终结果
    result_path = ddcolor_colorizer.save_debug_image(final_image, "api_final")
    
    strategy += " -> 再修复"
    
    print(f"\n✅ API模拟完成")
    print(f"   - 处理策略: {strategy}")
    print(f"   - 最终结果: {result_path}")
    
    return {
        'original': image,
        'colorized': colorized_image if original_is_grayscale else None,
        'final': final_image,
        'strategy': strategy
    }

def test_direct_colorization(image_path):
    """
    测试直接上色（类似test_ddcolor.py的方式）
    """
    print("\n=== 测试直接上色 ===")
    
    colorizer = DDColorColorizer()
    if not colorizer.is_available():
        print("❌ DDColor不可用")
        return None
    
    # 直接使用URL进行上色（和test_ddcolor.py相同）
    print("使用官方示例URL上色...")
    success, message = colorizer.test_colorization()
    
    if success:
        print(f"✅ URL上色成功: {message}")
    else:
        print(f"❌ URL上色失败: {message}")
    
    # 对本地图像进行直接上色
    print(f"对本地图像直接上色: {image_path}")
    success, message = colorizer.test_local_image(image_path)
    
    if success:
        print(f"✅ 本地图像上色成功: {message}")
    else:
        print(f"❌ 本地图像上色失败: {message}")

def compare_results():
    """
    对比分析结果
    """
    print("\n=== 结果对比分析 ===")
    
    # 列出生成的调试图像
    debug_files = []
    for f in os.listdir("."):
        if f.endswith("_debug.webp"):
            debug_files.append(f)
    
    if debug_files:
        print("生成的调试文件:")
        for f in sorted(debug_files):
            print(f"  - {f}")
        
        print("\n对比建议:")
        print("1. 比较 original_debug.webp 和 api_original_debug.webp")
        print("2. 比较 local_colorized_debug.webp 和 api_final_debug.webp")
        print("3. 检查 api_restored_debug.webp 的修复效果")
        print("4. 观察 ddcolor_test_result.webp 的官方示例效果")
    else:
        print("未找到调试文件")

def analyze_image_quality(image_path):
    """
    分析图像质量指标
    """
    print(f"\n=== 分析图像质量: {image_path} ===")
    
    if not os.path.exists(image_path):
        print(f"文件不存在: {image_path}")
        return
    
    image = cv2.imread(image_path)
    if image is None:
        print(f"无法读取图像: {image_path}")
        return
    
    # 基本信息
    h, w, c = image.shape
    print(f"尺寸: {w}x{h}, 通道数: {c}")
    
    # 亮度分析
    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    mean_brightness = np.mean(gray)
    print(f"平均亮度: {mean_brightness:.2f}")
    
    # 对比度分析
    contrast = np.std(gray)
    print(f"对比度(标准差): {contrast:.2f}")
    
    # 色彩分析
    hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
    mean_saturation = np.mean(hsv[:, :, 1])
    print(f"平均饱和度: {mean_saturation:.2f}")
    
    # 锐度分析（拉普拉斯算子）
    laplacian = cv2.Laplacian(gray, cv2.CV_64F)
    sharpness = np.var(laplacian)
    print(f"锐度: {sharpness:.2f}")

def main():
    """主函数"""
    print("DDColor 上色效果调试工具")
    print("=" * 60)
    
    # 可以指定测试图像路径，或使用默认路径
    test_image_path = "/path/to/your/test/image.jpg"  # 替换为实际路径
    
    if len(sys.argv) > 1:
        test_image_path = sys.argv[1]
    
    print(f"测试图像路径: {test_image_path}")
    
    if not os.path.exists(test_image_path):
        print("⚠️  测试图像不存在，将只运行URL测试")
        
        # 只测试直接上色
        test_direct_colorization(None)
        
    else:
        # 分析原图质量
        analyze_image_quality(test_image_path)
        
        # 测试直接上色
        test_direct_colorization(test_image_path)
        
        # 模拟API处理
        api_result = simulate_api_processing(test_image_path)
        
        # 分析结果图像质量
        if os.path.exists("api_final_debug.webp"):
            print("\n--- API处理结果质量分析 ---")
            analyze_image_quality("api_final_debug.webp")
        
        if os.path.exists("local_colorized_debug.webp"):
            print("\n--- 直接上色结果质量分析 ---")
            analyze_image_quality("local_colorized_debug.webp")
    
    # 对比分析
    compare_results()
    
    print("\n调试完成！")
    print("请检查生成的调试图像来识别问题所在。")

if __name__ == "__main__":
    main()