139 lines
4.6 KiB
Python
139 lines
4.6 KiB
Python
#!/usr/bin/env python3
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"""
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測試超像素區域保存功能
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Test script for superpixel region saving functionality
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"""
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import os
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import sys
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import numpy as np
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from PIL import Image
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import matplotlib.pyplot as plt
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from skimage.segmentation import slic
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from skimage.util import img_as_float
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# Add current directory to path
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sys.path.append(os.path.dirname(os.path.abspath(__file__)))
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from Density_Peak_Algorithm import save_superpixel_regions, compute_decision_graph
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def test_superpixel_regions_save():
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"""測試超像素區域保存功能"""
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print("🧪 開始測試超像素區域保存功能...")
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# 創建測試目錄
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test_dir = "test_superpixel_regions_results"
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os.makedirs(test_dir, exist_ok=True)
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# 創建一個簡單的測試圖像
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test_image_path = os.path.join(test_dir, "test_image.png")
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# 創建一個彩色測試圖像 (200x200)
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height, width = 200, 200
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test_img = np.zeros((height, width, 3), dtype=np.uint8)
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# 添加不同顏色的區域
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test_img[:100, :100] = [255, 0, 0] # 紅色
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test_img[:100, 100:] = [0, 255, 0] # 綠色
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test_img[100:, :100] = [0, 0, 255] # 藍色
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test_img[100:, 100:] = [255, 255, 0] # 黃色
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# 添加一些噪聲
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noise = np.random.randint(-30, 30, (height, width, 3))
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test_img = np.clip(test_img.astype(int) + noise, 0, 255).astype(np.uint8)
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# 保存測試圖像
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Image.fromarray(test_img).save(test_image_path)
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print(f"✅ 創建測試圖像: {test_image_path}")
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# 測試1: 直接使用save_superpixel_regions函數
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print("\n📋 測試1: 直接測試save_superpixel_regions函數")
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# 轉換為浮點數並應用SLIC
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img_array = test_img.astype(np.float32) / 255.0
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segments = slic(img_array, n_segments=20, compactness=10, start_label=1, enforce_connectivity=True)
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# 保存超像素區域
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regions_dir = os.path.join(test_dir, "direct_test_regions")
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saved_regions = save_superpixel_regions(test_img, segments, regions_dir, "test_image", max_regions=20)
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print(f"✅ 直接測試完成，保存了 {len(saved_regions)} 個區域")
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# 測試2: 通過compute_decision_graph函數測試
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print("\n📋 測試2: 通過compute_decision_graph函數測試")
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result = compute_decision_graph(
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test_image_path,
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test_dir,
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use_superpixels=True,
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n_segments=15,
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compactness=10,
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save_regions=True,
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max_regions=15
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)
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print(f"✅ compute_decision_graph測試完成")
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# 測試3: 使用真實圖像（如果存在）
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print("\n📋 測試3: 使用真實圖像測試（如果存在）")
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# 嘗試找到一個真實圖像
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possible_image_paths = [
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"../Data/A01.jpg",
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"sample_image_1.png",
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"../sample_image_1.png"
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]
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real_image_path = None
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for path in possible_image_paths:
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if os.path.exists(path):
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real_image_path = path
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break
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if real_image_path:
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print(f"🖼️ 找到真實圖像: {real_image_path}")
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try:
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# 測試真實圖像
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real_result = compute_decision_graph(
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real_image_path,
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test_dir,
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use_superpixels=True,
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n_segments=50,
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compactness=10,
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save_regions=True,
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max_regions=30 # 限制保存數量
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)
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print(f"✅ 真實圖像測試完成")
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except Exception as e:
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print(f"❌ 真實圖像測試失敗: {e}")
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else:
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print("⚠️ 未找到真實圖像，跳過此測試")
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# 顯示結果統計
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print(f"\n📊 測試結果統計:")
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print(f"測試目錄: {test_dir}")
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# 統計生成的文件
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total_files = 0
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for root, dirs, files in os.walk(test_dir):
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for file in files:
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if file.endswith('.png'):
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total_files += 1
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print(f"總共生成的PNG文件數量: {total_files}")
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# 列出所有子目錄
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subdirs = [d for d in os.listdir(test_dir) if os.path.isdir(os.path.join(test_dir, d))]
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if subdirs:
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print(f"生成的子目錄: {subdirs}")
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for subdir in subdirs:
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subdir_path = os.path.join(test_dir, subdir)
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files_in_subdir = [f for f in os.listdir(subdir_path) if f.endswith('.png')]
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print(f" {subdir}: {len(files_in_subdir)} 個PNG文件")
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print(f"\n🎉 超像素區域保存功能測試完成！")
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print(f"📁 請查看 {test_dir} 目錄中的結果")
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if __name__ == "__main__":
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test_superpixel_regions_save() |