diff --git a/.idea/.gitignore b/.idea/.gitignore
new file mode 100644
index 0000000..359bb53
--- /dev/null
+++ b/.idea/.gitignore
@@ -0,0 +1,3 @@
+# 默认忽略的文件
+/shelf/
+/workspace.xml
diff --git a/.idea/LAB.iml b/.idea/LAB.iml
new file mode 100644
index 0000000..c69f1c9
--- /dev/null
+++ b/.idea/LAB.iml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/inspectionProfiles/profiles_settings.xml b/.idea/inspectionProfiles/profiles_settings.xml
new file mode 100644
index 0000000..105ce2d
--- /dev/null
+++ b/.idea/inspectionProfiles/profiles_settings.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
new file mode 100644
index 0000000..782eb56
--- /dev/null
+++ b/.idea/misc.xml
@@ -0,0 +1,7 @@
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 0000000..1f83f51
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+
+
+
+
+
+
+
+
\ No newline at end of file
diff --git a/.idea/vcs.xml b/.idea/vcs.xml
new file mode 100644
index 0000000..94a25f7
--- /dev/null
+++ b/.idea/vcs.xml
@@ -0,0 +1,6 @@
+
+
+
+
+
+
\ No newline at end of file
diff --git a/1.py b/1.py
index e69de29..0aa4715 100644
--- a/1.py
+++ b/1.py
@@ -0,0 +1,3 @@
+git add .
+
+
diff --git a/git_commannd b/git_commannd
new file mode 100644
index 0000000..1df4812
--- /dev/null
+++ b/git_commannd
@@ -0,0 +1,3 @@
+git add .
+git commit -m "commit message"
+git push origin master
\ No newline at end of file
diff --git a/r1.py b/r1.py
new file mode 100644
index 0000000..24a6b58
--- /dev/null
+++ b/r1.py
@@ -0,0 +1,327 @@
+import argparse
+import numpy as np
+from PIL import Image
+import os
+import sys
+
+
+def load_image(image_path):
+ """
+ 加载图像文件并转换为numpy数组
+
+ Args:
+ image_path (str): 输入图像路径
+
+ Returns:
+ numpy.ndarray: 图像数据 (H, W, C)
+
+ Raises:
+ FileNotFoundError: 当图像文件不存在时
+ ValueError: 当图像格式不支持时
+ """
+ try:
+ # 检查文件是否存在
+ if not os.path.exists(image_path):
+ raise FileNotFoundError(f"图像文件不存在: {image_path}")
+
+ image = Image.open(image_path)
+ image_array = np.array(image)
+ print(f"✅ 成功加载图像: {image_path}")
+ print(f"📐 原始图像尺寸: {image_array.shape} (高度, 宽度, 通道数)")
+ return image_array
+ except FileNotFoundError as e:
+ raise e
+ except Exception as e:
+ raise ValueError(f"❌ 无法加载图像 '{image_path}': {e}")
+
+
+def save_image(image_array, output_path):
+ """
+ 将numpy数组保存为图像文件
+
+ Args:
+ image_array (numpy.ndarray): 图像数据
+ output_path (str): 输出图像路径
+ """
+ try:
+ # 确保输出目录存在
+ os.makedirs(os.path.dirname(output_path) if os.path.dirname(output_path) else '.', exist_ok=True)
+
+ image = Image.fromarray(image_array.astype(np.uint8))
+ image.save(output_path)
+ print(f"✅ 成功保存图像: {output_path}")
+ print(f"📐 输出图像尺寸: {image_array.shape}")
+ except Exception as e:
+ raise ValueError(f"❌ 无法保存图像 '{output_path}': {e}")
+
+
+def process_image(input_array, output_size, anchor_point):
+ """
+ 核心处理函数：根据输出尺寸和基准点进行裁剪或填充
+
+ Args:
+ input_array (numpy.ndarray): 输入图像数组 (H, W, C)
+ output_size (int): 输出图像尺寸（正方形）
+ anchor_point (int): 基准点 (0-8)
+
+ Returns:
+ numpy.ndarray: 处理后的图像数组
+
+ Raises:
+ ValueError: 当参数无效时
+ """
+ # 输入验证
+ if len(input_array.shape) != 3 or input_array.shape[2] not in [3, 4]:
+ raise ValueError("❌ 输入必须是3通道(RGB)或4通道(RGBA)彩色图像")
+
+ if output_size <= 0:
+ raise ValueError("❌ 输出尺寸必须为正整数")
+
+ if anchor_point not in range(0, 9):
+ raise ValueError("❌ 基准点必须在0-8范围内")
+
+ input_height, input_width, channels = input_array.shape
+ print(f"📥 输入尺寸: {input_width}x{input_height}, 通道数: {channels}")
+ print(f"🎯 目标尺寸: {output_size}x{output_size}, 基准点: {anchor_point}")
+
+ # 创建输出数组（初始为黑色）
+ output_array = np.zeros((output_size, output_size, channels), dtype=input_array.dtype)
+
+ # 计算裁剪或填充的区域
+ crop_height = min(input_height, output_size)
+ crop_width = min(input_width, output_size)
+
+ print(f"✂️ 实际复制区域: {crop_width}x{crop_height}")
+
+ # 根据基准点计算源和目标坐标
+ src_x, src_y, dst_x, dst_y = calculate_coordinates(
+ input_width, input_height, output_size, crop_width, crop_height, anchor_point
+ )
+
+ # 执行裁剪或填充操作
+ output_array[dst_y:dst_y + crop_height, dst_x:dst_x + crop_width] = \
+ input_array[src_y:src_y + crop_height, src_x:src_x + crop_width]
+
+ # 判断是裁剪还是填充
+ if output_size > input_width or output_size > input_height:
+ operation = "填充(Padding)"
+ elif output_size < input_width or output_size < input_height:
+ operation = "裁剪(Cropping)"
+ else:
+ operation = "尺寸不变"
+
+ print(f"🔄 操作类型: {operation}")
+
+ return output_array
+
+
+def calculate_coordinates(input_width, input_height, output_size, crop_width, crop_height, anchor_point):
+ """
+ 根据基准点计算源图像和目标图像的坐标
+
+ Args:
+ input_width (int): 输入图像宽度
+ input_height (int): 输入图像高度
+ output_size (int): 输出图像尺寸
+ crop_width (int): 裁剪宽度
+ crop_height (int): 裁剪高度
+ anchor_point (int): 基准点
+
+ Returns:
+ tuple: (src_x, src_y, dst_x, dst_y)
+ """
+ # 基准点映射说明：
+ anchor_names = {
+ 0: "左上(Top-Left)", 1: "中上(Top-Center)", 2: "右上(Top-Right)",
+ 3: "左中(Middle-Left)", 4: "中心(Center)", 5: "右中(Middle-Right)",
+ 6: "左下(Bottom-Left)", 7: "中下(Bottom-Center)", 8: "右下(Bottom-Right)"
+ }
+
+ print(f"📍 基准点: {anchor_point} - {anchor_names[anchor_point]}")
+
+ # 源图像起始坐标（从输入图像中裁剪的部分）
+ if anchor_point in [0, 3, 6]: # 左对齐
+ src_x = 0
+ elif anchor_point in [1, 4, 7]: # 水平居中
+ src_x = (input_width - crop_width) // 2
+ else: # 右对齐 [2, 5, 8]
+ src_x = input_width - crop_width
+
+ if anchor_point in [0, 1, 2]: # 上对齐
+ src_y = 0
+ elif anchor_point in [3, 4, 5]: # 垂直居中
+ src_y = (input_height - crop_height) // 2
+ else: # 下对齐 [6, 7, 8]
+ src_y = input_height - crop_height
+
+ # 目标图像起始坐标（在输出图像中放置的位置）
+ if anchor_point in [0, 3, 6]: # 左对齐
+ dst_x = 0
+ elif anchor_point in [1, 4, 7]: # 水平居中
+ dst_x = (output_size - crop_width) // 2
+ else: # 右对齐 [2, 5, 8]
+ dst_x = output_size - crop_width
+
+ if anchor_point in [0, 1, 2]: # 上对齐
+ dst_y = 0
+ elif anchor_point in [3, 4, 5]: # 垂直居中
+ dst_y = (output_size - crop_height) // 2
+ else: # 下对齐 [6, 7, 8]
+ dst_y = output_size - crop_height
+
+ print(f"🎯 源坐标: ({src_x}, {src_y}), 目标坐标: ({dst_x}, {dst_y})")
+
+ return src_x, src_y, dst_x, dst_y
+
+
+def setup_argument_parser():
+ """
+ 设置命令行参数解析器
+
+ Returns:
+ argparse.ArgumentParser: 配置好的参数解析器
+ """
+ parser = argparse.ArgumentParser(
+ description="🎨 彩色图像裁剪与填充处理器",
+ formatter_class=argparse.RawDescriptionHelpFormatter,
+ epilog="""
+📋 基准点选项 (0-8):
+ 0: 左上(Top-Left) 1: 中上(Top-Center) 2: 右上(Top-Right)
+ 3: 左中(Middle-Left) 4: 中心(Center) 5: 右中(Middle-Right)
+ 6: 左下(Bottom-Left) 7: 中下(Bottom-Center) 8: 右下(Bottom-Right)
+
+💡 使用示例:
+ python image_processor.py input.jpg output.jpg 512 4
+ python image_processor.py photo.png result.png 256 0
+
+🚀 完整演示:
+ python image_processor.py "/Users/liulaolao/SkillSemi2025/pepper.png" "output_512_center.jpg" 512 4
+ """
+ )
+
+ parser.add_argument(
+ "input_image",
+ help="📥 输入图像文件路径"
+ )
+
+ parser.add_argument(
+ "output_image",
+ help="📤 输出图像文件路径"
+ )
+
+ parser.add_argument(
+ "output_size",
+ type=int,
+ help="📐 输出图像尺寸（正方形边长）"
+ )
+
+ parser.add_argument(
+ "anchor_point",
+ type=int,
+ choices=range(0, 9), # 0-8
+ help="📍 裁剪/填充基准点 (0-8)"
+ )
+
+ return parser
+
+
+def demo_with_fixed_path():
+ """
+ 使用固定路径进行演示的函数
+ """
+ print("🚀 开始使用固定路径演示图像处理...")
+
+ # 固定参数设置
+ input_path = "/Users/liulaolao/SkillSemi2025/pepper.png"
+ output_sizes = [300, 512, 800] # 测试不同尺寸
+ anchor_points = [0, 4, 8] # 测试不同基准点：左上、中心、右下
+
+ try:
+ # 加载图像
+ original_image = load_image(input_path)
+ original_height, original_width, _ = original_image.shape
+
+ print(f"\n📊 原始图像信息:")
+ print(f" 宽度: {original_width}px")
+ print(f" 高度: {original_height}px")
+
+ for size in output_sizes:
+ for anchor in anchor_points:
+ print(f"\n{'=' * 60}")
+ print(f"🔄 处理中: 尺寸={size}, 基准点={anchor}")
+ print(f"{'=' * 60}")
+
+ # 处理图像
+ processed_image = process_image(original_image, size, anchor)
+
+ # 生成输出文件名
+ output_filename = f"pepper_{size}px_anchor{anchor}.jpg"
+ output_path = os.path.join("/Users/liulaolao/SkillSemi2025/", output_filename)
+
+ # 保存结果
+ save_image(processed_image, output_path)
+
+ print(f"\n🎉 所有演示完成！生成的图像已保存到 SkillSemi2025 目录")
+
+ except Exception as e:
+ print(f"❌ 演示过程中发生错误: {e}")
+ return False
+
+ return True
+
+
+def main():
+ """主函数"""
+ try:
+ # 设置参数解析器
+ parser = setup_argument_parser()
+
+ # 检查是否有命令行参数
+ if len(sys.argv) == 1:
+ # 没有参数时，运行固定路径演示
+ print("🎮 未提供命令行参数，运行固定路径演示模式...")
+ demo_with_fixed_path()
+ else:
+ # 有参数时，正常解析并运行
+ args = parser.parse_args()
+
+ print("=" * 60)
+ print("🎨 彩色图像裁剪与填充处理器")
+ print("=" * 60)
+
+ # 显示参数信息
+ print(f"📥 输入文件: {args.input_image}")
+ print(f"📤 输出文件: {args.output_image}")
+ print(f"📐 输出尺寸: {args.output_size}")
+ print(f"📍 基准点: {args.anchor_point}")
+ print("-" * 60)
+
+ # 加载图像
+ input_array = load_image(args.input_image)
+
+ # 处理图像
+ output_array = process_image(input_array, args.output_size, args.anchor_point)
+
+ # 保存结果
+ save_image(output_array, args.output_image)
+
+ print("-" * 60)
+ print("✅ 处理完成！")
+ print("=" * 60)
+
+ except FileNotFoundError as e:
+ print(f"❌ 文件错误: {e}")
+ sys.exit(1)
+ except ValueError as e:
+ print(f"❌ 参数错误: {e}")
+ sys.exit(1)
+ except KeyboardInterrupt:
+ print("\n⏹️ 程序被用户中断")
+ sys.exit(1)
+ except Exception as e:
+ print(f"❌ 未知错误: {e}")
+ sys.exit(1)
+
+
+if __name__ == "__main__":
+ main()
\ No newline at end of file