From 7a0ea130aa5f6921bcb9e061981bb0042c70a305 Mon Sep 17 00:00:00 2001
From: 2509165030 <2509165030@student.edu.cn>
Date: Thu, 16 Apr 2026 16:05:17 +0800
Subject: [PATCH] =?UTF-8?q?=E5=AE=8C=E6=88=90=E4=BD=9C=E4=B8=9AX?=
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---
 9999.py                 | 32 ++++++++++++++++++++++++++++++++
 import numpy as nddd.py | 21 +++++++++++++++++++++
 import numpy as np.py   | 30 ++++++++++++++++++++++++++++++
 3 files changed, 83 insertions(+)
 create mode 100644 9999.py
 create mode 100644 import numpy as nddd.py
 create mode 100644 import numpy as np.py

diff --git a/9999.py b/9999.py
new file mode 100644
index 0000000..7834707
--- /dev/null
+++ b/9999.py
@@ -0,0 +1,32 @@
+import numpy as np
+
+# 定义4×4图像矩阵
+img = np.array([
+    [255, 255, 0,   0  ],
+    [255, 255, 0,   0  ],
+    [0,   0,   255, 255],
+    [0,   0,   255, 255]
+], dtype=np.uint8)
+
+print("原始图像：")
+print(img)
+print("-" * 30)
+
+# 1. 统计白色(255)和黑色(0)像素数量
+white_pixels = np.sum(img == 255)  # 等于255的元素求和
+black_pixels = np.sum(img == 0)    # 等于0的元素求和
+print(f"白色像素(255)数量：{white_pixels}")
+print(f"黑色像素(0)数量：{black_pixels}")
+print("-" * 30)
+
+# 2. 水平翻转（左右翻转）
+img_flip_lr = np.fliplr(img)
+print("水平翻转后的图像：")
+print(img_flip_lr)
+print("-" * 30)
+
+# 3. 逆时针旋转90度（转置 + 上下翻转）
+# 方法：先转置，再上下翻转
+img_rot90_ccw = np.flipud(img.T)
+print("逆时针旋转90度后的图像:")
+print(img_rot90_ccw)
\ No newline at end of file
diff --git a/import numpy as nddd.py b/import numpy as nddd.py
new file mode 100644
index 0000000..d1606a7
--- /dev/null
+++ b/import numpy as nddd.py	
@@ -0,0 +1,21 @@
+import numpy as np
+
+# 假设这是从图像中提取的2个特征图
+feature_map1 = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
+feature_map2 = np.array([[1, 1, 1], [1, 0, 0], [1, 0, 0]])
+
+# 补全代码：将特征图展平为向量
+vector1 = feature_map1.flatten()  # 展平
+vector2 = feature_map2.flatten()  # 展平
+
+print("vector1:", vector1)
+print("vector2:", vector2)
+print("-" * 40)
+
+# 1. 计算欧几里得距离
+euclidean_dist = np.linalg.norm(vector1 - vector2)
+print("欧几里得距离：", euclidean_dist)
+
+# 2. 计算余弦相似度
+cos_sim = np.dot(vector1, vector2) / (np.linalg.norm(vector1) * np.linalg.norm(vector2))
+print("余弦相似度：", cos_sim)
\ No newline at end of file
diff --git a/import numpy as np.py b/import numpy as np.py
new file mode 100644
index 0000000..e065bbd
--- /dev/null
+++ b/import numpy as np.py	
@@ -0,0 +1,30 @@
+import numpy as np
+
+# 定义原始图像矩阵
+image = np.array([
+    [100, 150, 200],
+    [80,  120, 180],
+    [60,  90,  140]
+], dtype=np.uint8)
+
+print("原图：")
+print(image)
+print("-" * 20)
+
+# 1. 变暗20：每个像素值减20
+# 注意：uint8类型不会出现负数，自动取模（小于0会变成255附近）
+image_dark = image - 20
+print("1. 变暗20后的图像:")
+print(image_dark)
+print("-" * 20)
+
+# 2. 裁剪左上角：保留 image[0:2, 0:2]
+image_crop = image[0:2, 0:2]
+print("2. 裁剪左上角2*2区域:")
+print(image_crop)
+print("-" * 20)
+
+# 3. 水平翻转：使用 np.fliplr()
+image_flip = np.fliplr(image)
+print("3. 水平翻转后的图像：")
+print(image_flip)
\ No newline at end of file