import os
import numpy as np
from PIL import Image, ImageDraw
import matplotlib.pyplot as plt# ============== 1) 使用你的图片 ==============
CUSTOM_PATH = "apple.png"def load_or_make_image():if CUSTOM_PATH and os.path.exists(CUSTOM_PATH):img = Image.open(CUSTOM_PATH).convert("L")title = f"Original ({os.path.basename(CUSTOM_PATH)})"else:# 备用: 生成一张灰度示例图H, W = 128, 128img = Image.new("L", (W, H), color=220)draw = ImageDraw.Draw(img)draw.rectangle([20, 20, 100, 60], fill=40)draw.ellipse([60, 70, 110, 120], fill=255)draw.line([0, 0, 127, 127], fill=90, width=3)title = "Original (synthetic)"return img, titleimg, orig_title = load_or_make_image()
img_np = np.array(img, dtype=np.float32)# ============== 2) 多个 3×3 卷积核 ==============
kernels = {"Sobel X": np.array([[1, 0, -1],[1, 0, -1],[1, 0, -1]], dtype=np.float32),"Sobel Y": np.array([[ 1, 1, 1],[ 0, 0, 0],[-1, -1, -1]], dtype=np.float32),"Laplacian": np.array([[0, -1, 0],[-1, 4, -1],[0, -1, 0]], dtype=np.float32),"Sharpen": np.array([[0, -1, 0],[-1, 5, -1],[0, -1, 0]], dtype=np.float32),"Box Blur": (1/9.0) * np.ones((3,3), dtype=np.float32),"Emboss": np.array([[-2, -1, 0],[-1, 1, 1],[ 0, 1, 2]], dtype=np.float32),"Outline": np.array([[-1, -1, -1],[-1, 8, -1],[-1, -1, -1]], dtype=np.float32),
}# ============== 3) 卷积函数 ==============
def conv2d_same(image: np.ndarray, kernel: np.ndarray) -> np.ndarray:kh, kw = kernel.shapepad_h, pad_w = kh//2, kw//2padded = np.pad(image, ((pad_h, pad_h), (pad_w, pad_w)), mode='constant')out = np.zeros_like(image, dtype=np.float32)for i in range(out.shape[0]):for j in range(out.shape[1]):region = padded[i:i+kh, j:j+kw]out[i, j] = float(np.sum(region * kernel))return outdef to_uint8_for_display(arr: np.ndarray) -> np.ndarray:a_min, a_max = float(arr.min()), float(arr.max())if a_max - a_min < 1e-6:return np.zeros_like(arr, dtype=np.uint8)norm = (arr - a_min) / (a_max - a_min) * 255.0return norm.astype(np.uint8)# ============== 4) 卷积 + 展示 ==============
results = {"Original": img_np}
for name, k in kernels.items():conv = conv2d_same(img_np, k)results[name] = to_uint8_for_display(conv)# 展示：所有图像在一行或两行
n = len(results)
cols = 4 # 每行展示 4 张图，可以改成 3 或 5
rows = int(np.ceil(n / cols))plt.figure(figsize=(4*cols, 4*rows))
for idx, (name, arr) in enumerate(results.items(), 1):plt.subplot(rows, cols, idx)plt.imshow(arr, cmap="gray")plt.title(name)plt.axis("off")plt.tight_layout()
plt.show()
苹果原图
不同的filter卷积之后展示的图片效果
