一、简单的黑白边界检测

代码实现

import matplotlib.pyplot as plt
import numpy as np
import paddle
from paddle.nn import Conv2D
from paddle.nn.initializer import Assign

# 创建初始化权重参数w
w = np.array([0, -1, 1], dtype='float32')
# 将权重参数调整成维度为[count, cin, kh, kw]的四维张量
w = w.reshape([1, 1, 1, 3])
# 创建卷积算子，设置输出通道数，卷积核大小，和初始化权重参数
# kernel_size = [1, 3]表示kh = 1, kw=3
# 创建卷积算子的时候，通过参数属性weight_attr指定参数初始化方式
# 这里的初始化方式时，从numpy.ndarray初始化卷积参数
conv = Conv2D(in_channels=1, out_channels=1, kernel_size=[1, 3],
              weight_attr=paddle.ParamAttr(initializer=Assign(value=w)))
# 创建输入图片，图片左边的像素点取值为1，右边的像素点取值为0
img = np.ones([55, 55], dtype='float32')
img[:, 25:] = 0.
# 将图片形状调整为[N, C, H, W]的形式
x1 = img.reshape([1, 1, 55, 55])
# 将numpy.ndarray转化成paddle中的tensor
x2 = paddle.to_tensor(x1)
# 使用卷积算子作用在输入图片上
y = conv(x2)
# 将输出tensor转化为numpy.ndarray
out = y.numpy()
f = plt.subplot(121)
f.set_title('input image', fontsize=20)
plt.imshow(img, cmap='gray')
f = plt.subplot(122)
f.set_title('output featuremap', fontsize=20)
# 卷积算子Conv2D输出数据形状为[N, C, H, W]形式
# 此处N, C=1，输出数据形状为[1, 1, H, W]，是4维数组
# 但是画图函数plt.imshow画灰度图时，只接受2维数组
# 通过numpy.squeeze函数将大小为1的维度消除
plt.imshow(out.squeeze(), cmap='gray')
plt.show()

运行截图

二、图像中物体边缘检测

代码实现

import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
import paddle
from paddle.nn import Conv2D
from paddle.nn.initializer import Assign
import os

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

Ha_Shiqi = Image.open('img/hashiqi.jpg')

# 设置卷积核参数
w = np.array([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype='float32') / 8
w = w.reshape([1, 1, 3, 3])
# 由于输入通道数是3，将卷积核的形状从[1,1,3,3]调整为[1,3,3,3]
w = np.repeat(w, 3, axis=1)
# 创建卷积算子，输出通道数为1，卷积核大小为3x3，
# 并使用上面的设置好的数值作为卷积核权重的初始化参数
conv = Conv2D(in_channels=3, out_channels=1, kernel_size=[3, 3],
              weight_attr=paddle.ParamAttr(initializer=Assign(value=w)))
# 将读入的图片转化为float32类型的numpy.ndarray
x = np.array(Ha_Shiqi).astype('float32')
# 图片读入成ndarray时，形状是[H,W,3],将通道这一维度调整到最前面
x = np.transpose(x, (2, 0, 1))
# 将数据形状调整成[N,C,H,W]格式
x = x.reshape(1, 3, Ha_Shiqi.height, Ha_Shiqi.width)
x = paddle.to_tensor(x)
y = conv(x)
out = y.numpy()
plt.figure(figsize=(20, 10))
f = plt.subplot(121)
f.set_title('input image', fontsize=20)
plt.imshow(Ha_Shiqi)
f = plt.subplot(122)
f.set_title('output feature image', fontsize=20)
plt.imshow(out.squeeze(), cmap='gray')
plt.show()

运行结果

原图

边缘检测图

三、图像均值模糊

代码实现

import paddle
import matplotlib.pyplot as plt
from PIL import Image
import numpy as np
from paddle.nn import Conv2D
from paddle.nn.initializer import Assign
import os

os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"

# 读入图片并转换成numpy.ndarray
# 将原图片转换成灰度图
boshimao = Image.open('img/boshimiao.jpg').convert('L')
boshimao = np.array(boshimao)

# 创建初始化参数
w = np.ones([1, 1, 5, 5], dtype='float32') / 25
conv = Conv2D(in_channels=1, out_channels=1, kernel_size=[5, 5],
              weight_attr=paddle.ParamAttr(initializer=Assign(value=w)))
x = boshimao.astype('float32')
x = x.reshape([1, 1, boshimao.shape[0], boshimao.shape[1]])
x = paddle.to_tensor(x)
y = conv(x)
out = y.numpy()

plt.figure(figsize=(20, 12))
f = plt.subplot(121)
f.set_title('input image', fontsize=20)
plt.imshow(boshimao, cmap='gray')

f = plt.subplot(122)
f.set_title('output feature map', fontsize=20)
out = out.squeeze()
plt.imshow(out, cmap='gray')
plt.show()

运行结果