educoder之Python数值计算库Numpy图像处理详解

from PIL import Image
import numpy as np
import matplotlib.pyplot as plt
# 读入图片
img = Image.open('test.jpg') 
# 显示图片
plt.imshow(np.asarray(img))
plt.show()

from scipy import interpolate
# 将图像放大两倍
scale_factor = 2
image_arr = np.array(img)
width, height = image_arr.shape[:2]
new_width, new_height = int(scale_factor * width), int(scale_factor * height)
# 采用scipy.interpolate的interpolate()方法实现缩放
a, b = np.linspace(0, width, width), np.linspace(0, height, height)
x, y = np.meshgrid(a,b)
f = interpolate.interp2d(x, y, image_arr, kind='cubic')
new_x, new_y = np.linspace(0, width, new_width), np.linspace(0, height, new_height)
new_image_arr = f(new_x, new_y)
# 重新转为图像格式，显示出来观察。
new_image = Image.fromarray(np.uint8(new_image_arr))
plt.imshow(np.asarray(new_image))
plt.show()

# 将图像水平翻转
img_flip_horizontal = np.fliplr(image_arr)
# 将图像垂直翻转
img_flip_vertical = np.flipud(image_arr)

# 图像缩放并裁剪
scale_factor = 2
crop_area = (50, 100, 250, 350)
image_arr = np.array(img)
width, height = image_arr.shape[1], image_arr.shape[0]
new_width, new_height = int(scale_factor * width), int(scale_factor * height)
new_image_arr = np.asarray(Image.fromarray(image_arr).resize((new_width, new_height)))
# 裁剪图像
left, upper, right, lower = crop_area
cropped_image_arr = new_image_arr[upper:lower, left:right]
# 显示处理过的图片
plt.imshow(np.asarray(Image.fromarray(cropped_image_arr)))

# 灰度化
gray_image_arr = np.dot(image_arr, [0.2989, 0.5870, 0.1140])
gray_image = Image.fromarray(np.uint8(gray_image_arr))
plt.imshow(np.asarray(gray_image))
# 颜色通道处理
red_channel_arr = np.zeros_like(image_arr)
red_channel_arr[:, :, 0] = image_arr[:, :, 0]
red_channel_image = Image.fromarray(np.uint8(red_channel_arr))
plt.imshow(np.asarray(red_channel_image))
green_channel_arr = np.zeros_like(image_arr)
green_channel_arr[:, :, 1] = image_arr[:, :, 1]
green_channel_image = Image.fromarray(np.uint8(green_channel_arr))
plt.imshow(np.asarray(green_channel_image))
blue_channel_arr = np.zeros_like(image_arr)
blue_channel_arr[:, :, 2] = image_arr[:, :, 2]
blue_channel_image = Image.fromarray(np.uint8(blue_channel_arr))
plt.imshow(np.asarray(blue_channel_image))

# 图像平滑化
kernel = np.ones((3, 3), np.float32) / 9
smooth_image_arr = cv2.filter2D(image_arr, -1, kernel)
# 显示滤波过后的图片
plt.imshow(smooth_image_arr)