python 将原图像变成以55*55像素为单位的图像
时间: 2023-11-27 09:52:59 浏览: 28
你可以使用 Python 的 Pillow 库来将原图像变成以 55*55 像素为单位的图像。以下是示例代码:
```python
from PIL import Image
# 打开原图像
img = Image.open('original_image.jpg')
# 将图像转换为 55*55 像素为单位的大小
img_resized = img.resize((img.size[0] // 55 * 55, img.size[1] // 55 * 55))
# 显示调整后的图像
img_resized.show()
# 保存调整后的图像
img_resized.save('resized_image.jpg')
```
在这个示例中,我们首先使用 `PIL.Image` 模块中的 `Image.open()` 函数打开原始图像。然后,我们使用 `Image.resize()` 函数将图像的大小转换为以 55*55 像素为单位的大小。最后,我们使用 `
相关问题
请手动实现给一个RGB图像进行JPEG压缩(手动实现),Python
JPEG压缩是一种有损的压缩方式,它的基本思路是将图像分成若干个8x8的小块,对每个小块进行变换和量化,再进行哈夫曼编码实现压缩。下面是一个简单的手动实现示例:
1.导入相关库
```python
import numpy as np
from scipy import fftpack
from scipy import ndimage
```
2.加载并转换图像
```python
# 加载图像
img = ndimage.imread('image.jpg')
# 将图像转换为YCbCr格式
img = np.float32(img) / 255.0
img_ycc = np.zeros_like(img)
img_ycc[:,:,0] = 0.299*img[:,:,0] + 0.587*img[:,:,1] + 0.114*img[:,:,2]
img_ycc[:,:,1] = -0.1687*img[:,:,0] - 0.3313*img[:,:,1] + 0.5*img[:,:,2]
img_ycc[:,:,2] = 0.5*img[:,:,0] - 0.4187*img[:,:,1] - 0.0813*img[:,:,2]
```
3.分割图像并进行离散余弦变换
```python
# 定义8x8小块的数量
h, w, _ = img_ycc.shape
bh, bw = h // 8, w // 8
# 生成离散余弦变换矩阵
T = np.zeros((8, 8))
for i in range(8):
for j in range(8):
if i == 0:
T[i][j] = 1 / np.sqrt(8)
else:
T[i][j] = 0.5 * np.cos((2*j+1)*i*np.pi/16) * np.sqrt(2/8)
# 将图像分割成8x8小块并进行离散余弦变换
img_dct = np.zeros_like(img_ycc)
for i in range(bh):
for j in range(bw):
block = img_ycc[i*8:(i+1)*8, j*8:(j+1)*8, 0]
img_dct[i*8:(i+1)*8, j*8:(j+1)*8, 0] = fftpack.dct(fftpack.dct(block.T, norm='ortho').T, norm='ortho')
```
4.量化
```python
# 定义量化矩阵
Q = np.array([[16, 11, 10, 16, 24, 40, 51, 61],
[12, 12, 14, 19, 26, 58, 60, 55],
[14, 13, 16, 24, 40, 57, 69, 56],
[14, 17, 22, 29, 51, 87, 80, 62],
[18, 22, 37, 56, 68, 109, 103, 77],
[24, 35, 55, 64, 81, 104, 113, 92],
[49, 64, 78, 87, 103, 121, 120, 101],
[72, 92, 95, 98, 112, 100, 103, 99]])
# 进行量化
img_quant = np.zeros_like(img_dct)
for i in range(bh):
for j in range(bw):
block = img_dct[i*8:(i+1)*8, j*8:(j+1)*8, 0]
img_quant[i*8:(i+1)*8, j*8:(j+1)*8, 0] = np.round(block / (Q * 10))
```
5.反量化和反离散余弦变换
```python
# 反量化
img_dequant = np.zeros_like(img_quant)
for i in range(bh):
for j in range(bw):
block = img_quant[i*8:(i+1)*8, j*8:(j+1)*8, 0]
img_dequant[i*8:(i+1)*8, j*8:(j+1)*8, 0] = block * (Q * 10)
# 反离散余弦变换
img_idct = np.zeros_like(img_ycc)
for i in range(bh):
for j in range(bw):
block = img_dequant[i*8:(i+1)*8, j*8:(j+1)*8, 0]
img_idct[i*8:(i+1)*8, j*8:(j+1)*8, 0] = fftpack.idct(fftpack.idct(block.T, norm='ortho').T, norm='ortho')
```
6.将图像转换回RGB格式并保存
```python
# 将图像转换为RGB格式
img_rgb = np.zeros_like(img)
img_rgb[:,:,0] = img_idct[:,:,0] + 1.402*img_idct[:,:,2]
img_rgb[:,:,1] = img_idct[:,:,0] - 0.34414*img_idct[:,:,1] - 0.71414*img_idct[:,:,2]
img_rgb[:,:,2] = img_idct[:,:,0] + 1.772*img_idct[:,:,1]
# 将像素值限制在0-255之间
img_rgb = np.clip(img_rgb, 0, 1)
img_rgb = np.uint8(img_rgb * 255.0)
# 保存图像
ndimage.imsave('image_compressed.jpg', img_rgb)
```
以上就是一个简单的手动实现JPEG压缩的示例,由于在量化过程中我们将浮点数转换为整数,因此压缩后的图像可能出现一些失真。在实际应用中,我们可以使用更加复杂的压缩方法来减少失真并提高压缩效率。
用Python写一个程序,对一个灰度图像进行图像分割8X8,并将各像素值减去128,进行D CT正向变换,再通过量化,最后进行Z字形重排序
您好,我是 CSDN 开发的 C 知道,关于您的问题,我可以回答。以下是 Python 代码实现:
```python
import numpy as np
from scipy.fftpack import dct
# 读取灰度图像
img = cv2.imread('gray_image.jpg', )
# 图像分割8X8
h, w = img.shape
img = img[:h//8*8, :w//8*8]
blocks = img.reshape(h//8, 8, -1, 8).swapaxes(1, 2).reshape(-1, 8, 8)
# 将各像素值减去128
blocks = blocks - 128
# DCT正向变换
dct_blocks = np.zeros_like(blocks)
for i in range(blocks.shape[]):
dct_blocks[i] = dct(dct(blocks[i], axis=, norm='ortho'), axis=1, norm='ortho')
# 量化
quantization_table = np.array([[16, 11, 10, 16, 24, 40, 51, 61],
[12, 12, 14, 19, 26, 58, 60, 55],
[14, 13, 16, 24, 40, 57, 69, 56],
[14, 17, 22, 29, 51, 87, 80, 62],
[18, 22, 37, 56, 68, 109, 103, 77],
[24, 35, 55, 64, 81, 104, 113, 92],
[49, 64, 78, 87, 103, 121, 120, 101],
[72, 92, 95, 98, 112, 100, 103, 99]])
quantized_blocks = np.round(dct_blocks / quantization_table)
# Z字形重排序
zigzag_order = np.array([[, 1, 5, 6, 14, 15, 27, 28],
[2, 4, 7, 13, 16, 26, 29, 42],
[3, 8, 12, 17, 25, 30, 41, 43],
[9, 11, 18, 24, 31, 40, 44, 53],
[10, 19, 23, 32, 39, 45, 52, 54],
[20, 22, 33, 38, 46, 51, 55, 60],
[21, 34, 37, 47, 50, 56, 59, 61],
[35, 36, 48, 49, 57, 58, 62, 63]])
zigzag_blocks = np.zeros_like(quantized_blocks)
for i in range(quantized_blocks.shape[]):
zigzag_blocks[i] = quantized_blocks[i][zigzag_order.flatten()].reshape(8, 8)
# 输出结果
print(zigzag_blocks)
```
希望能对您有所帮助。
相关推荐
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![-](https://csdnimg.cn/download_wenku/file_type_lunwen.png)
![-](https://csdnimg.cn/download_wenku/file_type_lunwen.png)
![-](https://csdnimg.cn/download_wenku/file_type_column_c1.png)
![-](https://csdnimg.cn/download_wenku/file_type_lunwen.png)
![-](https://csdnimg.cn/download_wenku/file_type_lunwen.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![rar](https://img-home.csdnimg.cn/images/20210720083606.png)
![pdf](https://img-home.csdnimg.cn/images/20210720083512.png)