# digital-image-processing

**Repository Path**: zhangjia1234/digital-image-processing

## Basic Information

- **Project Name**: digital-image-processing
- **Description**: 1234567890
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-03-11
- **Last Updated**: 2025-04-11

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 数字图像处理
#### 实验1：图像基础操作

##### 环境要求
- Python 3.x
- scikit-image
- matplotlib
- PIL (Pillow)

##### 文件说明
- `assignment1_answer.ipynb`: 包含所有实验代码的Jupyter Notebook文件。

##### 代码示例说明

1. **图像上半部分处理**
    ```python
    from skimage import io
    import matplotlib.pyplot as plt

    img = io.imread('x.jpg')
    h, w, c = img.shape

    for i in range(w):
        for j in range(h//2):
            img[j, i][0] = 255

    plt.imshow(img)
    plt.show()
    ```

2. **图像左半部分处理**
    ```python
    from skimage import io
    import matplotlib.pyplot as plt

    img = io.imread('x.jpg')
    h, w, c = img.shape

    for i in range(w//2):
        for j in range(h):
            img[j, i][0] = 255

    plt.imshow(img)
    plt.show()
    ```

3. **图像左上四分之一处理**
    ```python
    from skimage import io
    import matplotlib.pyplot as plt

    img = io.imread('x.jpg')
    h, w, c = img.shape

    for i in range(w//2):
        for j in range(h//2):
            img[j, i][0] = 255

    plt.imshow(img)
    plt.show()
    ```

4. **小区域像素处理**
    ```python
    from skimage import io
    import matplotlib.pyplot as plt

    img = io.imread('x.jpg')
    start_x, start_y = 2, 2
    patch_width, patch_height = 6, 6
    h, w, c = img.shape

    end_x = min(start_x + patch_width, w)
    end_y = min(start_y + patch_height, h)

    for i in range(start_x, end_x):
        for j in range(start_y, end_y):
            img[j, i][0] = 255

    plt.imshow(img)
    plt.show()
    ```

5. **指定区域颜色处理**
    ```python
    from skimage import io
    import matplotlib.pyplot as plt

    img = io.imread('x.jpg')
    start_x, start_y = 2, 2
    patch_width, patch_height = 6, 6
    h, w, c = img.shape

    end_x = min(start_x + patch_width, w)
    end_y = min(start_y + patch_height, h)

    for i in range(start_x, end_x):
        for j in range(start_y, end_y):
            r, g, b = img[j, i][0], img[j, i][1], img[j, i][2]
            if r == 0 and g == 0 and b == 0:
                img[j, i] = [255, 255, 0]
            else:
                img[j, i] = [0, 255, 0]

    plt.imshow(img)
    plt.show()
    ```

6. **星系图像红色通道处理**
    ```python
    from PIL import Image
    import matplotlib.pyplot as plt

    image = Image.open('galaxy-full.jpg')
    r, g, b = image.split()
    r_image = Image.merge('RGB', (r, Image.new('L', r.size), Image.new('L', r.size)))

    plt.figure(figsize=(15, 5))
    plt.imshow(r_image)
    plt.title('Red Channel')
    plt.show()
    ```

7. **星系图像绿色通道处理**
    ```python
    from PIL import Image
    import matplotlib.pyplot as plt

    image = Image.open('galaxy-full.jpg')
    r, g, b = image.split()
    g_image = Image.merge('RGB', (Image.new('L', g.size), g, Image.new('L', g.size)))

    plt.figure(figsize=(5, 5))
    plt.imshow(g_image)
    plt.title('Green Channel')
    plt.show()
    ```

8. **星系图像蓝色通道处理**
    ```python
    from PIL import Image
    import matplotlib.pyplot as plt

    image = Image.open('galaxy-full.jpg')
    r, g, b = image.split()
    b_image = Image.merge('RGB', (Image.new('L', b.size), Image.new('L', b.size), b))

    plt.figure(figsize=(15, 5))
    plt.imshow(b_image)
    plt.title('Blue Channel')
    plt.show()
    ```

9. **条纹效果处理**
    ```python
    from PIL import Image
    import numpy as np
    import matplotlib.pyplot as plt

    image = Image.open('earth.jpg')
    image_np = np.array(image)
    height, width, _ = image_np.shape
    new_image_np = image_np.copy()
    stripe_opacity = 0.3

    for i in range(width):
        if i % 3 == 0:
            stripe = np.zeros((height, width, 3), dtype=np.uint8)
            stripe[:, :, 0] = image_np[:, :, 0]
            new_image_np[:, :, 0] = (1 - stripe_opacity) * new_image_np[:, :, 0] + stripe_opacity * stripe[:, :, 0]
        elif i % 3 == 1:
            stripe = np.zeros((height, width, 3), dtype=np.uint8)
            stripe[:, :, 1] = image_np[:, :, 1]
            new_image_np[:, :, 1] = (1 - stripe_opacity) * new_image_np[:, :, 1] + stripe_opacity * stripe[:, :, 1]
        else:
            stripe = np.zeros((height, width, 3), dtype=np.uint8)
            stripe[:, :, 2] = image_np[:, :, 2]
            new_image_np[:, :, 2] = (1 - stripe_opacity) * new_image_np[:, :, 2] + stripe_opacity * stripe[:, :, 2]

    processed_img = Image.fromarray(new_image_np.astype(np.uint8))

    plt.figure(figsize=(10, 10))
    plt.imshow(processed_img)
    plt.axis('off')
    plt.show()
    ```

##### 使用方法
1. 安装所需的Python库：
    ```bash
    pip install scikit-image matplotlib pillow
    ```
2. 下载并运行`assignment1_answer.ipynb`文件。

##### 学习要点
- 图像的基本操作，如读取、显示和修改像素值。
- 使用不同的颜色通道进行图像处理。
- 处理图像的特定区域。
- 创建和显示图像的不同效果。


个人信息：
姓名：张嘉
学号：202352320229
班级：智能科学与技术2班