【计算机科学前沿】第五章答案 2022 - 图像处理

第5章

5.1 图像的像素和色彩

5.1.1 图片显示

fig() + image(img_gray, cmap='gray')

5.1.2 图片数据结构分析

print(type(img_gray))
print(img_gray.shape)
printMat2D(img_gray)

5.1.3 像素 - 单像素读取

val = img_gray[18, 16]
print(val)

5.1.4 像素 - 单像素修改

img_gray_copy = image_copy(img_gray)
img_gray_copy[18, 16] = 255
fig() + image(img_gray_copy, cmap='gray')

5.1.5 像素 - 行修改

img_gray_copy = image_copy(img_gray)
img_gray_copy[18:25, 16] = 255
fig() + image(img_gray_copy, cmap='gray')

5.1.6 像素 - 区域修改

img_gray_copy = image_copy(img_gray)
img_gray_copy[18:25, 16:25] = 255
fig() + image(img_gray_copy, cmap='gray')

5.1.7 像素 - 区域修改简化写法

img_gray_copy = image_copy(img_gray)
img_gray_copy[8:25, 8:25] = 255
fig() + image(img_gray_copy, cmap='gray')

5.1.8 练习 - 花边框

img_gray_copy = image_copy(img_gray)
img_gray_copy[3:23, 22] = 255
img_gray_copy[2:23, 7] = 255
img_gray_copy[23, 7:23] = 255
img_gray_copy[2, 8:23] = 255
fig() + image(img_gray_copy, cmap='gray')

5.1.9 彩色图片

fig() + image(img_shibe)
print(img_shibe.shape)

5.1.10 色彩通道拆分

red, green, blue = color_split(img_shibe)
fig(1, 3) + [image(red, cmap='Reds'), image(green, cmap='Greens'), image(blue, cmap='Blues')]

merged_img = color_merge(red, green, blue)
fig() + image(merged_img)

5.1.11 色彩通道拆分 - 另一种方法

r = img_shibe[:, :, 0]
g = img_shibe[:, :, 1]
b = img_shibe[:, :, 2]
fig(1, 3) + [image(r, cmap='Reds'), image(g, cmap='Greens'), image(b, cmap='Blues')]

5.2 图像的色彩变换

5.2.1 色彩变换热身1 - 通道对调

fig() + image(img_raw)
img_bgr = image_copy(img_raw)

tmp = image_copy(img_bgr[:, :, 0])
img_bgr[:, :, 0] = img_bgr[:, :, 2]
img_bgr[:, :, 2] = tmp

fig() + image(img_bgr)

5.2.2 色彩变换热身2 - 暖色系

img_warm = image_copy(img_raw)

img_warm = image_int2float(img_warm)
img_warm[:, :, 2] = img_warm[:, :, 2] / 2
img_warm = image_float2int(img_warm)

fig() + image(img_warm)

5.2.3 矩阵形式 - 通道对调

mat = [
    [0, 0, 1],
    [0, 1, 0],
    [1, 0, 0]
]

img_bgr = image_copy(img_raw)
img_bgr = map_color_space(img_bgr, mat)
fig() + image(img_bgr)

5.2.4 练习 - 矩阵形式实现暖色系

mat = [[0, 0, 1],
     [0, 1, 0], 
     [1, 0, 0]]

img_bgr = image_copy(img_raw)
img_bar_warm = image_int2float(img_bgr)
img_bar_warm[:, :, 2] /= 2
img_bar_warm = image_float2int(img_bar_warm)

fig() + image(img_bar_warm)

5.2.5 矩阵形式 - 复古效果

mat = [
    [0.393, 0.769, 0.189],
    [0.349, 0.686, 0.168],
    [0.272, 0.534, 0.131]
]

img_retro = image_copy(img_raw)
img_retro = image_int2float(img_retro)
img_retro = map_color_space(img_retro, mat)
img_retro = bound(img_retro, 0, 255)
img_retro = image_float2int(img_retro)
fig() + image(img_retro)

5.2.6 矩阵形式 - 红绿色盲模拟

rgb2lms_mat = [
    [17.8824, 43.5161, 4.11935],
    [3.45565, 27.1554, 3.86714],
    [0.0299566, 0.184309, 1.46709]
]

lms2rgb_mat = [
    [8.09444479e-02, -1.30504409e-01, 1.16721066e-01],
    [-1.02485335e-02, 5.40193266e-02, -1.13614708e-01],
    [-3.65296938e-04, -4.12161469e-03, 6.93511405e-01]
]

lms_mix_mat = [
    [0, 2.02344, -2.52581],
    [0, 1, 0],
    [0, 0, 1]
]

img_cb = image_copy(img_raw)
img_cb = image_int2float(img_cb)

img_cb = map_color_space(img_cb, rgb2lms_mat)
img_cb = map_color_space(img_cb, lms_mix_mat)
img_cb = map_color_space(img_cb, lms2rgb_mat)

img_cb = bound(img_cb, 0, 255)
img_cb = image_float2int(img_cb)

fig() + image(img_raw)
fig() + image(img_cb)

5.2.7 练习 - 自己设计变换方式

import itertools
import random
mat = [
    [],
    [],
    []
]

for item, _ in itertools.product(mat, range(3)):
    item.append(random.random())

img_retro = image_copy(img_raw)
img_retro = image_int2float(img_retro)
img_retro = map_color_space(img_retro, mat)
img_retro = bound(img_retro, 0, 255)
img_retro = image_float2int(img_retro)
fig() + image(img_retro)

5.2.8 从线性变换到非线性变换 - 木雕效果

img_woodcut = image_copy(img_wzj)
img_woodcut = rgb2gray(img_woodcut)
img_woodcut = binary_threshold(img_woodcut, 170, 0, 255)
fig() + image(img_woodcut, cmap='gray')

5.3 图像特效的制作

5.3.1 毛玻璃特效

def groundglass_code(img, k=3):
    res = image_copy(img)
    H, W = get_image_size(img)
    
    for i in range(H):
        for j in range(W):
            offset_i = random_int(-k, k)
            offset_j = random_int(-k, k)
            
            src_i = i + offset_i
            src_j = j + offset_j
            
            src_i = bound(src_i, 0, H - 1)
            src_j = bound(src_j, 0, W - 1)
            
            res[i, j, :] = img[src_i, src_j, :]
            return res
        
img_groundglass = groundglass(img_sand,3)
fig() + image(img_sand)
fig() + image(img_groundglass)

5.3.2 毛玻璃拓展 - 调整参数

img_groundglass_ex = groundglass(img_sand,8)
fig() + image(img_sand)
fig() + image(img_groundglass_ex)

5.3.3 油画特效

def oil_painting_code(img, num_bin=2, region_size =4):
    H, W = get_image_size(img)
    res = image_copy(img)
    bin_assignment = compute_bin_assignment(img, num_bin)
    for i in range(0, H):
        for j in range(0, W):
            bb = get_bounding_box(i, j, region_size, H, W)
            y1, y2, x1, x2 = bb
            
            img_region = img[y1:y2, x1:x2, :]
            bin_assignment_region = bin_assignment[y1:y2, x1:x2]  
            region_r, region_g, region_b = get_most_frequent_color(img_region, bin_assignment_region)
            
            res[y1:y2, x1:x2, 0] = region_r
            res[y1:y2, x1:x2, 1] = region_g
            res[y1:y2, x1:x2, 2] = region_b
            return res
  
img_painting = oil_painting(img_sand,3,4)
fig() + image(img_sand)
fig() + image(img_painting)

5.3.4 油画效果拓展 - 调整参数

img_painting = oil_painting(img_sand,20,4)
fig() + image(img_sand)
fig() + image(img_painting)

5.4 图像扭曲

5.4.1 映射函数公式：

回顾图像扭曲的映射函数公式：
$$u,v=T(x,y)$$

T()将原图的（x, y）对应到新图的(u, v)像素点上去。然而，在计算机实现的时候，这个过程是相反的：对于新图上的一个像素点，我们希望找到它在原图上对应的源点。因此，编写程序时，映射函数公式其实应该写成：

$$x,y=T^{-1}(u,v)$$

5.4.2 全局扭曲1 - 坍缩映射函数

坍缩映射函数：
$$x^{\prime }=x\ast (1-p\ast r)/(1-p)$$

$$y^{\prime }=y\ast (1-p\ast r)/(1-p)$$

$$r=\sqrt{x^2+y^2}$$

def warp1(x, y):
    p = 0.4
    r = (x**2 + y**2)**0.5
    xp = x * (1 - p*r) / (1 - p)
    yp = y * (1 - p*r) / (1 - p)
    return xp, yp

img_anchor_warp = global_len_warp(img_anchor, warp1)

fig() + image(img_anchor)
fig() + image(img_anchor_warp)

5.4.3 全局扭曲1 - 坍缩扭曲效果

def warp1(x, y):
    p = 0.4
    r = (x**2 + y**2)**0.5
    xp = x * (1 - p*r) / (1 - p)
    yp = y * (1 - p*r) / (1 - p)
    return xp, yp

img_warp_boy = global_len_warp(img_boy, warp1)
fig() + image(img_boy)
fig() + image(img_warp_boy)

5.4.4 全局扭曲1 - 坍缩练习

def warp1_ex(x, y):
    p = 0.2
    r = (x**2 + y**2)**0.5
    xp = x * (1 - p*r) / (1 - p)
    yp = y * (1 - p*r) / (1 - p)
    return xp, yp

img_warp_boy_ex = global_len_warp(img_boy, warp1_ex)

fig() + image(img_boy)
fig() + image(img_warp_boy_ex)

5.4.5 全局扭曲2 - 棱镜扭曲函数

棱镜扭曲函数：
$$x^{\prime }=sin(x)\ast x^2$$

$$y^{\prime }=sin(y)\ast y^2$$

def warp2(x, y):
    xp = sin(x) * x**2
    yp = sin(y) * y**2
    return xp, yp

img_warp_anchor_ex2 = global_len_warp(img_anchor, warp2)
fig() + image(img_warp_anchor_ex2)

5.4.6 全局扭曲2 - 棱镜扭曲效果

def warp2(x, y):
    xp = sin(x) * x**2
    yp = sin(y) * y**2
    return xp, yp

img_warp_boy2 = global_len_warp(img_boy, warp2)
fig() + image(img_warp_boy2)

5.4.7 全局扭曲 - 疯狂模式

def warp_ex(x, y):
    xp = sin(x) * x
    yp = sin(y) * y    
    return xp, yp

img_warp_boy_insane = global_len_warp(img_boy, warp_ex)
fig() + image(img_warp_boy_insane)

5.4.8 局部扭曲 - 拉伸扭曲函数

warp_params = [(125, 125), (0, 80), 120]
img_warp_local_anchor = local_warp_image(img_anchor, warp_params)

warp_params = [(125, 125), (0, 80), 60]
img_warp_local_anchor2 = local_warp_image(img_anchor, warp_params)

fig() + image(img_warp_local_anchor)
fig() + image(img_warp_local_anchor2)

5.4.9 局部扭曲 - 拉伸扭曲效果

warp_params = [(100, 100), (0, 20), 50]
img_warp_local_boy = local_warp_image(img_boy, warp_params)

fig() + image(img_warp_local_boy)

5.5 人脸关键点检测

5.5.1 人脸关键点

在一系列关于人脸研究中，科学家们发现人脸部某些重要位置坐标的定位对于后续任务，如人脸对齐、分割等十分重要，这些点被称为人脸关键点。按照精度上升，有68，104，和240点。下图就是68点的分布情况：

[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-SI8gmBw5-1667613871201)(https://gitee.com/liuyun492/frontiers_in_-computer_-science/raw/img/Typora/%E4%B8%8B%E8%BD%BD.png)]

5.5.2 人脸关键点检测

points = detect_keypoints(img_boy)
print(points)

5.5.3 人脸关键点可视化

img_boy_with_points = draw_points(img_boy, points)
fig() + image(img_boy_with_points)

5.5.4 人来年关键点应用 - 找出嘴角和眉心的位置

mouth_left = points[48]
mouth_right = points[54]

left_eyebrow = points[19]
right_eyebrow = points[24]

cust_points = [mouth_left, mouth_right, left_eyebrow, right_eyebrow]
img_boy_cust = draw_points(img_boy, cust_points)
fig() + image(img_boy_cust)

5.5.5 练习 - 勾勒眼睛和嘴巴

left_eye = points[36:42]
right_eye = points[42:48]
mouth = points[48:60]

img_boy_face = draw_points(img_boy, left_eye + right_eye + mouth)
fig() + image(img_boy_face)

5.5.6 练习 - 运用关键点计算嘴部中心位置

mouth_up = points[51]
mouth_down = points[57]

mouth_center_y = int((mouth_up[0] + mouth_down[0] + mouth_left[0] + mouth_right[0]) / 4)

mouth_center_x = int((mouth_up[1] + mouth_down[1] + mouth_left[1] + mouth_right[1]) / 4)
mouth_center = (mouth_center_y, mouth_center_x)

img_boy_mouth = draw_points(img_boy, mouth_center)
fig() + image(img_boy_mouth)

5.6 动态表情包制作

5.6.1. 检测关键点

points = detect_keypoints(img_wzj)
img_with_points = draw_points(img_wzj, points)
fig() + image(img_with_points)

5.6.2 可视化表情控制点

mouth_left = points[48]
mouth_right = points[54]
left_eyebrow = points[19]
right_eyebrow = points[24]

cust_points = [mouth_left, mouth_right, left_eyebrow, right_eyebrow]
img_with_points = draw_points(img_wzj, cust_points)
fig() + image(img_with_points)

5.6.3. 设置拉伸函数并制作表情包

warp_params = [
    [left_eyebrow, (0, -5), 35],
    [right_eyebrow, (0, -5), 35],
    [mouth_left, (-5, -5), 40],
    [mouth_right, (5, -5), 40]
]

sticker = make_sticker(img_wzj, warp_params)
fig() + gif(sticker)

5.6.4 上传图片并制作属于自己的表情包

这个大家就自己做吧，其实也不用非得写的，随便一张网图的url就行

url = "https://xxxxx"
img_url = imread(url)