###### tags: `影像處理`
# *2022/05/24 影像處理 HW10*
## Coding Part
### 實戰(2)
#### 題目：

#實戰(2)
from IPython.core.pylabtools import figsize
import numpy as np
import cv2
import math
from numpy.random import *
from matplotlib import pyplot as plt
from google.colab import drive
from google.colab.patches import *

drive.mount('/content/drive')
img = cv2.imread("/content/drive/My Drive/Colab Notebooks/imgs/IMG_3643.JPG",-1)
print(img.shape)
img1 = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
img = cv2.cvtColor(img1,cv2.COLOR_BGR2GRAY)

new_img1 = cv2.applyColorMap(img,cv2.COLORMAP_AUTUMN)
new_img2 = cv2.applyColorMap(img,cv2.COLORMAP_JET)
new_img3 = cv2.applyColorMap(img,cv2.COLORMAP_BONE)

plt.figure(figsize=(20,5))
titles = ['Original','Gray','Autumn','Jet','Bone']
plt.subplot(151)
plt.title(titles[0],fontsize=15,color='r')
plt.imshow(img1)
plt.subplot(152)
plt.title(titles[1],fontsize=15,color='r')
plt.imshow(img,cmap='gray')
plt.subplot(153)
plt.title(titles[2],fontsize=15,color='r')
plt.imshow(new_img1)
plt.subplot(154)
plt.title(titles[3],fontsize=15,color='r')
plt.imshow(new_img2)
plt.subplot(155)
plt.title(titles[4],fontsize=15,color='r')
plt.imshow(new_img3)

plt.tight_layout()
plt.show()

##### 輸出結果:

### 實戰(3)
#### 題目：

#實戰(2)
from IPython.core.pylabtools import figsize
import numpy as np
import cv2
import math
from numpy.random import *
from matplotlib import pyplot as plt
from google.colab import drive
from google.colab.patches import *

drive.mount('/content/drive')
img = cv2.imread("/content/drive/My Drive/Colab Notebooks/imgs/IMG_3644.JPG",-1)
img1 = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)

#色彩校正函數
def RGB_gamma_correction(f,channel,gamma):
    g = f.copy()
    nr,nc = f.shape[:2]
    c = 255.0/(255.0**gamma)
    table = np.zeros(256)
    for i in range(256):
        table[i] = round(i**gamma*c,0)
    if channel ==1:
        k=2
    elif channel ==2:
        k=1
    else:
        k=0
    for x in range(nr):
        for y in range(nc):
            g[x,y,k]=table[f[x,y,k]]
    return g

#直方圖校正
def RGB_histogram_equalization(f):
    g=f.copy()
    for k in range(3):
        g[:,:,k]=cv2.equalizeHist(f[:,:,k])
    return g


new_img1 = RGB_gamma_correction(img1,1,0.5)
new_img2 = RGB_gamma_correction(img1,2,0.5)
new_img3 = RGB_gamma_correction(img1,3,0.5)
new_img4 = RGB_histogram_equalization(img1)
new_img5 = cv2.GaussianBlur(img1,(5,5),0)

plt.figure(figsize=(15,15))
titles = ['Original','R Channel(r=0.5)','G Channel(r=0.5)','B Channel(r=0.5)','Histogram_equalization','Gaussian Filter']

plt.subplot(3,3,1)
plt.title(titles[0],fontsize=15,color='r')
plt.imshow(img1)

plt.subplot(3,3,4)
plt.title(titles[1],fontsize=15,color='r')
plt.imshow(new_img1)
plt.subplot(3,3,5)
plt.title(titles[2],fontsize=15,color='r')
plt.imshow(new_img2)
plt.subplot(3,3,6)
plt.title(titles[3],fontsize=15,color='r')
plt.imshow(new_img3)

plt.subplot(3,3,7)
plt.title(titles[4],fontsize=15,color='r')
plt.imshow(new_img4)

plt.subplot(3,3,8)
plt.title(titles[5],fontsize=15,color='r')
plt.imshow(new_img5)


plt.tight_layout()
plt.show()

##### 輸出結果:

### 實戰(4)
#### 題目：

#實戰(3)
from IPython.core.pylabtools import figsize
import numpy as np
import cv2
import math
from numpy.random import *
from matplotlib import pyplot as plt
from google.colab import drive
from google.colab.patches import *

drive.mount('/content/drive')
img = cv2.imread("/content/drive/My Drive/Colab Notebooks/imgs/IMG_3646.JPG",-1)
img1 = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)

def RGB_to_HSI(R,G,B):
    r = R/255
    g = G/255
    b = B/255
    if R==G and G==B:
        H = -1.0
        S = 0.0
        I = (r+g+b)/3
    else:
        x = (0.5*((r-g)+(r-b)))/np.sqrt((r-g)**2+(r-b)*(g-b))
        if x < -1.0:
            x = -1.0
        if x > 1.0:
            x = 1.0
        theta = np.arccos(x)*180/np.pi
        if B <= G:
            H = theta
        else:
            H = 360.0 - theta
        S = 1.0-3.0/(r+g+b)*min(r,g,b)
        I = (r+g+b)/3
    return H,S,I

def HSI_to_RGB(H,S,I):
    if H==-1.0:
        r = I
        g = I
        b = I

    elif H>=0 and H <120:
        HH = H
        b = I*(1-S)
        r = I*(1+(S*np.cos(HH*np.pi/180))/np.cos((60-HH)*np.pi/180))
        g = 3.0*I-(r+b)

    elif H>=120 and H <240:
        HH = H - 120.0
        r = I*(1-S)
        g = I*(1+(S*np.cos(HH*np.pi/180))/np.cos((60-HH)*np.pi/180))
        b = 3*I-(r+g)
    else:
        HH = H - 240
        g = I*(1 - S)
        b = I * (1+(S*np.cos(HH*np.pi/180))/np.cos((60-HH)*np.pi/180))
        r = 3 * I-(g+b)
        
    rr = round(r*255)
    gg = round(g*255)
    bb = round(b*255)
    R = np.uint8(np.clip(rr,0,255))
    G = np.uint8(np.clip(gg,0,255))
    B = np.uint8(np.clip(bb,0,255))

    return R,G,B

def HSI_processing(f,angle = 0,saturation = 100,intensity = 100):
    g = f.copy()
    nr,nc = f.shape[:2]

    for x in range(nr):
        for y in range(nc):
            H,S,I = RGB_to_HSI(f[x,y,2],f[x,y,1],f[x,y,0])
            H = H+angle
            if H>360:
                H = H-360
            S = S*saturation/100
            I = I*intensity/100
            R,G,B = HSI_to_RGB(H,S,I)
            g[x,y,0] = B
            g[x,y,1] = G
            g[x,y,2] = R
    return g

new_img1 = HSI_processing(img1,180,100,100)
new_img2 = HSI_processing(img1,0,50,100)
new_img3 = HSI_processing(img1,0,100,50)
new_img4 = HSI_processing(img1,0,100,100)


plt.figure(figsize=(20,5))
titles = ['Original','Hue 180','Saturation 50%','Intensity 50%','Saturation 100% && Intensity 100%']
plt.subplot(151)
plt.title(titles[0],fontsize=15,color='r')
plt.imshow(img1)
plt.subplot(152)
plt.title(titles[1],fontsize=15,color='r')
plt.imshow(new_img1)
plt.subplot(153)
plt.title(titles[2],fontsize=15,color='r')
plt.imshow(new_img2)
plt.subplot(154)
plt.title(titles[3],fontsize=15,color='r')
plt.imshow(new_img3)
plt.subplot(155)
plt.title(titles[4],fontsize=15,color='r')
plt.imshow(new_img4)

plt.tight_layout()
plt.show()

##### 輸出結果:

### 實戰(5)
#### 題目：

from IPython.core.pylabtools import figsize
import numpy as np
import cv2
import math
from numpy.random import *
from matplotlib import pyplot as plt
from google.colab import drive
from google.colab.patches import *

drive.mount('/content/drive')
img = cv2.imread("/content/drive/My Drive/Colab Notebooks/imgs/Baboon.bmp",-1)
img1 = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
img2 = cv2.cvtColor(img,cv2.COLOR_BGR2HSV)

img3 = cv2.imread("/content/drive/My Drive/Colab Notebooks/imgs/Jenny.bmp",-1)
img4 = cv2.cvtColor(img3,cv2.COLOR_BGR2RGB)
img5 = cv2.cvtColor(img3,cv2.COLOR_BGR2HSV)

def IMG_Process(f1,f2):
    hsv = f1.copy()
    nr,nc = f1.shape[:2]
    hsv = cv2.cvtColor(f1,cv2.COLOR_BGR2HSV)
    for x in range(nr):
        for y in range(nc):
            hsv[x,y,0] = f1[x,y,0]
            hsv[x,y,1] = f2[x,y,1]
            hsv[x,y,2] = f2[x,y,2]
    hsv = cv2.cvtColor(hsv,cv2.COLOR_HSV2BGR)
    return hsv

output = IMG_Process(img5,img2)
output2 = IMG_Process(img2,img5)


plt.figure(figsize=(15,15))
titles = ['Baboon Original','Baboon Output','Jenny Original','Jenny Output']
plt.subplot(2,2,1)
plt.title(titles[0],fontsize=15,color='r')
plt.imshow(img1)
plt.subplot(2,2,2)
plt.title(titles[1],fontsize=15,color='r')
plt.imshow(output)

plt.subplot(2,2,3)
plt.title(titles[2],fontsize=15,color='r')
plt.imshow(img4)
plt.subplot(2,2,4)
plt.title(titles[3],fontsize=15,color='r')
plt.imshow(output2)

plt.tight_layout()
plt.show()

##### 輸出結果:

## 簡答題:
##### a.
* 身體部分變為**黑色**，因為紅色是M+Y，青色是C，所以C+M+Y根據調色盤是黑色，以下為Adobe Color官方調色盤工具驗證
* 鈴鐺會變為**紅色**，因為紅色本身就是M+Y，所以再加黃色仍舊是紅色，以下是Adobe Color官方調色工具驗證

##### b.
* H, S, I = 60, 0.8, 0.7:因為360除以6得60，0為紅色，60為黃色，而S、I是飽和度與強度，不影響色調，所以答案是**黃色**
* H, S, I = 120, 0.8, 0.8:承上題，120是**綠色**
* H, S, I = 240, 0.8, 0.6:承上題，240是**藍色**

##### c.

影像濾波套用在色彩影像，是將RGB三通道視為**獨立**的數位影像，再分別套用影像濾波技術，例如平均濾波、高斯慮波