# 前處理
[hough](https://hackmd.io/Zm31qOqDSWiKz3ScXQBAQw)

## 多次處理

from os import listdir
from os.path import isfile, join
mypath = "C:/Users/mua89/hough/Dataset/Test/Cataract"
file_names = [f for f in listdir(mypath) if isfile(join(mypath, f))]
print(str(len(file_names)) + ' images loaded')
# Read image as gray-scale
import cv2
import numpy as np
size=64
for n, file in enumerate(file_names):
    img = cv2.imread(mypath+"/"+file,0)
    h,w=img.shape
    if(h>128 and w>128):
        img = cv2.resize(img, (size, size), interpolation = cv2.INTER_AREA)
        img = cv2.medianBlur(img,5)
        cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
        circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,20,
                                    param1=50,param2=30,minRadius=0,maxRadius=0)
        if circles is not None:
            circles = np.uint16(np.around(circles))
            for i in circles[0,:]:
                # draw the outer circle
                cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
                # draw the center of the circle
                cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
            print(file)
            cv2.resizeWindow('image', 600,600)
            cv2.imshow('detected circles',cimg)
            cv2.waitKey(0)
            cv2.destroyAllWindows()

## 切割照片

import cv2
import numpy as np
from matplotlib import pyplot as plt

bgr_img = cv2.imread('eye.jpg') # read as it is

if bgr_img.shape[-1] == 3:           # color image
    b,g,r = cv2.split(bgr_img)       # get b,g,r
    rgb_img = cv2.merge([r,g,b])     # switch it to rgb
    gray_img = cv2.cvtColor(bgr_img, cv2.COLOR_BGR2GRAY)
else:
    gray_img = bgr_img

img = cv2.medianBlur(gray_img, 5)
cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)

circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,100,
                            param1=100,param2=30,minRadius=10,maxRadius=65)

circles = np.uint16(np.around(circles))
#print(circles)
for i in circles[0,:]:
    # draw the outer circle
    cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
    #print(cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2))
    # draw the center of the circle
    cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
    
    
plt.subplot(121),plt.imshow(rgb_img)

plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(cimg)

plt.title('Hough Transform'), plt.xticks([]), plt.yticks([])
plt.show()

#crop
max_circle = max(circles[0,:], key=lambda x:x[2])
# print(max_circle)

# # Create mask
height,width = img.shape
mask = np.zeros((height,width), np.uint8)


for i in [max_circle]:
    cv2.circle(mask,(i[0],i[1]),i[2],(255,255,255),thickness=-1)  


masked_data = cv2.bitwise_and(img, img, mask=mask)

_,thresh = cv2.threshold(mask,1,255,cv2.THRESH_BINARY)

# Find Contour
contours = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[0]
x,y,w,h = cv2.boundingRect(contours[0])

# Crop masked_data
crop = masked_data[y:y+h,x:x+w]

#Code to close Window
cv2.imshow('OG',img)
cv2.imshow('Cropped ROI',crop)

cv2.imwrite("test1.jpg", crop)
cv2.waitKey(0)
cv2.destroyAllWindows()

## final

import cv2
import numpy as np
from matplotlib import pyplot as plt
from os import listdir
from os.path import isfile, join
import datetime
mypath = "C:/Users/mua89/hough/123"
file_names = [f for f in listdir(mypath) if isfile(join(mypath, f))]
print(str(len(file_names)) + ' images loaded')
# Read image as gray-scale
size=64
start = datetime.datetime.now()
for n, file in enumerate(file_names):
    img = cv2.imread(mypath+"/"+file,0)
    h,w=img.shape
    if(h>128 and w>128):
        img = cv2.resize(img, (size, size), interpolation = cv2.INTER_AREA)
        img = cv2.medianBlur(img,5)
        cimg = cv2.cvtColor(img,cv2.COLOR_GRAY2BGR)
        circles = cv2.HoughCircles(img,cv2.HOUGH_GRADIENT,1,10,
                                    param1=100,param2=30,minRadius=5,maxRadius=50)
        if circles is not None:
            circles = np.uint16(np.around(circles))
            for i in circles[0,:]:
                # draw the outer circle
                cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
                # draw the center of the circle
                cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
            print(file)
            
            #crop
            max_circle = max(circles[0,:], key=lambda x:x[2])
            # print(max_circle)

            # # Create mask
            height,width = img.shape
            mask = np.zeros((height,width), np.uint8)


            for i in [max_circle]:
                cv2.circle(mask,(i[0],i[1]),i[2],(255,255,255),thickness=-1)  


            masked_data = cv2.bitwise_and(img, img, mask=mask)

            _,thresh = cv2.threshold(mask,1,255,cv2.THRESH_BINARY)

            # Find Contour
            contours = cv2.findContours(thresh,cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)[0]
            x,y,w,h = cv2.boundingRect(contours[0])

            # Crop masked_data
            crop = masked_data[y:y+h,x:x+w]

            #cv2.resizeWindow('image', 600,600)
            #cv2.imshow('detected circles',cimg)
            #cv2.waitKey(0)
            #cv2.destroyAllWindows()
           
            cv2.imwrite("C:/Users/mua89/hough/test/test"+str(n)+'.jpg' , crop)
end = datetime.datetime.now()
print("執行時間：", end - start)
           

## 反光處理
- illuminationChange
https://blog.csdn.net/fangyan90617/article/details/100533808
- <另外>
https://blog.csdn.net/qq_43555843/article/details/102510425

import  cv2
import os,shutil
#找亮光位置
def create_mask(imgpath):
    image = cv2.imread(imgpath, cv2.IMREAD_GRAYSCALE)
    _, mask = cv2.threshold(image, 200, 255, cv2.THRESH_BINARY)
    return mask
#修复图片
def xiufu(imgpath,maskpath):
    src_ = cv2.imread(imgpath)
    mask = cv2.imread(maskpath, cv2.IMREAD_GRAYSCALE)
    #缩放因子(fx,fy)
    res_ = cv2.resize(src_,None,fx=0.6, fy=0.6, interpolation = cv2.INTER_CUBIC)
    mask = cv2.resize(mask,None,fx=0.6, fy=0.6, interpolation = cv2.INTER_CUBIC)
    dst = cv2.inpaint(res_, mask, 10, cv2.INPAINT_TELEA)
    return dst

if __name__=='__main__':
    rootpath = 'C:/Users/Nan/orange.jpg'
    masksavepath= 'C:/Users/Nan/mask.jpg'
    savepath = 'C:/Users/Nan/result.jpg'
    imgfiles = os.listdir(rootpath)
    for i in range(0, len(imgfiles)):
        path = os.path.join(rootpath, imgfiles[i])
        print(imgfiles[i])
        if os.path.isfile(path):
            if (imgfiles[i].endswith("jpg") or imgfiles[i].endswith("JPG")):
                maskpath =os.path.join(masksavepath, "mask_"+imgfiles[i])
                cv2.imwrite(maskpath, create_mask(path))
                dst=xiufu(path,maskpath)
                newname = 'xiufu_' + imgfiles[i].split(".")[0]
                cv2.imwrite(os.path.join(savepath, newname + ".jpg"), dst)
                shutil.copyfile(os.path.join(rootpath, imgfiles[i].split(".")[0] + ".xml"),
                                os.path.join(savepath, newname + ".xml"))


### 處理方式
https://download.csdn.net/download/weixin_38530846/15090328
- 根據 Paper，有以下做法:

- 多角度拍照，重疊照片互補(可行)
- 根據反射量，霧化反光區域(可行)
- 物質表面特徵(暫不使用)
- 調整曝光時間，使閃光處不飽和(待了解)

- 醫師建議

- 建模時，刪除反光重疊到瞳孔的部分資料
- 請user重新拍照