import numpy as np
import matplotlib.pyplot as plt
import math
 
if __name__ == '__main__':
    pic_path = 'lenna.png' 
    img = plt.imread(pic_path)
    if pic_path[-4:] == '.png':  # .png圖片在這里的存儲格式是0到1的浮點(diǎn)數(shù)，所以要擴(kuò)展到255再計算
        img = img * 255  # 還是浮點(diǎn)數(shù)類型
    img = img.mean(axis=-1)  # 取均值就是灰度化了
 
    # 1、高斯平滑
    #sigma = 1.52  # 高斯平滑時的高斯核參數(shù)，標(biāo)準(zhǔn)差，可調(diào)
    sigma = 0.5  # 高斯平滑時的高斯核參數(shù)，標(biāo)準(zhǔn)差，可調(diào)
    dim = int(np.round(6 * sigma + 1))  # round是四舍五入函數(shù)，根據(jù)標(biāo)準(zhǔn)差求高斯核是幾乘幾的，也就是維度
    if dim % 2 == 0:  # 最好是奇數(shù),不是的話加一
        dim += 1
    Gaussian_filter = np.zeros([dim, dim])  # 存儲高斯核，這是數(shù)組不是列表了
    tmp = [i-dim//2 for i in range(dim)]  # 生成一個序列
    n1 = 1/(2*math.pi*sigma**2)  # 計算高斯核
    n2 = -1/(2*sigma**2)
    for i in range(dim):
        for j in range(dim):
            Gaussian_filter[i, j] = n1*math.exp(n2*(tmp[i]**2+tmp[j]**2))
    Gaussian_filter = Gaussian_filter / Gaussian_filter.sum()
    dx, dy = img.shape
    img_new = np.zeros(img.shape)  # 存儲平滑之后的圖像，zeros函數(shù)得到的是浮點(diǎn)型數(shù)據(jù)
    tmp = dim//2
    img_pad = np.pad(img, ((tmp, tmp), (tmp, tmp)), 'constant')  # 邊緣填補(bǔ)
    for i in range(dx):
        for j in range(dy):
            img_new[i, j] = np.sum(img_pad[i:i+dim, j:j+dim]*Gaussian_filter)
    plt.figure(1)
    plt.imshow(img_new.astype(np.uint8), cmap='gray')  # 此時的img_new是255的浮點(diǎn)型數(shù)據(jù)，強(qiáng)制類型轉(zhuǎn)換才可以，gray灰階
    plt.axis('off')
 
    # 2、求梯度。以下兩個是濾波求梯度用的sobel矩陣（檢測圖像中的水平、垂直和對角邊緣）
    sobel_kernel_x = np.array([[-1, 0, 1], [-2, 0, 2], [-1, 0, 1]])
    sobel_kernel_y = np.array([[1, 2, 1], [0, 0, 0], [-1, -2, -1]])
    img_tidu_x = np.zeros(img_new.shape)  # 存儲梯度圖像
    img_tidu_y = np.zeros([dx, dy])
    img_tidu = np.zeros(img_new.shape)
    img_pad = np.pad(img_new, ((1, 1), (1, 1)), 'constant')  # 邊緣填補(bǔ)，根據(jù)上面矩陣結(jié)構(gòu)所以寫1
    for i in range(dx):
        for j in range(dy):
            img_tidu_x[i, j] = np.sum(img_pad[i:i+3, j:j+3]*sobel_kernel_x)  # x方向
            img_tidu_y[i, j] = np.sum(img_pad[i:i+3, j:j+3]*sobel_kernel_y)  # y方向
            img_tidu[i, j] = np.sqrt(img_tidu_x[i, j]**2 + img_tidu_y[i, j]**2)
    img_tidu_x[img_tidu_x == 0] = 0.00000001
    angle = img_tidu_y/img_tidu_x
    plt.figure(2)
    plt.imshow(img_tidu.astype(np.uint8), cmap='gray')
    plt.axis('off')
 
    # 3、非極大值抑制
    img_yizhi = np.zeros(img_tidu.shape)
    for i in range(1, dx-1):
        for j in range(1, dy-1):
            flag = True  # 在8鄰域內(nèi)是否要抹去做個標(biāo)記
            temp = img_tidu[i-1:i+2, j-1:j+2]  # 梯度幅值的8鄰域矩陣
            if angle[i, j] <= -1:  # 使用線性插值法判斷抑制與否
                num_1 = (temp[0, 1] - temp[0, 0]) / angle[i, j] + temp[0, 1]
                num_2 = (temp[2, 1] - temp[2, 2]) / angle[i, j] + temp[2, 1]
                if not (img_tidu[i, j] > num_1 and img_tidu[i, j] > num_2):
                    flag = False
            elif angle[i, j] >= 1:
                num_1 = (temp[0, 2] - temp[0, 1]) / angle[i, j] + temp[0, 1]
                num_2 = (temp[2, 0] - temp[2, 1]) / angle[i, j] + temp[2, 1]
                if not (img_tidu[i, j] > num_1 and img_tidu[i, j] > num_2):
                    flag = False
            elif angle[i, j] > 0:
                num_1 = (temp[0, 2] - temp[1, 2]) * angle[i, j] + temp[1, 2]
                num_2 = (temp[2, 0] - temp[1, 0]) * angle[i, j] + temp[1, 0]
                if not (img_tidu[i, j] > num_1 and img_tidu[i, j] > num_2):
                    flag = False
            elif angle[i, j] < 0:
                num_1 = (temp[1, 0] - temp[0, 0]) * angle[i, j] + temp[1, 0]
                num_2 = (temp[1, 2] - temp[2, 2]) * angle[i, j] + temp[1, 2]
                if not (img_tidu[i, j] > num_1 and img_tidu[i, j] > num_2):
                    flag = False
            if flag:
                img_yizhi[i, j] = img_tidu[i, j]
    plt.figure(3)
    plt.imshow(img_yizhi.astype(np.uint8), cmap='gray')
    plt.axis('off')
 
    # 4、雙閾值檢測，連接邊緣。遍歷所有一定是邊的點(diǎn),查看8鄰域是否存在有可能是邊的點(diǎn)，進(jìn)棧
    lower_boundary = img_tidu.mean() * 0.5
    high_boundary = lower_boundary * 3  # 這里我設(shè)置高閾值是低閾值的三倍
    zhan = []
    for i in range(1, img_yizhi.shape[0]-1):  # 外圈不考慮了
        for j in range(1, img_yizhi.shape[1]-1):
            if img_yizhi[i, j] >= high_boundary:  # 取，一定是邊的點(diǎn)
                img_yizhi[i, j] = 255
                zhan.append([i, j])
            elif img_yizhi[i, j] <= lower_boundary:  # 舍
                img_yizhi[i, j] = 0
 
    while not len(zhan) == 0:
        temp_1, temp_2 = zhan.pop()  # 出棧
        a = img_yizhi[temp_1-1:temp_1+2, temp_2-1:temp_2+2]
        if (a[0, 0] < high_boundary) and (a[0, 0] > lower_boundary):
            img_yizhi[temp_1-1, temp_2-1] = 255  # 這個像素點(diǎn)標(biāo)記為邊緣
            zhan.append([temp_1-1, temp_2-1])  # 進(jìn)棧
        if (a[0, 1] < high_boundary) and (a[0, 1] > lower_boundary):
            img_yizhi[temp_1 - 1, temp_2] = 255
            zhan.append([temp_1 - 1, temp_2])
        if (a[0, 2] < high_boundary) and (a[0, 2] > lower_boundary):
            img_yizhi[temp_1 - 1, temp_2 + 1] = 255
            zhan.append([temp_1 - 1, temp_2 + 1])
        if (a[1, 0] < high_boundary) and (a[1, 0] > lower_boundary):
            img_yizhi[temp_1, temp_2 - 1] = 255
            zhan.append([temp_1, temp_2 - 1])
        if (a[1, 2] < high_boundary) and (a[1, 2] > lower_boundary):
            img_yizhi[temp_1, temp_2 + 1] = 255
            zhan.append([temp_1, temp_2 + 1])
        if (a[2, 0] < high_boundary) and (a[2, 0] > lower_boundary):
            img_yizhi[temp_1 + 1, temp_2 - 1] = 255
            zhan.append([temp_1 + 1, temp_2 - 1])
        if (a[2, 1] < high_boundary) and (a[2, 1] > lower_boundary):
            img_yizhi[temp_1 + 1, temp_2] = 255
            zhan.append([temp_1 + 1, temp_2])
        if (a[2, 2] < high_boundary) and (a[2, 2] > lower_boundary):
            img_yizhi[temp_1 + 1, temp_2 + 1] = 255
            zhan.append([temp_1 + 1, temp_2 + 1])
 
    for i in range(img_yizhi.shape[0]):
        for j in range(img_yizhi.shape[1]):
            if img_yizhi[i, j] != 0 and img_yizhi[i, j] != 255:
                img_yizhi[i, j] = 0
 
    # 繪圖
    plt.figure(4)
    plt.imshow(img_yizhi.astype(np.uint8), cmap='gray')
    plt.axis('off')  # 關(guān)閉坐標(biāo)刻度值
    plt.show()

#!/usr/bin/env python
# encoding=gbk

import cv2
import numpy as np

'''
cv2.Canny(image, threshold1, threshold2[, edges[, apertureSize[, L2gradient ]]])   
必要參數(shù)：
第一個參數(shù)是需要處理的原圖像，該圖像必須為單通道的灰度圖；
第二個參數(shù)是滯后閾值1；
第三個參數(shù)是滯后閾值2。
'''

img = cv2.imread("lenna.png", 1)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv2.imshow("canny", cv2.Canny(gray, 200, 300))
cv2.waitKey()
cv2.destroyAllWindows()

#!/usr/bin/env python
# encoding=gbk
 
'''
Canny邊緣檢測：優(yōu)化的程序
'''
import cv2
import numpy as np 
 
def CannyThreshold(lowThreshold):  
    detected_edges = cv2.GaussianBlur(gray,(3,3),0) #高斯濾波 
    detected_edges = cv2.Canny(detected_edges,
            lowThreshold,
            lowThreshold*ratio,
            apertureSize = kernel_size)  #邊緣檢測
 
     # just add some colours to edges from original image.  
    dst = cv2.bitwise_and(img,img,mask = detected_edges)  #用原始顏色添加到檢測的邊緣上
    cv2.imshow('canny demo',dst)  
  
 
lowThreshold = 0  
max_lowThreshold = 100  
ratio = 3  
kernel_size = 3  
  
img = cv2.imread('lenna.png')  
gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)  #轉(zhuǎn)換彩色圖像為灰度圖
  
cv2.namedWindow('canny demo')  
  
#設(shè)置調(diào)節(jié)杠,
'''
下面是第二個函數(shù)，cv2.createTrackbar()
共有5個參數(shù)，其實這五個參數(shù)看變量名就大概能知道是什么意思了
第一個參數(shù)，是這個trackbar對象的名字
第二個參數(shù)，是這個trackbar對象所在面板的名字
第三個參數(shù)，是這個trackbar的默認(rèn)值,也是調(diào)節(jié)的對象
第四個參數(shù)，是這個trackbar上調(diào)節(jié)的范圍(0~count)
第五個參數(shù)，是調(diào)節(jié)trackbar時調(diào)用的回調(diào)函數(shù)名
'''
cv2.createTrackbar('Min threshold','canny demo',lowThreshold, max_lowThreshold, CannyThreshold)  
  
CannyThreshold(0)  # initialization  
if cv2.waitKey(0) == 27:  #wait for ESC key to exit cv2
    cv2.destroyAllWindows()