python-tensorflow-opencv《计算机视觉的深度学习实践》代码笔记
python-tensorflow-opencv《计算机视觉的深度学习实践》代码笔记
01 课程概述
tf_test:
import tensorflow as tf
# 创建2个矩阵，前者1行2列，后者2行1列，然后矩阵相乘：
matrix1 = tf.constant([[3,3]])
matrix2 = tf.constant([[2], [2]])
product = tf.matmul(matrix1,matrix2)
# 上边的操作是定义图，然后用会话Session去计算：
with tf.Session() as sess:
result2 = sess.run(product)
print(result2)
print("-----------------------------")
print(matrix1)
[[12]]
-----------------------------
Tensor("Const_2:0", shape=(1, 2), dtype=int32)
opencv_test:
import cv2 as cv
#读取图像，支持 bmp、jpg、png、tiff 等常用格式
img = cv.imread("D:/vcprojects/images/lena.png")
#创建窗口并显示图像
cv.namedWindow("Image")
cv.imshow("Image",img)
cv.waitKey(0)
#释放窗口
cv.destroyAllWindows()
02 图像预处理
f1.py程序：
import cv2
import numpy as np
import matplotlib.pyplot as plt
img = cv2.imread('D:/pictures/opencv.png',0) #直接读为灰度图像
for i in range(2000): #添加点噪声
temp_x = np.random.randint(0,img.shape[0])
temp_y = np.random.randint(0,img.shape[1])
img[temp_x][temp_y] = 255
blur_1 = cv2.GaussianBlur(img,(5,5),0)
blur_2 = cv2.medianBlur(img,5)
plt.subplot(1,3,1),plt.imshow(img,'gray'),plt.title("img")#默认彩色，另一种彩色bgr
plt.subplot(1,3,2),plt.imshow(blur_1,'gray'),plt.title("blur1")
plt.subplot(1,3,3),plt.imshow(blur_2,'gray'),plt.title("blur2")
plt.show()
f1.py结果：
f2.py程序：
import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
img = cv.imread('D:/pictures/dave.png',0)
laplacian = cv.Laplacian(img,cv.CV_64F)
sobelx = cv.Sobel(img,cv.CV_64F,1,0,ksize=5)
sobely = cv.Sobel(img,cv.CV_64F,0,1,ksize=5)
plt.subplot(2,2,1),plt.imshow(img,cmap = 'gray')
plt.title('Original'), plt.xticks([]), plt.yticks([])
plt.subplot(2,2,2),plt.imshow(laplacian,cmap = 'gray')
plt.title('Laplacian'), plt.xticks([]), plt.yticks([])
plt.subplot(2,2,3),plt.imshow(sobelx,cmap = 'gray')
plt.title('Sobel X'), plt.xticks([]), plt.yticks([])
plt.subplot(2,2,4),plt.imshow(sobely,cmap = 'gray')
plt.title('Sobel Y'), plt.xticks([]), plt.yticks([])
plt.show()
f2.py结果：
fft.py程序：
import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
img = cv.imread('D:/pictures/opencv.png',0)
f = np.fft.fft2(img)
fshift = np.fft.fftshift(f)
magnitude_spectrum = 20*np.log(np.abs(fshift))
plt.subplot(121),plt.imshow(img, cmap = 'gray')
plt.title('Input Image'), plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(magnitude_spectrum, cmap = 'gray')
plt.title('Magnitude Spectrum'), plt.xticks([]), plt.yticks([])
plt.show()
fft.py结果：
clahe.py程序：
import cv2
import matplotlib.pyplot as plt
img = cv2.imread('D:/pictures/timg.jpg',0) #直接读为灰度图像
res = cv2.equalizeHist(img)#直接直方图均衡化
clahe = cv2.createCLAHE(clipLimit=2,tileGridSize=(10,10))
cl1 = clahe.apply(img)# 限制对比度自适应直方图均衡
plt.subplot(131),plt.imshow(img,'gray'),plt.title('img')
plt.subplot(132),plt.imshow(res,'gray'),plt.title('res')
plt.subplot(133),plt.imshow(cl1,'gray'),plt.title('cl1')
plt.show()
clahe.py结果：
filter_1.ipynb程序：
import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
img = cv.imread('D:/pictures/opencv.png')
kernel = np.ones((5,5),np.float32)/25
dst = cv.filter2D(img,-1,kernel)
plt.subplot(121),plt.imshow(img),plt.title('Original')
plt.xticks([]), plt.yticks([])
plt.subplot(122),plt.imshow(dst),plt.title('Averaging')
plt.xticks([]), plt.yticks([])
plt.show()
kernel
filter_1.ipynb结果：
array([[0.04, 0.04, 0.04, 0.04, 0.04],
[0.04, 0.04, 0.04, 0.04, 0.04],
[0.04, 0.04, 0.04, 0.04, 0.04],
[0.04, 0.04, 0.04, 0.04, 0.04],
[0.04, 0.04, 0.04, 0.04, 0.04]], dtype=float32)
03 图像特征与描述
imagestiching.py程序：
import numpy as np
import cv2
class Stitcher:
#拼接函数
def stitch(self, images, ratio=0.75, reprojThresh=4.0,showMatches=False):
#获取输入图片
(imageB, imageA) = images
#检测A、B图片的SIFT关键特征点，并计算特征描述子
(kpsA, featuresA) = self.detectAndDescribe(imageA)
(kpsB, featuresB) = self.detectAndDescribe(imageB)
# 匹配两张图片的所有特征点，返回匹配结果
M = self.matchKeypoints(kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh)
# 如果返回结果为空，没有匹配成功的特征点，退出算法
if M is None:
return None
# 否则，提取匹配结果
# H是3x3视角变换矩阵
(matches, H, status) = M
# 将图片A进行视角变换，result是变换后图片
result = cv2.warpPerspective(imageA, H, (imageA.shape[1] + imageB.shape[1], imageA.shape[0]))
# 将图片B传入result图片最左端
result[0:imageB.shape[0], 0:imageB.shape[1]] = imageB
# 检测是否需要显示图片匹配
if showMatches:
# 生成匹配图片
vis = self.drawMatches(imageA, imageB, kpsA, kpsB, matches, status)
# 返回结果
return (result, vis)
# 返回匹配结果
return result
def detectAndDescribe(self, image):
# 将彩色图片转换成灰度图
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
# 建立SIFT生成器
descriptor = cv2.xfeatures2d.SIFT_create()
# 检测SIFT特征点，并计算描述子
(kps, features) = descriptor.detectAndCompute(image, None)
# 将结果转换成NumPy数组
kps = np.float32([kp.pt for kp in kps])
# 返回特征点集，及对应的描述特征
return (kps, features)
def matchKeypoints(self, kpsA, kpsB, featuresA, featuresB, ratio, reprojThresh):
# 建立暴力匹配器
matcher = cv2.DescriptorMatcher_create("BruteForce")
# 使用KNN检测来自A、B图的SIFT特征匹配对，K=2
rawMatches = matcher.knnMatch(featuresA, featuresB, 2)
matches = []
for m in rawMatches:
# 当最近距离跟次近距离的比值小于ratio值时，保留此匹配对
if len(m) == 2 and m[0].distance < m[1].distance * ratio:
# 存储两个点在featuresA, featuresB中的索引值
matches.append((m[0].trainIdx, m[0].queryIdx))
# 当筛选后的匹配对大于4时，计算视角变换矩阵
if len(matches) > 4:
# 获取匹配对的点坐标
ptsA = np.float32([kpsA[i] for (_, i) in matches])
ptsB = np.float32([kpsB[i] for (i, _) in matches])
# 计算视角变换矩阵
(H, status) = cv2.findHomography(ptsA, ptsB, cv2.RANSAC, reprojThresh)
# 返回结果
return (matches, H, status)
# 如果匹配对小于4时，返回None
return None
def drawMatches(self, imageA, imageB, kpsA, kpsB, matches, status):
# 初始化可视化图片，将A、B图左右连接到一起
(hA, wA) = imageA.shape[:2]
(hB, wB) = imageB.shape[:2]
vis = np.zeros((max(hA, hB), wA + wB, 3), dtype="uint8")
vis[0:hA, 0:wA] = imageA
vis[0:hB, wA:] = imageB
# 联合遍历，画出匹配对
for ((trainIdx, queryIdx), s) in zip(matches, status):
# 当点对匹配成功时，画到可视化图上
if s == 1:
# 画出匹配对
ptA = (int(kpsA[queryIdx][0]), int(kpsA[queryIdx][1]))
ptB = (int(kpsB[trainIdx][0]) + wA, int(kpsB[trainIdx][1]))
cv2.line(vis, ptA, ptB, (0, 255, 0), 1)
# 返回可视化结果
return vis
# 读取拼接图片
imageA = cv2.imread("./left_01.png")
imageB = cv2.imread("./right_01.png")
# 把图片拼接成全景图
stitcher = Stitcher()
(result, vis) = stitcher.stitch([imageA, imageB], showMatches=True)
# 显示所有图片
cv2.imshow("Image A", imageA)
cv2.imshow("Image B", imageB)
cv2.imshow("Keypoint Matches", vis)
cv2.imshow("Result", result)
cv2.waitKey(0)
cv2.destroyAllWindows()
imagestiching.py结果：
harris_corner.py程序：
import numpy as np
import cv2 as cv
filename = './chessboard.png'
img = cv.imread(filename)
gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
gray = np.float32(gray)
dst = cv.cornerHarris(gray,2,3,0.04)
#result is dilated for marking the corners, not important
dst = cv.dilate(dst,None)
# Threshold for an optimal value, it may vary depending on the image.
img[dst>0.01*dst.max()]=[0,0,255]
#cv.imshow('dst',img)
cv.imwrite("D:/dst.jpg",img)
if cv.waitKey(0) & 0xff == 27:
cv.destroyAllWindows()
harris_corner.py结果：
surf.py程序：
import numpy as np
import cv2 as cv
img = cv.imread('./butterfly.jpg',0)
surf = cv.xfeatures2d.SURF_create(400)
#kp, des = surf.detectAndCompute(img,None)
surf.setHessianThreshold(50000)
kp, des = surf.detectAndCompute(img,None)
img2 = cv.drawKeypoints(img,kp,None,(255,0,0),4)
cv.imshow('surf',img2)
cv.waitKey(0)
cv.destroyAllWindows()
surf.py结果：
sift.py程序：
import numpy as np
import cv2 as cv
img = cv.imread('./home.jpg')
gray= cv.cvtColor(img,cv.COLOR_BGR2GRAY)
sift = cv.xfeatures2d.SIFT_create()
kp = sift.detect(gray,None)
img=cv.drawKeypoints(gray,kp,img)
cv.imshow("SIFT", img)
cv.imwrite('sift_keypoints.jpg',img)
cv.waitKey(0)
cv.destroyAllWindows()
sift.py结果：
orb.py程序：
import numpy as np
import cv2 as cv
import matplotlib.pyplot as plt
img1 = cv.imread('./box.png',0) # queryImage
img2 = cv.imread('./box_in_scene.png',0) # trainImage
# Initiate ORB detector
orb = cv.ORB_create()
# find the keypoints and descriptors with ORB
kp1, des1 = orb.detectAndCompute(img1,None)
kp2, des2 = orb.detectAndCompute(img2,None)
# create BFMatcher object
bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)
# Match descriptors.
matches = bf.match(des1,des2)
# Sort them in the order of their distance.
matches = sorted(matches, key = lambda x:x.distance)
# Draw first 10 matches.
img3 = cv.drawMatches(img1,kp1,img2,kp2,matches[:20],None, flags=2)
plt.imshow(img3),plt.show()
orb.py结果：
laplacian_sharpen.py程序：
from PIL import Image
import numpy as np
# 读入原图像
img = Image.open('./lena.png')
# img.show()
# 为了减少计算的维度，因此将图像转为灰度图
img_gray = img.convert('L')
img_gray.show()
# 得到转换后灰度图的像素矩阵
img_arr = np.array(img_gray)
h = img_arr.shape[0] # 行
w = img_arr.shape[1] # 列
# 拉普拉斯算子锐化图像，用二阶微分
new_img_arr = np.zeros((h, w)) # 拉普拉斯锐化后的图像像素矩阵
for i in range(2, h-1):
for j in range(2, w-1):
new_img_arr[i][j] = img_arr[i+1, j] + img_arr[i-1, j] + \
img_arr[i, j+1] + img_arr[i, j-1] - \
4*img_arr[i, j]
# 拉普拉斯锐化后图像和原图像相加
laplace_img_arr = np.zeros((h, w)) # 拉普拉斯锐化图像和原图像相加所得的像素矩阵
for i in range(0, h):
for j in range(0, w):
laplace_img_arr[i][j] = new_img_arr[i][j] + img_arr[i][j]
img_laplace = Image.fromarray(np.uint8(new_img_arr))
img_laplace.show()
img_laplace2 = Image.fromarray(np.uint8(laplace_img_arr))
img_laplace2.show()
laplacian_sharpen.py结果：
