OpenCV学习笔记

import cv2 # opencv读取的格式是BGR
img = cv2.imread("test.jpg"，cv.IMREAD_COLOR)

def cv_show(name, img):
    """图像显示"""
    cv2.imshow(name,img)
    cv2.waitKey(0) # 按任意键退出
    cv2.destroyAllWindows()

cv2.imwrite("test.png", img)

vc = cv2.VideoCapture(0)
while 1:
    ret, frame = vc.read()
    cv2.imshow("frame", frame)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
vc.release()
cv2.destroyAllWindows()

cat = img[0:50,0:200]
cv_show("cat",cat)

b, g, r = cv2.split(img)
hmerge = np.hstack((b, g, r))
cv_show("hmerge", hmerge)
img_new = cv2.merge((b, g, r))
cv_show("img_new", img_new)

top_size, bottom_size, left_size, right_size = (50, 50, 50, 50)
replicate = cv2.copyMakeBorder(img, top_size, bottom_size, left_size, right_size, borderType=cv2.BORDER_REPLICATE)
cv_show("img_new", replicate)

img_1 = img + 10  # 各像素点数值加1
print(img[:5, :, 0])
print(img_1[:5, :, 0])

print((img + img_1)[:5, :, 0])  # 各像素点数值相加，除256取余
print(cv2.add(img, img_1))  # 各像素点数值相加，超过255取255

img_2 = cv2.resize(img, img.shape[:2])  # 指定尺寸修改
img_3 = cv2.resize(img, (0, 0), fx=3, fy=3)  # 同比例放大
cv_show("img_2", img_2)
cv_show("img_3", img_3)

img = cv2.imread("test.png")
img1 = cv2.imread("test1.png")
img_2 = cv2.resize(img1, img.shape[:2])  # 指定尺寸修改
res = cv2.addWeighted(img, 0.4, img_2, 0.6, 0)
cv_show("res", res)

img_gray = cv2.imread("test.png", cv2.IMREAD_GRAYSCALE)
ret, dst = cv2.threshold(img_gray, 127, 255, cv2.THRESH_BINARY)
cv_show("dst", dst)

blur = cv2.blur(img, (3,3))  # 均值滤波，简单的平均卷积操作，需要设定核的大小
box = cv2.boxFilter(img,-1,(3,3), normalize=True)  # 方框滤波，基本和均值一样，可以选择归一化
gaussian = cv2.GaussianBlur(img, (5,5), 1)  # 高斯滤波，高斯模糊的卷积核里的数值是满足高斯分布的，相当于更重视中间的
median = cv2.medianBlur(img, 5)  # 中值滤波，相当于用中值代替

kernel = np.ones((5,5), np.uint8)
erosion = cv2.erode(img, kernel, iterations=1)
cv_show("erosion", erosion)

kernel = np.ones((5,5), np.uint8)
img_dilate = cv2.dilate(erosion, kernel, iterations=1)
cv_show("img_dilate", img_dilate)

kernel = np.ones((5,5), np.uint8)
# 开运算：先腐蚀，再膨胀
img_open = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
cv_show("open", img_open)
# 闭运算：先膨胀，在腐蚀
img_close = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
cv_show("close", img_close)

# 梯度运算 = 膨胀 - 腐蚀
img = cv2.imread("test.png")
kernel = np.ones((3, 3), np.uint8)
erosion = cv2.erode(img, kernel, iterations=1)
img_dilate = cv2.dilate(erosion, kernel, iterations=1)
gradient = cv2.morphologyEx(img_dilate, cv2.MORPH_GRADIENT, kernel)
cv_show("gradient", gradient)

kernel = np.ones((3, 3), np.uint8)
# 礼貌 = 原始输入 - 开运算结果
tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)
# 黑帽 = 闭运算 - 原始输入
blackhat = cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, kernel)
cv_show("tophat", tophat)
cv_show("blackhat", blackhat)

sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=3)
# 白到黑是正数，黑到白就是负数了，所有的负数会被截断成0，所以要绝对值。
sobelx = cv2.convertScaleAbs(sobelx)
cv_show(sobelx, "sobelx")
sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=3)
sobely = cv2.convertScaleAbs(sobely)
cv_show(sobely, "sobely")
# 分别计算x和y,再求和，不建议直接计算。
sobelxy = cv2.addWeighted(sobelx, 0.5, sobely, 0.5, 0)
cv_show(sobelxy, "sobelxy")

img_1 = cv2.Canny(img, 80, 150)
img_2 = cv2.Canny(img, 80, 100)
res = np.hstack((img_1, img_2))
cv_show("res", res)

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
# 传入绘制图像，轮廓，轮廓索引，颜色模式，线条厚度
# 注意需要copy，要不原图会变
draw_img = img.copy()
res = cv2.drawContours(draw_img, contours, -1, (0, 0, 255), 2)

cnt = contours[0]
cv2.contours(cnt)  # 面积
cv2.arcLength(cnt, True)  # 周长，True表示闭合的

epsilon = 0.1*cv2.arcLength(cnt, True)
approx = cv2.approxPolyDP(cnt, epsilon, True)
draw_img = img.copy()
res = cv2.drawContours(draw_img, [approx], -1, (0, 0, 255), 2)

x, y, w, h = cv2.boundingRect(cnt)
img = cv2.rectangle(img, (x,y), (x+w,y+h), (0,255,0), 2)

(x,y),radius = cv2.minEnclosingCircle(cnt)
center = (int(x), int(y))
radius = int(radius)
img = cv2.circle(img, center, radius, (0,255,0),2)

img = cv2.imread("img.jpg",0)  # 原始图像
template = cv2.imread("template.jpg",0)  # 模板
h, w = template.shape[:2]
res = cv2.matchTemplate(img, template, cv2.TM_SQDIFF)
min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)

img = cv2.imread("img.jpg",0)
hist = cv2.calcHist([img], [0], None, [256], [0,256])

equ = cv2.equalizeHist(img)
plt.hist(equ.ravel(),256)

clach = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8,8))
res_clach = clach.apply(img)
res = np.hstack((img, res_clach))

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

img = cv2.imread("img.jpg",0)

img_float = np.float32(img)

dft = cv2.dft(img_float, flags = cv2.DFT_COMPLEX_OUTPUT)
dft_shift = np.fft.fftshift(dft)

# 得到灰度图能表示的形式

magnitude_spectrum = 20*np.log(cv2.magnitude(dft_shift[:,:,0],dft_shift[:,:,1]))

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()

from imutils import contours
import numpy as np
import argparse
import imutils
import cv2
import myutils

# 设置参数
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required=True, help="Path to input image")
ap.add_argument("-t", "--template", required=True, help="Path to template OCR_A image")
args = vars(ap.parse_args())

# 指定信用卡类型
FIRST_NUMBER = {
    "3": "American Express",
    "4": "Visa",
    "5": "MasterCard",
    "6": "Discover Card"
}

# 绘图展示
def cv_show(name, img):
    cv2.imshow(name, img)
    cv2.waitKey(0)
    cv2.destroyAllWindows()
# 读取一个模板图像
img = cv2.imread(args["template"])
cv_show("img", img)
# 灰度图
ref = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
cv_show("ref", ref)
# 二值图像
ref = cv2.threshold(ref, 10, 255, cv2.THRESH_BINARY_INV)[1]
cv_show("ref",ref)
# 计算轮廓
# cv2.findContours()函数接受的参数为二值图，即黑白的（不是灰度图），cv2.RETR_EXTERNAL只检测外轮廓，cv2.CHAIN_APPROX_SIMPLE只保留终点坐标
# 返回的list中每个元素都是图像中的一个轮廓。
ref_,refCnts,hierarchy = cv2.findContours(ref.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(ref, refCnts, -1, (0,255,0), 3)
cv_show("img", img)
print(np.array(refCnts).shape)
refCnts = myutils.sort_contours(refCnts, method="left-to-right")[0]
digits = {}