数字图像与机器视觉--基于python+opencv识别硬币和细胞数量以及条形码检测
一、用奇异值分解(SVD)对一张图片进行特征值提取(降维)处理
奇异值分解(Singular Value Decomposition,以下简称SVD)是在机器学习领域广泛应用的算法,它不光可以用于降维算法中的特征分解,还可以用于推荐系统,以及自然语言处理等领域。
具体代码如下
import numpy as np
import os
from PIL import Image
import matplotlib.pyplot as plt
import matplotlib as mpl
from pprint import pprint
def restore1(sigma, u, v, K): # 奇异值、左特征向量、右特征向量
m = len(u)
n = len(v[0])
a = np.zeros((m, n))
for k in range(K):
uk = u[:, k].reshape(m, 1)
vk = v[k].reshape(1, n)
a += sigma[k] * np.dot(uk, vk)
a[a < 0] = 0
a[a > 255] = 255
# a = a.clip(0, 255)
return np.rint(a).astype(\'uint8\')
def restore2(sigma, u, v, K): # 奇异值、左特征向量、右特征向量
m = len(u)
n = len(v[0])
a = np.zeros((m, n))
for k in range(K+1):
for i in range(m):
a[i] += sigma[k] * u[i][k] * v[k]
a[a < 0] = 0
a[a > 255] = 255
return np.rint(a).astype(\'uint8\')
if __name__ == "__main__":
A = Image.open("C:/Users/86199/Pictures/lena/lena.jpg", \'r\')
print(A)
output_path = r\'./SVD_Output\'
if not os.path.exists(output_path):
os.mkdir(output_path)
a = np.array(A)
print(a.shape)
K = 50
u_r, sigma_r, v_r = np.linalg.svd(a[:, :, 0])
u_g, sigma_g, v_g = np.linalg.svd(a[:, :, 1])
u_b, sigma_b, v_b = np.linalg.svd(a[:, :, 2])
plt.figure(figsize=(11, 9), facecolor=\'w\')
mpl.rcParams[\'font.sans-serif\'] = [\'simHei\']
mpl.rcParams[\'axes.unicode_minus\'] = False
for k in range(1, K+1):
print(k)
R = restore1(sigma_r, u_r, v_r, k)
G = restore1(sigma_g, u_g, v_g, k)
B = restore1(sigma_b, u_b, v_b, k)
I = np.stack((R, G, B), axis=2)
Image.fromarray(I).save(\'%s\\svd_%d.png\' % (output_path, k))
if k <= 12:
plt.subplot(3, 4, k)
plt.imshow(I)
plt.axis(\'off\')
plt.title(\'奇异值个数:%d\' % k)
plt.suptitle(\'SVD与图像分解\', fontsize=20)
plt.tight_layout()
# plt.subplots_adjust(top=0.9)
plt.show()
运行代码:
可以直观地发现当奇异值越少,图片越模糊,查看文件信息也可以看到图片大小依次变大。
二、采用图像的开闭运算,检测出2个样本图像中硬币、细胞的个数
操作起来十分简单,步骤分别为
读取图片–>转为灰度图片–>二值化–>腐蚀–>膨胀–>找中心点–>标识–>显示
在中途可能腐蚀膨胀多次效果更好,具体代码如下
这里我用到的原图是以下两张
import cv2
import numpy as np
def stackImages(scale, imgArray):
"""
将多张图像压入同一个窗口显示
:param scale:float类型,输出图像显示百分比,控制缩放比例,0.5=图像分辨率缩小一半
:param imgArray:元组嵌套列表,需要排列的图像矩阵
:return:输出图像
"""
rows = len(imgArray)
cols = len(imgArray[0])
rowsAvailable = isinstance(imgArray[0], list)
width = imgArray[0][0].shape[1]
height = imgArray[0][0].shape[0]
if rowsAvailable:
for x in range(0, rows):
for y in range(0, cols):
if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
else:
imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]),
None, scale, scale)
if len(imgArray[x][y].shape) == 2: imgArray[x][y] = cv2.cvtColor(imgArray[x][y], cv2.COLOR_GRAY2BGR)
imageBlank = np.zeros((height, width, 3), np.uint8)
hor = [imageBlank] * rows
hor_con = [imageBlank] * rows
for x in range(0, rows):
hor[x] = np.hstack(imgArray[x])
ver = np.vstack(hor)
else:
for x in range(0, rows):
if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
else:
imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None, scale, scale)
if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
hor = np.hstack(imgArray)
ver = hor
return ver
#读取图片
src = cv2.imread("cell.png")
img = src.copy()
#灰度
img_1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#二值化
ret, img_2 = cv2.threshold(img_1, 127, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
#腐蚀
kernel = np.ones((20, 20), int)
img_3 = cv2.erode(img_2, kernel, iterations=1)
#膨胀
kernel = np.ones((3, 3), int)
img_4 = cv2.dilate(img_3, kernel, iterations=1)
#找到硬币中心
contours, hierarchy = cv2.findContours(img_4, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2:]
#标识硬币
cv2.drawContours(img, contours, -1, (0, 0, 255), 5)
#显示图片
cv2.putText(img, "count:{}".format(len(contours)), (0, 30), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 3)
cv2.putText(src, "src", (0, 30), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 3)
cv2.putText(img_1, "gray", (0, 30), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 3)
cv2.putText(img_2, "thresh", (0, 30), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 3)
cv2.putText(img_3, "erode", (0, 30), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 3)
cv2.putText(img_4, "dilate", (0, 30), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (255, 0, 0), 3)
imgStack = stackImages(1, ([src, img_1, img_2], [img_3, img_4, img]))
cv2.imshow("imgStack", imgStack)
cv2.waitKey(0)
# -*- coding: utf-8 -*-
"""
Created on Fri Dec 10 15:07:39 2021
@author: 13774
"""
#导入相关包
import cv2
import numpy as np
import os
#定义文件路径
cell_file="coin.png"
coin_file="cell.png"
cell_read=cv2.imread(cell_file)
coin_read=cv2.imread(coin_file)
cell_gauss=cv2.GaussianBlur(cell_read,(5,5),0)#高斯降噪
cell_gray=cv2.cvtColor(cell_gauss,cv2.COLOR_BGR2GRAY)#灰度化
cell_att=cell_gray
for j in range(3,342):
low=int(sum(cell_gray[j]))
high=int(sum(cell_gray[335]))
add=(high-low)/len(cell_read[3])
for i in range(len(cell_read[3])):
if int(cell_att[j][i])+add>255:
cell_att[j][i]=255
else :
cell_att[j][i]+=add
thre,cell_bw=cv2.threshold(cell_att,170,255,cv2.THRESH_BINARY_INV)#二值化
#闭运算,填充空洞
#构造一个全1的5*5的矩阵
kernel=np.ones((3,3),int)#设置形态学操作卷积的大小
cell_close=cv2.morphologyEx(cell_bw,cv2.MORPH_CLOSE,kernel)
#开运算
#构造一个全1的5*5的矩阵
kernel=np.ones((20,20),int)#设置形态学操作卷积的大小
cell_open=cv2.morphologyEx(cell_close,cv2.MORPH_OPEN,kernel)
#腐蚀运算
#构造一个全1的5*5的矩阵
#腐蚀运算
#构造一个全1的5*5的矩阵
kernel=np.ones((20,20),int)#设置形态学操作卷积的大小
cell_corr=cv2.erode(cell_open,kernel,iterations=1)
kernel=np.ones((11,11),int)#设置形态学操作卷积的大小
cell_open=cv2.morphologyEx(cell_corr,cv2.MORPH_OPEN,kernel)
cell_count,hir=cv2.findContours(cell_open,cv2.RETR_TREE,cv2.CHAIN_APPROX_NONE)
#检测所有轮廓,所有轮廓建立一个等级树结构。
print(len(cell_count))
cell_read=cv2.imread(cell_file)
result = cv2.drawContours(cell_read, cell_count, -1 ,(0, 255, 0), 1)
cv2.imshow(\'I\',result)
cv2.waitKey(0)
三、采用图像梯度、开闭、轮廓运算等,对图片中的条形码进行定位提取;再调用条码库获得条码字符。
这里使用的图片是
具体代码如下
import cv2
import numpy as np
import imutils
from pyzbar import pyzbar
def stackImages(scale, imgArray):
"""
将多张图像压入同一个窗口显示
:param scale:float类型,输出图像显示百分比,控制缩放比例,0.5=图像分辨率缩小一半
:param imgArray:元组嵌套列表,需要排列的图像矩阵
:return:输出图像
"""
rows = len(imgArray)
cols = len(imgArray[0])
rowsAvailable = isinstance(imgArray[0], list)
width = imgArray[0][0].shape[1]
height = imgArray[0][0].shape[0]
if rowsAvailable:
for x in range(0, rows):
for y in range(0, cols):
if imgArray[x][y].shape[:2] == imgArray[0][0].shape[:2]:
imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
else:
imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]),
None, scale, scale)
if len(imgArray[x][y].shape) == 2: imgArray[x][y] = cv2.cvtColor(imgArray[x][y], cv2.COLOR_GRAY2BGR)
imageBlank = np.zeros((height, width, 3), np.uint8)
hor = [imageBlank] * rows
hor_con = [imageBlank] * rows
for x in range(0, rows):
hor[x] = np.hstack(imgArray[x])
ver = np.vstack(hor)
else:
for x in range(0, rows):
if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
else:
imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None, scale, scale)
if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
hor = np.hstack(imgArray)
ver = hor
return ver
#读取图片
src = cv2.imread("C:/Users/86199/Pictures/computer/tm.png")
img = src.copy()
#灰度
img_1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#高斯滤波
img_2 = cv2.GaussianBlur(img_1, (5, 5), 1)
#Sobel算子
sobel_x = cv2.Sobel(img_2, cv2.CV_64F, 1, 0, ksize=3)
sobel_y = cv2.Sobel(img_2, cv2.CV_64F, 0, 1, ksize=3)
sobel_x = cv2.convertScaleAbs(sobel_x)
sobel_y = cv2.convertScaleAbs(sobel_y)
img_3 = cv2.addWeighted(sobel_x, 0.5, sobel_y, 0.5, 0)
#均值方波
img_4 = cv2.blur(img_3, (5, 5))
#二值化
ret, img_5 = cv2.threshold(img_4, 127, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
#闭运算
kernel = np.ones((18, 18), int)
img_6 = cv2.morphologyEx(img_5, cv2.MORPH_CLOSE, kernel)
#开运算
kernel = np.ones((100,100), int)
img_7 = cv2.morphologyEx(img_6, cv2.MORPH_OPEN, kernel)
#绘制条形码区域
contours = cv2.findContours(img_7, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = imutils.grab_contours(contours)
c = sorted(contours, key = cv2.contourArea, reverse = True)[0]
rect = cv2.minAreaRect(c)
box = cv2.cv.BoxPoints(rect) if imutils.is_cv2() else cv2.boxPoints(rect)
box = np.int0(box)
cv2.drawContours(img, [box], -1, (0,255,0), 6)
#显示图片信息
cv2.putText(img, "results", (30, 30), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255, 0, 0), 3)
cv2.putText(img_1, "gray", (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255, 0, 0), 3)
cv2.putText(img_2, "GaussianBlur",(40, 40), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255, 0, 0), 3)
cv2.putText(img_3, "Sobel", (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255, 0, 0), 3)
cv2.putText(img_4, "blur", (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255, 0, 0), 3)
cv2.putText(img_5, "threshold", (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255, 0, 0), 3)
cv2.putText(img_6, "close", (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255, 0, 0), 3)
cv2.putText(img_7, "open", (40, 40), cv2.FONT_HERSHEY_SIMPLEX, 2.0, (255, 0, 0), 3)
#输出条形码
barcodes = pyzbar.decode(src)
for barcode in barcodes:
barcodeData = barcode.data.decode("utf-8")
cv2.putText(img, barcodeData, (50, 70), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 3)
#显示所有图片
imgStack = stackImages(0.5, ([img_1, img_2,img_3,img_4],[img_5,img_6,img_7,img]))
cv2.imshow("imgStack", imgStack)
cv2.waitKey(0)
四、参考资料
https://blog.csdn.net/junseven164/article/details/121711581?spm=1001.2014.3001.5501