[人工智能]OpenCV+Python简单实践之硬币检测以及条形码检测

一、简单图片格式

1.位图

把一张彩色图片分别保存为 32位、16位彩色和256色、16色、单色的位图
notepad打开位图
img1.bmp单色位图
文件头如图

img16.bmp16色位图
img256.bmp256色位图
img16rgb16位彩图

img32rgb32位彩图

位图大小计算公式为；长×高×位深度
比如下面的图，可以计算得大小约为（220×220×16）/8/1024=94.53125kb，不包括文件头等其他信息。
0~1 两个字节为文件类型，0x4d42为固定BM
2~5 四个字节为文件大小，0x184e，即6222
6~9 四个字节为保留字段，全0
a~d 四个字节为从文件头到实际的位图数据的偏移字节数
12~15 四个字节表示图片宽度，0xdc为220
16~19 四个字节表示图片高度，0xdc为220
1a~1b 两个字节，恒定为0x1
1c~1d 两个字节表示像素占的比特，这里为0x1即两种颜色，16色为0x4即16种颜色，256色为0x8即256种颜色
1e~21 四个字节表示图片是否压缩，0x0表示不压缩
22~25 四个表示图像大小，0x1810为6160
26~29 四个字节表示水平分辨率
2a~2d 四个字节表示垂直分辨率
23~31 四个字节表示实际使用的颜色索引数
32~35 四个字节表示重要的颜色索引数

可以发现文件头一共占40个字节，为十六进制。
对于不同的图片，文件大小、长、宽、像素占比都不同。

2.文件压缩比

分别保存jpg，png，gif，bmp四种格式的图片，原图为24位img.bmp，大小为468kb。
经过jpg转换后大小变为22.6kb，压缩率在5%
经过gif转换后大小变为10.9kb，压缩率在3%
经过png转换后大小变为74.1kb，压缩率在16%
经过256色位图转换后大小变为157kb，压缩率在36%
当然，这里的图片空白部分偏多，压缩率很低。

二、用奇异值分解（SVD）对一张图片进行特征值提取（降维）处理

1.代码

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("./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()

2.效果

随着奇异值的减少图片变得模糊

三、采用图像的开闭运算(腐蚀-膨胀)，检测出2个样本图像中硬币、细胞的个数

1.硬币检测代码

读取图片

#读取图片
src = cv2.imread("img_1.png")
img = src.copy()

原图

转为灰度图片

#灰度
img_1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

图片二值化

#二值化
ret, img_2 = cv2.threshold(img_1, 127, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)

腐蚀

腐蚀主要为了把每个硬币区分开。过大会造成缺失，过低会无法区分开。参数可以自己设置以达到合适。

#腐蚀
kernel = np.ones((25, 25), int)
img_3 = cv2.erode(img_2, kernel, iterations=1)

膨胀

膨胀到合适的值，这样每一个白色区域就是一个硬币。

#膨胀
kernel = np.ones((10, 10), int)
img_4 = cv2.dilate(img_3, kernel, iterations=1)

找到硬币中心

#找到硬币中心
contours, hierarchy = cv2.findContours(img_4, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[-2:]

标识硬币

#标识硬币
cv2.drawContours(img, contours, -1, (0, 0, 255), 5)

显示结果

#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(0.7, ([src, img_1, img_2], [img_3, img_4, img]))
cv2.imshow("imgStack", imgStack)
cv2.waitKey(0)

最终结果

源码

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("img_1.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)

2.细胞检测

和硬币检测原理和过程一致

四、 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

读取图片

#读取图片
src = cv2.imread("img_3.jpg")
img = src.copy()

转为灰度图片

#灰度
img_1 = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

高斯滤波处理

#高斯滤波
img_2 = cv2.GaussianBlur(img_1, (5, 5), 1)

Sobel算子计算梯度差

#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)

均值方波消除高频噪声

#均值方波
img_4 = cv2.blur(img_3, (5, 5))

二值化

#二值化
ret, img_5 = cv2.threshold(img_4, 127, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

闭运算填充空隙

#闭运算
kernel = np.ones((100, 100), int)
img_6 = cv2.morphologyEx(img_5, cv2.MORPH_CLOSE, kernel)

开运算得到最终区域

#开运算
kernel = np.ones((200, 200), int)
img_7 = cv2.morphologyEx(img_6, cv2.MORPH_OPEN, kernel)

绘制结果

#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), 20)

#显示图片信息
cv2.putText(img, "results", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_1, "gray", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_2, "GaussianBlur",(200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_3, "Sobel", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_4, "blur", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_5, "threshold", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_6, "close", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_7, "open", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)

#输出条形码
barcodes = pyzbar.decode(src)
for barcode in barcodes:
    barcodeData = barcode.data.decode("utf-8")
    cv2.putText(img, barcodeData, (200, 600), cv2.FONT_HERSHEY_SIMPLEX, 5.0, (0, 255, 0), 30)

#显示所有图片
imgStack = stackImages(0.1, ([img_1, img_2,img_3,img_4],[img_5,img_6,img_7,img]))
cv2.imshow("imgStack", imgStack)
cv2.waitKey(0)

结果

源码

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("img_3.jpg")
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((100, 100), int)
img_6 = cv2.morphologyEx(img_5, cv2.MORPH_CLOSE, kernel)

#开运算
kernel = np.ones((200, 200), 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), 20)

#显示图片信息
cv2.putText(img, "results", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_1, "gray", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_2, "GaussianBlur",(200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_3, "Sobel", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_4, "blur", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_5, "threshold", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_6, "close", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)
cv2.putText(img_7, "open", (200, 200), cv2.FONT_HERSHEY_SIMPLEX, 10.0, (255, 0, 0), 30)

#输出条形码
barcodes = pyzbar.decode(src)
for barcode in barcodes:
    barcodeData = barcode.data.decode("utf-8")
    cv2.putText(img, barcodeData, (200, 600), cv2.FONT_HERSHEY_SIMPLEX, 5.0, (0, 255, 0), 30)

#显示所有图片
imgStack = stackImages(0.1, ([img_1, img_2,img_3,img_4],[img_5,img_6,img_7,img]))
cv2.imshow("imgStack", imgStack)
cv2.waitKey(0)

四、总结

学会使用OpenCV对于处理图像有很大的帮助

五、参考

https://blog.csdn.net/qq_43279579/article/details/120782545
https://blog.csdn.net/qq_43279579/article/details/120766454
https://blog.csdn.net/qq_43279579/article/details/120785076
https://blog.csdn.net/weixin_45602979/article/details/108831760
https://blog.csdn.net/yl_best/article/details/90666104
https://blog.csdn.net/qq_47281915/article/details/121640725?spm=1001.2014.3001.5501