python目标定位（借鉴csdn上大神）

写博客是为了记录下来，毕竟好多东西记不住，看过就忘了，收藏又太多，还不如搬运到自己博客下面，随时可翻~~~

近期再学目标识别与定位，看着原理都很简单，但是真自己做，又觉得困难重重。

csdn上一个大神发了一个虫子的定位切割程序，跑了一下效果不错，因此记录下来，可以在此基础上改进。

import cv2
import numpy as np


def get_image(path):
    #获取图片
    img=cv2.imread(path)
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)

    return img, gray

def Gaussian_Blur(gray):
    # 高斯去噪
    blurred = cv2.GaussianBlur(gray, (9, 9),0)

    return blurred

def Sobel_gradient(blurred):
    # 索比尔算子来计算x、y方向梯度
    gradX = cv2.Sobel(blurred, ddepth=cv2.CV_32F, dx=1, dy=0)
    gradY = cv2.Sobel(blurred, ddepth=cv2.CV_32F, dx=0, dy=1)

    gradient = cv2.subtract(gradX, gradY)
    gradient = cv2.convertScaleAbs(gradient)

    return gradX, gradY, gradient

def Thresh_and_blur(gradient):  #滤波，二值化

    blurred = cv2.GaussianBlur(gradient, (9, 9),0)
    (_, thresh) = cv2.threshold(blurred, 90, 255, cv2.THRESH_BINARY)

    return thresh

def image_morphology(thresh):   #形态学，补齐边缘
    # 建立一个椭圆核函数
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (25, 25))    #返回指定形状和尺寸的结构元素，
    # 定义一个25x25的椭圆形内核
    # 执行图像形态学, 细节直接查文档，很简单
    closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)    #开运算，开运算即为先腐蚀再膨胀，目的是消除白色点点
    closed = cv2.erode(closed, None, iterations=4)    #腐蚀
    closed = cv2.dilate(closed, None, iterations=4)   #膨胀

    return closed

def findcnts_and_box_point(closed):  #寻找目标轮廓和中心点
    # 这里opencv3返回的是三个参数
    (_, cnts, _) = cv2.findContours(closed.copy(),
        cv2.RETR_LIST,
        cv2.CHAIN_APPROX_SIMPLE)
    c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]    #倒序排列
#cv2.contourArea是轮廓的面积，因此是按照面积由大到小的顺序排序
    # compute the rotated bounding box of the largest contour
    rect = cv2.minAreaRect(c)
    #旋转的边界矩形，面积最小，返回值为Box2D结构，分别为左上角坐标、宽和高，旋转角度
    box = np.int0(cv2.boxPoints(rect))
#搭配cv2.minAreaRect函数，用于绘制旋转边界矩形
    return box

def drawcnts_and_cut(original_img, box):   #画轮廓
    # 因为这个函数有极强的破坏性，所有需要在img.copy()上画
    # draw a bounding box arounded the detected barcode and display the image
    draw_img = cv2.drawContours(original_img.copy(), [box], -1, (0, 0, 255), 3)    #绘制所有轮廓

    Xs = [i[0] for i in box]
    Ys = [i[1] for i in box]
    x1 = min(Xs)
    x2 = max(Xs)
    y1 = min(Ys)
    y2 = max(Ys)
    hight = y2 - y1
    width = x2 - x1
    crop_img = original_img[y1:y1+hight, x1:x1+width]

    return draw_img, crop_img

def walk():

    img_path = r'F:\pycharm\test\iterable\cz.png'
    save_path = r'F:\pycharm\test\iterable\cz_save.png'
    original_img, gray = get_image(img_path)
    blurred = Gaussian_Blur(gray)
    gradX, gradY, gradient = Sobel_gradient(blurred)
    thresh = Thresh_and_blur(gradient)
    closed = image_morphology(thresh)
    box = findcnts_and_box_point(closed)
    draw_img, crop_img = drawcnts_and_cut(original_img,box)
    # 暴力一点，把它们都显示出来看看
    cv2.imshow('original_img', original_img)
    cv2.imshow('blurred', blurred)
    cv2.imshow('gradX', gradX)
    cv2.imshow('gradY', gradY)
    cv2.imshow('final', gradient)
    cv2.imshow('thresh', thresh)
    cv2.imshow('closed', closed)
    cv2.imshow('draw_img', draw_img)
    cv2.imshow('crop_img', crop_img)
    cv2.waitKey(20171219)
    cv2.imwrite(save_path, crop_img)

walk()

希望自己早日写出实现功能的代码。

原作网址为：https://blog.csdn.net/sinat_36458870/article/details/78825571。

感谢。