OpenCV—Python目标區域圖像分割

使用OpenCV截取目标區域

關于灰階圖二值化：​​javascript:void(0)​​

#encoding:utf-8
import cv2
import numpy as np


def get_image(path):       #擷取圖檔
    img=cv2.imread(path)
    gray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
    return img, gray

def Gaussian_Blur(gray):    # 高斯去噪(去除圖像中的噪點)
   """
   高斯模糊本質上是低通濾波器:
   輸出圖像的每個像素點是原圖像上對應像素點與周圍像素點的權重和
   
   高斯矩陣的尺寸和标準差:
   (9, 9)表示高斯矩陣的長與寬，标準差取0時OpenCV會根據高斯矩陣的尺寸自己計算。
   高斯矩陣的尺寸越大，标準差越大，處理過的圖像模糊程度越大。
   """
    blurred = cv2.GaussianBlur(gray, (9, 9),0)
    return blurred

def Sobel_gradient(blurred): 
   """
    索比爾算子來計算x、y方向梯度
    關于算子請檢視:https://blog.csdn.net/wsp_1138886114/article/details/81368890
   """
    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):  #設定門檻值
    blurred = cv2.GaussianBlur(gradient, (9, 9),0)
    (_, thresh) = cv2.threshold(blurred, 90, 255, cv2.THRESH_BINARY)
    """
    cv2.threshold(src,thresh,maxval,type[,dst])->retval,dst (二進制值的灰階圖)
    src：  一般輸入灰階圖
   thresh:門檻值，
   maxval:在二進制門檻值THRESH_BINARY和
          逆二進制門檻值THRESH_BINARY_INV中使用的最大值 
   type:  使用的門檻值類型
    傳回值  retval其實就是門檻值 
   """
    return thresh

def image_morphology(thresh):
   """
    建立一個橢圓核函數
    執行圖像形态學, 細節直接查文檔，很簡單
   """
    kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (25, 25))
    closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
    closed = cv2.erode(closed, None, iterations=4)
    closed = cv2.dilate(closed, None, iterations=4)
    return closed

def findcnts_and_box_point(closed):
    # 這裡opencv3傳回的是三個參數
    (_, cnts, _) = cv2.findContours(closed.copy(),
        cv2.RETR_LIST,
        cv2.CHAIN_APPROX_SIMPLE)
    c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
    # 計算最大輪廓的旋轉包圍盒
    rect = cv2.minAreaRect(c)
    box = np.int0(cv2.boxPoints(rect))
    return box

def drawcnts_and_cut(original_img, box): #目标圖像裁剪
    # 因為這個函數有極強的破壞性，所有需要在img.copy()上畫
    draw_img = cv2.drawContours(original_img.copy(), [box], -1, (0, 0, 255), 3)

    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'./2010.png'
    save_path = r'./cat_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)

    # 暴力一點，把它們都顯示出來看看
    cv2.imshow('original_img', original_img)
    cv2.imshow('GaussianBlur', 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()      

執行效果