提取图片中红框矩形内容(python)

一.效果图展示

原图：

 提取后：

 二.源码

import cv2 as cv
import numpy as np
import os
import shutil

def canny_demo(image):
    t = 80
    canny_output = cv.Canny(image, t, t * 2)

    return canny_output

def GetRed(img):
    """
    提取图中的红色部分
    """
    # 转化为hsv空间
    hsv = cv.cvtColor(img, cv.COLOR_BGR2HSV)
    # print(hsv.shape)
    # 颜色在HSV空间下的上下限
    low_hsv = np.array([0, 180, 80])
    high_hsv = np.array([10, 255, 255])

    # 使用opencv的inRange函数提取颜色
    mask = cv.inRange(hsv, lowerb=low_hsv, upperb=high_hsv)
    Red = cv.bitwise_and(img, img, mask=mask)
    return Red

def get_roi(base_path):
    for filename in os.listdir(base_path):
        src_path = os.path.join(base_path, filename)
        img = cv.imread(src_path)

        src = GetRed(img)
        binary = canny_demo(src)
        k = np.ones((3, 3), dtype=np.uint8)
        binary = cv.morphologyEx(binary, cv.MORPH_DILATE, k)

        contours, hierarchy = cv.findContours(binary, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)
        for c in range(len(contours)):
            area = cv.contourArea(contours[c])
            arclen = cv.arcLength(contours[c], True)
            if area < 100 or arclen < 100:
                continue
            rect = cv.minAreaRect(contours[c])
            cx, cy = rect[0]

            box = cv.boxPoints(rect)
            box = np.int0(box)
            listX = [box[0][0], box[1][0], box[2][0], box[3][0]]
            listY = [box[0][1], box[1][1], box[2][1], box[3][1]]
            x1 = min(listX)
            y1 = min(listY)
            x2 = max(listX)
            y2 = max(listY)
            # print(x1, y1, x2, y2)
            width = np.int32(x2 - x1)
            height = np.int32(y2 - y1)

            roi = img[y1 + 5: y2 - 5, x1 + 5:x2 - 5]
            # print(width, height)
            # print(x1,y1,x2,y2)
            if width < 80 or height < 80:
                continue

            cv.imshow("roi", roi)
            cv.waitKey(0)

    cv.destroyAllWindows()
    
#根目录，可批量处理多张图片
base_path = r"F:\test"
get_roi(base_path)