Question

我问了一个类似的问题here，但这更多地集中在tesseract上。

我有一个示例图片如下。我想将白色方块设为我感兴趣的区域，然后裁剪掉那个部分（正方形）并用它创建一个新图像。我将处理不同的图像，因此正方形不会始终位于所有图像中的相同位置。所以我需要以某种方式检测广场的边缘。

enter image description here

我可以执行哪些预处理方法来实现结果？

Answer 1

使用您的测试图片我能够通过简单的erosion 操作删除所有噪音。

之后，在Mat上进行简单的迭代以找到角点像素是微不足道的，我在this answer上谈到了这一点。出于测试目的，我们可以在这些点之间绘制绿色线，以在原始图像中显示我们感兴趣的区域：

最后，我在原始图片中设置ROI并裁剪该部分。

最终结果显示在下图中：

我编写了一个示例代码，使用OpenCV的 C ++接口执行此任务。我对您将此代码转换为Python的技能充满信心。如果你做不到，请忘记代码并坚持使用我在此答案中分享的roadmap。

#include <cv.h> #include <highgui.h> int main(int argc, char* argv[]) { cv::Mat img = cv::imread(argv[1]); std::cout << "Original image size: " << img.size() << std::endl; // Convert RGB Mat to GRAY cv::Mat gray; cv::cvtColor(img, gray, CV_BGR2GRAY); std::cout << "Gray image size: " << gray.size() << std::endl; // Erode image to remove unwanted noises int erosion_size = 5; cv::Mat element = cv::getStructuringElement(cv::MORPH_CROSS, cv::Size(2 * erosion_size + 1, 2 * erosion_size + 1), cv::Point(erosion_size, erosion_size) ); cv::erode(gray, gray, element); // Scan the image searching for points and store them in a vector std::vector<cv::Point> points; cv::Mat_<uchar>::iterator it = gray.begin<uchar>(); cv::Mat_<uchar>::iterator end = gray.end<uchar>(); for (; it != end; it++) { if (*it) points.push_back(it.pos()); } // From the points, figure out the size of the ROI int left, right, top, bottom; for (int i = 0; i < points.size(); i++) { if (i == 0) // initialize corner values { left = right = points[i].x; top = bottom = points[i].y; } if (points[i].x < left) left = points[i].x; if (points[i].x > right) right = points[i].x; if (points[i].y < top) top = points[i].y; if (points[i].y > bottom) bottom = points[i].y; } std::vector<cv::Point> box_points; box_points.push_back(cv::Point(left, top)); box_points.push_back(cv::Point(left, bottom)); box_points.push_back(cv::Point(right, bottom)); box_points.push_back(cv::Point(right, top)); // Compute minimal bounding box for the ROI // Note: for some unknown reason, width/height of the box are switched. cv::RotatedRect box = cv::minAreaRect(cv::Mat(box_points)); std::cout << "box w:" << box.size.width << " h:" << box.size.height << std::endl; // Draw bounding box in the original image (debugging purposes) //cv::Point2f vertices[4]; //box.points(vertices); //for (int i = 0; i < 4; ++i) //{ // cv::line(img, vertices[i], vertices[(i + 1) % 4], cv::Scalar(0, 255, 0), 1, CV_AA); //} //cv::imshow("Original", img); //cv::waitKey(0); // Set the ROI to the area defined by the box // Note: because the width/height of the box are switched, // they were switched manually in the code below: cv::Rect roi; roi.x = box.center.x - (box.size.height / 2); roi.y = box.center.y - (box.size.width / 2); roi.width = box.size.height; roi.height = box.size.width; std::cout << "roi @ " << roi.x << "," << roi.y << " " << roi.width << "x" << roi.height << std::endl; // Crop the original image to the defined ROI cv::Mat crop = img(roi); // Display cropped ROI cv::imshow("Cropped ROI", crop); cv::waitKey(0); return 0; }

Answer 2

看到文本是唯一的大斑点，而其他所有内容都只是一个像素，一个简单的形态开放就足够了

你可以这样做in opencv 或with imagemagic

之后，白色矩形应该是图像中唯一剩下的东西。您可以使用opencvs findcontours，opencv的CvBlobs库或imagemagick -crop函数找到它

这是您的图像，有两个侵蚀步骤，然后是两个扩张步骤： enter image description here 您只需将此图像插入opencv findContours函数，就像在Squares tutorial example中一样，以获取位置

Answer 3

input

#objective:
#1)compress large images to less than 1000x1000
#2)identify region of interests
#3)save rois in top to bottom order
import cv2
import os

def get_contour_precedence(contour, cols):
    tolerance_factor = 10
    origin = cv2.boundingRect(contour)
    return ((origin[1] // tolerance_factor) * tolerance_factor) * cols + origin[0]

# Load image, grayscale, Gaussian blur, adaptive threshold
image = cv2.imread('./images/sample_0.jpg')

#compress the image if image size is >than 1000x1000
height, width, color = image.shape #unpacking tuple (height, width, colour) returned by image.shape
while(width > 1000):
    height = height/2
    width = width/2
print(int(height), int(width))
height = int(height)
width = int(width)
image = cv2.resize(image, (width, height))

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
blur = cv2.GaussianBlur(gray, (9,9), 0)
thresh = cv2.adaptiveThreshold(gray,255,cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY_INV,11,30)
# Dilate to combine adjacent text contours
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9,9))
ret,thresh3 = cv2.threshold(image,127,255,cv2.THRESH_BINARY_INV)
dilate = cv2.dilate(thresh, kernel, iterations=4)

# Find contours, highlight text areas, and extract ROIs
cnts = cv2.findContours(dilate, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
#cnts = cv2.findContours(thresh3, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

cnts = cnts[0] if len(cnts) == 2 else cnts[1]

#ORDER CONTOURS top to bottom
cnts.sort(key=lambda x:get_contour_precedence(x, image.shape[1]))

#delete previous roi images in folder roi to avoid
dir = './roi/'
for f in os.listdir(dir):
    os.remove(os.path.join(dir, f))

ROI_number = 0
for c in cnts:
    area = cv2.contourArea(c)
    if area > 10000:
        x,y,w,h = cv2.boundingRect(c)
        #cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 3)
        cv2.rectangle(image, (x, y), (x + w, y + h), (100,100,100), 1)
        #use below code to write roi when results are good
        ROI = image[y:y+h, x:x+w]
        cv2.imwrite('roi/ROI_{}.jpg'.format(ROI_number), ROI)
        ROI_number += 1

cv2.imshow('thresh', thresh)
cv2.imshow('dilate', dilate)
cv2.imshow('image', image)
cv2.waitKey()

roi detection output

如何确定感兴趣的区域，然后使用OpenCV裁剪图像

3 个答案: