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python - 如何从屏幕中提取选定的矩形作为顶视图图像？

问题描述

我编写了一些代码来检测图像中的计算机屏幕。我需要对位于该选定矩形中心的像素做一些工作。如何将选定的矩形提取为矩形图像？ 

import imutils
import cv2
image = cv2.imread('test-img/imgRec3.jpg')
ratio = image.shape[0] / 300.0
image = imutils.resize(image, height=300)
realImage = image.copy()

# convert the image to grayscale, blur it, and find edges in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.bilateralFilter(gray, 11, 17, 17)
edged = cv2.Canny(gray, 30, 200)

cnts = cv2.findContours(edged.copy(), cv2.RETR_TREE, 
cv2.CHAIN_APPROX_SIMPLE)
cnts = imutils.grab_contours(cnts)
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:10]
screenCnt = None

# loop over our contours
for c in cnts:
   # approximate the contour
   peri = cv2.arcLength(c, True)
   approx = cv2.approxPolyDP(c, 0.015 * peri, True)

   if len(approx) == 4:
        screenCnt = approx
        break

cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 3)
cv2.imshow("image", realImage)
cv2.imshow("Screen Rec", image)
cv2.waitKey(0)

提前感谢您的帮助。

标签： pythonopencvcomputer-visioncv2

解决方案

使用 skimage 你可以这样做：

def transform(intersections, image):
    w,h = get_orientation()
    a = np.array([0,h])
    b = np.array([w,h])
    c = np.array([w,0])
    d = np.array([0,0])
    tf = skimage.transform.estimate_transform("projective",
        dst=np.vstack((a,b,c,d)),
        src=intersections)
    invtf = tf.inverse
    transformedImage = skimage.transform.warp(image=image,inverse_map=invtf, output_shape=(h,w))
    return transformedImage

fig,(ax0,ax1) = plt.subplots(ncols=2, figsize=(15,8))

transformed_image = transform(sorted_intersec, img[index] )
ax0.imshow(transformed_image,cmap="gray")

ax1.imshow(img[index])

交叉点是你的 4 条边。请记住，您必须以正确的顺序放置它们。

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