计算机视觉入门之构建一个扫描仪

最新推荐文章于 2024-05-27 21:45:18 发布

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最新推荐文章于 2024-05-27 21:45:18 发布 · 842 阅读

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文章标签：

#计算机视觉 #边缘检测 #opencv #cv #python

这篇博客介绍了如何利用计算机视觉技术，通过边缘检测和透视变换将图像转化为扫描仪效果。具体步骤包括图像的边缘检测，寻找边缘轮廓，以及应用透视变换和阈值处理，以实现对图像的扫描仪功能。

源代码：

# import the necessary packages
from transform import four_point_transform
from skimage.filters import threshold_local
import numpy as np
import argparse
import cv2
import imutils
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True,
	help = "Path to the image to be scanned")
args = vars(ap.parse_args())
# load the image and compute the ratio of the old height
# to the new height, clone it, and resize it
image = cv2.imread(args["image"])
ratio = image.shape[0] / 500.0
orig = image.copy()
image = imutils.resize(image, height = 500)
# convert the image to grayscale, blur it, and find edges
# in the image
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)
# show the original image and the edge detected image
print("STEP 1: Edge Detection")
cv2.imshow("Image", image)
cv2.imshow("Edged", edged)
cv2.waitKey(0)
cv2.destroyAllWindows()

图像边缘检测

1.＃导入必要的软件包

from pyimagesearch.transform import four_point_transform
from skimage.filters import threshold_local
import numpy as np
import argparse
import cv2
import imutils

构造参数解析器并解析参数

ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True,
	help = "Pat

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