opencv笔记（9）—— 文档扫描[实战]

目录

一、包的导入

二、step1：边缘检测

三、step2：获取轮廓

四、step3：透视变换

五、其他函数

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一、包的导入

# 导入工具包
import numpy as np
import argparse
import cv2

# 设置参数
# ap = argparse.ArgumentParser()
# ap.add_argument("-i", "--image", required = True,
# 	help = "Path to the image to be scanned")
# args = vars(ap.parse_args())

args={"image": "./images/page.jpg"}

二、step1：边缘检测

# 读取输入
image = cv2.imread(args["image"])
#坐标也会相同变化
ratio = image.shape[0] / 500.0 # h
orig = image.copy()


image = resize(orig, height = 500)

# 预处理、边缘检测
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (5, 5), 0)
edged = cv2.Canny(gray, 75, 200)

# 展示预处理结果
print("STEP 1: 边缘检测")
cv2.imshow("Image", image)
cv2.imshow("Edged", edged)
cv2.waitKey(0)
cv2.destroyAllWindows()

三、step2：获取轮廓

# 轮廓检测
cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0]
cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5]

# 先进行排序，其实可以直接取排序好的【0】就是最外层轮廓，但是避免最外层的轮廓不是闭合的，所以就进行了一个这样的操作
# 遍历轮廓
for c in cnts:
	# 计算轮廓近似
	peri = cv2.arcLength(c, True)
	# C表示输入的点集
	# epsilon表示从原始轮廓到近似轮廓的最大距离，它是一个准确度参数
	# True表示封闭的
	approx = cv2.approxPolyDP(c, 0.02 * peri, True)

	# 4个点的时候就拿出来
	if len(approx) == 4:
		screenCnt = approx
		break

# 展示结果
print("STEP 2: 获取轮廓")
cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2)
cv2.imshow("Outline", image)
cv2.waitKey(0)
cv2.destroyAllWindows()

四、step3：透视变换

# 透视变换
warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio) # orig是原始输入的图像，（4，2）是每个点都是x，y

# 二值处理
warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY)
ref = cv2.threshold(warped, 100, 255, cv2.THRESH_BINARY)[1]
cv2.imwrite('scan.jpg', ref)
# 展示结果
print("STEP 3: 变换")
cv2.imshow("Original", resize(orig, height = 650))
cv2.imshow("Scanned", resize(ref, height = 650))
cv2.waitKey(0)

五、其他函数

# 获取输入图像在原图的坐标点
def order_points(pts):
	# 一共4个坐标点
	rect = np.zeros((4, 2), dtype = "float32")

	# (1, 2), (3, 2), (1, 5), (3, 5)
	# 按顺序找到对应坐标0123分别是 左上，右上，右下，左下
	# 计算左上，右下
	s = pts.sum(axis = 1) # x
	rect[0] = pts[np.argmin(s)]
	rect[2] = pts[np.argmax(s)]

	# 计算右上和左下
	diff = np.diff(pts, axis = 1) # y - x 差分
	rect[1] = pts[np.argmin(diff)]
	rect[3] = pts[np.argmax(diff)]

	return rect

# 返回变换后的图像的像素
def four_point_transform(image, pts):
	# 获取输入坐标点
	rect = order_points(pts)
	(tl, tr, br, bl) = rect

	# 计算输入的w和h值
	widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2))
	widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2))
	maxWidth = max(int(widthA), int(widthB)) # 用大的，因为不保证是标准的矩形

	heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2))
	heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2))
	maxHeight = max(int(heightA), int(heightB))

	# 变换后对应坐标位置
	dst = np.array([
		[0, 0],
		[maxWidth - 1, 0],
		[maxWidth - 1, maxHeight - 1],
		[0, maxHeight - 1]], dtype = "float32")

	# 计算变换矩阵，2d->3d->2d
	# [x/z, y/z] = [x, y, z] = M * [x, y, 1]
	# M = [h11, h12, h13,
	# 	   h21, h22, h23
	# 	   h31, h32, 1	]
	# 就是求解M矩阵，知道了输入、输出的四组坐标点
	M = cv2.getPerspectiveTransform(rect, dst)
	warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight))

	# 返回变换后结果
	return warped

def resize(image, width=None, height=None, inter=cv2.INTER_AREA):
	dim = None
	(h, w) = image.shape[:2]
	if width is None and height is None:
		return image
	if width is None:
		r = height / float(h)
		dim = (int(w * r), height)
	else:
		r = width / float(w)
		dim = (width, int(h * r))
	resized = cv2.resize(image, dim, interpolation=inter)
	return resized